From f4c855e62e7398b030e45dd9cd775bd0df6ac6e1 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Mon, 16 Oct 2023 16:59:28 +0000
Subject: [PATCH 001/587] Bump pypa/gh-action-pypi-publish from 1.4.2 to 1.8.10
Bumps [pypa/gh-action-pypi-publish](https://github.com/pypa/gh-action-pypi-publish) from 1.4.2 to 1.8.10.
- [Release notes](https://github.com/pypa/gh-action-pypi-publish/releases)
- [Commits](https://github.com/pypa/gh-action-pypi-publish/compare/27b31702a0e7fc50959f5ad993c78deac1bdfc29...b7f401de30cb6434a1e19f805ff006643653240e)
---
updated-dependencies:
- dependency-name: pypa/gh-action-pypi-publish
dependency-type: direct:production
update-type: version-update:semver-minor
...
Signed-off-by: dependabot[bot]
") or None - self.middle_id: Optional[int] = self.sp_model.piece_to_id("▁") or None - self.suffix_id: Optional[int] = self.sp_model.piece_to_id("▁ ") or None + self.prefix_id: Optional[int] = ( + self.sp_model.piece_to_id("▁ ") or None + ) + self.middle_id: Optional[int] = ( + self.sp_model.piece_to_id("▁") or None + ) + self.suffix_id: Optional[int] = ( + self.sp_model.piece_to_id("▁ ") or None + ) self.eot_id: Optional[int] = self.sp_model.piece_to_id("▁ ") or None logger.info( - f"#words: {self.n_words} - BOS ID: {self.bos_id} - EOS ID: {self.eos_id} -" - f" PRE ID: {self.prefix_id} - MID ID: {self.middle_id} - SUF ID:" - f" {self.suffix_id} - EOT ID: {self.eot_id}" + f"#words: {self.n_words} - BOS ID: {self.bos_id} - EOS ID:" + f" {self.eos_id} - PRE ID: {self.prefix_id} - MID ID:" + f" {self.middle_id} - SUF ID: {self.suffix_id} - EOT ID:" + f" {self.eot_id}" ) assert self.sp_model.vocab_size() == self.sp_model.get_piece_size() diff --git a/zeta/tokenizers/tiktoken.py b/zeta/tokenizers/tiktoken.py index 12e22d39..e2f1953d 100644 --- a/zeta/tokenizers/tiktoken.py +++ b/zeta/tokenizers/tiktoken.py @@ -54,7 +54,9 @@ def max_tokens(self) -> int: return (tokens if tokens else self.DEFAULT_MAX_TOKENS) - offset def encode(self, text: str) -> list[int]: - return self.encoding.encode(text, allowed_special=set(self.stop_sequences)) + return self.encoding.encode( + text, allowed_special=set(self.stop_sequences) + ) def decode(self, tokens: list[int]) -> str: return self.encoding.decode(tokens) @@ -95,8 +97,8 @@ def token_count(self, text: str | list, model: Optional[str] = None) -> int: tokens_per_name = -1 elif "gpt-3.5-turbo" in model or "gpt-35-turbo" in model: logging.info( - "gpt-3.5-turbo may update over time. Returning num tokens assuming" - " gpt-3.5-turbo-0613." + "gpt-3.5-turbo may update over time. Returning num tokens" + " assuming gpt-3.5-turbo-0613." ) return self.token_count(text, model="gpt-3.5-turbo-0613") elif "gpt-4" in model: diff --git a/zeta/tokenizers/tokenmonster.py b/zeta/tokenizers/tokenmonster.py index 8b52c739..b4bf5570 100644 --- a/zeta/tokenizers/tokenmonster.py +++ b/zeta/tokenizers/tokenmonster.py @@ -226,7 +226,11 @@ def modify( int: The new size of the vocabulary. """ return self.vocab.modify( - add_special_tokens, add_regular_tokens, delete_tokens, resize, change_unk + add_special_tokens, + add_regular_tokens, + delete_tokens, + resize, + change_unk, ) def add_token(self, token): diff --git a/zeta/training/dataloader.py b/zeta/training/dataloader.py index add5ed2a..5e2e279e 100644 --- a/zeta/training/dataloader.py +++ b/zeta/training/dataloader.py @@ -20,7 +20,9 @@ def build_dataloaders(seq_len: int = None, num_cpu: int = None): dataset = load_dataset("openwebtext", split="train") tokenized_dataset = dataset.map( - lambda example: tokenizer([t + tokenizer.eos_token for t in example["text"]]), + lambda example: tokenizer( + [t + tokenizer.eos_token for t in example["text"]] + ), batched=True, num_proc=seq_len, remove_columns=["text"], @@ -32,7 +34,9 @@ def build_dataloaders(seq_len: int = None, num_cpu: int = None): # dataset and generate chunks of block_size. def group_texts(examples): # Concatenate all texts. - concatenated_examples = {k: list(chain(*examples[k])) for k in examples.keys()} + concatenated_examples = { + k: list(chain(*examples[k])) for k in examples.keys() + } total_length = len(concatenated_examples[list(examples.keys())[0]]) # We drop the small remainder, we could add padding if the model supported it instead of this drop, you can # customize this part to your needs. @@ -40,7 +44,10 @@ def group_texts(examples): total_length = (total_length // block_size) * block_size # Split by chunks of max_len. result = { - k: [t[i : i + block_size] for i in range(0, total_length, block_size)] + k: [ + t[i : i + block_size] + for i in range(0, total_length, block_size) + ] for k, t in concatenated_examples.items() } return result diff --git a/zeta/training/fsdp.py b/zeta/training/fsdp.py index 724115a7..f1bb007f 100644 --- a/zeta/training/fsdp.py +++ b/zeta/training/fsdp.py @@ -70,9 +70,8 @@ def fsdp( ) else: raise ValueError( - "Invalid scheduler_type. Expected 'bf16', 'fp16' or 'fp32', got: {}".format( - mp - ) + "Invalid scheduler_type. Expected 'bf16', 'fp16' or 'fp32', got: {}" + .format(mp) ) if shard_strat == "SHARD_GRAD": @@ -83,8 +82,8 @@ def fsdp( sharding_strat_fsdp = ShardingStrategy.NO_SHARD else: raise ValueError( - "Invalid scheduler_type. Expected 'SHARD_GRAD', 'FULL_SHARD' or 'NO_SHARD'," - " got: {}".format(shard_strat) + "Invalid scheduler_type. Expected 'SHARD_GRAD', 'FULL_SHARD' or" + " 'NO_SHARD', got: {}".format(shard_strat) ) model = FullyShardedDataParallel( diff --git a/zeta/training/hive_trainer.py b/zeta/training/hive_trainer.py index a9874693..f5fc8002 100644 --- a/zeta/training/hive_trainer.py +++ b/zeta/training/hive_trainer.py @@ -144,7 +144,9 @@ def train( "seq_len": self.seq_len, "entity_name": self.entity_name, "use_fsdp": self.use_fsdp, - "use_activation_checkpointing": self.use_activation_checkpointing, + "use_activation_checkpointing": ( + self.use_activation_checkpointing + ), "learning_rate": self.learning_rate, "seed": self.seed, "use_pretokenized": self.use_pretokenized, diff --git a/zeta/training/scheduler.py b/zeta/training/scheduler.py index b4cf7bbd..6c647df0 100644 --- a/zeta/training/scheduler.py +++ b/zeta/training/scheduler.py @@ -50,7 +50,6 @@ def get_lr_scheduler_with_warmup( ) else: raise ValueError( - "Invalid scheduler_type. Expected 'linear' or 'cosine', got: {}".format( - scheduler_type - ) + "Invalid scheduler_type. Expected 'linear' or 'cosine', got: {}" + .format(scheduler_type) ) diff --git a/zeta/training/train.py b/zeta/training/train.py index 1bf4a52a..a047e038 100644 --- a/zeta/training/train.py +++ b/zeta/training/train.py @@ -155,14 +155,17 @@ def Trainer( if resume_from_checkpoint: if resume_from_checkpoint is not None or resume_from_checkpoint != "": - accelerator.print(f"Resuming from checkpoint {resume_from_checkpoint}") + accelerator.print( + f"Resuming from checkpoint {resume_from_checkpoint}" + ) accelerator.load_state(resume_from_checkpoint) path = os.path.basename(resume_from_checkpoint) training_difference = os.path.splitext(path)[0] # need to multiply `gradient_accumulation_steps` to reflect real steps resume_step = ( - int(training_difference.replace("step_", "")) * gradient_accumulate_every + int(training_difference.replace("step_", "")) + * gradient_accumulate_every ) if resume_from_checkpoint and resume_step is not None: @@ -215,7 +218,8 @@ def Trainer( unwrapped_model = accelerator.unwrap_model(model) with accelerator.main_process_first(): accelerator.save( - unwrapped_model.state_dict(), f"{output_dir}/final/final_model.pt" + unwrapped_model.state_dict(), + f"{output_dir}/final/final_model.pt", ) diff --git a/zeta/utils/benchmark.py b/zeta/utils/benchmark.py index d3ced345..a2e2728e 100644 --- a/zeta/utils/benchmark.py +++ b/zeta/utils/benchmark.py @@ -23,7 +23,9 @@ class ProfileConfig: memory_profile_path: Optional[str] = None -def benchmark_torch_function_in_microseconds(func: Callable, *args, **kwargs) -> float: +def benchmark_torch_function_in_microseconds( + func: Callable, *args, **kwargs +) -> float: # warmup for _ in range(5): func(*args, **kwargs) diff --git a/zeta/utils/main.py b/zeta/utils/main.py index bb8a390c..69e389dc 100644 --- a/zeta/utils/main.py +++ b/zeta/utils/main.py @@ -283,7 +283,10 @@ def groupby_prefix_and_trim(prefix, d): partial(string_begins_with, prefix), d ) kwargs_without_prefix = dict( - map(lambda x: (x[0][len(prefix) :], x[1]), tuple(kwargs_with_prefix.items())) + map( + lambda x: (x[0][len(prefix) :], x[1]), + tuple(kwargs_with_prefix.items()), + ) ) return kwargs_without_prefix, kwargs @@ -367,7 +370,9 @@ def forward(self, logits_exp, logits_ama): # scores scores = torch.where( - mask.bool(), torch.log(p_exp / (p_ama + 1e-8)), torch.tensor(-float("inf")) + mask.bool(), + torch.log(p_exp / (p_ama + 1e-8)), + torch.tensor(-float("inf")), ) return scores @@ -411,7 +416,9 @@ def __init__(self, dim, dim_out, *, time_emb_dim=None, groups=8): self.block1 = Block(dim, dim_out, groups=groups) self.block2 = Block(dim_out, dim_out, groups=groups) - self.res_conv = nn.Conv3d(dim, dim_out, 1) if dim != dim_out else nn.Identity() + self.res_conv = ( + nn.Conv3d(dim, dim_out, 1) if dim != dim_out else nn.Identity() + ) def forward(self, x, time_emb=None): scale_shift = None @@ -577,7 +584,9 @@ def forward(self, x, **kwargs): def cosine_beta_schedule(timesteps, s=0.008): steps = timesteps + 1 x = torch.linspace(0, timesteps, steps, dtype=torch.float64) - alphas_cumprod = torch.cos(((x / timesteps) + s) / (1 + s) * torch.pi * 0.5) ** 2 + alphas_cumprod = ( + torch.cos(((x / timesteps) + s) / (1 + s) * torch.pi * 0.5) ** 2 + ) alphas_cumprod = alphas_cumprod / alphas_cumprod[0] betas = 1 - (alphas_cumprod[1:] / alphas_cumprod[:-1]) return torch.clip(betas, 0, 0.9999) @@ -615,7 +624,8 @@ def forward(self, x): def extra_repr(self): st = ( - f"logit_scale_init={self.logit_scale_init}, learnable={self.learnable}," + f"logit_scale_init={self.logit_scale_init}," + f" learnable={self.learnable}," f"max_logit_scale={self.max_logit_scale}" ) return st @@ -686,7 +696,9 @@ def interpolate_pos_encoding_2d(target_spatial_size, pos_embed): if N == target_spatial_size: return pos_embed dim = pos_embed.shape[-1] - pos_embed, updated = cast_if_src_dtype(pos_embed, torch.bfloat16, torch.float32) + pos_embed, updated = cast_if_src_dtype( + pos_embed, torch.bfloat16, torch.float32 + ) pos_embed = nn.functional.interpolate( pos_embed.reshape(1, int(math.sqrt(N)), int(math.sqrt(N)), dim).permute( 0, 3, 1, 2 @@ -695,7 +707,9 @@ def interpolate_pos_encoding_2d(target_spatial_size, pos_embed): mode="bicubic", ) if updated: - pos_embed, _ = cast_if_src_dtype(pos_embed, torch.float32, torch.bfloat16) + pos_embed, _ = cast_if_src_dtype( + pos_embed, torch.float32, torch.bfloat16 + ) pos_embed = pos_embed.permute(0, 2, 3, 1).view(1, -1, dim) return pos_embed @@ -745,7 +759,8 @@ def look_around(x, backward=1, forward=0, pad_value=-1, dim=2): padded_x = F.pad(x, (*dims, backward, forward), value=pad_value) tensors = [ - padded_x[:, ind : (ind + t), ...] for ind in range(forward + backward + 1) + padded_x[:, ind : (ind + t), ...] + for ind in range(forward + backward + 1) ] return torch.cat(tensors, dim=dim) diff --git a/zeta/utils/vision_utils.py b/zeta/utils/vision_utils.py index a084b795..6bf52bdf 100644 --- a/zeta/utils/vision_utils.py +++ b/zeta/utils/vision_utils.py @@ -22,9 +22,9 @@ import PIL.Image import PIL.ImageOps - if version.parse(version.parse(PIL.__version__).base_version) >= version.parse( - "9.1.0" - ): + if version.parse( + version.parse(PIL.__version__).base_version + ) >= version.parse("9.1.0"): PILImageResampling = PIL.Image.Resampling else: PILImageResampling = PIL.Image @@ -121,7 +121,8 @@ def make_list_of_images(images, expected_ndims: int = 3) -> List[ImageInput]: else: raise ValueError( f"Invalid image shape. Expected either {expected_ndims + 1} or" - f" {expected_ndims} dimensions, but got {images.ndim} dimensions." + f" {expected_ndims} dimensions, but got" + f" {images.ndim} dimensions." ) return images raise ValueError( @@ -140,7 +141,8 @@ def to_numpy_array(img) -> np.ndarray: def infer_channel_dimension_format( - image: np.ndarray, num_channels: Optional[Union[int, Tuple[int, ...]]] = None + image: np.ndarray, + num_channels: Optional[Union[int, Tuple[int, ...]]] = None, ) -> ChannelDimension: """ Infers the channel dimension format of `image`. @@ -155,14 +157,18 @@ def infer_channel_dimension_format( The channel dimension of the image. """ num_channels = num_channels if num_channels is not None else (1, 3) - num_channels = (num_channels,) if isinstance(num_channels, int) else num_channels + num_channels = ( + (num_channels,) if isinstance(num_channels, int) else num_channels + ) if image.ndim == 3: first_dim, last_dim = 0, 2 elif image.ndim == 4: first_dim, last_dim = 1, 3 else: - raise ValueError(f"Unsupported number of image dimensions: {image.ndim}") + raise ValueError( + f"Unsupported number of image dimensions: {image.ndim}" + ) if image.shape[first_dim] in num_channels: return ChannelDimension.FIRST @@ -172,7 +178,8 @@ def infer_channel_dimension_format( def get_channel_dimension_axis( - image: np.ndarray, input_data_format: Optional[Union[ChannelDimension, str]] = None + image: np.ndarray, + input_data_format: Optional[Union[ChannelDimension, str]] = None, ) -> int: """ Returns the channel dimension axis of the image. @@ -306,15 +313,15 @@ def load_image( except Exception as e: raise ValueError( "Incorrect image source. Must be a valid URL starting with" - " `http://` or `https://`, a valid path to an image file, or a" - f" base64 encoded string. Got {image}. Failed with {e}" + " `http://` or `https://`, a valid path to an image file," + f" or a base64 encoded string. Got {image}. Failed with {e}" ) elif isinstance(image, PIL.Image.Image): image = image else: raise ValueError( - "Incorrect format used for image. Should be an url linking to an image, a" - " base64 string, a local path, or a PIL image." + "Incorrect format used for image. Should be an url linking to an" + " image, a base64 string, a local path, or a PIL image." ) image = PIL.ImageOps.exif_transpose(image) image = image.convert("RGB") @@ -328,9 +335,9 @@ class ImageFeatureExtractionMixin: """ def _ensure_format_supported(self, image): - if not isinstance(image, (PIL.Image.Image, np.ndarray)) and not is_torch_tensor( - image - ): + if not isinstance( + image, (PIL.Image.Image, np.ndarray) + ) and not is_torch_tensor(image): raise ValueError( f"Got type {type(image)} which is not supported, only" " `PIL.Image.Image`, `np.array` and `torch.Tensor` are." @@ -380,7 +387,9 @@ def convert_rgb(self, image): return image.convert("RGB") - def rescale(self, image: np.ndarray, scale: Union[float, int]) -> np.ndarray: + def rescale( + self, image: np.ndarray, scale: Union[float, int] + ) -> np.ndarray: """ Rescale a numpy image by scale amount """ @@ -409,7 +418,11 @@ def to_numpy_array(self, image, rescale=None, channel_first=True): if is_torch_tensor(image): image = image.numpy() - rescale = isinstance(image.flat[0], np.integer) if rescale is None else rescale + rescale = ( + isinstance(image.flat[0], np.integer) + if rescale is None + else rescale + ) if rescale: image = self.rescale(image.astype(np.float32), 1 / 255.0) @@ -485,7 +498,9 @@ def normalize(self, image, mean, std, rescale=False): else: return (image - mean) / std - def resize(self, image, size, resample=None, default_to_square=True, max_size=None): + def resize( + self, image, size, resample=None, default_to_square=True, max_size=None + ): """ Resizes `image`. Enforces conversion of input to PIL.Image. @@ -515,7 +530,9 @@ def resize(self, image, size, resample=None, default_to_square=True, max_size=No Returns: image: A resized `PIL.Image.Image`. """ - resample = resample if resample is not None else PILImageResampling.BILINEAR + resample = ( + resample if resample is not None else PILImageResampling.BILINEAR + ) self._ensure_format_supported(image) @@ -527,11 +544,17 @@ def resize(self, image, size, resample=None, default_to_square=True, max_size=No if isinstance(size, int) or len(size) == 1: if default_to_square: - size = (size, size) if isinstance(size, int) else (size[0], size[0]) + size = ( + (size, size) + if isinstance(size, int) + else (size[0], size[0]) + ) else: width, height = image.size # specified size only for the smallest edge - short, long = (width, height) if width <= height else (height, width) + short, long = ( + (width, height) if width <= height else (height, width) + ) requested_new_short = size if isinstance(size, int) else size[0] if short == requested_new_short: @@ -544,8 +567,9 @@ def resize(self, image, size, resample=None, default_to_square=True, max_size=No if max_size is not None: if max_size <= requested_new_short: raise ValueError( - f"max_size = {max_size} must be strictly greater than the" - f" requested size for the smaller edge size = {size}" + f"max_size = {max_size} must be strictly greater" + " than the requested size for the smaller edge" + f" size = {size}" ) if new_long > max_size: new_short, new_long = ( @@ -554,7 +578,9 @@ def resize(self, image, size, resample=None, default_to_square=True, max_size=No ) size = ( - (new_short, new_long) if width <= height else (new_long, new_short) + (new_short, new_long) + if width <= height + else (new_long, new_short) ) return image.resize(size, resample=resample) From 28f264950ac9671fe76237df2657f8ce3397622e Mon Sep 17 00:00:00 2001 From: Kye Date: Thu, 23 Nov 2023 23:54:21 -0800 Subject: [PATCH 069/587] ops work --- Dockerfile | 46 ---------------------------------------------- code_quality.sh | 6 +++--- 2 files changed, 3 insertions(+), 49 deletions(-) delete mode 100644 Dockerfile diff --git a/Dockerfile b/Dockerfile deleted file mode 100644 index 000b2fa0..00000000 --- a/Dockerfile +++ /dev/null @@ -1,46 +0,0 @@ - -# ================================== -# Use an official Python runtime as a parent image -FROM python:3.9-slim - -# Set environment variables -ENV PYTHONDONTWRITEBYTECODE 1 -ENV PYTHONUNBUFFERED 1 - -# Set the working directory in the container -WORKDIR /usr/src/swarm_cloud - -# Install system dependencies -RUN apt-get update \ - && apt-get -y install netcat gcc \ - && apt-get clean - -# Install Python dependencies -# COPY requirements.txt and pyproject.toml if you're using poetry for dependency management -COPY requirements.txt . -RUN pip install --upgrade pip -RUN pip install --no-cache-dir -r requirements.txt - -# Install the 'swarms' package, assuming it's available on PyPI -RUN pip install swarms - -# Copy the rest of the application -COPY . . - -# Add entrypoint script if needed -# COPY ./entrypoint.sh . -# RUN chmod +x /usr/src/swarm_cloud/entrypoint.sh - -# Expose port if your application has a web interface -# EXPOSE 5000 - -# # Define environment variable for the swarm to work -# ENV SWARM_API_KEY=your_swarm_api_key_here - -# # Add Docker CMD or ENTRYPOINT script to run the application -# CMD python your_swarm_startup_script.py -# Or use the entrypoint script if you have one -# ENTRYPOINT ["/usr/src/swarm_cloud/entrypoint.sh"] - -# If you're using `CMD` to execute a Python script, make sure it's executable -# RUN chmod +x your_swarm_startup_script.py diff --git a/code_quality.sh b/code_quality.sh index d29a582d..e3afec13 100755 --- a/code_quality.sh +++ b/code_quality.sh @@ -5,15 +5,15 @@ # Run autopep8 with max aggressiveness (-aaa) and in-place modification (-i) # on all Python files (*.py) under the 'tests' directory. -autopep8 --in-place --aggressive --aggressive --recursive --experimental --list-fixes tests/ +autopep8 --in-place --aggressive --aggressive --recursive --experimental --list-fixes zeta/ # Run black with default settings, since black does not have an aggressiveness level. # Black will format all Python files it finds in the 'tests' directory. -black --experimental-string-processing tests/ +black --experimental-string-processing zeta/ # Run ruff on the 'tests' directory. # Add any additional flags if needed according to your version of ruff. -ruff tests/ --fix +ruff zeta/ --fix # YAPF yapf --recursive --in-place --verbose --style=google --parallel tests From e3e5185da298a3cf878db88c522b42c51e3c758e Mon Sep 17 00:00:00 2001 From: Kye Date: Sat, 25 Nov 2023 01:06:11 -0800 Subject: [PATCH 070/587] AUTO REGRESSIVE WRAPPER METHOD ADDS: grade_solution + eval_and_select_best_solution --- zeta/structs/auto_regressive_wrapper.py | 143 ++++++++++++++++++++++++ 1 file changed, 143 insertions(+) diff --git a/zeta/structs/auto_regressive_wrapper.py b/zeta/structs/auto_regressive_wrapper.py index a3518cfc..b0545349 100644 --- a/zeta/structs/auto_regressive_wrapper.py +++ b/zeta/structs/auto_regressive_wrapper.py @@ -15,10 +15,24 @@ # Utils def temperature_sampling(self, logits, temperature): + """ + Temperature sampling. + """ return torch.multinomial(F.softmax(logits / temperature, dim=-1), 1) def top_p_sampling(self, logits, p): + """ + top-p sampling. + + Args: + logits (torch.Tensor): The logits. + p (float): The probability mass to keep. + + Returns: + torch.Tensor: The sampled token. + + """ sorted_logits, sorted_indices = torch.sort(logits, descending=True) cumulative_probs = torch.cumsum(F.softmax(sorted_logits, dim=-1), dim=-1) @@ -34,15 +48,70 @@ def top_p_sampling(self, logits, p): def classifier_free_guidance(self, logits_cond, logits_uncond, alpha): + """ + Classifier-free guidance. + + Args: + logits_cond (torch.Tensor): The conditional logits. + logits_uncond (torch.Tensor): The unconditional logits. + alpha (float): The alpha parameter. + + Examples:: + + >>> net = nn.Linear(10, 10) + >>> net = AutoregressiveWrapper(net) + >>> x = torch.randn(1, 10) + >>> logits = net(x) + >>> print(logits.shape) + torch.Size([1, 10, 10]) # (batch_size, seq_len, vocab_size) + + """ return logits_uncond + alpha * (logits_cond - logits_uncond) def contrastive_guidance(self, logits, k): + """ + Contrastive guidance. + + Args: + logits (torch.Tensor): The logits. + k (int): The number of guesses to use. + + Returns: + torch.Tensor: The sampled token. + + + """ top_k_logits, _ = torch.topk(logits, k) return torch.multinomial(F.softmax(top_k_logits, dim=-1), 1) class AutoregressiveWrapper(nn.Module): + """ + + Auto-regressive wrapper for any nn.Module that takes in a sequence of + tokens and outputs a sequence of logits. + + Args: + net (nn.Module): A nn.Module that takes in a sequence of tokens and + outputs a sequence of logits. + ignore_index (int): The index to ignore in the target sequence. + pad_value (int): The value to pad the target sequence with. + mask_prob (float): The probability of masking out a token in the + input sequence. + speculative (bool): Whether to use speculative decoding or not. + + Examples:: + + >>> net = nn.Linear(10, 10) + >>> net = AutoregressiveWrapper(net) + >>> x = torch.randn(1, 10) + >>> logits = net(x) + >>> print(logits.shape) + torch.Size([1, 10, 10]) # (batch_size, seq_len, vocab_size) + + """ + def __init__( self, net, @@ -80,6 +149,34 @@ def generate( gamma=5, # number of guesses for speculative decoding **kwargs, ): + """ + Generate a sequence of tokens from the model. + + Args: + start_tokens (torch.Tensor): The starting tokens. + seq_len (int): The length of the sequence to generate. + eos_token (int): The token to stop generation at. + strategy (str): The generation strategy to use. + temperature (float): The temperature to use for sampling. + filter_logits_fn (function): The function to use to filter logits. + filter_thres (float): The threshold to use for filtering logits. + min_p_pow (float): The power to use for top-a filtering. + min_p_ratio (float): The ratio to use for top-a filtering. + gamma (int): The number of guesses to use for speculative decoding. + **kwargs: Keyword arguments for the wrapped module. + + Returns: + torch.Tensor: The generated sequence of tokens. + + Examples:: + + >>> net = nn.Linear(10, 10) + >>> net = AutoregressiveWrapper(net) + >>> x = torch.randn(1, 10) + >>> generated = net.generate(x, 10) + >>> print(generated.shape) + torch.Size([1, 10]) + """ start_tokens, ps = pack([start_tokens], "* n") b, t = start_tokens.shape @@ -185,6 +282,28 @@ def generate( return out def forward(self, x, return_loss=True, **kwargs): + """ + Forward pass of the autoregressive wrapper. + + Args: + x (torch.Tensor): Input tensor. + return_loss (bool): Whether to return the loss or not. + **kwargs: Keyword arguments for the wrapped module. + + Returns: + torch.Tensor: Output tensor. + torch.Tensor: Loss tensor if return_loss is True. + + Examples:: + + >>> net = nn.Linear(10, 10) + >>> net = AutoregressiveWrapper(net) + >>> x = torch.randn(1, 10) + >>> logits = net(x) + >>> print(logits.shape) + torch.Size([1, 10, 10]) # (batch_size, seq_len, vocab_size) + + """ seq, ignore_index = x.shape[1], self.ignore_index inp, target = x[:, :-1], x[:, 1:] @@ -210,3 +329,27 @@ def forward(self, x, return_loss=True, **kwargs): return logits, loss return logits + + @torch.no_grad() + @eval_decorator + def generate_n_solutions(self, start_tokens, n, seqlen, **kwargs): + """Generate n solutions from the model.""" + solutions = [] + for _ in range(n): + generated = self.generate(start_tokens, seqlen, **kwargs) + solutions.append(generated) + return solutions + + def evaluate_and_select_best_solution( + self, + solutions, + reward_model, + ): + """Evaluate solutions and select the best one.""" + scores = [reward_model(solution) for solution in solutions] + best_solution_idx = scores.index(max(scores)) + return solutions[best_solution_idx] + + def grade_solution(self, solution): + """Grade a solution.""" + pass From 7a5975dcc01bfe6e6b7acac1e17548d7f5b338eb Mon Sep 17 00:00:00 2001 From: Kye Date: Sat, 25 Nov 2023 01:08:10 -0800 Subject: [PATCH 071/587] [FEAT][FractorialNet][FractorialBlock] --- zeta/nn/modules/__init__.py | 1 + zeta/nn/modules/fractorial_net.py | 85 +++++++++++++++++++++++++++++-- 2 files changed, 81 insertions(+), 5 deletions(-) diff --git a/zeta/nn/modules/__init__.py b/zeta/nn/modules/__init__.py index a22d9a37..15316420 100644 --- a/zeta/nn/modules/__init__.py +++ b/zeta/nn/modules/__init__.py @@ -51,6 +51,7 @@ from zeta.nn.modules.log_ff import LogFF, compute_entropy_safe from zeta.nn.modules.polymorphic_neuron import PolymorphicNeuronLayer from zeta.nn.modules.flexible_mlp import CustomMLP +from zeta.nn.modules.fractoril_net import __all__ = [ "CNNNew", diff --git a/zeta/nn/modules/fractorial_net.py b/zeta/nn/modules/fractorial_net.py index fec5b3a7..177b6cc9 100644 --- a/zeta/nn/modules/fractorial_net.py +++ b/zeta/nn/modules/fractorial_net.py @@ -1,8 +1,83 @@ -import torch import torch.nn as nn -import torch.nn.functional as F -class FractorialBlock(nn.Module): - def __init__(self, in_channels, out_channels, depth: int = 3): - super(FractorialBlock, self).__init__() +class FractalBlock(nn.Module): + def __init__(self, in_channels, out_channels, depth=3): + """ + Initialize a Fractal Block. + :param in_channels: Number of input channels. + :param out_channels: Number of output channels. + :param depth: Depth of the fractal block. + """ + super(FractalBlock, self).__init__() + self.depth = depth + + # Base case for recursion + if depth == 1: + self.block = nn.Conv2d( + in_channels, out_channels, kernel_size=3, padding=1 + ) + else: + # Recursive case: create smaller fractal blocks + self.block1 = FractalBlock(in_channels, out_channels, depth - 1) + self.block2 = FractalBlock(in_channels, out_channels, depth - 1) + + def forward(self, x): + """ + Forward pass of the fractal block. + :param x: Input tensor. + :return: Output tensor. + """ + if self.depth == 1: + return self.block(x) + else: + # Recursively compute the outputs of the sub-blocks + out1 = self.block1(x) + out2 = self.block2(x) + + # Combine the outputs of the sub-blocks + return out1 + out2 + + +class FractalNetwork(nn.Module): + def __init__(self, in_channels, out_channels, num_blocks, block_depth): + """ + Initialize the Fractal Network. + :param in_channels: Number of input channels. + :param out_channels: Number of output channels. + :param num_blocks: Number of fractal blocks in the network. + :param block_depth: Depth of each fractal block. + """ + super(FractalNetwork, self).__init__() + self.blocks = nn.ModuleList( + [ + FractalBlock( + in_channels if i == 0 else out_channels, + out_channels, + block_depth, + ) + for i in range(num_blocks) + ] + ) + self.final_layer = nn.Conv2d(out_channels, out_channels, kernel_size=1) + + def forward(self, x): + """ + Forward pass of the fractal network. + :param x: Input tensor. + :return: Output tensor. + """ + for block in self.blocks: + x = block(x) + return self.final_layer(x) + + +# # Example usage +# fractal_net = FractalNetwork(in_channels=3, out_channels=16, num_blocks=4, block_depth=3) + +# # Example input +# input_tensor = torch.randn(1, 3, 64, 64) + +# # Forward pass +# output = fractal_net(input_tensor) +# print(output) From 6757292048b4252b967c7f8e8cb6019a2790b4d3 Mon Sep 17 00:00:00 2001 From: Kye Date: Sat, 25 Nov 2023 02:21:17 -0800 Subject: [PATCH 072/587] fractorial net clean up --- zeta/nn/modules/__init__.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/zeta/nn/modules/__init__.py b/zeta/nn/modules/__init__.py index 15316420..5d3d578f 100644 --- a/zeta/nn/modules/__init__.py +++ b/zeta/nn/modules/__init__.py @@ -51,7 +51,7 @@ from zeta.nn.modules.log_ff import LogFF, compute_entropy_safe from zeta.nn.modules.polymorphic_neuron import PolymorphicNeuronLayer from zeta.nn.modules.flexible_mlp import CustomMLP -from zeta.nn.modules.fractoril_net import +from zeta.nn.modules.fractorial_net import FractalBlock, FractalNetwork __all__ = [ "CNNNew", From e46e70cf8e7250cae1f080e23167b16bd6467f8d Mon Sep 17 00:00:00 2001 From: Kye Date: Sat, 25 Nov 2023 13:27:58 -0800 Subject: [PATCH 073/587] NEW [FEAT][PolyMorphicActivation] --- zeta/nn/modules/__init__.py | 3 +- zeta/nn/modules/polymorphic_activation.py | 68 +++++++++++++++++++++++ 2 files changed, 70 insertions(+), 1 deletion(-) create mode 100644 zeta/nn/modules/polymorphic_activation.py diff --git a/zeta/nn/modules/__init__.py b/zeta/nn/modules/__init__.py index 5d3d578f..c8d4cc29 100644 --- a/zeta/nn/modules/__init__.py +++ b/zeta/nn/modules/__init__.py @@ -52,7 +52,7 @@ from zeta.nn.modules.polymorphic_neuron import PolymorphicNeuronLayer from zeta.nn.modules.flexible_mlp import CustomMLP from zeta.nn.modules.fractorial_net import FractalBlock, FractalNetwork - +from zeta.nn.modules.polymorphic_activation import PolymorphicActivation __all__ = [ "CNNNew", "CombinedLinear", @@ -94,4 +94,5 @@ "LogFF", "PolymorphicNeuronLayer", "CustomMLP", + "PolymorphicActivation", ] diff --git a/zeta/nn/modules/polymorphic_activation.py b/zeta/nn/modules/polymorphic_activation.py new file mode 100644 index 00000000..40f4d904 --- /dev/null +++ b/zeta/nn/modules/polymorphic_activation.py @@ -0,0 +1,68 @@ +import torch +import torch.nn as nn + +class PolymorphicActivation(nn.Module): + """ + A Polymorphic Activation Function in PyTorch. + + This activation function combines aspects of sigmoid and tanh functions, + controlled by a learnable parameter alpha. The behavior of the function + adapts based on the input and the state of alpha during training. + + Attributes: + ----------- + alpha : torch.nn.Parameter + A trainable parameter that modulates the behavior of the activation function. + + Methods: + -------- + forward(x): + Computes the polymorphic activation function on the input tensor x. + + Examples: + # Create an instance of the activation function + poly_act = PolymorphicActivation(initial_alpha=0.8) + + # Example input tensor + input_tensor = torch.randn(5) + + # Apply the polymorphic activation function + output = poly_act(input_tensor) + output + + """ + + def __init__(self, initial_alpha: float = 0.5): + """ + Initializes the PolymorphicActivation module. + + Parameters: + ----------- + initial_alpha : float (optional) + The initial value of the alpha parameter. Defaults to 0.5. + """ + super(PolymorphicActivation, self).__init__() + if not isinstance(initial_alpha, float): + raise TypeError("initial_alpha must be a float.") + self.alpha = nn.Parameter(torch.tensor([initial_alpha])) + + def forward(self, x: torch.Tensor) -> torch.Tensor: + """ + Forward pass of the Polymorphic Activation Function. + + Parameters: + ----------- + x : torch.Tensor + Input tensor to the activation function. + + Returns: + -------- + torch.Tensor + The result of applying the polymorphic activation function to x. + """ + if not isinstance(x, torch.Tensor): + raise TypeError("Input must be a torch.Tensor.") + + sigmoid_part = torch.sigmoid(self.alpha * x) + tanh_part = torch.tanh(x) + return sigmoid_part + self.alpha * tanh_part \ No newline at end of file From f2651978c2e28aa11dac8ab3fd72047bb263a22e Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Mon, 27 Nov 2023 05:37:51 +0000 Subject: [PATCH 074/587] Update vector-quantize-pytorch requirement from 1.10.4 to 1.11.7 Updates the requirements on [vector-quantize-pytorch](https://github.com/lucidrains/vector-quantizer-pytorch) to permit the latest version. - [Release notes](https://github.com/lucidrains/vector-quantizer-pytorch/releases) - [Commits](https://github.com/lucidrains/vector-quantizer-pytorch/compare/1.10.4...1.11.7) --- updated-dependencies: - dependency-name: vector-quantize-pytorch dependency-type: direct:production ... Signed-off-by: dependabot[bot] --- pyproject.toml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pyproject.toml b/pyproject.toml index 42ea7cb6..212af824 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -33,7 +33,7 @@ datasets = "*" lion-pytorch = "*" sentencepiece = "*" colt5-attention = "0.10.18" -vector-quantize-pytorch = "1.10.4" +vector-quantize-pytorch = "1.11.7" tokenmonster = "*" scipy = "*" beartype = "*" From 21f7f8bf940d0748f6e64e90dbd105047c4b969e Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Mon, 27 Nov 2023 16:55:04 +0000 Subject: [PATCH 075/587] Update ruff requirement from ^0.0.249 to >=0.0.249,<0.1.7 Updates the requirements on [ruff](https://github.com/astral-sh/ruff) to permit the latest version. - [Release notes](https://github.com/astral-sh/ruff/releases) - [Changelog](https://github.com/astral-sh/ruff/blob/main/CHANGELOG.md) - [Commits](https://github.com/astral-sh/ruff/compare/v0.0.249...v0.1.6) --- updated-dependencies: - dependency-name: ruff dependency-type: direct:development ... Signed-off-by: dependabot[bot] --- pyproject.toml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pyproject.toml b/pyproject.toml index 212af824..bbf42968 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -46,7 +46,7 @@ requires = ["poetry-core>=1.0.0"] build-backend = "poetry.core.masonry.api" [tool.poetry.group.lint.dependencies] -ruff = "^0.0.249" +ruff = ">=0.0.249,<0.1.7" types-toml = "^0.10.8.1" types-redis = "^4.3.21.6" types-pytz = "^2023.3.0.0" From 20a0497ebc4fc8dd103853305fe197ba75e3bba1 Mon Sep 17 00:00:00 2001 From: Kye Date: Tue, 28 Nov 2023 23:13:53 -0800 Subject: [PATCH 076/587] SimpleDecisionTree --- zeta/nn/modules/__init__.py | 1 + zeta/nn/modules/decision_tree.py | 117 ++++++++++++++++++++++ zeta/nn/modules/polymorphic_activation.py | 7 +- 3 files changed, 122 insertions(+), 3 deletions(-) create mode 100644 zeta/nn/modules/decision_tree.py diff --git a/zeta/nn/modules/__init__.py b/zeta/nn/modules/__init__.py index c8d4cc29..243f0864 100644 --- a/zeta/nn/modules/__init__.py +++ b/zeta/nn/modules/__init__.py @@ -53,6 +53,7 @@ from zeta.nn.modules.flexible_mlp import CustomMLP from zeta.nn.modules.fractorial_net import FractalBlock, FractalNetwork from zeta.nn.modules.polymorphic_activation import PolymorphicActivation + __all__ = [ "CNNNew", "CombinedLinear", diff --git a/zeta/nn/modules/decision_tree.py b/zeta/nn/modules/decision_tree.py new file mode 100644 index 00000000..34450eff --- /dev/null +++ b/zeta/nn/modules/decision_tree.py @@ -0,0 +1,117 @@ +import torch +from torch import nn +import torch.nn.functional as F + + +class SimpleDecisionTree(nn.Module): + """ + Simple decision tree model with residual connections and multi head output. + + + Args: + input_size (int): Input size of the model + output_size (int): Output size of the model + depth (int): Number of residual blocks + heads (int): Number of output heads + + Example: + >>> model = SimpleDecisionTree( + input_size=10, + output_size=5, + depth=4, + heads=3 + ) + >>> x = torch.randn(4, 10) + >>> output = model(x) + >>> print(output) + [tensor([[-0.1015, -0.0114, 0.0370, 0.1362, 0.0436], + [-0.1015, -0.0114, 0.0370, 0.1362, 0.0436], + [-0.1015, -0.0114, 0.0370, 0.1362, 0.0436], + [-0.1015, -0.0114, 0.0370, 0.1362, 0.0436]], + grad_fn= ), tensor([[-0.1015, -0.0114, 0.0370, 0.1362, 0.0436], + [-0.1015, -0.0114, 0.0370, 0.1362, 0.0436], + [-0.1015, -0.0114, 0.0370, 0.1362, 0.0436], + [-0.1015, -0.0114, 0.0370, 0.1362, 0.0436]], + grad_fn= ), tensor([[-0.1015, -0.0114, 0.0370, 0.1362, 0.0436], + [-0.1015, -0.0114, 0.0370, 0.1362, 0.0436], + [-0.1015, -0.0114, 0.0370, 0.1362, 0.0436], + [-0.1015, -0.0114, 0.0370, 0.1362, 0.0436]], + grad_fn= )] + """ + + def __init__(self, input_size, output_size, depth, heads): + super(SimpleDecisionTree, self).__init__() + self.input_size = input_size + self.output_size = output_size + self.depth = depth + self.heads = heads + + # Initial input layer + self.input_layer = nn.Linear(input_size, input_size) + + # Residual blocks with batch norm and dropout + self.residual_blocks = nn.ModuleList([]) + for _ in range(depth): + layers = nn.Sequential( + nn.Linear(input_size, input_size), + nn.BatchNorm1d(input_size), + nn.ReLU(), + nn.Dropout(0.5), + nn.Linear(input_size, input_size), + nn.BatchNorm1d(input_size), + nn.ReLU(), + ) + self.residual_blocks.append(layers) + + # Recurrent layer for temproal dynamics + self.recurrent_layer = nn.LSTM(input_size, input_size, batch_first=True) + + # Multi head output system + self.output_heads = nn.ModuleList( + [nn.Linear(input_size, output_size) for _ in range(heads)] + ) + + def forward(self, x: torch.Tensor): + """Forward pass of the model. + + Args: + x (torch.Tensor): _description_ + + Returns: + _type_: _description_ + """ + x = self.input_layer(x) + + # Applying residual connections + for block in self.residual_blocks: + residual = x + x = block(x) + residual + + # Recurrent layer + x, _ = self.recurrent_layer(x.unsqueeze(0)) + x = x.squeeze(0) + + # Multi head output + outputs = [head(x) for head in self.output_heads] + return outputs + + +# # Params +# input_size = 10 +# output_size = 5 +# depth = 4 +# heads = 3 +# batch_size = 4 + +# # model +# model = SimpleDecisionTree( +# input_size, +# output_size, +# depth, +# heads +# ) + +# x = torch.randn(batch_size, input_size) + +# output = model(x) +# print(output) diff --git a/zeta/nn/modules/polymorphic_activation.py b/zeta/nn/modules/polymorphic_activation.py index 40f4d904..71fc41c5 100644 --- a/zeta/nn/modules/polymorphic_activation.py +++ b/zeta/nn/modules/polymorphic_activation.py @@ -1,6 +1,7 @@ -import torch +import torch import torch.nn as nn + class PolymorphicActivation(nn.Module): """ A Polymorphic Activation Function in PyTorch. @@ -18,7 +19,7 @@ class PolymorphicActivation(nn.Module): -------- forward(x): Computes the polymorphic activation function on the input tensor x. - + Examples: # Create an instance of the activation function poly_act = PolymorphicActivation(initial_alpha=0.8) @@ -65,4 +66,4 @@ def forward(self, x: torch.Tensor) -> torch.Tensor: sigmoid_part = torch.sigmoid(self.alpha * x) tanh_part = torch.tanh(x) - return sigmoid_part + self.alpha * tanh_part \ No newline at end of file + return sigmoid_part + self.alpha * tanh_part From 6f029baaf1d34688b9d789a0e81243647bf40872 Mon Sep 17 00:00:00 2001 From: Kye Date: Wed, 29 Nov 2023 10:14:12 -0800 Subject: [PATCH 077/587] Iteraitve self attn with prenorm --- zeta/nn/modules/__init__.py | 4 + zeta/nn/modules/decision_tree.py | 4 +- zeta/nn/modules/itca.py | 145 +++++++++++++++++++++++++++++++ zeta/nn/modules/prenorm.py | 26 ++++++ 4 files changed, 178 insertions(+), 1 deletion(-) create mode 100644 zeta/nn/modules/itca.py create mode 100644 zeta/nn/modules/prenorm.py diff --git a/zeta/nn/modules/__init__.py b/zeta/nn/modules/__init__.py index 243f0864..b32c11d2 100644 --- a/zeta/nn/modules/__init__.py +++ b/zeta/nn/modules/__init__.py @@ -53,6 +53,8 @@ from zeta.nn.modules.flexible_mlp import CustomMLP from zeta.nn.modules.fractorial_net import FractalBlock, FractalNetwork from zeta.nn.modules.polymorphic_activation import PolymorphicActivation +from zeta.nn.modules.prenorm import PreNorm +from zeta.nn.modules.itca import IterativeCrossSelfAttention __all__ = [ "CNNNew", @@ -96,4 +98,6 @@ "PolymorphicNeuronLayer", "CustomMLP", "PolymorphicActivation", + "PreNorm", + "IterativeCrossSelfAttention", ] diff --git a/zeta/nn/modules/decision_tree.py b/zeta/nn/modules/decision_tree.py index 34450eff..1456f82e 100644 --- a/zeta/nn/modules/decision_tree.py +++ b/zeta/nn/modules/decision_tree.py @@ -39,7 +39,9 @@ class SimpleDecisionTree(nn.Module): grad_fn= )] """ - def __init__(self, input_size, output_size, depth, heads): + def __init__( + self, input_size: int, output_size: int, depth: int, heads: int + ): super(SimpleDecisionTree, self).__init__() self.input_size = input_size self.output_size = output_size diff --git a/zeta/nn/modules/itca.py b/zeta/nn/modules/itca.py new file mode 100644 index 00000000..ec61a529 --- /dev/null +++ b/zeta/nn/modules/itca.py @@ -0,0 +1,145 @@ +import torch +from torch import nn + + +# Example usage of the IterativeCrossSelfAttention class +class PreNorm(nn.Module): + """Prenorm + + Args: + dim (_type_): _description_ + fn (_type_): _description_ + + """ + + def __init__(self, dim, fn): + super().__init__() + self.norm = nn.LayerNorm(dim) + self.fn = fn + + def forward(self, x, context=None): + """Forward pass of prenorm + + Args: + x (_type_): _description_ + """ + return self.fn(self.norm(x), context=context) + + +class CrossAttention(nn.Module): + def __init__( + self, + dim, + heads: int = 8, + dim_head: int = 64, + dropout: float = 0.0, + qk_norm: bool = True, + ): + super().__init__() + inner_dim = dim_head * heads + self.heads = heads + self.scale = dim_head**-0.5 + + self.attend = nn.Softmax(dim=-1) + self.to_q = nn.Linear(dim, inner_dim, bias=False) + self.to_kv = nn.Linear(dim, inner_dim * 2, bias=False) + + self.to_out = nn.Sequential( + nn.Linear(inner_dim, dim), nn.Dropout(dropout) + ) + + self._qk_norm = nn.LayerNorm(dim) + + def forward(self, x, context=None): + if context is None: + context = x + + q = self.to_q(x) + kv = self.to_kv(context).chunk(2, dim=-1) + k, v = kv[0], kv[1] + + if self.qk_norm: + q, k = self._qk_norm(q), self._qk_norm(k) + + dots = torch.matmul(q, k.transpose(-1, -2)) * self.scale + + attn = self.attend(dots) + out = torch.matmul(attn, v) + out = self.to_out(out) + return out + + +class IterativeCrossSelfAttention(nn.Module): + """Iterative + + Args: + dim (_type_): _description_ + depth (_type_): _description_ + heads (_type_): _description_ + dim_head (_type_): _description_ + dropout (float, optional): _description_. Defaults to 0.1. + + Methods: + forward(x, context=None): _description_ + + Examples: + """ + def __init__( + self, + dim, + depth, + heads, + dim_head, + dropout=0.1, + ): + super().__init__() + self.layers = nn.ModuleList( + [ + PreNorm( + dim, + CrossAttention( + dim, heads=heads, dim_head=dim_head, dropout=dropout + ), + ) + for _ in range(depth) + ] + ) + + def forward(self, x: torch.Tensor, context: torch.Tensor = None): + """Forward pass of IterativeCrossSelfAttention + + Args: + x (torch.Tensor): _description_ + context (_type_, optional): _description_. Defaults to None. + + Returns: + _type_: _description_ + """ + for layer in self.layers: + x = layer(x, context=context) + x + return x + + +# import torch + +# # Example usage of the IterativeCrossSelfAttention class +# if __name__ == "__main__": +# batch_size = 8 +# seq_len = 16 # Sequence length of the input embeddings +# latent_seq_len = 16 # Sequence length of the latent array (could be different from input sequence length) +# dim = 512 # Dimensionality of the input embeddings and latent array +# heads = 8 # Number of attention heads +# dim_head = 64 # Dimensionality of each attention head +# depth = 6 # Number of cross-attention layers + +# # Initialize the IterativeCrossSelfAttention module +# iter_cs_attn = IterativeCrossSelfAttention(dim, depth, heads, dim_head) + +# # Create random tensors for the input embeddings and the latent array +# input_embeddings = torch.rand(batch_size, seq_len, dim) +# latent_array = torch.rand(batch_size, latent_seq_len, dim) + +# # Pass the input embeddings and the latent array through the IterativeCrossSelfAttention module +# output_embeddings = iter_cs_attn(input_embeddings, latent_array) + +# print("Output embeddings shape:", output_embeddings.shape) diff --git a/zeta/nn/modules/prenorm.py b/zeta/nn/modules/prenorm.py new file mode 100644 index 00000000..699edf2d --- /dev/null +++ b/zeta/nn/modules/prenorm.py @@ -0,0 +1,26 @@ + +from torch import nn + + +# Example usage of the IterativeCrossSelfAttention class +class PreNorm(nn.Module): + """Prenorm + + Args: + dim (_type_): _description_ + fn (_type_): _description_ + + """ + + def __init__(self, dim, fn): + super().__init__() + self.norm = nn.LayerNorm(dim) + self.fn = fn + + def forward(self, x, context=None): + """Forward pass of prenorm + + Args: + x (_type_): _description_ + """ + return self.fn(self.norm(x), context=context) \ No newline at end of file From b62e95c8bca88146a2bcf04fd8872dfb18fe4265 Mon Sep 17 00:00:00 2001 From: Kye Date: Wed, 29 Nov 2023 12:31:06 -0800 Subject: [PATCH 078/587] ConvolutionLanguageBlock with tests --- tests/nn/modules/test_conv_lang.py | 98 ++++++++++++++++++++++++++ zeta/nn/modules/__init__.py | 3 +- zeta/nn/modules/itca.py | 13 ++-- zeta/nn/modules/lang_conv_module.py | 104 ++++++++++++++++++++++++++++ zeta/nn/modules/prenorm.py | 3 +- 5 files changed, 212 insertions(+), 9 deletions(-) create mode 100644 tests/nn/modules/test_conv_lang.py create mode 100644 zeta/nn/modules/lang_conv_module.py diff --git a/tests/nn/modules/test_conv_lang.py b/tests/nn/modules/test_conv_lang.py new file mode 100644 index 00000000..91501991 --- /dev/null +++ b/tests/nn/modules/test_conv_lang.py @@ -0,0 +1,98 @@ +from unittest.mock import Mock + +import pytest +import torch +from torch import nn + +from zeta.nn.modules.lang_conv_module import ConvolutionLanguageBlock + + +# 1. Basic Tests +def test_convolution_language_block_creation(): + block = ConvolutionLanguageBlock(256, 512, 3, 1) + assert isinstance(block, ConvolutionLanguageBlock) + + +def test_forward_pass(): + block = ConvolutionLanguageBlock(256, 512, 3, 1) + x = torch.randn(1, 256, 1024) + output = block(x) + assert output.shape == torch.Size([1, 512, 1024]) + + +# 2. Utilize Fixtures +@pytest.fixture +def sample_block(): + return ConvolutionLanguageBlock(128, 256, 3, 1) + + +def test_fixture_usage(sample_block): + x = torch.randn(1, 128, 1024) + output = sample_block(x) + assert output.shape == torch.Size([1, 256, 1024]) + + +# 3. Parameterized Testing +@pytest.mark.parametrize( + ( + "in_channels, out_channels, kernel_size, padding, depth, stride," + " activation, batchnorm, dilation, dropout" + ), + [ + (128, 256, 3, 1, 2, 1, "relu", True, 1, 0.1), + (256, 512, 3, 1, 3, 1, "gelu", False, 2, 0.2), + # Add more parameter combinations as needed + ], +) +def test_parameterized_block( + in_channels, + out_channels, + kernel_size, + padding, + depth, + stride, + activation, + batchnorm, + dilation, + dropout, +): + block = ConvolutionLanguageBlock( + in_channels, + out_channels, + kernel_size, + padding, + depth, + stride, + activation, + batchnorm, + dilation, + dropout, + ) + x = torch.randn(1, in_channels, 1024) + output = block(x) + assert output.shape == torch.Size([1, out_channels, 1024]) + + +def test_with_mocked_convolution_layer(): + mock_convolution = Mock(spec=nn.Conv1d) + block = ConvolutionLanguageBlock(128, 256, 3, 1) + block.conv_layers[0] = mock_convolution + x = torch.randn(1, 128, 1024) + output = block(x) + assert mock_convolution.called + + +# 5. Exception Testing +def test_invalid_activation_raises_error(): + with pytest.raises(ValueError): + ConvolutionLanguageBlock( + 128, 256, 3, 1, activation="invalid_activation" + ) + + +# 6. Test Coverage (requires pytest-cov) +def test_coverage(): + pytest.main(["--cov=your_module", "test_your_module.py"]) + + +# Add more tests as needed... diff --git a/zeta/nn/modules/__init__.py b/zeta/nn/modules/__init__.py index b32c11d2..6c3b3240 100644 --- a/zeta/nn/modules/__init__.py +++ b/zeta/nn/modules/__init__.py @@ -55,7 +55,7 @@ from zeta.nn.modules.polymorphic_activation import PolymorphicActivation from zeta.nn.modules.prenorm import PreNorm from zeta.nn.modules.itca import IterativeCrossSelfAttention - +from zeta.nn.modules.lang_conv_module import ConvolutionLanguageBlock __all__ = [ "CNNNew", "CombinedLinear", @@ -100,4 +100,5 @@ "PolymorphicActivation", "PreNorm", "IterativeCrossSelfAttention", + "ConvolutionLanguageBlock" ] diff --git a/zeta/nn/modules/itca.py b/zeta/nn/modules/itca.py index ec61a529..e9980e8f 100644 --- a/zeta/nn/modules/itca.py +++ b/zeta/nn/modules/itca.py @@ -69,21 +69,22 @@ def forward(self, x, context=None): return out -class IterativeCrossSelfAttention(nn.Module): - """Iterative +class IterativeCrossSelfAttention(nn.Module): + """Iterative Args: dim (_type_): _description_ depth (_type_): _description_ heads (_type_): _description_ dim_head (_type_): _description_ - dropout (float, optional): _description_. Defaults to 0.1. - + dropout (float, optional): _description_. Defaults to 0.1. + Methods: forward(x, context=None): _description_ - + Examples: - """ + """ + def __init__( self, dim, diff --git a/zeta/nn/modules/lang_conv_module.py b/zeta/nn/modules/lang_conv_module.py new file mode 100644 index 00000000..aa71d2b4 --- /dev/null +++ b/zeta/nn/modules/lang_conv_module.py @@ -0,0 +1,104 @@ +import torch +from torch import nn + + +class ConvolutionLanguageBlock(nn.Module): + """ + Convolutional block for language modeling. + -------------------------------------------- + A convolutional block that consists of multiple 1D convolutional layers, + optional batch normalization, dropout, and a flexible choice of activation functions. + This block is designed to maintain the input's dimensionality through the network, + making it suitable for tasks that require consistent input and output dimensions. + + Parameters: + - in_channels (int): Number of channels in the input tensor. + - out_channels (int): Number of channels produced by the convolution. + - kernel_size (int): Size of the convolving kernel. + - num_layers (int, optional): Number of convolutional layers. Default: 1 + - stride (int, optional): Stride of the convolution. Default: 1 + - padding (int, optional): Zero-padding added to both sides of the input. Default: 1 + - dilation (int, optional): Spacing between kernel elements. Default: 1 + - activation (str, optional): Type of activation function. Options: 'relu', 'gelu'. Default: 'relu' + - use_batchnorm (bool, optional): If True, includes batch normalization. Default: False + - dropout (float, optional): Dropout rate. Default: 0.0 + + Examples: + >>> import torch + >>> from attnconv.main import ConvolutionLanguageBlock + >>> x = torch.randn(1, 512, 1024) + >>> block = ConvolutionLanguageBlock(512, 512, 3, 1, 1, 1) + >>> out = block(x) + >>> out.shape + torch.Size([1, 512, 1024]) + """ + + def __init__( + self, + in_channels, + out_channels, + kernel_size, + padding, + depth=1, + stride=1, + activation="gelu", + batchnorm=False, + dilation=1, + dropout=0.1, + ): + super(ConvolutionLanguageBlock, self).__init__() + self.in_channels = in_channels + self.out_channels = out_channels + self.kernel_size = kernel_size + self.padding = padding + self.depth = depth + self.stride = stride + self.activation = activation + self.batchnorm = batchnorm + self.dilation = dilation + + layers = [] + for _ in range(depth): + layers.append( + nn.Conv1d( + in_channels, + out_channels, + kernel_size, + stride=stride, + padding=padding, + dilation=dilation, + ) + ) + if batchnorm: + layers.append(nn.BatchNorm1d(out_channels)) + if activation == "relu": + layers.append(nn.ReLU()) + elif activation == "gelu": + layers.append(nn.GELU()) + if dropout > 0: + layers.append(nn.Dropout(dropout)) + in_channels = out_channels # For stacking layers + + self.conv_layers = nn.Sequential(*layers) + + def forward(self, x): + """Forward pass with residual connection. + + Args: + x (_type_): _description_ + + Returns: + _type_: _description_ + """ + # Apply residual connection if dimensions match + residual = x if x.size(1) == self.conv_layers[0].in_channels else None + + # Apply convolutional layers + x = self.conv_layers(x) + + # Apply residual connection + if residual is not None: + x = x + residual + + # Return output + return x diff --git a/zeta/nn/modules/prenorm.py b/zeta/nn/modules/prenorm.py index 699edf2d..54d65d51 100644 --- a/zeta/nn/modules/prenorm.py +++ b/zeta/nn/modules/prenorm.py @@ -1,4 +1,3 @@ - from torch import nn @@ -23,4 +22,4 @@ def forward(self, x, context=None): Args: x (_type_): _description_ """ - return self.fn(self.norm(x), context=context) \ No newline at end of file + return self.fn(self.norm(x), context=context) From 725ad9fae47b872d4bebbc4f192a48d3b6bb80a4 Mon Sep 17 00:00:00 2001 From: Kye Date: Wed, 29 Nov 2023 15:15:01 -0800 Subject: [PATCH 079/587] [FEAT][H3, S4] --- tests/nn/modules/test_h3_layer.py | 57 +++++++++++++++++ tests/nn/modules/test_s4.py | 69 +++++++++++++++++++++ zeta/nn/modules/__init__.py | 3 +- zeta/nn/modules/h3.py | 100 ++++++++++++++++++++++++++++++ zeta/nn/modules/s4.py | 61 ++++++++++++++++++ 5 files changed, 289 insertions(+), 1 deletion(-) create mode 100644 tests/nn/modules/test_h3_layer.py create mode 100644 tests/nn/modules/test_s4.py create mode 100644 zeta/nn/modules/h3.py create mode 100644 zeta/nn/modules/s4.py diff --git a/tests/nn/modules/test_h3_layer.py b/tests/nn/modules/test_h3_layer.py new file mode 100644 index 00000000..d06fb1fa --- /dev/null +++ b/tests/nn/modules/test_h3_layer.py @@ -0,0 +1,57 @@ + +from unittest.mock import Mock + +import pytest +import torch + +from zeta.nn.modules.h3 import H3Layer + + +# 1. Basic Tests +def test_h3_layer_creation(): + layer = H3Layer(256) + assert isinstance(layer, H3Layer) + +def test_forward_pass(): + layer = H3Layer(256) + x = torch.randn(1, 256, 1024) + output = layer(x) + assert output.shape == torch.Size([1, 256, 1024]) + +# 2. Utilize Fixtures +@pytest.fixture +def sample_layer(): + return H3Layer(128) + +def test_fixture_usage(sample_layer): + x = torch.randn(1, 128, 1024) + output = sample_layer(x) + assert output.shape == torch.Size([1, 128, 1024]) + +# 3. Parameterized Testing +@pytest.mark.parametrize("dim", [128, 256, 512]) +def test_parameterized_layer(dim): + layer = H3Layer(dim) + x = torch.randn(1, dim, 1024) + output = layer(x) + assert output.shape == torch.Size([1, dim, 1024]) + + +def test_with_mocked_ssm(): + mock_ssm = Mock() + layer = H3Layer(128) + layer.diagonal_ssm = mock_ssm + x = torch.randn(1, 128, 1024) + layer(x) + assert mock_ssm.called + +# 5. Exception Testing +def test_invalid_dimension_raises_error(): + with pytest.raises(ValueError): + H3Layer(0) + +# 6. Test Coverage (requires pytest-cov) +def test_coverage(): + pytest.main(["--cov=your_module", "test_your_module.py"]) + +# Add more tests as needed... diff --git a/tests/nn/modules/test_s4.py b/tests/nn/modules/test_s4.py new file mode 100644 index 00000000..0f4a5628 --- /dev/null +++ b/tests/nn/modules/test_s4.py @@ -0,0 +1,69 @@ +import torch +import pytest +from zeta.nn.modules.s4 import s4d_kernel + +# Test cases for s4d_kernel function + +# Test 1: Basic test with valid inputs +def test_s4d_kernel_basic(): + A = torch.tensor([[1.0, 2.0, 3.0]]) + B = torch.tensor([[0.5, 1.0, 1.5]]) + C = torch.tensor([[0.2, 0.4, 0.6]]) + dt = 0.1 + L = 5 + result = s4d_kernel(A, B, C, dt, L) + assert result.shape == (1, 5, 3) + assert torch.allclose( + result, + torch.tensor([[[0.2, 0.4, 0.6], [0.2602, 0.5488, 0.8617], [0.3293, 0.6978, 1.0947], [0.4072, 0.8661, 1.3574], [0.4938, 1.0461, 1.6424]]]), + atol=1e-4, + ) + +# Test 2: Test with incompatible tensor dimensions +def test_s4d_kernel_incompatible_dimensions(): + A = torch.tensor([[1.0, 2.0, 3.0]]) + B = torch.tensor([[0.5, 1.0, 1.5]]) + C = torch.tensor([[0.2, 0.4, 0.6]]) + dt = 0.1 + L = 5 + # Make A and B incompatible by adding an extra dimension to A + A = A.unsqueeze(0) + with pytest.raises(ValueError): + s4d_kernel(A, B, C, dt, L) + +# Test 3: Test with invalid data type for dt +def test_s4d_kernel_invalid_dt_type(): + A = torch.tensor([[1.0, 2.0, 3.0]]) + B = torch.tensor([[0.5, 1.0, 1.5]]) + C = torch.tensor([[0.2, 0.4, 0.6]]) + dt = "0.1" # Should be a float, but provided as a string + L = 5 + with pytest.raises(TypeError): + s4d_kernel(A, B, C, dt, L) + +# Test 4: Test with invalid data type for L +def test_s4d_kernel_invalid_L_type(): + A = torch.tensor([[1.0, 2.0, 3.0]]) + B = torch.tensor([[0.5, 1.0, 1.5]]) + C = torch.tensor([[0.2, 0.4, 0.6]]) + dt = 0.1 + L = 5.5 # Should be an integer, but provided as a float + with pytest.raises(TypeError): + s4d_kernel(A, B, C, dt, L) + +# Test 5: Test with zero-dimensional tensors +def test_s4d_kernel_zero_dimensional_tensors(): + A = torch.tensor(1.0) + B = torch.tensor(0.5) + C = torch.tensor(0.2) + dt = 0.1 + L = 5 + result = s4d_kernel(A, B, C, dt, L) + assert result.shape == (1, 5, 1) + assert torch.allclose( + result, + torch.tensor([[[0.2], [0.2], [0.2], [0.2], [0.2]]]), + atol=1e-4, + ) + +# Add more test cases as needed... diff --git a/zeta/nn/modules/__init__.py b/zeta/nn/modules/__init__.py index 6c3b3240..2707065f 100644 --- a/zeta/nn/modules/__init__.py +++ b/zeta/nn/modules/__init__.py @@ -56,6 +56,7 @@ from zeta.nn.modules.prenorm import PreNorm from zeta.nn.modules.itca import IterativeCrossSelfAttention from zeta.nn.modules.lang_conv_module import ConvolutionLanguageBlock +from zeta.nn.modules.h3 import s4d_kernel __all__ = [ "CNNNew", "CombinedLinear", @@ -100,5 +101,5 @@ "PolymorphicActivation", "PreNorm", "IterativeCrossSelfAttention", - "ConvolutionLanguageBlock" + "ConvolutionLanguageBlock", ] diff --git a/zeta/nn/modules/h3.py b/zeta/nn/modules/h3.py new file mode 100644 index 00000000..92ed3092 --- /dev/null +++ b/zeta/nn/modules/h3.py @@ -0,0 +1,100 @@ +import torch +import torch.nn as nn + +class DiagonalSSM(nn.Module): + """DiagonalSSM is a module that implements the Diagonal SSM operation. + + Args: + nn (_type_): _description_ + """ + def __init__(self, dim): + super().__init__() + # A diagonal matrix represented as a vector for ease of multiplication + self.diag = nn.Parameter(torch.ones(dim)) + + def forward(self, x): + """Forward + + Args: + x (_type_): _description_ + + Returns: + _type_: _description_ + """ + # Multiplication with a diagonal matrix can be done element-wise + return x * self.diag + +class ShiftSSM(nn.Module): + """ShiftSSM is a module that implements the Shift SSM operation. + + Args: + nn (_type_): _description_ + """ + def __init__(self, dim): + super().__init__() + # A shift matrix operation + self.dim = dim + + def forward(self, x): + """Forward pass of the module. + + Args: + x (_type_): _description_ + + Returns: + _type_: _description_ + """ + # Shift the last dimension of x by one + return torch.cat((x[..., -1:], x[..., :-1]), dim=-1) + +class H3Layer(nn.Module): + """H3Layer is a layer that implements the H3 associative memory model. + + + Attributes: + dim (int): The dimensionality of the input and output tensors. + + Methods: + forward(x): Performs a forward pass through the layer. + + Examples: + >>> import torch + >>> from zeta.nn.modules.h3 import H3Layer + >>> x = torch.randn(1, 512, 1024) + >>> layer = H3Layer(512) + >>> out = layer(x) + >>> out.shape + torch.Size([1, 512, 1024]) + """ + def __init__(self, dim: int): + super().__init__() + self.diagonal_ssm = DiagonalSSM(dim) + self.shift_ssm = ShiftSSM(dim) + + self.q_proj = nn.Linear(dim, dim) + self.k_proj = nn.Linear(dim, dim) + self.v_proj = nn.Linear(dim, dim) + + def forward(self, x): + # Linear projections + q = self.q_proj(x) + k = self.k_proj(x) + v = self.v_proj(x) + + # Apply Shift SSM to k + k = self.shift_ssm(k) + + # Element-wise multiplication for associative recall + combined = q * k + + # Apply Diagonal SSM to combined tensor + output = self.diagonal_ssm(combined) * v + + return output + +# # Example usage: +# batch_size, seq_len, dim = 32, 40, 512 +# x = torch.rand(batch_size, seq_len, dim) +# h3_layer = H3Layer(dim) +# output = h3_layer(x) +# print(output.shape) # Expected shape: (batch_size, seq_len, dim) diff --git a/zeta/nn/modules/s4.py b/zeta/nn/modules/s4.py new file mode 100644 index 00000000..d834fe15 --- /dev/null +++ b/zeta/nn/modules/s4.py @@ -0,0 +1,61 @@ +import torch +from typing import Tuple + +def s4d_kernel(A: torch.Tensor, B: torch.Tensor, C: torch.Tensor, dt: float, L: int) -> torch.Tensor: + """ + Compute the S4D convolution kernel for state space models on 3D tensors with shape (batch_size, seqlen, dim). + + Parameters: + A (torch.Tensor): A tensor of shape (batch_size, dim) containing the eigenvalues of the state update matrix. + B (torch.Tensor): A tensor of shape (batch_size, dim) containing the input-to-state weights. + C (torch.Tensor): A tensor of shape (batch_size, dim) containing the state-to-output weights. + dt (float): A scalar that represents the time step in the discrete-time SSM. + L (int): The length of the sequence over which the convolution will be performed. + + Returns: + torch.Tensor: A tensor of shape (batch_size, seqlen, dim) that represents the convolution of the inputs through the SSM. + + Raises: + ValueError: If the dimensions of A, B, or C are not compatible. + TypeError: If dt is not a float or L is not an integer. + """ + + # Ensure A, B, and C have the same size in the last dimension and compatible batch dimensions + if A.size(-1) != B.size(-1) or A.size(-1) != C.size(-1) or A.shape[:-1] != B.shape[:-1] or A.shape[:-1] != C.shape[:-1]: + raise ValueError("The last dimension of tensors A, B, and C must match and have compatible batch dimensions.") + + # Check that dt is a float and L is an integer + if not isinstance(dt, float): + raise TypeError("The time step dt must be a float.") + if not isinstance(L, int): + raise TypeError("The sequence length L must be an integer.") + + # Create a range of values from 0 to L-1 and reshape for broadcasting + arange_L = torch.arange(L, dtype=A.dtype, device=A.device).view(L, 1) + + # Expand A and B for broadcasting with the sequence length + A_expanded = A.unsqueeze(1) # Shape: (batch_size, 1, dim) + B_expanded = B.unsqueeze(1) # Shape: (batch_size, 1, dim) + + # Perform the convolution kernel operation with proper broadcasting + vandermonde = torch.exp(arange_L * dt * A_expanded) # Shape: (seqlen, batch_size, dim) + result = torch.sum(vandermonde * B_expanded * (torch.exp(dt * A_expanded) - 1) / A_expanded, dim=0) + result = C.unsqueeze(1) * result # Shape: (batch_size, seqlen, dim) + + return result + +# # Example usage with random tensors: +# torch.manual_seed(0) # For reproducibility +# batch_size = 5 # Example batch size +# N = 10 # Size of the state space +# L = 100 # Sequence length + +# # Randomly generated tensors for A, B, and C with the correct shape and a random float for dt +# A_random = torch.randn(batch_size, N) +# B_random = torch.randn(batch_size, N) +# C_random = torch.randn(batch_size, N) +# dt_random = float(torch.rand(1).item()) + +# # Call the s4d_kernel function with the random tensors and parameters +# output = s4d_kernel(A_random, B_random, C_random, dt_random, L) +# print("Output of the s4d_kernel with random inputs:", output) From 90659226f0a17098ef5b9ac1beca5a92c5b113bc Mon Sep 17 00:00:00 2001 From: Kye Date: Wed, 29 Nov 2023 15:34:09 -0800 Subject: [PATCH 080/587] init cleaup --- zeta/nn/__init__.py | 10 ++++------ zeta/nn/modules/__init__.py | 3 +++ 2 files changed, 7 insertions(+), 6 deletions(-) diff --git a/zeta/nn/__init__.py b/zeta/nn/__init__.py index a1fafd5d..6e3768f6 100644 --- a/zeta/nn/__init__.py +++ b/zeta/nn/__init__.py @@ -1,18 +1,16 @@ # Attention # from zeta.nn.attention import * -from zeta.nn import attention +from zeta.nn.attention import * -# architecture -import zeta.structs as architecture # embeddings # from zeta.nn.embeddings import * -from zeta.nn import embeddings +from zeta.nn.embeddings import * # modules # from zeta.nn.modules import * -from zeta.nn import modules +from zeta.nn.modules import * # biases # from zeta.nn.biases import * -from zeta.nn import biases +from zeta.nn.biases import * diff --git a/zeta/nn/modules/__init__.py b/zeta/nn/modules/__init__.py index 2707065f..9cc211fd 100644 --- a/zeta/nn/modules/__init__.py +++ b/zeta/nn/modules/__init__.py @@ -57,6 +57,8 @@ from zeta.nn.modules.itca import IterativeCrossSelfAttention from zeta.nn.modules.lang_conv_module import ConvolutionLanguageBlock from zeta.nn.modules.h3 import s4d_kernel +from zeta.nn.modules.h3 import H3Layer + __all__ = [ "CNNNew", "CombinedLinear", @@ -102,4 +104,5 @@ "PreNorm", "IterativeCrossSelfAttention", "ConvolutionLanguageBlock", + "H3Layer", ] From 546382c7f17fb6cdeea4c7c11afcae05e8c130f7 Mon Sep 17 00:00:00 2001 From: Kye Date: Wed, 29 Nov 2023 22:52:35 -0800 Subject: [PATCH 081/587] [__INIT__][CLEAN UP] --- pyproject.toml | 2 +- zeta/__init__.py | 18 +++++++++++------- 2 files changed, 12 insertions(+), 8 deletions(-) diff --git a/pyproject.toml b/pyproject.toml index 212af824..aced8e7c 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,6 +1,6 @@ [tool.poetry] name = "zetascale" -version = "0.8.6" +version = "0.8.7" description = "Transformers at zeta scales" authors = ["Zeta Team "] license = "MIT" diff --git a/zeta/__init__.py b/zeta/__init__.py index 378649ad..f083fb4d 100644 --- a/zeta/__init__.py +++ b/zeta/__init__.py @@ -26,10 +26,14 @@ def filter(self, record): logger.addFilter(f) from zeta.nn import * -from zeta import models -from zeta import utils -from zeta import training -from zeta import tokenizers -from zeta import rl -from zeta import optim -from zeta import ops +from zeta.models import * +from zeta.utils import * +from zeta.training import * +from zeta.tokenizers import * +from zeta.rl import * +from zeta.optim import * +from zeta.ops import * +from zeta.quant import * + + + From 7be55b09685611857347274d68d61af63434e496 Mon Sep 17 00:00:00 2001 From: Kye Date: Wed, 29 Nov 2023 23:07:44 -0800 Subject: [PATCH 082/587] [CLEANUP][CHORES] --- pyproject.toml | 2 +- zeta/nn/modules/__init__.py | 29 +++++++++++++++-------------- zeta/nn/modules/rmsnorm.py | 32 -------------------------------- zeta/quant/__init__.py | 2 +- zeta/structs/__init__.py | 3 +-- 5 files changed, 18 insertions(+), 50 deletions(-) delete mode 100644 zeta/nn/modules/rmsnorm.py diff --git a/pyproject.toml b/pyproject.toml index aced8e7c..da785df6 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,6 +1,6 @@ [tool.poetry] name = "zetascale" -version = "0.8.7" +version = "0.8.8" description = "Transformers at zeta scales" authors = ["Zeta Team "] license = "MIT" diff --git a/zeta/nn/modules/__init__.py b/zeta/nn/modules/__init__.py index 9cc211fd..70f467e8 100644 --- a/zeta/nn/modules/__init__.py +++ b/zeta/nn/modules/__init__.py @@ -1,4 +1,3 @@ -# Description: __init__ file for modules from zeta.nn.modules.cnn_text import CNNNew from zeta.nn.modules.combined_linear import CombinedLinear from zeta.nn.modules.convnet import ConvNet @@ -31,19 +30,6 @@ from zeta.nn.modules.simple_res_block import SimpleResBlock from zeta.nn.modules.sig_lip import SigLipLoss from zeta.nn.modules.simple_feedforward import SimpleFeedForward - -# from zeta.nn.modules.img_reshape import image_reshape -# from zeta.nn.modules.flatten_features import flatten_features -# from zeta.nn.modules.scaled_sinusoidal import ScaledSinuosidalEmbedding -# from zeta.nn.modules.scale import Scale -# from zeta.nn.modules.scalenorm import ScaleNorm -# from zeta.nn.modules.simple_rmsnorm import SimpleRMSNorm -# from zeta.nn.modules.gru_gating import GRUGating -# from zeta.nn.modules.shift_tokens import ShiftTokens -# from zeta.nn.modules.swarmalator import simulate_swarmalators -# from zeta.nn.modules.transformations import image_transform -# from zeta.nn.modules.squeeze_excitation import SqueezeExcitation -# from zeta.nn.modules.clex import Clex from zeta.nn.modules.unet import Unet from zeta.nn.modules.visual_expert import VisualExpert from zeta.nn.modules.feedforward import FeedForward @@ -59,6 +45,21 @@ from zeta.nn.modules.h3 import s4d_kernel from zeta.nn.modules.h3 import H3Layer + + +# from zeta.nn.modules.img_reshape import image_reshape +# from zeta.nn.modules.flatten_features import flatten_features +# from zeta.nn.modules.scaled_sinusoidal import ScaledSinuosidalEmbedding +# from zeta.nn.modules.scale import Scale +# from zeta.nn.modules.scalenorm import ScaleNorm +# from zeta.nn.modules.simple_rmsnorm import SimpleRMSNorm +# from zeta.nn.modules.gru_gating import GRUGating +# from zeta.nn.modules.shift_tokens import ShiftTokens +# from zeta.nn.modules.swarmalator import simulate_swarmalators +# from zeta.nn.modules.transformations import image_transform +# from zeta.nn.modules.squeeze_excitation import SqueezeExcitation +# from zeta.nn.modules.clex import Clex + __all__ = [ "CNNNew", "CombinedLinear", diff --git a/zeta/nn/modules/rmsnorm.py b/zeta/nn/modules/rmsnorm.py deleted file mode 100644 index 54f37679..00000000 --- a/zeta/nn/modules/rmsnorm.py +++ /dev/null @@ -1,32 +0,0 @@ -import torch.nn.functional as F -from torch import nn - - -class RMSNorm(nn.Module): - """ - RMSNorm - - Args: - dim (int): dimension of the embedding - - - Attributes: - g (nn.Parameter): scaling parameter - eps (float): epsilon value - - Usage: - We can use RMSNorm as a layer in a neural network as follows: - >>> x = torch.randn(1, 10, 512) - >>> rms_norm = RMSNorm(dim=512) - >>> rms_norm(x).shape - torch.Size([1, 10, 512]) - - - """ - - def __init__(self, dim): - super().__init__() - self.scale = dim**-0.5 - - def forward(self, x): - return F.normalize(x, dim=-1) * self.scale * self.g diff --git a/zeta/quant/__init__.py b/zeta/quant/__init__.py index 01c46f57..98a70445 100644 --- a/zeta/quant/__init__.py +++ b/zeta/quant/__init__.py @@ -3,4 +3,4 @@ from zeta.quant.ste import STE from zeta.quant.qlora import QloraLinear -__all__ = ["QUIK", "absmax_quantize", "BitLinear", "STE"] +__all__ = ["QUIK", "absmax_quantize", "BitLinear", "STE", "QloraLinear"] \ No newline at end of file diff --git a/zeta/structs/__init__.py b/zeta/structs/__init__.py index 34badf18..8f1c4d99 100644 --- a/zeta/structs/__init__.py +++ b/zeta/structs/__init__.py @@ -15,8 +15,7 @@ from zeta.structs.multi_modal_projector import build_vision_projector from zeta.structs.simple_transformer import SimpleTransformer -# from zeta.structs.efficent_net import EfficientNet -from zeta.structs.efficient_net import EfficientNet +# from zeta.structs.efficient_net import EfficientNet __all__ = [ "AutoregressiveWrapper", From 55118bbe1372dc7ce5a85012f65ac70028e96a46 Mon Sep 17 00:00:00 2001 From: Kye Date: Wed, 29 Nov 2023 23:10:13 -0800 Subject: [PATCH 083/587] [CLEANUP][Fixes of __init__] --- pyproject.toml | 2 +- zeta/nn/__init__.py | 12 ------------ zeta/nn/modules/__init__.py | 2 +- 3 files changed, 2 insertions(+), 14 deletions(-) diff --git a/pyproject.toml b/pyproject.toml index da785df6..16d022d3 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,6 +1,6 @@ [tool.poetry] name = "zetascale" -version = "0.8.8" +version = "0.8.9" description = "Transformers at zeta scales" authors = ["Zeta Team "] license = "MIT" diff --git a/zeta/nn/__init__.py b/zeta/nn/__init__.py index 6e3768f6..799bb6b6 100644 --- a/zeta/nn/__init__.py +++ b/zeta/nn/__init__.py @@ -1,16 +1,4 @@ -# Attention -# from zeta.nn.attention import * from zeta.nn.attention import * - - -# embeddings -# from zeta.nn.embeddings import * from zeta.nn.embeddings import * - -# modules -# from zeta.nn.modules import * from zeta.nn.modules import * - -# biases -# from zeta.nn.biases import * from zeta.nn.biases import * diff --git a/zeta/nn/modules/__init__.py b/zeta/nn/modules/__init__.py index 70f467e8..57abba76 100644 --- a/zeta/nn/modules/__init__.py +++ b/zeta/nn/modules/__init__.py @@ -42,7 +42,7 @@ from zeta.nn.modules.prenorm import PreNorm from zeta.nn.modules.itca import IterativeCrossSelfAttention from zeta.nn.modules.lang_conv_module import ConvolutionLanguageBlock -from zeta.nn.modules.h3 import s4d_kernel +from zeta.nn.modules.s4 import s4d_kernel from zeta.nn.modules.h3 import H3Layer From 2c21eadb5535b99187c0bef03a318b310a6e05d4 Mon Sep 17 00:00:00 2001 From: Kye Date: Wed, 29 Nov 2023 23:51:57 -0800 Subject: [PATCH 084/587] [TESTS][FIXUP for Pytests], [FIX][Gradient Ascent, GradientEquillibrum] --- test_name.sh | 6 + tests/nn/attentions/mha.py | 47 ---- .../attentions/{attend.py => test_attend.py} | 0 .../{cross_attn.py => test_cross_attn.py} | 0 ...modal.py => test_cross_attn_multimodal.py} | 0 ...cal_attn_mha.py => test_local_attn_mha.py} | 0 tests/nn/attentions/{mgqa.py => test_mgqa.py} | 0 tests/nn/attentions/test_mha.py | 208 ++++-------------- tests/nn/attentions/{mqa.py => test_mqa.py} | 0 .../{shaped_attn.py => test_shaped_attn.py} | 0 .../{sparse_attn.py => test_sparse_attn.py} | 0 tests/nn/attentions/test_test_mha.py | 167 ++++++++++++++ .../{xc_attention.py => test_xc_attention.py} | 0 tests/nn/biases/{alibi.py => test_alibi.py} | 0 ...c_relative.py => test_dynamic_relative.py} | 0 ...bias.py => test_relative_position_bias.py} | 0 .../{abc_pos_emb.py => test_abc_pos_emb.py} | 0 ...h_embedding.py => test_patch_embedding.py} | 0 ...dings.py => test_positional_embeddings.py} | 0 tests/nn/embeddings/{rope.py => test_rope.py} | 0 .../embeddings/{rotary.py => test_rotary.py} | 0 ...l_embs.py => test_sine_positional_embs.py} | 0 ...ry_emb.py => test_truncated_rotary_emb.py} | 0 ...mbeddings.py => test_vision_embeddings.py} | 0 ...ings.py => test_vision_lang_embeddings.py} | 0 tests/nn/embeddings/{xpos.py => test_xpos.py} | 0 tests/nn/embeddings/{yarn.py => test_yarn.py} | 0 ...aptive_param.py => test_adaptive_param.py} | 0 .../{alr_block.py => test_alr_block.py} | 0 .../{bitlinear.py => test_bitlinear.py} | 0 ...tn_images.py => test_cross_attn_images.py} | 0 .../{custom_mlp.py => test_custom_mlp.py} | 0 ...namic_module.py => test_dynamic_module.py} | 0 .../nn/modules/{expert.py => test_expert.py} | 0 .../{feedforward.py => test_feedforward.py} | 0 ...eedforward.py => test_full_feedforward.py} | 0 .../modules/{hebbian.py => test_hebbian.py} | 0 ...e_projector.py => test_image_projector.py} | 0 .../nn/modules/{log_ff.py => test_log_ff.py} | 0 .../nn/modules/{mbconv.py => test_mbconv.py} | 0 tests/nn/modules/{mlp.py => test_mlp.py} | 0 .../{mm_adapter.py => test_mm_adapter.py} | 0 ...c_neuron.py => test_polymorphic_neuron.py} | 0 ...dforward.py => test_simple_feedforward.py} | 0 ...st_conv_lang.py => test_test_conv_lang.py} | 0 ...test_h3_layer.py => test_test_h3_layer.py} | 0 .../modules/{test_s4.py => test_test_s4.py} | 0 ...token_learner.py => test_token_learner.py} | 0 ...sformations.py => test_transformations.py} | 0 tests/nn/modules/{unet.py => test_unet.py} | 0 ...visual_expert.py => test_visual_expert.py} | 0 ...nops_from_to.py => test_einops_from_to.py} | 0 .../{einops_poly.py => test_einops_poly.py} | 0 tests/ops/{mos.py => test_mos.py} | 0 ...coupled_lion.py => test_decoupled_lion.py} | 0 ...ient_ascent.py => test_gradient_ascent.py} | 2 +- ...librum.py => test_gradient_equillibrum.py} | 2 +- .../{stable_adamw.py => test_stable_adamw.py} | 0 tests/quant/{qlora.py => test_qlora.py} | 0 ...d_model.py => test_vision_reward_model.py} | 0 ...efficient_net.py => test_efficient_net.py} | 2 +- tests/{__init__.py => test_test___init__.py} | 0 tests/{example.py => test_test_example.py} | 0 ...el_wrapper.py => test_parallel_wrapper.py} | 0 64 files changed, 220 insertions(+), 214 deletions(-) create mode 100755 test_name.sh delete mode 100644 tests/nn/attentions/mha.py rename tests/nn/attentions/{attend.py => test_attend.py} (100%) rename tests/nn/attentions/{cross_attn.py => test_cross_attn.py} (100%) rename tests/nn/attentions/{cross_attn_multimodal.py => test_cross_attn_multimodal.py} (100%) rename tests/nn/attentions/{local_attn_mha.py => test_local_attn_mha.py} (100%) rename tests/nn/attentions/{mgqa.py => test_mgqa.py} (100%) rename tests/nn/attentions/{mqa.py => test_mqa.py} (100%) rename tests/nn/attentions/{shaped_attn.py => test_shaped_attn.py} (100%) rename tests/nn/attentions/{sparse_attn.py => test_sparse_attn.py} (100%) create mode 100644 tests/nn/attentions/test_test_mha.py rename tests/nn/attentions/{xc_attention.py => test_xc_attention.py} (100%) rename tests/nn/biases/{alibi.py => test_alibi.py} (100%) rename tests/nn/biases/{dynamic_relative.py => test_dynamic_relative.py} (100%) rename tests/nn/biases/{relative_position_bias.py => test_relative_position_bias.py} (100%) rename tests/nn/embeddings/{abc_pos_emb.py => test_abc_pos_emb.py} (100%) rename tests/nn/embeddings/{patch_embedding.py => test_patch_embedding.py} (100%) rename tests/nn/embeddings/{positional_embeddings.py => test_positional_embeddings.py} (100%) rename tests/nn/embeddings/{rope.py => test_rope.py} (100%) rename tests/nn/embeddings/{rotary.py => test_rotary.py} (100%) rename tests/nn/embeddings/{sine_positional_embs.py => test_sine_positional_embs.py} (100%) rename tests/nn/embeddings/{truncated_rotary_emb.py => test_truncated_rotary_emb.py} (100%) rename tests/nn/embeddings/{vision_embeddings.py => test_vision_embeddings.py} (100%) rename tests/nn/embeddings/{vision_lang_embeddings.py => test_vision_lang_embeddings.py} (100%) rename tests/nn/embeddings/{xpos.py => test_xpos.py} (100%) rename tests/nn/embeddings/{yarn.py => test_yarn.py} (100%) rename tests/nn/modules/{adaptive_param.py => test_adaptive_param.py} (100%) rename tests/nn/modules/{alr_block.py => test_alr_block.py} (100%) rename tests/nn/modules/{bitlinear.py => test_bitlinear.py} (100%) rename tests/nn/modules/{cross_attn_images.py => test_cross_attn_images.py} (100%) rename tests/nn/modules/{custom_mlp.py => test_custom_mlp.py} (100%) rename tests/nn/modules/{dynamic_module.py => test_dynamic_module.py} (100%) rename tests/nn/modules/{expert.py => test_expert.py} (100%) rename tests/nn/modules/{feedforward.py => test_feedforward.py} (100%) rename tests/nn/modules/{full_feedforward.py => test_full_feedforward.py} (100%) rename tests/nn/modules/{hebbian.py => test_hebbian.py} (100%) rename tests/nn/modules/{image_projector.py => test_image_projector.py} (100%) rename tests/nn/modules/{log_ff.py => test_log_ff.py} (100%) rename tests/nn/modules/{mbconv.py => test_mbconv.py} (100%) rename tests/nn/modules/{mlp.py => test_mlp.py} (100%) rename tests/nn/modules/{mm_adapter.py => test_mm_adapter.py} (100%) rename tests/nn/modules/{polymorphic_neuron.py => test_polymorphic_neuron.py} (100%) rename tests/nn/modules/{simple_feedforward.py => test_simple_feedforward.py} (100%) rename tests/nn/modules/{test_conv_lang.py => test_test_conv_lang.py} (100%) rename tests/nn/modules/{test_h3_layer.py => test_test_h3_layer.py} (100%) rename tests/nn/modules/{test_s4.py => test_test_s4.py} (100%) rename tests/nn/modules/{token_learner.py => test_token_learner.py} (100%) rename tests/nn/modules/{transformations.py => test_transformations.py} (100%) rename tests/nn/modules/{unet.py => test_unet.py} (100%) rename tests/nn/modules/{visual_expert.py => test_visual_expert.py} (100%) rename tests/ops/{einops_from_to.py => test_einops_from_to.py} (100%) rename tests/ops/{einops_poly.py => test_einops_poly.py} (100%) rename tests/ops/{mos.py => test_mos.py} (100%) rename tests/optim/{decoupled_lion.py => test_decoupled_lion.py} (100%) rename tests/optim/{gradient_ascent.py => test_gradient_ascent.py} (98%) rename tests/optim/{gradient_equillibrum.py => test_gradient_equillibrum.py} (99%) rename tests/optim/{stable_adamw.py => test_stable_adamw.py} (100%) rename tests/quant/{qlora.py => test_qlora.py} (100%) rename tests/rl/{vision_reward_model.py => test_vision_reward_model.py} (100%) rename tests/structs/{efficient_net.py => test_efficient_net.py} (98%) rename tests/{__init__.py => test_test___init__.py} (100%) rename tests/{example.py => test_test_example.py} (100%) rename tests/training/{parallel_wrapper.py => test_parallel_wrapper.py} (100%) diff --git a/test_name.sh b/test_name.sh new file mode 100755 index 00000000..d894e4aa --- /dev/null +++ b/test_name.sh @@ -0,0 +1,6 @@ +find ./tests -name "*.py" -type f | while read file +do + filename=$(basename "$file") + dir=$(dirname "$file") + mv "$file" "$dir/test_$filename" +done \ No newline at end of file diff --git a/tests/nn/attentions/mha.py b/tests/nn/attentions/mha.py deleted file mode 100644 index cd54d88b..00000000 --- a/tests/nn/attentions/mha.py +++ /dev/null @@ -1,47 +0,0 @@ -import pytest -import torch -from zeta.nn.attention.multihead_attention import MultiheadAttention - - -def test_multiheadattention_initialization(): - args = {"layernorm_eps": 1e-5, "xpos_rel_pos": False} - model = MultiheadAttention(args, embed_dim=512, num_heads=8) - assert isinstance(model, MultiheadAttention) - assert model.embed_dim == 512 - assert model.num_heads == 8 - assert model.head_dim == 64 - assert model.scaling == 1 / 8 - - -def test_multiheadattention_forward(): - args = {"layernorm_eps": 1e-5, "xpos_rel_pos": False} - model = MultiheadAttention(args, embed_dim=512, num_heads=8) - query = torch.randn(1, 10, 512) - key = torch.randn(1, 10, 512) - value = torch.randn(1, 10, 512) - output, attn_weights = model(query, key, value) - assert output.shape == (1, 10, 512) - assert attn_weights.shape == (8, 1, 10, 10) - - -@pytest.mark.parametrize( - "query_len, key_len, value_len", [(0, 10, 10), (10, 0, 10), (10, 10, 0)] -) -def test_multiheadattention_forward_edge_cases(query_len, key_len, value_len): - args = {"layernorm_eps": 1e-5, "xpos_rel_pos": False} - model = MultiheadAttention(args, embed_dim=512, num_heads=8) - query = torch.randn(1, query_len, 512) - key = torch.randn(1, key_len, 512) - value = torch.randn(1, value_len, 512) - with pytest.raises(Exception): - model(query, key, value) - - -def test_multiheadattention_forward_invalid_dimensions(): - args = {"layernorm_eps": 1e-5, "xpos_rel_pos": False} - model = MultiheadAttention(args, embed_dim=512, num_heads=8) - query = torch.randn(1, 10, 256) - key = torch.randn(1, 10, 512) - value = torch.randn(1, 10, 512) - with pytest.raises(Exception): - model(query, key, value) diff --git a/tests/nn/attentions/attend.py b/tests/nn/attentions/test_attend.py similarity index 100% rename from tests/nn/attentions/attend.py rename to tests/nn/attentions/test_attend.py diff --git a/tests/nn/attentions/cross_attn.py b/tests/nn/attentions/test_cross_attn.py similarity index 100% rename from tests/nn/attentions/cross_attn.py rename to tests/nn/attentions/test_cross_attn.py diff --git a/tests/nn/attentions/cross_attn_multimodal.py b/tests/nn/attentions/test_cross_attn_multimodal.py similarity index 100% rename from tests/nn/attentions/cross_attn_multimodal.py rename to tests/nn/attentions/test_cross_attn_multimodal.py diff --git a/tests/nn/attentions/local_attn_mha.py b/tests/nn/attentions/test_local_attn_mha.py similarity index 100% rename from tests/nn/attentions/local_attn_mha.py rename to tests/nn/attentions/test_local_attn_mha.py diff --git a/tests/nn/attentions/mgqa.py b/tests/nn/attentions/test_mgqa.py similarity index 100% rename from tests/nn/attentions/mgqa.py rename to tests/nn/attentions/test_mgqa.py diff --git a/tests/nn/attentions/test_mha.py b/tests/nn/attentions/test_mha.py index 44ef5d73..cd54d88b 100644 --- a/tests/nn/attentions/test_mha.py +++ b/tests/nn/attentions/test_mha.py @@ -1,167 +1,47 @@ -from zeta.nn.attention.multihead_attention import MultiheadAttention +import pytest import torch -import unittest - - -class TestMultiheadAttention(unittest.TestCase): - def setUp(self): - self.args = { - "xpos_rel_pos": True, - "xpos_scale_base": 2, - "layernorm_eps": 1e-5, - } - self.embed_dim = 64 - self.num_heads = 4 - self.multihead_attn = MultiheadAttention( - self.args, self.embed_dim, self.num_heads - ) - - def test_forward_shape(self): - query = torch.rand(16, 20, self.embed_dim) - key = torch.rand(16, 20, self.embed_dim) - value = torch.rand(16, 20, self.embed_dim) - attn, attn_weights = self.multihead_attn(query, key, value) - self.assertEqual(attn.shape, (16, 20, self.embed_dim)) - self.assertEqual(attn_weights.shape, (self.num_heads, 16, 20, 20)) - - def test_forward_incremental_state(self): - query = torch.rand(16, 20, self.embed_dim) - key = torch.rand(16, 20, self.embed_dim) - value = torch.rand(16, 20, self.embed_dim) - incremental_state = { - "prev_key": torch.rand( - 16, self.num_heads, 10, self.embed_dim // self.num_heads - ), - "prev_value": torch.rand( - 16, self.num_heads, 10, self.embed_dim // self.num_heads - ), - } - attn, attn_weights = self.multihead_attn( - query, key, value, incremental_state=incremental_state - ) - self.assertEqual(attn.shape, (16, 20, self.embed_dim)) - self.assertEqual(attn_weights.shape, (self.num_heads, 16, 20, 30)) - - def test_forward_attn_mask(self): - query = torch.rand(16, 20, self.embed_dim) - key = torch.rand(16, 20, self.embed_dim) - value = torch.rand(16, 20, self.embed_dim) - attn_mask = torch.ones(20, 20) - attn, attn_weights = self.multihead_attn( - query, key, value, attn_mask=attn_mask - ) - self.assertEqual(attn.shape, (16, 20, self.embed_dim)) - self.assertEqual(attn_weights.shape, (self.num_heads, 16, 20, 20)) - - def test_forward_key_padding_mask(self): - query = torch.rand(16, 20, self.embed_dim) - key = torch.rand(16, 20, self.embed_dim) - value = torch.rand(16, 20, self.embed_dim) - key_padding_mask = torch.ones(16, 20) - attn, attn_weights = self.multihead_attn( - query, key, value, key_padding_mask=key_padding_mask - ) - self.assertEqual(attn.shape, (16, 20, self.embed_dim)) - self.assertEqual(attn_weights.shape, (self.num_heads, 16, 20, 20)) - - def test_forward_rel_pos(self): - query = torch.rand(16, 20, self.embed_dim) - key = torch.rand(16, 20, self.embed_dim) - value = torch.rand(16, 20, self.embed_dim) - rel_pos = torch.rand(16, self.num_heads, 20, 20) - attn, attn_weights = self.multihead_attn( - query, key, value, rel_pos=rel_pos - ) - self.assertEqual(attn.shape, (16, 20, self.embed_dim)) - self.assertEqual(attn_weights.shape, (self.num_heads, 16, 20, 20)) - - def test_forward_is_first_step(self): - query = torch.rand(16, 20, self.embed_dim) - key = torch.rand(16, 20, self.embed_dim) - value = torch.rand(16, 20, self.embed_dim) - attn, attn_weights = self.multihead_attn( - query, key, value, is_first_step=True - ) - self.assertEqual(attn.shape, (16, 20, self.embed_dim)) - self.assertEqual(attn_weights.shape, (self.num_heads, 16, 20, 20)) - - def test_forward_is_not_first_step(self): - query = torch.rand(16, 20, self.embed_dim) - key = torch.rand(16, 20, self.embed_dim) - value = torch.rand(16, 20, self.embed_dim) - attn, attn_weights = self.multihead_attn( - query, key, value, is_first_step=False - ) - self.assertEqual(attn.shape, (16, 20, self.embed_dim)) - self.assertEqual(attn_weights.shape, (self.num_heads, 16, 20, 20)) - - def test_forward_different_query_key_value_size(self): - query = torch.rand(16, 20, self.embed_dim) - key = torch.rand(16, 30, self.embed_dim) - value = torch.rand(16, 30, self.embed_dim) - with self.assertRaises(AssertionError): - self.multihead_attn(query, key, value) - - def test_forward_different_batch_size(self): - query = torch.rand(16, 20, self.embed_dim) - key = torch.rand(32, 20, self.embed_dim) - value = torch.rand(32, 20, self.embed_dim) - with self.assertRaises(AssertionError): - self.multihead_attn(query, key, value) - - def test_forward_different_embed_dim(self): - query = torch.rand(16, 20, 128) - key = torch.rand(16, 20, 128) - value = torch.rand(16, 20, 128) - with self.assertRaises(AssertionError): - self.multihead_attn(query, key, value) - - def test_forward_no_value(self): - query = torch.rand(16, 20, self.embed_dim) - key = torch.rand(16, 20, self.embed_dim) - with self.assertRaises(AssertionError): - self.multihead_attn(query, key, None) - - def test_forward_no_key(self): - query = torch.rand(16, 20, self.embed_dim) - value = torch.rand(16, 20, self.embed_dim) - with self.assertRaises(AssertionError): - self.multihead_attn(query, None, value) - - def test_forward_no_query(self): - key = torch.rand(16, 20, self.embed_dim) - value = torch.rand(16, 20, self.embed_dim) - with self.assertRaises(AssertionError): - self.multihead_attn(None, key, value) - - def test_forward_no_input(self): - with self.assertRaises(AssertionError): - self.multihead_attn(None, None, None) - - def test_forward_zero_length_input(self): - query = torch.rand(16, 0, self.embed_dim) - key = torch.rand(16, 0, self.embed_dim) - value = torch.rand(16, 0, self.embed_dim) - attn, attn_weights = self.multihead_attn(query, key, value) - self.assertEqual(attn.shape, (16, 0, self.embed_dim)) - self.assertEqual(attn_weights.shape, (self.num_heads, 16, 0, 0)) - - def test_forward_one_length_input(self): - query = torch.rand(16, 1, self.embed_dim) - key = torch.rand(16, 1, self.embed_dim) - value = torch.rand(16, 1, self.embed_dim) - attn, attn_weights = self.multihead_attn(query, key, value) - self.assertEqual(attn.shape, (16, 1, self.embed_dim)) - self.assertEqual(attn_weights.shape, (self.num_heads, 16, 1, 1)) - - def test_forward_large_input(self): - query = torch.rand(16, 1000, self.embed_dim) - key = torch.rand(16, 1000, self.embed_dim) - value = torch.rand(16, 1000, self.embed_dim) - attn, attn_weights = self.multihead_attn(query, key, value) - self.assertEqual(attn.shape, (16, 1000, self.embed_dim)) - self.assertEqual(attn_weights.shape, (self.num_heads, 16, 1000, 1000)) +from zeta.nn.attention.multihead_attention import MultiheadAttention -if __name__ == "__main__": - unittest.main() +def test_multiheadattention_initialization(): + args = {"layernorm_eps": 1e-5, "xpos_rel_pos": False} + model = MultiheadAttention(args, embed_dim=512, num_heads=8) + assert isinstance(model, MultiheadAttention) + assert model.embed_dim == 512 + assert model.num_heads == 8 + assert model.head_dim == 64 + assert model.scaling == 1 / 8 + + +def test_multiheadattention_forward(): + args = {"layernorm_eps": 1e-5, "xpos_rel_pos": False} + model = MultiheadAttention(args, embed_dim=512, num_heads=8) + query = torch.randn(1, 10, 512) + key = torch.randn(1, 10, 512) + value = torch.randn(1, 10, 512) + output, attn_weights = model(query, key, value) + assert output.shape == (1, 10, 512) + assert attn_weights.shape == (8, 1, 10, 10) + + +@pytest.mark.parametrize( + "query_len, key_len, value_len", [(0, 10, 10), (10, 0, 10), (10, 10, 0)] +) +def test_multiheadattention_forward_edge_cases(query_len, key_len, value_len): + args = {"layernorm_eps": 1e-5, "xpos_rel_pos": False} + model = MultiheadAttention(args, embed_dim=512, num_heads=8) + query = torch.randn(1, query_len, 512) + key = torch.randn(1, key_len, 512) + value = torch.randn(1, value_len, 512) + with pytest.raises(Exception): + model(query, key, value) + + +def test_multiheadattention_forward_invalid_dimensions(): + args = {"layernorm_eps": 1e-5, "xpos_rel_pos": False} + model = MultiheadAttention(args, embed_dim=512, num_heads=8) + query = torch.randn(1, 10, 256) + key = torch.randn(1, 10, 512) + value = torch.randn(1, 10, 512) + with pytest.raises(Exception): + model(query, key, value) diff --git a/tests/nn/attentions/mqa.py b/tests/nn/attentions/test_mqa.py similarity index 100% rename from tests/nn/attentions/mqa.py rename to tests/nn/attentions/test_mqa.py diff --git a/tests/nn/attentions/shaped_attn.py b/tests/nn/attentions/test_shaped_attn.py similarity index 100% rename from tests/nn/attentions/shaped_attn.py rename to tests/nn/attentions/test_shaped_attn.py diff --git a/tests/nn/attentions/sparse_attn.py b/tests/nn/attentions/test_sparse_attn.py similarity index 100% rename from tests/nn/attentions/sparse_attn.py rename to tests/nn/attentions/test_sparse_attn.py diff --git a/tests/nn/attentions/test_test_mha.py b/tests/nn/attentions/test_test_mha.py new file mode 100644 index 00000000..44ef5d73 --- /dev/null +++ b/tests/nn/attentions/test_test_mha.py @@ -0,0 +1,167 @@ +from zeta.nn.attention.multihead_attention import MultiheadAttention +import torch +import unittest + + +class TestMultiheadAttention(unittest.TestCase): + def setUp(self): + self.args = { + "xpos_rel_pos": True, + "xpos_scale_base": 2, + "layernorm_eps": 1e-5, + } + self.embed_dim = 64 + self.num_heads = 4 + self.multihead_attn = MultiheadAttention( + self.args, self.embed_dim, self.num_heads + ) + + def test_forward_shape(self): + query = torch.rand(16, 20, self.embed_dim) + key = torch.rand(16, 20, self.embed_dim) + value = torch.rand(16, 20, self.embed_dim) + attn, attn_weights = self.multihead_attn(query, key, value) + self.assertEqual(attn.shape, (16, 20, self.embed_dim)) + self.assertEqual(attn_weights.shape, (self.num_heads, 16, 20, 20)) + + def test_forward_incremental_state(self): + query = torch.rand(16, 20, self.embed_dim) + key = torch.rand(16, 20, self.embed_dim) + value = torch.rand(16, 20, self.embed_dim) + incremental_state = { + "prev_key": torch.rand( + 16, self.num_heads, 10, self.embed_dim // self.num_heads + ), + "prev_value": torch.rand( + 16, self.num_heads, 10, self.embed_dim // self.num_heads + ), + } + attn, attn_weights = self.multihead_attn( + query, key, value, incremental_state=incremental_state + ) + self.assertEqual(attn.shape, (16, 20, self.embed_dim)) + self.assertEqual(attn_weights.shape, (self.num_heads, 16, 20, 30)) + + def test_forward_attn_mask(self): + query = torch.rand(16, 20, self.embed_dim) + key = torch.rand(16, 20, self.embed_dim) + value = torch.rand(16, 20, self.embed_dim) + attn_mask = torch.ones(20, 20) + attn, attn_weights = self.multihead_attn( + query, key, value, attn_mask=attn_mask + ) + self.assertEqual(attn.shape, (16, 20, self.embed_dim)) + self.assertEqual(attn_weights.shape, (self.num_heads, 16, 20, 20)) + + def test_forward_key_padding_mask(self): + query = torch.rand(16, 20, self.embed_dim) + key = torch.rand(16, 20, self.embed_dim) + value = torch.rand(16, 20, self.embed_dim) + key_padding_mask = torch.ones(16, 20) + attn, attn_weights = self.multihead_attn( + query, key, value, key_padding_mask=key_padding_mask + ) + self.assertEqual(attn.shape, (16, 20, self.embed_dim)) + self.assertEqual(attn_weights.shape, (self.num_heads, 16, 20, 20)) + + def test_forward_rel_pos(self): + query = torch.rand(16, 20, self.embed_dim) + key = torch.rand(16, 20, self.embed_dim) + value = torch.rand(16, 20, self.embed_dim) + rel_pos = torch.rand(16, self.num_heads, 20, 20) + attn, attn_weights = self.multihead_attn( + query, key, value, rel_pos=rel_pos + ) + self.assertEqual(attn.shape, (16, 20, self.embed_dim)) + self.assertEqual(attn_weights.shape, (self.num_heads, 16, 20, 20)) + + def test_forward_is_first_step(self): + query = torch.rand(16, 20, self.embed_dim) + key = torch.rand(16, 20, self.embed_dim) + value = torch.rand(16, 20, self.embed_dim) + attn, attn_weights = self.multihead_attn( + query, key, value, is_first_step=True + ) + self.assertEqual(attn.shape, (16, 20, self.embed_dim)) + self.assertEqual(attn_weights.shape, (self.num_heads, 16, 20, 20)) + + def test_forward_is_not_first_step(self): + query = torch.rand(16, 20, self.embed_dim) + key = torch.rand(16, 20, self.embed_dim) + value = torch.rand(16, 20, self.embed_dim) + attn, attn_weights = self.multihead_attn( + query, key, value, is_first_step=False + ) + self.assertEqual(attn.shape, (16, 20, self.embed_dim)) + self.assertEqual(attn_weights.shape, (self.num_heads, 16, 20, 20)) + + def test_forward_different_query_key_value_size(self): + query = torch.rand(16, 20, self.embed_dim) + key = torch.rand(16, 30, self.embed_dim) + value = torch.rand(16, 30, self.embed_dim) + with self.assertRaises(AssertionError): + self.multihead_attn(query, key, value) + + def test_forward_different_batch_size(self): + query = torch.rand(16, 20, self.embed_dim) + key = torch.rand(32, 20, self.embed_dim) + value = torch.rand(32, 20, self.embed_dim) + with self.assertRaises(AssertionError): + self.multihead_attn(query, key, value) + + def test_forward_different_embed_dim(self): + query = torch.rand(16, 20, 128) + key = torch.rand(16, 20, 128) + value = torch.rand(16, 20, 128) + with self.assertRaises(AssertionError): + self.multihead_attn(query, key, value) + + def test_forward_no_value(self): + query = torch.rand(16, 20, self.embed_dim) + key = torch.rand(16, 20, self.embed_dim) + with self.assertRaises(AssertionError): + self.multihead_attn(query, key, None) + + def test_forward_no_key(self): + query = torch.rand(16, 20, self.embed_dim) + value = torch.rand(16, 20, self.embed_dim) + with self.assertRaises(AssertionError): + self.multihead_attn(query, None, value) + + def test_forward_no_query(self): + key = torch.rand(16, 20, self.embed_dim) + value = torch.rand(16, 20, self.embed_dim) + with self.assertRaises(AssertionError): + self.multihead_attn(None, key, value) + + def test_forward_no_input(self): + with self.assertRaises(AssertionError): + self.multihead_attn(None, None, None) + + def test_forward_zero_length_input(self): + query = torch.rand(16, 0, self.embed_dim) + key = torch.rand(16, 0, self.embed_dim) + value = torch.rand(16, 0, self.embed_dim) + attn, attn_weights = self.multihead_attn(query, key, value) + self.assertEqual(attn.shape, (16, 0, self.embed_dim)) + self.assertEqual(attn_weights.shape, (self.num_heads, 16, 0, 0)) + + def test_forward_one_length_input(self): + query = torch.rand(16, 1, self.embed_dim) + key = torch.rand(16, 1, self.embed_dim) + value = torch.rand(16, 1, self.embed_dim) + attn, attn_weights = self.multihead_attn(query, key, value) + self.assertEqual(attn.shape, (16, 1, self.embed_dim)) + self.assertEqual(attn_weights.shape, (self.num_heads, 16, 1, 1)) + + def test_forward_large_input(self): + query = torch.rand(16, 1000, self.embed_dim) + key = torch.rand(16, 1000, self.embed_dim) + value = torch.rand(16, 1000, self.embed_dim) + attn, attn_weights = self.multihead_attn(query, key, value) + self.assertEqual(attn.shape, (16, 1000, self.embed_dim)) + self.assertEqual(attn_weights.shape, (self.num_heads, 16, 1000, 1000)) + + +if __name__ == "__main__": + unittest.main() diff --git a/tests/nn/attentions/xc_attention.py b/tests/nn/attentions/test_xc_attention.py similarity index 100% rename from tests/nn/attentions/xc_attention.py rename to tests/nn/attentions/test_xc_attention.py diff --git a/tests/nn/biases/alibi.py b/tests/nn/biases/test_alibi.py similarity index 100% rename from tests/nn/biases/alibi.py rename to tests/nn/biases/test_alibi.py diff --git a/tests/nn/biases/dynamic_relative.py b/tests/nn/biases/test_dynamic_relative.py similarity index 100% rename from tests/nn/biases/dynamic_relative.py rename to tests/nn/biases/test_dynamic_relative.py diff --git a/tests/nn/biases/relative_position_bias.py b/tests/nn/biases/test_relative_position_bias.py similarity index 100% rename from tests/nn/biases/relative_position_bias.py rename to tests/nn/biases/test_relative_position_bias.py diff --git a/tests/nn/embeddings/abc_pos_emb.py b/tests/nn/embeddings/test_abc_pos_emb.py similarity index 100% rename from tests/nn/embeddings/abc_pos_emb.py rename to tests/nn/embeddings/test_abc_pos_emb.py diff --git a/tests/nn/embeddings/patch_embedding.py b/tests/nn/embeddings/test_patch_embedding.py similarity index 100% rename from tests/nn/embeddings/patch_embedding.py rename to tests/nn/embeddings/test_patch_embedding.py diff --git a/tests/nn/embeddings/positional_embeddings.py b/tests/nn/embeddings/test_positional_embeddings.py similarity index 100% rename from tests/nn/embeddings/positional_embeddings.py rename to tests/nn/embeddings/test_positional_embeddings.py diff --git a/tests/nn/embeddings/rope.py b/tests/nn/embeddings/test_rope.py similarity index 100% rename from tests/nn/embeddings/rope.py rename to tests/nn/embeddings/test_rope.py diff --git a/tests/nn/embeddings/rotary.py b/tests/nn/embeddings/test_rotary.py similarity index 100% rename from tests/nn/embeddings/rotary.py rename to tests/nn/embeddings/test_rotary.py diff --git a/tests/nn/embeddings/sine_positional_embs.py b/tests/nn/embeddings/test_sine_positional_embs.py similarity index 100% rename from tests/nn/embeddings/sine_positional_embs.py rename to tests/nn/embeddings/test_sine_positional_embs.py diff --git a/tests/nn/embeddings/truncated_rotary_emb.py b/tests/nn/embeddings/test_truncated_rotary_emb.py similarity index 100% rename from tests/nn/embeddings/truncated_rotary_emb.py rename to tests/nn/embeddings/test_truncated_rotary_emb.py diff --git a/tests/nn/embeddings/vision_embeddings.py b/tests/nn/embeddings/test_vision_embeddings.py similarity index 100% rename from tests/nn/embeddings/vision_embeddings.py rename to tests/nn/embeddings/test_vision_embeddings.py diff --git a/tests/nn/embeddings/vision_lang_embeddings.py b/tests/nn/embeddings/test_vision_lang_embeddings.py similarity index 100% rename from tests/nn/embeddings/vision_lang_embeddings.py rename to tests/nn/embeddings/test_vision_lang_embeddings.py diff --git a/tests/nn/embeddings/xpos.py b/tests/nn/embeddings/test_xpos.py similarity index 100% rename from tests/nn/embeddings/xpos.py rename to tests/nn/embeddings/test_xpos.py diff --git a/tests/nn/embeddings/yarn.py b/tests/nn/embeddings/test_yarn.py similarity index 100% rename from tests/nn/embeddings/yarn.py rename to tests/nn/embeddings/test_yarn.py diff --git a/tests/nn/modules/adaptive_param.py b/tests/nn/modules/test_adaptive_param.py similarity index 100% rename from tests/nn/modules/adaptive_param.py rename to tests/nn/modules/test_adaptive_param.py diff --git a/tests/nn/modules/alr_block.py b/tests/nn/modules/test_alr_block.py similarity index 100% rename from tests/nn/modules/alr_block.py rename to tests/nn/modules/test_alr_block.py diff --git a/tests/nn/modules/bitlinear.py b/tests/nn/modules/test_bitlinear.py similarity index 100% rename from tests/nn/modules/bitlinear.py rename to tests/nn/modules/test_bitlinear.py diff --git a/tests/nn/modules/cross_attn_images.py b/tests/nn/modules/test_cross_attn_images.py similarity index 100% rename from tests/nn/modules/cross_attn_images.py rename to tests/nn/modules/test_cross_attn_images.py diff --git a/tests/nn/modules/custom_mlp.py b/tests/nn/modules/test_custom_mlp.py similarity index 100% rename from tests/nn/modules/custom_mlp.py rename to tests/nn/modules/test_custom_mlp.py diff --git a/tests/nn/modules/dynamic_module.py b/tests/nn/modules/test_dynamic_module.py similarity index 100% rename from tests/nn/modules/dynamic_module.py rename to tests/nn/modules/test_dynamic_module.py diff --git a/tests/nn/modules/expert.py b/tests/nn/modules/test_expert.py similarity index 100% rename from tests/nn/modules/expert.py rename to tests/nn/modules/test_expert.py diff --git a/tests/nn/modules/feedforward.py b/tests/nn/modules/test_feedforward.py similarity index 100% rename from tests/nn/modules/feedforward.py rename to tests/nn/modules/test_feedforward.py diff --git a/tests/nn/modules/full_feedforward.py b/tests/nn/modules/test_full_feedforward.py similarity index 100% rename from tests/nn/modules/full_feedforward.py rename to tests/nn/modules/test_full_feedforward.py diff --git a/tests/nn/modules/hebbian.py b/tests/nn/modules/test_hebbian.py similarity index 100% rename from tests/nn/modules/hebbian.py rename to tests/nn/modules/test_hebbian.py diff --git a/tests/nn/modules/image_projector.py b/tests/nn/modules/test_image_projector.py similarity index 100% rename from tests/nn/modules/image_projector.py rename to tests/nn/modules/test_image_projector.py diff --git a/tests/nn/modules/log_ff.py b/tests/nn/modules/test_log_ff.py similarity index 100% rename from tests/nn/modules/log_ff.py rename to tests/nn/modules/test_log_ff.py diff --git a/tests/nn/modules/mbconv.py b/tests/nn/modules/test_mbconv.py similarity index 100% rename from tests/nn/modules/mbconv.py rename to tests/nn/modules/test_mbconv.py diff --git a/tests/nn/modules/mlp.py b/tests/nn/modules/test_mlp.py similarity index 100% rename from tests/nn/modules/mlp.py rename to tests/nn/modules/test_mlp.py diff --git a/tests/nn/modules/mm_adapter.py b/tests/nn/modules/test_mm_adapter.py similarity index 100% rename from tests/nn/modules/mm_adapter.py rename to tests/nn/modules/test_mm_adapter.py diff --git a/tests/nn/modules/polymorphic_neuron.py b/tests/nn/modules/test_polymorphic_neuron.py similarity index 100% rename from tests/nn/modules/polymorphic_neuron.py rename to tests/nn/modules/test_polymorphic_neuron.py diff --git a/tests/nn/modules/simple_feedforward.py b/tests/nn/modules/test_simple_feedforward.py similarity index 100% rename from tests/nn/modules/simple_feedforward.py rename to tests/nn/modules/test_simple_feedforward.py diff --git a/tests/nn/modules/test_conv_lang.py b/tests/nn/modules/test_test_conv_lang.py similarity index 100% rename from tests/nn/modules/test_conv_lang.py rename to tests/nn/modules/test_test_conv_lang.py diff --git a/tests/nn/modules/test_h3_layer.py b/tests/nn/modules/test_test_h3_layer.py similarity index 100% rename from tests/nn/modules/test_h3_layer.py rename to tests/nn/modules/test_test_h3_layer.py diff --git a/tests/nn/modules/test_s4.py b/tests/nn/modules/test_test_s4.py similarity index 100% rename from tests/nn/modules/test_s4.py rename to tests/nn/modules/test_test_s4.py diff --git a/tests/nn/modules/token_learner.py b/tests/nn/modules/test_token_learner.py similarity index 100% rename from tests/nn/modules/token_learner.py rename to tests/nn/modules/test_token_learner.py diff --git a/tests/nn/modules/transformations.py b/tests/nn/modules/test_transformations.py similarity index 100% rename from tests/nn/modules/transformations.py rename to tests/nn/modules/test_transformations.py diff --git a/tests/nn/modules/unet.py b/tests/nn/modules/test_unet.py similarity index 100% rename from tests/nn/modules/unet.py rename to tests/nn/modules/test_unet.py diff --git a/tests/nn/modules/visual_expert.py b/tests/nn/modules/test_visual_expert.py similarity index 100% rename from tests/nn/modules/visual_expert.py rename to tests/nn/modules/test_visual_expert.py diff --git a/tests/ops/einops_from_to.py b/tests/ops/test_einops_from_to.py similarity index 100% rename from tests/ops/einops_from_to.py rename to tests/ops/test_einops_from_to.py diff --git a/tests/ops/einops_poly.py b/tests/ops/test_einops_poly.py similarity index 100% rename from tests/ops/einops_poly.py rename to tests/ops/test_einops_poly.py diff --git a/tests/ops/mos.py b/tests/ops/test_mos.py similarity index 100% rename from tests/ops/mos.py rename to tests/ops/test_mos.py diff --git a/tests/optim/decoupled_lion.py b/tests/optim/test_decoupled_lion.py similarity index 100% rename from tests/optim/decoupled_lion.py rename to tests/optim/test_decoupled_lion.py diff --git a/tests/optim/gradient_ascent.py b/tests/optim/test_gradient_ascent.py similarity index 98% rename from tests/optim/gradient_ascent.py rename to tests/optim/test_gradient_ascent.py index 48a85710..0af93833 100644 --- a/tests/optim/gradient_ascent.py +++ b/tests/optim/test_gradient_ascent.py @@ -1,6 +1,6 @@ import pytest import torch -from gradient_ascent import GradientAscent +from zeta.optim.gradient_ascent import GradientAscent def mock_module(): diff --git a/tests/optim/gradient_equillibrum.py b/tests/optim/test_gradient_equillibrum.py similarity index 99% rename from tests/optim/gradient_equillibrum.py rename to tests/optim/test_gradient_equillibrum.py index 1c60e068..256549b4 100644 --- a/tests/optim/gradient_equillibrum.py +++ b/tests/optim/test_gradient_equillibrum.py @@ -3,7 +3,7 @@ from torch import nn from torch.optim import SGD -from ge.main import GradientEquilibrum +from zeta.optim.gradient_equillibrum import GradientEquilibrum # Helper function to create a simple model and loss for testing diff --git a/tests/optim/stable_adamw.py b/tests/optim/test_stable_adamw.py similarity index 100% rename from tests/optim/stable_adamw.py rename to tests/optim/test_stable_adamw.py diff --git a/tests/quant/qlora.py b/tests/quant/test_qlora.py similarity index 100% rename from tests/quant/qlora.py rename to tests/quant/test_qlora.py diff --git a/tests/rl/vision_reward_model.py b/tests/rl/test_vision_reward_model.py similarity index 100% rename from tests/rl/vision_reward_model.py rename to tests/rl/test_vision_reward_model.py diff --git a/tests/structs/efficient_net.py b/tests/structs/test_efficient_net.py similarity index 98% rename from tests/structs/efficient_net.py rename to tests/structs/test_efficient_net.py index 50cfe255..1cdd5621 100644 --- a/tests/structs/efficient_net.py +++ b/tests/structs/test_efficient_net.py @@ -1,7 +1,7 @@ import pytest import torch import torch.nn as nn -from zeta.structs import EfficientNet +from zeta.structs.efficient_net import EfficientNet @pytest.fixture diff --git a/tests/__init__.py b/tests/test_test___init__.py similarity index 100% rename from tests/__init__.py rename to tests/test_test___init__.py diff --git a/tests/example.py b/tests/test_test_example.py similarity index 100% rename from tests/example.py rename to tests/test_test_example.py diff --git a/tests/training/parallel_wrapper.py b/tests/training/test_parallel_wrapper.py similarity index 100% rename from tests/training/parallel_wrapper.py rename to tests/training/test_parallel_wrapper.py From 70d1f1340b9281b6c073d07dd7e285924d250d77 Mon Sep 17 00:00:00 2001 From: Kye Date: Wed, 29 Nov 2023 23:54:30 -0800 Subject: [PATCH 085/587] [FEAT][Scripts] --- code_quality.sh => scripts/code_quality.sh | 0 test_name.sh => scripts/test_name.sh | 4 +++- tests.sh => scripts/tests.sh | 0 3 files changed, 3 insertions(+), 1 deletion(-) rename code_quality.sh => scripts/code_quality.sh (100%) rename test_name.sh => scripts/test_name.sh (59%) rename tests.sh => scripts/tests.sh (100%) diff --git a/code_quality.sh b/scripts/code_quality.sh similarity index 100% rename from code_quality.sh rename to scripts/code_quality.sh diff --git a/test_name.sh b/scripts/test_name.sh similarity index 59% rename from test_name.sh rename to scripts/test_name.sh index d894e4aa..cdc6a013 100755 --- a/test_name.sh +++ b/scripts/test_name.sh @@ -2,5 +2,7 @@ find ./tests -name "*.py" -type f | while read file do filename=$(basename "$file") dir=$(dirname "$file") - mv "$file" "$dir/test_$filename" + if [[ $filename != test_* ]]; then + mv "$file" "$dir/test_$filename" + fi done \ No newline at end of file diff --git a/tests.sh b/scripts/tests.sh similarity index 100% rename from tests.sh rename to scripts/tests.sh From 45ef726e0e0603f9226f4eb63421f33b46962a29 Mon Sep 17 00:00:00 2001 From: Kye Date: Thu, 30 Nov 2023 00:03:11 -0800 Subject: [PATCH 086/587] [TESTS][__init__] --- tests/nn/modules/test_test_h3_layer.py | 8 ++++++- tests/nn/modules/test_test_s4.py | 18 ++++++++++++++- tests/test_init.py | 25 ++++++++++++++++++++ tests/test_test___init__.py | 2 -- zeta/__init__.py | 3 --- zeta/nn/modules/__init__.py | 1 - zeta/nn/modules/h3.py | 27 ++++++++++++++-------- zeta/nn/modules/s4.py | 32 +++++++++++++++++++++----- zeta/quant/__init__.py | 2 +- 9 files changed, 93 insertions(+), 25 deletions(-) create mode 100644 tests/test_init.py delete mode 100644 tests/test_test___init__.py diff --git a/tests/nn/modules/test_test_h3_layer.py b/tests/nn/modules/test_test_h3_layer.py index d06fb1fa..3ac54264 100644 --- a/tests/nn/modules/test_test_h3_layer.py +++ b/tests/nn/modules/test_test_h3_layer.py @@ -1,4 +1,3 @@ - from unittest.mock import Mock import pytest @@ -12,22 +11,26 @@ def test_h3_layer_creation(): layer = H3Layer(256) assert isinstance(layer, H3Layer) + def test_forward_pass(): layer = H3Layer(256) x = torch.randn(1, 256, 1024) output = layer(x) assert output.shape == torch.Size([1, 256, 1024]) + # 2. Utilize Fixtures @pytest.fixture def sample_layer(): return H3Layer(128) + def test_fixture_usage(sample_layer): x = torch.randn(1, 128, 1024) output = sample_layer(x) assert output.shape == torch.Size([1, 128, 1024]) + # 3. Parameterized Testing @pytest.mark.parametrize("dim", [128, 256, 512]) def test_parameterized_layer(dim): @@ -45,13 +48,16 @@ def test_with_mocked_ssm(): layer(x) assert mock_ssm.called + # 5. Exception Testing def test_invalid_dimension_raises_error(): with pytest.raises(ValueError): H3Layer(0) + # 6. Test Coverage (requires pytest-cov) def test_coverage(): pytest.main(["--cov=your_module", "test_your_module.py"]) + # Add more tests as needed... diff --git a/tests/nn/modules/test_test_s4.py b/tests/nn/modules/test_test_s4.py index 0f4a5628..6b33ac37 100644 --- a/tests/nn/modules/test_test_s4.py +++ b/tests/nn/modules/test_test_s4.py @@ -4,6 +4,7 @@ # Test cases for s4d_kernel function + # Test 1: Basic test with valid inputs def test_s4d_kernel_basic(): A = torch.tensor([[1.0, 2.0, 3.0]]) @@ -15,10 +16,21 @@ def test_s4d_kernel_basic(): assert result.shape == (1, 5, 3) assert torch.allclose( result, - torch.tensor([[[0.2, 0.4, 0.6], [0.2602, 0.5488, 0.8617], [0.3293, 0.6978, 1.0947], [0.4072, 0.8661, 1.3574], [0.4938, 1.0461, 1.6424]]]), + torch.tensor( + [ + [ + [0.2, 0.4, 0.6], + [0.2602, 0.5488, 0.8617], + [0.3293, 0.6978, 1.0947], + [0.4072, 0.8661, 1.3574], + [0.4938, 1.0461, 1.6424], + ] + ] + ), atol=1e-4, ) + # Test 2: Test with incompatible tensor dimensions def test_s4d_kernel_incompatible_dimensions(): A = torch.tensor([[1.0, 2.0, 3.0]]) @@ -31,6 +43,7 @@ def test_s4d_kernel_incompatible_dimensions(): with pytest.raises(ValueError): s4d_kernel(A, B, C, dt, L) + # Test 3: Test with invalid data type for dt def test_s4d_kernel_invalid_dt_type(): A = torch.tensor([[1.0, 2.0, 3.0]]) @@ -41,6 +54,7 @@ def test_s4d_kernel_invalid_dt_type(): with pytest.raises(TypeError): s4d_kernel(A, B, C, dt, L) + # Test 4: Test with invalid data type for L def test_s4d_kernel_invalid_L_type(): A = torch.tensor([[1.0, 2.0, 3.0]]) @@ -51,6 +65,7 @@ def test_s4d_kernel_invalid_L_type(): with pytest.raises(TypeError): s4d_kernel(A, B, C, dt, L) + # Test 5: Test with zero-dimensional tensors def test_s4d_kernel_zero_dimensional_tensors(): A = torch.tensor(1.0) @@ -66,4 +81,5 @@ def test_s4d_kernel_zero_dimensional_tensors(): atol=1e-4, ) + # Add more test cases as needed... diff --git a/tests/test_init.py b/tests/test_init.py new file mode 100644 index 00000000..2a97119b --- /dev/null +++ b/tests/test_init.py @@ -0,0 +1,25 @@ +import pytest +import zeta + + +def test_imports(): + modules = [ + "nn", + "structs", + "models", + "utils", + "training", + "tokenizers", + "rl", + "optim", + "ops", + "quant", + ] + missing_modules = [] + for module in modules: + if not hasattr(zeta, module): + missing_modules.append(module) + + assert ( + not missing_modules + ), f"Modules {', '.join(missing_modules)} not found in zeta package" diff --git a/tests/test_test___init__.py b/tests/test_test___init__.py deleted file mode 100644 index 73dbf876..00000000 --- a/tests/test_test___init__.py +++ /dev/null @@ -1,2 +0,0 @@ -# Copyright (c) 2022 Agora -# Licensed under The MIT License [see LICENSE for details] diff --git a/zeta/__init__.py b/zeta/__init__.py index f083fb4d..5fbcfce8 100644 --- a/zeta/__init__.py +++ b/zeta/__init__.py @@ -34,6 +34,3 @@ def filter(self, record): from zeta.optim import * from zeta.ops import * from zeta.quant import * - - - diff --git a/zeta/nn/modules/__init__.py b/zeta/nn/modules/__init__.py index 57abba76..c8d1fee3 100644 --- a/zeta/nn/modules/__init__.py +++ b/zeta/nn/modules/__init__.py @@ -46,7 +46,6 @@ from zeta.nn.modules.h3 import H3Layer - # from zeta.nn.modules.img_reshape import image_reshape # from zeta.nn.modules.flatten_features import flatten_features # from zeta.nn.modules.scaled_sinusoidal import ScaledSinuosidalEmbedding diff --git a/zeta/nn/modules/h3.py b/zeta/nn/modules/h3.py index 92ed3092..1a4b3931 100644 --- a/zeta/nn/modules/h3.py +++ b/zeta/nn/modules/h3.py @@ -1,12 +1,14 @@ import torch import torch.nn as nn + class DiagonalSSM(nn.Module): """DiagonalSSM is a module that implements the Diagonal SSM operation. Args: nn (_type_): _description_ """ + def __init__(self, dim): super().__init__() # A diagonal matrix represented as a vector for ease of multiplication @@ -24,12 +26,14 @@ def forward(self, x): # Multiplication with a diagonal matrix can be done element-wise return x * self.diag + class ShiftSSM(nn.Module): """ShiftSSM is a module that implements the Shift SSM operation. Args: nn (_type_): _description_ """ + def __init__(self, dim): super().__init__() # A shift matrix operation @@ -47,16 +51,17 @@ def forward(self, x): # Shift the last dimension of x by one return torch.cat((x[..., -1:], x[..., :-1]), dim=-1) + class H3Layer(nn.Module): """H3Layer is a layer that implements the H3 associative memory model. - - + + Attributes: dim (int): The dimensionality of the input and output tensors. - + Methods: forward(x): Performs a forward pass through the layer. - + Examples: >>> import torch >>> from zeta.nn.modules.h3 import H3Layer @@ -66,32 +71,34 @@ class H3Layer(nn.Module): >>> out.shape torch.Size([1, 512, 1024]) """ + def __init__(self, dim: int): super().__init__() self.diagonal_ssm = DiagonalSSM(dim) self.shift_ssm = ShiftSSM(dim) - + self.q_proj = nn.Linear(dim, dim) self.k_proj = nn.Linear(dim, dim) self.v_proj = nn.Linear(dim, dim) - + def forward(self, x): # Linear projections q = self.q_proj(x) k = self.k_proj(x) v = self.v_proj(x) - + # Apply Shift SSM to k k = self.shift_ssm(k) - + # Element-wise multiplication for associative recall combined = q * k - + # Apply Diagonal SSM to combined tensor output = self.diagonal_ssm(combined) * v - + return output + # # Example usage: # batch_size, seq_len, dim = 32, 40, 512 # x = torch.rand(batch_size, seq_len, dim) diff --git a/zeta/nn/modules/s4.py b/zeta/nn/modules/s4.py index d834fe15..dd41d306 100644 --- a/zeta/nn/modules/s4.py +++ b/zeta/nn/modules/s4.py @@ -1,7 +1,10 @@ import torch from typing import Tuple -def s4d_kernel(A: torch.Tensor, B: torch.Tensor, C: torch.Tensor, dt: float, L: int) -> torch.Tensor: + +def s4d_kernel( + A: torch.Tensor, B: torch.Tensor, C: torch.Tensor, dt: float, L: int +) -> torch.Tensor: """ Compute the S4D convolution kernel for state space models on 3D tensors with shape (batch_size, seqlen, dim). @@ -21,9 +24,17 @@ def s4d_kernel(A: torch.Tensor, B: torch.Tensor, C: torch.Tensor, dt: float, L: """ # Ensure A, B, and C have the same size in the last dimension and compatible batch dimensions - if A.size(-1) != B.size(-1) or A.size(-1) != C.size(-1) or A.shape[:-1] != B.shape[:-1] or A.shape[:-1] != C.shape[:-1]: - raise ValueError("The last dimension of tensors A, B, and C must match and have compatible batch dimensions.") - + if ( + A.size(-1) != B.size(-1) + or A.size(-1) != C.size(-1) + or A.shape[:-1] != B.shape[:-1] + or A.shape[:-1] != C.shape[:-1] + ): + raise ValueError( + "The last dimension of tensors A, B, and C must match and have" + " compatible batch dimensions." + ) + # Check that dt is a float and L is an integer if not isinstance(dt, float): raise TypeError("The time step dt must be a float.") @@ -38,12 +49,21 @@ def s4d_kernel(A: torch.Tensor, B: torch.Tensor, C: torch.Tensor, dt: float, L: B_expanded = B.unsqueeze(1) # Shape: (batch_size, 1, dim) # Perform the convolution kernel operation with proper broadcasting - vandermonde = torch.exp(arange_L * dt * A_expanded) # Shape: (seqlen, batch_size, dim) - result = torch.sum(vandermonde * B_expanded * (torch.exp(dt * A_expanded) - 1) / A_expanded, dim=0) + vandermonde = torch.exp( + arange_L * dt * A_expanded + ) # Shape: (seqlen, batch_size, dim) + result = torch.sum( + vandermonde + * B_expanded + * (torch.exp(dt * A_expanded) - 1) + / A_expanded, + dim=0, + ) result = C.unsqueeze(1) * result # Shape: (batch_size, seqlen, dim) return result + # # Example usage with random tensors: # torch.manual_seed(0) # For reproducibility # batch_size = 5 # Example batch size diff --git a/zeta/quant/__init__.py b/zeta/quant/__init__.py index 98a70445..4a393157 100644 --- a/zeta/quant/__init__.py +++ b/zeta/quant/__init__.py @@ -3,4 +3,4 @@ from zeta.quant.ste import STE from zeta.quant.qlora import QloraLinear -__all__ = ["QUIK", "absmax_quantize", "BitLinear", "STE", "QloraLinear"] \ No newline at end of file +__all__ = ["QUIK", "absmax_quantize", "BitLinear", "STE", "QloraLinear"] From deb2513f7152955947431250efce47381ad44ceb Mon Sep 17 00:00:00 2001 From: Kye Date: Thu, 30 Nov 2023 11:20:59 -0800 Subject: [PATCH 087/587] git ignore --- .gitignore | 1 + 1 file changed, 1 insertion(+) diff --git a/.gitignore b/.gitignore index 1c21c0cd..d5aec461 100644 --- a/.gitignore +++ b/.gitignore @@ -11,6 +11,7 @@ data # Distribution / packaging .Python build/ +.vscode develop-eggs/ dist/ downloads/ From 1457dcc3cd35b4b8578fa55cf631bb0705a0207f Mon Sep 17 00:00:00 2001 From: Kye Date: Fri, 1 Dec 2023 13:26:17 -0800 Subject: [PATCH 088/587] [MLPMixer] --- tests/nn/modules/test_kv_cache.py | 165 ++++++++++++++++++++++++++++++ zeta/nn/modules/__init__.py | 2 + zeta/nn/modules/kv_cache.py | 157 ++++++++++++++++++++++++++++ zeta/nn/modules/mlp_mixer.py | 146 ++++++++++++++++++++++++++ 4 files changed, 470 insertions(+) create mode 100644 tests/nn/modules/test_kv_cache.py create mode 100644 zeta/nn/modules/kv_cache.py create mode 100644 zeta/nn/modules/mlp_mixer.py diff --git a/tests/nn/modules/test_kv_cache.py b/tests/nn/modules/test_kv_cache.py new file mode 100644 index 00000000..7efeb3f8 --- /dev/null +++ b/tests/nn/modules/test_kv_cache.py @@ -0,0 +1,165 @@ +from unittest.mock import Mock +import pytest +import torch + +from zeta.nn.modules.kv_cache import ( + KVCache, + find_multiple, + precompute_freq_cis, + setup_cache, +) + + +# 1. Basic Tests +def test_find_multiple(): + assert find_multiple(10, 3) == 12 + assert find_multiple(15, 5) == 15 + assert find_multiple(20, 7) == 21 + + +def test_precompute_freq_cis(): + seq_len = 128 + n_elem = 64 + freqs = precompute_freq_cis(seq_len, n_elem) + assert freqs.shape == torch.Size([seq_len, n_elem, 2]) + + +def test_kv_cache_creation(): + cache = KVCache(32, 128, 8, 64) + assert isinstance(cache, KVCache) + + +# 2. Utilize Fixtures +@pytest.fixture +def sample_cache(): + return KVCache(16, 64, 4, 32) + + +def test_kv_cache_update(sample_cache): + input_pos = torch.randint(0, 64, (5,)) + k_val = torch.randn(16, 4, 64, 32) + v_val = torch.randn(16, 4, 64, 32) + k_out, v_out = sample_cache.update(input_pos, k_val, v_val) + assert k_out.shape == torch.Size([16, 4, 64, 32]) + assert v_out.shape == torch.Size([16, 4, 64, 32]) + + +# 3. Parameterized Testing +@pytest.mark.parametrize( + "max_batch_size, max_seq_len, heads, head_dim", + [(32, 128, 8, 64), (16, 64, 4, 32)], +) +def test_setup_cache(max_batch_size, max_seq_len, heads, head_dim): + layers = [ + Mock(attention=Mock(kw_cache=None)), + Mock(attention=Mock(kw_cache=None)), + ] + block_size = 64 + rope_base = 1000 + setup_cache( + max_batch_size, + max_seq_len, + head_dim * heads, + heads, + layers, + block_size, + rope_base, + ) + for layer in layers: + assert isinstance(layer.attention.kw_cache, KVCache) + + +# 1. Edge Cases +def test_find_multiple_edge_cases(): + assert find_multiple(0, 5) == 0 + assert find_multiple(5, 0) == 5 + assert find_multiple(0, 0) == 0 + + +def test_precompute_freq_cis_edge_cases(): + seq_len = 128 + n_elem = 0 + freqs = precompute_freq_cis(seq_len, n_elem) + assert freqs.shape == torch.Size([seq_len, 0, 2]) + + +# 2. Additional KVCache Tests +def test_kv_cache_update_empty_input(): + cache = KVCache(32, 128, 8, 64) + input_pos = torch.tensor([], dtype=torch.int64) + k_val = torch.randn(32, 8, 64, 64) + v_val = torch.randn(32, 8, 64, 64) + k_out, v_out = cache.update(input_pos, k_val, v_val) + assert k_out.shape == torch.Size([32, 8, 128, 64]) + assert v_out.shape == torch.Size([32, 8, 128, 64]) + + +def test_kv_cache_update_out_of_bounds_input(): + cache = KVCache(32, 128, 8, 64) + input_pos = torch.tensor([140, 160, 200], dtype=torch.int64) + k_val = torch.randn(32, 8, 64, 64) + v_val = torch.randn(32, 8, 64, 64) + k_out, v_out = cache.update(input_pos, k_val, v_val) + assert k_out.shape == torch.Size([32, 8, 128, 64]) + assert v_out.shape == torch.Size([32, 8, 128, 64]) + + +# 3. Additional setup_cache Tests +def test_setup_cache_max_seq_len_greater_than_max(): + layers = [ + Mock(attention=Mock(kw_cache=None)), + Mock(attention=Mock(kw_cache=None)), + ] + max_batch_size = 16 + max_seq_len = 64 + heads = 4 + head_dim = 32 + block_size = 32 + rope_base = 1000 + setup_cache( + max_batch_size, + max_seq_len + 10, + head_dim * heads, + heads, + layers, + block_size, + rope_base, + ) + for layer in layers: + assert isinstance(layer.attention.kw_cache, KVCache) + assert layer.attention.kw_cache.k_cache.shape == torch.Size( + [max_batch_size, heads, max_seq_len + 10, head_dim] + ) + assert layer.attention.kw_cache.v_cache.shape == torch.Size( + [max_batch_size, heads, max_seq_len + 10, head_dim] + ) + + +def test_setup_cache_max_batch_size_greater_than_max(): + layers = [ + Mock(attention=Mock(kw_cache=None)), + Mock(attention=Mock(kw_cache=None)), + ] + max_batch_size = 64 + max_seq_len = 32 + heads = 4 + head_dim = 32 + block_size = 32 + rope_base = 1000 + setup_cache( + max_batch_size + 10, + max_seq_len, + head_dim * heads, + heads, + layers, + block_size, + rope_base, + ) + for layer in layers: + assert isinstance(layer.attention.kw_cache, KVCache) + assert layer.attention.kw_cache.k_cache.shape == torch.Size( + [max_batch_size + 10, heads, max_seq_len, head_dim] + ) + assert layer.attention.kw_cache.v_cache.shape == torch.Size( + [max_batch_size + 10, heads, max_seq_len, head_dim] + ) diff --git a/zeta/nn/modules/__init__.py b/zeta/nn/modules/__init__.py index c8d1fee3..e169194b 100644 --- a/zeta/nn/modules/__init__.py +++ b/zeta/nn/modules/__init__.py @@ -44,6 +44,7 @@ from zeta.nn.modules.lang_conv_module import ConvolutionLanguageBlock from zeta.nn.modules.s4 import s4d_kernel from zeta.nn.modules.h3 import H3Layer +from zeta.nn.modules.mlp_mixer import MLPMixer # from zeta.nn.modules.img_reshape import image_reshape @@ -105,4 +106,5 @@ "IterativeCrossSelfAttention", "ConvolutionLanguageBlock", "H3Layer", + "MLPMixer", ] diff --git a/zeta/nn/modules/kv_cache.py b/zeta/nn/modules/kv_cache.py new file mode 100644 index 00000000..7e6c8fba --- /dev/null +++ b/zeta/nn/modules/kv_cache.py @@ -0,0 +1,157 @@ +import torch +from torch import nn, Tensor + + +# Helpers +def find_multiple(n: int, k: int) -> int: + """Finds the smallest multiple of k that is greater than or equal to n. + + Args: + n (int): _description_ + k (int): _description_ + + Returns: + int: _description_ + """ + if n % k == 0: + return n + return n + k - (n % k) + + +def precompute_freq_cis(seq_len: int, n_elem: int, base: int = 10000) -> Tensor: + """Precomputes the frequency values for the positional encodings. + + Args: + seq_len (int): _description_ + n_elem (int): _description_ + base (int, optional): _description_. Defaults to 10000. + + Returns: + Tensor: _description_ + """ + freqs = 1.0 / ( + base ** (torch.arange(0, n_elem, 2)[: (n_elem // 2)].float() / n_elem) + ) + t = torch.arange(seq_len, device=freqs.device) + freqs = torch.outer(t, freqs) + freqs_cis = torch.polar(torch.ones_like(freqs), freqs) + cache = torch.stack([freqs_cis.real, freqs_cis.imag], dim=-1) + return cache.to(dtype=torch.bfloat16) + + +class KVCache(nn.Module): + """ + KVCache is a module that stores the key and value tensors for each + position in the input sequence. This is used in the decoder of the + Transformer model to store the key and value tensors for each position + in the encoder output sequence. + + The cache is updated by calling the update method, which takes the + input positions and the key and value tensors for those positions. + + The cache is a tensor of shape [B, H, S, D], where B is the batch size, + H is the number of heads, S is the maximum sequence length, and D is + the head dimension. + + Args: + max_batch_size: The maximum batch size of the model. + max_seq_len: The maximum sequence length of the model. + heads: The number of heads in the model. + head_dim: The dimension of each head. + dtype: The datatype of the cache. + + Attributes: + k_cache: The key cache. + v_cache: The value cache. + + Methods: + update: Updates the cache with the given input positions and key + and value tensors. + + Input Shapes: + input_pos: [S] + k_val: [B, H, S, D] + v_val: [B, H, S, D] + + Output Shapes: + k_out: [B, H, S, D] + v_out: [B, H, S, D] + + Examples: + >>> from zeta.nn import KVCache + >>> cache = KVCache(32, 128, 8, 64) + >>> k_val = torch.randn(32, 8, 128, 64) + >>> v_val = torch.randn(32, 8, 128, 64) + >>> input_pos = torch.randint(0, 128, (5,)) + >>> k_out, v_out = cache.update(input_pos, k_val, v_val) + >>> k_out.shape + torch.Size([32, 8, 128, 64]) + """ + + def __init__( + self, + max_batch_size: int, + max_seq_len: int, + heads: int, + head_dim: int, + dtype=torch.bfloat16, + ): + super().__init__() + cache_shape = (max_batch_size, heads, max_seq_len, head_dim) + self.register_buffer("k_cache", torch.zeros(cache_shape, dtype=dtype)) + self.register_buffer("v_cache", torch.zeros(cache_shape, dtype=dtype)) + + def update(self, input_pos, k_val, v_val): + """ + Updates the cache with the given input positions and key and value. + + Args: + input_pos (_type_): _description_ + k_val (_type_): _description_ + v_val (_type_): _description_ + + Returns: + _type_: _description_ + """ + # Input pos: [5], k_val: [B, H, S, D] + assert input_pos.shape[0] == k_val.shape[2] + + k_out = self.k_cache + v_out = self.v_cache + k_out[:, :, input_pos, :] = k_val + v_out[:, :, input_pos, :] = v_val + + return k_out, v_out + + +def setup_cache( + max_batch_size, max_seq_len, dim, heads, layers, block_size, rope_base +): + """Sets up the cache for the given model. + + Args: + max_batch_size (_type_): _description_ + max_seq_len (_type_): _description_ + dim (_type_): _description_ + heads (_type_): _description_ + layers (_type_): _description_ + block_size (_type_): _description_ + rope_base (_type_): _description_ + """ + if max_seq_len >= max_seq_len and max_batch_size >= max_batch_size: + return + + head_dim = dim // heads + max_seq_len = find_multiple(max_seq_len, 8) + + for b in layers: + b.attention.kv_cache = KVCache( + max_batch_size, max_seq_len, heads, head_dim + ) + + freq_cis = precompute_freq_cis(block_size, dim // heads, rope_base) + causal_mask = torch.tril( + torch.ones(max_seq_len, max_seq_len, dtype=torch.bool) + ) + + return causal_mask, freq_cis diff --git a/zeta/nn/modules/mlp_mixer.py b/zeta/nn/modules/mlp_mixer.py new file mode 100644 index 00000000..e48a5e26 --- /dev/null +++ b/zeta/nn/modules/mlp_mixer.py @@ -0,0 +1,146 @@ +import torch +import torch.nn.functional as F +from einops import rearrange +from torch import nn + + +class MLPBlock(nn.Module): + """MLPBlock + + Args: + dim (int): [description] + """ + + def __init__(self, dim: int): + super(MLPBlock, self).__init__() + self.dense1 = nn.Linear(dim, dim) + self.dense2 = nn.Linear(dim, dim) + + def forward(self, x: torch.Tensor) -> torch.Tensor: + """Forward pass of MLPBlock + + Args: + x (torch.Tensor): _description_ + + Returns: + torch.Tensor: _description_ + """ + y = self.dense1(x) + y = F.gelu(y) + return self.dense(y) + + +class MixerBlock(nn.Module): + """MixerBlock + + + Args: + mlp_dim (int): [description] + channels_dim (int): [description] + """ + + def __init__(self, mlp_dim: int, channels_dim: int): + super(MixerBlock, self).__init__() + self.norm1 = nn.LayerNorm(channels_dim) + self.tokens_mlp = MLPBlock(mlp_dim) + + self.norm2 = nn.LayerNorm(channels_dim) + self.channel_mlp = MLPBlock(mlp_dim) + + def forward(self, x: torch.Tensor) -> torch.Tensor: + """Forward pass of MixerBlock + + Args: + x (torch.Tensor): _description_ + + Returns: + torch.Tensor: _description_ + """ + y = self.norm1(x) + y = rearrange(y, "n c t -> n t c") + y = self.tokens_mlp(y) + y = rearrange(y, "n t c -> n c t") + x = x + y + y = self.norm2(x) + return x + self.channel_mlp(y) + + +class MLPMixer(nn.Module): + """MLPMixer + + Args: + num_classes (int): [description] + num_blocks (int): [description] + patch_size (int): [description] + hidden_dim (int): [description] + tokens_mlp_dim (int): [description] + channels_mlp_dim (int): [description] + + Examples: + >>> from zeta.nn import MLPMixer + >>> model = MLPMixer(10, 8, 16, 32, 64, 64) + >>> x = torch.randn(32, 3, 224, 224) + >>> model(x).shape + torch.Size([32, 10]) + + + """ + + def __init__( + self, + num_classes: int, + num_blocks: int, + patch_size: int, + hidden_dim: int, + tokens_mlp_dim: int, + channels_mlp_dim: int, + ): + super(MLPMixer, self).__init__() + self.stem = nn.Conv2d( + hidden_dim, hidden_dim, kernel_size=patch_size, stride=patch_size + ) + self.mixer_blocks = nn.ModuleList( + [ + MixerBlock(tokens_mlp_dim, channels_mlp_dim) + for _ in range(num_blocks) + ] + ) + self.pred_head_layernorm = nn.LayerNorm(hidden_dim) + self.head = nn.Linear(hidden_dim, num_classes) + + def forward(self, x: torch.Tensor) -> torch.Tensor: + """Forward pass of MLPMixer + + Args: + x (torch.Tensor): _description_ + + Returns: + torch.Tensor: _description_ + """ + x = self.stem(x) + x = rearrange(x, "n c h w -> n (h w) c") + for mixer_block in self.mixer_blocks: + x = mixer_block(x) + x = self.pred_head_layernorm(x) + x = x.mean(dim=1) + return self.head(x) + + +# Example of creating a model instance +mlp_mixer = MLPMixer( + num_classes=10, + num_blocks=8, + patch_size=16, + hidden_dim=512, + tokens_mlp_dim=256, + channels_mlp_dim=512, +) + +# Example input tensor +example_input = torch.randn( + 1, 512, 32, 32 +) # Batch size of 1, 512 channels, 32x32 image +output = mlp_mixer(example_input) +print( + output.shape +) # Should output the shape corresponding to the number of classes From f38f932a9e4a9c28006884d000d2a0ee42134cd9 Mon Sep 17 00:00:00 2001 From: Kye Date: Sat, 2 Dec 2023 11:28:12 -0800 Subject: [PATCH 089/587] [LeakyRelu] --- zeta/nn/modules/__init__.py | 2 ++ zeta/nn/modules/leaky_relu.py | 52 +++++++++++++++++++++++++++++++++++ zeta/nn/modules/mlp_mixer.py | 16 ++++++----- 3 files changed, 63 insertions(+), 7 deletions(-) create mode 100644 zeta/nn/modules/leaky_relu.py diff --git a/zeta/nn/modules/__init__.py b/zeta/nn/modules/__init__.py index e169194b..b252eb86 100644 --- a/zeta/nn/modules/__init__.py +++ b/zeta/nn/modules/__init__.py @@ -45,6 +45,7 @@ from zeta.nn.modules.s4 import s4d_kernel from zeta.nn.modules.h3 import H3Layer from zeta.nn.modules.mlp_mixer import MLPMixer +from zeta.nn.modules.leaky_relu import LeakyRELU # from zeta.nn.modules.img_reshape import image_reshape @@ -107,4 +108,5 @@ "ConvolutionLanguageBlock", "H3Layer", "MLPMixer", + "LeakyRELU", ] diff --git a/zeta/nn/modules/leaky_relu.py b/zeta/nn/modules/leaky_relu.py new file mode 100644 index 00000000..5952412b --- /dev/null +++ b/zeta/nn/modules/leaky_relu.py @@ -0,0 +1,52 @@ +import torch +from torch import nn + + +class LeakyRELU(nn.Module): + """LeakyReLU activation function. + + Args: + nn (_type_): _description_ + + Returns: + _type_: _description_ + """ + __constants__ = ["inplace", "negative_slope"] + inplace: bool + negative_sloop: float + + def __init__( + self, + negative_slope: float = 1e-2, + inplace: bool = False, + ) -> None: + super().__init__() + self.negative_slope = negative_slope + self.inplace = inplace + + def forward( + self, + input: torch.Tensor, + ) -> torch.Tensor: + """Forward pass of the LeakyReLU module. + + Args: + input (torch.Tensor): _description_ + + Returns: + torch.Tensor: _description_ + """ + return torch.where( + input >= 0.0, + input, + input * self.negative_slope + ) + + def extra_repr(self) -> str: + """Extra information about this module. + + Returns: + str: _description_ + """ + inplace_str = ", inplace=True" if self.inplace else "" + return "negative_slope={}{}".format(self.negative_slope, inplace_str) \ No newline at end of file diff --git a/zeta/nn/modules/mlp_mixer.py b/zeta/nn/modules/mlp_mixer.py index e48a5e26..f45e7c39 100644 --- a/zeta/nn/modules/mlp_mixer.py +++ b/zeta/nn/modules/mlp_mixer.py @@ -11,10 +11,12 @@ class MLPBlock(nn.Module): dim (int): [description] """ - def __init__(self, dim: int): + def __init__(self, dim: int, hidden_dim: int): super(MLPBlock, self).__init__() - self.dense1 = nn.Linear(dim, dim) - self.dense2 = nn.Linear(dim, dim) + self.dim = dim + self.hidden_dim = hidden_dim + self.dense1 = nn.Linear(dim, hidden_dim) + self.dense2 = nn.Linear(hidden_dim, dim) def forward(self, x: torch.Tensor) -> torch.Tensor: """Forward pass of MLPBlock @@ -27,7 +29,7 @@ def forward(self, x: torch.Tensor) -> torch.Tensor: """ y = self.dense1(x) y = F.gelu(y) - return self.dense(y) + return self.dense2(y) class MixerBlock(nn.Module): @@ -42,10 +44,10 @@ class MixerBlock(nn.Module): def __init__(self, mlp_dim: int, channels_dim: int): super(MixerBlock, self).__init__() self.norm1 = nn.LayerNorm(channels_dim) - self.tokens_mlp = MLPBlock(mlp_dim) + self.tokens_mlp = MLPBlock(mlp_dim, mlp_dim) self.norm2 = nn.LayerNorm(channels_dim) - self.channel_mlp = MLPBlock(mlp_dim) + self.channel_mlp = MLPBlock(mlp_dim, mlp_dim) def forward(self, x: torch.Tensor) -> torch.Tensor: """Forward pass of MixerBlock @@ -132,7 +134,7 @@ def forward(self, x: torch.Tensor) -> torch.Tensor: num_blocks=8, patch_size=16, hidden_dim=512, - tokens_mlp_dim=256, + tokens_mlp_dim=512, channels_mlp_dim=512, ) From 93cc490d68bf5bac991b7b08c2fbf74633490b72 Mon Sep 17 00:00:00 2001 From: Kye Date: Sun, 3 Dec 2023 16:49:39 -0800 Subject: [PATCH 090/587] residual vector q --- tests/quant/resudual_vq.py | 30 +++++++++++++++++ zeta/nn/modules/leaky_relu.py | 15 ++++----- zeta/quant/residual_vq.py | 63 +++++++++++++++++++++++++++++++++++ 3 files changed, 99 insertions(+), 9 deletions(-) create mode 100644 tests/quant/resudual_vq.py create mode 100644 zeta/quant/residual_vq.py diff --git a/tests/quant/resudual_vq.py b/tests/quant/resudual_vq.py new file mode 100644 index 00000000..a9ca1e2d --- /dev/null +++ b/tests/quant/resudual_vq.py @@ -0,0 +1,30 @@ +import torch +import torch.nn as nn +from zeta.quant.residual_vq import ResidualVectorQuantizer + +def test_residual_vector_quantizer_init(): + model = ResidualVectorQuantizer(4, 4, 4) + assert isinstance(model, nn.Module) + assert model.dim == 4 + assert model.dim_out == 4 + assert model.n_embed == 4 + assert isinstance(model.embed, nn.Embedding) + assert isinstance(model.proj, nn.Linear) + +def test_residual_vector_quantizer_forward(): + model = ResidualVectorQuantizer(4, 4, 4) + x = torch.randn(2, 4) + out = model(x) + assert out.shape == torch.Size([2, 4]) + +def test_residual_vector_quantizer_forward_zero(): + model = ResidualVectorQuantizer(4, 4, 4) + x = torch.zeros(2, 4) + out = model(x) + assert torch.all(out == 0) + +def test_residual_vector_quantizer_forward_one(): + model = ResidualVectorQuantizer(4, 4, 4) + x = torch.ones(2, 4) + out = model(x) + assert torch.all(out == 1) \ No newline at end of file diff --git a/zeta/nn/modules/leaky_relu.py b/zeta/nn/modules/leaky_relu.py index 5952412b..1ad97b89 100644 --- a/zeta/nn/modules/leaky_relu.py +++ b/zeta/nn/modules/leaky_relu.py @@ -11,10 +11,11 @@ class LeakyRELU(nn.Module): Returns: _type_: _description_ """ + __constants__ = ["inplace", "negative_slope"] inplace: bool negative_sloop: float - + def __init__( self, negative_slope: float = 1e-2, @@ -23,7 +24,7 @@ def __init__( super().__init__() self.negative_slope = negative_slope self.inplace = inplace - + def forward( self, input: torch.Tensor, @@ -36,12 +37,8 @@ def forward( Returns: torch.Tensor: _description_ """ - return torch.where( - input >= 0.0, - input, - input * self.negative_slope - ) - + return torch.where(input >= 0.0, input, input * self.negative_slope) + def extra_repr(self) -> str: """Extra information about this module. @@ -49,4 +46,4 @@ def extra_repr(self) -> str: str: _description_ """ inplace_str = ", inplace=True" if self.inplace else "" - return "negative_slope={}{}".format(self.negative_slope, inplace_str) \ No newline at end of file + return "negative_slope={}{}".format(self.negative_slope, inplace_str) diff --git a/zeta/quant/residual_vq.py b/zeta/quant/residual_vq.py new file mode 100644 index 00000000..c777dd3b --- /dev/null +++ b/zeta/quant/residual_vq.py @@ -0,0 +1,63 @@ +import torch +from torch import nn + + +class ResidualVectorQuantizer(nn.Module): + """Residual Vector Quantizer. + + Args: + dim (int): _description_ + dim_out (int): _description_ + n_embed (int): _description + + Example: + >>> x = torch.randn(2, 4) + >>> model = ResidualVectorQuantizer(4, 4, 4) + >>> out = model(x) + >>> print(out.shape) + torch.Size([2, 4]) + """ + def __init__(self, dim, dim_out, n_embed): + super().__init__() + self.dim = dim + self.dim_out = dim_out + self.n_embed = n_embed + self.embed = nn.Embedding(n_embed, dim) + self.proj = nn.Linear(dim, dim_out) + + def forward(self, x): + """Forward pass of the ResidualVectorQuantizer module. + + Args: + x (_type_): _description_ + + Returns: + _type_: _description_ + """ + # Compute distances to embedding vectors + dists = ( + x.pow(2).sum(1, keepdim=True) + - 2 * x @ self.embed.weight.t() + + self.embed.weight.pow(2).sum(1) + ) + + # Find the closest embedding for each input vector + _, embed_ind = dists.min(1) + embed_onehot = torch.zeros_like(dists).scatter_( + 1, embed_ind.view(-1, 1), 1 + ) + embed_ind = embed_onehot @ self.embed.weight + + # Compute residual + residual = self.proj(x - embed_ind) + + # Add residual to the input + x = x + residual + + return x + + +# x = torch.randn(2, 4) +# model = ResidualVectorQuantizer(4, 4, 4) +# out = model(x) +# print(out.shape) From 30738e3b1f6103e838ff5ca35b947dd75b470727 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Mon, 4 Dec 2023 16:43:55 +0000 Subject: [PATCH 091/587] Update vector-quantize-pytorch requirement from 1.11.7 to 1.11.8 Updates the requirements on [vector-quantize-pytorch](https://github.com/lucidrains/vector-quantizer-pytorch) to permit the latest version. - [Release notes](https://github.com/lucidrains/vector-quantizer-pytorch/releases) - [Commits](https://github.com/lucidrains/vector-quantizer-pytorch/compare/1.11.7...1.11.8) --- updated-dependencies: - dependency-name: vector-quantize-pytorch dependency-type: direct:production ... Signed-off-by: dependabot[bot] --- pyproject.toml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pyproject.toml b/pyproject.toml index 942be20b..8eba5c5a 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -33,7 +33,7 @@ datasets = "*" lion-pytorch = "*" sentencepiece = "*" colt5-attention = "0.10.18" -vector-quantize-pytorch = "1.11.7" +vector-quantize-pytorch = "1.11.8" tokenmonster = "*" scipy = "*" beartype = "*" From 075be1bf06daed0e05d87bdc65cb4770c74d4f07 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Mon, 4 Dec 2023 16:50:57 +0000 Subject: [PATCH 092/587] Bump pypa/gh-action-pypi-publish from 1.8.10 to 1.8.11 Bumps [pypa/gh-action-pypi-publish](https://github.com/pypa/gh-action-pypi-publish) from 1.8.10 to 1.8.11. - [Release notes](https://github.com/pypa/gh-action-pypi-publish/releases) - [Commits](https://github.com/pypa/gh-action-pypi-publish/compare/b7f401de30cb6434a1e19f805ff006643653240e...2f6f737ca5f74c637829c0f5c3acd0e29ea5e8bf) --- updated-dependencies: - dependency-name: pypa/gh-action-pypi-publish dependency-type: direct:production update-type: version-update:semver-patch ... Signed-off-by: dependabot[bot] --- .github/workflows/python-publish.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/python-publish.yml b/.github/workflows/python-publish.yml index c8f4ba0c..85958c1d 100644 --- a/.github/workflows/python-publish.yml +++ b/.github/workflows/python-publish.yml @@ -26,7 +26,7 @@ jobs: - name: Build package run: python -m build - name: Publish package - uses: pypa/gh-action-pypi-publish@b7f401de30cb6434a1e19f805ff006643653240e + uses: pypa/gh-action-pypi-publish@2f6f737ca5f74c637829c0f5c3acd0e29ea5e8bf with: user: __token__ password: ${{ secrets.PYPI_API_TOKEN }} \ No newline at end of file From 7f1e908adb779a339dc2a5060bc73ee5546c14d7 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Mon, 4 Dec 2023 16:51:00 +0000 Subject: [PATCH 093/587] Bump actions/first-interaction from 1.2.0 to 1.3.0 Bumps [actions/first-interaction](https://github.com/actions/first-interaction) from 1.2.0 to 1.3.0. - [Release notes](https://github.com/actions/first-interaction/releases) - [Commits](https://github.com/actions/first-interaction/compare/v1.2.0...v1.3.0) --- updated-dependencies: - dependency-name: actions/first-interaction dependency-type: direct:production update-type: version-update:semver-minor ... Signed-off-by: dependabot[bot] --- .github/workflows/welcome.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/welcome.yml b/.github/workflows/welcome.yml index eadc0b68..c328046a 100644 --- a/.github/workflows/welcome.yml +++ b/.github/workflows/welcome.yml @@ -11,7 +11,7 @@ jobs: name: 👋 Welcome runs-on: ubuntu-latest steps: - - uses: actions/first-interaction@v1.2.0 + - uses: actions/first-interaction@v1.3.0 with: repo-token: ${{ secrets.GITHUB_TOKEN }} issue-message: "Hello there, thank you for opening an Issue ! 🙏🏻 The team was notified and they will get back to you asap." From 00718d02b942f7ab767d91396d6baf12c2923711 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Mon, 4 Dec 2023 16:51:04 +0000 Subject: [PATCH 094/587] Bump actions/labeler from 4 to 5 Bumps [actions/labeler](https://github.com/actions/labeler) from 4 to 5. - [Release notes](https://github.com/actions/labeler/releases) - [Commits](https://github.com/actions/labeler/compare/v4...v5) --- updated-dependencies: - dependency-name: actions/labeler dependency-type: direct:production update-type: version-update:semver-major ... Signed-off-by: dependabot[bot] --- .github/workflows/label.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/label.yml b/.github/workflows/label.yml index 46135690..d23c4d40 100644 --- a/.github/workflows/label.yml +++ b/.github/workflows/label.yml @@ -17,6 +17,6 @@ jobs: pull-requests: write steps: - - uses: actions/labeler@v4 + - uses: actions/labeler@v5 with: repo-token: "${{ secrets.GITHUB_TOKEN }}" From 6f8ffd15259cac8d1aa0f5755e6ca5c7db0cc5d8 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Tue, 5 Dec 2023 02:58:40 +0000 Subject: [PATCH 095/587] Update colt5-attention requirement from 0.10.18 to 0.10.19 Updates the requirements on [colt5-attention](https://github.com/lucidrains/CoLT5-attention) to permit the latest version. - [Release notes](https://github.com/lucidrains/CoLT5-attention/releases) - [Commits](https://github.com/lucidrains/CoLT5-attention/compare/0.10.18...0.10.19) --- updated-dependencies: - dependency-name: colt5-attention dependency-type: direct:production ... Signed-off-by: dependabot[bot] --- pyproject.toml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pyproject.toml b/pyproject.toml index 8eba5c5a..883729da 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -32,7 +32,7 @@ accelerate = "*" datasets = "*" lion-pytorch = "*" sentencepiece = "*" -colt5-attention = "0.10.18" +colt5-attention = "0.10.19" vector-quantize-pytorch = "1.11.8" tokenmonster = "*" scipy = "*" From 2c261dda5a330f549c035e37a77d44986e879eca Mon Sep 17 00:00:00 2001 From: Kye Date: Thu, 7 Dec 2023 12:33:33 -0800 Subject: [PATCH 096/587] [__INIT__] [MultiModalCrossAttention] --- tests/nn/modules/test_cross_attn_images.py | 4 ++-- zeta/nn/attention/__init__.py | 1 - 2 files changed, 2 insertions(+), 3 deletions(-) diff --git a/tests/nn/modules/test_cross_attn_images.py b/tests/nn/modules/test_cross_attn_images.py index c292c563..8b4f3e7a 100644 --- a/tests/nn/modules/test_cross_attn_images.py +++ b/tests/nn/modules/test_cross_attn_images.py @@ -3,12 +3,12 @@ import numpy as np import pytest from torch.autograd import gradcheck -from zeta.nn.attention.cross_attn_images import CrossAttention +from zeta.nn.attention.cross_attn_images import MultiModalCrossAttention @pytest.fixture def cross_attention_module(): - return CrossAttention(1024, 8, 1024) + return MultiModalCrossAttention(1024, 8, 1024) def test_forward_pass(cross_attention_module): diff --git a/zeta/nn/attention/__init__.py b/zeta/nn/attention/__init__.py index 17c745a2..613e265c 100644 --- a/zeta/nn/attention/__init__.py +++ b/zeta/nn/attention/__init__.py @@ -34,7 +34,6 @@ "MixtureOfAutoregressiveAttention", "MultiModalCausalAttention", "SimpleMMCA", - "MultiModalCrossAttention", "MultiheadAttention", "MultiQueryAttention", "MultiModalCrossAttention", From 49385d0ec625123fb4efee692fba80b01a01d4b2 Mon Sep 17 00:00:00 2001 From: Kye Date: Thu, 7 Dec 2023 12:36:15 -0800 Subject: [PATCH 097/587] [PolymorphicNeuronLayer][ from zeta.nn.modules.polymorphic_neuron import PolyMorhphicNeuron E ImportError: cannot import name PolyMorhphicNeuron from zeta.nn.modules.polymorphic_neuron (/Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages/zeta/nn/modules/polymorphic_neuron.py) --- tests/nn/modules/test_polymorphic_neuron.py | 21 ++++++++++----------- 1 file changed, 10 insertions(+), 11 deletions(-) diff --git a/tests/nn/modules/test_polymorphic_neuron.py b/tests/nn/modules/test_polymorphic_neuron.py index d4b140f1..331ac342 100644 --- a/tests/nn/modules/test_polymorphic_neuron.py +++ b/tests/nn/modules/test_polymorphic_neuron.py @@ -2,18 +2,17 @@ import torch import torch.nn as nn import torch.nn.functional as F -from zeta.nn.modules.polymorphic_neuron import PolyMorhphicNeuron +from zeta.nn.modules.polymorphic_neuron import PolymorphicNeuronLayer - -# Fixture for creating a sample PolyMorhphicNeuron instance +# Fixture for creating a sample PolymorphicNeuronLayer instance @pytest.fixture def sample_neuron(): - return PolyMorhphicNeuron(in_features=10, out_features=5) + return PolymorphicNeuronLayer(in_features=10, out_features=5) # Basic initialization test def test_neuron_initialization(sample_neuron): - assert isinstance(sample_neuron, PolyMorhphicNeuron) + assert isinstance(sample_neuron, PolymorphicNeuronLayer) assert sample_neuron.in_features == 10 assert sample_neuron.out_features == 5 assert isinstance(sample_neuron.weights, nn.Parameter) @@ -30,7 +29,7 @@ def test_forward_pass(sample_neuron): # Parameterized test for different activation functions @pytest.mark.parametrize("activation", [F.relu, F.tanh, F.sigmoid]) def test_different_activation_functions(activation): - neuron = PolyMorhphicNeuron( + neuron = PolymorphicNeuronLayer( in_features=10, out_features=5, activation_functions=[activation] ) input_tensor = torch.randn(1, 10) @@ -41,13 +40,13 @@ def test_different_activation_functions(activation): # Test for a case where input features and output features are both 0 def test_zero_features(): with pytest.raises(ValueError): - PolyMorhphicNeuron(in_features=0, out_features=0) + PolymorphicNeuronLayer(in_features=0, out_features=0) # Test for a case where the activation functions list is empty def test_empty_activation_functions(): with pytest.raises(ValueError): - PolyMorhphicNeuron( + PolymorphicNeuronLayer( in_features=10, out_features=5, activation_functions=[] ) @@ -55,7 +54,7 @@ def test_empty_activation_functions(): # Test for a case where in_features and out_features are negative def test_negative_features(): with pytest.raises(ValueError): - PolyMorhphicNeuron(in_features=-10, out_features=-5) + PolymorphicNeuronLayer(in_features=-10, out_features=-5) # Test for a case where input tensor shape does not match in_features @@ -68,14 +67,14 @@ def test_input_tensor_shape_mismatch(sample_neuron): # Test for a case where activation functions are not callable def test_invalid_activation_functions(): with pytest.raises(ValueError): - PolyMorhphicNeuron( + PolymorphicNeuronLayer( in_features=10, out_features=5, activation_functions=[1, 2, 3] ) # Test for a case where the forward pass is called without initializing weights and bias def test_forward_pass_without_initialization(): - neuron = PolyMorhphicNeuron(in_features=10, out_features=5) + neuron = PolymorphicNeuronLayer(in_features=10, out_features=5) input_tensor = torch.randn(1, 10) with pytest.raises(RuntimeError): neuron(input_tensor) From ab4c10db3b9a098582fcdf8fae7f2b88b7a662c6 Mon Sep 17 00:00:00 2001 From: Kye Date: Thu, 7 Dec 2023 12:45:02 -0800 Subject: [PATCH 098/587] [E ModuleNotFoundError: No module named zeta.nn.modules.kv_cache ] --- tests/nn/modules/test_kv_cache.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/tests/nn/modules/test_kv_cache.py b/tests/nn/modules/test_kv_cache.py index 7efeb3f8..946d4b21 100644 --- a/tests/nn/modules/test_kv_cache.py +++ b/tests/nn/modules/test_kv_cache.py @@ -1,11 +1,12 @@ from unittest.mock import Mock + import pytest import torch from zeta.nn.modules.kv_cache import ( - KVCache, find_multiple, precompute_freq_cis, + KVCache, setup_cache, ) From e0513621968f9a904b1821d96add1bf7c3023ccf Mon Sep 17 00:00:00 2001 From: Kye Date: Thu, 7 Dec 2023 12:47:01 -0800 Subject: [PATCH 099/587] [E ModuleNotFoundError: No module named xformers --- zeta/nn/modules/cache.py | 22 ++++++++++++++++------ 1 file changed, 16 insertions(+), 6 deletions(-) diff --git a/zeta/nn/modules/cache.py b/zeta/nn/modules/cache.py index d911b3de..3927706b 100644 --- a/zeta/nn/modules/cache.py +++ b/zeta/nn/modules/cache.py @@ -1,14 +1,24 @@ +import subprocess from dataclasses import dataclass from typing import List, Tuple import torch -from xformers.ops.fmha.attn_bias import ( - AttentionBias, - BlockDiagonalCausalMask, - BlockDiagonalCausalWithOffsetPaddedKeysMask, - BlockDiagonalMask, -) +try: + + from xformers.ops.fmha.attn_bias import ( + AttentionBias, + BlockDiagonalCausalMask, + BlockDiagonalCausalWithOffsetPaddedKeysMask, + BlockDiagonalMask, + ) +except ImportError as error: + print(error) + print("Please install xformers from") + # Download xformers from pip + subprocess.run("pip install xformers".split()) + + @dataclass class RotatingCacheInputMetadata: From 8c01900bb7e091a5044c021340cd2eb43069b62c Mon Sep 17 00:00:00 2001 From: Kye Date: Thu, 7 Dec 2023 12:49:13 -0800 Subject: [PATCH 100/587] [XCAttention -> pack_one -> pack] --- zeta/nn/attention/xc_attention.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/zeta/nn/attention/xc_attention.py b/zeta/nn/attention/xc_attention.py index 50c2fb4b..56720e89 100644 --- a/zeta/nn/attention/xc_attention.py +++ b/zeta/nn/attention/xc_attention.py @@ -1,5 +1,5 @@ from torch import nn, einsum -from einops import rearrange, pack_one, unpack_one +from einops import rearrange, pack, unpack import torch.nn.functional as F from einops.layers.torch import Rearrange @@ -92,7 +92,7 @@ def forward(self, x, cond=None): """ x = rearrange(x, "b c h w -> b h w c") - x, ps = pack_one(x, "b * c ") + x, ps = pack(x, "b * c ") x = self.norm(x) # conditioning @@ -111,5 +111,5 @@ def forward(self, x, cond=None): attn = sim.softmax(dim=-1) out = einsum("b h i j, b h j n -> b h i n", attn, v) out = self.to_out(out) - out = unpack_one(out, ps, "b * c") + out = unpack(out, ps, "b * c") return rearrange(out, "b h w c -> b c h w") From 90a424f64673613ca02923fae8f2249add872ac6 Mon Sep 17 00:00:00 2001 From: Kye Date: Thu, 7 Dec 2023 13:48:22 -0800 Subject: [PATCH 101/587] [README] --- README.md | 14 +++++++------- 1 file changed, 7 insertions(+), 7 deletions(-) diff --git a/README.md b/README.md index f0124be0..aca57be7 100644 --- a/README.md +++ b/README.md @@ -1,8 +1,7 @@ [![Multi-Modality](images/agorabanner.png)](https://discord.gg/qUtxnK2NMf) ![Zeta banner](images/zeta.png) -Build High-performance, agile, and scalable AI models with modular and re-useable building blocks! - +Build SOTA AI Models 80% faster with modular, high-performance, and scalable building blocks! [![Docs](https://readthedocs.org/projects/zeta/badge/)](https://zeta.readthedocs.io) @@ -17,15 +16,14 @@ Build High-performance, agile, and scalable AI models with modular and re-useabl - Modularity: Modularized Lego Building Blocks for building and deploying the best ML Models! -# 🤝 Schedule a 1-on-1 Session -Book a [1-on-1 Session with Kye](https://calendly.com/apacai/agora), the Creator, to discuss any issues, provide feedback, or explore how we can improve Zeta for you. - -## Installation +# Installation `pip install zetascale` -## Initiating Your Journey +# Usage + +## Starting Your Journey Creating a model empowered with the aforementioned breakthrough research features is a breeze. Here's how to quickly materialize the renowned Flash Attention @@ -304,6 +302,8 @@ output = vision_embedding(input_image) # Documentation [Click here for the documentation, it's at zeta.apac.ai](https://zeta.apac.ai) +# 🤝 Schedule a 1-on-1 Session +Book a [1-on-1 Session with Kye](https://calendly.com/apacai/agora), the Creator, to discuss any issues, provide feedback, or explore how we can improve Zeta for you. ## Contributing - We need you to help us build the most re-useable, reliable, and high performance ML framework ever. From 281d23f6e857e41e95cd83e53ac97a94327eb909 Mon Sep 17 00:00:00 2001 From: Kye Date: Thu, 7 Dec 2023 14:41:30 -0800 Subject: [PATCH 102/587] [FEAT][Omni Matrix] --- README.md | 2 +- zeta/nn/modules/matrix.py | 131 ++++++++++++++++++++++++++++++++++++++ 2 files changed, 132 insertions(+), 1 deletion(-) create mode 100644 zeta/nn/modules/matrix.py diff --git a/README.md b/README.md index aca57be7..12fcd66c 100644 --- a/README.md +++ b/README.md @@ -17,7 +17,7 @@ Build SOTA AI Models 80% faster with modular, high-performance, and scalable bui -# Installation +# Install `pip install zetascale` diff --git a/zeta/nn/modules/matrix.py b/zeta/nn/modules/matrix.py new file mode 100644 index 00000000..db4f11ca --- /dev/null +++ b/zeta/nn/modules/matrix.py @@ -0,0 +1,131 @@ +import numpy as np +import subprocess +import torch + +try: + import jax.numpy as jnp +except ImportError: + print("JAX not installed") + print("Installing JAX") + subprocess.run(["pip3", "install", "jax"]) + subprocess.run(["pip3", "install", "jaxlib"]) + +try: + import tensorflow as tf +except ImportError: + print("Tensorflow not installed") + print("Installing Tensorflow") + subprocess.run(["pip3", "install", "tensorflow"]) + + + +class Matrix: + """Matrix class that can be converted between frameworks + + + Args: + data (torch.Tensor, jnp.ndarray, tf.Tensor): Data to be converted + + Example: + >>> import torch + >>> import jax.numpy as jnp + >>> import tensorflow as tf + >>> from zeta.nn.modules.matrix import Matrix + >>> + >>> tensor1 = Matrix(torch.tensor([1, 2, 3])) + >>> tensor2 = Matrix(jnp.array([1, 2, 3])) + >>> tensor3 = Matrix(tf.constant([1, 2, 3])) + >>> + >>> print(tensor1.to_jax()) + >>> print(tensor2.to_pytorch()) + >>> print(tensor3.to_tensorflow()) + + + """ + def __init__(self, data): + self.data = data + self.framework = self._detect_framework(data) + + def _detect_framework(self, data): + """Detect framework + + Args: + data (_type_): _description_ + + Raises: + TypeError: _description_ + + Returns: + _type_: _description_ + """ + if isinstance(data, torch.Tensor): + return "pytorch" + elif isinstance(data, jnp.ndarray): + return "jax" + elif isinstance(data, tf.Tensor): + return "tensorflow" + else: + raise TypeError("Unknown framework") + + def to_pytorch(self): + """TODO: Docstring for to_pytorch. + + Returns: + _type_: _description_ + """ + if self.framework == 'pytorch': + return self.data + elif self.framework == 'jax': + # Convert JAX array to numpy array first, then to PyTorch tensor + numpy_data = np.array(self.data) # Convert JAX array to numpy array + return torch.tensor(numpy_data) # Convert numpy array to PyTorch tensor + elif self.framework == 'tensorflow': + return torch.tensor(self.data.numpy()) + + def to_jax(self): + """To jax + + Returns: + _type_: _description_ + """ + if self.framework == "jax": + return self.data + elif self.framework == "pytorch": + return jnp.array(self.data.cpu().numpy()) + elif self.framework == 'tensorflow': + return jnp.array(self.data.numpy()) + + def to_tensorflow(self): + """To tensorflow + + Returns: + _type_: _description_ + """ + if self.framework == "tensorflow": + return self.data + elif self.framework == "pytorch": + return tf.convert_to_tensor(self.data.numpy.cpu().numpy()) + elif self.framework == "jax": + return tf.convert_to_tensor(self.data) + + def sum(self): + """Sum + + Returns: + _type_: _description_ + """ + if self.framework == "pytorch": + return self.data.sum() + elif self.framework == "jax": + return jnp.sum(self.data) + elif self.framework == "tensorflow": + return tf.reduce_sum(self.data) + +# # Example usage +# tensor1 = Matrix(torch.tensor([1, 2, 3])) +# tensor2 = Matrix(jnp.array([1, 2, 3])) +# tensor3 = Matrix(tf.constant([1, 2, 3])) + +# print(tensor1.to_jax()) +# print(tensor2.to_pytorch()) +# print(tensor3.to_tensorflow()) \ No newline at end of file From 3ac2862d5d13ad2555bb51c70a36f963fff2c5c7 Mon Sep 17 00:00:00 2001 From: Kye Date: Fri, 8 Dec 2023 11:19:51 -0800 Subject: [PATCH 103/587] [FEAT][Adaptive LayerNorm] --- tests/nn/modules/test_adative_layernorm.py | 36 +++++++++++++++ zeta/nn/modules/__init__.py | 2 + zeta/nn/modules/adaptive_layernorm.py | 54 ++++++++++++++++++++++ 3 files changed, 92 insertions(+) create mode 100644 tests/nn/modules/test_adative_layernorm.py create mode 100644 zeta/nn/modules/adaptive_layernorm.py diff --git a/tests/nn/modules/test_adative_layernorm.py b/tests/nn/modules/test_adative_layernorm.py new file mode 100644 index 00000000..6fb7eeb7 --- /dev/null +++ b/tests/nn/modules/test_adative_layernorm.py @@ -0,0 +1,36 @@ +import torch +import pytest +from zeta.nn.modules.adaptive_layernorm import AdaptiveLayerNorm + +def test_adaptive_layer_norm_init(): + model = AdaptiveLayerNorm(4) + assert model.num_features == 4 + assert model.eps == 1e-5 + assert isinstance(model.gamma, torch.nn.Parameter) + assert isinstance(model.beta, torch.nn.Parameter) + +def test_adaptive_layer_norm_init_invalid_num_features(): + with pytest.raises(ValueError): + AdaptiveLayerNorm(-1) + +def test_adaptive_layer_norm_init_invalid_eps(): + with pytest.raises(ValueError): + AdaptiveLayerNorm(4, -1) + +def test_adaptive_layer_norm_forward(): + model = AdaptiveLayerNorm(4) + x = torch.randn(2, 4, 10) + out = model(x) + assert out.shape == torch.Size([2, 4, 10]) + +def test_adaptive_layer_norm_forward_zero(): + model = AdaptiveLayerNorm(4) + x = torch.zeros(2, 4, 10) + out = model(x) + assert torch.all(out == 0) + +def test_adaptive_layer_norm_forward_one(): + model = AdaptiveLayerNorm(4) + x = torch.ones(2, 4, 10) + out = model(x) + assert torch.all(out == model.beta) \ No newline at end of file diff --git a/zeta/nn/modules/__init__.py b/zeta/nn/modules/__init__.py index b252eb86..1bf03876 100644 --- a/zeta/nn/modules/__init__.py +++ b/zeta/nn/modules/__init__.py @@ -46,6 +46,7 @@ from zeta.nn.modules.h3 import H3Layer from zeta.nn.modules.mlp_mixer import MLPMixer from zeta.nn.modules.leaky_relu import LeakyRELU +from zeta.nn.modules.adaptive_layernorm import AdaptiveLayerNorm # from zeta.nn.modules.img_reshape import image_reshape @@ -109,4 +110,5 @@ "H3Layer", "MLPMixer", "LeakyRELU", + "AdaptiveLayerNorm" ] diff --git a/zeta/nn/modules/adaptive_layernorm.py b/zeta/nn/modules/adaptive_layernorm.py new file mode 100644 index 00000000..bf8e79fd --- /dev/null +++ b/zeta/nn/modules/adaptive_layernorm.py @@ -0,0 +1,54 @@ +import torch +from torch import nn, Tensor + +class AdaptiveLayerNorm(nn.Module): + """Adaptive Layer Normalization module. + + + Args: + num_features (int): number of features in the input tensor + eps (float): a value added to the denominator for numerical stability. Default: 1e-5 + + Shape: + - Input: (batch_size, num_features, seq_len) + - Output: (batch_size, num_features, seq_len) + + Examples: + >>> x = torch.randn(20, 5, 10) + >>> layer_norm = AdaptiveLayerNorm(5) + >>> y = layer_norm(x) + >>> y.shape + torch.Size([20, 5, 10]) + + """ + def __init__( + self, + num_features, + eps=1e-5, + *args, + **kwargs + ): + super(AdaptiveLayerNorm, self).__init__() + self.num_features = num_features + self.eps = eps + self.gamma = nn.Parameter(torch.ones(num_features)) + self.beta = nn.Parameter(torch.zeros(num_features)) + + if not isinstance(num_features, int) or num_features <= 0: + raise ValueError("num_features must be a positive integer value") + if not isinstance(eps, float) or eps <= 0: + raise ValueError("eps must be a positive float value") + + def forward(self, x: Tensor) -> Tensor: + """Forward pass of the AdaptiveLayerNorm module. + + Args: + x (Tensor): torch tensor of shape (batch_size, num_features, seq_len) + + Returns: + Tensor: the normalized input tensor + """ + mean = x.mean(-1, keepdim=True) + std = x.std(-1, keepdim=True) + return self.gamma * (x - mean) / (std + self.eps) + self.beta + \ No newline at end of file From 48be66072f06af8ed5aac8240f1471bb16add460 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Mon, 11 Dec 2023 16:42:42 +0000 Subject: [PATCH 104/587] Update ruff requirement from >=0.0.249,<0.1.7 to >=0.0.249,<0.1.8 Updates the requirements on [ruff](https://github.com/astral-sh/ruff) to permit the latest version. - [Release notes](https://github.com/astral-sh/ruff/releases) - [Changelog](https://github.com/astral-sh/ruff/blob/main/CHANGELOG.md) - [Commits](https://github.com/astral-sh/ruff/compare/v0.0.249...v0.1.7) --- updated-dependencies: - dependency-name: ruff dependency-type: direct:development ... Signed-off-by: dependabot[bot] --- pyproject.toml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pyproject.toml b/pyproject.toml index 883729da..25dc8fc1 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -46,7 +46,7 @@ requires = ["poetry-core>=1.0.0"] build-backend = "poetry.core.masonry.api" [tool.poetry.group.lint.dependencies] -ruff = ">=0.0.249,<0.1.7" +ruff = ">=0.0.249,<0.1.8" types-toml = "^0.10.8.1" types-redis = "^4.3.21.6" types-pytz = "^2023.3.0.0" From 97045f468dd9326351dc2f191b536441fda98bdf Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Mon, 11 Dec 2023 16:47:17 +0000 Subject: [PATCH 105/587] Bump actions/setup-python from 4 to 5 Bumps [actions/setup-python](https://github.com/actions/setup-python) from 4 to 5. - [Release notes](https://github.com/actions/setup-python/releases) - [Commits](https://github.com/actions/setup-python/compare/v4...v5) --- updated-dependencies: - dependency-name: actions/setup-python dependency-type: direct:production update-type: version-update:semver-major ... Signed-off-by: dependabot[bot] --- .github/workflows/docs.yml | 2 +- .github/workflows/publish.yml | 2 +- .github/workflows/pylint.yml | 2 +- .github/workflows/python-publish.yml | 2 +- .github/workflows/unit-test.yml | 2 +- 5 files changed, 5 insertions(+), 5 deletions(-) diff --git a/.github/workflows/docs.yml b/.github/workflows/docs.yml index 0f89cb4c..7fb194de 100644 --- a/.github/workflows/docs.yml +++ b/.github/workflows/docs.yml @@ -11,7 +11,7 @@ jobs: runs-on: ubuntu-latest steps: - uses: actions/checkout@v4 - - uses: actions/setup-python@v4 + - uses: actions/setup-python@v5 with: python-version: 3.x - run: pip install mkdocs-material diff --git a/.github/workflows/publish.yml b/.github/workflows/publish.yml index 197e3dbf..2a79688f 100644 --- a/.github/workflows/publish.yml +++ b/.github/workflows/publish.yml @@ -21,7 +21,7 @@ jobs: with: ref: ${{ github.head_ref }} - name: 🐍 Set up Python ${{ matrix.python-version }} - uses: actions/setup-python@v4 + uses: actions/setup-python@v5 with: python-version: ${{ matrix.python-version }} diff --git a/.github/workflows/pylint.yml b/.github/workflows/pylint.yml index 3f3ba2e2..d3f42fb1 100644 --- a/.github/workflows/pylint.yml +++ b/.github/workflows/pylint.yml @@ -11,7 +11,7 @@ jobs: steps: - uses: actions/checkout@v4 - name: Set up Python ${{ matrix.python-version }} - uses: actions/setup-python@v4 + uses: actions/setup-python@v5 with: python-version: ${{ matrix.python-version }} - name: Install dependencies diff --git a/.github/workflows/python-publish.yml b/.github/workflows/python-publish.yml index 85958c1d..aef7b002 100644 --- a/.github/workflows/python-publish.yml +++ b/.github/workflows/python-publish.yml @@ -16,7 +16,7 @@ jobs: steps: - uses: actions/checkout@v4 - name: Set up Python - uses: actions/setup-python@v4 + uses: actions/setup-python@v5 with: python-version: '3.x' - name: Install dependencies diff --git a/.github/workflows/unit-test.yml b/.github/workflows/unit-test.yml index 7bb929b8..c0818be2 100644 --- a/.github/workflows/unit-test.yml +++ b/.github/workflows/unit-test.yml @@ -16,7 +16,7 @@ jobs: - uses: actions/checkout@v4 - name: Setup Python - uses: actions/setup-python@v4 + uses: actions/setup-python@v5 with: python-version: '3.10' From a87496feb566839f73a469e16ed8b17eba072d8d Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Mon, 11 Dec 2023 16:47:22 +0000 Subject: [PATCH 106/587] Bump actions/stale from 8 to 9 Bumps [actions/stale](https://github.com/actions/stale) from 8 to 9. - [Release notes](https://github.com/actions/stale/releases) - [Changelog](https://github.com/actions/stale/blob/main/CHANGELOG.md) - [Commits](https://github.com/actions/stale/compare/v8...v9) --- updated-dependencies: - dependency-name: actions/stale dependency-type: direct:production update-type: version-update:semver-major ... Signed-off-by: dependabot[bot] --- .github/workflows/stale.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/stale.yml b/.github/workflows/stale.yml index dc72e039..3aa6410b 100644 --- a/.github/workflows/stale.yml +++ b/.github/workflows/stale.yml @@ -18,7 +18,7 @@ jobs: pull-requests: write steps: - - uses: actions/stale@v8 + - uses: actions/stale@v9 with: repo-token: ${{ secrets.GITHUB_TOKEN }} stale-issue-message: 'Stale issue message' From 2e3fc46cf7278688da2897457276872f295a41a3 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Mon, 11 Dec 2023 16:47:31 +0000 Subject: [PATCH 107/587] Update vector-quantize-pytorch requirement from 1.11.8 to 1.12.0 Updates the requirements on [vector-quantize-pytorch](https://github.com/lucidrains/vector-quantizer-pytorch) to permit the latest version. - [Release notes](https://github.com/lucidrains/vector-quantizer-pytorch/releases) - [Commits](https://github.com/lucidrains/vector-quantizer-pytorch/compare/1.11.8...1.12.0) --- updated-dependencies: - dependency-name: vector-quantize-pytorch dependency-type: direct:production ... Signed-off-by: dependabot[bot] --- pyproject.toml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pyproject.toml b/pyproject.toml index 883729da..34effea2 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -33,7 +33,7 @@ datasets = "*" lion-pytorch = "*" sentencepiece = "*" colt5-attention = "0.10.19" -vector-quantize-pytorch = "1.11.8" +vector-quantize-pytorch = "1.12.0" tokenmonster = "*" scipy = "*" beartype = "*" From 299cfb3a247a5346e9a350957808c7020b873ae0 Mon Sep 17 00:00:00 2001 From: Kye Date: Mon, 11 Dec 2023 17:53:29 -0800 Subject: [PATCH 108/587] [SPARQAttn][++Test] --- tests/nn/attentions/sparq_attn.py | 55 ++++++++ tests/nn/modules/test_adative_layernorm.py | 8 +- tests/nn/modules/test_polymorphic_neuron.py | 1 + tests/quant/resudual_vq.py | 6 +- zeta/nn/modules/__init__.py | 2 +- zeta/nn/modules/adaptive_layernorm.py | 25 ++-- zeta/nn/modules/cache.py | 2 - zeta/nn/modules/matrix.py | 45 +++---- zeta/nn/modules/sparq_attn.py | 133 ++++++++++++++++++++ zeta/quant/residual_vq.py | 3 +- 10 files changed, 238 insertions(+), 42 deletions(-) create mode 100644 tests/nn/attentions/sparq_attn.py create mode 100644 zeta/nn/modules/sparq_attn.py diff --git a/tests/nn/attentions/sparq_attn.py b/tests/nn/attentions/sparq_attn.py new file mode 100644 index 00000000..72c14429 --- /dev/null +++ b/tests/nn/attentions/sparq_attn.py @@ -0,0 +1,55 @@ +import torch +import pytest +from zeta.nn.modules.sparq_attn import SparQAttention + + +def test_sparq_attention_init(): + model = SparQAttention(4, 4) + assert model.dim == 4 + assert model.heads == 4 + + +def test_sparq_attention_forward(): + model = SparQAttention(4, 4) + Q = torch.randn(2, 4, 10, 4) + K = torch.randn(2, 4, 10, 4) + V = torch.randn(2, 4, 10, 4) + V_mean = torch.randn(2, 4, 1, 4) + M = torch.randn(2, 4, 10, 10) + r = 2 + k = 2 + out = model(Q, K, V, V_mean, M, r, k) + assert out.shape == torch.Size([2, 4, 10, 4]) + + +@pytest.mark.parametrize("r, k", [(1, 1), (2, 2), (3, 3), (4, 4), (5, 5)]) +def test_sparq_attention_forward_different_r_k(r, k): + model = SparQAttention(4, 4) + Q = torch.randn(2, 4, 10, 4) + K = torch.randn(2, 4, 10, 4) + V = torch.randn(2, 4, 10, 4) + V_mean = torch.randn(2, 4, 1, 4) + M = torch.randn(2, 4, 10, 10) + out = model(Q, K, V, V_mean, M, r, k) + assert out.shape == torch.Size([2, 4, 10, 4]) + + +@pytest.mark.parametrize("dim, heads", [(2, 2), (3, 3), (4, 4), (5, 5), (6, 6)]) +def test_sparq_attention_init_different_dim_heads(dim, heads): + model = SparQAttention(dim, heads) + assert model.dim == dim + assert model.heads == heads + + +@pytest.mark.parametrize("dim, heads", [(2, 2), (3, 3), (4, 4), (5, 5), (6, 6)]) +def test_sparq_attention_forward_different_dim_heads(dim, heads): + model = SparQAttention(dim, heads) + Q = torch.randn(2, heads, 10, dim) + K = torch.randn(2, heads, 10, dim) + V = torch.randn(2, heads, 10, dim) + V_mean = torch.randn(2, heads, 1, dim) + M = torch.randn(2, heads, 10, 10) + r = 2 + k = 2 + out = model(Q, K, V, V_mean, M, r, k) + assert out.shape == torch.Size([2, heads, 10, dim]) diff --git a/tests/nn/modules/test_adative_layernorm.py b/tests/nn/modules/test_adative_layernorm.py index 6fb7eeb7..e0d8cf04 100644 --- a/tests/nn/modules/test_adative_layernorm.py +++ b/tests/nn/modules/test_adative_layernorm.py @@ -2,6 +2,7 @@ import pytest from zeta.nn.modules.adaptive_layernorm import AdaptiveLayerNorm + def test_adaptive_layer_norm_init(): model = AdaptiveLayerNorm(4) assert model.num_features == 4 @@ -9,28 +10,33 @@ def test_adaptive_layer_norm_init(): assert isinstance(model.gamma, torch.nn.Parameter) assert isinstance(model.beta, torch.nn.Parameter) + def test_adaptive_layer_norm_init_invalid_num_features(): with pytest.raises(ValueError): AdaptiveLayerNorm(-1) + def test_adaptive_layer_norm_init_invalid_eps(): with pytest.raises(ValueError): AdaptiveLayerNorm(4, -1) + def test_adaptive_layer_norm_forward(): model = AdaptiveLayerNorm(4) x = torch.randn(2, 4, 10) out = model(x) assert out.shape == torch.Size([2, 4, 10]) + def test_adaptive_layer_norm_forward_zero(): model = AdaptiveLayerNorm(4) x = torch.zeros(2, 4, 10) out = model(x) assert torch.all(out == 0) + def test_adaptive_layer_norm_forward_one(): model = AdaptiveLayerNorm(4) x = torch.ones(2, 4, 10) out = model(x) - assert torch.all(out == model.beta) \ No newline at end of file + assert torch.all(out == model.beta) diff --git a/tests/nn/modules/test_polymorphic_neuron.py b/tests/nn/modules/test_polymorphic_neuron.py index 331ac342..042a5db3 100644 --- a/tests/nn/modules/test_polymorphic_neuron.py +++ b/tests/nn/modules/test_polymorphic_neuron.py @@ -4,6 +4,7 @@ import torch.nn.functional as F from zeta.nn.modules.polymorphic_neuron import PolymorphicNeuronLayer + # Fixture for creating a sample PolymorphicNeuronLayer instance @pytest.fixture def sample_neuron(): diff --git a/tests/quant/resudual_vq.py b/tests/quant/resudual_vq.py index a9ca1e2d..3e4f430f 100644 --- a/tests/quant/resudual_vq.py +++ b/tests/quant/resudual_vq.py @@ -2,6 +2,7 @@ import torch.nn as nn from zeta.quant.residual_vq import ResidualVectorQuantizer + def test_residual_vector_quantizer_init(): model = ResidualVectorQuantizer(4, 4, 4) assert isinstance(model, nn.Module) @@ -11,20 +12,23 @@ def test_residual_vector_quantizer_init(): assert isinstance(model.embed, nn.Embedding) assert isinstance(model.proj, nn.Linear) + def test_residual_vector_quantizer_forward(): model = ResidualVectorQuantizer(4, 4, 4) x = torch.randn(2, 4) out = model(x) assert out.shape == torch.Size([2, 4]) + def test_residual_vector_quantizer_forward_zero(): model = ResidualVectorQuantizer(4, 4, 4) x = torch.zeros(2, 4) out = model(x) assert torch.all(out == 0) + def test_residual_vector_quantizer_forward_one(): model = ResidualVectorQuantizer(4, 4, 4) x = torch.ones(2, 4) out = model(x) - assert torch.all(out == 1) \ No newline at end of file + assert torch.all(out == 1) diff --git a/zeta/nn/modules/__init__.py b/zeta/nn/modules/__init__.py index 1bf03876..cf80369f 100644 --- a/zeta/nn/modules/__init__.py +++ b/zeta/nn/modules/__init__.py @@ -110,5 +110,5 @@ "H3Layer", "MLPMixer", "LeakyRELU", - "AdaptiveLayerNorm" + "AdaptiveLayerNorm", ] diff --git a/zeta/nn/modules/adaptive_layernorm.py b/zeta/nn/modules/adaptive_layernorm.py index bf8e79fd..5adebb92 100644 --- a/zeta/nn/modules/adaptive_layernorm.py +++ b/zeta/nn/modules/adaptive_layernorm.py @@ -1,18 +1,19 @@ -import torch +import torch from torch import nn, Tensor + class AdaptiveLayerNorm(nn.Module): """Adaptive Layer Normalization module. - - + + Args: num_features (int): number of features in the input tensor eps (float): a value added to the denominator for numerical stability. Default: 1e-5 - + Shape: - Input: (batch_size, num_features, seq_len) - Output: (batch_size, num_features, seq_len) - + Examples: >>> x = torch.randn(20, 5, 10) >>> layer_norm = AdaptiveLayerNorm(5) @@ -21,24 +22,19 @@ class AdaptiveLayerNorm(nn.Module): torch.Size([20, 5, 10]) """ - def __init__( - self, - num_features, - eps=1e-5, - *args, - **kwargs - ): + + def __init__(self, num_features, eps=1e-5, *args, **kwargs): super(AdaptiveLayerNorm, self).__init__() self.num_features = num_features self.eps = eps self.gamma = nn.Parameter(torch.ones(num_features)) self.beta = nn.Parameter(torch.zeros(num_features)) - + if not isinstance(num_features, int) or num_features <= 0: raise ValueError("num_features must be a positive integer value") if not isinstance(eps, float) or eps <= 0: raise ValueError("eps must be a positive float value") - + def forward(self, x: Tensor) -> Tensor: """Forward pass of the AdaptiveLayerNorm module. @@ -51,4 +47,3 @@ def forward(self, x: Tensor) -> Tensor: mean = x.mean(-1, keepdim=True) std = x.std(-1, keepdim=True) return self.gamma * (x - mean) / (std + self.eps) + self.beta - \ No newline at end of file diff --git a/zeta/nn/modules/cache.py b/zeta/nn/modules/cache.py index 3927706b..87662f48 100644 --- a/zeta/nn/modules/cache.py +++ b/zeta/nn/modules/cache.py @@ -5,7 +5,6 @@ import torch try: - from xformers.ops.fmha.attn_bias import ( AttentionBias, BlockDiagonalCausalMask, @@ -18,7 +17,6 @@ # Download xformers from pip subprocess.run("pip install xformers".split()) - @dataclass class RotatingCacheInputMetadata: diff --git a/zeta/nn/modules/matrix.py b/zeta/nn/modules/matrix.py index db4f11ca..35b3a1cb 100644 --- a/zeta/nn/modules/matrix.py +++ b/zeta/nn/modules/matrix.py @@ -1,6 +1,6 @@ -import numpy as np +import numpy as np import subprocess -import torch +import torch try: import jax.numpy as jnp @@ -9,23 +9,22 @@ print("Installing JAX") subprocess.run(["pip3", "install", "jax"]) subprocess.run(["pip3", "install", "jaxlib"]) - + try: import tensorflow as tf except ImportError: print("Tensorflow not installed") print("Installing Tensorflow") subprocess.run(["pip3", "install", "tensorflow"]) - class Matrix: """Matrix class that can be converted between frameworks - - + + Args: data (torch.Tensor, jnp.ndarray, tf.Tensor): Data to be converted - + Example: >>> import torch >>> import jax.numpy as jnp @@ -39,13 +38,14 @@ class Matrix: >>> print(tensor1.to_jax()) >>> print(tensor2.to_pytorch()) >>> print(tensor3.to_tensorflow()) - - + + """ + def __init__(self, data): self.data = data self.framework = self._detect_framework(data) - + def _detect_framework(self, data): """Detect framework @@ -66,22 +66,24 @@ def _detect_framework(self, data): return "tensorflow" else: raise TypeError("Unknown framework") - + def to_pytorch(self): """TODO: Docstring for to_pytorch. Returns: _type_: _description_ """ - if self.framework == 'pytorch': + if self.framework == "pytorch": return self.data - elif self.framework == 'jax': + elif self.framework == "jax": # Convert JAX array to numpy array first, then to PyTorch tensor numpy_data = np.array(self.data) # Convert JAX array to numpy array - return torch.tensor(numpy_data) # Convert numpy array to PyTorch tensor - elif self.framework == 'tensorflow': + return torch.tensor( + numpy_data + ) # Convert numpy array to PyTorch tensor + elif self.framework == "tensorflow": return torch.tensor(self.data.numpy()) - + def to_jax(self): """To jax @@ -92,9 +94,9 @@ def to_jax(self): return self.data elif self.framework == "pytorch": return jnp.array(self.data.cpu().numpy()) - elif self.framework == 'tensorflow': + elif self.framework == "tensorflow": return jnp.array(self.data.numpy()) - + def to_tensorflow(self): """To tensorflow @@ -107,7 +109,7 @@ def to_tensorflow(self): return tf.convert_to_tensor(self.data.numpy.cpu().numpy()) elif self.framework == "jax": return tf.convert_to_tensor(self.data) - + def sum(self): """Sum @@ -120,7 +122,8 @@ def sum(self): return jnp.sum(self.data) elif self.framework == "tensorflow": return tf.reduce_sum(self.data) - + + # # Example usage # tensor1 = Matrix(torch.tensor([1, 2, 3])) # tensor2 = Matrix(jnp.array([1, 2, 3])) @@ -128,4 +131,4 @@ def sum(self): # print(tensor1.to_jax()) # print(tensor2.to_pytorch()) -# print(tensor3.to_tensorflow()) \ No newline at end of file +# print(tensor3.to_tensorflow()) diff --git a/zeta/nn/modules/sparq_attn.py b/zeta/nn/modules/sparq_attn.py new file mode 100644 index 00000000..4a3337b1 --- /dev/null +++ b/zeta/nn/modules/sparq_attn.py @@ -0,0 +1,133 @@ +import torch +from torch import nn +from torch import abs, softmax, sqrt, tensor, topk + + +class SparQAttention(nn.Module): + """ + Sparse and Quantized Attention (SparQAttention) is a novel attention mechanism + that approximates the attention scores using the r largest components of the query matrix + and then gathers the top k positions based on the approximate attention scores. + + + Methods: + forward(Q, K, V, V_mean, M, r, k): Computes the Sparse and Quantized attention. + + Examples: + >>> import torch + >>> from zeta.nn.modules import SparQAttention + >>> attention = SparQAttention() + >>> batch_size, heads, seq_length, dim = 2, 4, 10, 64 + >>> Q = torch.randn(batch_size, heads, seq_length, dim) + >>> K = torch.randn(batch_size, heads, seq_length, dim) + >>> V = torch.randn(batch_size, heads, seq_length, dim) + >>> V_mean = torch.randn(batch_size, heads, 1, dim) + >>> M = torch.randn(batch_size, heads, seq_length, seq_length) + >>> r = 5 # Number of largest components for approximation + >>> k = 5 # Number of top positions for attention + >>> output = attention.forward(Q, K, V, V_mean, M, r, k) + >>> print(output) + + + + + """ + + def __init__(self, dim: int = None, heads: int = None, *args, **kwargs): + """Initialize the SparQAttention class.""" + super().__init__(*args, **kwargs) + self.dim = dim + self.heads = heads + + def forward( + self, + Q: torch.Tensor, + K: torch.Tensor, + V: torch.Tensor, + V_mean: torch.Tensor, + M: torch.Tensor, + r: int, + k: int, + *args, + **kwargs, + ): + """ + Computes the Sparse and Quantized attention. + + Args: + Q (Tensor): Query matrix. + K (Tensor): Key matrix. + V (Tensor): Value matrix. + V_mean (Tensor): Mean of values. + M (Tensor): Mask. + r (int): Number of largest components for approximation. + k (int): Number of top positions for attention. + + Returns: + Tensor: The result of applying sparse quantized attention. + """ + try: + # # Make sure that the input tensors match the specified dimensions + # assert Q.size(1) == self.heads and Q.size(-1) == self.dim, \ + # "Query tensor dimensions do not match the specified number of heads and head dimension" + # assert K.size(1) == self.heads and K.size(-1) == self.dim, \ + # "Key tensor dimensions do not match the specified number of heads and head dimension" + # assert V.size(1) == self.heads and V.size(-1) == self.dim, \ + # "Value tensor dimensions do not match the specified number of heads and head dimension" + + # Gather function + def gather(t, dim, i): + dim += (dim < 0) * t.dim() + return t.gather( + dim, + i.expand(*t.shape[:dim], i.shape[dim], *t.shape[dim + 1 :]), + ) + + # Attention function + def attn(q, k, v, m): + s = q @ k.transpose(-1, -2) / sqrt(tensor(q.shape[-1])) + m + return softmax(s, dim=-1) @ v + + # 1. Approximate attention scores using r largest components of Q + i1 = topk(abs(Q), r, -1).indices + Q_hat, K_hat = gather(Q, -1, i1), gather(K, -1, i1) + scale = sqrt( + Q.shape[-1] + * abs(Q_hat).sum(dim=-1, keepdim=True) + / abs(Q).sum(dim=-1, keepdim=True) + ) + s_hat = softmax(Q_hat @ K_hat.transpose(-1, -2) / scale + M, dim=-1) + + # 2. Gather top k positions based on approximate attention scores & run attention + i2 = topk(s_hat, k, -1).indices + iKV = i2[..., 0, :, None] + K, V, M = gather(K, -2, iKV), gather(V, -2, iKV), gather(M, -1, i2) + y_ = attn(Q, K, V, M) + + # 3. Estimate the total score of the top k, and interpolate with V_mean + alpha = gather(s_hat, -1, i2).sum(-1, keepdim=True) + return alpha * y_ + (1 - alpha) * V_mean + except Exception as e: + raise ValueError(f"Error in SPARQ attention computation: {e}") + + +# Example usage +num_heads = 4 +head_dim = 64 +attention = SparQAttention(num_heads, head_dim) + +# Generate random tensors with the specified dimensions +batch_size, seq_length = 2, 10 +Q = torch.randn(batch_size, num_heads, seq_length, head_dim) +K = torch.randn(batch_size, num_heads, seq_length, head_dim) +V = torch.randn(batch_size, num_heads, seq_length, head_dim) +V_mean = torch.randn(batch_size, num_heads, 1, head_dim) +M = torch.randn(batch_size, num_heads, seq_length, seq_length) + +# Compute the Sparse and Quantized attention +r = 5 # Number of largest components for approximation +k = 5 # Number of top positions for attention +output = attention.forward(Q, K, V, V_mean, M, r, k) + +# Output tensor +print(output) diff --git a/zeta/quant/residual_vq.py b/zeta/quant/residual_vq.py index c777dd3b..cb21eb66 100644 --- a/zeta/quant/residual_vq.py +++ b/zeta/quant/residual_vq.py @@ -9,7 +9,7 @@ class ResidualVectorQuantizer(nn.Module): dim (int): _description_ dim_out (int): _description_ n_embed (int): _description - + Example: >>> x = torch.randn(2, 4) >>> model = ResidualVectorQuantizer(4, 4, 4) @@ -17,6 +17,7 @@ class ResidualVectorQuantizer(nn.Module): >>> print(out.shape) torch.Size([2, 4]) """ + def __init__(self, dim, dim_out, n_embed): super().__init__() self.dim = dim From cd07d6d53922b5b2d66395c6cc23518630c26dbb Mon Sep 17 00:00:00 2001 From: Eternal Reclaimer <98760976+kyegomez@users.noreply.github.com> Date: Mon, 11 Dec 2023 20:17:10 -0800 Subject: [PATCH 109/587] Update README.md --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 12fcd66c..c7eeb5c6 100644 --- a/README.md +++ b/README.md @@ -314,4 +314,4 @@ Book a [1-on-1 Session with Kye](https://calendly.com/apacai/agora), the Creator # License -- MIT \ No newline at end of file +- Apache From 612efd06edeef1600218946f14cee89114d57e63 Mon Sep 17 00:00:00 2001 From: Eternal Reclaimer <98760976+kyegomez@users.noreply.github.com> Date: Mon, 11 Dec 2023 20:19:41 -0800 Subject: [PATCH 110/587] Update README.md --- README.md | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/README.md b/README.md index c7eeb5c6..65825a9e 100644 --- a/README.md +++ b/README.md @@ -10,11 +10,11 @@ Build SOTA AI Models 80% faster with modular, high-performance, and scalable bui -# Design Principles -- Fluid Experimentation: Zeta aims to be effortless for researchers and industrial AI engineers to rapidly experiment with the latest modules and components like `MultiGroupedQueryAttention` or `Unet` and many others! -- Production-Grade Reliability: Facilitate reproducibility with bleeding-edge performance. -- Modularity: Modularized Lego Building Blocks for building and deploying the best ML Models! +[![GitHub issues](https://img.shields.io/github/issues/kyegomez/zeta)](https://github.com/kyegomez/zeta/issues) [![GitHub forks](https://img.shields.io/github/forks/kyegomez/zeta)](https://github.com/kyegomez/zeta/network) [![GitHub stars](https://img.shields.io/github/stars/kyegomez/zeta)](https://github.com/kyegomez/zeta/stargazers) [![GitHub license](https://img.shields.io/github/license/kyegomez/zeta)](https://github.com/kyegomez/zeta/blob/main/LICENSE)[![GitHub star chart](https://img.shields.io/github/stars/kyegomez/zeta?style=social)](https://star-history.com/#kyegomez/zeta)[![Dependency Status](https://img.shields.io/librariesio/github/kyegomez/zeta)](https://libraries.io/github/kyegomez/zeta) [![Downloads](https://static.pepy.tech/badge/zeta/month)](https://pepy.tech/project/zeta) +[![Join the Agora discord](https://img.shields.io/discord/1110910277110743103?label=Discord&logo=discord&logoColor=white&style=plastic&color=d7b023)![Share on Twitter](https://img.shields.io/twitter/url/https/twitter.com/cloudposse.svg?style=social&label=Share%20%40kyegomez/zeta)](https://twitter.com/intent/tweet?text=Check%20out%20this%20amazing%20AI%20project:%20&url=https%3A%2F%2Fgithub.com%2Fkyegomez%2Fzeta) [![Share on Facebook](https://img.shields.io/badge/Share-%20facebook-blue)](https://www.facebook.com/sharer/sharer.php?u=https%3A%2F%2Fgithub.com%2Fkyegomez%2Fzeta) [![Share on LinkedIn](https://img.shields.io/badge/Share-%20linkedin-blue)](https://www.linkedin.com/shareArticle?mini=true&url=https%3A%2F%2Fgithub.com%2Fkyegomez%2Fzeta&title=&summary=&source=) + +[![Share on Reddit](https://img.shields.io/badge/-Share%20on%20Reddit-orange)](https://www.reddit.com/submit?url=https%3A%2F%2Fgithub.com%2Fkyegomez%2Fzeta&title=zeta%20-%20the%20future%20of%20AI) [![Share on Hacker News](https://img.shields.io/badge/-Share%20on%20Hacker%20News-orange)](https://news.ycombinator.com/submitlink?u=https%3A%2F%2Fgithub.com%2Fkyegomez%2Fzeta&t=zeta%20-%20the%20future%20of%20AI) [![Share on Pinterest](https://img.shields.io/badge/-Share%20on%20Pinterest-red)](https://pinterest.com/pin/create/button/?url=https%3A%2F%2Fgithub.com%2Fkyegomez%2Fzeta&media=https%3A%2F%2Fexample.com%2Fimage.jpg&description=zeta%20-%20the%20future%20of%20AI) [![Share on WhatsApp](https://img.shields.io/badge/-Share%20on%20WhatsApp-green)](https://api.whatsapp.com/send?text=Check%20out%20zeta%20-%20the%20future%20of%20AI%20%23zeta%20%23AI%0A%0Ahttps%3A%2F%2Fgithub.com%2Fkyegomez%2Fzeta) # Install From 68f4e32a2bc8e4051a8e35243d7521e5966af8f1 Mon Sep 17 00:00:00 2001 From: Kye Date: Mon, 11 Dec 2023 23:05:02 -0800 Subject: [PATCH 111/587] [TRAINER] --- zeta/training/train.py | 50 ++++++++++++++++++++++++++++++++++++++---- 1 file changed, 46 insertions(+), 4 deletions(-) diff --git a/zeta/training/train.py b/zeta/training/train.py index a047e038..5fbe1342 100644 --- a/zeta/training/train.py +++ b/zeta/training/train.py @@ -17,6 +17,7 @@ def print_num_params(model, accelerator: Accelerator): + """Print number of parameters in model""" # n_params = sum(p.numel() for p in model.parameters() if p.requires_grad) n_params = sum(p.numel() for p in model.parameters() if p.requires_grad) accelerator.print(f"Number of parameters in model: {n_params}") @@ -26,6 +27,7 @@ def Trainer( gradient_accumulate_every: int = None, batch_size: int = None, seq_len: int = None, + model_name: str = None, entity_name: str = None, model=None, use_fsdp: bool = False, @@ -36,9 +38,49 @@ def Trainer( resume_from_checkpoint=None, checkpointing_steps=None, output_dir=None, + optimizer_type: str = "Adam8bit", weight_decay=None, use_deepspeed=None, ): + """Trainer + + Args: + gradient_accumulate_every (int, optional): _description_. Defaults to None. + batch_size (int, optional): _description_. Defaults to None. + seq_len (int, optional): _description_. Defaults to None. + entity_name (str, optional): _description_. Defaults to None. + model (_type_, optional): _description_. Defaults to None. + use_fsdp (bool, optional): _description_. Defaults to False. + use_activation_checkpointing (bool, optional): _description_. Defaults to False. + learning_rate (_type_, optional): _description_. Defaults to None. + seed (_type_, optional): _description_. Defaults to None. + use_pretokenized (bool, optional): _description_. Defaults to False. + resume_from_checkpoint (_type_, optional): _description_. Defaults to None. + checkpointing_steps (_type_, optional): _description_. Defaults to None. + output_dir (_type_, optional): _description_. Defaults to None. + weight_decay (_type_, optional): _description_. Defaults to None. + use_deepspeed (_type_, optional): _description_. Defaults to None. + + Examples: + >>> Trainer( + >>> gradient_accumulate_every=gradient_accumulate_every, + >>> batch_size=batch_size, + >>> seq_len=seq_len, + >>> entity_name=entity_name, + >>> model=model, + >>> use_fsdp=use_fsdp, + >>> use_activation_checkpointing=use_activation_checkpointing, + >>> learning_rate=learning_rate, + >>> seed=seed, + >>> use_pretokenized=use_pretokenized, + >>> resume_from_checkpoint=resume_from_checkpoint, + >>> checkpointing_steps=checkpointing_steps, + >>> output_dir=output_dir, + >>> weight_decay=weight_decay, + >>> use_deepspeed=use_deepspeed, + >>> ) + + """ # accelerator timeout = InitProcessGroupKwargs(timeout=timedelta(seconds=1_000_000)) @@ -52,7 +94,7 @@ def Trainer( # AcceleratorState().deepspeed_plugin.deepspeed_config['train_micro_batch_ accelerator.init_trackers( - project_name="LongNet", + project_name=model_name, config={ "batch_size": batch_size, "gradient_accumulate_every": gradient_accumulate_every, @@ -101,7 +143,7 @@ def Trainer( weight_decay=weight_decay, beta_1=0.90, beta_2=0.95, - optimizer_type="Adam8bit", + optimizer_type=optimizer_type, use_fsdp=True, accelerator=accelerator, ) @@ -207,12 +249,12 @@ def Trainer( # end training - # accelerator.print(f"Training Finished") + accelerator.print("Training Finished") accelerator.end_training() # save final model - # accelerator.print(f"Saving model to {output_dir}") + accelerator.print(f"Saving model to {output_dir}") if output_dir is not None: accelerator.wait_for_everyone() unwrapped_model = accelerator.unwrap_model(model) From 5ab9041b77de420cce7f91e0c3c8f0e39d3d8b68 Mon Sep 17 00:00:00 2001 From: Kye Date: Mon, 11 Dec 2023 23:06:32 -0800 Subject: [PATCH 112/587] [CLEANUP] --- pyproject.toml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pyproject.toml b/pyproject.toml index f1ff6e20..1b8e08c0 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,6 +1,6 @@ [tool.poetry] name = "zetascale" -version = "0.8.9" +version = "0.9.0" description = "Transformers at zeta scales" authors = ["Zeta Team "] license = "MIT" From 8a29d971efcd2174bef9ac3aa4681f8db8c184aa Mon Sep 17 00:00:00 2001 From: Kye Date: Mon, 11 Dec 2023 23:10:11 -0800 Subject: [PATCH 113/587] [LLamaTokenizer] --- zeta/tokenizers/__init__.py | 3 +- zeta/tokenizers/llama_sentencepiece.py | 90 ++++++++++++++++++++++++++ 2 files changed, 92 insertions(+), 1 deletion(-) create mode 100644 zeta/tokenizers/llama_sentencepiece.py diff --git a/zeta/tokenizers/__init__.py b/zeta/tokenizers/__init__.py index ec8c22b5..aabf0cd3 100644 --- a/zeta/tokenizers/__init__.py +++ b/zeta/tokenizers/__init__.py @@ -2,7 +2,7 @@ from zeta.tokenizers.multi_modal_tokenizer import MultiModalTokenizer from zeta.tokenizers.sentence_piece import SentencePieceTokenizer from zeta.tokenizers.tokenmonster import TokenMonster - +from zeta.tokenizers.llama_sentencepiece import LLamaTokenizer # from zeta.tokenizers.tiktoken import TikToken __all__ = [ @@ -10,5 +10,6 @@ "MultiModalTokenizer", "SentencePieceTokenizer", "TokenMonster", + "LLamaTokenizer", # "TikToken", ] diff --git a/zeta/tokenizers/llama_sentencepiece.py b/zeta/tokenizers/llama_sentencepiece.py new file mode 100644 index 00000000..4e10802d --- /dev/null +++ b/zeta/tokenizers/llama_sentencepiece.py @@ -0,0 +1,90 @@ +# Using LLAMA tokenizer +import os +import requests +from logging import getLogger + +from sentencepiece import SentencePieceProcessor + +logger = getLogger() + +PRETRAINED_VOCAB_FILES_MAP = { + "vocab_file": { + "hf-internal-testing/llama-tokenizer": "https://huggingface.co/hf-internal-testing/llama-tokenizer/resolve/main/tokenizer.model", + }, + "tokenizer_file": { + "hf-internal-testing/llama-tokenizer": "https://huggingface.co/hf-internal-testing/llama-tokenizer/resolve/main/tokenizer_config.json", + }, +} + + +class LLamaTokenizer: + """ + A tokenizer that uses a pretrained SentencePiece model for text tokenization. + + Args: + model_path: Path to a pretrained SentencePiece model file. + tokenizer_name: Name of a pretrained SentencePiece model hosted on HuggingFace Hub. + + Examples: + >>> tokenizer_name = "hf-internal-testing/llama-tokenizer" + >>> tokenizer = Tokenizer(tokenizer_name=tokenizer_name) + >>> encoded_text = tokenizer.encode("This is a sample text") + >>> decoded_text = tokenizer.decode(encoded_text) + >>> print("Encoded text:", encoded_text) + >>> print("Decoded text:", decoded_text) + """ + + def __init__(self, model_path: str = None, tokenizer_name: str = None): + if model_path: + assert os.path.isfile(model_path), model_path + elif tokenizer_name: + model_path = self.download_tokenizer(tokenizer_name) + else: + raise ValueError("Either model_path or tokenizer_name must be provided.") + + self.sp_model = SentencePieceProcessor(model_file=model_path) + logger.info(f"Reloaded SentencePiece model from {model_path}") + + @staticmethod + def download_tokenizer(tokenizer_name: str) -> str: + if tokenizer_name not in PRETRAINED_VOCAB_FILES_MAP["vocab_file"]: + raise ValueError(f"Tokenizer {tokenizer_name} is not available.") + + model_url = PRETRAINED_VOCAB_FILES_MAP["vocab_file"][tokenizer_name] + model_path = os.path.join("data", "tokenizer.model") + + if not os.path.exists("data"): + os.makedirs("data") + + # Downloading the tokenizer model file + response = requests.get(model_url) + if response.status_code == 200: + with open(model_path, "wb") as file: + file.write(response.content) + logger.info(f"Downloaded SentencePiece model to {model_path}") + else: + raise Exception(f"Failed to download model from {model_url}") + + return model_path + + def encode(self, s: str) -> [int]: + """Encodes a string into a list of token ids. + + Args: + s (str): _description_ + + Returns: + [int]: _description_ + """ + return self.sp_model.encode(s, out_type=int) + + def decode(self, ids: [int]) -> str: + """decodes a list of token ids into a string. + + Args: + ids (int]): _description_ + + Returns: + str: _description_ + """ + return self.sp_model.decode(ids) From c7c9a7922f0f0d4a5cfe4dd8fc2b56ab1e69ab4d Mon Sep 17 00:00:00 2001 From: Kye Date: Mon, 11 Dec 2023 23:12:58 -0800 Subject: [PATCH 114/587] [LLamaTokenizer] --- pyproject.toml | 2 +- tests/tokenizers/test_llama_tokenizer.py | 76 ++++++++++++++++++++++++ zeta/tokenizers/__init__.py | 1 + zeta/tokenizers/llama_sentencepiece.py | 4 +- zeta/training/train.py | 4 +- 5 files changed, 83 insertions(+), 4 deletions(-) create mode 100644 tests/tokenizers/test_llama_tokenizer.py diff --git a/pyproject.toml b/pyproject.toml index 1b8e08c0..95aac5c2 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,6 +1,6 @@ [tool.poetry] name = "zetascale" -version = "0.9.0" +version = "0.9.1" description = "Transformers at zeta scales" authors = ["Zeta Team "] license = "MIT" diff --git a/tests/tokenizers/test_llama_tokenizer.py b/tests/tokenizers/test_llama_tokenizer.py new file mode 100644 index 00000000..726c193e --- /dev/null +++ b/tests/tokenizers/test_llama_tokenizer.py @@ -0,0 +1,76 @@ +import pytest +import os +from zeta.tokenizers.llama_sentencepiece import LLamaTokenizer + + +def test_llama_tokenizer_init_model_path(): + model_path = "/path/to/model" + tokenizer = LLamaTokenizer(model_path=model_path) + assert tokenizer.sp_model is not None + + +def test_llama_tokenizer_init_tokenizer_name(): + tokenizer_name = "hf-internal-testing/llama-tokenizer" + tokenizer = LLamaTokenizer(tokenizer_name=tokenizer_name) + assert tokenizer.sp_model is not None + + +def test_llama_tokenizer_init_no_args(): + with pytest.raises(ValueError): + LLamaTokenizer() + + +def test_llama_tokenizer_encode(): + model_path = "/path/to/model" + tokenizer = LLamaTokenizer(model_path=model_path) + encoded_text = tokenizer.encode("This is a sample text") + assert isinstance(encoded_text, list) + assert all(isinstance(i, int) for i in encoded_text) + + +def test_llama_tokenizer_decode(): + model_path = "/path/to/model" + tokenizer = LLamaTokenizer(model_path=model_path) + decoded_text = tokenizer.decode([1, 2, 3]) + assert isinstance(decoded_text, str) + + +@pytest.mark.parametrize("text", ["", " ", " ", "\t", "\n"]) +def test_llama_tokenizer_encode_empty(text): + model_path = "/path/to/model" + tokenizer = LLamaTokenizer(model_path=model_path) + encoded_text = tokenizer.encode(text) + assert encoded_text == [] + + +@pytest.mark.parametrize("ids", [[], [0], [0, 1], [0, 1, 2]]) +def test_llama_tokenizer_decode_empty(ids): + model_path = "/path/to/model" + tokenizer = LLamaTokenizer(model_path=model_path) + decoded_text = tokenizer.decode(ids) + assert isinstance(decoded_text, str) + + +@pytest.mark.parametrize( + "text", + ["This is a sample text", "Another sample text", "Yet another sample text"], +) +def test_llama_tokenizer_encode_decode(text): + model_path = "/path/to/model" + tokenizer = LLamaTokenizer(model_path=model_path) + encoded_text = tokenizer.encode(text) + decoded_text = tokenizer.decode(encoded_text) + assert text == decoded_text + + +@pytest.mark.parametrize( + "tokenizer_name", + [ + "hf-internal-testing/llama-tokenizer", + "another-tokenizer", + "yet-another-tokenizer", + ], +) +def test_llama_tokenizer_download_tokenizer(tokenizer_name): + tokenizer = LLamaTokenizer(tokenizer_name=tokenizer_name) + assert os.path.isfile("data/tokenizer.model") diff --git a/zeta/tokenizers/__init__.py b/zeta/tokenizers/__init__.py index aabf0cd3..71527045 100644 --- a/zeta/tokenizers/__init__.py +++ b/zeta/tokenizers/__init__.py @@ -3,6 +3,7 @@ from zeta.tokenizers.sentence_piece import SentencePieceTokenizer from zeta.tokenizers.tokenmonster import TokenMonster from zeta.tokenizers.llama_sentencepiece import LLamaTokenizer + # from zeta.tokenizers.tiktoken import TikToken __all__ = [ diff --git a/zeta/tokenizers/llama_sentencepiece.py b/zeta/tokenizers/llama_sentencepiece.py index 4e10802d..abf2bb5d 100644 --- a/zeta/tokenizers/llama_sentencepiece.py +++ b/zeta/tokenizers/llama_sentencepiece.py @@ -40,7 +40,9 @@ def __init__(self, model_path: str = None, tokenizer_name: str = None): elif tokenizer_name: model_path = self.download_tokenizer(tokenizer_name) else: - raise ValueError("Either model_path or tokenizer_name must be provided.") + raise ValueError( + "Either model_path or tokenizer_name must be provided." + ) self.sp_model = SentencePieceProcessor(model_file=model_path) logger.info(f"Reloaded SentencePiece model from {model_path}") diff --git a/zeta/training/train.py b/zeta/training/train.py index 5fbe1342..a391c7e6 100644 --- a/zeta/training/train.py +++ b/zeta/training/train.py @@ -60,7 +60,7 @@ def Trainer( output_dir (_type_, optional): _description_. Defaults to None. weight_decay (_type_, optional): _description_. Defaults to None. use_deepspeed (_type_, optional): _description_. Defaults to None. - + Examples: >>> Trainer( >>> gradient_accumulate_every=gradient_accumulate_every, @@ -79,7 +79,7 @@ def Trainer( >>> weight_decay=weight_decay, >>> use_deepspeed=use_deepspeed, >>> ) - + """ # accelerator From 9160096a18b83fbc18baf579ca0f87985cf73c43 Mon Sep 17 00:00:00 2001 From: Kye Date: Wed, 13 Dec 2023 23:30:41 -0800 Subject: [PATCH 115/587] [REQUIREMENTS] --- pyproject.toml | 47 ++++++++++++----------- requirements.txt | 50 ++++++++++++------------- scripts/get_package_requirements.py | 39 +++++++++++++++++++ scripts/requirementstxt_to_pyproject.py | 40 ++++++++++++++++++++ zeta/training/train.py | 32 +++++++++------- 5 files changed, 148 insertions(+), 60 deletions(-) create mode 100644 scripts/get_package_requirements.py create mode 100644 scripts/requirementstxt_to_pyproject.py diff --git a/pyproject.toml b/pyproject.toml index 95aac5c2..bd68782b 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -17,29 +17,29 @@ packages = [ [tool.poetry.dependencies] python = "^3.8" -torch = "*" -fairscale = "*" -timm = "*" -torchdiffeq = "*" -pytest = "*" -einops = "*" -bitsandbytes = "*" -typing = "*" -transformers = "*" -einops-exts = "*" -torchvision = "*" -accelerate = "*" -datasets = "*" -lion-pytorch = "*" -sentencepiece = "*" +torch = "2.1.1" +fairscale = "0.4.0" +timm = "0.6.13" +torchdiffeq = "0.2.3" +pytest = "7.4.2" +einops = "0.7.0" +bitsandbytes = "0.38.1" +typing = "3.7.4.3" +transformers = "4.35.0" +einops-exts = "0.0.4" +torchvision = "0.16.1" +accelerate = "0.22.0" +datasets = "2.10.1" +lion-pytorch = "0.0.7" +sentencepiece = "0.1.98" colt5-attention = "0.10.19" vector-quantize-pytorch = "1.12.0" -tokenmonster = "*" -scipy = "*" -beartype = "*" -tiktoken = "*" -tqdm = "*" -rich = "*" +tokenmonster = "1.1.12" +scipy = "1.9.3" +beartype = "0.15.0" +tiktoken = "0.4.0" +tqdm = "4.66.1" +rich = "13.5.2" [build-system] requires = ["poetry-core>=1.0.0"] @@ -71,3 +71,8 @@ target-version = ['py38'] preview = true + + + + + diff --git a/requirements.txt b/requirements.txt index 2aa5161e..e36d446c 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,32 +1,30 @@ - -torch -fairscale -timm -einops +torch==2.1.1 +fairscale==0.4.0 +timm==0.6.13 +einops==0.7.0 apex memory-profiler -triton -lion-pytorch -bitsandbytes -typing -einops-exts -torchvision -tokenmonster -accelerate -datasets -torchdiffeq -lion-pytorch -sentencepiece -beartype +lion-pytorch==0.0.7 +bitsandbytes==0.38.1 +typing==3.7.4.3 +einops-exts==0.0.4 +torchvision==0.16.1 +tokenmonster==1.1.12 +accelerate==0.22.0 +datasets==2.10.1 +torchdiffeq==0.2.3 +lion-pytorch==0.0.7 +sentencepiece==0.1.98 +beartype==0.15.0 xformers -vector-quantize-pytorch -scipy -rich -tiktoken +vector-quantize-pytorch==1.12.0 +scipy==1.9.3 +rich==13.5.2 +tiktoken==0.4.0 autopep8 -transformers -tqdm -torchaudio +transformers==4.35.0 +tqdm==4.66.1 +torchaudio==2.1.1 mkdocs mkdocs-material -mkdocs-glightbox \ No newline at end of file +mkdocs-glightbox diff --git a/scripts/get_package_requirements.py b/scripts/get_package_requirements.py new file mode 100644 index 00000000..9494409b --- /dev/null +++ b/scripts/get_package_requirements.py @@ -0,0 +1,39 @@ +import pkg_resources + + +def get_package_versions(requirements_path, output_path): + try: + with open(requirements_path, "r") as file: + requirements = file.readlines() + except FileNotFoundError: + print(f"Error: The file '{requirements_path}' was not found.") + return + + package_versions = [] + + for requirement in requirements: + # Skip empty lines and comments + if ( + requirement.strip() == "" + or requirement.strip().startswith("#") + ): + continue + + # Extract package name + package_name = requirement.split("==")[0].strip() + try: + version = pkg_resources.get_distribution( + package_name + ).version + package_versions.append(f"{package_name}=={version}") + except pkg_resources.DistributionNotFound: + package_versions.append(f"{package_name}: not installed") + + with open(output_path, "w") as file: + for package_version in package_versions: + file.write(package_version + "\n") + print(f"Versions written to {output_path}") + + +# Usage +get_package_versions("requirements.txt", "installed_versions.txt") diff --git a/scripts/requirementstxt_to_pyproject.py b/scripts/requirementstxt_to_pyproject.py new file mode 100644 index 00000000..5710db61 --- /dev/null +++ b/scripts/requirementstxt_to_pyproject.py @@ -0,0 +1,40 @@ +import toml +import pkg_resources + + +def update_pyproject_versions(pyproject_path): + try: + with open(pyproject_path, "r") as file: + data = toml.load(file) + except FileNotFoundError: + print(f"Error: The file '{pyproject_path}' was not found.") + return + except toml.TomlDecodeError: + print( + f"Error: The file '{pyproject_path}' is not a valid TOML" + " file." + ) + return + + dependencies = ( + data.get("tool", {}).get("poetry", {}).get("dependencies", {}) + ) + + for package in dependencies: + if package.lower() == "python": + continue # Skip the Python version dependency + + try: + version = pkg_resources.get_distribution(package).version + dependencies[package] = version + except pkg_resources.DistributionNotFound: + print(f"Warning: Package '{package}' not installed.") + + with open(pyproject_path, "w") as file: + toml.dump(data, file) + + print(f"Updated versions written to {pyproject_path}") + + +# Usage +update_pyproject_versions("pyproject.toml") diff --git a/zeta/training/train.py b/zeta/training/train.py index a391c7e6..270c5fad 100644 --- a/zeta/training/train.py +++ b/zeta/training/train.py @@ -24,23 +24,24 @@ def print_num_params(model, accelerator: Accelerator): def Trainer( - gradient_accumulate_every: int = None, + gradient_accumulate_every: int = 2, batch_size: int = None, seq_len: int = None, - model_name: str = None, - entity_name: str = None, + entity_name: str = "zeta", model=None, use_fsdp: bool = False, use_activation_checkpointing: bool = False, - learning_rate=None, - seed=None, + learning_rate: float = None, + seed: int = None, use_pretokenized: bool = False, - resume_from_checkpoint=None, + resume_from_checkpoint: bool = None, checkpointing_steps=None, - output_dir=None, + output_dir: str = "checlpoints/", optimizer_type: str = "Adam8bit", - weight_decay=None, + weight_decay: float = 0.1, use_deepspeed=None, + *args, + **kwargs ): """Trainer @@ -94,7 +95,7 @@ def Trainer( # AcceleratorState().deepspeed_plugin.deepspeed_config['train_micro_batch_ accelerator.init_trackers( - project_name=model_name, + project_name=entity_name, config={ "batch_size": batch_size, "gradient_accumulate_every": gradient_accumulate_every, @@ -265,17 +266,22 @@ def Trainer( ) -def train(MASTER_ADDR=None, MASTER_PORT=None, RANK=None, WORLD_SIZE=None): +def train( + MASTER_ADDR=None, + MASTER_PORT=None, + RANK=None, + WORLD_SIZE=None, + *args, + **kwargs, +): os.environ["MASTER_ADDR"] or MASTER_ADDR # = 'localhost' os.environ["MASTER_PORT"] or MASTER_PORT # = '9994' # # [CRITICAL] Pay attention to this when scaling to multiple GPUs and clusters - # # Pay attention to this, use "accelerate config" - os.environ["RANK"] or RANK # = str(0) # Number of nodes (servers) os.environ["WORLD_SIZE"] or WORLD_SIZE # = str(torch.cuda.device_count()) torch.distributed.init_process_group() - Trainer() + Trainer(*args, **kwargs) From 19dac5381ad21a08f9ddd043899dd48c601bdab7 Mon Sep 17 00:00:00 2001 From: Kye Date: Wed, 13 Dec 2023 23:31:43 -0800 Subject: [PATCH 116/587] [V] --- pyproject.toml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pyproject.toml b/pyproject.toml index bd68782b..445f6d44 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,6 +1,6 @@ [tool.poetry] name = "zetascale" -version = "0.9.1" +version = "0.9.2" description = "Transformers at zeta scales" authors = ["Zeta Team "] license = "MIT" From 630f749c5f968db22703fd7b24f443852ab97353 Mon Sep 17 00:00:00 2001 From: Kye Date: Thu, 14 Dec 2023 23:59:37 -0800 Subject: [PATCH 117/587] [SwiGLUStacked] --- zeta/nn/modules/swiglu.py | 65 +++++++++++++++++++++++++++++++++++++++ 1 file changed, 65 insertions(+) diff --git a/zeta/nn/modules/swiglu.py b/zeta/nn/modules/swiglu.py index e61662a5..6b36fe5d 100644 --- a/zeta/nn/modules/swiglu.py +++ b/zeta/nn/modules/swiglu.py @@ -3,6 +3,71 @@ class SwiGLU(nn.Module): + """_summary_ + + Args: + nn (_type_): _description_ + """ def forward(self, x): + """Forward + + Args: + x (_type_): _description_ + + Returns: + _type_: _description_ + """ x, gate = x.chunk(2, dim=-1) return F.silu(gate) * x + + +class SwiGLUStacked(nn.Module): + """SwiGLUStacked + + Args: + nn (_type_): _description_ + + Examples: + >>> from zeta.nn.modules.swiglu import SwiGLUStacked + >>> import torch + >>> x = torch.randn(5, 10) + >>> swiglu = SwiGLUStacked(10, 20) + >>> swiglu(x).shape + torch.Size([5, 10]) + """ + def __init__( + self, + dim: int, + hidden_dim: int = None, + dropout: float = None, + bias: bool = False, + *args, + **kwargs + ): + self.w1 = nn.Linear( + dim, + hidden_dim, + bias=bias + ) + self.w2 = nn.Linear( + hidden_dim, + dim, + bias=bias + ) + self.w3 = nn.Linear( + dim, + hidden_dim, + bias=bias + ) + + def forward(self, x): + """Forward + + Args: + x (_type_): _description_ + + Returns: + _type_: _description_ + """ + x = self.w2(F.silu(self.w1(x)) * self.w3(x)) + return x \ No newline at end of file From db8de360773919ab1745ce4db07dd6a00a7d2896 Mon Sep 17 00:00:00 2001 From: Kye Date: Fri, 15 Dec 2023 00:04:53 -0800 Subject: [PATCH 118/587] [SwiGLU] --- scripts/get_package_requirements.py | 9 +---- scripts/requirementstxt_to_pyproject.py | 5 +-- zeta/nn/modules/__init__.py | 52 +++++++++++++------------ zeta/nn/modules/swiglu.py | 28 +++++-------- zeta/training/train.py | 4 +- 5 files changed, 41 insertions(+), 57 deletions(-) diff --git a/scripts/get_package_requirements.py b/scripts/get_package_requirements.py index 9494409b..0d57c028 100644 --- a/scripts/get_package_requirements.py +++ b/scripts/get_package_requirements.py @@ -13,18 +13,13 @@ def get_package_versions(requirements_path, output_path): for requirement in requirements: # Skip empty lines and comments - if ( - requirement.strip() == "" - or requirement.strip().startswith("#") - ): + if requirement.strip() == "" or requirement.strip().startswith("#"): continue # Extract package name package_name = requirement.split("==")[0].strip() try: - version = pkg_resources.get_distribution( - package_name - ).version + version = pkg_resources.get_distribution(package_name).version package_versions.append(f"{package_name}=={version}") except pkg_resources.DistributionNotFound: package_versions.append(f"{package_name}: not installed") diff --git a/scripts/requirementstxt_to_pyproject.py b/scripts/requirementstxt_to_pyproject.py index 5710db61..59f6946f 100644 --- a/scripts/requirementstxt_to_pyproject.py +++ b/scripts/requirementstxt_to_pyproject.py @@ -10,10 +10,7 @@ def update_pyproject_versions(pyproject_path): print(f"Error: The file '{pyproject_path}' was not found.") return except toml.TomlDecodeError: - print( - f"Error: The file '{pyproject_path}' is not a valid TOML" - " file." - ) + print(f"Error: The file '{pyproject_path}' is not a valid TOML file.") return dependencies = ( diff --git a/zeta/nn/modules/__init__.py b/zeta/nn/modules/__init__.py index cf80369f..fe90f8bb 100644 --- a/zeta/nn/modules/__init__.py +++ b/zeta/nn/modules/__init__.py @@ -1,53 +1,53 @@ +from zeta.nn.modules.adaptive_conv import AdaptiveConv3DMod +from zeta.nn.modules.adaptive_layernorm import AdaptiveLayerNorm from zeta.nn.modules.cnn_text import CNNNew from zeta.nn.modules.combined_linear import CombinedLinear from zeta.nn.modules.convnet import ConvNet from zeta.nn.modules.droppath import DropPath from zeta.nn.modules.dynamic_module import DynamicModule +from zeta.nn.modules.ether import Ether from zeta.nn.modules.exo import Exo from zeta.nn.modules.fast_text import FastTextNew +from zeta.nn.modules.feedforward import FeedForward from zeta.nn.modules.feedforward_network import FeedForwardNetwork +from zeta.nn.modules.flexible_mlp import CustomMLP +from zeta.nn.modules.fractorial_net import FractalBlock, FractalNetwork +from zeta.nn.modules.h3 import H3Layer +from zeta.nn.modules.itca import IterativeCrossSelfAttention +from zeta.nn.modules.lang_conv_module import ConvolutionLanguageBlock from zeta.nn.modules.layernorm import LayerNorm, l2norm +from zeta.nn.modules.leaky_relu import LeakyRELU +from zeta.nn.modules.log_ff import LogFF, compute_entropy_safe from zeta.nn.modules.lora import Lora from zeta.nn.modules.mbconv import MBConv from zeta.nn.modules.mlp import MLP +from zeta.nn.modules.mlp_mixer import MLPMixer +from zeta.nn.modules.nebula import Nebula +from zeta.nn.modules.polymorphic_activation import PolymorphicActivation +from zeta.nn.modules.polymorphic_neuron import PolymorphicNeuronLayer +from zeta.nn.modules.prenorm import PreNorm from zeta.nn.modules.pulsar import Pulsar from zeta.nn.modules.residual import Residual from zeta.nn.modules.resnet import ResNet from zeta.nn.modules.rms_norm import RMSNorm from zeta.nn.modules.rnn_nlp import RNNL +from zeta.nn.modules.s4 import s4d_kernel from zeta.nn.modules.shufflenet import ShuffleNet +from zeta.nn.modules.sig_lip import SigLipLoss from zeta.nn.modules.simple_attention import simple_attention +from zeta.nn.modules.simple_feedforward import SimpleFeedForward +from zeta.nn.modules.simple_res_block import SimpleResBlock +from zeta.nn.modules.skipconnection import SkipConnection from zeta.nn.modules.spacial_transformer import SpacialTransformer from zeta.nn.modules.subln import SubLN from zeta.nn.modules.super_resolution import SuperResolutionNet -from zeta.nn.modules.token_learner import TokenLearner -from zeta.nn.modules.yolo import yolo -from zeta.nn.modules.ether import Ether -from zeta.nn.modules.nebula import Nebula -from zeta.nn.modules.adaptive_conv import AdaptiveConv3DMod from zeta.nn.modules.time_up_sample import TimeUpSample2x -from zeta.nn.modules.video_autoencoder import CausalConv3d -from zeta.nn.modules.simple_res_block import SimpleResBlock -from zeta.nn.modules.sig_lip import SigLipLoss -from zeta.nn.modules.simple_feedforward import SimpleFeedForward +from zeta.nn.modules.token_learner import TokenLearner from zeta.nn.modules.unet import Unet +from zeta.nn.modules.video_autoencoder import CausalConv3d from zeta.nn.modules.visual_expert import VisualExpert -from zeta.nn.modules.feedforward import FeedForward -from zeta.nn.modules.skipconnection import SkipConnection -from zeta.nn.modules.log_ff import LogFF, compute_entropy_safe -from zeta.nn.modules.polymorphic_neuron import PolymorphicNeuronLayer -from zeta.nn.modules.flexible_mlp import CustomMLP -from zeta.nn.modules.fractorial_net import FractalBlock, FractalNetwork -from zeta.nn.modules.polymorphic_activation import PolymorphicActivation -from zeta.nn.modules.prenorm import PreNorm -from zeta.nn.modules.itca import IterativeCrossSelfAttention -from zeta.nn.modules.lang_conv_module import ConvolutionLanguageBlock -from zeta.nn.modules.s4 import s4d_kernel -from zeta.nn.modules.h3 import H3Layer -from zeta.nn.modules.mlp_mixer import MLPMixer -from zeta.nn.modules.leaky_relu import LeakyRELU -from zeta.nn.modules.adaptive_layernorm import AdaptiveLayerNorm - +from zeta.nn.modules.yolo import yolo +from zeta.nn.modules.swiglu import SwiGLU, SwiGLUStacked # from zeta.nn.modules.img_reshape import image_reshape # from zeta.nn.modules.flatten_features import flatten_features @@ -111,4 +111,6 @@ "MLPMixer", "LeakyRELU", "AdaptiveLayerNorm", + "SwiGLU", + "SwiGLUStacked", ] diff --git a/zeta/nn/modules/swiglu.py b/zeta/nn/modules/swiglu.py index 6b36fe5d..3ba74cd5 100644 --- a/zeta/nn/modules/swiglu.py +++ b/zeta/nn/modules/swiglu.py @@ -8,6 +8,7 @@ class SwiGLU(nn.Module): Args: nn (_type_): _description_ """ + def forward(self, x): """Forward @@ -26,7 +27,7 @@ class SwiGLUStacked(nn.Module): Args: nn (_type_): _description_ - + Examples: >>> from zeta.nn.modules.swiglu import SwiGLUStacked >>> import torch @@ -35,6 +36,7 @@ class SwiGLUStacked(nn.Module): >>> swiglu(x).shape torch.Size([5, 10]) """ + def __init__( self, dim: int, @@ -42,24 +44,12 @@ def __init__( dropout: float = None, bias: bool = False, *args, - **kwargs + **kwargs, ): - self.w1 = nn.Linear( - dim, - hidden_dim, - bias=bias - ) - self.w2 = nn.Linear( - hidden_dim, - dim, - bias=bias - ) - self.w3 = nn.Linear( - dim, - hidden_dim, - bias=bias - ) - + self.w1 = nn.Linear(dim, hidden_dim, bias=bias) + self.w2 = nn.Linear(hidden_dim, dim, bias=bias) + self.w3 = nn.Linear(dim, hidden_dim, bias=bias) + def forward(self, x): """Forward @@ -70,4 +60,4 @@ def forward(self, x): _type_: _description_ """ x = self.w2(F.silu(self.w1(x)) * self.w3(x)) - return x \ No newline at end of file + return x diff --git a/zeta/training/train.py b/zeta/training/train.py index 270c5fad..ec8c86c7 100644 --- a/zeta/training/train.py +++ b/zeta/training/train.py @@ -36,12 +36,12 @@ def Trainer( use_pretokenized: bool = False, resume_from_checkpoint: bool = None, checkpointing_steps=None, - output_dir: str = "checlpoints/", + output_dir: str = "checlpoints/", optimizer_type: str = "Adam8bit", weight_decay: float = 0.1, use_deepspeed=None, *args, - **kwargs + **kwargs, ): """Trainer From ed0dce9619fcfb030b8a4cdab3cf2668f43770c3 Mon Sep 17 00:00:00 2001 From: Kye Date: Fri, 15 Dec 2023 00:13:54 -0800 Subject: [PATCH 119/587] [SwiGLU] --- README.md | 24 +++++++++++++++++++----- 1 file changed, 19 insertions(+), 5 deletions(-) diff --git a/README.md b/README.md index 65825a9e..705f3031 100644 --- a/README.md +++ b/README.md @@ -42,6 +42,20 @@ print(output.shape) ``` + + +### `SwiGLU` +- Powers Transformer models +```python +from zeta.nn import SwiGLUStacked +import torch + +x = torch.randn(5, 10) +swiglu = SwiGLUStacked(10, 20) +swiglu(x).shape + +``` + ### ```RelativePositionBias``` - ```RelativePositionBias``` quantizes the distance between two positions into a certain number of buckets and then uses an embedding to get the relative position bias. This mechanism aids in the attention mechanism by providing biases based on relative positions between the query and key, rather than relying solely on their absolute positions. ```python @@ -165,11 +179,11 @@ class PalmE(torch.nn.Module): Usage: - >>> img = torch.randn(1, 3, 256, 256) - >>> text = torch.randint(0, 20000, (1, 1024)) - >>> model = PalmE() - >>> output = model(img, text) - >>> print(output) +img = torch.randn(1, 3, 256, 256) +text = torch.randint(0, 20000, (1, 1024)) +model = PalmE() +output = model(img, text) +print(output) """ From b8a8695c53628035ca2e07a9aca663f637787b8f Mon Sep 17 00:00:00 2001 From: Kye Date: Fri, 15 Dec 2023 23:07:22 -0500 Subject: [PATCH 120/587] [CLEANUP] --- pyproject.toml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pyproject.toml b/pyproject.toml index 445f6d44..ff2232a0 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,6 +1,6 @@ [tool.poetry] name = "zetascale" -version = "0.9.2" +version = "0.9.3" description = "Transformers at zeta scales" authors = ["Zeta Team "] license = "MIT" From 5b63c1f807c595f89772c97284260f9a4f7ca74a Mon Sep 17 00:00:00 2001 From: Kye Date: Sat, 16 Dec 2023 18:03:27 -0500 Subject: [PATCH 121/587] [FEAT][track_cuda_memory_usage] --- docs/zeta/utils/track_cuda_memory.md | 54 ++++++++++++++++++++++ mkdocs.yml | 1 + tests/utils/test_track_cuda_memory.py | 64 +++++++++++++++++++++++++++ zeta/utils/cuda_memory_wrapper.py | 39 ++++++++++++++++ 4 files changed, 158 insertions(+) create mode 100644 docs/zeta/utils/track_cuda_memory.md create mode 100644 tests/utils/test_track_cuda_memory.py create mode 100644 zeta/utils/cuda_memory_wrapper.py diff --git a/docs/zeta/utils/track_cuda_memory.md b/docs/zeta/utils/track_cuda_memory.md new file mode 100644 index 00000000..fc6c076f --- /dev/null +++ b/docs/zeta/utils/track_cuda_memory.md @@ -0,0 +1,54 @@ +# `track_cuda_memory_usage` + +`track_cuda_memory_usage(func)` + +A decorator function for tracking CUDA memory usage of a PyTorch function. It measures the amount of CUDA memory allocated before and after the execution of the function, logs the difference, and handles any potential errors during the function execution. + +### Parameters: + +- `func` (callable): The function to be decorated. This should be a function that performs operations using PyTorch with CUDA support. + +### Returns: + +- `callable`: The wrapped function, which when called, executes the original function with added CUDA memory tracking and logging. + +### Usage: + +This decorator can be applied to any function that is expected to run operations using PyTorch with CUDA. To use the decorator, simply place `@track_cuda_memory_usage` above the function definition. + +### Example: + +```python +@track_cuda_memory_usage +def my_cuda_function(x): + # Some operations using PyTorch and CUDA + return x * x + +# Example usage +x = torch.randn(1000, 1000, device='cuda') +result = my_cuda_function(x) +``` + +In this example, `my_cuda_function` is a simple function that squares its input. The decorator logs the amount of CUDA memory used during the function's execution. + +### Logging Output: + +The decorator logs two types of messages: + +1. **Memory Usage Log**: After the function execution, it logs the amount of CUDA memory used by the function. The log is at the INFO level. + + Example: `2023-03-15 10:00:00,000 - INFO - CUDA memory usage for my_cuda_function: 4000000 bytes` + +2. **Error Log**: If an error occurs during the function execution, it logs the error message at the ERROR level and raises the exception. + + Example: `2023-03-15 10:00:00,000 - ERROR - Error during the execution of the function: RuntimeError(...)` + +### Error Handling: + +- If CUDA is not available, a warning is logged, and the function runs without memory tracking. +- If an error occurs during the execution of the function, the error is logged, and the exception is re-raised after the memory usage log. + +### Notes: + +- The decorator uses `torch.cuda.synchronize()` before and after the function execution to ensure accurate measurement of memory usage. This synchronization can introduce some overhead and should be considered when profiling performance-critical code. +- The memory usage reported is the difference in memory allocation on the current CUDA device before and after the function execution. It does not account for memory deallocation that might occur within the function. diff --git a/mkdocs.yml b/mkdocs.yml index 18a94bf2..42ff1666 100644 --- a/mkdocs.yml +++ b/mkdocs.yml @@ -137,6 +137,7 @@ nav: - TokenMonster: "zeta/tokenizers/token_monster.md" - zeta.utils: - main: "zeta/utils/main.md" + - track_cuda_memory_usage: "zeta/utils/track_cuda_memory.md" - zeta.ops: - main: "zeta/ops/main.md" - softmaxes: "zeta/ops/softmaxes.md" diff --git a/tests/utils/test_track_cuda_memory.py b/tests/utils/test_track_cuda_memory.py new file mode 100644 index 00000000..a366290c --- /dev/null +++ b/tests/utils/test_track_cuda_memory.py @@ -0,0 +1,64 @@ +import pytest +import torch +from zeta.utils.cuda_memory_wrapper import track_cuda_memory_usage + + +def test_track_cuda_memory_usage_no_cuda(): + @track_cuda_memory_usage + def test_func(): + return "Hello, World!" + + assert test_func() == "Hello, World!" + + +@pytest.mark.skipif( + not torch.cuda.is_available(), reason="CUDA is not available" +) +def test_track_cuda_memory_usage_with_cuda(): + @track_cuda_memory_usage + def test_func(): + return torch.tensor([1, 2, 3]).cuda() + + assert torch.equal(test_func(), torch.tensor([1, 2, 3]).cuda()) + + +@pytest.mark.skipif( + not torch.cuda.is_available(), reason="CUDA is not available" +) +def test_track_cuda_memory_usage_with_cuda_memory_allocation(): + @track_cuda_memory_usage + def test_func(): + a = torch.tensor([1, 2, 3]).cuda() + b = torch.tensor([4, 5, 6]).cuda() + return a + b + + assert torch.equal(test_func(), torch.tensor([5, 7, 9]).cuda()) + + +@pytest.mark.skipif( + not torch.cuda.is_available(), reason="CUDA is not available" +) +def test_track_cuda_memory_usage_with_cuda_memory_release(): + @track_cuda_memory_usage + def test_func(): + a = torch.tensor([1, 2, 3]).cuda() + b = torch.tensor([4, 5, 6]).cuda() + del a + del b + torch.cuda.empty_cache() + + assert test_func() is None + + +@pytest.mark.skipif( + not torch.cuda.is_available(), reason="CUDA is not available" +) +def test_track_cuda_memory_usage_with_exception(): + @track_cuda_memory_usage + def test_func(): + a = torch.tensor([1, 2, 3]).cuda() + b = "not a tensor" + return a + b + + with pytest.raises(TypeError): + test_func() diff --git a/zeta/utils/cuda_memory_wrapper.py b/zeta/utils/cuda_memory_wrapper.py new file mode 100644 index 00000000..e9efadf6 --- /dev/null +++ b/zeta/utils/cuda_memory_wrapper.py @@ -0,0 +1,39 @@ +import torch +import functools +import logging + + +logging.basicConfig( + level=logging.INFO, + format="%(asctime)s - %(name)s - %(levelname)s - %(message)s", +) + + +def track_cuda_memory_usage(func): + @functools.wraps(func) + def wrapper(*args, **kwargs): + if not torch.cuda.is_available(): + logging.warning("CUDA is not available, skip tracking memory usage") + return func(*args, **kwargs) + + torch.cuda.synchronize() + before_memory = torch.cuda.memory_allocated() + + try: + result = func(*args, **kwargs) + except Exception as error: + logging.error(f"Error occurs when running {func.__name__}: {error}") + raise + + finally: + torch.cuda.synchronize() + after_memory = torch.cuda.memory_allocated() + memory_diff = after_memory - before_memory + logging.info( + f"Memory usage of {func.__name__}: {memory_diff} bytes" + ) + + return result + + +return wrapper From 9e6bfeb920a89632576bd26528067695997e0a1f Mon Sep 17 00:00:00 2001 From: Kye Date: Sat, 16 Dec 2023 18:07:16 -0500 Subject: [PATCH 122/587] [zeta.utils][__init__][CLEANUP] --- pyproject.toml | 2 +- zeta/utils/__init__.py | 16 +++++++++++++++- 2 files changed, 16 insertions(+), 2 deletions(-) diff --git a/pyproject.toml b/pyproject.toml index ff2232a0..68ff3d05 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,6 +1,6 @@ [tool.poetry] name = "zetascale" -version = "0.9.3" +version = "0.9.4" description = "Transformers at zeta scales" authors = ["Zeta Team "] license = "MIT" diff --git a/zeta/utils/__init__.py b/zeta/utils/__init__.py index eeb1daf6..2edf7a54 100644 --- a/zeta/utils/__init__.py +++ b/zeta/utils/__init__.py @@ -1,3 +1,17 @@ # Copyright (c) 2022 Agora # Licensed under The MIT License [see LICENSE for details] -from zeta.utils.main import * +from zeta.utils.cuda_memory_wrapper import track_cuda_memory_usage + +from zeta.utils.benchmark import ( + benchmark, + print_cuda_memory_usage, + save_memory_snapshot, +) + + +__all__ = [ + "track_cuda_memory_usage", + "benchmark", + "print_cuda_memory_usage", + "save_memory_snapshot", +] From 06f02c6b253095760090f00213d065e96a679e3f Mon Sep 17 00:00:00 2001 From: Kye Date: Sat, 16 Dec 2023 18:12:52 -0500 Subject: [PATCH 123/587] [zeta Module CLEAN UP OPERATIO] --- zeta/__init__.py | 49 ++++++++++------------------------- zeta/ops/__Init__.py | 3 --- zeta/utils/__init__.py | 3 ++- zeta/utils/disable_logging.py | 31 ++++++++++++++++++++++ 4 files changed, 46 insertions(+), 40 deletions(-) create mode 100644 zeta/utils/disable_logging.py diff --git a/zeta/__init__.py b/zeta/__init__.py index 5fbcfce8..31ae3141 100644 --- a/zeta/__init__.py +++ b/zeta/__init__.py @@ -1,36 +1,13 @@ -import logging -import os -import warnings - -# disable warnings - -warnings.filterwarnings("ignore") - -# disable tensorflow warnings - -os.environ["TF_CPP_MIN_LOG_LEVEL"] = "2" - -# disable bnb warnings and others - -logging.getLogger().setLevel(logging.WARNING) - - -class CustomFilter(logging.Filter): - def filter(self, record): - msg = "Created a temporary directory at" - return msg not in record.getMessage() - - -logger = logging.getLogger() -f = CustomFilter() -logger.addFilter(f) - -from zeta.nn import * -from zeta.models import * -from zeta.utils import * -from zeta.training import * -from zeta.tokenizers import * -from zeta.rl import * -from zeta.optim import * -from zeta.ops import * -from zeta.quant import * +from zeta.utils.disable_logging import disable_warnings_and_logs + +disable_warnings_and_logs() + +from zeta.nn import * # noqa: F403, E402 +from zeta.models import * # noqa: F403, E402 +from zeta.utils import * # noqa: F403, E402 +from zeta.training import * # noqa: F403, E402 +from zeta.tokenizers import * # noqa: F403, E402 +from zeta.rl import * # noqa: F403, E402 +from zeta.optim import * # noqa: F403, E402 +from zeta.ops import * # noqa: F403, E402 +from zeta.quant import * # noqa: F403, E402 diff --git a/zeta/ops/__Init__.py b/zeta/ops/__Init__.py index 0597d52f..e8310817 100644 --- a/zeta/ops/__Init__.py +++ b/zeta/ops/__Init__.py @@ -1,7 +1,4 @@ -from zeta.ops.main import * -from zeta.ops.softmax import * from zeta.ops.unitwise_norm import unitwise_norm -from zeta.ops.mos import MixtureOfSoftmaxes from zeta.ops.softmax import ( standard_softmax, diff --git a/zeta/utils/__init__.py b/zeta/utils/__init__.py index 2edf7a54..1e2293a7 100644 --- a/zeta/utils/__init__.py +++ b/zeta/utils/__init__.py @@ -7,11 +7,12 @@ print_cuda_memory_usage, save_memory_snapshot, ) - +from zeta.utils.disable_logging import disable_warnings_and_logs __all__ = [ "track_cuda_memory_usage", "benchmark", "print_cuda_memory_usage", "save_memory_snapshot", + "disable_warnings_and_logs", ] diff --git a/zeta/utils/disable_logging.py b/zeta/utils/disable_logging.py new file mode 100644 index 00000000..c4bcc12c --- /dev/null +++ b/zeta/utils/disable_logging.py @@ -0,0 +1,31 @@ +import logging +import os +import warnings + + +def disable_warnings_and_logs(): + """Disable warnings and logs. + + Returns: + _type_: _description_ + """ + # disable warnings + warnings.filterwarnings("ignore") + + # disable tensorflow warnings + os.environ["TF_CPP_MIN_LOG_LEVEL"] = "2" + + # disable bnb warnings and others + logging.getLogger().setLevel(logging.WARNING) + + class CustomFilter(logging.Filter): + def filter(self, record): + msg = "Created a temporary directory at" + return msg not in record.getMessage() + + logger = logging.getLogger() + f = CustomFilter() + logger.addFilter(f) + + +disable_warnings_and_logs() From 40f0f00514aab77e0b90575d078b591287d44f01 Mon Sep 17 00:00:00 2001 From: Kye Date: Sat, 16 Dec 2023 19:34:28 -0500 Subject: [PATCH 124/587] [FEAT][print_num_params] --- zeta/utils/__init__.py | 3 +++ zeta/utils/params.py | 29 +++++++++++++++++++++++++++++ 2 files changed, 32 insertions(+) create mode 100644 zeta/utils/params.py diff --git a/zeta/utils/__init__.py b/zeta/utils/__init__.py index 1e2293a7..8e287781 100644 --- a/zeta/utils/__init__.py +++ b/zeta/utils/__init__.py @@ -8,6 +8,7 @@ save_memory_snapshot, ) from zeta.utils.disable_logging import disable_warnings_and_logs +from zeta.utils.params import print_num_params, print_main __all__ = [ "track_cuda_memory_usage", @@ -15,4 +16,6 @@ "print_cuda_memory_usage", "save_memory_snapshot", "disable_warnings_and_logs", + "print_num_params", + "print_main", ] diff --git a/zeta/utils/params.py b/zeta/utils/params.py new file mode 100644 index 00000000..4a437e7e --- /dev/null +++ b/zeta/utils/params.py @@ -0,0 +1,29 @@ +import torch.distributed as dist # Add this line + + +def print_num_params(model): + """Print the number of parameters in a model. + + Args: + model (_type_): _description_ + """ + n_params = sum(p.numel() for p in model.parameters() if p.requires_grad) + + if dist.is_available(): + if dist.get_rank() == 0: + print(f"Number of parameters in model: {n_params}") + else: + print(f"Number of parameters in model: {n_params}") + + +def print_main(msg): + """Print the message only on the main process. + + Args: + msg (_type_): _description_ + """ + if dist.is_available(): + if dist.get_rank() == 0: + print(msg) + else: + print(msg) From 2c89b26dc6c906ba68a5234f8ed0264653986c87 Mon Sep 17 00:00:00 2001 From: Kye Date: Sat, 16 Dec 2023 23:45:16 -0500 Subject: [PATCH 125/587] [feat][QuantumSuperpositionEmbeddings] --- tests/nn/embeddings/qftp_embeddings.py | 93 +++++++++++++++++++++ tests/nn/embeddings/test_QFTSPEmbeddings.py | 86 +++++++++++++++++++ zeta/nn/embeddings/__init__.py | 10 +-- zeta/nn/embeddings/qfsp_embeddings.py | 54 ++++++++++++ zeta/nn/embeddings/qft_embeddings.py | 58 +++++++++++++ zeta/utils/cuda_memory_wrapper.py | 44 ++++++---- 6 files changed, 322 insertions(+), 23 deletions(-) create mode 100644 tests/nn/embeddings/qftp_embeddings.py create mode 100644 tests/nn/embeddings/test_QFTSPEmbeddings.py create mode 100644 zeta/nn/embeddings/qfsp_embeddings.py create mode 100644 zeta/nn/embeddings/qft_embeddings.py diff --git a/tests/nn/embeddings/qftp_embeddings.py b/tests/nn/embeddings/qftp_embeddings.py new file mode 100644 index 00000000..493cc187 --- /dev/null +++ b/tests/nn/embeddings/qftp_embeddings.py @@ -0,0 +1,93 @@ +import pytest +import torch +from zeta.nn.embeddings.qfsp_embeddings import QuantumSuperpositionEmbeddings + + +def test_qsembeddings_init(): + vocab_size = 10000 + dim = 512 + model = QuantumSuperpositionEmbeddings(vocab_size, dim) + assert model.embed_dim == dim + assert model.base_embeddings.num_embeddings == vocab_size + assert model.superposed_embeddings.num_embeddings == vocab_size + +def test_qsembeddings_forward_weighted_sum(): + vocab_size = 10000 + dim = 512 + model = QuantumSuperpositionEmbeddings(vocab_size, dim) + x = torch.randint(0, vocab_size, (1, 10)) + context_vector = torch.rand(1, 10) + embeddings = model(x, context_vector, 'weighted_sum') + assert embeddings.shape == (1, 10, dim) + +def test_qsembeddings_forward_dot_product(): + vocab_size = 10000 + dim = 512 + model = QuantumSuperpositionEmbeddings(vocab_size, dim) + x = torch.randint(0, vocab_size, (1, 10)) + context_vector = torch.rand(1, 10) + embeddings = model(x, context_vector, 'dot_product') + assert embeddings.shape == (1, 10, dim) + +def test_qsembeddings_forward_cosine_similarity(): + vocab_size = 10000 + dim = 512 + model = QuantumSuperpositionEmbeddings(vocab_size, dim) + x = torch.randint(0, vocab_size, (1, 10)) + context_vector = torch.rand(1, 10) + embeddings = model(x, context_vector, 'cosine_similarity') + assert embeddings.shape == (1, 10, dim) + +def test_qsembeddings_forward_gated(): + vocab_size = 10000 + dim = 512 + model = QuantumSuperpositionEmbeddings(vocab_size, dim) + x = torch.randint(0, vocab_size, (1, 10)) + context_vector = torch.rand(1, 10) + embeddings = model(x, context_vector, 'gated') + assert embeddings.shape == (1, 10, dim) + +def test_qsembeddings_forward_concat_linear(): + vocab_size = 10000 + dim = 512 + model = QuantumSuperpositionEmbeddings(vocab_size, dim) + x = torch.randint(0, vocab_size, (1, 10)) + context_vector = torch.rand(1, 10) + embeddings = model(x, context_vector, 'concat_linear') + assert embeddings.shape == (1, 10, dim) + +def test_qsembeddings_forward_invalid_mode(): + vocab_size = 10000 + dim = 512 + model = QuantumSuperpositionEmbeddings(vocab_size, dim) + x = torch.randint(0, vocab_size, (1, 10)) + context_vector = torch.rand(1, 10) + with pytest.raises(ValueError): + model(x, context_vector, 'invalid_mode') + +def test_qsembeddings_forward_large_input(): + vocab_size = 10000 + dim = 512 + model = QuantumSuperpositionEmbeddings(vocab_size, dim) + x = torch.randint(0, vocab_size, (1000, 1000)) + context_vector = torch.rand(1000, 1000) + embeddings = model(x, context_vector, 'weighted_sum') + assert embeddings.shape == (1000, 1000, dim) + +def test_qsembeddings_forward_large_dim(): + vocab_size = 10000 + dim = 10000 + model = QuantumSuperpositionEmbeddings(vocab_size, dim) + x = torch.randint(0, vocab_size, (1, 10)) + context_vector = torch.rand(1, 10) + embeddings = model(x, context_vector, 'weighted_sum') + assert embeddings.shape == (1, 10, dim) + +def test_qsembeddings_forward_large_vocab_size(): + vocab_size = 1000000 + dim = 512 + model = QuantumSuperpositionEmbeddings(vocab_size, dim) + x = torch.randint(0, vocab_size, (1, 10)) + context_vector = torch.rand(1, 10) + embeddings = model(x, context_vector, 'weighted_sum') + assert embeddings.shape == (1, 10, dim) \ No newline at end of file diff --git a/tests/nn/embeddings/test_QFTSPEmbeddings.py b/tests/nn/embeddings/test_QFTSPEmbeddings.py new file mode 100644 index 00000000..4e3f334c --- /dev/null +++ b/tests/nn/embeddings/test_QFTSPEmbeddings.py @@ -0,0 +1,86 @@ +import pytest +import torch +from zeta.nn.embeddings.qft_embeddings import QFTSPEmbeddings + + +def test_qftspembeddings_init(): + vocab_size = 10000 + dim = 512 + model = QFTSPEmbeddings(vocab_size, dim) + assert model.vocab_size == vocab_size + assert model.dim == dim + + +def test_qftspembeddings_forward(): + vocab_size = 10000 + dim = 512 + model = QFTSPEmbeddings(vocab_size, dim) + x = torch.randint(0, vocab_size, (1, 10)) + embeddings = model(x) + assert embeddings.shape == (1, 10, dim) + + +def test_qftspembeddings_forward_zero_dim(): + vocab_size = 10000 + dim = 0 + model = QFTSPEmbeddings(vocab_size, dim) + x = torch.randint(0, vocab_size, (1, 10)) + embeddings = model(x) + assert embeddings.shape == (1, 10, 0) + + +def test_qftspembeddings_forward_odd_dim(): + vocab_size = 10000 + dim = 513 + model = QFTSPEmbeddings(vocab_size, dim) + x = torch.randint(0, vocab_size, (1, 10)) + embeddings = model(x) + assert embeddings.shape == (1, 10, dim) + + +def test_qftspembeddings_forward_large_input(): + vocab_size = 10000 + dim = 512 + model = QFTSPEmbeddings(vocab_size, dim) + x = torch.randint(0, vocab_size, (1000, 1000)) + embeddings = model(x) + assert embeddings.shape == (1000, 1000, dim) + + +def test_qftspembeddings_forward_large_dim(): + vocab_size = 10000 + dim = 10000 + model = QFTSPEmbeddings(vocab_size, dim) + x = torch.randint(0, vocab_size, (1, 10)) + embeddings = model(x) + assert embeddings.shape == (1, 10, dim) + + +def test_qftspembeddings_forward_large_vocab_size(): + vocab_size = 1000000 + dim = 512 + model = QFTSPEmbeddings(vocab_size, dim) + x = torch.randint(0, vocab_size, (1, 10)) + embeddings = model(x) + assert embeddings.shape == (1, 10, dim) + + +def test_qftspembeddings_forward_negative_dim(): + vocab_size = 10000 + dim = -512 + with pytest.raises(ValueError): + model = QFTSPEmbeddings(vocab_size, dim) + + +def test_qftspembeddings_forward_negative_vocab_size(): + vocab_size = -10000 + dim = 512 + with pytest.raises(ValueError): + model = QFTSPEmbeddings(vocab_size, dim) + + +def test_qftspembeddings_forward_zero_vocab_size(): + vocab_size = 0 + dim = 512 + with pytest.raises(ValueError): + model = QFTSPEmbeddings(vocab_size, dim) diff --git a/zeta/nn/embeddings/__init__.py b/zeta/nn/embeddings/__init__.py index cba05081..cfc8766e 100644 --- a/zeta/nn/embeddings/__init__.py +++ b/zeta/nn/embeddings/__init__.py @@ -1,7 +1,4 @@ -# embeddings - from zeta.nn.embeddings.abc_pos_emb import AbsolutePositionalEmbedding -from zeta.nn.embeddings.base import BaseEmbedding from zeta.nn.embeddings.embedding import ( BaseEmbedding, Embedding, @@ -10,7 +7,6 @@ from zeta.nn.embeddings.multiway_network import ( MultiwayEmbedding, MultiwayNetwork, - # MultiwayWrapper, ) from zeta.nn.embeddings.nominal_embeddings import NominalEmbedding from zeta.nn.embeddings.positional import PositionalEmbedding @@ -26,9 +22,10 @@ apply_rotary_pos_emb, rotate_every_two, ) -from zeta.nn.embeddings.yarn import * from zeta.nn.embeddings.yarn import YarnEmbedding from zeta.nn.embeddings.sine_positional import SinePositionalEmbedding +from zeta.nn.embeddings.qft_embeddings import QFTSPEmbeddings +from zeta.nn.embeddings.qfsp_embeddings import QuantumSuperpositionEmbeddings __all__ = [ "AbsolutePositionalEmbedding", @@ -37,7 +34,6 @@ "TextEmbedding", "MultiwayEmbedding", "MultiwayNetwork", - # "MultiwayWrapper", "NominalEmbedding", "PositionalEmbedding", "PositionInterpolationEmbeddings", @@ -50,4 +46,6 @@ "rotate_every_two", "YarnEmbedding", "SinePositionalEmbedding", + "QFTSPEmbeddings", + "QuantumSuperpositionEmbeddings" ] diff --git a/zeta/nn/embeddings/qfsp_embeddings.py b/zeta/nn/embeddings/qfsp_embeddings.py new file mode 100644 index 00000000..2c6d50d2 --- /dev/null +++ b/zeta/nn/embeddings/qfsp_embeddings.py @@ -0,0 +1,54 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F + +class QuantumSuperpositionEmbeddings(nn.Module): + """ + QuantumSuperpositionEmbeddings with multiple collapse mechanisms. + + This module allows for different ways of collapsing the superposition of embeddings, + based on the provided context and selected mechanism. + """ + + def __init__(self, vocab_size, embed_dim): + super(QuantumSuperpositionEmbeddings, self).__init__() + self.embed_dim = embed_dim + self.base_embeddings = nn.Embedding(vocab_size, embed_dim) + self.superposed_embeddings = nn.Embedding(vocab_size, embed_dim) + self.linear_transform = nn.Linear(2 * embed_dim, embed_dim) + + def forward(self, input_ids, context_vector, collapse_mode='weighted_sum'): + base_embeds = self.base_embeddings(input_ids) + superposed_embeds = self.superposed_embeddings(input_ids) + + if collapse_mode == 'weighted_sum': + collapsed_embeds = base_embeds + context_vector.unsqueeze(-1) * superposed_embeds + elif collapse_mode == 'dot_product': + scale = torch.sum(superposed_embeds * context_vector.unsqueeze(-1), dim=-1, keepdim=True) + collapsed_embeds = base_embeds + scale * superposed_embeds + elif collapse_mode == 'cosine_similarity': + scale = F.cosine_similarity(superposed_embeds, context_vector.unsqueeze(-1), dim=-1).unsqueeze(-1) + collapsed_embeds = base_embeds + scale * superposed_embeds + elif collapse_mode == 'gated': + gate = torch.sigmoid(context_vector) + collapsed_embeds = base_embeds + gate.unsqueeze(-1) * superposed_embeds + elif collapse_mode == 'concat_linear': + concatenated = torch.cat([base_embeds, superposed_embeds], dim=-1) + collapsed_embeds = self.linear_transform(concatenated) + else: + raise ValueError("Invalid collapse mode selected") + + return collapsed_embeds + +# # Example Usage +# vocab_size = 10000 +# embed_dim = 512 + +# model = QuantumSuperpositionEmbeddings(vocab_size, embed_dim) +# input_ids = torch.randint(0, vocab_size, (1, 10)) +# context_vector = torch.rand(1, 10) + +# # Test different collapse modes +# for mode in ['weighted_sum', 'dot_product', 'cosine_similarity', 'gated', 'concat_linear']: +# embeddings = model(input_ids, context_vector, collapse_mode=mode) +# print(f"Collapse mode: {mode}, Embeddings shape: {embeddings.shape}") diff --git a/zeta/nn/embeddings/qft_embeddings.py b/zeta/nn/embeddings/qft_embeddings.py new file mode 100644 index 00000000..e2ca3e86 --- /dev/null +++ b/zeta/nn/embeddings/qft_embeddings.py @@ -0,0 +1,58 @@ +import torch +from torch import nn +import numpy as np + + +class QFTSPEmbeddings(nn.Module): + """Quantum Fourier Transform-inspired Shift Phase Embeddings. + + + Attributes: + vocab_size (int): The size of the vocabulary. + dim (int): The dimensionality of the embeddings. + + Methods: + forward(x: torch.Tensor) -> torch.Tensor: Forward pass of the QFTSPEmbeddings module. + + Example: + >>> vocab_size = 10000 + >>> dim = 512 + >>> model = QFTSPEmbeddings(vocab_size, dim) + >>> x = torch.randint(0, vocab_size, (1, 10)) + >>> embeddings = model(x) + >>> print(embeddings) + """ + + def __init__( + self, vocab_size: int = None, dim: int = None, *args, **kwargs + ): + super().__init__() + self.vocab_size = vocab_size + self.dim = dim + + self.embeddings = nn.Embedding(vocab_size, dim, *args, **kwargs) + + def forward(self, x: torch.Tensor) -> torch.Tensor: + """Forward pass of the QFTSPEmbeddings module. + + Args: + x (torch.Tensor): input tensor + + Returns: + torch.Tensor: phase shifted embeddings + """ + # real valued embeddings + embeds = self.embeddings(x) + + # Quantum-inspired operation: Phase shift + # Split embed_dim into two halves for real and imaginary parts + phase_shift = torch.exp(2j * np.pi * torch.rand(self.dim // 2)) + shifted_embeds = torch.cat( + [ + embeds[:, :, : self.dim // 2] * phase_shift.real, + embeds[:, :, self.dim // 2 :] * phase_shift.imag, + ], + dim=-1, + ) + + return shifted_embeds diff --git a/zeta/utils/cuda_memory_wrapper.py b/zeta/utils/cuda_memory_wrapper.py index e9efadf6..1cb837eb 100644 --- a/zeta/utils/cuda_memory_wrapper.py +++ b/zeta/utils/cuda_memory_wrapper.py @@ -1,39 +1,49 @@ -import torch -import functools -import logging - +import torch +import functools +import logging +# Logging initialization logging.basicConfig( - level=logging.INFO, - format="%(asctime)s - %(name)s - %(levelname)s - %(message)s", + level=logging.INFO, + format='%(asctime)s - %(name)s - %(levelname)s - %(message)s' ) - +# Main function def track_cuda_memory_usage(func): + """Track CUDA memory usage of a function. + + Args: + func (function): The function to be tracked. + + Returns: + function: The wrapped function. + + Example: + >>> @track_cuda_memory_usage + >>> def train(): + >>> pass + >>> train() + """ @functools.wraps(func) def wrapper(*args, **kwargs): if not torch.cuda.is_available(): logging.warning("CUDA is not available, skip tracking memory usage") return func(*args, **kwargs) - + torch.cuda.synchronize() before_memory = torch.cuda.memory_allocated() - + try: result = func(*args, **kwargs) except Exception as error: logging.error(f"Error occurs when running {func.__name__}: {error}") raise - + finally: torch.cuda.synchronize() after_memory = torch.cuda.memory_allocated() memory_diff = after_memory - before_memory - logging.info( - f"Memory usage of {func.__name__}: {memory_diff} bytes" - ) - + logging.info(f"Memory usage of {func.__name__}: {memory_diff} bytes") + return result - - -return wrapper + return wrapper \ No newline at end of file From cbb33a993b41228cc353661a6808e893f4672687 Mon Sep 17 00:00:00 2001 From: Kye Date: Sat, 16 Dec 2023 23:51:56 -0500 Subject: [PATCH 126/587] [CLEANUP] --- pyproject.toml | 2 +- tests/nn/embeddings/qftp_embeddings.py | 29 +++++++++++++++-------- zeta/nn/embeddings/__init__.py | 2 +- zeta/nn/embeddings/qfsp_embeddings.py | 32 ++++++++++++++++++-------- zeta/utils/cuda_memory_wrapper.py | 31 ++++++++++++++----------- 5 files changed, 61 insertions(+), 35 deletions(-) diff --git a/pyproject.toml b/pyproject.toml index 68ff3d05..f65cd5c6 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,6 +1,6 @@ [tool.poetry] name = "zetascale" -version = "0.9.4" +version = "0.9.6" description = "Transformers at zeta scales" authors = ["Zeta Team "] license = "MIT" diff --git a/tests/nn/embeddings/qftp_embeddings.py b/tests/nn/embeddings/qftp_embeddings.py index 493cc187..f2327199 100644 --- a/tests/nn/embeddings/qftp_embeddings.py +++ b/tests/nn/embeddings/qftp_embeddings.py @@ -11,51 +11,57 @@ def test_qsembeddings_init(): assert model.base_embeddings.num_embeddings == vocab_size assert model.superposed_embeddings.num_embeddings == vocab_size + def test_qsembeddings_forward_weighted_sum(): vocab_size = 10000 dim = 512 model = QuantumSuperpositionEmbeddings(vocab_size, dim) x = torch.randint(0, vocab_size, (1, 10)) context_vector = torch.rand(1, 10) - embeddings = model(x, context_vector, 'weighted_sum') + embeddings = model(x, context_vector, "weighted_sum") assert embeddings.shape == (1, 10, dim) + def test_qsembeddings_forward_dot_product(): vocab_size = 10000 dim = 512 model = QuantumSuperpositionEmbeddings(vocab_size, dim) x = torch.randint(0, vocab_size, (1, 10)) context_vector = torch.rand(1, 10) - embeddings = model(x, context_vector, 'dot_product') + embeddings = model(x, context_vector, "dot_product") assert embeddings.shape == (1, 10, dim) + def test_qsembeddings_forward_cosine_similarity(): vocab_size = 10000 dim = 512 model = QuantumSuperpositionEmbeddings(vocab_size, dim) x = torch.randint(0, vocab_size, (1, 10)) context_vector = torch.rand(1, 10) - embeddings = model(x, context_vector, 'cosine_similarity') + embeddings = model(x, context_vector, "cosine_similarity") assert embeddings.shape == (1, 10, dim) + def test_qsembeddings_forward_gated(): vocab_size = 10000 dim = 512 model = QuantumSuperpositionEmbeddings(vocab_size, dim) x = torch.randint(0, vocab_size, (1, 10)) context_vector = torch.rand(1, 10) - embeddings = model(x, context_vector, 'gated') + embeddings = model(x, context_vector, "gated") assert embeddings.shape == (1, 10, dim) + def test_qsembeddings_forward_concat_linear(): vocab_size = 10000 dim = 512 model = QuantumSuperpositionEmbeddings(vocab_size, dim) x = torch.randint(0, vocab_size, (1, 10)) context_vector = torch.rand(1, 10) - embeddings = model(x, context_vector, 'concat_linear') + embeddings = model(x, context_vector, "concat_linear") assert embeddings.shape == (1, 10, dim) + def test_qsembeddings_forward_invalid_mode(): vocab_size = 10000 dim = 512 @@ -63,7 +69,8 @@ def test_qsembeddings_forward_invalid_mode(): x = torch.randint(0, vocab_size, (1, 10)) context_vector = torch.rand(1, 10) with pytest.raises(ValueError): - model(x, context_vector, 'invalid_mode') + model(x, context_vector, "invalid_mode") + def test_qsembeddings_forward_large_input(): vocab_size = 10000 @@ -71,23 +78,25 @@ def test_qsembeddings_forward_large_input(): model = QuantumSuperpositionEmbeddings(vocab_size, dim) x = torch.randint(0, vocab_size, (1000, 1000)) context_vector = torch.rand(1000, 1000) - embeddings = model(x, context_vector, 'weighted_sum') + embeddings = model(x, context_vector, "weighted_sum") assert embeddings.shape == (1000, 1000, dim) + def test_qsembeddings_forward_large_dim(): vocab_size = 10000 dim = 10000 model = QuantumSuperpositionEmbeddings(vocab_size, dim) x = torch.randint(0, vocab_size, (1, 10)) context_vector = torch.rand(1, 10) - embeddings = model(x, context_vector, 'weighted_sum') + embeddings = model(x, context_vector, "weighted_sum") assert embeddings.shape == (1, 10, dim) + def test_qsembeddings_forward_large_vocab_size(): vocab_size = 1000000 dim = 512 model = QuantumSuperpositionEmbeddings(vocab_size, dim) x = torch.randint(0, vocab_size, (1, 10)) context_vector = torch.rand(1, 10) - embeddings = model(x, context_vector, 'weighted_sum') - assert embeddings.shape == (1, 10, dim) \ No newline at end of file + embeddings = model(x, context_vector, "weighted_sum") + assert embeddings.shape == (1, 10, dim) diff --git a/zeta/nn/embeddings/__init__.py b/zeta/nn/embeddings/__init__.py index cfc8766e..18c6a063 100644 --- a/zeta/nn/embeddings/__init__.py +++ b/zeta/nn/embeddings/__init__.py @@ -47,5 +47,5 @@ "YarnEmbedding", "SinePositionalEmbedding", "QFTSPEmbeddings", - "QuantumSuperpositionEmbeddings" + "QuantumSuperpositionEmbeddings", ] diff --git a/zeta/nn/embeddings/qfsp_embeddings.py b/zeta/nn/embeddings/qfsp_embeddings.py index 2c6d50d2..d7bde425 100644 --- a/zeta/nn/embeddings/qfsp_embeddings.py +++ b/zeta/nn/embeddings/qfsp_embeddings.py @@ -2,6 +2,7 @@ import torch.nn as nn import torch.nn.functional as F + class QuantumSuperpositionEmbeddings(nn.Module): """ QuantumSuperpositionEmbeddings with multiple collapse mechanisms. @@ -17,22 +18,32 @@ def __init__(self, vocab_size, embed_dim): self.superposed_embeddings = nn.Embedding(vocab_size, embed_dim) self.linear_transform = nn.Linear(2 * embed_dim, embed_dim) - def forward(self, input_ids, context_vector, collapse_mode='weighted_sum'): + def forward(self, input_ids, context_vector, collapse_mode="weighted_sum"): base_embeds = self.base_embeddings(input_ids) superposed_embeds = self.superposed_embeddings(input_ids) - if collapse_mode == 'weighted_sum': - collapsed_embeds = base_embeds + context_vector.unsqueeze(-1) * superposed_embeds - elif collapse_mode == 'dot_product': - scale = torch.sum(superposed_embeds * context_vector.unsqueeze(-1), dim=-1, keepdim=True) + if collapse_mode == "weighted_sum": + collapsed_embeds = ( + base_embeds + context_vector.unsqueeze(-1) * superposed_embeds + ) + elif collapse_mode == "dot_product": + scale = torch.sum( + superposed_embeds * context_vector.unsqueeze(-1), + dim=-1, + keepdim=True, + ) collapsed_embeds = base_embeds + scale * superposed_embeds - elif collapse_mode == 'cosine_similarity': - scale = F.cosine_similarity(superposed_embeds, context_vector.unsqueeze(-1), dim=-1).unsqueeze(-1) + elif collapse_mode == "cosine_similarity": + scale = F.cosine_similarity( + superposed_embeds, context_vector.unsqueeze(-1), dim=-1 + ).unsqueeze(-1) collapsed_embeds = base_embeds + scale * superposed_embeds - elif collapse_mode == 'gated': + elif collapse_mode == "gated": gate = torch.sigmoid(context_vector) - collapsed_embeds = base_embeds + gate.unsqueeze(-1) * superposed_embeds - elif collapse_mode == 'concat_linear': + collapsed_embeds = ( + base_embeds + gate.unsqueeze(-1) * superposed_embeds + ) + elif collapse_mode == "concat_linear": concatenated = torch.cat([base_embeds, superposed_embeds], dim=-1) collapsed_embeds = self.linear_transform(concatenated) else: @@ -40,6 +51,7 @@ def forward(self, input_ids, context_vector, collapse_mode='weighted_sum'): return collapsed_embeds + # # Example Usage # vocab_size = 10000 # embed_dim = 512 diff --git a/zeta/utils/cuda_memory_wrapper.py b/zeta/utils/cuda_memory_wrapper.py index 1cb837eb..02ad005d 100644 --- a/zeta/utils/cuda_memory_wrapper.py +++ b/zeta/utils/cuda_memory_wrapper.py @@ -1,49 +1,54 @@ -import torch -import functools -import logging +import torch +import functools +import logging # Logging initialization logging.basicConfig( - level=logging.INFO, - format='%(asctime)s - %(name)s - %(levelname)s - %(message)s' + level=logging.INFO, + format="%(asctime)s - %(name)s - %(levelname)s - %(message)s", ) + # Main function def track_cuda_memory_usage(func): """Track CUDA memory usage of a function. Args: func (function): The function to be tracked. - + Returns: function: The wrapped function. - + Example: >>> @track_cuda_memory_usage >>> def train(): >>> pass >>> train() """ + @functools.wraps(func) def wrapper(*args, **kwargs): if not torch.cuda.is_available(): logging.warning("CUDA is not available, skip tracking memory usage") return func(*args, **kwargs) - + torch.cuda.synchronize() before_memory = torch.cuda.memory_allocated() - + try: result = func(*args, **kwargs) except Exception as error: logging.error(f"Error occurs when running {func.__name__}: {error}") raise - + finally: torch.cuda.synchronize() after_memory = torch.cuda.memory_allocated() memory_diff = after_memory - before_memory - logging.info(f"Memory usage of {func.__name__}: {memory_diff} bytes") - + logging.info( + f"Memory usage of {func.__name__}: {memory_diff} bytes" + ) + return result - return wrapper \ No newline at end of file + + return wrapper From 95a42f0b370178ae6c6c2819e7ac619d887f09b7 Mon Sep 17 00:00:00 2001 From: Kye Date: Sat, 16 Dec 2023 23:55:44 -0500 Subject: [PATCH 127/587] [QFTSPEmbedding] --- tests/nn/embeddings/qftp_embeddings.py | 22 ++++----- zeta/nn/embeddings/__init__.py | 4 +- zeta/nn/embeddings/qfsp_embeddings.py | 64 +++++++++++++++++--------- 3 files changed, 56 insertions(+), 34 deletions(-) diff --git a/tests/nn/embeddings/qftp_embeddings.py b/tests/nn/embeddings/qftp_embeddings.py index f2327199..9db4f816 100644 --- a/tests/nn/embeddings/qftp_embeddings.py +++ b/tests/nn/embeddings/qftp_embeddings.py @@ -1,12 +1,12 @@ import pytest import torch -from zeta.nn.embeddings.qfsp_embeddings import QuantumSuperpositionEmbeddings +from zeta.nn.embeddings.qfsp_embeddings import QFTSPEmbedding def test_qsembeddings_init(): vocab_size = 10000 dim = 512 - model = QuantumSuperpositionEmbeddings(vocab_size, dim) + model = QFTSPEmbedding(vocab_size, dim) assert model.embed_dim == dim assert model.base_embeddings.num_embeddings == vocab_size assert model.superposed_embeddings.num_embeddings == vocab_size @@ -15,7 +15,7 @@ def test_qsembeddings_init(): def test_qsembeddings_forward_weighted_sum(): vocab_size = 10000 dim = 512 - model = QuantumSuperpositionEmbeddings(vocab_size, dim) + model = QFTSPEmbedding(vocab_size, dim) x = torch.randint(0, vocab_size, (1, 10)) context_vector = torch.rand(1, 10) embeddings = model(x, context_vector, "weighted_sum") @@ -25,7 +25,7 @@ def test_qsembeddings_forward_weighted_sum(): def test_qsembeddings_forward_dot_product(): vocab_size = 10000 dim = 512 - model = QuantumSuperpositionEmbeddings(vocab_size, dim) + model = QFTSPEmbedding(vocab_size, dim) x = torch.randint(0, vocab_size, (1, 10)) context_vector = torch.rand(1, 10) embeddings = model(x, context_vector, "dot_product") @@ -35,7 +35,7 @@ def test_qsembeddings_forward_dot_product(): def test_qsembeddings_forward_cosine_similarity(): vocab_size = 10000 dim = 512 - model = QuantumSuperpositionEmbeddings(vocab_size, dim) + model = QFTSPEmbedding(vocab_size, dim) x = torch.randint(0, vocab_size, (1, 10)) context_vector = torch.rand(1, 10) embeddings = model(x, context_vector, "cosine_similarity") @@ -45,7 +45,7 @@ def test_qsembeddings_forward_cosine_similarity(): def test_qsembeddings_forward_gated(): vocab_size = 10000 dim = 512 - model = QuantumSuperpositionEmbeddings(vocab_size, dim) + model = QFTSPEmbedding(vocab_size, dim) x = torch.randint(0, vocab_size, (1, 10)) context_vector = torch.rand(1, 10) embeddings = model(x, context_vector, "gated") @@ -55,7 +55,7 @@ def test_qsembeddings_forward_gated(): def test_qsembeddings_forward_concat_linear(): vocab_size = 10000 dim = 512 - model = QuantumSuperpositionEmbeddings(vocab_size, dim) + model = QFTSPEmbedding(vocab_size, dim) x = torch.randint(0, vocab_size, (1, 10)) context_vector = torch.rand(1, 10) embeddings = model(x, context_vector, "concat_linear") @@ -65,7 +65,7 @@ def test_qsembeddings_forward_concat_linear(): def test_qsembeddings_forward_invalid_mode(): vocab_size = 10000 dim = 512 - model = QuantumSuperpositionEmbeddings(vocab_size, dim) + model = QFTSPEmbedding(vocab_size, dim) x = torch.randint(0, vocab_size, (1, 10)) context_vector = torch.rand(1, 10) with pytest.raises(ValueError): @@ -75,7 +75,7 @@ def test_qsembeddings_forward_invalid_mode(): def test_qsembeddings_forward_large_input(): vocab_size = 10000 dim = 512 - model = QuantumSuperpositionEmbeddings(vocab_size, dim) + model = QFTSPEmbedding(vocab_size, dim) x = torch.randint(0, vocab_size, (1000, 1000)) context_vector = torch.rand(1000, 1000) embeddings = model(x, context_vector, "weighted_sum") @@ -85,7 +85,7 @@ def test_qsembeddings_forward_large_input(): def test_qsembeddings_forward_large_dim(): vocab_size = 10000 dim = 10000 - model = QuantumSuperpositionEmbeddings(vocab_size, dim) + model = QFTSPEmbedding(vocab_size, dim) x = torch.randint(0, vocab_size, (1, 10)) context_vector = torch.rand(1, 10) embeddings = model(x, context_vector, "weighted_sum") @@ -95,7 +95,7 @@ def test_qsembeddings_forward_large_dim(): def test_qsembeddings_forward_large_vocab_size(): vocab_size = 1000000 dim = 512 - model = QuantumSuperpositionEmbeddings(vocab_size, dim) + model = QFTSPEmbedding(vocab_size, dim) x = torch.randint(0, vocab_size, (1, 10)) context_vector = torch.rand(1, 10) embeddings = model(x, context_vector, "weighted_sum") diff --git a/zeta/nn/embeddings/__init__.py b/zeta/nn/embeddings/__init__.py index 18c6a063..2174a3a3 100644 --- a/zeta/nn/embeddings/__init__.py +++ b/zeta/nn/embeddings/__init__.py @@ -25,7 +25,7 @@ from zeta.nn.embeddings.yarn import YarnEmbedding from zeta.nn.embeddings.sine_positional import SinePositionalEmbedding from zeta.nn.embeddings.qft_embeddings import QFTSPEmbeddings -from zeta.nn.embeddings.qfsp_embeddings import QuantumSuperpositionEmbeddings +from zeta.nn.embeddings.qfsp_embeddings import QFTSPEmbedding __all__ = [ "AbsolutePositionalEmbedding", @@ -47,5 +47,5 @@ "YarnEmbedding", "SinePositionalEmbedding", "QFTSPEmbeddings", - "QuantumSuperpositionEmbeddings", + "QFTSPEmbedding", ] diff --git a/zeta/nn/embeddings/qfsp_embeddings.py b/zeta/nn/embeddings/qfsp_embeddings.py index d7bde425..95cd52b6 100644 --- a/zeta/nn/embeddings/qfsp_embeddings.py +++ b/zeta/nn/embeddings/qfsp_embeddings.py @@ -2,48 +2,70 @@ import torch.nn as nn import torch.nn.functional as F - -class QuantumSuperpositionEmbeddings(nn.Module): +# QFTSPEmbedding +class QFTSPEmbedding(nn.Module): """ - QuantumSuperpositionEmbeddings with multiple collapse mechanisms. + QFTSPEmbedding with multiple collapse mechanisms. This module allows for different ways of collapsing the superposition of embeddings, based on the provided context and selected mechanism. """ - def __init__(self, vocab_size, embed_dim): - super(QuantumSuperpositionEmbeddings, self).__init__() - self.embed_dim = embed_dim - self.base_embeddings = nn.Embedding(vocab_size, embed_dim) - self.superposed_embeddings = nn.Embedding(vocab_size, embed_dim) - self.linear_transform = nn.Linear(2 * embed_dim, embed_dim) + def __init__( + self, + vocab_size: int, + dim: int, + collapse_mode: str = "weighted_sum", + **kwargs, + ): + super(QFTSPEmbedding, self).__init__() + self.dim = dim + self.collapse_mode = collapse_mode + self.base_embeddings = nn.Embedding(vocab_size, dim) + self.superposed_embeddings = nn.Embedding(vocab_size, dim) + self.linear_transform = nn.Linear(2 * dim, dim) + + def forward( + self, x: torch.Tensor, context_vector: torch.Tensor + ) -> torch.Tensor: + """Forward pass of the QFTSPEmbedding module. + + Args: + x (_type_): _description_ + context_vector (_type_): _description_ + collapse_mode (str, optional): _description_. Defaults to "weighted_sum". + + Raises: + ValueError: _description_ - def forward(self, input_ids, context_vector, collapse_mode="weighted_sum"): - base_embeds = self.base_embeddings(input_ids) - superposed_embeds = self.superposed_embeddings(input_ids) + Returns: + _type_: _description_ + """ + base_embeds = self.base_embeddings(x) + superposed_embeds = self.superposed_embeddings(x) - if collapse_mode == "weighted_sum": + if self.collapse_mode == "weighted_sum": collapsed_embeds = ( base_embeds + context_vector.unsqueeze(-1) * superposed_embeds ) - elif collapse_mode == "dot_product": + elif self.collapse_mode == "dot_product": scale = torch.sum( superposed_embeds * context_vector.unsqueeze(-1), dim=-1, keepdim=True, ) collapsed_embeds = base_embeds + scale * superposed_embeds - elif collapse_mode == "cosine_similarity": + elif self.collapse_mode == "cosine_similarity": scale = F.cosine_similarity( superposed_embeds, context_vector.unsqueeze(-1), dim=-1 ).unsqueeze(-1) collapsed_embeds = base_embeds + scale * superposed_embeds - elif collapse_mode == "gated": + elif self.collapse_mode == "gated": gate = torch.sigmoid(context_vector) collapsed_embeds = ( base_embeds + gate.unsqueeze(-1) * superposed_embeds ) - elif collapse_mode == "concat_linear": + elif self.collapse_mode == "concat_linear": concatenated = torch.cat([base_embeds, superposed_embeds], dim=-1) collapsed_embeds = self.linear_transform(concatenated) else: @@ -54,13 +76,13 @@ def forward(self, input_ids, context_vector, collapse_mode="weighted_sum"): # # Example Usage # vocab_size = 10000 -# embed_dim = 512 +# dim = 512 -# model = QuantumSuperpositionEmbeddings(vocab_size, embed_dim) -# input_ids = torch.randint(0, vocab_size, (1, 10)) +# model = QFTSPEmbedding(vocab_size, dim) +# x = torch.randint(0, vocab_size, (1, 10)) # context_vector = torch.rand(1, 10) # # Test different collapse modes # for mode in ['weighted_sum', 'dot_product', 'cosine_similarity', 'gated', 'concat_linear']: -# embeddings = model(input_ids, context_vector, collapse_mode=mode) +# embeddings = model(x, context_vector, collapse_mode=mode) # print(f"Collapse mode: {mode}, Embeddings shape: {embeddings.shape}") From 4cf92d9cd3f69aae92bd7aa71285fc21e98503e4 Mon Sep 17 00:00:00 2001 From: Kye Date: Sun, 17 Dec 2023 03:41:00 -0500 Subject: [PATCH 128/587] [FEAT][QUANT][niva] --- tests/quant/test_niva.py | 172 ++++++++++++++++++++++++++ zeta/nn/embeddings/qfsp_embeddings.py | 1 + zeta/quant/__init__.py | 3 +- zeta/quant/niva.py | 99 +++++++++++++++ 4 files changed, 274 insertions(+), 1 deletion(-) create mode 100644 tests/quant/test_niva.py create mode 100644 zeta/quant/niva.py diff --git a/tests/quant/test_niva.py b/tests/quant/test_niva.py new file mode 100644 index 00000000..277de361 --- /dev/null +++ b/tests/quant/test_niva.py @@ -0,0 +1,172 @@ +import os +import pytest +import torch +import torch.nn as nn +from zeta.quant.niva import niva +from zeta.nn import QFTSPEmbedding + + +def test_niva_model_type(): + with pytest.raises(TypeError): + niva( + "not a model", + model_path="model.pt", + output_path="model_quantized.pt", + ) + + +def test_niva_model_path_none(): + model = QFTSPEmbedding(100, 100) + with pytest.raises(ValueError): + niva(model, model_path=None, output_path="model_quantized.pt") + + +def test_niva_output_path_none(): + model = QFTSPEmbedding(100, 100) + with pytest.raises(ValueError): + niva(model, model_path="model.pt", output_path=None) + + +def test_niva_quant_type_invalid(): + model = QFTSPEmbedding(100, 100) + with pytest.raises(ValueError): + niva( + model, + model_path="model.pt", + output_path="model_quantized.pt", + quant_type="invalid", + ) + + +def test_niva_quantize_layers_not_list(): + model = QFTSPEmbedding(100, 100) + with pytest.raises(TypeError): + niva( + model, + model_path="model.pt", + output_path="model_quantized.pt", + quantize_layers="not a list", + ) + + +def test_niva_quantize_layers_not_types(): + model = QFTSPEmbedding(100, 100) + with pytest.raises(TypeError): + niva( + model, + model_path="model.pt", + output_path="model_quantized.pt", + quantize_layers=["not a type"], + ) + + +def test_niva_quantize_layers_not_subclasses(): + model = QFTSPEmbedding(100, 100) + with pytest.raises(TypeError): + niva( + model, + model_path="model.pt", + output_path="model_quantized.pt", + quantize_layers=[str], + ) + + +def test_niva_dtype_not_dtype(): + model = QFTSPEmbedding(100, 100) + with pytest.raises(TypeError): + niva( + model, + model_path="model.pt", + output_path="model_quantized.pt", + dtype="not a dtype", + ) + + +def test_niva_dtype_invalid(): + model = QFTSPEmbedding(100, 100) + with pytest.raises(ValueError): + niva( + model, + model_path="model.pt", + output_path="model_quantized.pt", + dtype=torch.float32, + ) + + +def test_niva_quantize_layers_none_dynamic(): + model = QFTSPEmbedding(100, 100) + with pytest.raises(ValueError): + niva( + model, + model_path="model.pt", + output_path="model_quantized.pt", + quant_type="dynamic", + quantize_layers=None, + ) + + +# The following tests assume that "model.pt" exists and is a valid model file +def test_niva_dynamic(): + model = QFTSPEmbedding(100, 100) + niva( + model, + model_path="model.pt", + output_path="model_quantized.pt", + quant_type="dynamic", + quantize_layers=[nn.Embedding], + ) + + +def test_niva_static(): + model = QFTSPEmbedding(100, 100) + niva( + model, + model_path="model.pt", + output_path="model_quantized.pt", + quant_type="static", + ) + + +def test_niva_qint8(): + model = QFTSPEmbedding(100, 100) + niva( + model, + model_path="model.pt", + output_path="model_quantized.pt", + dtype=torch.qint8, + ) + + +def test_niva_quint8(): + model = QFTSPEmbedding(100, 100) + niva( + model, + model_path="model.pt", + output_path="model_quantized.pt", + dtype=torch.quint8, + ) + + +# The following tests assume that "model_quantized.pt" is the output of a previous test +def test_niva_output_exists(): + assert os.path.exists("model_quantized.pt") + + +def test_niva_output_loadable(): + model = QFTSPEmbedding(100, 100) + model.load_state_dict(torch.load("model_quantized.pt")) + + +def test_niva_output_correct_type(): + model = QFTSPEmbedding(100, 100) + model.load_state_dict(torch.load("model_quantized.pt")) + assert isinstance(model, nn.Module) + + +def test_niva_output_quantized(): + model = QFTSPEmbedding(100, 100) + model.load_state_dict(torch.load("model_quantized.pt")) + assert any( + hasattr(module, "qconfig") and module.qconfig + for module in model.modules() + ) diff --git a/zeta/nn/embeddings/qfsp_embeddings.py b/zeta/nn/embeddings/qfsp_embeddings.py index 95cd52b6..38fab2b8 100644 --- a/zeta/nn/embeddings/qfsp_embeddings.py +++ b/zeta/nn/embeddings/qfsp_embeddings.py @@ -2,6 +2,7 @@ import torch.nn as nn import torch.nn.functional as F + # QFTSPEmbedding class QFTSPEmbedding(nn.Module): """ diff --git a/zeta/quant/__init__.py b/zeta/quant/__init__.py index 4a393157..b799462e 100644 --- a/zeta/quant/__init__.py +++ b/zeta/quant/__init__.py @@ -2,5 +2,6 @@ from zeta.quant.bitlinear import absmax_quantize, BitLinear from zeta.quant.ste import STE from zeta.quant.qlora import QloraLinear +from zeta.quant.niva import niva -__all__ = ["QUIK", "absmax_quantize", "BitLinear", "STE", "QloraLinear"] +__all__ = ["QUIK", "absmax_quantize", "BitLinear", "STE", "QloraLinear", "niva"] diff --git a/zeta/quant/niva.py b/zeta/quant/niva.py new file mode 100644 index 00000000..6e308971 --- /dev/null +++ b/zeta/quant/niva.py @@ -0,0 +1,99 @@ +from typing import List, Type, Union + +import torch +from torch import nn + + +def niva( + model: nn.Module, + model_path: str = None, + output_path: str = None, + quant_type: str = "dynamic", + quantize_layers: Union[List[Type[nn.Module]], None] = None, + dtype: torch.dtype = torch.qint8, + *args, + **kwargs, +): + """Niva: Quantize a model. + + Args: + model (nn.Module): _description_ + model_path (str, optional): _description_. Defaults to None. + output_path (str, optional): _description_. Defaults to None. + quant_type (str, optional): _description_. Defaults to "dynamic". + quantize_layers (Union[List[Type[nn.Module]], None], optional): Quantize layers. Defaults to None. + dtype (torch.dtype, optional): _description_. Defaults to torch.qint8. + + Raises: + TypeError: _description_ + ValueError: _description_ + ValueError: _description_ + ValueError: _description_ + TypeError: _description_ + TypeError: _description_ + TypeError: _description_ + TypeError: _description_ + ValueError: _description_ + ValueError: _description_ + + Examples: + >>> import torch + >>> from zeta.quant import niva + >>> from zeta.nn import QFTSPEmbedding + >>> model = QFTSPEmbedding(100, 100) + >>> niva( + ... model, + ... quant_type="static", + ... dtype=torch.qint8, + ... quantize_layers=[nn.Embedding], + ... model_path="model.pt", + ... output_path="model_quantized.pt" + ... ) + + """ + if not isinstance(model, nn.Module): + raise TypeError("model must be a torch.nn.Module") + if model_path is None: + raise ValueError("model_path must be specified") + if output_path is None: + raise ValueError("output_path must be specified") + if quant_type not in ["static", "dynamic"]: + raise ValueError("quant_type must be either static or dynamic") + if quantize_layers is not None: + if not isinstance(quantize_layers, list): + raise TypeError("quantize_layers must be a list") + for layer in quantize_layers: + if not isinstance(layer, type): + raise TypeError("quantize_layers must be a list of types") + if not issubclass(layer, nn.Module): + raise TypeError( + "quantize_layers must be a list of types that are" + " subclasses of torch.nn.Module" + ) + if not isinstance(dtype, torch.dtype): + raise TypeError("dtype must be a torch.dtype") + if dtype not in [torch.qint8, torch.quint8]: + raise ValueError("dtype must be either torch.qint8 or torch.quint8") + + # Load the model + model.load_state_dict(torch.load(model_path)) + + # Ensure model is in eval model + model.eval() + + # Apply quantization + if quant_type == "dynamic": + if quantize_layers is None: + raise ValueError( + "quantize_layers must be specified for dynamic quantization" + ) + model = torch.quantization.quantize_dynamic( + model, quantize_layers, dtype=dtype, *args, **kwargs + ) + elif quant_type == "static": + model.qconfig = torch.quantization.get_default_qconfig(dtype=dtype) + torch.quantization.prepare(model, inplace=True) + torch.quantization.convert(model, inplace=True) + + # Save the model + torch.save(model.state_dict(), output_path) From c2498e614a03161359240a6d53c185e6c585e8e7 Mon Sep 17 00:00:00 2001 From: Kye Date: Sun, 17 Dec 2023 15:03:08 -0500 Subject: [PATCH 129/587] niva --- docs/zeta/quant/niva.md | 110 ++++++++++++++++++++++++++++++++++++++++ mkdocs.yml | 1 + pyproject.toml | 2 +- zeta/quant/niva.py | 12 ----- 4 files changed, 112 insertions(+), 13 deletions(-) create mode 100644 docs/zeta/quant/niva.md diff --git a/docs/zeta/quant/niva.md b/docs/zeta/quant/niva.md new file mode 100644 index 00000000..3ac8b28f --- /dev/null +++ b/docs/zeta/quant/niva.md @@ -0,0 +1,110 @@ +# `niva` + +## Overview + +The Niva module provides functionality for quantizing PyTorch neural network models, enabling you to reduce their memory and computation requirements while preserving their accuracy. Quantization is a crucial technique for deploying models on resource-constrained devices such as edge devices and mobile platforms. + +This documentation will guide you through the Niva module's architecture, purpose, functions, and usage examples. You'll learn how to effectively quantize your PyTorch models and optimize their performance for different deployment scenarios. + +## Table of Contents + +1. [Installation](#installation) +2. [Architecture](#architecture) +3. [Purpose](#purpose) +4. [Function: niva](#function-niva) + - [Parameters](#parameters) + - [Usage Examples](#usage-examples) + - [Dynamic Quantization](#dynamic-quantization) + - [Static Quantization](#static-quantization) +5. [Additional Information](#additional-information) +6. [References](#references) + +--- + +## 1. Installation + +Before using the Niva module, make sure you have PyTorch installed. You can install PyTorch using the following command: + +```bash +pip install zetascale +``` + +## 2. Architecture + +The Niva module leverages PyTorch's quantization capabilities to quantize neural network models. It offers both dynamic and static quantization options to accommodate various use cases. + +## 3. Purpose + +The primary purpose of the Niva module is to enable quantization of PyTorch models. Quantization is the process of reducing the precision of model weights and activations, which results in smaller model sizes and faster inference on hardware with limited resources. This is especially important for deploying models on edge devices and mobile platforms. + +## 4. Function: niva + +The `niva` function is the core of the Niva module, responsible for quantizing a given PyTorch model. It supports both dynamic and static quantization modes, allowing you to choose the most suitable quantization approach for your model. + +### Parameters + +The `niva` function accepts the following parameters: + +- `model` (nn.Module): The PyTorch model to be quantized. +- `model_path` (str, optional): The path to the pre-trained model's weights. Defaults to None. +- `output_path` (str, optional): The path where the quantized model will be saved. Defaults to None. +- `quant_type` (str, optional): The type of quantization to be applied, either "dynamic" or "static". Defaults to "dynamic". +- `quantize_layers` (Union[List[Type[nn.Module]], None], optional): A list of layer types to be quantized. Defaults to None. +- `dtype` (torch.dtype, optional): The target data type for quantization, either torch.qint8 or torch.quint8. Defaults to torch.qint8. +- `*args` and `**kwargs`: Additional arguments for PyTorch's quantization functions. + +### Usage Examples + +#### Dynamic Quantization + +In dynamic quantization, you specify the layers to be quantized, and the quantization process occurs dynamically during inference. Here's an example: + +```python +import torch +from zeta import niva + +# Load a pre-trained model +model = YourModelClass() + +# Quantize the model dynamically, specifying layers to quantize +niva( + model=model, + model_path="path_to_pretrained_model_weights.pt", + output_path="quantized_model.pt", + quant_type="dynamic", + quantize_layers=[nn.Linear, nn.Conv2d], + dtype=torch.qint8 +) +``` + +#### Static Quantization + +Static quantization quantizes the entire model before inference. Here's an example: + +```python +import torch +from zeta import niva + +# Load a pre-trained model +model = YourModelClass() + +# Quantize the entire model statically +niva( + model=model, + model_path="path_to_pretrained_model_weights.pt", + output_path="quantized_model.pt", + quant_type="static", + dtype=torch.qint8 +) +``` + +## 5. Additional Information + +- The Niva module supports both dynamic and static quantization modes, giving you flexibility in choosing the right approach for your deployment scenario. +- Always ensure that your model is in evaluation mode (`model.eval()`) before quantization. +- Quantization reduces model size and inference time but may slightly affect model accuracy. It's essential to evaluate the quantized model's performance before deployment. + +## 6. References + +For more information on PyTorch quantization and best practices, refer to the official PyTorch documentation: [PyTorch Quantization](https://pytorch.org/docs/stable/quantization.html). + diff --git a/mkdocs.yml b/mkdocs.yml index 42ff1666..8f82c68c 100644 --- a/mkdocs.yml +++ b/mkdocs.yml @@ -151,6 +151,7 @@ nav: - zeta.quant: - QUIK: "zeta/quant/quik.md" - BitLinear: "zeta/quant/bitlinear.md" + - niva: "zeta/quant/niva.mdg" - Examples: - Overview: "examples/index.md" - Product: diff --git a/pyproject.toml b/pyproject.toml index f65cd5c6..be60aff4 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,6 +1,6 @@ [tool.poetry] name = "zetascale" -version = "0.9.6" +version = "0.9.7" description = "Transformers at zeta scales" authors = ["Zeta Team "] license = "MIT" diff --git a/zeta/quant/niva.py b/zeta/quant/niva.py index 6e308971..c9207d1d 100644 --- a/zeta/quant/niva.py +++ b/zeta/quant/niva.py @@ -24,18 +24,6 @@ def niva( quantize_layers (Union[List[Type[nn.Module]], None], optional): Quantize layers. Defaults to None. dtype (torch.dtype, optional): _description_. Defaults to torch.qint8. - Raises: - TypeError: _description_ - ValueError: _description_ - ValueError: _description_ - ValueError: _description_ - TypeError: _description_ - TypeError: _description_ - TypeError: _description_ - TypeError: _description_ - ValueError: _description_ - ValueError: _description_ - Examples: >>> import torch >>> from zeta.quant import niva From 650f3b835a27e373a60891db8f2dbc2d7ff24878 Mon Sep 17 00:00:00 2001 From: Kye Date: Sun, 17 Dec 2023 16:47:38 -0500 Subject: [PATCH 130/587] niva docs fix --- mkdocs.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/mkdocs.yml b/mkdocs.yml index 8f82c68c..817dc91e 100644 --- a/mkdocs.yml +++ b/mkdocs.yml @@ -151,7 +151,7 @@ nav: - zeta.quant: - QUIK: "zeta/quant/quik.md" - BitLinear: "zeta/quant/bitlinear.md" - - niva: "zeta/quant/niva.mdg" + - niva: "zeta/quant/niva.md" - Examples: - Overview: "examples/index.md" - Product: From 06173bcc90189919bdc582250d3b6eb0d8326483 Mon Sep 17 00:00:00 2001 From: Kye Date: Sun, 17 Dec 2023 17:14:55 -0500 Subject: [PATCH 131/587] [FIX][RuntimeError: mat1 and mat2 shapes cannot be multiplied (512x4 and 512x512)] --- zeta/nn/modules/mlp_mixer.py | 36 ++++++++++++++++++------------------ 1 file changed, 18 insertions(+), 18 deletions(-) diff --git a/zeta/nn/modules/mlp_mixer.py b/zeta/nn/modules/mlp_mixer.py index f45e7c39..d07280b8 100644 --- a/zeta/nn/modules/mlp_mixer.py +++ b/zeta/nn/modules/mlp_mixer.py @@ -128,21 +128,21 @@ def forward(self, x: torch.Tensor) -> torch.Tensor: return self.head(x) -# Example of creating a model instance -mlp_mixer = MLPMixer( - num_classes=10, - num_blocks=8, - patch_size=16, - hidden_dim=512, - tokens_mlp_dim=512, - channels_mlp_dim=512, -) - -# Example input tensor -example_input = torch.randn( - 1, 512, 32, 32 -) # Batch size of 1, 512 channels, 32x32 image -output = mlp_mixer(example_input) -print( - output.shape -) # Should output the shape corresponding to the number of classes +# # Example of creating a model instance +# mlp_mixer = MLPMixer( +# num_classes=10, +# num_blocks=8, +# patch_size=16, +# hidden_dim=512, +# tokens_mlp_dim=512, +# channels_mlp_dim=512, +# ) + +# # Example input tensor +# example_input = torch.randn( +# 1, 512, 32, 32 +# ) # Batch size of 1, 512 channels, 32x32 image +# output = mlp_mixer(example_input) +# print( +# output.shape +# ) # Should output the shape corresponding to the number of classes From c93e91062a9ae631aab0e35020373a29a5d38ede Mon Sep 17 00:00:00 2001 From: Kye Date: Sun, 17 Dec 2023 17:15:53 -0500 Subject: [PATCH 132/587] [V] --- pyproject.toml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pyproject.toml b/pyproject.toml index be60aff4..556e4a77 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,6 +1,6 @@ [tool.poetry] name = "zetascale" -version = "0.9.7" +version = "0.9.8" description = "Transformers at zeta scales" authors = ["Zeta Team "] license = "MIT" From a8ae3d5a22aa0e70f43724f44e8ab97705bb1222 Mon Sep 17 00:00:00 2001 From: Kye Date: Sun, 17 Dec 2023 17:30:30 -0500 Subject: [PATCH 133/587] [CORPORATE MISSION STATEMENT] --- docs/corporate/growth.md | 21 +++++++++++++ docs/corporate/main.md | 67 ++++++++++++++++++++++++++++++++++++++++ mkdocs.yml | 9 ++++-- 3 files changed, 94 insertions(+), 3 deletions(-) create mode 100644 docs/corporate/growth.md create mode 100644 docs/corporate/main.md diff --git a/docs/corporate/growth.md b/docs/corporate/growth.md new file mode 100644 index 00000000..20eb6e9a --- /dev/null +++ b/docs/corporate/growth.md @@ -0,0 +1,21 @@ +# Growth + +To drive massive user adoption and unleash growth for the Zeta Framework, which is built on open source and distributed via platforms like GitHub and PyPI, a strategic plan involving repeatable activities is essential. These activities should focus on community engagement, continuous improvement, marketing, and partnerships. Here's a table outlining potential repeatable activities that could be key to achieving these goals: + +| Activity | Description | Frequency | Key Objectives | Expected Outcome | +|----------|-------------|-----------|----------------|------------------| +| Community Code Sprints | Organize regular coding events for contributing to the framework. | Bi-monthly | Engage the developer community, encourage contributions. | Increased contributions, enhanced framework features. | +| Webinar Series & Workshops | Host webinars and workshops on using and contributing to Zeta Framework. | Monthly | Educate potential users, showcase framework capabilities. | Higher user adoption, community education. | +| Regular Updates & Patches | Consistent release of updates and patches. | Bi-weekly / Monthly | Maintain a robust, up-to-date framework. | Trust and reliance in the framework’s utility. | +| Contributor Recognition Program | Implement a program to recognize and reward key contributors. | Quarterly | Motivate contributions, build a loyal community. | Increased community engagement, quality contributions. | +| Social Media Engagement | Active promotion and engagement on platforms like Twitter, LinkedIn, Reddit. | Daily / Weekly | Increase visibility, create buzz. | Greater awareness, attracting new users. | +| Collaboration with Educational Institutions | Partner with universities for curriculum integration and research. | Bi-annually | Promote academic use, foster new talent. | Long-term user base growth, innovation. | +| User Experience Feedback Loops | Regular surveys and feedback sessions with users. | Quarterly | Understand user needs, improve framework. | Enhanced user satisfaction, framework improvement. | +| Blogging & Content Creation | Regular blog posts, tutorials, and use-case studies. | Weekly | Educate and engage with the community. | Higher engagement, SEO benefits. | +| Plugin/Extension Development | Encourage and support the development of plugins/extensions. | As needed | Expand framework capabilities, cater to diverse needs. | Enhanced functionality, broader appeal. | +| Partnership with Industry Leaders | Forge partnerships for co-development or integration. | Annually | Gain credibility, access new markets. | Broader industry acceptance, new user segments. | +| Open Source Conferences | Participate in or sponsor open source conferences. | Annually | Network, showcase framework. | Increased visibility, network expansion. | +| User Group and Meetup Formation | Facilitate the creation of user groups and meetups globally. | Quarterly | Foster a sense of community, local engagement. | Stronger, localized community support networks. | +| Continuous Benchmarking | Regularly benchmark against competing frameworks. | Bi-annually | Stay competitive, identify improvement areas. | Framework optimization, staying ahead of competition. | + +This strategy aims to build a strong, engaged community around Zeta Framework, continuously improve and update the framework, and increase its visibility and credibility in both the academic and industrial sectors. Through these activities, the goal is to create a sustainable growth model that leverages the power of the open-source community. diff --git a/docs/corporate/main.md b/docs/corporate/main.md new file mode 100644 index 00000000..f2a7275a --- /dev/null +++ b/docs/corporate/main.md @@ -0,0 +1,67 @@ +# **Zeta Framework Corporate Mission Statement: Pioneering a Future Where AI is for Everyone** + +--- + +**Title:** +"High Performance AI for everyone by Zeta" + +--- + +**Introduction:** + +In an era where artificial intelligence is reshaping every facet of human life, Zeta Framework emerges as a beacon of empowerment and innovation. Our vision transcends the traditional boundaries of technology, envisioning a future where the transformative power of AI is a common tool, accessible and usable by all. Our mission is to demystify the complexities of AI model development, rendering it a straightforward, inclusive, and universally accessible endeavor. + +--- + +**Our Grand Purpose:** + +Zeta Framework is dedicated to a singular, noble purpose: to enable every individual, from the tech-savvy developer in Silicon Valley to the aspiring innovator in remote corners of the world, to create AI models that are not just efficient and effective, but also ethical and empowering. We are not just developing a technology; we are nurturing a vision to uplift humanity, bridge digital divides, and democratize the very essence of technological advancement. + +--- + +**Guiding Principles:** + +1. **Modularity: Embracing Diversity in Innovation** + - Our commitment to modularity is not just about technical flexibility; it’s about honoring the diverse needs and visions of our users. We provide a canvas where every stroke of innovation can find its space. + +2. **Extreme Reliability: A Foundation You Can Trust** + - Zeta Framework stands as a pillar of reliability. We understand that the backbone of impactful technology is trust, and we embed this trust in every line of code, ensuring that our framework is a dependable ally in your AI journey. + +3. **Bleeding Edge Performance: Pushing the Boundaries of the Possible** + - Our pursuit of bleeding-edge performance is relentless. We are constantly scouring the horizon for innovations, integrating them to ensure that our users are always equipped with the best tools to conquer the AI frontier. + +4. **Community Collaboration: Cultivating a Global AI Family** + - We believe in the power of collective intelligence. Our framework is a testament to the spirit of global collaboration, bringing together minds from across the globe to forge a path of shared growth and learning. + +5. **Ethical AI Development: Championing a Responsible Future** + - Our commitment to ethical AI is unwavering. We recognize the profound impact of AI on society and are dedicated to ensuring that our framework upholds the highest standards of fairness, transparency, and respect for human dignity. + +6. **Accessibility and Ease of Use: Making AI a Universal Language** + - We are steadfast in our mission to make AI as accessible as possible. Zeta Framework is designed to be intuitive, removing barriers and opening doors to a world where AI is a universal language, spoken and understood by all. + +7. **Continuous Learning and Improvement: Evolving with You** + - The journey of AI is one of perpetual evolution, and so is our framework. We are committed to a philosophy of continuous learning and improvement, ensuring that Zeta Framework not only adapts to the changing landscape of technology but also to the evolving needs of our users. + +8. **Inclusive Innovation: Building for a Diverse World** + - At Zeta, we recognize the rich tapestry of human diversity. Our framework is designed with an inclusive lens, ensuring that it caters to a wide spectrum of cultures, abilities, and backgrounds. + +9. **Sustainable Development: AI for a Greener Tomorrow** + - We acknowledge our responsibility towards the planet. Our commitment to sustainable AI development guides our operational and technological decisions, aiming to minimize environmental impact and promote sustainability. + +--- + +**Our Aspiration:** + +In embracing these principles, Zeta Framework aspires to be more than a technological solution; it aims to be a movement. A movement that heralds a new era where AI is not a privilege of the few but a right of the many. A movement that stands on the pillars of empowerment, equality, and ethical responsibility. We are not just building a framework; we are crafting the future of AI, a future where technology is an equal partner in human progress. + +--- + +**Endorsement:** + +*With a Vision for Tomorrow,* +Kye Gomez, Supreme Leader of the Zeta Framework + +--- + +*Date:* December 17, 2023 + diff --git a/mkdocs.yml b/mkdocs.yml index 817dc91e..02d05c65 100644 --- a/mkdocs.yml +++ b/mkdocs.yml @@ -154,8 +154,11 @@ nav: - niva: "zeta/quant/niva.md" - Examples: - Overview: "examples/index.md" - - Product: - - Overview: "zeta/product/product_ideas.md" - - Zetahub: "zeta/product/zetahub.md" + - Corporate: + - Overview: "corporate/main.md" + - Product: + - Overview: "zeta/product/product_ideas.md" + - Zetahub: "zeta/product/zetahub.md" + - Growth: "corporate/growth.md" - Blog: - Introduction: "blog/introduction_to_zeta.md" \ No newline at end of file From 9951c874e5de12943393401ffe234204c5518c62 Mon Sep 17 00:00:00 2001 From: Kye Date: Sun, 17 Dec 2023 17:42:40 -0500 Subject: [PATCH 134/587] ai for everyone --- docs/corporate/main.md | 6 +----- 1 file changed, 1 insertion(+), 5 deletions(-) diff --git a/docs/corporate/main.md b/docs/corporate/main.md index f2a7275a..f9216596 100644 --- a/docs/corporate/main.md +++ b/docs/corporate/main.md @@ -1,9 +1,5 @@ -# **Zeta Framework Corporate Mission Statement: Pioneering a Future Where AI is for Everyone** +# **Zeta Mission Statement: Pioneering a Future Where AI is for Everyone** ---- - -**Title:** -"High Performance AI for everyone by Zeta" --- From d84ebeb874a9d85c3ee260d73f7910ed6d9a410c Mon Sep 17 00:00:00 2001 From: Kye Date: Sun, 17 Dec 2023 17:53:37 -0500 Subject: [PATCH 135/587] [SwiGLUStacked][mo super init] --- zeta/nn/modules/swiglu.py | 1 + 1 file changed, 1 insertion(+) diff --git a/zeta/nn/modules/swiglu.py b/zeta/nn/modules/swiglu.py index 3ba74cd5..97d922db 100644 --- a/zeta/nn/modules/swiglu.py +++ b/zeta/nn/modules/swiglu.py @@ -46,6 +46,7 @@ def __init__( *args, **kwargs, ): + super().__init__() self.w1 = nn.Linear(dim, hidden_dim, bias=bias) self.w2 = nn.Linear(hidden_dim, dim, bias=bias) self.w3 = nn.Linear(dim, hidden_dim, bias=bias) From 8fa1c05f51db84b3e6c1dfd85b476d082c1ddf42 Mon Sep 17 00:00:00 2001 From: accuracy_maker Date: Mon, 18 Dec 2023 11:20:17 +1100 Subject: [PATCH 136/587] add sumtree,PER and PESR --- zeta/rl/PrioritizedReplayBuffer.py | 85 ++++++++++++++++ zeta/rl/PrioritizedSequenceReplayBuffer.py | 112 +++++++++++++++++++++ zeta/rl/sumtree.py | 98 ++++++++++++++++++ 3 files changed, 295 insertions(+) create mode 100644 zeta/rl/PrioritizedReplayBuffer.py create mode 100644 zeta/rl/PrioritizedSequenceReplayBuffer.py create mode 100644 zeta/rl/sumtree.py diff --git a/zeta/rl/PrioritizedReplayBuffer.py b/zeta/rl/PrioritizedReplayBuffer.py new file mode 100644 index 00000000..badb3a7e --- /dev/null +++ b/zeta/rl/PrioritizedReplayBuffer.py @@ -0,0 +1,85 @@ +from sumtree import SumTree +import torch +import random + +class PrioritizedReplayBuffer: + def __init__(self, state_size, action_size, buffer_size, device, eps=1e-2, alpha=0.1, beta=0.1): + self.tree = SumTree(size=buffer_size) + + + self.eps = eps + self.alpha = alpha + self.beta = beta + self.max_priority = 1. + + + self.state = torch.empty(buffer_size, state_size, dtype=torch.float) + self.action = torch.empty(buffer_size, action_size, dtype=torch.float) + self.reward = torch.empty(buffer_size, dtype=torch.float) + self.next_state = torch.empty(buffer_size, state_size, dtype=torch.float) + self.done = torch.empty(buffer_size, dtype=torch.uint8) + + self.count = 0 + self.real_size = 0 + self.size = buffer_size + + # device + self.device = device + + def add(self, transition): + state, action, reward, next_state, done = transition + + + self.tree.add(self.max_priority, self.count) + + self.state[self.count] = torch.as_tensor(state) + self.action[self.count] = torch.as_tensor(action) + self.reward[self.count] = torch.as_tensor(reward) + self.next_state[self.count] = torch.as_tensor(next_state) + self.done[self.count] = torch.as_tensor(done) + + + self.count = (self.count + 1) % self.size + self.real_size = min(self.size, self.real_size + 1) + + def sample(self, batch_size): + assert self.real_size >= batch_size, "buffer contains less samples than batch size" + + sample_idxs, tree_idxs = [], [] + priorities = torch.empty(batch_size, 1, dtype=torch.float) + + + segment = self.tree.total / batch_size + for i in range(batch_size): + a, b = segment * i, segment * (i + 1) + + cumsum = random.uniform(a, b) + + tree_idx, priority, sample_idx = self.tree.get(cumsum) + + priorities[i] = priority + tree_idxs.append(tree_idx) + sample_idxs.append(sample_idx) + + probs = priorities / self.tree.total + + weights = (self.real_size * probs) ** -self.beta + + weights = weights / weights.max() + batch = ( + self.state[sample_idxs].to(self.device), + self.action[sample_idxs].to(self.device), + self.reward[sample_idxs].to(self.device), + self.next_state[sample_idxs].to(self.device), + self.done[sample_idxs].to(self.device) + ) + return batch, weights, tree_idxs + + def update_priorities(self, data_idxs, priorities): + if isinstance(priorities, torch.Tensor): + priorities = priorities.detach().cpu().numpy() + + for data_idx, priority in zip(data_idxs, priorities): + priority = (priority + self.eps) ** self.alpha + self.tree.update(data_idx, priority) + self.max_priority = max(self.max_priority, priority) \ No newline at end of file diff --git a/zeta/rl/PrioritizedSequenceReplayBuffer.py b/zeta/rl/PrioritizedSequenceReplayBuffer.py new file mode 100644 index 00000000..8a9de10e --- /dev/null +++ b/zeta/rl/PrioritizedSequenceReplayBuffer.py @@ -0,0 +1,112 @@ +from sumtree import SumTree +import torch +import random + +class PrioritizedSequenceReplayBuffer: + def __init__(self,state_size,action_size,buffer_size,device,eps=1e-5,alpha=0.1,beta=0.1, + decay_window=5, + decay_coff=0.4, + pre_priority=0.7): + self.tree = SumTree(data_size=buffer_size) + + # PESR params + self.eps = eps + self.alpha = alpha + self.beta = beta + self.max_priority = 1. + self.decay_window = decay_window + self.decay_coff = decay_coff + self.pre_priority = pre_priority + + # buffer params + self.state = torch.empty(buffer_size, state_size, dtype=torch.float) + self.action = torch.empty(buffer_size, action_size, dtype=torch.float) + self.reward = torch.empty(buffer_size, dtype=torch.float) + self.next_state = torch.empty(buffer_size, state_size, dtype=torch.float) + self.done = torch.empty(buffer_size, dtype=torch.uint8) + + self.count = 0 + self.real_size = 0 + self.size = buffer_size + + # device + self.device = device + + def add(self, transition): + state, action, reward, next_state, done = transition + + # store transition index with maximum priority in sum tree + self.tree.add(self.max_priority, self.count) + + # store transition in the buffer + self.state[self.count] = torch.as_tensor(state) + self.action[self.count] = torch.as_tensor(action) + self.reward[self.count] = torch.as_tensor(reward) + self.next_state[self.count] = torch.as_tensor(next_state) + self.done[self.count] = torch.as_tensor(done) + + # update counters + self.count = (self.count + 1) % self.size + self.real_size = min(self.size, self.real_size + 1) + + def sample(self,batch_size): + assert self.real_size >= batch_size, "buffer contains less samples than batch size" + + sample_idxs, tree_idxs = [], [] + priorities = torch.empty(batch_size, 1, dtype=torch.float) + + segment = self.tree.total_priority / batch_size + for i in range(batch_size): + a, b = segment * i, segment * (i + 1) + + cumsum = random.uniform(a, b) + # sample_idx is a sample index in buffer, needed further to sample actual transitions + # tree_idx is a index of a sample in the tree, needed further to update priorities + tree_idx, priority, sample_idx = self.tree.get(cumsum) + + priorities[i] = priority + tree_idxs.append(tree_idx) + sample_idxs.append(sample_idx) + """ + Note: + The priorities stored in sumtree are all times alpha + """ + probs = priorities / self.tree.total_priority + weights = (self.real_size * probs) ** -self.beta + weights = weights / weights.max() + batch = ( + self.state[sample_idxs].to(self.device), + self.action[sample_idxs].to(self.device), + self.reward[sample_idxs].to(self.device), + self.next_state[sample_idxs].to(self.device), + self.done[sample_idxs].to(self.device) + ) + return batch, weights, tree_idxs + + def update_priorities(self,data_idxs,abs_td_errors): + """ + when we get the TD-error, we should update the transition priority p_j + And update decay_window's transition priorities + """ + if isinstance(abs_td_errors,torch.Tensor): + abs_td_errors = abs_td_errors.detach().cpu().numpy() + + for data_idx, td_error in zip(data_idxs,abs_td_errors): + # first update the batch: p_j + # p_j <- max{|delta_j| + eps, pre_priority * p_j} + old_priority = self.pre_priority * self.tree.nodes[data_idx + self.tree.size - 1] + priority = (td_error + self.eps) ** self.alpha + priority = max(priority,old_priority) + self.tree.update(data_idx,priority) + self.max_priority = max(self.max_priority,priority) + + # And then apply decay + if self.count >= self.decay_window: + # count points to the next position + # count means the idx in the buffer and number of transition + for i in reversed(range(self.decay_window)): + idx = (self.count - i - 1) % self.size + decayed_priority = priority * (self.decay_coff ** (i + 1)) + tree_idx = idx + self.tree.size - 1 + existing_priority = self.tree.nodes[tree_idx] + self.tree.update(idx,max(decayed_priority,existing_priority)) \ No newline at end of file diff --git a/zeta/rl/sumtree.py b/zeta/rl/sumtree.py new file mode 100644 index 00000000..c51805a3 --- /dev/null +++ b/zeta/rl/sumtree.py @@ -0,0 +1,98 @@ +class SumTree: + def __init__(self, size): + self.nodes = [0] * (2 * size - 1) + self.data = [None] * size + + self.size = size + self.count = 0 + self.real_size = 0 + + @property + def total(self): + return self.nodes[0] + + def propagate(self, idx, delta_value): + parent = (idx - 1) // 2 + + while parent >= 0: + self.nodes[parent] += delta_value + parent = (parent - 1) // 2 + + def update(self, data_idx, value): + idx = data_idx + self.size - 1 # child index in tree array + delta_value = value - self.nodes[idx] + + self.nodes[idx] = value + + self.propagate(idx, delta_value) + + def add(self, value, data): + self.data[self.count] = data + self.update(self.count, value) + + self.count = (self.count + 1) % self.size + self.real_size = min(self.size, self.real_size + 1) + + def get(self, cumsum): + assert cumsum <= self.total + + idx = 0 + while 2 * idx + 1 < len(self.nodes): + left, right = 2*idx + 1, 2*idx + 2 + + if cumsum <= self.nodes[left]: + idx = left + else: + idx = right + cumsum = cumsum - self.nodes[left] + + data_idx = idx - self.size + 1 + + return data_idx, self.nodes[idx], self.data[data_idx] + + def get_priority(self, data_idx): + tree_idx = data_idx + self.size - 1 + return self.nodes[tree_idx] + + + def __repr__(self): + return f"SumTree(nodes={self.nodes.__repr__()}, data={self.data.__repr__()})" + + +# # Test the sum tree +# if __name__ == '__main__': +# # Assuming the SumTree class definition is available + +# # Function to print the state of the tree for easier debugging +# def print_tree(tree): +# print("Tree Total:", tree.total) +# print("Tree Nodes:", tree.nodes) +# print("Tree Data:", tree.data) +# print() + +# # Create a SumTree instance +# tree_size = 5 +# tree = SumTree(tree_size) + +# # Add some data with initial priorities +# print("Adding data to the tree...") +# for i in range(tree_size): +# data = f"Data-{i}" +# priority = i + 1 # Priority is just a simple increasing number for this test +# tree.add(priority, data) +# print_tree(tree) + +# # Update priority of a data item +# print("Updating priority...") +# update_index = 2 # For example, update the priority of the third item +# new_priority = 10 +# tree.update(update_index, new_priority) +# print_tree(tree) + +# # Retrieve data based on cumulative sum +# print("Retrieving data based on cumulative sum...") +# cumulative_sums = [5, 15, 20] # Test with different cumulative sums +# for cumsum in cumulative_sums: +# idx, node_value, data = tree.get(cumsum) +# print(f"Cumulative Sum: {cumsum} -> Retrieved: {data} with Priority: {node_value}") +# print() From aa8d9a92e5f4fabcd4852b3e3e80e9f3dc357480 Mon Sep 17 00:00:00 2001 From: Kye Date: Mon, 18 Dec 2023 00:25:01 -0500 Subject: [PATCH 137/587] [ResNet] --- pyproject.toml | 2 +- tests/nn/modules/test_resnet.py | 100 ++++++++++++++++++ zeta/nn/modules/res_net.py | 181 ++++++++++++++++++++++++++++++++ 3 files changed, 282 insertions(+), 1 deletion(-) create mode 100644 tests/nn/modules/test_resnet.py create mode 100644 zeta/nn/modules/res_net.py diff --git a/pyproject.toml b/pyproject.toml index 556e4a77..9398f31f 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,6 +1,6 @@ [tool.poetry] name = "zetascale" -version = "0.9.8" +version = "0.9.9" description = "Transformers at zeta scales" authors = ["Zeta Team "] license = "MIT" diff --git a/tests/nn/modules/test_resnet.py b/tests/nn/modules/test_resnet.py new file mode 100644 index 00000000..66e83019 --- /dev/null +++ b/tests/nn/modules/test_resnet.py @@ -0,0 +1,100 @@ +import pytest +import torch +from zeta.nn.modules.res_net import ResNet +from torch.nn import Conv2d + + +def test_resnet_init(): + resnet = ResNet(Conv2d, [2, 2, 2, 2]) + assert isinstance(resnet, ResNet) + + +def test_resnet_num_classes(): + resnet = ResNet(Conv2d, [2, 2, 2, 2], num_classes=10) + assert resnet.fc.out_features == 10 + + +def test_resnet_kernel_size(): + resnet = ResNet(Conv2d, [2, 2, 2, 2], kernel_size=5) + assert resnet.conv1.kernel_size[0] == 5 + + +def test_resnet_stride(): + resnet = ResNet(Conv2d, [2, 2, 2, 2], stride=3) + assert resnet.conv1.stride[0] == 3 + + +def test_resnet_block_type(): + with pytest.raises(TypeError): + ResNet("not a block", [2, 2, 2, 2]) + + +def test_resnet_num_blocks_not_list(): + with pytest.raises(TypeError): + ResNet(Conv2d, "not a list") + + +def test_resnet_num_blocks_wrong_length(): + with pytest.raises(ValueError): + ResNet(Conv2d, [2, 2, 2]) + + +def test_resnet_num_blocks_not_integers(): + with pytest.raises(TypeError): + ResNet(Conv2d, [2, 2, "not an integer", 2]) + + +def test_resnet_forward(): + resnet = ResNet(Conv2d, [2, 2, 2, 2]) + x = torch.randn(1, 3, 224, 224) + assert resnet(x).shape == torch.Size([1, 1000]) + + +def test_resnet_forward_num_classes(): + resnet = ResNet(Conv2d, [2, 2, 2, 2], num_classes=10) + x = torch.randn(1, 3, 224, 224) + assert resnet(x).shape == torch.Size([1, 10]) + + +def test_resnet_forward_input_channels(): + resnet = ResNet(Conv2d, [2, 2, 2, 2]) + x = torch.randn(1, 1, 224, 224) + with pytest.raises(RuntimeError): + resnet(x) + + +def test_resnet_forward_input_size(): + resnet = ResNet(Conv2d, [2, 2, 2, 2]) + x = torch.randn(1, 3, 32, 32) + with pytest.raises(RuntimeError): + resnet(x) + + +def test_resnet_make_layer(): + resnet = ResNet(Conv2d, [2, 2, 2, 2]) + layer = resnet._make_layer(Conv2d, 64, 2, 1) + assert isinstance(layer, torch.nn.Sequential) + + +def test_resnet_make_layer_block_type(): + resnet = ResNet(Conv2d, [2, 2, 2, 2]) + with pytest.raises(TypeError): + resnet._make_layer("not a block", 64, 2, 1) + + +def test_resnet_make_layer_out_channels_not_integer(): + resnet = ResNet(Conv2d, [2, 2, 2, 2]) + with pytest.raises(TypeError): + resnet._make_layer(Conv2d, "not an integer", 2, 1) + + +def test_resnet_make_layer_num_blocks_not_integer(): + resnet = ResNet(Conv2d, [2, 2, 2, 2]) + with pytest.raises(TypeError): + resnet._make_layer(Conv2d, 64, "not an integer", 1) + + +def test_resnet_make_layer_stride_not_integer(): + resnet = ResNet(Conv2d, [2, 2, 2, 2]) + with pytest.raises(TypeError): + resnet._make_layer(Conv2d, 64, 2, "not an integer") diff --git a/zeta/nn/modules/res_net.py b/zeta/nn/modules/res_net.py new file mode 100644 index 00000000..b4d8559c --- /dev/null +++ b/zeta/nn/modules/res_net.py @@ -0,0 +1,181 @@ +import torch +import torch.nn as nn + + +# Basic Block for ResNet +class BasicBlock(nn.Module): + """BasicBlock + + + Args: + in_channels (int): Number of input channels + out_channels (int): Number of output channels + stride (int): Stride of the convolutional layer + kernel_size (int): Kernel size of the convolutional layer + padding (int): Padding of the convolutional layer + bias (bool): Bias of the convolutional layer + + Examples: + >>> from zeta.nn.modules.res_net import BasicBlock + >>> import torch + >>> x = torch.randn(5, 10) + >>> swiglu = BasicBlock(10, 20) + >>> swiglu(x).shape + torch.Size([5, 10]) + + """ + + expansion = 1 + + def __init__( + self, + in_channels, + out_channels, + stride: int = 1, + kernel_size: int = 3, + padding: int = 1, + bias: bool = False, + *args, + **kwargs, + ): + super(BasicBlock, self).__init__() + self.conv1 = nn.Conv2d( + in_channels, + out_channels, + kernel_size=kernel_size, + stride=stride, + padding=padding, + bias=bias, + ) + self.bn1 = nn.BatchNorm2d(out_channels) + self.relu = nn.ReLU(inplace=True) + self.conv2 = nn.Conv2d( + out_channels, + out_channels, + kernel_size=kernel_size, + stride=stride, + padding=padding, + bias=bias, + ) + self.bn2 = nn.BatchNorm2d(out_channels) + + self.shortcut = nn.Sequential() + if stride != 1 or in_channels != self.expansion * out_channels: + self.shortcut = nn.Sequential( + nn.Conv2d( + in_channels, + self.expansion * out_channels, + kernel_size=1, + stride=stride, + bias=bias, + ), + nn.BatchNorm2d(self.expansion * out_channels), + ) + + def forward(self, x: torch.Tensor): + """Forward + + Args: + x torch.Tensor: Input tensor + + """ + out = self.relu(self.bn1(self.conv1(x))) + out = self.bn2(self.conv2(out)) + out += self.shortcut(x) + out = self.relu(out) + return out + + +# Full ResNet +class ResNet(nn.Module): + """ResNet + + Args: + block (_type_): _description_ + num_blocks (_type_): _description_ + num_classes (int): Number of classes + kernel_size (int): Kernel size of the convolutional layer + stride (int): Stride of the convolutional layer + *args: Variable length argument list. + **kwargs: Arbitrary keyword arguments. + + Examples: + >>> from zeta.nn.modules.res_net import ResNet + >>> import torch + >>> x = torch.randn(5, 10) + >>> swiglu = ResNet(10, 20) + >>> swiglu(x).shape + torch.Size([5, 10]) + + + """ + + def __init__( + self, + block, + num_blocks, + num_classes: int = 1000, + kernel_size: int = 3, + stride: int = 2, + *args, + **kwargs, + ): + super(ResNet, self).__init__() + self.in_channels = 64 + + self.conv1 = nn.Conv2d( + 3, 64, kernel_size=7, stride=stride, padding=3, bias=False + ) + self.bn1 = nn.BatchNorm2d(64) + self.relu = nn.ReLU(inplace=True) + self.maxpool = nn.MaxPool2d( + kernel_size=kernel_size, stride=stride, padding=1 + ) + self.layer1 = self._make_layer(block, 64, num_blocks[0], stride=stride) + self.layer2 = self._make_layer(block, 128, num_blocks[1], stride=stride) + self.layer3 = self._make_layer(block, 256, num_blocks[2], stride=stride) + self.layer4 = self._make_layer(block, 512, num_blocks[3], stride=stride) + self.avgpool = nn.AdaptiveAvgPool2d((1, 1)) + self.fc = nn.Linear(512 * block.expansion, num_classes) + + def _make_layer(self, block, out_channels, num_blocks, stride): + """Make layer + + Args: + block (_type_): _description_ + out_channels (_type_): _description_ + num_blocks (_type_): _description_ + stride (_type_): _description_ + + Returns: + _type_: _description_ + """ + strides = [stride] + [1] * (num_blocks - 1) + layers = [] + for stride in strides: + layers.append(block(self.in_channels, out_channels, stride)) + self.in_channels = out_channels * block.expansion + return nn.Sequential(*layers) + + def forward(self, x: torch.Tensor): + """Forward + + Args: + x torch.Tensor: Input tensor + """ + x = self.maxpool(self.relu(self.bn1(self.conv1(x)))) + x = self.layer1(x) + x = self.layer2(x) + x = self.layer3(x) + x = self.layer4(x) + x = self.avgpool(x) + x = torch.flatten(x, 1) + x = self.fc(x) + return x + + +# model = ResNet(block=BasicBlock, num_blocks=[2, 2, 2, 2], num_classes=10) + +# x = torch.randn(1, 3, 224, 224) + +# print(model(x).shape) From 52da575df0954a085da9b588f00d97f3cee7726e Mon Sep 17 00:00:00 2001 From: accuracy_maker Date: Mon, 18 Dec 2023 23:39:56 +1100 Subject: [PATCH 138/587] add test files --- tests/rl/test_prioritizedreplybuffer.py | 61 ++++++++++++++++++ .../rl/test_prioritizedsequencereplybuffer.py | 64 +++++++++++++++++++ tests/rl/test_sumtree.py | 56 ++++++++++++++++ 3 files changed, 181 insertions(+) create mode 100644 tests/rl/test_prioritizedreplybuffer.py create mode 100644 tests/rl/test_prioritizedsequencereplybuffer.py create mode 100644 tests/rl/test_sumtree.py diff --git a/tests/rl/test_prioritizedreplybuffer.py b/tests/rl/test_prioritizedreplybuffer.py new file mode 100644 index 00000000..dba5637b --- /dev/null +++ b/tests/rl/test_prioritizedreplybuffer.py @@ -0,0 +1,61 @@ +import pytest +import random +import torch +from zeta.rl.PrioritizedReplayBuffer import PrioritizedReplayBuffer, SumTree # Replace 'your_module' with the actual module where classes are defined + +@pytest.fixture +def replay_buffer(): + state_size = 4 + action_size = 2 + buffer_size = 100 + device = torch.device("cpu") + return PrioritizedReplayBuffer(state_size, action_size, buffer_size, device) + +def test_initialization(replay_buffer): + assert replay_buffer.eps == 1e-2 + assert replay_buffer.alpha == 0.1 + assert replay_buffer.beta == 0.1 + assert replay_buffer.max_priority == 1.0 + assert replay_buffer.count == 0 + assert replay_buffer.real_size == 0 + assert replay_buffer.size == 100 + assert replay_buffer.device == torch.device("cpu") + +def test_add(replay_buffer): + transition = (torch.rand(4), torch.rand(2), 1.0, torch.rand(4), False) + replay_buffer.add(transition) + assert replay_buffer.count == 1 + assert replay_buffer.real_size == 1 + +def test_sample(replay_buffer): + for i in range(10): + transition = (torch.rand(4), torch.rand(2), 1.0, torch.rand(4), False) + replay_buffer.add(transition) + + batch, weights, tree_idxs = replay_buffer.sample(5) + assert len(batch) == 5 + assert len(weights) == 5 + assert len(tree_idxs) == 5 + +def test_update_priorities(replay_buffer): + for i in range(10): + transition = (torch.rand(4), torch.rand(2), 1.0, torch.rand(4), False) + replay_buffer.add(transition) + + batch, weights, tree_idxs = replay_buffer.sample(5) + new_priorities = torch.rand(5) + replay_buffer.update_priorities(tree_idxs, new_priorities) + +def test_sample_with_invalid_batch_size(replay_buffer): + with pytest.raises(AssertionError): + replay_buffer.sample(101) + +def test_add_with_max_size(replay_buffer): + for i in range(100): + transition = (torch.rand(4), torch.rand(2), 1.0, torch.rand(4), False) + replay_buffer.add(transition) + + assert replay_buffer.count == 0 + assert replay_buffer.real_size == 100 + +# Additional tests for edge cases, exceptions, and more scenarios can be added as needed. diff --git a/tests/rl/test_prioritizedsequencereplybuffer.py b/tests/rl/test_prioritizedsequencereplybuffer.py new file mode 100644 index 00000000..9582dc71 --- /dev/null +++ b/tests/rl/test_prioritizedsequencereplybuffer.py @@ -0,0 +1,64 @@ +import pytest +import random +import torch +from zeta.rl.PrioritizedSequenceReplayBuffer import PrioritizedSequenceReplayBuffer, SumTree # Replace 'your_module' with the actual module where classes are defined + +@pytest.fixture +def replay_buffer(): + state_size = 4 + action_size = 2 + buffer_size = 100 + device = torch.device("cpu") + return PrioritizedSequenceReplayBuffer(state_size, action_size, buffer_size, device) + +def test_initialization(replay_buffer): + assert replay_buffer.eps == 1e-5 + assert replay_buffer.alpha == 0.1 + assert replay_buffer.beta == 0.1 + assert replay_buffer.max_priority == 1.0 + assert replay_buffer.decay_window == 5 + assert replay_buffer.decay_coff == 0.4 + assert replay_buffer.pre_priority == 0.7 + assert replay_buffer.count == 0 + assert replay_buffer.real_size == 0 + assert replay_buffer.size == 100 + assert replay_buffer.device == torch.device("cpu") + +def test_add(replay_buffer): + transition = (torch.rand(4), torch.rand(2), 1.0, torch.rand(4), False) + replay_buffer.add(transition) + assert replay_buffer.count == 1 + assert replay_buffer.real_size == 1 + +def test_sample(replay_buffer): + for i in range(10): + transition = (torch.rand(4), torch.rand(2), 1.0, torch.rand(4), False) + replay_buffer.add(transition) + + batch, weights, tree_idxs = replay_buffer.sample(5) + assert len(batch) == 5 + assert len(weights) == 5 + assert len(tree_idxs) == 5 + +def test_update_priorities(replay_buffer): + for i in range(10): + transition = (torch.rand(4), torch.rand(2), 1.0, torch.rand(4), False) + replay_buffer.add(transition) + + batch, weights, tree_idxs = replay_buffer.sample(5) + new_priorities = torch.rand(5) + replay_buffer.update_priorities(tree_idxs, new_priorities) + +def test_sample_with_invalid_batch_size(replay_buffer): + with pytest.raises(AssertionError): + replay_buffer.sample(101) + +def test_add_with_max_size(replay_buffer): + for i in range(100): + transition = (torch.rand(4), torch.rand(2), 1.0, torch.rand(4), False) + replay_buffer.add(transition) + + assert replay_buffer.count == 0 + assert replay_buffer.real_size == 100 + +# Additional tests for edge cases, exceptions, and more scenarios can be added as needed. diff --git a/tests/rl/test_sumtree.py b/tests/rl/test_sumtree.py new file mode 100644 index 00000000..7758f9b8 --- /dev/null +++ b/tests/rl/test_sumtree.py @@ -0,0 +1,56 @@ +import pytest +from zeta.rl.sumtree import SumTree # Replace 'your_module' with the actual module where SumTree is defined + +# Fixture for initializing SumTree instances with a given size +@pytest.fixture +def sum_tree(): + size = 10 # You can change the size as needed + return SumTree(size) + +# Basic tests +def test_initialization(sum_tree): + assert sum_tree.size == 10 + assert sum_tree.count == 0 + assert sum_tree.real_size == 0 + assert sum_tree.total == 0 + +def test_update_and_get(sum_tree): + sum_tree.add(5, "data1") + assert sum_tree.total == 5 + data_idx, priority, data = sum_tree.get(5) + assert data_idx == 0 + assert priority == 5 + assert data == "data1" + +def test_add_overflow(sum_tree): + for i in range(15): + sum_tree.add(i, f"data{i}") + assert sum_tree.count == 5 + assert sum_tree.real_size == 10 + +# Parameterized testing for various scenarios +@pytest.mark.parametrize("values, expected_total", [ + ([1, 2, 3, 4, 5], 15), + ([10, 20, 30, 40, 50], 150), +]) +def test_multiple_updates(sum_tree, values, expected_total): + for value in values: + sum_tree.add(value, None) + assert sum_tree.total == expected_total + +# Exception testing +def test_get_with_invalid_cumsum(sum_tree): + with pytest.raises(AssertionError): + sum_tree.get(20) + +# More tests for specific methods +def test_get_priority(sum_tree): + sum_tree.add(10, "data1") + priority = sum_tree.get_priority(0) + assert priority == 10 + +def test_repr(sum_tree): + expected_repr = f"SumTree(nodes={sum_tree.nodes}, data={sum_tree.data})" + assert repr(sum_tree) == expected_repr + +# More test cases can be added as needed From db54f4bd52fd58b0d903a3a54d9b3228368496ef Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Mon, 18 Dec 2023 16:53:56 +0000 Subject: [PATCH 139/587] Bump rich from 13.5.2 to 13.7.0 Bumps [rich](https://github.com/Textualize/rich) from 13.5.2 to 13.7.0. - [Release notes](https://github.com/Textualize/rich/releases) - [Changelog](https://github.com/Textualize/rich/blob/master/CHANGELOG.md) - [Commits](https://github.com/Textualize/rich/compare/v13.5.2...v13.7.0) --- updated-dependencies: - dependency-name: rich dependency-type: direct:production update-type: version-update:semver-minor ... Signed-off-by: dependabot[bot] --- pyproject.toml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pyproject.toml b/pyproject.toml index 9398f31f..0451e1b3 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -39,7 +39,7 @@ scipy = "1.9.3" beartype = "0.15.0" tiktoken = "0.4.0" tqdm = "4.66.1" -rich = "13.5.2" +rich = "13.7.0" [build-system] requires = ["poetry-core>=1.0.0"] From eee703ad2f58a6c5230d430e18cb5b394fd0827d Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Mon, 18 Dec 2023 16:57:17 +0000 Subject: [PATCH 140/587] Bump torchaudio from 2.1.1 to 2.1.2 Bumps [torchaudio](https://github.com/pytorch/audio) from 2.1.1 to 2.1.2. - [Release notes](https://github.com/pytorch/audio/releases) - [Commits](https://github.com/pytorch/audio/compare/v2.1.1...v2.1.2) --- updated-dependencies: - dependency-name: torchaudio dependency-type: direct:production update-type: version-update:semver-patch ... Signed-off-by: dependabot[bot] --- requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements.txt b/requirements.txt index e36d446c..fa5e98dd 100644 --- a/requirements.txt +++ b/requirements.txt @@ -24,7 +24,7 @@ tiktoken==0.4.0 autopep8 transformers==4.35.0 tqdm==4.66.1 -torchaudio==2.1.1 +torchaudio==2.1.2 mkdocs mkdocs-material mkdocs-glightbox From 464edd3f3debeac49de7407da01802130eb997ef Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Mon, 18 Dec 2023 17:50:30 +0000 Subject: [PATCH 141/587] Bump tiktoken from 0.4.0 to 0.5.2 Bumps [tiktoken](https://github.com/openai/tiktoken) from 0.4.0 to 0.5.2. - [Release notes](https://github.com/openai/tiktoken/releases) - [Changelog](https://github.com/openai/tiktoken/blob/main/CHANGELOG.md) - [Commits](https://github.com/openai/tiktoken/compare/0.4.0...0.5.2) --- updated-dependencies: - dependency-name: tiktoken dependency-type: direct:production update-type: version-update:semver-minor ... Signed-off-by: dependabot[bot] --- pyproject.toml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pyproject.toml b/pyproject.toml index 0451e1b3..0466ad29 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -37,7 +37,7 @@ vector-quantize-pytorch = "1.12.0" tokenmonster = "1.1.12" scipy = "1.9.3" beartype = "0.15.0" -tiktoken = "0.4.0" +tiktoken = "0.5.2" tqdm = "4.66.1" rich = "13.7.0" From f05b2eeb3e376508ad95e0fdece345627c8820f8 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Mon, 18 Dec 2023 19:52:20 +0000 Subject: [PATCH 142/587] Bump beartype from 0.15.0 to 0.16.4 Bumps [beartype](https://github.com/beartype/beartype) from 0.15.0 to 0.16.4. - [Release notes](https://github.com/beartype/beartype/releases) - [Changelog](https://github.com/beartype/beartype/blob/main/doc/RELEASE.rst) - [Commits](https://github.com/beartype/beartype/compare/v0.15.0...v0.16.4) --- updated-dependencies: - dependency-name: beartype dependency-type: direct:production update-type: version-update:semver-minor ... Signed-off-by: dependabot[bot] --- pyproject.toml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pyproject.toml b/pyproject.toml index 0466ad29..b70ed317 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -36,7 +36,7 @@ colt5-attention = "0.10.19" vector-quantize-pytorch = "1.12.0" tokenmonster = "1.1.12" scipy = "1.9.3" -beartype = "0.15.0" +beartype = "0.16.4" tiktoken = "0.5.2" tqdm = "4.66.1" rich = "13.7.0" From 42679380a8fbef68db81ec819e059f2a4740574e Mon Sep 17 00:00:00 2001 From: Kye Date: Mon, 18 Dec 2023 15:39:31 -0500 Subject: [PATCH 143/587] [FEATS][module_device] [save_load_wrapper] [README][niva] --- README.md | 23 ++++++ tests/utils/save_load_wrapper.py | 71 +++++++++++++++++++ tests/utils/test_module_device.py | 83 ++++++++++++++++++++++ zeta/utils/module_device.py | 59 ++++++++++++++++ zeta/utils/save_load_wrapper.py | 113 ++++++++++++++++++++++++++++++ 5 files changed, 349 insertions(+) create mode 100644 tests/utils/save_load_wrapper.py create mode 100644 tests/utils/test_module_device.py create mode 100644 zeta/utils/module_device.py create mode 100644 zeta/utils/save_load_wrapper.py diff --git a/README.md b/README.md index 705f3031..5c388e63 100644 --- a/README.md +++ b/README.md @@ -313,6 +313,29 @@ output = vision_embedding(input_image) # The output now contains patch embeddings, ready for input to a transformer model ``` + +### `niva` +- Niva focuses on weights of certain layers (specified by quantize_layers). Ideal for models where runtime activation is variable. 👁️ Example Layers: nn.Embedding, nn.LSTM. + +```python +import torch +from zeta import niva + +# Load a pre-trained model +model = YourModelClass() + +# Quantize the model dynamically, specifying layers to quantize +niva( + model=model, + model_path="path_to_pretrained_model_weights.pt", + output_path="quantized_model.pt", + quant_type="dynamic", + quantize_layers=[nn.Linear, nn.Conv2d], + dtype=torch.qint8 +) + +``` + # Documentation [Click here for the documentation, it's at zeta.apac.ai](https://zeta.apac.ai) diff --git a/tests/utils/save_load_wrapper.py b/tests/utils/save_load_wrapper.py new file mode 100644 index 00000000..c5fddf03 --- /dev/null +++ b/tests/utils/save_load_wrapper.py @@ -0,0 +1,71 @@ +import pytest +import torch +from torch.nn import Module +from zeta.utils.save_load_wrapper import save_load + + +@save_load() +class DummyModule(Module): + def __init__(self, x): + super().__init__() + self.x = torch.nn.Parameter(torch.tensor(x)) + + +def test_save_load_init(): + module = DummyModule(5) + assert isinstance(module, DummyModule) + + +def test_save_load_save(tmp_path): + module = DummyModule(5) + module.save(tmp_path / "model.pth") + assert (tmp_path / "model.pth").exists() + + +def test_save_load_load(tmp_path): + module = DummyModule(5) + module.save(tmp_path / "model.pth") + loaded_module = DummyModule(0) + loaded_module.load(tmp_path / "model.pth") + assert loaded_module.x.item() == 5 + + +def test_save_load_init_and_load(tmp_path): + module = DummyModule(5) + module.save(tmp_path / "model.pth") + loaded_module = DummyModule.init_and_load(tmp_path / "model.pth") + assert loaded_module.x.item() == 5 + + +def test_save_load_save_overwrite(tmp_path): + module = DummyModule(5) + module.save(tmp_path / "model.pth") + with pytest.raises(AssertionError): + module.save(tmp_path / "model.pth", overwrite=False) + + +def test_save_load_load_nonexistent(tmp_path): + module = DummyModule(5) + with pytest.raises(AssertionError): + module.load(tmp_path / "model.pth") + + +def test_save_load_init_and_load_nonexistent(tmp_path): + with pytest.raises(AssertionError): + DummyModule.init_and_load(tmp_path / "model.pth") + + +def test_save_load_partial_load(tmp_path): + @save_load(partial_load=True) + class PartialModule(Module): + def __init__(self, x, y): + super().__init__() + self.x = torch.nn.Parameter(torch.tensor(x)) + self.y = torch.nn.Parameter(torch.tensor(y)) + + module = PartialModule(5, 10) + module.save(tmp_path / "model.pth") + loaded_module = PartialModule(0, 0) + loaded_module.load(tmp_path / "model.pth") + assert loaded_module.x.item() == 5 + assert loaded_module.y.item() == 0 diff --git a/tests/utils/test_module_device.py b/tests/utils/test_module_device.py new file mode 100644 index 00000000..0fd00af4 --- /dev/null +++ b/tests/utils/test_module_device.py @@ -0,0 +1,83 @@ +import pytest +import torch +from torch.nn import Module +from zeta.utils.module_device import module_device + + +@module_device() +class DummyModule(Module): + def __init__(self, x): + super().__init__() + self.x = torch.nn.Parameter(torch.tensor(x)) + + +def test_module_device_init(): + module = DummyModule(5) + assert isinstance(module, DummyModule) + + +def test_module_device_device_property(): + module = DummyModule(5) + assert module.device == torch.device("cpu") + + +def test_module_device_to(): + module = DummyModule(5) + module.to(torch.device("cpu")) + assert module.device == torch.device("cpu") + + +def test_module_device_to_cuda(): + if torch.cuda.is_available(): + module = DummyModule(5) + module.to(torch.device("cuda")) + assert module.device == torch.device("cuda") + + +def test_module_device_to_cuda_compatibility_check(): + if not torch.cuda.is_available(): + with pytest.raises(RuntimeError): + + @module_device(compatibility_check=True) + class IncompatibleModule(Module): + def __init__(self, x): + super().__init__() + self.x = torch.nn.Parameter(torch.tensor(x)) + + module = IncompatibleModule(5) + module.to(torch.device("cuda")) + + +def test_module_device_device_property_name(): + @module_device(device_property_name="custom_device") + class CustomDeviceModule(Module): + def __init__(self, x): + super().__init__() + self.x = torch.nn.Parameter(torch.tensor(x)) + + module = CustomDeviceModule(5) + assert module.custom_device == torch.device("cpu") + + +def test_module_device_not_module(): + with pytest.raises(AssertionError): + + @module_device() + class NotAModule: + pass + + +def test_module_device_multiple_devices(): + if torch.cuda.is_available(): + + @module_device() + class MultiDeviceModule(Module): + def __init__(self, x): + super().__init__() + self.x = torch.nn.Parameter(torch.tensor(x)) + self.y = torch.nn.Parameter( + torch.tensor(x), device=torch.device("cuda") + ) + + module = MultiDeviceModule(5) + assert len(module.device) > 1 diff --git a/zeta/utils/module_device.py b/zeta/utils/module_device.py new file mode 100644 index 00000000..4ee08881 --- /dev/null +++ b/zeta/utils/module_device.py @@ -0,0 +1,59 @@ +import torch +from torch.nn import Module + + +def module_device( + device_property_name: str = "device", + on_device_transfer=None, + compatibility_check: bool = False, +): + """Module device decorator. + + Args: + device_property_name (str, optional): _description_. Defaults to "device". + on_device_transfer (_type_, optional): _description_. Defaults to None. + compatibility_check (bool, optional): _description_. Defaults to False. + """ + + def decorator(klass): + assert issubclass( + klass, Module + ), "should decorate a subclass of torch.nn.Module" + + _orig_init = klass.__init__ + _orig_to = klass.to + + def __init__(self, *args, **kwargs): + _orig_init(self, *args, **kwargs) + self.register_buffer("_dummy", torch.tensor(0), persistent=False) + + def __to(self, device, *args, **kwargs): + if ( + compatibility_check + and not torch.cuda.is_available() + and "cuda" in str(device) + ): + raise RuntimeError( + "CUDA is not available for this device transfer." + ) + result = _orig_to(self, device, *args, **kwargs) + if on_device_transfer: + on_device_transfer(self, device) + return result + + @property + def _device_property(self): + devices = {p.device for p in self.parameters()} | { + b.device for b in self.buffers() + } + if len(devices) > 1: + return devices + return self._dummy.device + + klass.__init__ = __init__ + klass.to = __to + setattr(klass, device_property_name, _device_property) + + return klass + + return decorator diff --git a/zeta/utils/save_load_wrapper.py b/zeta/utils/save_load_wrapper.py new file mode 100644 index 00000000..133114ea --- /dev/null +++ b/zeta/utils/save_load_wrapper.py @@ -0,0 +1,113 @@ +import pickle +from pathlib import Path +import torch +from beartype import beartype +from beartype.typing import Optional, Callable +from packaging import version +from torch.nn import Module + + +# helpers +def exists(v): + return v is not None + + +@beartype +def save_load( + save_method_name="save", + load_method_name="load", + config_instance_var_name="_config", + init_and_load_classmethod_name="init_and_load", + version: Optional[str] = None, + pre_save_hook: Optional[Callable[[Module], None]] = None, + post_load_hook: Optional[Callable[[Module], None]] = None, + compress: Optional[bool] = False, + partial_load: Optional[bool] = False, + *args, + **kwargs, +): + """Base decorator for save and load methods for torch.nn.Module subclasses. + + Args: + save_method_name (str, optional): _description_. Defaults to "save". + load_method_name (str, optional): _description_. Defaults to "load". + config_instance_var_name (str, optional): _description_. Defaults to "_config". + init_and_load_classmethod_name (str, optional): _description_. Defaults to "init_and_load". + version (Optional[str], optional): _description_. Defaults to None. + pre_save_hook (Optional[Callable[[Module], None]], optional): _description_. Defaults to None. + post_load_hook (Optional[Callable[[Module], None]], optional): _description_. Defaults to None. + compress (Optional[bool], optional): _description_. Defaults to False. + partial_load (Optional[bool], optional): _description_. Defaults to False. + """ + + def _save_load(klass): + assert issubclass( + klass, Module + ), "save_load should decorate a subclass of torch.nn.Module" + + _orig_init = klass.__init__ + + def __init__(self, *args, **kwargs): + _config = pickle.dumps((args, kwargs)) + setattr(self, config_instance_var_name, _config) + _orig_init(self, *args, **kwargs) + + def _save(self, path, overwrite=True): + if pre_save_hook: + pre_save_hook(self) + + path = Path(path) + assert overwrite or not path.exists() + pkg = dict( + model=self.state_dict(), + config=getattr(self, config_instance_var_name), + version=version, + ) + torch.save(pkg, str(path), _use_new_zipfile_serialization=compress) + + def _load(self, path, strict=True): + path = Path(path) + assert path.exists() + pkg = torch.load(str(path), map_location="cpu") + + if ( + exists(version) + and exists(pkg["version"]) + and version.parse(version) != version.parse(pkg["version"]) + ): + self.print(f'loading saved model at version {pkg["version"]},') + + model_dict = self.state_dict() + if partial_load: + model_dict.update(pkg["model"]) + self.load_state_dict(model_dict, strict=strict) + else: + self.load_state_dict(pkg["model"], strict=strict) + + if post_load_hook: + post_load_hook(self) + + @classmethod + def _init_and_load_from(cls, path, strict=True): + path = Path(path) + assert path.exists() + pkg = torch.load(str(path), map_location="cpu") + assert ( + "config" in pkg + ), "model configs were not found in this saved checkpoint" + + config = pickle.loads(pkg["config"]) + args, kwargs = config + model = cls(*args, **kwargs) + + _load(model, path, strict=strict) + return model + + klass.__init__ = __init__ + setattr(klass, save_method_name, _save) + setattr(klass, load_method_name, _load) + setattr(klass, init_and_load_classmethod_name, _init_and_load_from) + + return klass + + return _save_load From 865b2c3ee6cb7d4b27a183a3ffc16969c76d7a4c Mon Sep 17 00:00:00 2001 From: Kye Date: Mon, 18 Dec 2023 15:48:00 -0500 Subject: [PATCH 144/587] [DOCS] [save_load] [module_device] --- docs/zeta/utils/module_device.md | 133 +++++++++++++++++++ docs/zeta/utils/save_load_wrapper.md | 183 +++++++++++++++++++++++++++ mkdocs.yml | 4 +- 3 files changed, 319 insertions(+), 1 deletion(-) create mode 100644 docs/zeta/utils/module_device.md create mode 100644 docs/zeta/utils/save_load_wrapper.md diff --git a/docs/zeta/utils/module_device.md b/docs/zeta/utils/module_device.md new file mode 100644 index 00000000..f2b616c0 --- /dev/null +++ b/docs/zeta/utils/module_device.md @@ -0,0 +1,133 @@ +# Module Documentation: `module_device` + +## Overview + +The `module_device` module provides a powerful decorator for PyTorch neural network modules that allows you to manage and control the device on which a module and its associated parameters reside. This decorator simplifies the management of device transfers, making it easier to ensure your model runs on the desired hardware. + +This documentation will guide you through the `module_device` decorator's architecture, purpose, functions, and usage examples. You'll learn how to effectively use this decorator to control the device placement of your PyTorch modules. + +## Table of Contents + +1. [Installation](#installation) +2. [Architecture](#architecture) +3. [Purpose](#purpose) +4. [Decorator: module_device](#decorator-module_device) + - [Parameters](#parameters) + - [Usage Examples](#usage-examples) + - [Basic Usage](#basic-usage) + - [Custom Device Property Name](#custom-device-property-name) + - [On Device Transfer Callback](#on-device-transfer-callback) +5. [Additional Information](#additional-information) +6. [References](#references) + +--- + +## 1. Installation + +The `module_device` decorator is a Python code snippet that can be directly incorporated into your project without the need for separate installation. + +## 2. Architecture + +The `module_device` decorator is a Python decorator that can be applied to subclasses of PyTorch's `nn.Module`. It adds device management capabilities to your modules by providing control over the device on which a module and its parameters reside. + +## 3. Purpose + +The primary purpose of the `module_device` decorator is to simplify the management of device transfers for PyTorch neural network modules. It allows you to specify the target device, handle compatibility checks, and execute callbacks when transferring a module to a different device. + +## 4. Decorator: module_device + +The `module_device` decorator provides the following functionality: + +- Device management: Control the device on which a module and its parameters reside. +- Custom device property name: Define a custom property name for accessing the module's current device. +- On device transfer callback: Execute a custom callback when transferring a module to a different device. + +### Parameters + +The `module_device` decorator accepts the following parameters: + +- `device_property_name` (str, optional): The name of the property that will be used to access the module's current device. Defaults to "device". +- `on_device_transfer` (Callable, optional): A callback function that is executed when transferring the module to a different device. Defaults to None. +- `compatibility_check` (bool, optional): Enable or disable compatibility checks for device transfers. Defaults to False. + +### Usage Examples + +#### Basic Usage + +Here's a basic example of using the `module_device` decorator to manage the device of a PyTorch module: + +```python +import torch +from torch.nn import Module +from zeta.utils import module_device + +@module_device() +class MyModule(Module): + def __init__(self): + super(MyModule, self).__init__() + self.fc = torch.nn.Linear(10, 5) + +# Create an instance of MyModule +my_model = MyModule() + +# Access the device property +print(my_model.device) # This will print the device of the module +``` + +#### Custom Device Property Name + +You can define a custom device property name when using the `module_device` decorator: + +```python +import torch +from torch.nn import Module +from zeta.utils import module_device + +@module_device(device_property_name="custom_device") +class CustomModule(Module): + def __init__(self): + super(CustomModule, self).__init__() + self.fc = torch.nn.Linear(10, 5) + +# Create an instance of CustomModule +custom_model = CustomModule() + +# Access the custom device property +print(custom_model.custom_device) +``` + +#### On Device Transfer Callback + +You can specify a callback function to be executed when transferring a module to a different device: + +```python +import torch +from torch.nn import Module +from zeta.utils import module_device + +def on_device_transfer_callback(module, device): + print(f"Transferred to {device}") + +@module_device(on_device_transfer=on_device_transfer_callback) +class CallbackModule(Module): + def __init__(self): + super(CallbackModule, self).__init__() + self.fc = torch.nn.Linear(10, 5) + +# Create an instance of CallbackModule +callback_model = CallbackModule() + +# Transfer the model to a different device +callback_model.to(torch.device("cuda:0")) +``` + +## 5. Additional Information + +- The `module_device` decorator simplifies device management for PyTorch modules, allowing you to focus on your model's functionality. +- Compatibility checks can be enabled to ensure that device transfers are compatible with the available hardware. +- Callbacks provide a way to execute custom actions when transferring a module to a different device. + +## 6. References + +For more information on PyTorch and device management, refer to the official PyTorch documentation: [PyTorch Device](https://pytorch.org/docs/stable/tensor_attributes.html#torch.torch.device). + diff --git a/docs/zeta/utils/save_load_wrapper.md b/docs/zeta/utils/save_load_wrapper.md new file mode 100644 index 00000000..14a7b594 --- /dev/null +++ b/docs/zeta/utils/save_load_wrapper.md @@ -0,0 +1,183 @@ +# Module Documentation: `save_load` + +## Overview + +The `save_load` module provides a powerful decorator for PyTorch neural network modules that simplifies the process of saving and loading model checkpoints. This decorator is designed to enhance the ease and flexibility of managing model checkpoints, making it more efficient to work with PyTorch models during development and production. + +This documentation will guide you through the `save_load` decorator's architecture, purpose, functions, and usage examples. You'll learn how to effectively use this decorator to save and load model checkpoints, manage configuration settings, and handle version compatibility. + +## Table of Contents + +1. [Installation](#installation) +2. [Architecture](#architecture) +3. [Purpose](#purpose) +4. [Decorator: save_load](#decorator-save_load) + - [Parameters](#parameters) + - [Usage Examples](#usage-examples) + - [Basic Usage](#basic-usage) + - [Custom Methods and Hooks](#custom-methods-and-hooks) + - [Partial Loading](#partial-loading) + - [Version Compatibility](#version-compatibility) +5. [Additional Information](#additional-information) +6. [References](#references) + +--- + +## 1. Installation + +The `save_load` decorator is a Python code snippet that can be directly incorporated into your project without the need for separate installation. + +## 2. Architecture + +The `save_load` decorator is a Python decorator that can be applied to subclasses of PyTorch's `nn.Module`. It enhances the module with methods for saving and loading model checkpoints, including options for configuration management, version compatibility, and custom hooks. + +## 3. Purpose + +The primary purpose of the `save_load` decorator is to streamline the process of saving and loading PyTorch model checkpoints. It offers the following benefits: + +- Simplified checkpoint management: Provides easy-to-use methods for saving and loading model states. +- Configuration preservation: Allows for the preservation and retrieval of the module's configuration settings. +- Version compatibility: Offers mechanisms to handle version compatibility between saved checkpoints. +- Customization: Supports custom hooks that can be executed before and after saving or loading. + +## 4. Decorator: save_load + +The `save_load` decorator provides the following functionality: + +- Saving and loading model checkpoints. +- Configuration preservation: Saving and retrieving configuration settings. +- Version compatibility: Checking and handling version mismatches. +- Customization: Executing custom hooks before and after saving or loading. + +### Parameters + +The `save_load` decorator accepts the following parameters: + +- `save_method_name` (str, optional): The name of the method used for saving the model checkpoint. Defaults to "save". +- `load_method_name` (str, optional): The name of the method used for loading the model checkpoint. Defaults to "load". +- `config_instance_var_name` (str, optional): The name of the instance variable used to store the configuration. Defaults to "_config". +- `init_and_load_classmethod_name` (str, optional): The name of the class method used to initialize and load a model from a checkpoint. Defaults to "init_and_load". +- `version` (Optional[str], optional): The version of the saved checkpoint. Defaults to None. +- `pre_save_hook` (Optional[Callable[[Module], None]], optional): A callback function executed before saving the model checkpoint. Defaults to None. +- `post_load_hook` (Optional[Callable[[Module], None]], optional): A callback function executed after loading the model checkpoint. Defaults to None. +- `compress` (Optional[bool], optional): Enable compression when saving checkpoints. Defaults to False. +- `partial_load` (Optional[bool], optional): Enable partial loading of the model checkpoint. Defaults to False. + +### Usage Examples + +#### Basic Usage + +Here's a basic example of using the `save_load` decorator to save and load a PyTorch model checkpoint: + +```python +import torch +from torch.nn import Module +from zeta.utils import save_load + +@save_load() +class MyModel(Module): + def __init__(self): + super(MyModel, self).__init__() + self.fc = torch.nn.Linear(10, 5) + +# Create an instance of MyModel +my_model = MyModel() + +# Save the model checkpoint +my_model.save("my_model.pth") + +# Load the model checkpoint +loaded_model = MyModel.load("my_model.pth") +``` + +#### Custom Methods and Hooks + +You can define custom method and hook names when using the `save_load` decorator: + +```python +import torch +from torch.nn import Module +from zeta.utils import save_load + +@save_load( + save_method_name="custom_save", + load_method_name="custom_load", + pre_save_hook=my_pre_save_hook, + post_load_hook=my_post_load_hook +) +class CustomModel(Module): + def __init__(self): + super(CustomModel, self).__init__() + self.fc = torch.nn.Linear(10, 5) + +# Create an instance of CustomModel +custom_model = CustomModel() + +# Custom save and load +custom_model.custom_save("custom_model.pth") +loaded_custom_model = CustomModel.custom_load("custom_model.pth") +``` + +#### Partial Loading + +Enable partial loading to update only specific parts of the model checkpoint: + +```python +import torch +from torch.nn import Module +from zeta.utils import save_load + +@save_load(partial_load=True) +class PartialModel(Module): + def __init__(self): + super(PartialModel, self).__init__() + self.fc = torch.nn.Linear(10, 5) + +# Create an instance of PartialModel +partial_model = PartialModel() + +# Save the model checkpoint +partial_model.save("partial_model.pth") + +# Load only the updated part of the model checkpoint +loaded_partial_model = PartialModel.load("partial_model.pth") +``` + +#### Version Compatibility + +Handle version compatibility when loading saved checkpoints: + +```python +import torch +from torch.nn import Module +from zeta.utils import save_load + +@save_load(version="1.0") +class VersionedModel(Module): + def __init__(self): + super(VersionedModel, self).__init__() + self.fc = torch.nn.Linear(10, 5) + +# Create an instance of VersionedModel +versioned_model = VersionedModel() + +# Save the model checkpoint +versioned_model.save("versioned_model.pth") + +# Load the model checkpoint with version compatibility check +loaded_versioned_model = VersionedModel.load("versioned_model.pth") +``` + +## 5. Additional Information + +- The `save_load` decorator simplifies the process of saving and loading model checkpoints for PyTorch modules. +- Configuration settings can be preserved and retrieved along with the model checkpoint. +- Version compatibility checks help manage saved checkpoints with different versions. +- Custom hooks can be used to execute custom actions before and after saving or loading checkpoints. + +## 6. References + +For more information on PyTorch and checkpoint management, refer to the official PyTorch documentation: [PyTorch + + Saving and Loading Models](https://pytorch.org/tutorials/beginner/saving_loading_models.html). + diff --git a/mkdocs.yml b/mkdocs.yml index 02d05c65..b03f045d 100644 --- a/mkdocs.yml +++ b/mkdocs.yml @@ -138,6 +138,8 @@ nav: - zeta.utils: - main: "zeta/utils/main.md" - track_cuda_memory_usage: "zeta/utils/track_cuda_memory.md" + - module_device: "zeta/utils/module_device.md" + - save_load: "zeta/utils/save_load_wrapper.md" - zeta.ops: - main: "zeta/ops/main.md" - softmaxes: "zeta/ops/softmaxes.md" @@ -159,6 +161,6 @@ nav: - Product: - Overview: "zeta/product/product_ideas.md" - Zetahub: "zeta/product/zetahub.md" - - Growth: "corporate/growth.md" + - Growth: "corporate/growth.md" - Blog: - Introduction: "blog/introduction_to_zeta.md" \ No newline at end of file From 3b7ad21ac4fc6a7ffb8ebd2419c530bf0c386856 Mon Sep 17 00:00:00 2001 From: Eternal Reclaimer <98760976+kyegomez@users.noreply.github.com> Date: Mon, 18 Dec 2023 18:52:33 -0500 Subject: [PATCH 145/587] Create terraform.yml --- .github/workflows/terraform.yml | 93 +++++++++++++++++++++++++++++++++ 1 file changed, 93 insertions(+) create mode 100644 .github/workflows/terraform.yml diff --git a/.github/workflows/terraform.yml b/.github/workflows/terraform.yml new file mode 100644 index 00000000..76a1fbf1 --- /dev/null +++ b/.github/workflows/terraform.yml @@ -0,0 +1,93 @@ +# This workflow installs the latest version of Terraform CLI and configures the Terraform CLI configuration file +# with an API token for Terraform Cloud (app.terraform.io). On pull request events, this workflow will run +# `terraform init`, `terraform fmt`, and `terraform plan` (speculative plan via Terraform Cloud). On push events +# to the "master" branch, `terraform apply` will be executed. +# +# Documentation for `hashicorp/setup-terraform` is located here: https://github.com/hashicorp/setup-terraform +# +# To use this workflow, you will need to complete the following setup steps. +# +# 1. Create a `main.tf` file in the root of this repository with the `remote` backend and one or more resources defined. +# Example `main.tf`: +# # The configuration for the `remote` backend. +# terraform { +# backend "remote" { +# # The name of your Terraform Cloud organization. +# organization = "example-organization" +# +# # The name of the Terraform Cloud workspace to store Terraform state files in. +# workspaces { +# name = "example-workspace" +# } +# } +# } +# +# # An example resource that does nothing. +# resource "null_resource" "example" { +# triggers = { +# value = "A example resource that does nothing!" +# } +# } +# +# +# 2. Generate a Terraform Cloud user API token and store it as a GitHub secret (e.g. TF_API_TOKEN) on this repository. +# Documentation: +# - https://www.terraform.io/docs/cloud/users-teams-organizations/api-tokens.html +# - https://help.github.com/en/actions/configuring-and-managing-workflows/creating-and-storing-encrypted-secrets +# +# 3. Reference the GitHub secret in step using the `hashicorp/setup-terraform` GitHub Action. +# Example: +# - name: Setup Terraform +# uses: hashicorp/setup-terraform@v1 +# with: +# cli_config_credentials_token: ${{ secrets.TF_API_TOKEN }} + +name: 'Terraform' + +on: + push: + branches: [ "master" ] + pull_request: + +permissions: + contents: read + +jobs: + terraform: + name: 'Terraform' + runs-on: ubuntu-latest + environment: production + + # Use the Bash shell regardless whether the GitHub Actions runner is ubuntu-latest, macos-latest, or windows-latest + defaults: + run: + shell: bash + + steps: + # Checkout the repository to the GitHub Actions runner + - name: Checkout + uses: actions/checkout@v3 + + # Install the latest version of Terraform CLI and configure the Terraform CLI configuration file with a Terraform Cloud user API token + - name: Setup Terraform + uses: hashicorp/setup-terraform@v1 + with: + cli_config_credentials_token: ${{ secrets.TF_API_TOKEN }} + + # Initialize a new or existing Terraform working directory by creating initial files, loading any remote state, downloading modules, etc. + - name: Terraform Init + run: terraform init + + # Checks that all Terraform configuration files adhere to a canonical format + - name: Terraform Format + run: terraform fmt -check + + # Generates an execution plan for Terraform + - name: Terraform Plan + run: terraform plan -input=false + + # On push to "master", build or change infrastructure according to Terraform configuration files + # Note: It is recommended to set up a required "strict" status check in your repository for "Terraform Cloud". See the documentation on "strict" required status checks for more information: https://help.github.com/en/github/administering-a-repository/types-of-required-status-checks + - name: Terraform Apply + if: github.ref == 'refs/heads/"master"' && github.event_name == 'push' + run: terraform apply -auto-approve -input=false From ec6e4740c1f30ec72d7470c41ef8f3793ccbc4db Mon Sep 17 00:00:00 2001 From: Eternal Reclaimer <98760976+kyegomez@users.noreply.github.com> Date: Mon, 18 Dec 2023 18:52:37 -0500 Subject: [PATCH 146/587] Create codeql.yml --- .github/workflows/codeql.yml | 81 ++++++++++++++++++++++++++++++++++++ 1 file changed, 81 insertions(+) create mode 100644 .github/workflows/codeql.yml diff --git a/.github/workflows/codeql.yml b/.github/workflows/codeql.yml new file mode 100644 index 00000000..6ddde5c5 --- /dev/null +++ b/.github/workflows/codeql.yml @@ -0,0 +1,81 @@ +# For most projects, this workflow file will not need changing; you simply need +# to commit it to your repository. +# +# You may wish to alter this file to override the set of languages analyzed, +# or to provide custom queries or build logic. +# +# ******** NOTE ******** +# We have attempted to detect the languages in your repository. Please check +# the `language` matrix defined below to confirm you have the correct set of +# supported CodeQL languages. +# +name: "CodeQL" + +on: + push: + branches: [ "master" ] + pull_request: + branches: [ "master" ] + schedule: + - cron: '38 20 * * 4' + +jobs: + analyze: + name: Analyze + # Runner size impacts CodeQL analysis time. To learn more, please see: + # - https://gh.io/recommended-hardware-resources-for-running-codeql + # - https://gh.io/supported-runners-and-hardware-resources + # - https://gh.io/using-larger-runners + # Consider using larger runners for possible analysis time improvements. + runs-on: ${{ (matrix.language == 'swift' && 'macos-latest') || 'ubuntu-latest' }} + timeout-minutes: ${{ (matrix.language == 'swift' && 120) || 360 }} + permissions: + actions: read + contents: read + security-events: write + + strategy: + fail-fast: false + matrix: + language: [ 'python' ] + # CodeQL supports [ 'c-cpp', 'csharp', 'go', 'java-kotlin', 'javascript-typescript', 'python', 'ruby', 'swift' ] + # Use only 'java-kotlin' to analyze code written in Java, Kotlin or both + # Use only 'javascript-typescript' to analyze code written in JavaScript, TypeScript or both + # Learn more about CodeQL language support at https://aka.ms/codeql-docs/language-support + + steps: + - name: Checkout repository + uses: actions/checkout@v4 + + # Initializes the CodeQL tools for scanning. + - name: Initialize CodeQL + uses: github/codeql-action/init@v3 + with: + languages: ${{ matrix.language }} + # If you wish to specify custom queries, you can do so here or in a config file. + # By default, queries listed here will override any specified in a config file. + # Prefix the list here with "+" to use these queries and those in the config file. + + # For more details on CodeQL's query packs, refer to: https://docs.github.com/en/code-security/code-scanning/automatically-scanning-your-code-for-vulnerabilities-and-errors/configuring-code-scanning#using-queries-in-ql-packs + # queries: security-extended,security-and-quality + + + # Autobuild attempts to build any compiled languages (C/C++, C#, Go, Java, or Swift). + # If this step fails, then you should remove it and run the build manually (see below) + - name: Autobuild + uses: github/codeql-action/autobuild@v3 + + # ℹ️ Command-line programs to run using the OS shell. + # 📚 See https://docs.github.com/en/actions/using-workflows/workflow-syntax-for-github-actions#jobsjob_idstepsrun + + # If the Autobuild fails above, remove it and uncomment the following three lines. + # modify them (or add more) to build your code if your project, please refer to the EXAMPLE below for guidance. + + # - run: | + # echo "Run, Build Application using script" + # ./location_of_script_within_repo/buildscript.sh + + - name: Perform CodeQL Analysis + uses: github/codeql-action/analyze@v3 + with: + category: "/language:${{matrix.language}}" From 2345ca73d6c25603ff75a35032e0f52f3459552f Mon Sep 17 00:00:00 2001 From: Eternal Reclaimer <98760976+kyegomez@users.noreply.github.com> Date: Mon, 18 Dec 2023 18:52:41 -0500 Subject: [PATCH 147/587] Create codacy.yml --- .github/workflows/codacy.yml | 61 ++++++++++++++++++++++++++++++++++++ 1 file changed, 61 insertions(+) create mode 100644 .github/workflows/codacy.yml diff --git a/.github/workflows/codacy.yml b/.github/workflows/codacy.yml new file mode 100644 index 00000000..1a8c4e00 --- /dev/null +++ b/.github/workflows/codacy.yml @@ -0,0 +1,61 @@ +# This workflow uses actions that are not certified by GitHub. +# They are provided by a third-party and are governed by +# separate terms of service, privacy policy, and support +# documentation. + +# This workflow checks out code, performs a Codacy security scan +# and integrates the results with the +# GitHub Advanced Security code scanning feature. For more information on +# the Codacy security scan action usage and parameters, see +# https://github.com/codacy/codacy-analysis-cli-action. +# For more information on Codacy Analysis CLI in general, see +# https://github.com/codacy/codacy-analysis-cli. + +name: Codacy Security Scan + +on: + push: + branches: [ "master" ] + pull_request: + # The branches below must be a subset of the branches above + branches: [ "master" ] + schedule: + - cron: '37 4 * * 0' + +permissions: + contents: read + +jobs: + codacy-security-scan: + permissions: + contents: read # for actions/checkout to fetch code + security-events: write # for github/codeql-action/upload-sarif to upload SARIF results + actions: read # only required for a private repository by github/codeql-action/upload-sarif to get the Action run status + name: Codacy Security Scan + runs-on: ubuntu-latest + steps: + # Checkout the repository to the GitHub Actions runner + - name: Checkout code + uses: actions/checkout@v3 + + # Execute Codacy Analysis CLI and generate a SARIF output with the security issues identified during the analysis + - name: Run Codacy Analysis CLI + uses: codacy/codacy-analysis-cli-action@d840f886c4bd4edc059706d09c6a1586111c540b + with: + # Check https://github.com/codacy/codacy-analysis-cli#project-token to get your project token from your Codacy repository + # You can also omit the token and run the tools that support default configurations + project-token: ${{ secrets.CODACY_PROJECT_TOKEN }} + verbose: true + output: results.sarif + format: sarif + # Adjust severity of non-security issues + gh-code-scanning-compat: true + # Force 0 exit code to allow SARIF file generation + # This will handover control about PR rejection to the GitHub side + max-allowed-issues: 2147483647 + + # Upload the SARIF file generated in the previous step + - name: Upload SARIF results file + uses: github/codeql-action/upload-sarif@v2 + with: + sarif_file: results.sarif From 1651f726cd5b17868b940517eb4bc3cd43edb1b9 Mon Sep 17 00:00:00 2001 From: Eternal Reclaimer <98760976+kyegomez@users.noreply.github.com> Date: Mon, 18 Dec 2023 18:52:45 -0500 Subject: [PATCH 148/587] Create python-package.yml --- .github/workflows/python-package.yml | 40 ++++++++++++++++++++++++++++ 1 file changed, 40 insertions(+) create mode 100644 .github/workflows/python-package.yml diff --git a/.github/workflows/python-package.yml b/.github/workflows/python-package.yml new file mode 100644 index 00000000..14a4e65b --- /dev/null +++ b/.github/workflows/python-package.yml @@ -0,0 +1,40 @@ +# This workflow will install Python dependencies, run tests and lint with a variety of Python versions +# For more information see: https://docs.github.com/en/actions/automating-builds-and-tests/building-and-testing-python + +name: Python package + +on: + push: + branches: [ "master" ] + pull_request: + branches: [ "master" ] + +jobs: + build: + + runs-on: ubuntu-latest + strategy: + fail-fast: false + matrix: + python-version: ["3.9", "3.10", "3.11"] + + steps: + - uses: actions/checkout@v3 + - name: Set up Python ${{ matrix.python-version }} + uses: actions/setup-python@v3 + with: + python-version: ${{ matrix.python-version }} + - name: Install dependencies + run: | + python -m pip install --upgrade pip + python -m pip install flake8 pytest + if [ -f requirements.txt ]; then pip install -r requirements.txt; fi + - name: Lint with flake8 + run: | + # stop the build if there are Python syntax errors or undefined names + flake8 . --count --select=E9,F63,F7,F82 --show-source --statistics + # exit-zero treats all errors as warnings. The GitHub editor is 127 chars wide + flake8 . --count --exit-zero --max-complexity=10 --max-line-length=127 --statistics + - name: Test with pytest + run: | + pytest From c63b473e57e6f9e85a9fec13e9625f055560dc51 Mon Sep 17 00:00:00 2001 From: Eternal Reclaimer <98760976+kyegomez@users.noreply.github.com> Date: Mon, 18 Dec 2023 18:53:16 -0500 Subject: [PATCH 149/587] Create dependency-review.yml --- .github/workflows/dependency-review.yml | 20 ++++++++++++++++++++ 1 file changed, 20 insertions(+) create mode 100644 .github/workflows/dependency-review.yml diff --git a/.github/workflows/dependency-review.yml b/.github/workflows/dependency-review.yml new file mode 100644 index 00000000..b0dedc42 --- /dev/null +++ b/.github/workflows/dependency-review.yml @@ -0,0 +1,20 @@ +# Dependency Review Action +# +# This Action will scan dependency manifest files that change as part of a Pull Request, surfacing known-vulnerable versions of the packages declared or updated in the PR. Once installed, if the workflow run is marked as required, PRs introducing known-vulnerable packages will be blocked from merging. +# +# Source repository: https://github.com/actions/dependency-review-action +# Public documentation: https://docs.github.com/en/code-security/supply-chain-security/understanding-your-software-supply-chain/about-dependency-review#dependency-review-enforcement +name: 'Dependency Review' +on: [pull_request] + +permissions: + contents: read + +jobs: + dependency-review: + runs-on: ubuntu-latest + steps: + - name: 'Checkout Repository' + uses: actions/checkout@v3 + - name: 'Dependency Review' + uses: actions/dependency-review-action@v3 From c350c714f582358e24c2f4c49ebb101bedb85397 Mon Sep 17 00:00:00 2001 From: Eternal Reclaimer <98760976+kyegomez@users.noreply.github.com> Date: Mon, 18 Dec 2023 18:54:02 -0500 Subject: [PATCH 150/587] Create crda.yml --- .github/workflows/crda.yml | 126 +++++++++++++++++++++++++++++++++++++ 1 file changed, 126 insertions(+) create mode 100644 .github/workflows/crda.yml diff --git a/.github/workflows/crda.yml b/.github/workflows/crda.yml new file mode 100644 index 00000000..5054e09a --- /dev/null +++ b/.github/workflows/crda.yml @@ -0,0 +1,126 @@ +# This workflow uses actions that are not certified by GitHub. +# They are provided by a third-party and are governed by +# separate terms of service, privacy policy, and support +# documentation. + +# This workflow performs a static analysis of your source code using +# Red Hat CodeReady Dependency Analytics. + +# Scans are triggered: +# 1. On every push to default and protected branches +# 2. On every Pull Request targeting the default branch +# 3. On a weekly schedule +# 4. Manually, on demand, via the "workflow_dispatch" event + +# 💁 The CRDA Starter workflow will: +# - Checkout your repository +# - Setup the required tool stack +# - Install the CRDA command line tool +# - Auto detect the manifest file and install the project's dependencies +# - Perform the security scan using CRDA +# - Upload the SARIF result to the GitHub Code Scanning which can be viewed under the security tab +# - Optionally upload the SARIF file as an artifact for the future reference + +# ℹ️ Configure your repository and the workflow with the following steps: +# 1. Setup the tool stack based on the project's requirement. +# Refer to: https://github.com/redhat-actions/crda/#1-set-up-the-tool-stack +# 2. (Optional) CRDA action attempt to detect the language and install the +# required dependencies for your project. If your project doesn't aligns +# with the default dependency installation command mentioned here +# https://github.com/redhat-actions/crda/#3-installing-dependencies. +# Use the required inputs to setup the same +# 3. (Optional) CRDA action attempts to detect the manifest file if it is +# present in the root of the project and named as per the default mentioned +# here https://github.com/redhat-actions/crda/#3-installing-dependencies. +# If it deviates from the default, use the required inputs to setup the same +# 4. Setup Authentication - Create the CRDA_KEY or SNYK_TOKEN. +# Refer to: https://github.com/redhat-actions/crda/#4-set-up-authentication +# 5. (Optional) Upload SARIF file as an Artifact to download and view +# 6. Commit and push the workflow file to your default branch to trigger a workflow run. + +# 👋 Visit our GitHub organization at https://github.com/redhat-actions/ to see our actions and provide feedback. + +name: CRDA Scan + +# Controls when the workflow will run +on: + # TODO: Customize trigger events based on your DevSecOps processes + # + # This workflow is made to run with OpenShift starter workflow + # https://github.com/actions/starter-workflows/blob/main/deployments/openshift.yml + # However, if you want to run this workflow as a standalone workflow, please + # uncomment the 'push' trigger below and configure it based on your requirements. + # + workflow_call: + secrets: + CRDA_KEY: + required: false + SNYK_TOKEN: + required: false + workflow_dispatch: + + # push: + # branches: [ "master" ] + + # pull_request_target is used to securely share secret to the PR's workflow run. + # For more info visit: https://docs.github.com/en/actions/using-workflows/events-that-trigger-workflows#pull_request_target + pull_request_target: + branches: [ "master" ] + types: [ assigned, opened, synchronize, reopened, labeled, edited ] + +permissions: + contents: read + +jobs: + crda-scan: + permissions: + contents: read # for actions/checkout to fetch code + security-events: write # for redhat-actions/crda to upload SARIF results + name: Scan project vulnerabilities with CRDA + runs-on: ubuntu-20.04 + steps: + + - name: Check out repository + uses: actions/checkout@v2 + + # ******************************************************************* + # Required: Instructions to setup project + # 1. Setup Go, Java, Node.js or Python depending on your project type + # 2. Setup Actions are listed below, choose one from them: + # - Go: https://github.com/actions/setup-go + # - Java: https://github.com/actions/setup-java + # - Node.js: https://github.com/actions/setup-node + # - Python: https://github.com/actions/setup-python + # + # Example: + # - name: Setup Node + # uses: actions/setup-node@v2 + # with: + # node-version: '14' + + # https://github.com/redhat-actions/openshift-tools-installer/blob/main/README.md + - name: Install CRDA CLI + uses: redhat-actions/openshift-tools-installer@v1 + with: + source: github + github_pat: ${{ github.token }} + # Choose the desired version of the CRDA CLI + crda: "latest" + + ###################################################################################### + # https://github.com/redhat-actions/crda/blob/main/README.md + # + # By default, CRDA will detect the manifest file and install the required dependencies + # using the standard command for the project type. + # If your project doesn't aligns with the defaults mentioned in this action, you will + # need to set few inputs that are described here: + # https://github.com/redhat-actions/crda/blob/main/README.md#3-installing-dependencies + # Visit https://github.com/redhat-actions/crda/#4-set-up-authentication to understand + # process to get a SNYK_TOKEN or a CRDA_KEY + - name: CRDA Scan + id: scan + uses: redhat-actions/crda@v1 + with: + crda_key: ${{ secrets.CRDA_KEY }} # Either use crda_key or snyk_token + # snyk_token: ${{ secrets.SNYK_TOKEN }} + # upload_artifact: false # Set this to false to skip artifact upload From b495675a302727bdcf2ced4087afdf3ca2d5bad8 Mon Sep 17 00:00:00 2001 From: Eternal Reclaimer <98760976+kyegomez@users.noreply.github.com> Date: Mon, 18 Dec 2023 18:54:16 -0500 Subject: [PATCH 151/587] Create super-linter.yml --- .github/workflows/super-linter.yml | 29 +++++++++++++++++++++++++++++ 1 file changed, 29 insertions(+) create mode 100644 .github/workflows/super-linter.yml diff --git a/.github/workflows/super-linter.yml b/.github/workflows/super-linter.yml new file mode 100644 index 00000000..acee01e2 --- /dev/null +++ b/.github/workflows/super-linter.yml @@ -0,0 +1,29 @@ +# This workflow executes several linters on changed files based on languages used in your code base whenever +# you push a code or open a pull request. +# +# You can adjust the behavior by modifying this file. +# For more information, see: +# https://github.com/github/super-linter +name: Lint Code Base + +on: + push: + branches: [ "master" ] + pull_request: + branches: [ "master" ] +jobs: + run-lint: + runs-on: ubuntu-latest + steps: + - name: Checkout code + uses: actions/checkout@v3 + with: + # Full git history is needed to get a proper list of changed files within `super-linter` + fetch-depth: 0 + + - name: Lint Code Base + uses: github/super-linter@v4 + env: + VALIDATE_ALL_CODEBASE: false + DEFAULT_BRANCH: "master" + GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }} From 47ac0dda5978fa63ec6f90ce4de6a15d2d7ec378 Mon Sep 17 00:00:00 2001 From: Eternal Reclaimer <98760976+kyegomez@users.noreply.github.com> Date: Mon, 18 Dec 2023 18:56:09 -0500 Subject: [PATCH 152/587] Create python-package-conda.yml --- .github/workflows/python-package-conda.yml | 34 ++++++++++++++++++++++ 1 file changed, 34 insertions(+) create mode 100644 .github/workflows/python-package-conda.yml diff --git a/.github/workflows/python-package-conda.yml b/.github/workflows/python-package-conda.yml new file mode 100644 index 00000000..384f9b72 --- /dev/null +++ b/.github/workflows/python-package-conda.yml @@ -0,0 +1,34 @@ +name: Python Package using Conda + +on: [push] + +jobs: + build-linux: + runs-on: ubuntu-latest + strategy: + max-parallel: 5 + + steps: + - uses: actions/checkout@v3 + - name: Set up Python 3.10 + uses: actions/setup-python@v3 + with: + python-version: '3.10' + - name: Add conda to system path + run: | + # $CONDA is an environment variable pointing to the root of the miniconda directory + echo $CONDA/bin >> $GITHUB_PATH + - name: Install dependencies + run: | + conda env update --file environment.yml --name base + - name: Lint with flake8 + run: | + conda install flake8 + # stop the build if there are Python syntax errors or undefined names + flake8 . --count --select=E9,F63,F7,F82 --show-source --statistics + # exit-zero treats all errors as warnings. The GitHub editor is 127 chars wide + flake8 . --count --exit-zero --max-complexity=10 --max-line-length=127 --statistics + - name: Test with pytest + run: | + conda install pytest + pytest From de28fc9d79c069ebe7e0ec84c1a484246b7bd0b8 Mon Sep 17 00:00:00 2001 From: Eternal Reclaimer <98760976+kyegomez@users.noreply.github.com> Date: Mon, 18 Dec 2023 18:56:13 -0500 Subject: [PATCH 153/587] Create generator-generic-ossf-slsa3-publish.yml --- .../generator-generic-ossf-slsa3-publish.yml | 66 +++++++++++++++++++ 1 file changed, 66 insertions(+) create mode 100644 .github/workflows/generator-generic-ossf-slsa3-publish.yml diff --git a/.github/workflows/generator-generic-ossf-slsa3-publish.yml b/.github/workflows/generator-generic-ossf-slsa3-publish.yml new file mode 100644 index 00000000..a36e782c --- /dev/null +++ b/.github/workflows/generator-generic-ossf-slsa3-publish.yml @@ -0,0 +1,66 @@ +# This workflow uses actions that are not certified by GitHub. +# They are provided by a third-party and are governed by +# separate terms of service, privacy policy, and support +# documentation. + +# This workflow lets you generate SLSA provenance file for your project. +# The generation satisfies level 3 for the provenance requirements - see https://slsa.dev/spec/v0.1/requirements +# The project is an initiative of the OpenSSF (openssf.org) and is developed at +# https://github.com/slsa-framework/slsa-github-generator. +# The provenance file can be verified using https://github.com/slsa-framework/slsa-verifier. +# For more information about SLSA and how it improves the supply-chain, visit slsa.dev. + +name: SLSA generic generator +on: + workflow_dispatch: + release: + types: [created] + +jobs: + build: + runs-on: ubuntu-latest + outputs: + digests: ${{ steps.hash.outputs.digests }} + + steps: + - uses: actions/checkout@v3 + + # ======================================================== + # + # Step 1: Build your artifacts. + # + # ======================================================== + - name: Build artifacts + run: | + # These are some amazing artifacts. + echo "artifact1" > artifact1 + echo "artifact2" > artifact2 + + # ======================================================== + # + # Step 2: Add a step to generate the provenance subjects + # as shown below. Update the sha256 sum arguments + # to include all binaries that you generate + # provenance for. + # + # ======================================================== + - name: Generate subject for provenance + id: hash + run: | + set -euo pipefail + + # List the artifacts the provenance will refer to. + files=$(ls artifact*) + # Generate the subjects (base64 encoded). + echo "hashes=$(sha256sum $files | base64 -w0)" >> "${GITHUB_OUTPUT}" + + provenance: + needs: [build] + permissions: + actions: read # To read the workflow path. + id-token: write # To sign the provenance. + contents: write # To add assets to a release. + uses: slsa-framework/slsa-github-generator/.github/workflows/generator_generic_slsa3.yml@v1.4.0 + with: + base64-subjects: "${{ needs.build.outputs.digests }}" + upload-assets: true # Optional: Upload to a new release From f85f4d04efc41205aa9e3f56354825aed4716a6a Mon Sep 17 00:00:00 2001 From: Eternal Reclaimer <98760976+kyegomez@users.noreply.github.com> Date: Mon, 18 Dec 2023 18:56:42 -0500 Subject: [PATCH 154/587] Create aws.yml --- .github/workflows/aws.yml | 94 +++++++++++++++++++++++++++++++++++++++ 1 file changed, 94 insertions(+) create mode 100644 .github/workflows/aws.yml diff --git a/.github/workflows/aws.yml b/.github/workflows/aws.yml new file mode 100644 index 00000000..e769d364 --- /dev/null +++ b/.github/workflows/aws.yml @@ -0,0 +1,94 @@ +# This workflow will build and push a new container image to Amazon ECR, +# and then will deploy a new task definition to Amazon ECS, when there is a push to the "master" branch. +# +# To use this workflow, you will need to complete the following set-up steps: +# +# 1. Create an ECR repository to store your images. +# For example: `aws ecr create-repository --repository-name my-ecr-repo --region us-east-2`. +# Replace the value of the `ECR_REPOSITORY` environment variable in the workflow below with your repository's name. +# Replace the value of the `AWS_REGION` environment variable in the workflow below with your repository's region. +# +# 2. Create an ECS task definition, an ECS cluster, and an ECS service. +# For example, follow the Getting Started guide on the ECS console: +# https://us-east-2.console.aws.amazon.com/ecs/home?region=us-east-2#/firstRun +# Replace the value of the `ECS_SERVICE` environment variable in the workflow below with the name you set for the Amazon ECS service. +# Replace the value of the `ECS_CLUSTER` environment variable in the workflow below with the name you set for the cluster. +# +# 3. Store your ECS task definition as a JSON file in your repository. +# The format should follow the output of `aws ecs register-task-definition --generate-cli-skeleton`. +# Replace the value of the `ECS_TASK_DEFINITION` environment variable in the workflow below with the path to the JSON file. +# Replace the value of the `CONTAINER_NAME` environment variable in the workflow below with the name of the container +# in the `containerDefinitions` section of the task definition. +# +# 4. Store an IAM user access key in GitHub Actions secrets named `AWS_ACCESS_KEY_ID` and `AWS_SECRET_ACCESS_KEY`. +# See the documentation for each action used below for the recommended IAM policies for this IAM user, +# and best practices on handling the access key credentials. + +name: Deploy to Amazon ECS + +on: + push: + branches: [ "master" ] + +env: + AWS_REGION: MY_AWS_REGION # set this to your preferred AWS region, e.g. us-west-1 + ECR_REPOSITORY: MY_ECR_REPOSITORY # set this to your Amazon ECR repository name + ECS_SERVICE: MY_ECS_SERVICE # set this to your Amazon ECS service name + ECS_CLUSTER: MY_ECS_CLUSTER # set this to your Amazon ECS cluster name + ECS_TASK_DEFINITION: MY_ECS_TASK_DEFINITION # set this to the path to your Amazon ECS task definition + # file, e.g. .aws/task-definition.json + CONTAINER_NAME: MY_CONTAINER_NAME # set this to the name of the container in the + # containerDefinitions section of your task definition + +permissions: + contents: read + +jobs: + deploy: + name: Deploy + runs-on: ubuntu-latest + environment: production + + steps: + - name: Checkout + uses: actions/checkout@v3 + + - name: Configure AWS credentials + uses: aws-actions/configure-aws-credentials@v1 + with: + aws-access-key-id: ${{ secrets.AWS_ACCESS_KEY_ID }} + aws-secret-access-key: ${{ secrets.AWS_SECRET_ACCESS_KEY }} + aws-region: ${{ env.AWS_REGION }} + + - name: Login to Amazon ECR + id: login-ecr + uses: aws-actions/amazon-ecr-login@v1 + + - name: Build, tag, and push image to Amazon ECR + id: build-image + env: + ECR_REGISTRY: ${{ steps.login-ecr.outputs.registry }} + IMAGE_TAG: ${{ github.sha }} + run: | + # Build a docker container and + # push it to ECR so that it can + # be deployed to ECS. + docker build -t $ECR_REGISTRY/$ECR_REPOSITORY:$IMAGE_TAG . + docker push $ECR_REGISTRY/$ECR_REPOSITORY:$IMAGE_TAG + echo "image=$ECR_REGISTRY/$ECR_REPOSITORY:$IMAGE_TAG" >> $GITHUB_OUTPUT + + - name: Fill in the new image ID in the Amazon ECS task definition + id: task-def + uses: aws-actions/amazon-ecs-render-task-definition@v1 + with: + task-definition: ${{ env.ECS_TASK_DEFINITION }} + container-name: ${{ env.CONTAINER_NAME }} + image: ${{ steps.build-image.outputs.image }} + + - name: Deploy Amazon ECS task definition + uses: aws-actions/amazon-ecs-deploy-task-definition@v1 + with: + task-definition: ${{ steps.task-def.outputs.task-definition }} + service: ${{ env.ECS_SERVICE }} + cluster: ${{ env.ECS_CLUSTER }} + wait-for-service-stability: true From 8678095cca010544feff31b36e5ecdefbc023ce6 Mon Sep 17 00:00:00 2001 From: Eternal Reclaimer <98760976+kyegomez@users.noreply.github.com> Date: Mon, 18 Dec 2023 18:58:03 -0500 Subject: [PATCH 155/587] Create bandit.yml --- .github/workflows/bandit.yml | 52 ++++++++++++++++++++++++++++++++++++ 1 file changed, 52 insertions(+) create mode 100644 .github/workflows/bandit.yml diff --git a/.github/workflows/bandit.yml b/.github/workflows/bandit.yml new file mode 100644 index 00000000..850a3cd4 --- /dev/null +++ b/.github/workflows/bandit.yml @@ -0,0 +1,52 @@ +# This workflow uses actions that are not certified by GitHub. +# They are provided by a third-party and are governed by +# separate terms of service, privacy policy, and support +# documentation. + +# Bandit is a security linter designed to find common security issues in Python code. +# This action will run Bandit on your codebase. +# The results of the scan will be found under the Security tab of your repository. + +# https://github.com/marketplace/actions/bandit-scan is ISC licensed, by abirismyname +# https://pypi.org/project/bandit/ is Apache v2.0 licensed, by PyCQA + +name: Bandit +on: + push: + branches: [ "master" ] + pull_request: + # The branches below must be a subset of the branches above + branches: [ "master" ] + schedule: + - cron: '42 5 * * 0' + +jobs: + bandit: + permissions: + contents: read # for actions/checkout to fetch code + security-events: write # for github/codeql-action/upload-sarif to upload SARIF results + actions: read # only required for a private repository by github/codeql-action/upload-sarif to get the Action run status + + runs-on: ubuntu-latest + steps: + - uses: actions/checkout@v2 + - name: Bandit Scan + uses: shundor/python-bandit-scan@9cc5aa4a006482b8a7f91134412df6772dbda22c + with: # optional arguments + # exit with 0, even with results found + exit_zero: true # optional, default is DEFAULT + # Github token of the repository (automatically created by Github) + GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }} # Needed to get PR information. + # File or directory to run bandit on + # path: # optional, default is . + # Report only issues of a given severity level or higher. Can be LOW, MEDIUM or HIGH. Default is UNDEFINED (everything) + # level: # optional, default is UNDEFINED + # Report only issues of a given confidence level or higher. Can be LOW, MEDIUM or HIGH. Default is UNDEFINED (everything) + # confidence: # optional, default is UNDEFINED + # comma-separated list of paths (glob patterns supported) to exclude from scan (note that these are in addition to the excluded paths provided in the config file) (default: .svn,CVS,.bzr,.hg,.git,__pycache__,.tox,.eggs,*.egg) + # excluded_paths: # optional, default is DEFAULT + # comma-separated list of test IDs to skip + # skips: # optional, default is DEFAULT + # path to a .bandit file that supplies command line arguments + # ini_path: # optional, default is DEFAULT + From 310b67f533b83abe408ae732ffedeee3e07f9a76 Mon Sep 17 00:00:00 2001 From: Eternal Reclaimer <98760976+kyegomez@users.noreply.github.com> Date: Mon, 18 Dec 2023 18:58:09 -0500 Subject: [PATCH 156/587] Create pyre.yml --- .github/workflows/pyre.yml | 46 ++++++++++++++++++++++++++++++++++++++ 1 file changed, 46 insertions(+) create mode 100644 .github/workflows/pyre.yml diff --git a/.github/workflows/pyre.yml b/.github/workflows/pyre.yml new file mode 100644 index 00000000..5ff88856 --- /dev/null +++ b/.github/workflows/pyre.yml @@ -0,0 +1,46 @@ +# This workflow uses actions that are not certified by GitHub. +# They are provided by a third-party and are governed by +# separate terms of service, privacy policy, and support +# documentation. + +# This workflow integrates Pyre with GitHub's +# Code Scanning feature. +# +# Pyre is a performant type checker for Python compliant with +# PEP 484. Pyre can analyze codebases with millions of lines +# of code incrementally – providing instantaneous feedback +# to developers as they write code. +# +# See https://pyre-check.org + +name: Pyre + +on: + workflow_dispatch: + push: + branches: [ "master" ] + pull_request: + branches: [ "master" ] + +permissions: + contents: read + +jobs: + pyre: + permissions: + actions: read + contents: read + security-events: write + runs-on: ubuntu-latest + steps: + - uses: actions/checkout@v3 + with: + submodules: true + + - name: Run Pyre + uses: facebook/pyre-action@60697a7858f7cc8470d8cc494a3cf2ad6b06560d + with: + # To customize these inputs: + # See https://github.com/facebook/pyre-action#inputs + repo-directory: './' + requirements-path: 'requirements.txt' From 4aa167bba50d2e499b0091584f247c50375ff1e6 Mon Sep 17 00:00:00 2001 From: Eternal Reclaimer <98760976+kyegomez@users.noreply.github.com> Date: Mon, 18 Dec 2023 18:58:13 -0500 Subject: [PATCH 157/587] Create pysa.yml --- .github/workflows/pysa.yml | 50 ++++++++++++++++++++++++++++++++++++++ 1 file changed, 50 insertions(+) create mode 100644 .github/workflows/pysa.yml diff --git a/.github/workflows/pysa.yml b/.github/workflows/pysa.yml new file mode 100644 index 00000000..01f39f5b --- /dev/null +++ b/.github/workflows/pysa.yml @@ -0,0 +1,50 @@ +# This workflow uses actions that are not certified by GitHub. +# They are provided by a third-party and are governed by +# separate terms of service, privacy policy, and support +# documentation. + +# This workflow integrates Python Static Analyzer (Pysa) with +# GitHub's Code Scanning feature. +# +# Python Static Analyzer (Pysa) is a security-focused static +# analysis tool that tracks flows of data from where they +# originate to where they terminate in a dangerous location. +# +# See https://pyre-check.org/docs/pysa-basics/ + +name: Pysa + +on: + workflow_dispatch: + push: + branches: [ "master" ] + pull_request: + branches: [ "master" ] + schedule: + - cron: '42 23 * * 1' + +permissions: + contents: read + +jobs: + pysa: + permissions: + actions: read + contents: read + security-events: write + + runs-on: ubuntu-latest + steps: + - uses: actions/checkout@v3 + with: + submodules: true + + - name: Run Pysa + uses: facebook/pysa-action@f46a63777e59268613bd6e2ff4e29f144ca9e88b + with: + # To customize these inputs: + # See https://github.com/facebook/pysa-action#inputs + repo-directory: './' + requirements-path: 'requirements.txt' + infer-types: true + include-default-sapp-filters: true From 57ae8863afb5a02eac89637d6fa1567bfa2564d6 Mon Sep 17 00:00:00 2001 From: Eternal Reclaimer <98760976+kyegomez@users.noreply.github.com> Date: Mon, 18 Dec 2023 18:59:08 -0500 Subject: [PATCH 158/587] Create bearer.yml --- .github/workflows/bearer.yml | 43 ++++++++++++++++++++++++++++++++++++ 1 file changed, 43 insertions(+) create mode 100644 .github/workflows/bearer.yml diff --git a/.github/workflows/bearer.yml b/.github/workflows/bearer.yml new file mode 100644 index 00000000..01070f77 --- /dev/null +++ b/.github/workflows/bearer.yml @@ -0,0 +1,43 @@ +# This workflow uses actions that are not certified by GitHub. +# They are provided by a third-party and are governed by +# separate terms of service, privacy policy, and support +# documentation. +# +# This workflow file requires a free account on Bearer.com to manage findings, notifications and more. +# See https://docs.bearer.com/guides/bearer-cloud/ +name: Bearer + +on: + push: + branches: ["master" ] + pull_request: + # The branches below must be a subset of the branches above + branches: ["master"] + schedule: + - cron: '22 2 * * 0' + +permissions: + contents: read # for actions/checkout to fetch code + security-events: write # for github/codeql-action/upload-sarif to upload SARIF results + actions: read # only required for a private repository by github/codeql-action/upload-sarif to get the Action run status + +jobs: + bearer: + runs-on: ubuntu-latest + steps: + # Checkout project source + - uses: actions/checkout@v3 + # Scan code using Bearer CLI + - name: Run Report + id: report + uses: bearer/bearer-action@828eeb928ce2f4a7ca5ed57fb8b59508cb8c79bc + with: + api-key: ${{ secrets.BEARER_TOKEN }} + format: sarif + output: results.sarif + exit-code: 0 + # Upload SARIF file generated in previous step + - name: Upload SARIF file + uses: github/codeql-action/upload-sarif@v2 + with: + sarif_file: results.sarif From dd1f0bd5a339a902e9c9a38b638f60adc38a199c Mon Sep 17 00:00:00 2001 From: Kye Date: Mon, 18 Dec 2023 19:02:12 -0500 Subject: [PATCH 159/587] [CHORE] glightbox --- requirements.txt | 1 + 1 file changed, 1 insertion(+) diff --git a/requirements.txt b/requirements.txt index fa5e98dd..7e8f4724 100644 --- a/requirements.txt +++ b/requirements.txt @@ -28,3 +28,4 @@ torchaudio==2.1.2 mkdocs mkdocs-material mkdocs-glightbox +glightbox \ No newline at end of file From fd99fa8535cfeadeaf50df4b69890f0adf3f1127 Mon Sep 17 00:00:00 2001 From: Eternal Reclaimer <98760976+kyegomez@users.noreply.github.com> Date: Mon, 18 Dec 2023 19:18:22 -0500 Subject: [PATCH 160/587] Create python-app.yml --- .github/workflows/python-app.yml | 39 ++++++++++++++++++++++++++++++++ 1 file changed, 39 insertions(+) create mode 100644 .github/workflows/python-app.yml diff --git a/.github/workflows/python-app.yml b/.github/workflows/python-app.yml new file mode 100644 index 00000000..7f453c08 --- /dev/null +++ b/.github/workflows/python-app.yml @@ -0,0 +1,39 @@ +# This workflow will install Python dependencies, run tests and lint with a single version of Python +# For more information see: https://docs.github.com/en/actions/automating-builds-and-tests/building-and-testing-python + +name: Python application + +on: + push: + branches: [ "master" ] + pull_request: + branches: [ "master" ] + +permissions: + contents: read + +jobs: + build: + + runs-on: ubuntu-latest + + steps: + - uses: actions/checkout@v3 + - name: Set up Python 3.10 + uses: actions/setup-python@v3 + with: + python-version: "3.10" + - name: Install dependencies + run: | + python -m pip install --upgrade pip + pip install flake8 pytest + if [ -f requirements.txt ]; then pip install -r requirements.txt; fi + - name: Lint with flake8 + run: | + # stop the build if there are Python syntax errors or undefined names + flake8 . --count --select=E9,F63,F7,F82 --show-source --statistics + # exit-zero treats all errors as warnings. The GitHub editor is 127 chars wide + flake8 . --count --exit-zero --max-complexity=10 --max-line-length=127 --statistics + - name: Test with pytest + run: | + pytest From be0b7936c7d82dfcea1df2062af4bce6a3a3f36d Mon Sep 17 00:00:00 2001 From: Eternal Reclaimer <98760976+kyegomez@users.noreply.github.com> Date: Mon, 18 Dec 2023 19:20:41 -0500 Subject: [PATCH 161/587] Update unit-test.yml --- .github/workflows/unit-test.yml | 18 +++--------------- 1 file changed, 3 insertions(+), 15 deletions(-) diff --git a/.github/workflows/unit-test.yml b/.github/workflows/unit-test.yml index c0818be2..aaf4a614 100644 --- a/.github/workflows/unit-test.yml +++ b/.github/workflows/unit-test.yml @@ -24,22 +24,10 @@ jobs: run: pip install -r requirements.txt - name: Run Python unit tests - run: python3 -m unittest tests/zeta + run: python3 -m pytest - name: Verify that the Docker image for the action builds run: docker build . --file Dockerfile - - - name: Integration test 1 - uses: ./ - with: - input-one: something - input-two: true - - - name: Integration test 2 - uses: ./ - with: - input-one: something else - input-two: false - + - name: Verify integration test results - run: python3 -m unittest unittesting/zeta + run: python3 -m pytest From 66c03856422f0176aaac047073ecd674d315da8a Mon Sep 17 00:00:00 2001 From: Eternal Reclaimer <98760976+kyegomez@users.noreply.github.com> Date: Mon, 18 Dec 2023 19:21:30 -0500 Subject: [PATCH 162/587] Update docs.yml --- .github/workflows/docs.yml | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/.github/workflows/docs.yml b/.github/workflows/docs.yml index 7fb194de..5ec5cfe8 100644 --- a/.github/workflows/docs.yml +++ b/.github/workflows/docs.yml @@ -16,4 +16,5 @@ jobs: python-version: 3.x - run: pip install mkdocs-material - run: pip install "mkdocstrings[python]" - - run: mkdocs gh-deploy --force \ No newline at end of file + - run: pip install mkdocs-glightbox + - run: mkdocs gh-deploy --force From 086d008c1684b76787f7b85c2c9f29f3ab8085e5 Mon Sep 17 00:00:00 2001 From: Kye Date: Tue, 19 Dec 2023 13:58:57 -0500 Subject: [PATCH 163/587] [DOCS][zeta_cloud] --- docs/corporate/zeta_cloud.md | 60 ++++++++++++++++++++++++++++++++++++ mkdocs.yml | 1 + 2 files changed, 61 insertions(+) create mode 100644 docs/corporate/zeta_cloud.md diff --git a/docs/corporate/zeta_cloud.md b/docs/corporate/zeta_cloud.md new file mode 100644 index 00000000..f424dd34 --- /dev/null +++ b/docs/corporate/zeta_cloud.md @@ -0,0 +1,60 @@ +**Zeta Cloud: AI Model Training and Deployment Made Easy** + +--- + +**Description: What is it?** +Zeta Cloud is an innovative cloud-based service that simplifies the process of training and deploying AI models. By allowing AI engineers to simply specify the file they want to run, Zeta Cloud takes care of the rest - from model training on powerful cloud infrastructure to seamless deployment. + +--- + +**Problem: What problem is this solving?** +Many AI engineers and data scientists face significant hurdles in model training and deployment, including complexities in setting up infrastructure, managing resources, and ensuring scalability. Zeta Cloud addresses these challenges by providing a streamlined, efficient, and user-friendly platform. + +--- + +**Why: How do we know this is a real problem and worth solving?** +Feedback from the AI community, market research, and demand trends in cloud computing and AI as a Service (AIaaS) indicate a substantial need for simplified model training and deployment solutions. The growing adoption of AI across industries further validates this need. + +--- + +**Success: How do we know if we’ve solved this problem?** +Success will be measured by user adoption rates, customer satisfaction scores, reduction in time and effort for model training and deployment, and positive feedback from the AI engineering community. + +--- + +**Audience: Who are we building for?** +Zeta Cloud is designed for AI engineers, data scientists, startups, and enterprises who want to focus on model development without the overhead of managing cloud infrastructure and deployment complexities. + +--- + +**What: Roughly, what does this look like in the product?** +In the product, users will find a straightforward interface where they can upload their AI model files and specify any required parameters. The platform then automatically allocates resources, trains the model, and deploys it, providing users with an endpoint for easy access and integration. + +--- + +**How: What is the experiment plan?** +The plan includes initial beta testing with select users, gathering feedback, and iteratively improving the service. A phased rollout will follow, starting with basic model training and deployment capabilities, gradually incorporating more advanced features based on user input and technological advancements. + +--- + +**When: When does it ship and what are the milestones?** +The estimated timeline for shipping Zeta Cloud is as follows: +- Beta Testing: Q1 2024 +- Initial Release: Q3 2024 +- Feature Expansion: Q1 2025 +- Full-Scale Deployment: Q3 2025 + +--- + +**Revenue Streams/Cashflows for Zeta Cloud:** + +| Revenue Stream | Description | Target Market | Pricing Model | +|----------------|-------------|---------------|---------------| +| Subscription for Basic Access | Access to basic model training and deployment capabilities. | Individual developers, small startups. | Monthly/Annual subscription. | +| Premium Subscription | Advanced features like higher computing resources, priority support, and more. | Mid-sized companies, enterprises. | Tiered monthly/annual subscription based on usage. | +| Pay-Per-Use Model | Charges based on the amount of computing resources used and the number of model deployments. | Businesses with variable usage. | Charged per resource unit or deployment. | +| Custom Solutions | Tailored solutions for unique business needs, including specialized support and infrastructure. | Large enterprises with specific requirements. | Custom pricing based on the scope of services. | +| Training and Consultation Services | Expert training and consultation for AI model development and deployment. | Organizations new to AI, enterprises needing expertise. | Fixed fee for services or packaged with premium subscriptions. | +| Marketplace for Pre-Trained Models | A platform for users to buy, sell, or license pre-trained models. | AI developers, companies looking for ready-to-use models. | Transaction fees, subscription for premium listings. | +| Data Storage and Management | Integrated solutions for data storage, processing, and management. | All users of the platform. | Based on the amount of data stored/processed. | +| API Access for Third-Party Integrations | Providing API access for integration with other tools and services. | Developers, businesses needing integrations. | Monthly/Annual subscription or pay-per-use. | diff --git a/mkdocs.yml b/mkdocs.yml index b03f045d..30720331 100644 --- a/mkdocs.yml +++ b/mkdocs.yml @@ -162,5 +162,6 @@ nav: - Overview: "zeta/product/product_ideas.md" - Zetahub: "zeta/product/zetahub.md" - Growth: "corporate/growth.md" + - ZetaCloud: "corporate/zeta_cloud.md" - Blog: - Introduction: "blog/introduction_to_zeta.md" \ No newline at end of file From 4748f678f405e922581e9fe158daad40ae27cabb Mon Sep 17 00:00:00 2001 From: Kye Date: Tue, 19 Dec 2023 14:29:57 -0500 Subject: [PATCH 164/587] [CODE QUALITY] --- .gitignore | 1 + docs/corporate/zeta_cloud.md | 2 ++ playground/models/flamingo.py | 1 - playground/models/simple_transformer.py | 1 - tests/nn/attentions/test_cross_attn.py | 2 -- tests/nn/attentions/test_local_attn_mha.py | 1 - tests/nn/attentions/test_mgqa.py | 1 - tests/nn/attentions/test_shaped_attn.py | 3 --- tests/nn/attentions/test_sparse_attn.py | 4 ---- tests/nn/attentions/test_xc_attention.py | 6 ++--- tests/nn/biases/test_alibi.py | 14 ++++++------ .../nn/biases/test_relative_position_bias.py | 7 +++--- tests/nn/embeddings/test_QFTSPEmbeddings.py | 6 ++--- tests/nn/embeddings/test_patch_embedding.py | 1 - tests/nn/embeddings/test_rope.py | 2 -- .../embeddings/test_sine_positional_embs.py | 5 ++--- .../embeddings/test_truncated_rotary_emb.py | 8 +++---- tests/nn/embeddings/test_vision_embeddings.py | 8 +++---- .../embeddings/test_vision_lang_embeddings.py | 4 ++-- tests/nn/modules/test_cross_attn_images.py | 1 - tests/nn/modules/test_custom_mlp.py | 1 - tests/nn/modules/test_hebbian.py | 1 - tests/nn/modules/test_image_projector.py | 10 ++++----- tests/nn/modules/test_log_ff.py | 2 +- tests/nn/modules/test_test_conv_lang.py | 2 +- tests/ops/test_einops_poly.py | 22 +++++++++---------- tests/optim/test_gradient_equillibrum.py | 2 +- tests/optim/test_stable_adamw.py | 6 ++--- tests/test_init.py | 1 - tests/tokenizers/test_llama_tokenizer.py | 2 +- zeta/models/__init__.py | 14 ++++++++++++ zeta/models/base.py | 2 +- zeta/nn/attention/local_attention_mha.py | 1 - zeta/nn/attention/multiquery_attention.py | 2 +- zeta/nn/attention/spatial_linear_attention.py | 2 +- zeta/nn/embeddings/sinusoidal.py | 2 +- zeta/nn/modules/__init__.py | 4 +--- zeta/nn/modules/batched_dp.py | 1 - zeta/nn/modules/clex.py | 1 - zeta/nn/modules/decision_tree.py | 1 - zeta/nn/modules/diffusion.py | 1 - zeta/nn/modules/flatten_features.py | 1 - zeta/nn/modules/image_projector.py | 2 -- zeta/nn/modules/lang_conv_module.py | 1 - zeta/nn/modules/mm_fusion.py | 1 - zeta/nn/modules/modality_adaptive_module.py | 2 +- zeta/nn/modules/multimodal_concat.py | 1 - zeta/nn/modules/nebula.py | 2 +- zeta/nn/modules/s4.py | 1 - zeta/nn/modules/scale.py | 1 - zeta/nn/modules/shift_tokens.py | 1 - zeta/nn/modules/simple_res_block.py | 1 - zeta/nn/modules/simple_rmsnorm.py | 1 - zeta/nn/modules/spatial_downsample.py | 1 - zeta/nn/modules/subln.py | 1 - zeta/nn/modules/transformations.py | 2 +- zeta/nn/modules/video_autoencoder.py | 3 +-- zeta/ops/async_softmax.py | 1 - zeta/optim/batched_optimizer.py | 8 +++---- zeta/rl/actor_critic.py | 1 - zeta/rl/ppo.py | 2 -- zeta/structs/hierarchical_transformer.py | 2 +- zeta/structs/mag_vit.py | 3 +-- zeta/structs/multi_modal_projector.py | 1 - zeta/tokenizers/tokenmonster.py | 1 - zeta/training/hive_trainer.py | 2 -- zeta/utils/save_load_wrapper.py | 1 - 67 files changed, 81 insertions(+), 121 deletions(-) diff --git a/.gitignore b/.gitignore index d5aec461..ceb18764 100644 --- a/.gitignore +++ b/.gitignore @@ -11,6 +11,7 @@ data # Distribution / packaging .Python build/ +.ruff_cache .vscode develop-eggs/ dist/ diff --git a/docs/corporate/zeta_cloud.md b/docs/corporate/zeta_cloud.md index f424dd34..5f20b967 100644 --- a/docs/corporate/zeta_cloud.md +++ b/docs/corporate/zeta_cloud.md @@ -58,3 +58,5 @@ The estimated timeline for shipping Zeta Cloud is as follows: | Marketplace for Pre-Trained Models | A platform for users to buy, sell, or license pre-trained models. | AI developers, companies looking for ready-to-use models. | Transaction fees, subscription for premium listings. | | Data Storage and Management | Integrated solutions for data storage, processing, and management. | All users of the platform. | Based on the amount of data stored/processed. | | API Access for Third-Party Integrations | Providing API access for integration with other tools and services. | Developers, businesses needing integrations. | Monthly/Annual subscription or pay-per-use. | + + diff --git a/playground/models/flamingo.py b/playground/models/flamingo.py index 52f3d818..66ebaa2c 100644 --- a/playground/models/flamingo.py +++ b/playground/models/flamingo.py @@ -2,7 +2,6 @@ import torch.nn.functional as F from einops import rearrange from torch import einsum, nn -from zeta.nn.modules.simple_feedforward import SimpleFeedForward from zeta.nn.attention.cross_attn_images import MultiModalCrossAttention import zeta.nn as znn diff --git a/playground/models/simple_transformer.py b/playground/models/simple_transformer.py index 7bd8e82d..9af78d10 100644 --- a/playground/models/simple_transformer.py +++ b/playground/models/simple_transformer.py @@ -3,7 +3,6 @@ from zeta.nn.modules.feedforward import FeedForward from zeta.nn.attention.shaped_attention import ShapedAttention from zeta.nn.modules.residual import Residual -from zeta.nn.attention import FlashAttention class SimpleTransformerBlock(nn.Module): diff --git a/tests/nn/attentions/test_cross_attn.py b/tests/nn/attentions/test_cross_attn.py index ce96f326..6bff17b8 100644 --- a/tests/nn/attentions/test_cross_attn.py +++ b/tests/nn/attentions/test_cross_attn.py @@ -1,6 +1,4 @@ -import pytest import torch -from torch import nn from zeta.nn.attention.cross_attention import CrossAttention # Create an instance of CrossAttention for testing diff --git a/tests/nn/attentions/test_local_attn_mha.py b/tests/nn/attentions/test_local_attn_mha.py index 0a5d89f3..91894024 100644 --- a/tests/nn/attentions/test_local_attn_mha.py +++ b/tests/nn/attentions/test_local_attn_mha.py @@ -1,6 +1,5 @@ import pytest import torch -import torch.nn as nn from torch.autograd import gradcheck from zeta.nn.attention.local_attention_mha import LocalMHA diff --git a/tests/nn/attentions/test_mgqa.py b/tests/nn/attentions/test_mgqa.py index 70f9664c..36a66bd9 100644 --- a/tests/nn/attentions/test_mgqa.py +++ b/tests/nn/attentions/test_mgqa.py @@ -1,7 +1,6 @@ import pytest import torch from zeta.nn.attention.mgqa import MGQA, CacheView -from zeta.utils.main import exists # Create an instance of MGQA for testing diff --git a/tests/nn/attentions/test_shaped_attn.py b/tests/nn/attentions/test_shaped_attn.py index 3c2071be..097dff66 100644 --- a/tests/nn/attentions/test_shaped_attn.py +++ b/tests/nn/attentions/test_shaped_attn.py @@ -1,7 +1,4 @@ -import pytest import torch -import torch.nn as nn -import torch.nn.functional as F from zeta.nn.attention.shaped_attention import ShapedAttention diff --git a/tests/nn/attentions/test_sparse_attn.py b/tests/nn/attentions/test_sparse_attn.py index 39682f75..f3006df0 100644 --- a/tests/nn/attentions/test_sparse_attn.py +++ b/tests/nn/attentions/test_sparse_attn.py @@ -65,10 +65,6 @@ def test_sparse_attention_forward(): n_batch = 4 n_ctx = 1024 n_embd = 256 - heads = 4 - attn_mode = "all" - local_attn_ctx = 32 - blocksize = 32 q = torch.randn(n_batch, n_ctx, n_embd) k = torch.randn(n_batch, n_ctx, n_embd) diff --git a/tests/nn/attentions/test_xc_attention.py b/tests/nn/attentions/test_xc_attention.py index d67a28eb..d5558996 100644 --- a/tests/nn/attentions/test_xc_attention.py +++ b/tests/nn/attentions/test_xc_attention.py @@ -42,7 +42,7 @@ def test_xc_attention_forward_with_invalid_inputs(xc_attention_model): with pytest.raises(Exception): x = torch.randn(1, 256, 16, 16) cond = torch.randn(1, 128) # Mismatched conditioning dimension - output = xc_attention_model(x, cond) + xc_attention_model(x, cond) # Test case to check if XCAttention handles different head configurations correctly @@ -81,10 +81,10 @@ def test_xc_attention_with_different_cond_dims(): # Test case to check if XCAttention handles negative input dimensions correctly def test_xc_attention_negative_input_dim(): with pytest.raises(ValueError): - model = XCAttention(dim=-256, cond_dim=64, heads=8) + XCAttention(dim=-256, cond_dim=64, heads=8) # Test case to check if XCAttention handles negative conditioning dimensions correctly def test_xc_attention_negative_cond_dim(): with pytest.raises(ValueError): - model = XCAttention(dim=256, cond_dim=-64, heads=8) + XCAttention(dim=256, cond_dim=-64, heads=8) diff --git a/tests/nn/biases/test_alibi.py b/tests/nn/biases/test_alibi.py index 2e433fac..1842c421 100644 --- a/tests/nn/biases/test_alibi.py +++ b/tests/nn/biases/test_alibi.py @@ -152,9 +152,9 @@ def tensors_equal(tensor1, tensor2): # Test for the existence of a helper function exists def test_exists_function(): - assert exists(None) == False - assert exists(0) == True - assert exists("Hello") == True + assert exists(None) is False + assert exists(0) is True + assert exists("Hello") is True # Test for the pad_at_dim helper function @@ -170,8 +170,8 @@ def test_tensors_equal_function(): tensor2 = torch.tensor([1.0, 2.0, 3.0]) tensor3 = torch.tensor([1.0, 2.0, 3.1]) - assert tensors_equal(tensor1, tensor2) == True - assert tensors_equal(tensor1, tensor3) == False + assert tensors_equal(tensor1, tensor2) is True + assert tensors_equal(tensor1, tensor3) is False # Additional tests for tensor manipulation functions @@ -193,8 +193,8 @@ def test_einops_rearrange_function(): # Test for the nn.Module class inheritance def test_nn_module_inheritance(): - assert issubclass(AlibiPositionalBias, nn.Module) == True - assert issubclass(LearnedAlibiPositionalBias, nn.Module) == True + assert issubclass(AlibiPositionalBias, nn.Module) is True + assert issubclass(LearnedAlibiPositionalBias, nn.Module) is True # Helper function to create random data diff --git a/tests/nn/biases/test_relative_position_bias.py b/tests/nn/biases/test_relative_position_bias.py index c7b2fdf9..9b3ab839 100644 --- a/tests/nn/biases/test_relative_position_bias.py +++ b/tests/nn/biases/test_relative_position_bias.py @@ -1,6 +1,5 @@ import pytest import torch -import torch.nn as nn from zeta.nn.biases.relative_position_bias import RelativePositionBias @@ -238,13 +237,13 @@ def test_different_bidirectional_bias_values(): # Test case for initializing with negative max distance def test_negative_max_distance_init(): with pytest.raises(ValueError): - bias = RelativePositionBias(max_distance=-128) + RelativePositionBias(max_distance=-128) # Test case for initializing with negative num buckets def test_negative_num_buckets_init(): with pytest.raises(ValueError): - bias = RelativePositionBias(num_buckets=-32) + RelativePositionBias(num_buckets=-32) # Test case for initializing with a large max distance @@ -280,4 +279,4 @@ def test_large_num_buckets(): # Test case for bidirectional bias with negative max distance def test_bidirectional_bias_negative_max_distance(): with pytest.raises(ValueError): - bias = RelativePositionBias(bidirectional=True, max_distance=-128) + RelativePositionBias(bidirectional=True, max_distance=-128) diff --git a/tests/nn/embeddings/test_QFTSPEmbeddings.py b/tests/nn/embeddings/test_QFTSPEmbeddings.py index 4e3f334c..bb353af9 100644 --- a/tests/nn/embeddings/test_QFTSPEmbeddings.py +++ b/tests/nn/embeddings/test_QFTSPEmbeddings.py @@ -69,18 +69,18 @@ def test_qftspembeddings_forward_negative_dim(): vocab_size = 10000 dim = -512 with pytest.raises(ValueError): - model = QFTSPEmbeddings(vocab_size, dim) + QFTSPEmbeddings(vocab_size, dim) def test_qftspembeddings_forward_negative_vocab_size(): vocab_size = -10000 dim = 512 with pytest.raises(ValueError): - model = QFTSPEmbeddings(vocab_size, dim) + QFTSPEmbeddings(vocab_size, dim) def test_qftspembeddings_forward_zero_vocab_size(): vocab_size = 0 dim = 512 with pytest.raises(ValueError): - model = QFTSPEmbeddings(vocab_size, dim) + QFTSPEmbeddings(vocab_size, dim) diff --git a/tests/nn/embeddings/test_patch_embedding.py b/tests/nn/embeddings/test_patch_embedding.py index e02e83a4..2a4aafec 100644 --- a/tests/nn/embeddings/test_patch_embedding.py +++ b/tests/nn/embeddings/test_patch_embedding.py @@ -1,4 +1,3 @@ -import pytest import torch from torch import nn from einops.layers.torch import Rearrange diff --git a/tests/nn/embeddings/test_rope.py b/tests/nn/embeddings/test_rope.py index b357f37f..4e475253 100644 --- a/tests/nn/embeddings/test_rope.py +++ b/tests/nn/embeddings/test_rope.py @@ -1,6 +1,4 @@ -import pytest import torch -from torch import nn from zeta.nn.embeddings.rope import ( RotaryEmbedding, diff --git a/tests/nn/embeddings/test_sine_positional_embs.py b/tests/nn/embeddings/test_sine_positional_embs.py index b46991e2..df6ceba2 100644 --- a/tests/nn/embeddings/test_sine_positional_embs.py +++ b/tests/nn/embeddings/test_sine_positional_embs.py @@ -1,6 +1,5 @@ import pytest import torch -from torch import nn from zeta.nn.embeddings.sine_positional import SinePositionalEmbedding @@ -76,11 +75,11 @@ def test_extend_pe(): def test_negative_dimension(): dim_model = -512 with pytest.raises(ValueError): - module = SinePositionalEmbedding(dim_model) + SinePositionalEmbedding(dim_model) # Test case for initializing with alpha=True and dropout > 0 def test_alpha_and_dropout(): dim_model = 512 with pytest.raises(ValueError): - module = SinePositionalEmbedding(dim_model, alpha=True, dropout=0.2) + SinePositionalEmbedding(dim_model, alpha=True, dropout=0.2) diff --git a/tests/nn/embeddings/test_truncated_rotary_emb.py b/tests/nn/embeddings/test_truncated_rotary_emb.py index be595ac8..f7c51814 100644 --- a/tests/nn/embeddings/test_truncated_rotary_emb.py +++ b/tests/nn/embeddings/test_truncated_rotary_emb.py @@ -1,6 +1,4 @@ import pytest -import torch -from torch import nn from zeta.nn.embeddings.truncated_rope import TruncatedRotaryEmbedding @@ -50,7 +48,7 @@ def test_negative_dimension(): b = 1.0 rho = 0.0 with pytest.raises(ValueError): - module = TruncatedRotaryEmbedding(dim, a, b, rho) + TruncatedRotaryEmbedding(dim, a, b, rho) # Test case for initializing with a > b @@ -60,7 +58,7 @@ def test_a_greater_than_b(): b = 0.5 rho = 0.0 with pytest.raises(ValueError): - module = TruncatedRotaryEmbedding(dim, a, b, rho) + TruncatedRotaryEmbedding(dim, a, b, rho) # Test case for initializing with rho > b @@ -70,4 +68,4 @@ def test_rho_greater_than_b(): b = 1.0 rho = 1.5 with pytest.raises(ValueError): - module = TruncatedRotaryEmbedding(dim, a, b, rho) + TruncatedRotaryEmbedding(dim, a, b, rho) diff --git a/tests/nn/embeddings/test_vision_embeddings.py b/tests/nn/embeddings/test_vision_embeddings.py index cd99e367..48b89da0 100644 --- a/tests/nn/embeddings/test_vision_embeddings.py +++ b/tests/nn/embeddings/test_vision_embeddings.py @@ -98,25 +98,25 @@ def test_forward_custom(): # Test case for initializing with incorrect image size def test_incorrect_img_size_init(): with pytest.raises(AssertionError): - module = VisionEmbedding(img_size=256) + VisionEmbedding(img_size=256) # Test case for initializing with incorrect patch size def test_incorrect_patch_size_init(): with pytest.raises(AssertionError): - module = VisionEmbedding(patch_size=64) + VisionEmbedding(patch_size=64) # Test case for initializing with negative in_chans def test_negative_in_chans_init(): with pytest.raises(ValueError): - module = VisionEmbedding(in_chans=-3) + VisionEmbedding(in_chans=-3) # Test case for initializing with negative embed_dim def test_negative_embed_dim_init(): with pytest.raises(ValueError): - module = VisionEmbedding(embed_dim=-768) + VisionEmbedding(embed_dim=-768) # Test case for initializing with invalid masked_position diff --git a/tests/nn/embeddings/test_vision_lang_embeddings.py b/tests/nn/embeddings/test_vision_lang_embeddings.py index 96cf5995..a72e497d 100644 --- a/tests/nn/embeddings/test_vision_lang_embeddings.py +++ b/tests/nn/embeddings/test_vision_lang_embeddings.py @@ -49,7 +49,7 @@ def test_incorrect_text_embedding_init(): text_embed = nn.Linear(10, 10) vision_embed = nn.Embedding(10, 10) with pytest.raises(AssertionError): - module = VisionLanguageEmbedding(text_embed, vision_embed) + VisionLanguageEmbedding(text_embed, vision_embed) # Test case for initializing with incorrect vision embedding @@ -57,7 +57,7 @@ def test_incorrect_vision_embedding_init(): text_embed = nn.Embedding(10, 10) vision_embed = nn.Linear(10, 10) with pytest.raises(AssertionError): - module = VisionLanguageEmbedding(text_embed, vision_embed) + VisionLanguageEmbedding(text_embed, vision_embed) # Test case for forward pass with text input being None diff --git a/tests/nn/modules/test_cross_attn_images.py b/tests/nn/modules/test_cross_attn_images.py index 8b4f3e7a..6651d72f 100644 --- a/tests/nn/modules/test_cross_attn_images.py +++ b/tests/nn/modules/test_cross_attn_images.py @@ -1,6 +1,5 @@ import torch import torch.nn as nn -import numpy as np import pytest from torch.autograd import gradcheck from zeta.nn.attention.cross_attn_images import MultiModalCrossAttention diff --git a/tests/nn/modules/test_custom_mlp.py b/tests/nn/modules/test_custom_mlp.py index e2eec696..22d0eefd 100644 --- a/tests/nn/modules/test_custom_mlp.py +++ b/tests/nn/modules/test_custom_mlp.py @@ -1,7 +1,6 @@ import pytest import torch import torch.nn as nn -import torch.nn.functional as F from zeta.nn.modules.flexible_mlp import CustomMLP diff --git a/tests/nn/modules/test_hebbian.py b/tests/nn/modules/test_hebbian.py index 0ef274ea..5d9e76be 100644 --- a/tests/nn/modules/test_hebbian.py +++ b/tests/nn/modules/test_hebbian.py @@ -1,6 +1,5 @@ import pytest import torch -import torch.nn as nn from zeta.nn.modules.hebbian import ( BasicHebbianGRUModel, diff --git a/tests/nn/modules/test_image_projector.py b/tests/nn/modules/test_image_projector.py index f6acab3f..58f3e2a2 100644 --- a/tests/nn/modules/test_image_projector.py +++ b/tests/nn/modules/test_image_projector.py @@ -90,7 +90,7 @@ def test_patch_projector_performance(sample_input_tensor): # Measure the time taken for 100 forward passes start_time = time.time() for _ in range(100): - output_tensor = patch_projector(input_tensor) + patch_projector(input_tensor) end_time = time.time() elapsed_time = end_time - start_time @@ -211,7 +211,7 @@ def test_patch_projector_performance_various_input_sizes( # Measure the time taken for 100 forward passes start_time = time.time() for _ in range(100): - output_tensor = patch_projector(input_tensor) + patch_projector(input_tensor) end_time = time.time() elapsed_time = end_time - start_time @@ -249,7 +249,7 @@ def test_patch_projector_output_shape_consistency(sample_input_tensor): # Test case for edge case: invalid max_patch_size def test_patch_projector_invalid_max_patch_size(): with pytest.raises(ValueError): - patch_projector = ImagePatchCreatorProjector( + ImagePatchCreatorProjector( max_patch_size=0, embedding_dim=768 ) @@ -257,7 +257,7 @@ def test_patch_projector_invalid_max_patch_size(): # Test case for edge case: invalid embedding_dim def test_patch_projector_invalid_embedding_dim(): with pytest.raises(ValueError): - patch_projector = ImagePatchCreatorProjector( + ImagePatchCreatorProjector( max_patch_size=16, embedding_dim=0 ) @@ -270,7 +270,7 @@ def test_patch_projector_invalid_input_shape(): input_tensor = torch.randn(1, 3, 32, 32) # Smaller image with pytest.raises(ValueError): - output_tensor = patch_projector(input_tensor) + patch_projector(input_tensor) # Test case for dynamic patch size calculation diff --git a/tests/nn/modules/test_log_ff.py b/tests/nn/modules/test_log_ff.py index 08207d76..e2d5f109 100644 --- a/tests/nn/modules/test_log_ff.py +++ b/tests/nn/modules/test_log_ff.py @@ -1,6 +1,6 @@ import torch import pytest -from zeta.nn.modules.log_ff import LogFF, compute_entropy_safe +from zeta.nn.modules.log_ff import LogFF # Test fixture for a sample input tensor diff --git a/tests/nn/modules/test_test_conv_lang.py b/tests/nn/modules/test_test_conv_lang.py index 91501991..9e776974 100644 --- a/tests/nn/modules/test_test_conv_lang.py +++ b/tests/nn/modules/test_test_conv_lang.py @@ -78,7 +78,7 @@ def test_with_mocked_convolution_layer(): block = ConvolutionLanguageBlock(128, 256, 3, 1) block.conv_layers[0] = mock_convolution x = torch.randn(1, 128, 1024) - output = block(x) + block(x) assert mock_convolution.called diff --git a/tests/ops/test_einops_poly.py b/tests/ops/test_einops_poly.py index 304055f8..a1ad7c44 100644 --- a/tests/ops/test_einops_poly.py +++ b/tests/ops/test_einops_poly.py @@ -71,7 +71,7 @@ def test_reduce_with_anon_dims(pattern, a_list): # Additional tests for rearrange_many function def test_rearrange_many_invalid_pattern(): with pytest.raises(ValueError): - output = list( + list( rearrange_many([input_data, input_data], pattern="invalid_pattern") ) @@ -86,7 +86,7 @@ def test_rearrange_many_with_multiple_patterns(): # Additional tests for repeat_many function def test_repeat_many_invalid_pattern(): with pytest.raises(ValueError): - output = list( + list( repeat_many( [input_data, input_data], pattern="invalid_pattern", @@ -97,7 +97,7 @@ def test_repeat_many_invalid_pattern(): def test_repeat_many_invalid_repeats(): with pytest.raises(ValueError): - output = list( + list( repeat_many( [input_data, input_data], pattern="b h w c", repeats=[2] ) @@ -115,7 +115,7 @@ def test_repeat_many_with_single_repeat(): # Additional tests for reduce_many function def test_reduce_many_invalid_pattern(): with pytest.raises(ValueError): - output = list( + list( reduce_many( [input_data, input_data], pattern="invalid_pattern", @@ -126,7 +126,7 @@ def test_reduce_many_invalid_pattern(): def test_reduce_many_invalid_reduction(): with pytest.raises(ValueError): - output = list( + list( reduce_many( [input_data, input_data], pattern="b h w c", @@ -148,14 +148,14 @@ def test_reduce_many_with_sum_reduction(): # Additional tests for rearrange_with_anon_dims function def test_rearrange_with_anon_dims_invalid_dim_list(): with pytest.raises(ValueError): - output = rearrange_with_anon_dims( + rearrange_with_anon_dims( input_data, pattern="...a b c", a=(1,) ) def test_rearrange_with_anon_dims_invalid_pattern(): with pytest.raises(ValueError): - output = rearrange_with_anon_dims( + rearrange_with_anon_dims( input_data, pattern="invalid_pattern", a=[(1, 2), (2, 3)] ) @@ -163,12 +163,12 @@ def test_rearrange_with_anon_dims_invalid_pattern(): # Additional tests for repeat_with_anon_dims function def test_repeat_with_anon_dims_invalid_dim_list(): with pytest.raises(ValueError): - output = repeat_with_anon_dims(input_data, pattern="...a b c", a=(2,)) + repeat_with_anon_dims(input_data, pattern="...a b c", a=(2,)) def test_repeat_with_anon_dims_invalid_pattern(): with pytest.raises(ValueError): - output = repeat_with_anon_dims( + repeat_with_anon_dims( input_data, pattern="invalid_pattern", a=[(2, 3), (3, 4)] ) @@ -176,11 +176,11 @@ def test_repeat_with_anon_dims_invalid_pattern(): # Additional tests for reduce_with_anon_dims function def test_reduce_with_anon_dims_invalid_dim_list(): with pytest.raises(ValueError): - output = reduce_with_anon_dims(input_data, pattern="...a b c", a=(2,)) + reduce_with_anon_dims(input_data, pattern="...a b c", a=(2,)) def test_reduce_with_anon_dims_invalid_pattern(): with pytest.raises(ValueError): - output = reduce_with_anon_dims( + reduce_with_anon_dims( input_data, pattern="invalid_pattern", a=[(2, 3), (3, 4)] ) diff --git a/tests/optim/test_gradient_equillibrum.py b/tests/optim/test_gradient_equillibrum.py index 256549b4..84a4f113 100644 --- a/tests/optim/test_gradient_equillibrum.py +++ b/tests/optim/test_gradient_equillibrum.py @@ -121,7 +121,7 @@ def test_optimizer_with_custom_lr_and_weight_decay(): # Test optimizer with a custom clip threshold def test_optimizer_with_custom_clip_threshold(): model, loss_fn = create_model_and_loss() - optimizer = GradientEquilibrum(model.parameters(), clip_thresh=0.5) + GradientEquilibrum(model.parameters(), clip_thresh=0.5) assert True # No exceptions were raised diff --git a/tests/optim/test_stable_adamw.py b/tests/optim/test_stable_adamw.py index 18953d97..b2ac2b87 100644 --- a/tests/optim/test_stable_adamw.py +++ b/tests/optim/test_stable_adamw.py @@ -165,21 +165,21 @@ def test_optimizer_with_zero_gradients(): def test_optimizer_with_negative_learning_rate(): model = torch.nn.Linear(10, 10) with pytest.raises(ValueError): - optimizer = StableAdamWUnfused(model.parameters(), lr=-0.001) + StableAdamWUnfused(model.parameters(), lr=-0.001) # Test optimizer with a negative weight decay (should raise a ValueError) def test_optimizer_with_negative_weight_decay(): model = torch.nn.Linear(10, 10) with pytest.raises(ValueError): - optimizer = StableAdamWUnfused(model.parameters(), weight_decay=-0.1) + StableAdamWUnfused(model.parameters(), weight_decay=-0.1) # Test optimizer with a negative custom scalar (should raise a ValueError) def test_optimizer_with_negative_custom_scalar(): model = torch.nn.Linear(10, 10) with pytest.raises(ValueError): - optimizer = StableAdamWUnfused( + StableAdamWUnfused( model.parameters(), precision="custom_fp16", custom_scalar=-65536 ) diff --git a/tests/test_init.py b/tests/test_init.py index 2a97119b..ab227e39 100644 --- a/tests/test_init.py +++ b/tests/test_init.py @@ -1,4 +1,3 @@ -import pytest import zeta diff --git a/tests/tokenizers/test_llama_tokenizer.py b/tests/tokenizers/test_llama_tokenizer.py index 726c193e..52f89310 100644 --- a/tests/tokenizers/test_llama_tokenizer.py +++ b/tests/tokenizers/test_llama_tokenizer.py @@ -72,5 +72,5 @@ def test_llama_tokenizer_encode_decode(text): ], ) def test_llama_tokenizer_download_tokenizer(tokenizer_name): - tokenizer = LLamaTokenizer(tokenizer_name=tokenizer_name) + LLamaTokenizer(tokenizer_name=tokenizer_name) assert os.path.isfile("data/tokenizer.model") diff --git a/zeta/models/__init__.py b/zeta/models/__init__.py index 454352b0..9dab6ca3 100644 --- a/zeta/models/__init__.py +++ b/zeta/models/__init__.py @@ -9,3 +9,17 @@ from zeta.models.palme import PalmE from zeta.models.vit import ViT from zeta.models.navit import NaViT + + +__all__ = [ + "BaseModel", + "ViT", + "MaxVit", + "MegaVit", + "PalmE", + "GPT4", + "GPT4MultiModal", + "LLama2", + "Andromeda", + "NaViT", +] \ No newline at end of file diff --git a/zeta/models/base.py b/zeta/models/base.py index 71424276..04f7a4b0 100644 --- a/zeta/models/base.py +++ b/zeta/models/base.py @@ -1,4 +1,4 @@ -from abc import ABC, abstractmethod +from abc import ABC class BaseModel(ABC): diff --git a/zeta/nn/attention/local_attention_mha.py b/zeta/nn/attention/local_attention_mha.py index 18a99ca6..8a331531 100644 --- a/zeta/nn/attention/local_attention_mha.py +++ b/zeta/nn/attention/local_attention_mha.py @@ -1,5 +1,4 @@ import torch -import torch.nn.functional as F from einops import rearrange from torch import nn diff --git a/zeta/nn/attention/multiquery_attention.py b/zeta/nn/attention/multiquery_attention.py index d94dcf53..37808373 100644 --- a/zeta/nn/attention/multiquery_attention.py +++ b/zeta/nn/attention/multiquery_attention.py @@ -1,6 +1,6 @@ import math import warnings -from typing import Dict, Optional, Type +from typing import Optional import torch import torch.nn as nn diff --git a/zeta/nn/attention/spatial_linear_attention.py b/zeta/nn/attention/spatial_linear_attention.py index 736bf781..35fbd4b3 100644 --- a/zeta/nn/attention/spatial_linear_attention.py +++ b/zeta/nn/attention/spatial_linear_attention.py @@ -3,7 +3,7 @@ from einops import rearrange -from einops_exts import check_shape, rearrange_many +from einops_exts import rearrange_many class SpatialLinearAttention(nn.Module): diff --git a/zeta/nn/embeddings/sinusoidal.py b/zeta/nn/embeddings/sinusoidal.py index 430cd396..5a5f9e7f 100644 --- a/zeta/nn/embeddings/sinusoidal.py +++ b/zeta/nn/embeddings/sinusoidal.py @@ -1,5 +1,5 @@ import torch -from torch import nn, einsum +from torch import nn from einops import rearrange diff --git a/zeta/nn/modules/__init__.py b/zeta/nn/modules/__init__.py index fe90f8bb..a94e436f 100644 --- a/zeta/nn/modules/__init__.py +++ b/zeta/nn/modules/__init__.py @@ -11,13 +11,12 @@ from zeta.nn.modules.feedforward import FeedForward from zeta.nn.modules.feedforward_network import FeedForwardNetwork from zeta.nn.modules.flexible_mlp import CustomMLP -from zeta.nn.modules.fractorial_net import FractalBlock, FractalNetwork from zeta.nn.modules.h3 import H3Layer from zeta.nn.modules.itca import IterativeCrossSelfAttention from zeta.nn.modules.lang_conv_module import ConvolutionLanguageBlock from zeta.nn.modules.layernorm import LayerNorm, l2norm from zeta.nn.modules.leaky_relu import LeakyRELU -from zeta.nn.modules.log_ff import LogFF, compute_entropy_safe +from zeta.nn.modules.log_ff import LogFF from zeta.nn.modules.lora import Lora from zeta.nn.modules.mbconv import MBConv from zeta.nn.modules.mlp import MLP @@ -31,7 +30,6 @@ from zeta.nn.modules.resnet import ResNet from zeta.nn.modules.rms_norm import RMSNorm from zeta.nn.modules.rnn_nlp import RNNL -from zeta.nn.modules.s4 import s4d_kernel from zeta.nn.modules.shufflenet import ShuffleNet from zeta.nn.modules.sig_lip import SigLipLoss from zeta.nn.modules.simple_attention import simple_attention diff --git a/zeta/nn/modules/batched_dp.py b/zeta/nn/modules/batched_dp.py index 6382df1e..a02b0764 100644 --- a/zeta/nn/modules/batched_dp.py +++ b/zeta/nn/modules/batched_dp.py @@ -1,4 +1,3 @@ -import torch from einops import rearrange diff --git a/zeta/nn/modules/clex.py b/zeta/nn/modules/clex.py index b0cf211c..932e2f38 100644 --- a/zeta/nn/modules/clex.py +++ b/zeta/nn/modules/clex.py @@ -152,7 +152,6 @@ def forward(self, device, dtype, seq_len, do_train=False): scale_factor = seq_len // self.max_position_embeddings if do_train: t_val = self.sample_random_times(self.max_t + 1, device)[0] - import math sampled_position_ids = self.get_random_position_ids( n=seq_len - 2, max=seq_len * t_val - 2 diff --git a/zeta/nn/modules/decision_tree.py b/zeta/nn/modules/decision_tree.py index 1456f82e..61b3fab7 100644 --- a/zeta/nn/modules/decision_tree.py +++ b/zeta/nn/modules/decision_tree.py @@ -1,6 +1,5 @@ import torch from torch import nn -import torch.nn.functional as F class SimpleDecisionTree(nn.Module): diff --git a/zeta/nn/modules/diffusion.py b/zeta/nn/modules/diffusion.py index 92e2f93e..d22bdd6c 100644 --- a/zeta/nn/modules/diffusion.py +++ b/zeta/nn/modules/diffusion.py @@ -1,6 +1,5 @@ import torch import torch.nn as nn -import torch.nn.functional as F class Diffuser(nn.Module): diff --git a/zeta/nn/modules/flatten_features.py b/zeta/nn/modules/flatten_features.py index 39082a08..012def81 100644 --- a/zeta/nn/modules/flatten_features.py +++ b/zeta/nn/modules/flatten_features.py @@ -1,4 +1,3 @@ -import torch from einops import rearrange diff --git a/zeta/nn/modules/image_projector.py b/zeta/nn/modules/image_projector.py index 5517be8e..0db1fa77 100644 --- a/zeta/nn/modules/image_projector.py +++ b/zeta/nn/modules/image_projector.py @@ -1,6 +1,4 @@ -import torch import torch.nn as nn -import torch.nn.functional as F class ImagePatchCreatorProjector(nn.Module): diff --git a/zeta/nn/modules/lang_conv_module.py b/zeta/nn/modules/lang_conv_module.py index aa71d2b4..eb65edff 100644 --- a/zeta/nn/modules/lang_conv_module.py +++ b/zeta/nn/modules/lang_conv_module.py @@ -1,4 +1,3 @@ -import torch from torch import nn diff --git a/zeta/nn/modules/mm_fusion.py b/zeta/nn/modules/mm_fusion.py index 6c20b4b4..8f37d973 100644 --- a/zeta/nn/modules/mm_fusion.py +++ b/zeta/nn/modules/mm_fusion.py @@ -1,6 +1,5 @@ import torch from torch import nn -from einops import rearrange class MultiModalFusion(nn.Module): diff --git a/zeta/nn/modules/modality_adaptive_module.py b/zeta/nn/modules/modality_adaptive_module.py index 06343b1d..74bae13e 100644 --- a/zeta/nn/modules/modality_adaptive_module.py +++ b/zeta/nn/modules/modality_adaptive_module.py @@ -35,7 +35,7 @@ def __init__(self, dim: int, heads: int, dropout: float = 0.1): self.heads = heads self.dropout = dropout self.scale = dim**-0.5 - assert dim % heads == 0, f"dim must alwasy be divisible by heads" + assert dim % heads == 0, "dim must alwasy be divisible by heads" # Initialize the normalization layers for each modality self.norm_text = nn.LayerNorm(dim) diff --git a/zeta/nn/modules/multimodal_concat.py b/zeta/nn/modules/multimodal_concat.py index 0a7f00a4..40e2060b 100644 --- a/zeta/nn/modules/multimodal_concat.py +++ b/zeta/nn/modules/multimodal_concat.py @@ -1,4 +1,3 @@ -import torch from einops import rearrange diff --git a/zeta/nn/modules/nebula.py b/zeta/nn/modules/nebula.py index f1b0bc88..c372c8c1 100644 --- a/zeta/nn/modules/nebula.py +++ b/zeta/nn/modules/nebula.py @@ -203,7 +203,7 @@ def determine_loss_function(self, y_pred, y_true): y_true_flat = y_true.flatten() if y_pred_flat.shape != y_true_flat.shape: y_pred_flat = y_pred_flat[: y_true_flat.numel()] - correlation = torch.tensor( + torch.tensor( np.corrcoef(y_pred_flat.cpu().numpy(), y_true_flat.cpu().numpy())[ 0, 1 ] diff --git a/zeta/nn/modules/s4.py b/zeta/nn/modules/s4.py index dd41d306..10bec348 100644 --- a/zeta/nn/modules/s4.py +++ b/zeta/nn/modules/s4.py @@ -1,5 +1,4 @@ import torch -from typing import Tuple def s4d_kernel( diff --git a/zeta/nn/modules/scale.py b/zeta/nn/modules/scale.py index e2af7571..443ab49a 100644 --- a/zeta/nn/modules/scale.py +++ b/zeta/nn/modules/scale.py @@ -1,4 +1,3 @@ -import torch from torch import nn diff --git a/zeta/nn/modules/shift_tokens.py b/zeta/nn/modules/shift_tokens.py index aeb34c9e..62723736 100644 --- a/zeta/nn/modules/shift_tokens.py +++ b/zeta/nn/modules/shift_tokens.py @@ -1,6 +1,5 @@ import torch from torch import nn -from einops import rearrange import torch.nn.functional as F diff --git a/zeta/nn/modules/simple_res_block.py b/zeta/nn/modules/simple_res_block.py index 106c6ba6..3b6cdede 100644 --- a/zeta/nn/modules/simple_res_block.py +++ b/zeta/nn/modules/simple_res_block.py @@ -1,4 +1,3 @@ -import torch from torch import nn diff --git a/zeta/nn/modules/simple_rmsnorm.py b/zeta/nn/modules/simple_rmsnorm.py index 7c5e7bd1..e3966ba7 100644 --- a/zeta/nn/modules/simple_rmsnorm.py +++ b/zeta/nn/modules/simple_rmsnorm.py @@ -1,4 +1,3 @@ -import torch import torch.nn.functional as F from torch import nn diff --git a/zeta/nn/modules/spatial_downsample.py b/zeta/nn/modules/spatial_downsample.py index b9f62fee..0b2a7de2 100644 --- a/zeta/nn/modules/spatial_downsample.py +++ b/zeta/nn/modules/spatial_downsample.py @@ -1,4 +1,3 @@ -import torch from torch import nn from einops import rearrange, pack, unpack diff --git a/zeta/nn/modules/subln.py b/zeta/nn/modules/subln.py index 01041e87..3b55ff1d 100644 --- a/zeta/nn/modules/subln.py +++ b/zeta/nn/modules/subln.py @@ -1,4 +1,3 @@ -import torch from torch import nn diff --git a/zeta/nn/modules/transformations.py b/zeta/nn/modules/transformations.py index f938c179..d72c407f 100644 --- a/zeta/nn/modules/transformations.py +++ b/zeta/nn/modules/transformations.py @@ -1,6 +1,6 @@ # Copyright (c) Meta Platforms, Inc. and affiliates -from typing import Optional, Sequence, Tuple +from typing import Optional, Tuple import torch import torch.nn as nn diff --git a/zeta/nn/modules/video_autoencoder.py b/zeta/nn/modules/video_autoencoder.py index 3ead357d..3576c368 100644 --- a/zeta/nn/modules/video_autoencoder.py +++ b/zeta/nn/modules/video_autoencoder.py @@ -1,8 +1,7 @@ -import torch from torch import nn from typing import Union, Tuple import torch.nn.functional as F -from einops import rearrange, reduce, repeat, pack, unpack +from einops import pack, unpack # helper diff --git a/zeta/ops/async_softmax.py b/zeta/ops/async_softmax.py index 5fede6a9..85cac3c8 100644 --- a/zeta/ops/async_softmax.py +++ b/zeta/ops/async_softmax.py @@ -1,6 +1,5 @@ # Import necessary libraries import torch -import torch.nn.functional as F from torch import nn diff --git a/zeta/optim/batched_optimizer.py b/zeta/optim/batched_optimizer.py index 71248d7c..36cc0b5e 100644 --- a/zeta/optim/batched_optimizer.py +++ b/zeta/optim/batched_optimizer.py @@ -1,6 +1,5 @@ import contextlib import logging -import random from collections import defaultdict from typing import List, Optional, Tuple, Union @@ -207,7 +206,6 @@ def step(self, closure=None): with torch.enable_grad(): loss = closure() - batch = True for group, group_params_names in zip( self.param_groups, self.parameters_names @@ -471,7 +469,7 @@ def _step_one_batch( as a batch) state: state-dict for p, to look up the optimizer state """ - lr = group["lr"] + group["lr"] size_update_period = group["size_update_period"] beta1 = group["betas"][0] @@ -535,7 +533,7 @@ def _size_update( param_max_rms = group["param_max_rms"] eps = group["eps"] step = state["step"] - batch_size = p.shape[0] + p.shape[0] size_update_period = scale_grads.shape[0] # correct beta2 for the size update period: we will have @@ -596,7 +594,7 @@ def _step(self, group: dict, p: Tensor, state: dict): beta1, beta2 = group["betas"] eps = group["eps"] param_min_rms = group["param_min_rms"] - step = state["step"] + state["step"] exp_avg_sq = state["exp_avg_sq"] exp_avg_sq.mul_(beta2).addcmul_(grad, grad, value=(1 - beta2)) diff --git a/zeta/rl/actor_critic.py b/zeta/rl/actor_critic.py index 8b50b4c0..80e705a9 100644 --- a/zeta/rl/actor_critic.py +++ b/zeta/rl/actor_critic.py @@ -1,6 +1,5 @@ import torch from torch import nn -import torch.nn as optim class ActorCritic(nn.Module): diff --git a/zeta/rl/ppo.py b/zeta/rl/ppo.py index 0f4e5026..00bd243d 100644 --- a/zeta/rl/ppo.py +++ b/zeta/rl/ppo.py @@ -1,7 +1,5 @@ -import numpy as np import torch import torch.nn as nn -import torch.optim as optim class ActorCritic(nn.Module): diff --git a/zeta/structs/hierarchical_transformer.py b/zeta/structs/hierarchical_transformer.py index 7447c24e..d7c75d1b 100644 --- a/zeta/structs/hierarchical_transformer.py +++ b/zeta/structs/hierarchical_transformer.py @@ -7,7 +7,7 @@ import torch.nn.functional as F from einops import rearrange, repeat from einops.layers.torch import Rearrange -from torch import einsum, nn +from torch import nn from vector_quantize_pytorch import RandomProjectionQuantizer from zeta.structs.attn_layers import rotate_half diff --git a/zeta/structs/mag_vit.py b/zeta/structs/mag_vit.py index 4f5f102d..e31350d1 100644 --- a/zeta/structs/mag_vit.py +++ b/zeta/structs/mag_vit.py @@ -1,10 +1,9 @@ # from lucidrain -from math import log2 import torch import torch.nn.functional as F -from torch import nn, einsum, Tensor +from torch import nn, Tensor from torch.nn import Module, ModuleList from collections import namedtuple diff --git a/zeta/structs/multi_modal_projector.py b/zeta/structs/multi_modal_projector.py index 8ce56246..c5e3eefb 100644 --- a/zeta/structs/multi_modal_projector.py +++ b/zeta/structs/multi_modal_projector.py @@ -1,4 +1,3 @@ -import torch import torch.nn as nn import re diff --git a/zeta/tokenizers/tokenmonster.py b/zeta/tokenizers/tokenmonster.py index b4bf5570..b6302b4a 100644 --- a/zeta/tokenizers/tokenmonster.py +++ b/zeta/tokenizers/tokenmonster.py @@ -1,4 +1,3 @@ -import numpy as np import tokenmonster diff --git a/zeta/training/hive_trainer.py b/zeta/training/hive_trainer.py index f5fc8002..9496d8fd 100644 --- a/zeta/training/hive_trainer.py +++ b/zeta/training/hive_trainer.py @@ -17,8 +17,6 @@ """ -import torch -import torch.distributed as dist import threading from zeta.training.train import Trainer diff --git a/zeta/utils/save_load_wrapper.py b/zeta/utils/save_load_wrapper.py index 133114ea..b1d63e19 100644 --- a/zeta/utils/save_load_wrapper.py +++ b/zeta/utils/save_load_wrapper.py @@ -3,7 +3,6 @@ import torch from beartype import beartype from beartype.typing import Optional, Callable -from packaging import version from torch.nn import Module From bdc229aaadb4050287c5836773e8a457bd8a2696 Mon Sep 17 00:00:00 2001 From: Kye Date: Wed, 20 Dec 2023 03:12:35 -0500 Subject: [PATCH 165/587] [DecoupledLionW8Bit] [zeta.cli][zeta.main][zeta cloud] --- docs/corporate/zeta_cloud.md | 103 +++++ pyproject.toml | 8 +- requirements.txt | 3 +- tests/cloud/main.py | 101 +++++ tests/nn/modules/test_image_projector.py | 8 +- tests/ops/test_einops_poly.py | 4 +- tests/optim/lion8b.py | 131 ++++++ zeta/__init__.py | 1 + zeta/cli/__init__.py | 0 zeta/cli/main.py | 66 +++ zeta/cloud/__init__.py | 4 + zeta/cloud/main.py | 70 ++++ zeta/cloud/sky_api.py | 202 ++++++++++ zeta/models/__init__.py | 2 +- zeta/optim/__init__.py | 2 + zeta/optim/batched_optimizer.py | 1 - zeta/optim/lion8b.py | 490 +++++++++++++++++++++++ 17 files changed, 1182 insertions(+), 14 deletions(-) create mode 100644 tests/cloud/main.py create mode 100644 tests/optim/lion8b.py create mode 100644 zeta/cli/__init__.py create mode 100644 zeta/cli/main.py create mode 100644 zeta/cloud/__init__.py create mode 100644 zeta/cloud/main.py create mode 100644 zeta/cloud/sky_api.py create mode 100644 zeta/optim/lion8b.py diff --git a/docs/corporate/zeta_cloud.md b/docs/corporate/zeta_cloud.md index 5f20b967..61cce3e1 100644 --- a/docs/corporate/zeta_cloud.md +++ b/docs/corporate/zeta_cloud.md @@ -60,3 +60,106 @@ The estimated timeline for shipping Zeta Cloud is as follows: | API Access for Third-Party Integrations | Providing API access for integration with other tools and services. | Developers, businesses needing integrations. | Monthly/Annual subscription or pay-per-use. | + + +# GTM - Go To Market + +### **Contents** + +1. Positioning Statement +2. Early Adopter Segments +3. Branding +4. Channel Strategy +5. Initial Marketing Methods +6. Testing Plan +7. LTV/CAC + +--- + +### **1. Positioning Statement** + +*For AI engineers and data scientists who struggle with the complexities of model training and deployment, Zeta Cloud is a new cloud-based AI service that simplifies these processes. Unlike traditional cloud services, we offer an automated, user-friendly platform with a strong focus on accessibility and efficiency.* + +--- + +### **2. Early Adopter Segments** + +**Segment Characteristics:** +- Demographics: AI engineers and data scientists in mid-sized tech companies and startups. +- Unmet Needs: Simplification of AI model deployment, efficient resource management, cost-effective scaling. +- Behaviors: Active users of cloud computing services, frequent participants in tech forums and communities. +- Psychographics: Value innovation, efficiency, and user-friendly interfaces. +- Multi-party Decision Making: End users (engineers and scientists), economic buyers (company executives), and key influencers (tech thought leaders and industry experts). + +**Implications for Targeted Marketing:** +- Focused engagement in tech forums and communities. +- Tailored content marketing addressing specific needs and pain points. +- Leveraging influencers and thought leaders to reach decision-makers. + +--- + +### **3. Branding** + +**Strengths of Product Name:** +- 'Zeta Cloud' conveys a sense of technological advancement and cloud-based efficiency. + +**Brand Association Words:** +- Innovative, Efficient, User-Friendly, Accessible, Empowering, Reliable. + +**Aspirational Brand Similarities:** +- Brands like AWS, Google Cloud, and Azure for their technological prowess and market presence. + +--- + +### **4. Channel Strategy** + +**Channels:** +- Own Website: Primary channel for direct sales and customer engagement. +- Sales Force: Blend of inside sales for smaller accounts and field sales for larger, enterprise-level deals. +- Channel Partners: Collaborations with tech marketplaces and value-added resellers. + +**Partner Responsibilities and Margins:** +- Education and initial engagement by Zeta Cloud, with partners focusing on closing sales and after-sales service. +- Attractive margins to incentivize partner engagement and commitment. + +--- + +### **5. Initial Marketing Methods** + +**Hypothesized Effective Methods:** +1. **Content Marketing:** Strength - establishes thought leadership; Weakness - time-intensive. +2. **Social Media and Community Engagement:** Strength - builds brand awareness; Weakness - requires consistent, high-quality engagement. +3. **Paid Digital Advertising (e.g., Google Ads, LinkedIn):** Strength - targets specific segments; Weakness - can be costly. + +**Performance Metrics:** +- Engagement rates, conversion rates, customer acquisition costs. + +**Secondary Marketing Methods:** +- Email marketing, PR activities, and webinars; secondary due to longer lead times and higher resource requirements. + +--- + +### **6. Testing Plan** + +**Completed Tests:** +- Initial A/B testing on website messaging and layout. + +**Upcoming Tests:** +- Content marketing effectiveness: Measuring engagement and conversion rates from different content types. +- Social media ad campaigns: Assessing customer acquisition costs and conversion rates. +- Budget for tests: Approximately $20,000 over three months. + +--- + +### **7. LTV/CAC** + +**LTV Targets:** +- Average annual revenue per customer: $5,000. +- Variable contribution margin: 70%. +- Retention rate: 85% annually. + +**CAC Projections:** +- Mix of free and paid methods: 40% free methods (referrals), 60% paid methods. +- Viral coefficient: 0.5. +- CAC for paid methods: $500 - $1,000, varying by channel. + diff --git a/pyproject.toml b/pyproject.toml index b70ed317..bfe9dbe9 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,6 +1,6 @@ [tool.poetry] name = "zetascale" -version = "0.9.9" +version = "1.1.6" description = "Transformers at zeta scales" authors = ["Zeta Team "] license = "MIT" @@ -40,6 +40,9 @@ beartype = "0.16.4" tiktoken = "0.5.2" tqdm = "4.66.1" rich = "13.7.0" +argparse = "^1.4.0" +skypilot = "0.4.1" + [build-system] requires = ["poetry-core>=1.0.0"] @@ -73,6 +76,7 @@ preview = true - +[tool.poetry.scripts] +zeta = 'zeta.cli.main:main' diff --git a/requirements.txt b/requirements.txt index 7e8f4724..87e024db 100644 --- a/requirements.txt +++ b/requirements.txt @@ -28,4 +28,5 @@ torchaudio==2.1.2 mkdocs mkdocs-material mkdocs-glightbox -glightbox \ No newline at end of file +skypilot==0.4.1 +argparse \ No newline at end of file diff --git a/tests/cloud/main.py b/tests/cloud/main.py new file mode 100644 index 00000000..46a81395 --- /dev/null +++ b/tests/cloud/main.py @@ -0,0 +1,101 @@ +import pytest +from unittest.mock import MagicMock, patch +from zeta.cloud.main import zetacloud + + +@patch("zeta.cloud.main.skyapi") +@patch("zeta.cloud.main.logger") +def test_zetacloud_basic(mock_logger, mock_skyapi): + # Arrange + mock_task = MagicMock() + mock_skyapi.create_task.return_value = mock_task + + # Act + zetacloud(task_name="test_task") + + # Assert + mock_skyapi.create_task.assert_called_once_with( + name="test_task", + setup="pip install requirements.txt", + run="python train.py", + workdir=".", + ) + mock_logger.info.assert_called_with( + "Task: {} has been created".format(mock_task) + ) + mock_task.set_resources.assert_called_once() + mock_skyapi.launch.assert_called_once_with(mock_task, "[ZetaTrainingRun]") + + +# ... replicate this test with different arguments for thoroughness + + +@patch("zeta.cloud.main.skyapi") +@patch("zeta.cloud.main.logger") +def test_zetacloud_with_stop(mock_logger, mock_skyapi): + # Arrange + mock_task = MagicMock() + mock_skyapi.create_task.return_value = mock_task + + # Act + zetacloud(task_name="test_task", stop=True) + + # Assert + mock_skyapi.stop.assert_called_once_with("[ZetaTrainingRun]") + mock_logger.info.assert_called_with( + "Cluster: [ZetaTrainingRun] has been stopped" + ) + + +@patch("zeta.cloud.main.skyapi") +@patch("zeta.cloud.main.logger") +def test_zetacloud_with_down(mock_logger, mock_skyapi): + # Arrange + mock_task = MagicMock() + mock_skyapi.create_task.return_value = mock_task + + # Act + zetacloud(task_name="test_task", down=True) + + # Assert + mock_skyapi.down.assert_called_once_with("[ZetaTrainingRun]") + mock_logger.info.assert_called_with( + "Cluster: [ZetaTrainingRun] has been deleted" + ) + + +@patch("zeta.cloud.main.skyapi") +@patch("zeta.cloud.main.logger") +def test_zetacloud_with_status_report(mock_logger, mock_skyapi): + # Arrange + mock_task = MagicMock() + mock_skyapi.create_task.return_value = mock_task + + # Act + zetacloud(task_name="test_task", status_report=True) + + # Assert + mock_skyapi.status.assert_called_once_with( + cluster_names=["[ZetaTrainingRun]"] + ) + mock_logger.info.assert_called_with( + "Cluster: [ZetaTrainingRun] has been reported on" + ) + + +@patch("zeta.cloud.main.skyapi") +@patch("zeta.cloud.main.logger") +def test_zetacloud_with_exception(mock_logger, mock_skyapi): + # Arrange + mock_skyapi.create_task.side_effect = Exception("Test exception") + + # Act + with pytest.raises(Exception): + zetacloud(task_name="test_task") + + # Assert + mock_logger.error.assert_called_once() + + +# ... replicate similar tests with minor changes for thoroughness +# Examples: test different cloud providers, test other parameter combinations, etc. diff --git a/tests/nn/modules/test_image_projector.py b/tests/nn/modules/test_image_projector.py index 58f3e2a2..92d696d9 100644 --- a/tests/nn/modules/test_image_projector.py +++ b/tests/nn/modules/test_image_projector.py @@ -249,17 +249,13 @@ def test_patch_projector_output_shape_consistency(sample_input_tensor): # Test case for edge case: invalid max_patch_size def test_patch_projector_invalid_max_patch_size(): with pytest.raises(ValueError): - ImagePatchCreatorProjector( - max_patch_size=0, embedding_dim=768 - ) + ImagePatchCreatorProjector(max_patch_size=0, embedding_dim=768) # Test case for edge case: invalid embedding_dim def test_patch_projector_invalid_embedding_dim(): with pytest.raises(ValueError): - ImagePatchCreatorProjector( - max_patch_size=16, embedding_dim=0 - ) + ImagePatchCreatorProjector(max_patch_size=16, embedding_dim=0) # Test case for edge case: invalid input tensor shape diff --git a/tests/ops/test_einops_poly.py b/tests/ops/test_einops_poly.py index a1ad7c44..85f0f14e 100644 --- a/tests/ops/test_einops_poly.py +++ b/tests/ops/test_einops_poly.py @@ -148,9 +148,7 @@ def test_reduce_many_with_sum_reduction(): # Additional tests for rearrange_with_anon_dims function def test_rearrange_with_anon_dims_invalid_dim_list(): with pytest.raises(ValueError): - rearrange_with_anon_dims( - input_data, pattern="...a b c", a=(1,) - ) + rearrange_with_anon_dims(input_data, pattern="...a b c", a=(1,)) def test_rearrange_with_anon_dims_invalid_pattern(): diff --git a/tests/optim/lion8b.py b/tests/optim/lion8b.py new file mode 100644 index 00000000..75fa2b8b --- /dev/null +++ b/tests/optim/lion8b.py @@ -0,0 +1,131 @@ +import pytest +import torch +from zeta.optim.lion8b import DecoupledLionW_8bit + + +def test_optimizer_init(): + params = [torch.randn(3, 3, requires_grad=True) for _ in range(2)] + optimizer = DecoupledLionW_8bit(params) + + assert len(optimizer.param_groups) == 1 + assert optimizer.param_groups[0]["lr"] == 1e-3 + assert optimizer.param_groups[0]["betas"] == (0.9, 0.99) + assert optimizer.param_groups[0]["weight_decay"] == 0 + + +def test_optimizer_init_invalid_lr(): + params = [torch.randn(3, 3, requires_grad=True) for _ in range(2)] + with pytest.raises(ValueError): + DecoupledLionW_8bit(params, lr=-1) + + +def test_optimizer_init_invalid_betas(): + params = [torch.randn(3, 3, requires_grad=True) for _ in range(2)] + with pytest.raises(ValueError): + DecoupledLionW_8bit(params, betas=(-1, 0.99)) + with pytest.raises(ValueError): + DecoupledLionW_8bit(params, betas=(0.9, -1)) + + +def test_optimizer_init_invalid_weight_decay(): + params = [torch.randn(3, 3, requires_grad=True) for _ in range(2)] + with pytest.raises(ValueError): + DecoupledLionW_8bit(params, weight_decay=-1) + + +def test_step_without_closure(): + params = [torch.randn(3, 3, requires_grad=True) for _ in range(2)] + optimizer = DecoupledLionW_8bit(params) + loss = optimizer.step() + + assert loss is None + + +def test_step_with_closure(): + params = [torch.randn(3, 3, requires_grad=True) for _ in range(2)] + optimizer = DecoupledLionW_8bit(params) + closure = lambda: torch.sum(params[0] ** 2 + params[1] ** 2) + loss = optimizer.step(closure) + + assert loss is not None + assert loss == closure() + + +def test_step_param_no_grad(): + params = [torch.randn(3, 3, requires_grad=False) for _ in range(2)] + optimizer = DecoupledLionW_8bit(params) + optimizer.step_param(params[0], optimizer.param_groups[0]) + + assert params[0].grad is None + + +def test_step_param_with_grad(): + params = [torch.randn(3, 3, requires_grad=True) for _ in range(2)] + optimizer = DecoupledLionW_8bit(params) + closure = lambda: torch.sum(params[0] ** 2 + params[1] ** 2) + closure().backward() + optimizer.step_param(params[0], optimizer.param_groups[0]) + + assert params[0].grad is not None + + +def test_step_param_not_cuda(): + params = [torch.randn(3, 3, requires_grad=True) for _ in range(2)] + optimizer = DecoupledLionW_8bit(params, quantize=True) + closure = lambda: torch.sum(params[0] ** 2 + params[1] ** 2) + closure().backward() + + with pytest.raises(NotImplementedError): + optimizer.step_param(params[0], optimizer.param_groups[0]) + + +def test_optimizer_init_invalid_weight_decay(): + params = [torch.randn(3, 3, requires_grad=True) for _ in range(2)] + with pytest.raises(ValueError): + DecoupledLionW_8bit(params, weight_decay=-1) + + +def test_step_without_closure(): + params = [torch.randn(3, 3, requires_grad=True) for _ in range(2)] + optimizer = DecoupledLionW_8bit(params) + loss = optimizer.step() + + assert loss is None + + +def test_step_with_closure(): + params = [torch.randn(3, 3, requires_grad=True) for _ in range(2)] + optimizer = DecoupledLionW_8bit(params) + closure = lambda: torch.sum(params[0] ** 2 + params[1] ** 2) + loss = optimizer.step(closure) + + assert loss is not None + assert loss == closure() + + +def test_step_param_no_grad(): + params = [torch.randn(3, 3, requires_grad=False) for _ in range(2)] + optimizer = DecoupledLionW_8bit(params) + optimizer.step_param(params[0], optimizer.param_groups[0]) + + assert params[0].grad is None + + +def test_step_param_with_grad(): + params = [torch.randn(3, 3, requires_grad=True) for _ in range(2)] + optimizer = DecoupledLionW_8bit(params) + closure = lambda: torch.sum(params[0] ** 2 + params[1] ** 2) + closure().backward() + optimizer.step_param(params[0], optimizer.param_groups[0]) + + assert params[0].grad is not None + + +def test_step_param_not_cuda(): + params = [torch.randn(3, 3, requires_grad=True) for _ in range(2)] + optimizer = DecoupledLionW_8bit(params, quantize=True) + closure = lambda: torch.sum(params[0] ** 2 + params[1] ** 2) + closure().backward() + + with pytest.raises(NotImplementedError): + optimizer.step_param(params[0], optimizer.param_groups[0]) diff --git a/zeta/__init__.py b/zeta/__init__.py index 31ae3141..e0099777 100644 --- a/zeta/__init__.py +++ b/zeta/__init__.py @@ -11,3 +11,4 @@ from zeta.optim import * # noqa: F403, E402 from zeta.ops import * # noqa: F403, E402 from zeta.quant import * # noqa: F403, E402 +from zeta.cloud import * # noqa: F403, E402 diff --git a/zeta/cli/__init__.py b/zeta/cli/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/zeta/cli/main.py b/zeta/cli/main.py new file mode 100644 index 00000000..98b5e2dc --- /dev/null +++ b/zeta/cli/main.py @@ -0,0 +1,66 @@ +import argparse +from zeta.cloud.main import zetacloud + + +def main(): + """Main function for the CLI + + Args: + task_name (str, optional): _description_. Defaults to None. + cluster_name (str, optional): _description_. Defaults to "[ZetaTrainingRun]". + cloud (Any, optional): _description_. Defaults to AWS(). + gpus (str, optional): _description_. Defaults to None. + + Examples: + $ zetacloud -t "test" -c "[ZetaTrainingRun]" -cl AWS -g "1 V100" + + + """ + parser = argparse.ArgumentParser(description="Zetacloud CLI") + parser.add_argument("-t", "--task_name", type=str, help="Task name") + parser.add_argument( + "-c", + "--cluster_name", + type=str, + default="[ZetaTrainingRun]", + help="Cluster name", + ) + parser.add_argument( + "-cl", "--cloud", type=str, default="AWS", help="Cloud provider" + ) + parser.add_argument("-g", "--gpus", type=str, help="GPUs") + parser.add_argument( + "-f", "--filename", type=str, default="train.py", help="Filename" + ) + parser.add_argument("-s", "--stop", action="store_true", help="Stop flag") + parser.add_argument("-d", "--down", action="store_true", help="Down flag") + parser.add_argument( + "-sr", "--status_report", action="store_true", help="Status report flag" + ) + + # Generate API key + # parser.add_argument( + # "-k", "--generate_api_key", action="store_true", help="Generate key flag" + # ) + + # Sign In + # parser.add_argument( + # "-si", "--sign_in", action="store_true", help="Sign in flag" + # ) + + args = parser.parse_args() + + zetacloud( + task_name=args.task_name, + cluster_name=args.cluster_name, + cloud=args.cloud, + gpus=args.gpus, + filename=args.filename, + stop=args.stop, + down=args.down, + status_report=args.status_report, + ) + + +# if __name__ == "__main__": +# main() diff --git a/zeta/cloud/__init__.py b/zeta/cloud/__init__.py new file mode 100644 index 00000000..05c279eb --- /dev/null +++ b/zeta/cloud/__init__.py @@ -0,0 +1,4 @@ +from zeta.cloud.sky_api import SkyInterface +from zeta.cloud.main import zetacloud + +__all__ = ["zetacloud", "SkyInterface"] diff --git a/zeta/cloud/main.py b/zeta/cloud/main.py new file mode 100644 index 00000000..e2760272 --- /dev/null +++ b/zeta/cloud/main.py @@ -0,0 +1,70 @@ +import logging +from typing import Any +from sky import Resources, AWS +from zeta.cloud.sky_api import SkyInterface + +skyapi = SkyInterface(stream_logs_enabled=True) + + +# Logger +logger = logging.getLogger(__name__) +logger.setLevel(logging.INFO) + + +def zetacloud( + task_name: str = None, + cluster_name: str = "[ZetaTrainingRun]", + cloud: Any = AWS(), + gpus: str = None, + filename: str = "train.py", + stop: bool = False, + down: bool = False, + status_report: bool = False, + *args, + **kwargs, +): + """zetacloud + + Args: + task_name (str, optional): _description_. Defaults to None. + cluster_name (str, optional): _description_. Defaults to "[ZetaTrainingRun]". + cloud (Any, optional): _description_. Defaults to AWS(). + gpus (str, optional): _description_. Defaults to None. + """ + try: + task = skyapi.create_task( + name=task_name, + setup="pip install -r requirements.txt", + run=f"python {filename}", + workdir=".", + ) + logger.info(f"Task: {task} has been created") + + # Set the resources + task.set_resources(Resources(accelerators=gpus)) + # logger.info(f"Resources: {task.resources} have been set") + + # Execute the task on the cluster + execution = skyapi.launch(task, cluster_name) + print(execution) + logger.info( + f"Task: {task} has been launched on cluster: {cluster_name}" + ) + + if stop: + skyapi.stop(cluster_name) + logger.info(f"Cluster: {cluster_name} has been stopped") + + if down: + skyapi.down(cluster_name) + logger.info(f"Cluster: {cluster_name} has been deleted") + + if status_report: + skyapi.status(cluster_names=[cluster_name]) + logger.info(f"Cluster: {cluster_name} has been reported on") + + except Exception as error: + print( + f"There has been an error: {error} the root cause is:" + f" {error.__cause__}" + ) diff --git a/zeta/cloud/sky_api.py b/zeta/cloud/sky_api.py new file mode 100644 index 00000000..6fd1f776 --- /dev/null +++ b/zeta/cloud/sky_api.py @@ -0,0 +1,202 @@ +from typing import List + +import sky +from sky import Task + + +class SkyInterface: + """ + + SkyInterface is a wrapper around the sky Python API. It provides a + simplified interface for launching, executing, stopping, starting, and + tearing down clusters. + + Attributes: + clusters (dict): A dictionary of clusters that have been launched. + The keys are the names of the clusters and the values are the handles + to the clusters. + + Methods: + launch: Launch a cluster + execute: Execute a task on a cluster + stop: Stop a cluster + start: Start a cluster + down: Tear down a cluster + status: Get the status of a cluster + autostop: Set the autostop of a cluster + + Example: + >>> sky_interface = SkyInterface() + >>> job_id = sky_interface.launch("task", "cluster_name") + >>> sky_interface.execute("task", "cluster_name") + >>> sky_interface.stop("cluster_name") + >>> sky_interface.start("cluster_name") + >>> sky_interface.down("cluster_name") + >>> sky_interface.status() + >>> sky_interface.autostop("cluster_name") + + + """ + + def __init__( + self, + task_name: str = None, + cluster_name: str = None, + gpus: str = "T4:1", + stream_logs_enabled: bool = False, + *args, + **kwargs, + ): + self.task_name = task_name + self.cluster_name = cluster_name + self.gpus = gpus + self.stream_logs_enabled = stream_logs_enabled + self.clusters = {} + + def launch(self, task: Task = None, cluster_name: str = None, **kwargs): + """Launch a task on a cluster + + Args: + task (str): code to execute on the cluster + cluster_name (_type_, optional): _description_. Defaults to None. + + Returns: + _type_: _description_ + """ + cluster = None + try: + cluster = sky.launch( + task=task, + cluster_name=cluster_name, + stream_logs=self.stream_logs_enabled, + **kwargs, + ) + print(f"Launched job {cluster} on cluster {cluster_name}") + return cluster + except Exception as error: + # Deep error logging + print( + f"Error launching job {cluster} on cluster {cluster_name} with" + f" error {error}" + ) + raise error + + def execute(self, task: Task = None, cluster_name: str = None, **kwargs): + """Execute a task on a cluster + + Args: + task (_type_): _description_ + cluster_name (_type_): _description_ + + Raises: + ValueError: _description_ + + Returns: + _type_: _description_ + """ + if cluster_name not in self.clusters: + raise ValueError("Cluster {} does not exist".format(cluster_name)) + try: + return sky.exec( + task=task, + cluster_name=cluster_name, + stream_logs=self.stream_logs_enabled, + **kwargs, + ) + except Exception as e: + print("Error executing on cluster:", e) + + def stop(self, cluster_name: str = None, **kwargs): + """Stop a cluster + + Args: + cluster_name (str): name of the cluster to stop + """ + try: + sky.stop(cluster_name, **kwargs) + except (ValueError, RuntimeError) as e: + print("Error stopping cluster:", e) + + def start(self, cluster_name: str = None, **kwargs): + """start a cluster + + Args: + cluster_name (str): name of the cluster to start + """ + try: + sky.start(cluster_name, **kwargs) + except Exception as e: + print("Error starting cluster:", e) + + def down(self, cluster_name: str = None, **kwargs): + """Down a cluster + + Args: + cluster_name (str): name of the cluster to tear down + """ + try: + sky.down(cluster_name, **kwargs) + if cluster_name in self.clusters: + del self.clusters[cluster_name] + except (ValueError, RuntimeError) as e: + print("Error tearing down cluster:", e) + + def status(self, cluster_names: List[str] = None, **kwargs): + """Save a cluster + + Returns: + r: the status of the cluster + """ + try: + return sky.status(cluster_names, **kwargs) + except Exception as e: + print("Error getting status:", e) + + def autostop(self, cluster_name: str = None, **kwargs): + """Autostop a cluster + + Args: + cluster_name (str): name of the cluster to autostop + """ + try: + sky.autostop(cluster_name, **kwargs) + except Exception as e: + print("Error setting autostop:", e) + + def create_task( + self, + name: str = None, + setup: str = None, + run: str = None, + workdir: str = None, + task: str = None, + *args, + **kwargs, + ): + """_summary_ + + Args: + name (str, optional): _description_. Defaults to None. + setup (str, optional): _description_. Defaults to None. + run (str, optional): _description_. Defaults to None. + workdir (str, optional): _description_. Defaults to None. + task (str, optional): _description_. Defaults to None. + + Returns: + _type_: _description_ + + # A Task that will sync up local workdir '.', containing + # requirements.txt and train.py. + sky.Task(setup='pip install requirements.txt', + run='python train.py', + workdir='.') + + # An empty Task for provisioning a cluster. + task = sky.Task(num_nodes=n).set_resources(...) + + # Chaining setters. + sky.Task().set_resources(...).set_file_mounts(...) + """ + return Task( + name=name, setup=setup, run=run, workdir=workdir, *args, **kwargs + ) diff --git a/zeta/models/__init__.py b/zeta/models/__init__.py index 9dab6ca3..5d17fc25 100644 --- a/zeta/models/__init__.py +++ b/zeta/models/__init__.py @@ -22,4 +22,4 @@ "LLama2", "Andromeda", "NaViT", -] \ No newline at end of file +] diff --git a/zeta/optim/__init__.py b/zeta/optim/__init__.py index 5b6cea92..f9009c4f 100644 --- a/zeta/optim/__init__.py +++ b/zeta/optim/__init__.py @@ -12,6 +12,7 @@ from zeta.optim.stable_adam import StableAdamWUnfused from zeta.optim.gradient_ascent import GradientAscent from zeta.optim.gradient_equillibrum import GradientEquilibrum +from zeta.optim.lion8b import DecoupledLionW8Bit __all__ = [ "BatchedOptimizer", @@ -26,4 +27,5 @@ "StableAdamWUnfused", "GradientAscent", "GradientEquilibrum", + "DecoupledLionW8Bit" ] diff --git a/zeta/optim/batched_optimizer.py b/zeta/optim/batched_optimizer.py index 36cc0b5e..8b0300a8 100644 --- a/zeta/optim/batched_optimizer.py +++ b/zeta/optim/batched_optimizer.py @@ -206,7 +206,6 @@ def step(self, closure=None): with torch.enable_grad(): loss = closure() - for group, group_params_names in zip( self.param_groups, self.parameters_names ): diff --git a/zeta/optim/lion8b.py b/zeta/optim/lion8b.py new file mode 100644 index 00000000..31e147a1 --- /dev/null +++ b/zeta/optim/lion8b.py @@ -0,0 +1,490 @@ +from typing import Any, Callable, Dict, Iterable, Optional, Tuple, Union + +import torch + + +class DecoupledLionW8Bit(torch.optim.Optimizer): + """LION optimizer with ~8 bits of state per parameter. + + This optimizer is a drop-in replacement for our regular LION optimizer + with decoupled weight decay, but uses less memory, writes smaller + checkpoints, and offers almost-numerically-identical convergence. + + Its state saved per parameter is just an int8, though there are auxiliary + scaling factors that bring the total memory per parameter to ~8.5 bits. + The exact quantization scheme is considered an implementation detail + and may change. + + When training on CPUs, however, no quantization will actually take place. + + See the LION paper (https://arxiv.org/abs/2302.06675) for details about + the algorithm itself. + + Args: + params: iterable of parameters to optimize or dicts defining + parameter groups + lr: learning rate + betas: two coefficients between 0 and 1 used to combine the current + gradients and the momentum. The first coefficient is the weight + of the gradient when computing the update. The second is the + weight of the gradient when computing the new momentum. + weight decay: Weights are multiplied by 1 - `weight_decay` after + each optimizer step. Note that we use decoupled weight decay, + meaning that this decay does not contribute to the momentum. + compress_state_dict: if True, this optimizer's `state_dict` will + include quantized optimizer states. Otherwise, the optimizer + states are converted to bfloat16 Tensors matching the shapes of + their corresponding parameters. The former uses ~8.5 bits per + parameter while the latter uses 16 bits per parameter. However, + the former is less thoroughly tested and will not work with + FSDP or other weight sharding approaches. + quantize: If False, optimizer states will not actually be quantized. + This option is available so that one can easily debug whether + the quantization is causing any convergence issues. Because + quantization is only supported for CUDA parameters, attempting to + update a non-CUDA tensor will raise an error. + error_correction: If True, float16 and bfloat16 parameters will be + given an extra state variable, "errors." This tensor will be + of the same shape as the parameter but of dtype uint8. This + auxiliary variable is used to better approximate float32 updates + by retaining information across optimizer steps. + + Raises: + NotImplementedError - If any of `quantize`, `compress_state_dict`, + or `error_correction` are `True` and either a) there is no CUDA + device, or b) step() is executed on a non-CUDA parameter. + """ + + def __init__( + self, + params: Union[Iterable[torch.Tensor], Iterable[Dict[str, Any]]], + lr: float = 1e-3, + betas: Tuple[float, float] = (0.9, 0.99), + weight_decay: float = 0, + quantize: bool = True, + compress_state_dict: bool = False, + error_correction: bool = False, + _fused: bool = True, # XXX this flag is mostly for testing... + ): + if lr < 0.0: + raise ValueError("Invalid learning rate: {}".format(lr)) + if not 0.0 <= betas[0] <= 1.0: + raise ValueError( + "Invalid beta parameter at index 0: {}".format(betas[0]) + ) + if not 0.0 <= betas[1] <= 1.0: + raise ValueError( + "Invalid beta parameter at index 1: {}".format(betas[1]) + ) + if not 0.0 <= weight_decay: + raise ValueError( + "Invalid weight_decay value: {}".format(weight_decay) + ) + + if not torch.cuda.is_available(): + needs_cuda = " requires a CUDA device." + if quantize: + raise NotImplementedError("Quantization" + needs_cuda) + if error_correction: + raise NotImplementedError("Error correction" + needs_cuda) + if compress_state_dict: + raise NotImplementedError("Quantized state dict" + needs_cuda) + + _fused = _fused and quantize + self._quantize = quantize + self._error_correction = error_correction + self._compress_state_dict = compress_state_dict + + defaults = { + "lr": lr, + "initial_lr": lr, + "betas": betas, + "weight_decay": weight_decay, + "fused": _fused, + } + super().__init__(params, defaults) + + @torch.no_grad() + def step(self, closure: Optional[Callable] = None): + loss = None + if closure is not None: + with torch.enable_grad(): + loss = closure() + + for group in self.param_groups: + for p in group["params"]: + self.step_param(p, group) + + return loss + + def step_param(self, p: torch.Tensor, hparams: Dict[str, Any]) -> None: + if not p.requires_grad or p.grad is None: + return + if self._quantize and not p.is_cuda: + raise NotImplementedError( + f"Can't use quantization with param on {p.device} " + + f"({p.shape}, {p.dtype}). If you need " + + "to use DecoupledLionW_8bit without a CUDA device, try " + + "creating this optimizer with quantize=False." + ) + state = self.state[p] # type:ignore using tensor as key + if "exp_avg" not in state: + mom = torch.zeros_like(p) + state["exp_avg"] = _MaybeQuantizedTensor( + mom, try_quantize=self._quantize + ) + need_errs = (p.dtype != torch.float32) and self._error_correction + if state.get("errors") is None and need_errs: + numel = p.numel() + numel += numel % 2 # ensure even number of bytes + errors = torch.zeros(numel, dtype=torch.uint8, device=p.device) + # as of torch 2.1, FSDP can't shard ints for no reason + state["errors"] = errors.view(torch.bfloat16) + decay_factor = hparams["weight_decay"] + decay_factor *= hparams["lr"] / hparams["initial_lr"] + errors: Optional[torch.Tensor] = None + if "errors" in state: + errors = state["errors"] + assert errors is not None # pyright + errors = errors.view(dtype=torch.uint8) + errors = errors[: p.numel()].view( + p.shape + ) # strip padding + reshape + _lion8b_step( + momentums=state["exp_avg"], + weights=p, + grads=p.grad, + beta1=hparams["betas"][0], + beta2=hparams["betas"][1], + lr=hparams["lr"], + weight_decay=decay_factor, + fused=hparams["fused"], + errors=errors, + ) + + def __setstate__(self, state: Dict[str, Dict[str, Any]]) -> None: + # we override this function to quantize optimizer states when + # loading a state dict + opt_state, _ = state.values() # other val is param_groups + for param_id in opt_state: + param_state = opt_state[param_id] + new_state = {} + if any(k.startswith("exp_avg") for k in param_state): + # the keys can either be just "exp_avg" or + # "exp_avg::quantized" and "exp_avg::scales", depending on + # whether we saved it as quantized or not. The former case + # gives us interop with regular LION. + qtensor = _MaybeQuantizedTensor( + None, try_quantize=self._quantize + ) + qtensor.load_state_dict(param_state, name="exp_avg") + new_state["exp_avg"] = qtensor + if "errors" in param_state: + # we need to cast back to the correct dtype since optimizer + # load_state_dict casts to param dtype for fp params; see + # https://github.com/pytorch/pytorch/blob/a25eee1d77d93079614fab3ea4ac66e64fb2343b/torch/optim/optimizer.py#L626C7-L626C7 # noqa + errs = ( + param_state["errors"] + .to(dtype=torch.uint8) + .view(torch.bfloat16) + ) + new_state["errors"] = errs + opt_state[param_id] = new_state + super().__setstate__(state) + + def state_dict(self): + # If the user hasn't opted into storing compressed state dicts + # we have to make sure our states are regular torch.Tensors. This + # is mostly needed to make FSDP happy in the case that we want to + # resume training with a number of devices where + # (param numel / device count) % quantization group size != 0 + # for any param. + d = super().state_dict() + opt_state, _ = d.values() # other val is param_groups + for param_id in opt_state: + # make a copy so that we don't mutate our self.state; opt_state + # isn't the same as self.state, but its consituent dicts are + # the same as those in self.state + param_state = {k: v for k, v in opt_state[param_id].items()} + if "exp_avg" in param_state: # true if we've taken any steps + qtensor = param_state.pop("exp_avg") + assert isinstance(qtensor, _MaybeQuantizedTensor) # pyright + param_state.update( + qtensor.state_dict( + name="exp_avg", + allow_quantized=self._compress_state_dict, + ) + ) + if "errors" in param_state: + # fsdp apparently needs the states to be the same shape + # as the params + param_state["errors"] = ( + param_state["errors"] + .view(torch.uint8) + .to(dtype=torch.bfloat16) + ) + opt_state[param_id] = param_state + return d + + +class _MaybeQuantizedTensor: + """Helper class so 8b LION doesn't have to know quantization details. + + Important points about this class: + * It handles CPU tensors not being quantized + * It knows how to save + load state dicts, handling both the quantized + and not quantized cases + * It implements some parts of the torch.Tensor interface that we need, + but is not intended to be a full torch.Tensor replacement + """ + + def __init__(self, data: Optional[torch.Tensor], try_quantize: bool = True): + super().__init__() + self.data: Optional[torch.Tensor] = None + self.quantized: Optional[torch.Tensor] = None + self.scales: Optional[torch.Tensor] = None + self._try_quantize = try_quantize and torch.cuda.is_available() + + # conditionally import CUDA kernels + self._f_encode = None + self._f_decode = None + if self._try_quantize: + from turbo import dequantize8b, quantize8b + + self._f_encode = quantize8b + self._f_decode = dequantize8b + + if data is not None: + self.set_data(data) + + def state_dict( + self, name: str, allow_quantized: bool = False + ) -> Dict[str, torch.Tensor]: + if self.is_quantized() and allow_quantized: + assert self.quantized is not None # pyright + assert self.scales is not None # pyright + return { + f"{name}::quantized": self.quantized, + f"{name}::scales": self.scales, + } + return {name: self.materialize().to(dtype=torch.bfloat16)} + + def load_state_dict(self, d: Dict[str, torch.Tensor], name: str) -> None: + # we allow other keys in the state dict for convenience, so you can + # just pass this the whole opt state for a parameters + d = {k: v for k, v in d.items() if k.startswith(name)} + if name in d: + if len(d) != 1: + raise ValueError( + f"If state dict specifies {name}, it must not " + + f"specify other keys. Got {list(d.keys())}" + ) + self.set_data(d[name]) + return + + self.quantized = d[f"{name}::quantized"].to(dtype=torch.int8) + self.scales = d[f"{name}::scales"].to(dtype=torch.float16) + + def set_data(self, data: torch.Tensor) -> None: + if self._try_quantize: + if not data.is_cuda: + raise NotImplementedError( + f"Attempting to quantize a non-CUDA {data.dtype} tensor " + + f"on device {data.device} with shape {data.shape}." + ) + self.data = None + assert self._f_encode is not None # pyright + self.quantized, self.scales = self._f_encode(data) + else: + self.data = data.to(dtype=torch.float32) + self.quantized = None + self.scales = None + + def is_quantized(self) -> bool: + return self.data is None + + def materialize(self) -> torch.Tensor: + if not self.is_quantized(): + assert self.data is not None # pyright + return self.data + assert self._f_decode is not None # pyright + assert self.quantized is not None # pyright + assert self.scales is not None # pyright + return self._f_decode(self.quantized, self.scales) + + @property # property to mirror Tensor interface + def is_cuda(self) -> bool: + if self.is_quantized(): + assert self.quantized is not None # pyright + return self.quantized.is_cuda + assert self.data is not None # pyright + return self.data.is_cuda + + @property # property to mirror Tensor interface + def shape(self) -> Tuple[int]: + if self.is_quantized(): + assert self.quantized is not None # pyright + return self.quantized.shape + assert self.data is not None # pyright + return self.data.shape + + def numel(self) -> int: + if self.is_quantized(): + assert self.quantized is not None # pyright + return self.quantized.numel() + assert self.data is not None # pyright + return self.data.numel() + + def __repr__(self): + return ( + f"{self.__class__.__name__} quantized={self.is_quantized()} " + + f"shape={self.shape}" + ) + + +def lion_step_unfused( + grads: torch.Tensor, + weights: torch.Tensor, + momentums: torch.Tensor, + lr: float, + beta1: float, + beta2: float, + weight_decay: float = 0, +) -> torch.Tensor: + # f32 cast to match fused impl + for compatibility with f32 grads or weights + momentums = momentums.to(dtype=torch.float32) + grads = grads.to(dtype=torch.float32) + + update = momentums.lerp(grads, 1 - beta1).sign_() + if weight_decay > 0: + weights.mul_(1.0 - weight_decay) + + weights.add_(update, alpha=-lr) + momentums.lerp_(grads, 1.0 - beta2) + return momentums # f32 upcast means not necessarily modified in place + + +def lion8b_step_fused( + grads: torch.Tensor, + weights: torch.Tensor, + momentums: torch.Tensor, + scales: torch.Tensor, + lr: float, + beta1: float, + beta2: float, + weight_decay: float, + errors: Optional[torch.Tensor] = None, +) -> None: + # just to save space in lists of allowed dtypes + f16, bf16, f32 = torch.float16, torch.bfloat16, torch.float32 + + use_errors = (errors is not None) and (weights.dtype in (f16, bf16)) + orig_shape = weights.shape + + # ------------------------------------------------ wall of error checking + quantize_group_size = 32 + num_groups = ( + weights.numel() + quantize_group_size - 1 + ) // quantize_group_size + if num_groups != scales.numel(): + raise ValueError( + f"Expected {num_groups} quantization scales but " + + f" received {scales.numel()}" + ) + + for name, tensor, allowed_dtypes in [ + ("grad", grads, (f16, bf16, f32)), + ("param", weights, (f16, bf16, f32)), + ("momentum", momentums, [torch.int8]), + ("scales", scales, [f16]), + ("errors", errors, [torch.uint8]), + ]: + if name == "errors" and not use_errors: + continue + if not tensor.is_cuda: + raise ValueError( + f"{name} must be on a CUDA device, not {tensor.device}" + ) + if not tensor.is_contiguous(): + raise ValueError(f"{name} is not contiguous!") + strides_unequal = tensor.stride() != weights.stride() + if name not in ("scales", "errors") and strides_unequal: + raise ValueError( + f"{name} stride {tensor.stride()} != " + + f"param stride {weights.stride()}" + ) + if tensor.dtype not in allowed_dtypes: + raise ValueError( + f"{name} must have dtype {allowed_dtypes}, not " + + f"{tensor.dtype}" + ) + if (name != "scales") and (orig_shape != tensor.shape): + raise ValueError( + f"Param shape {orig_shape} != " + f"{name} shape {tensor.shape}" + ) + + if grads.dtype in (torch.float16, torch.bfloat16): + allowed_dtypes = (grads.dtype, torch.float32) + if weights.dtype not in allowed_dtypes: + raise ValueError( + f"Weights must be f32 or match grad dtype {grads.dtype}" + ) + + # ------------------------------------------------ actual function call + from turbo import lion8b_step_cuda + + return lion8b_step_cuda( + grads=grads, + weights=weights, + momentums=momentums, + scales=scales, + lr=lr, + beta1=beta1, + beta2=beta2, + weight_decay=weight_decay, + errors=errors, + ) + + +def _lion8b_step( + grads: torch.Tensor, + weights: torch.Tensor, + momentums: _MaybeQuantizedTensor, + lr: float, + beta1: float, + beta2: float, + weight_decay: float = 0, + errors: Optional[torch.Tensor] = None, + fused: bool = True, +) -> None: + if fused and not momentums.is_quantized(): + raise NotImplementedError( + "Fused LION step only implemented with quantization." + ) + + if momentums.is_quantized() and fused: + assert momentums.quantized is not None # pyright + assert momentums.scales is not None # pyright + return lion8b_step_fused( + grads=grads, + weights=weights, + momentums=momentums.quantized, + scales=momentums.scales, + lr=lr, + beta1=beta1, + beta2=beta2, + weight_decay=weight_decay, + errors=errors, + ) + + momentums_float = momentums.materialize() + new_momentums = lion_step_unfused( + grads=grads, + weights=weights, + momentums=momentums_float, + lr=lr, + beta1=beta1, + beta2=beta2, + weight_decay=weight_decay, + ) + momentums.set_data(new_momentums) From bddc2df3c2e403e84d0efa3eafad895d6d1d5c91 Mon Sep 17 00:00:00 2001 From: Kye Date: Wed, 20 Dec 2023 12:02:08 -0500 Subject: [PATCH 166/587] [DOCS][FusedDenseGELUDense] --- docs/zeta/nn/modules/fused_gelu_dense.md | 140 ++++++++++++++++++++++ mkdocs.yml | 1 + tests/nn/modules/test_fused_gelu_dense.py | 70 +++++++++++ zeta/cloud/main.py | 2 +- zeta/nn/modules/fused_gelu_dense.py | 98 +++++++++++++++ zeta/optim/__init__.py | 2 +- 6 files changed, 311 insertions(+), 2 deletions(-) create mode 100644 docs/zeta/nn/modules/fused_gelu_dense.md create mode 100644 tests/nn/modules/test_fused_gelu_dense.py create mode 100644 zeta/nn/modules/fused_gelu_dense.py diff --git a/docs/zeta/nn/modules/fused_gelu_dense.md b/docs/zeta/nn/modules/fused_gelu_dense.md new file mode 100644 index 00000000..77868b86 --- /dev/null +++ b/docs/zeta/nn/modules/fused_gelu_dense.md @@ -0,0 +1,140 @@ +# `FusedDenseGELUDense` + +## Overview + +The `FusedDenseGELUDense` module is a versatile neural network layer designed for efficient computation of dense layers with GELU (Gaussian Error Linear Unit) activations. This documentation will provide an in-depth understanding of the module's architecture, purpose, parameters, and usage examples. + +## Table of Contents + +1. [Introduction](#introduction) +2. [Architecture](#architecture) +3. [Purpose](#purpose) +4. [Class Definition](#class-definition) + - [Parameters](#parameters) + - [Internal Layers](#internal-layers) +5. [Functionality and Usage](#functionality-and-usage) + - [Forward Pass](#forward-pass) +6. [Examples](#examples) + - [Basic Usage](#basic-usage) + - [Custom Configuration](#custom-configuration) + - [Quantization with bitsandbytes](#quantization-with-bitsandbytes) +7. [Additional Information](#additional-information) +8. [References](#references) + +--- + +## 1. Introduction + +The `FusedDenseGELUDense` module combines dense layers with GELU activations in a single neural network layer. This fusion improves computational efficiency and is particularly useful in various deep learning applications. + +## 2. Architecture + +The `FusedDenseGELUDense` layer consists of two dense sub-layers, each followed by a GELU activation function. It takes an input tensor and passes it through these sub-layers to produce the final output. + +## 3. Purpose + +The primary purpose of the `FusedDenseGELUDense` layer is to efficiently compute dense transformations with GELU activations. It is designed for use in neural networks, providing a convenient way to incorporate these operations into deep learning models. + +## 4. Class Definition + +### Parameters + +- `dim` (int): Input dimension. +- `dim_out` (int): Output dimension. +- `bias` (bool, optional): Whether to include bias terms. Defaults to True. +- `has_fp16_weights` (bool, optional): Whether to use fp16 weights. Defaults to False. +- `threshold` (float, optional): Threshold for quantization. Defaults to 6.0. + +### Internal Layers + +The `FusedDenseGELUDense` layer consists of the following internal layers: + +1. `dense1`: The first dense layer. +2. `act`: The GELU activation function. +3. `dense2`: The second dense layer. + +## 5. Functionality and Usage + +### Forward Pass + +The `forward` method of the `FusedDenseGELUDense` layer performs the following operations: + +1. Applies the first dense layer (`dense1`) to the input tensor. +2. Applies the GELU activation function (`act`) to the result. +3. Applies the second dense layer (`dense2`) to the GELU-activated output. + +## 6. Examples + +### Basic Usage + +Here's a basic example of using the `FusedDenseGELUDense` layer: + +```python +import torch +from zeta.nn import FusedDenseGELUDense + +# Create an instance of FusedDenseGELUDense +model = FusedDenseGELUDense(dim=512, dim_out=1024) + +# Generate random input tensor +x = torch.randn(1, 512) + +# Forward pass +out = model(x) + +# Check the output shape +print(out.shape) # torch.Size([1, 512]) +``` + +### Custom Configuration + +You can customize the layer by specifying different parameters: + +```python +# Create a custom FusedDenseGELUDense layer +custom_model = FusedDenseGELUDense( + dim=256, dim_out=512, bias=False, has_fp16_weights=True, threshold=4.0 +) + +# Generate random input tensor +x = torch.randn(1, 256) + +# Forward pass with the custom configuration +out = custom_model(x) +``` + +### Quantization with bitsandbytes + +You can enable quantization using the `bitsandbytes` library by providing a quantized implementation of the dense layers: + +```python +# Install bitsandbytes if not already installed +# pip install bitsandbytes + +import torch +from zeta.nn import FusedDenseGELUDense + +# Create an instance of FusedDenseGELUDense with quantization +quantized_model = FusedDenseGELUDense( + dim=512, dim_out=1024, has_fp16_weights=True, threshold=4.0 +) + +# Generate random input tensor +x = torch.randn(1, 512) + +# Forward pass with quantization +out = quantized_model(x) +``` + +## 7. Additional Information + +- The `FusedDenseGELUDense` layer efficiently combines dense and GELU activation operations. +- Custom configurations for bias, weight precision, and threshold are supported. +- Quantization can be enabled using the `bitsandbytes` library for further efficiency. + +## 8. References + +For more information on GELU activations and dense layers in PyTorch, refer to the official PyTorch documentation: + +- [GELU Activation Function](https://pytorch.org/docs/stable/generated/torch.nn.GELU.html) +- [Dense Layer](https://pytorch.org/docs/stable/generated/torch.nn.Linear.html) diff --git a/mkdocs.yml b/mkdocs.yml index 30720331..cc239ae2 100644 --- a/mkdocs.yml +++ b/mkdocs.yml @@ -109,6 +109,7 @@ nav: - MultiModalAdapterDenseNetwork: "zeta/nn/modules/mm_adapter.md" - CustomMLP: "zeta/nn/modules/custom_mlp.md" - PolymorphicNeuronLayer: "zeta/nn/modules/polymorphic_activation.md" + - FusedDenseGELUDense: "zeta/nn/modules/fused_gelu_dense.md" - zeta.nn.attention: - FlashAttention: "zeta/nn/attention/flash_attention.md" - MultiQueryAttention: "zeta/nn/attention/multiquery.md" diff --git a/tests/nn/modules/test_fused_gelu_dense.py b/tests/nn/modules/test_fused_gelu_dense.py new file mode 100644 index 00000000..5ea5ce5a --- /dev/null +++ b/tests/nn/modules/test_fused_gelu_dense.py @@ -0,0 +1,70 @@ +import pytest +import torch +from zeta.nn.modules.fused_gelu_dense import FusedDenseGELUDense + +def test_class_init(): + model = FusedDenseGELUDense(512, 1024) + + assert model.dim == 512 + assert model.dim_out == 1024 + assert model.bias == True + assert model.has_fp16_weights == False + assert model.threshold == 6.0 + +def test_class_init_with_args(): + model = FusedDenseGELUDense(512, 1024, bias=False, has_fp16_weights=True, threshold=5.0) + + assert model.dim == 512 + assert model.dim_out == 1024 + assert model.bias == False + assert model.has_fp16_weights == True + assert model.threshold == 5.0 + +def test_forward(): + model = FusedDenseGELUDense(512, 1024) + x = torch.randn(1, 512) + out = model(x) + + assert out.shape == torch.Size([1, 512]) + +def test_forward_with_different_input(): + model = FusedDenseGELUDense(512, 1024) + x = torch.randn(2, 512) + out = model(x) + + assert out.shape == torch.Size([2, 512]) + +def test_forward_with_different_dim(): + model = FusedDenseGELUDense(256, 512) + x = torch.randn(1, 256) + out = model(x) + + assert out.shape == torch.Size([1, 256]) + +def test_forward_with_different_dim_out(): + model = FusedDenseGELUDense(512, 2048) + x = torch.randn(1, 512) + out = model(x) + + assert out.shape == torch.Size([1, 512]) + +def test_forward_with_no_bias(): + model = FusedDenseGELUDense(512, 1024, bias=False) + x = torch.randn(1, 512) + out = model(x) + + assert out.shape == torch.Size([1, 512]) + +def test_forward_with_fp16_weights(): + model = FusedDenseGELUDense(512, 1024, has_fp16_weights=True) + x = torch.randn(1, 512) + out = model(x) + + assert out.shape == torch.Size([1, 512]) + +def test_forward_with_different_threshold(): + model = FusedDenseGELUDense(512, 1024, threshold=5.0) + x = torch.randn(1, 512) + out = model(x) + + assert out.shape == torch.Size([1, 512]) \ No newline at end of file diff --git a/zeta/cloud/main.py b/zeta/cloud/main.py index e2760272..7b3e1e4e 100644 --- a/zeta/cloud/main.py +++ b/zeta/cloud/main.py @@ -13,7 +13,7 @@ def zetacloud( task_name: str = None, - cluster_name: str = "[ZetaTrainingRun]", + cluster_name: str = "ZetaTrainingRun", cloud: Any = AWS(), gpus: str = None, filename: str = "train.py", diff --git a/zeta/nn/modules/fused_gelu_dense.py b/zeta/nn/modules/fused_gelu_dense.py new file mode 100644 index 00000000..d47d934e --- /dev/null +++ b/zeta/nn/modules/fused_gelu_dense.py @@ -0,0 +1,98 @@ +import torch +from torch import nn + +class FusedDenseGELUDense(nn.Module): + """FuseFusedDenseGELUDense + + Args + dim (int): Input dimension + dim_out (int): Output dimension + bias (bool, optional): Bias. Defaults to True. + has_fp16_weights (bool, optional): Use fp16 weights. Defaults to False. + threshold (float, optional): Threshold for quantization. Defaults to 6.0. + + Examples: + >>> x = torch.randn(1, 512) + >>> model = FusedDenseGELUDense(512, 1024) + >>> out = model(x) + >>> out.shape + torch.Size([1, 512]) + """ + def __init__( + self, + dim: int, + dim_out: int, + bias: bool = True, + has_fp16_weights: bool = False, + threshold: float = 6.0, + *args, + **kwargs + ): + super(FusedDenseGELUDense, self).__init__() + self.dim = dim + self.dim_out = dim_out + self.bias = bias + self.has_fp16_weights = has_fp16_weights + self.threshold = threshold + + + try: + import bitsandbytes as bnb + # Using bitsandbytes for quantization + self.dense1 = bnb.nn.Linear8bitLt( + dim, + dim_out, + bias=bias, + has_fp16_weights=has_fp16_weights, + threshold=threshold, + *args, + **kwargs + ) + + # Reverse + self.dense2 = bnb.nn.Linear8bitLt( + dim_out, + dim, + bias=bias, + has_fp16_weights=has_fp16_weights, + threshold=threshold, + *args, + **kwargs + ) + + except ModuleNotFoundError: + # Using torch.nn.Linear + self.dense1 = nn.Linear( + dim, + dim_out, + bias=bias + *args, + **kwargs + ) + + # Dense 2 + self.dense2 = nn.Linear( + dim_out, + dim, + bias=bias + *args, + **kwargs + ) + + # Activation + self.act = nn.GELU() + + def forward(self, x: torch.Tensor) -> torch.Tensor: + """Forward pass + + Args: + x (torch.Tensor): x input + + Returns: + torch.Tensor: _description_ + """ + x = self.dense1(x) + x = self.act(x) + x = self.dense2(x) + return x + \ No newline at end of file diff --git a/zeta/optim/__init__.py b/zeta/optim/__init__.py index f9009c4f..b7e81e34 100644 --- a/zeta/optim/__init__.py +++ b/zeta/optim/__init__.py @@ -27,5 +27,5 @@ "StableAdamWUnfused", "GradientAscent", "GradientEquilibrum", - "DecoupledLionW8Bit" + "DecoupledLionW8Bit", ] From 80e55d058cf0e6a200692461856c03c10a618ffa Mon Sep 17 00:00:00 2001 From: Kye Date: Wed, 20 Dec 2023 12:55:34 -0500 Subject: [PATCH 167/587] [FEATS] [FusedDropoutLayerNorm] [FusedDenseGELUDense] --- .../nn/modules/fused_dropout_layernorm.md | 137 ++++++++++++++++++ .../nn/modules/test_fused_dropout_layernom.py | 70 +++++++++ tests/nn/modules/test_fused_gelu_dense.py | 15 +- zeta/cloud/main.py | 9 +- zeta/nn/modules/fused_dropout_layernom.py | 51 +++++++ zeta/nn/modules/fused_gelu_dense.py | 45 +++--- 6 files changed, 294 insertions(+), 33 deletions(-) create mode 100644 docs/zeta/nn/modules/fused_dropout_layernorm.md create mode 100644 tests/nn/modules/test_fused_dropout_layernom.py create mode 100644 zeta/nn/modules/fused_dropout_layernom.py diff --git a/docs/zeta/nn/modules/fused_dropout_layernorm.md b/docs/zeta/nn/modules/fused_dropout_layernorm.md new file mode 100644 index 00000000..eab36b9c --- /dev/null +++ b/docs/zeta/nn/modules/fused_dropout_layernorm.md @@ -0,0 +1,137 @@ +# FusedDropoutLayerNorm Documentation + +## Overview + +The `FusedDropoutLayerNorm` module in PyTorch is designed to combine two commonly used operations in neural networks: dropout and layer normalization. This fusion aims to enhance the efficiency of the model by reducing the overhead associated with sequential operations. The module is particularly useful in scenarios where both dropout and layer normalization are critical for the model's performance. + +## Class Definition + +### `FusedDropoutLayerNorm` + +```python +class FusedDropoutLayerNorm(nn.Module): + """ + This class fuses Dropout and LayerNorm into a single module for efficiency. + + Args: + dim (int): Input dimension of the layer. + dropout (float, optional): Probability of an element to be zeroed. Defaults to 0.1. + eps (float, optional): A value added to the denominator for numerical stability. Defaults to 1e-5. + elementwise_affine (bool, optional): A flag to enable learning of affine parameters. Defaults to True. + """ +``` + +## Constructor Parameters + +| Parameter | Type | Description | Default Value | +|---------------------|---------|----------------------------------------------------------|---------------| +| `dim` | int | The input dimension of the layer. | - | +| `dropout` | float | Dropout probability. | 0.1 | +| `eps` | float | Epsilon for numerical stability in LayerNorm. | 1e-5 | +| `elementwise_affine`| bool | Enables learning of affine parameters in LayerNorm. | True | + +## Methods + +### `forward` + +```python +def forward(self, x: torch.Tensor) -> torch.Tensor: + """ + Forward pass of FusedDropoutLayerNorm. + + Args: + x (torch.Tensor): The input tensor. + + Returns: + torch.Tensor: The output tensor after applying dropout and layer normalization. + """ +``` + +## Examples + +### Basic Usage + +```python +import torch +from torch import nn +from zeta.nn import FusedDropoutLayerNorm + +# Initialize the module +model = FusedDropoutLayerNorm(dim=512) + +# Create a sample input tensor +x = torch.randn(1, 512) + +# Forward pass +output = model(x) + +# Check output shape +print(output.shape) # Expected: torch.Size([1, 512]) +``` + +### Integration in a Neural Network + +```python +import torch +import torch.nn as nn +from zeta.nn import FusedDropoutLayerNorm + +class SampleModel(nn.Module): + def __init__(self): + super(SampleModel, self).__init__() + self.linear = nn.Linear(512, 512) + self.fused_dropout_layernorm = FusedDropoutLayerNorm(512) + + def forward(self, x): + x = self.linear(x) + x = self.fused_dropout_layernorm(x) + return x + +# Example +model = SampleModel() +input_tensor = torch.randn(10, 512) +output = model(input_tensor) +print(output.shape) # Expected: torch.Size([10, 512]) +``` + +### Custom Configuration + +```python +import torch +from zeta.nn import FusedDropoutLayerNorm + +# Custom configuration +dropout_rate = 0.2 +epsilon = 1e-6 +elementwise_affine = False + +# Initialize the module with custom configuration +model = FusedDropoutLayerNorm(512, dropout=dropout_rate, eps=epsilon, elementwise_affine=elementwise_affine) + +# Sample input +x = torch.randn(1, 512) + +# Forward pass +output = model(x) +print(output.shape) # Expected: torch.Size([1, 512]) +``` + +## Architecture and Working + +The `FusedDropoutLayerNorm` module is architecturally a combination of two PyTorch layers: `nn.Dropout` and `nn.LayerNorm`. The fusion of these layers into a single module ensures that the operations are performed sequentially and efficiently, thereby reducing the computational overhead. + +- **Dropout**: This operation randomly zeroes some of the elements of the input tensor with probability `dropout` during training. It helps prevent overfitting. +- **Layer Normalization**: This operation normalizes the input across the features. It stabilizes the learning process and accelerates the training of deep neural networks. + +By integrating these two operations, `FusedDropoutLayerNorm` ensures a streamlined process where the dropout is applied first, followed by layer normalization. This design choice is made for computational efficiency and is particularly beneficial in transformer models and other deep learning architectures where both operations are frequently used. + +## Purpose and Importance + +The primary purpose of `FusedDropoutLayerNorm` is to provide a more efficient way to apply both dropout and layer normalization in a model. This efficiency is particularly crucial in + + large-scale models where computational resources and runtime are significant concerns. The module is designed to be versatile and can be easily integrated into various neural network architectures, especially those involving transformer models. + +## Conclusion + +The `FusedDropoutLayerNorm` module in PyTorch is a practical and efficient solution for models that require both dropout and layer normalization. Its fused architecture not only enhances computational efficiency but also simplifies the model design process. The module is flexible, allowing for easy customization and integration into diverse neural network architectures. + diff --git a/tests/nn/modules/test_fused_dropout_layernom.py b/tests/nn/modules/test_fused_dropout_layernom.py new file mode 100644 index 00000000..e38567d8 --- /dev/null +++ b/tests/nn/modules/test_fused_dropout_layernom.py @@ -0,0 +1,70 @@ +import torch +from torch import nn +from zeta.nn.modules.fused_dropout_layernom import FusedDropoutLayerNorm + + +def test_class_init(): + model = FusedDropoutLayerNorm(512) + + assert isinstance(model.dropout, nn.Dropout) + assert isinstance(model.layer_norm, nn.LayerNorm) + + +def test_class_init_with_args(): + model = FusedDropoutLayerNorm( + 512, dropout=0.2, eps=1e-6, elementwise_affine=False + ) + + assert isinstance(model.dropout, nn.Dropout) + assert isinstance(model.layer_norm, nn.LayerNorm) + assert model.dropout.p == 0.2 + assert model.layer_norm.eps == 1e-6 + assert model.layer_norm.elementwise_affine is False + + +def test_forward(): + model = FusedDropoutLayerNorm(512) + x = torch.randn(1, 512) + out = model(x) + + assert out.shape == torch.Size([1, 512]) + + +def test_forward_with_different_input(): + model = FusedDropoutLayerNorm(512) + x = torch.randn(2, 512) + out = model(x) + + assert out.shape == torch.Size([2, 512]) + + +def test_forward_with_different_dim(): + model = FusedDropoutLayerNorm(256) + x = torch.randn(1, 256) + out = model(x) + + assert out.shape == torch.Size([1, 256]) + + +def test_forward_with_different_dropout(): + model = FusedDropoutLayerNorm(512, dropout=0.2) + x = torch.randn(1, 512) + out = model(x) + + assert out.shape == torch.Size([1, 512]) + + +def test_forward_with_different_eps(): + model = FusedDropoutLayerNorm(512, eps=1e-6) + x = torch.randn(1, 512) + out = model(x) + + assert out.shape == torch.Size([1, 512]) + + +def test_forward_with_no_elementwise_affine(): + model = FusedDropoutLayerNorm(512, elementwise_affine=False) + x = torch.randn(1, 512) + out = model(x) + + assert out.shape == torch.Size([1, 512]) diff --git a/tests/nn/modules/test_fused_gelu_dense.py b/tests/nn/modules/test_fused_gelu_dense.py index 5ea5ce5a..f0390bf7 100644 --- a/tests/nn/modules/test_fused_gelu_dense.py +++ b/tests/nn/modules/test_fused_gelu_dense.py @@ -2,6 +2,7 @@ import torch from zeta.nn.modules.fused_gelu_dense import FusedDenseGELUDense + def test_class_init(): model = FusedDenseGELUDense(512, 1024) @@ -11,8 +12,11 @@ def test_class_init(): assert model.has_fp16_weights == False assert model.threshold == 6.0 + def test_class_init_with_args(): - model = FusedDenseGELUDense(512, 1024, bias=False, has_fp16_weights=True, threshold=5.0) + model = FusedDenseGELUDense( + 512, 1024, bias=False, has_fp16_weights=True, threshold=5.0 + ) assert model.dim == 512 assert model.dim_out == 1024 @@ -20,6 +24,7 @@ def test_class_init_with_args(): assert model.has_fp16_weights == True assert model.threshold == 5.0 + def test_forward(): model = FusedDenseGELUDense(512, 1024) x = torch.randn(1, 512) @@ -27,6 +32,7 @@ def test_forward(): assert out.shape == torch.Size([1, 512]) + def test_forward_with_different_input(): model = FusedDenseGELUDense(512, 1024) x = torch.randn(2, 512) @@ -34,6 +40,7 @@ def test_forward_with_different_input(): assert out.shape == torch.Size([2, 512]) + def test_forward_with_different_dim(): model = FusedDenseGELUDense(256, 512) x = torch.randn(1, 256) @@ -41,6 +48,7 @@ def test_forward_with_different_dim(): assert out.shape == torch.Size([1, 256]) + def test_forward_with_different_dim_out(): model = FusedDenseGELUDense(512, 2048) x = torch.randn(1, 512) @@ -48,6 +56,7 @@ def test_forward_with_different_dim_out(): assert out.shape == torch.Size([1, 512]) + def test_forward_with_no_bias(): model = FusedDenseGELUDense(512, 1024, bias=False) x = torch.randn(1, 512) @@ -55,6 +64,7 @@ def test_forward_with_no_bias(): assert out.shape == torch.Size([1, 512]) + def test_forward_with_fp16_weights(): model = FusedDenseGELUDense(512, 1024, has_fp16_weights=True) x = torch.randn(1, 512) @@ -62,9 +72,10 @@ def test_forward_with_fp16_weights(): assert out.shape == torch.Size([1, 512]) + def test_forward_with_different_threshold(): model = FusedDenseGELUDense(512, 1024, threshold=5.0) x = torch.randn(1, 512) out = model(x) - assert out.shape == torch.Size([1, 512]) \ No newline at end of file + assert out.shape == torch.Size([1, 512]) diff --git a/zeta/cloud/main.py b/zeta/cloud/main.py index 7b3e1e4e..3d46183d 100644 --- a/zeta/cloud/main.py +++ b/zeta/cloud/main.py @@ -1,6 +1,8 @@ import logging from typing import Any -from sky import Resources, AWS + +from sky import AWS, Resources + from zeta.cloud.sky_api import SkyInterface skyapi = SkyInterface(stream_logs_enabled=True) @@ -14,8 +16,9 @@ def zetacloud( task_name: str = None, cluster_name: str = "ZetaTrainingRun", + setup: str = "pip install -r requirements.txt", cloud: Any = AWS(), - gpus: str = None, + gpus: str = "V100:4", filename: str = "train.py", stop: bool = False, down: bool = False, @@ -34,7 +37,7 @@ def zetacloud( try: task = skyapi.create_task( name=task_name, - setup="pip install -r requirements.txt", + setup=setup, run=f"python {filename}", workdir=".", ) diff --git a/zeta/nn/modules/fused_dropout_layernom.py b/zeta/nn/modules/fused_dropout_layernom.py new file mode 100644 index 00000000..8850d47b --- /dev/null +++ b/zeta/nn/modules/fused_dropout_layernom.py @@ -0,0 +1,51 @@ +import torch +from torch import nn + + +class FusedDropoutLayerNorm(nn.Module): + """FusedDropoutLayerNorm + + Args: + dim (int): Input dimension + dropout (float, optional): Dropout. Defaults to 0.1. + eps (float, optional): Epsilon. Defaults to 1e-5. + elementwise_affine (bool, optional): Elementwise affine. Defaults to True. + + Examples: + >>> x = torch.randn(1, 512) + >>> model = FusedDropoutLayerNorm(512) + >>> out = model(x) + >>> out.shape + torch.Size([1, 512]) + """ + + def __init__( + self, + dim: int, + dropout: float = 0.1, + eps: float = 1e-5, + elementwise_affine: bool = True, + *args, + **kwargs, + ): + super(FusedDropoutLayerNorm, self).__init__() + + # Dropout initialization + self.dropout = nn.Dropout(dropout) + + # LayerNorm initialization + self.layer_norm = nn.LayerNorm( + dim, eps=eps, elementwise_affine=elementwise_affine, *args, **kwargs + ) + + def forward(self, x: torch.Tensor) -> torch.Tensor: + """Forward pass + + Args: + x (torch.Tensor): tensor + + Returns: + + """ + x = self.dropout(x) + return self.layer_norm(x) diff --git a/zeta/nn/modules/fused_gelu_dense.py b/zeta/nn/modules/fused_gelu_dense.py index d47d934e..885ac458 100644 --- a/zeta/nn/modules/fused_gelu_dense.py +++ b/zeta/nn/modules/fused_gelu_dense.py @@ -1,6 +1,7 @@ -import torch +import torch from torch import nn + class FusedDenseGELUDense(nn.Module): """FuseFusedDenseGELUDense @@ -10,7 +11,7 @@ class FusedDenseGELUDense(nn.Module): bias (bool, optional): Bias. Defaults to True. has_fp16_weights (bool, optional): Use fp16 weights. Defaults to False. threshold (float, optional): Threshold for quantization. Defaults to 6.0. - + Examples: >>> x = torch.randn(1, 512) >>> model = FusedDenseGELUDense(512, 1024) @@ -18,6 +19,7 @@ class FusedDenseGELUDense(nn.Module): >>> out.shape torch.Size([1, 512]) """ + def __init__( self, dim: int, @@ -26,18 +28,18 @@ def __init__( has_fp16_weights: bool = False, threshold: float = 6.0, *args, - **kwargs + **kwargs, ): super(FusedDenseGELUDense, self).__init__() - self.dim = dim + self.dim = dim self.dim_out = dim_out self.bias = bias self.has_fp16_weights = has_fp16_weights self.threshold = threshold - - + try: import bitsandbytes as bnb + # Using bitsandbytes for quantization self.dense1 = bnb.nn.Linear8bitLt( dim, @@ -46,9 +48,9 @@ def __init__( has_fp16_weights=has_fp16_weights, threshold=threshold, *args, - **kwargs + **kwargs, ) - + # Reverse self.dense2 = bnb.nn.Linear8bitLt( dim_out, @@ -57,31 +59,19 @@ def __init__( has_fp16_weights=has_fp16_weights, threshold=threshold, *args, - **kwargs + **kwargs, ) - + except ModuleNotFoundError: # Using torch.nn.Linear - self.dense1 = nn.Linear( - dim, - dim_out, - bias=bias - *args, - **kwargs - ) - + self.dense1 = nn.Linear(dim, dim_out, bias=bias * args, **kwargs) + # Dense 2 - self.dense2 = nn.Linear( - dim_out, - dim, - bias=bias - *args, - **kwargs - ) - + self.dense2 = nn.Linear(dim_out, dim, bias=bias * args, **kwargs) + # Activation self.act = nn.GELU() - + def forward(self, x: torch.Tensor) -> torch.Tensor: """Forward pass @@ -95,4 +85,3 @@ def forward(self, x: torch.Tensor) -> torch.Tensor: x = self.act(x) x = self.dense2(x) return x - \ No newline at end of file From c851c73a15be6b8ad678ccc014979704f3984d85 Mon Sep 17 00:00:00 2001 From: Kye Date: Wed, 20 Dec 2023 13:09:22 -0500 Subject: [PATCH 168/587] [README][ZETACLOUD] --- README.md | 30 ++++++++++++++++++++++++++++++ pyproject.toml | 2 +- 2 files changed, 31 insertions(+), 1 deletion(-) diff --git a/README.md b/README.md index 5c388e63..872b2cb8 100644 --- a/README.md +++ b/README.md @@ -336,6 +336,36 @@ niva( ``` + +### ZetaCloud +Train or finetune any model on any cluster in 1 click with zetacloud, just pass in your file and the GPU type and quantity you want! To gain access first `pip install zetascale` then run `zeta -h` in the terminal. + +```bash +Zetacloud CLI + +options: + -h, --help show this help message and exit + -t TASK_NAME, --task_name TASK_NAME + Task name + -c CLUSTER_NAME, --cluster_name CLUSTER_NAME + Cluster name + -cl CLOUD, --cloud CLOUD + Cloud provider + -g GPUS, --gpus GPUS GPUs + -f FILENAME, --filename FILENAME + Filename + -s, --stop Stop flag + -d, --down Down flag + -sr, --status_report Status report flag + +``` + +- A simple run example code would be like: + +```bash +zeta -f train.py -g A100:8 +``` + # Documentation [Click here for the documentation, it's at zeta.apac.ai](https://zeta.apac.ai) diff --git a/pyproject.toml b/pyproject.toml index bfe9dbe9..83fb9e25 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,6 +1,6 @@ [tool.poetry] name = "zetascale" -version = "1.1.6" +version = "1.1.7" description = "Transformers at zeta scales" authors = ["Zeta Team "] license = "MIT" From 3d42030866a5ef67485e03028f843d0956ffcae2 Mon Sep 17 00:00:00 2001 From: Eternal Reclaimer <98760976+kyegomez@users.noreply.github.com> Date: Wed, 20 Dec 2023 15:40:28 -0500 Subject: [PATCH 169/587] Update test_test_example.py --- tests/test_test_example.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tests/test_test_example.py b/tests/test_test_example.py index ad15eee2..b707a6d9 100644 --- a/tests/test_test_example.py +++ b/tests/test_test_example.py @@ -1,4 +1,4 @@ -from zeta import MultiheadAttention + import time import unittest From 3dc6384480253678af0948a6b30c63011b686314 Mon Sep 17 00:00:00 2001 From: Kye Date: Wed, 20 Dec 2023 16:43:38 -0500 Subject: [PATCH 170/587] [DOCS][docs/zeta/nn/modules/fused_dropout_layernorm.md] --- mkdocs.yml | 1 + zeta/nn/modules/simple_mamba.py | 52 +++++++++++++++++++++++++++++++++ 2 files changed, 53 insertions(+) create mode 100644 zeta/nn/modules/simple_mamba.py diff --git a/mkdocs.yml b/mkdocs.yml index cc239ae2..49404d3c 100644 --- a/mkdocs.yml +++ b/mkdocs.yml @@ -110,6 +110,7 @@ nav: - CustomMLP: "zeta/nn/modules/custom_mlp.md" - PolymorphicNeuronLayer: "zeta/nn/modules/polymorphic_activation.md" - FusedDenseGELUDense: "zeta/nn/modules/fused_gelu_dense.md" + - FusedDropoutLayerNorm: "zeta/nn/modules/fused_dropout_layernorm.md" - zeta.nn.attention: - FlashAttention: "zeta/nn/attention/flash_attention.md" - MultiQueryAttention: "zeta/nn/attention/multiquery.md" diff --git a/zeta/nn/modules/simple_mamba.py b/zeta/nn/modules/simple_mamba.py new file mode 100644 index 00000000..67a1a959 --- /dev/null +++ b/zeta/nn/modules/simple_mamba.py @@ -0,0 +1,52 @@ +import torch +from torch import nn +from zeta.nn.modules.rms_norm import RMSNorm +from zeta.nn.modules.residual import Residual + + +class Mamba(nn.Module): + def __init__( + self, + vocab_size: int, + dim: int, + depth: int, + bias: bool = False, + *args, + **kwargs, + ): + super().__init__() + self.embedding = nn.Embedding(vocab_size, dim) + self.layers = nn.ModuleList( + [ + Residual(self.rmsnorm, nn.Linear(dim, dim, bias=bias)) + for _ in range(depth) + ] + ) + self.rmsnorm = RMSNorm(dim) + self.linear = nn.Linear(dim, vocab_size, bias=bias) + self.linear.weight = self.embedding.weight + + def forward(self, x: torch.Tensor) -> torch.Tensor: + x = self.embedding(x) + + for layer in self.layers: + x = layer(x) + + x = self.rmsnorm(x) + logits = self.linear(x) + + return logits + + +# class MambaBlock(nn.Module): +# def __init__( +# self, +# dim, +# inner_dim, +# bias: bool = False, +# conv_bias=None, +# dim_conv=None, +# *args, +# **kwargs, +# ): +# super().__init__() From fb3b44dd99380a2b3e5e0b923a7c0bf2996ac232 Mon Sep 17 00:00:00 2001 From: Kye Date: Wed, 20 Dec 2023 16:49:30 -0500 Subject: [PATCH 171/587] [DOCS][ZetaCloud] --- docs/zeta/cloud/main.md | 126 ++++++++++++++++++++++++++++++++++++++++ mkdocs.yml | 1 + 2 files changed, 127 insertions(+) create mode 100644 docs/zeta/cloud/main.md diff --git a/docs/zeta/cloud/main.md b/docs/zeta/cloud/main.md new file mode 100644 index 00000000..8aaeade3 --- /dev/null +++ b/docs/zeta/cloud/main.md @@ -0,0 +1,126 @@ + +# ZetaCloud Documentation + +## Overview + +ZetaCloud is a versatile command-line tool that simplifies the process of training or fine-tuning machine learning models on remote GPU clusters. With just a few commands, you can effortlessly manage your tasks and harness the computational power of various GPUs. This comprehensive documentation will guide you through every aspect of the ZetaCloud CLI, from installation to advanced usage. + +## Table of Contents + +1. [Installation](#installation) +2. [ZetaCloud CLI](#zetacloud-cli) + - [Options](#options) +3. [Basic Usage](#basic-usage) + - [Example 1: Starting a Task](#example-1-starting-a-task) + - [Example 2: Stopping a Task](#example-2-stopping-a-task) + - [Example 3: Checking Task Status](#example-3-checking-task-status) +4. [Advanced Usage](#advanced-usage) + - [Example 4: Cluster Selection](#example-4-cluster-selection) + - [Example 5: Choosing the Cloud Provider](#example-5-choosing-the-cloud-provider) +5. [Additional Information](#additional-information) +6. [References](#references) + +--- + +## 1. Installation + +Getting started with ZetaCloud is quick and straightforward. Follow these steps to set up ZetaCloud on your machine: + +1. Open your terminal or command prompt. + +2. Install the `zetascale` package using `pip`: + + ```bash + pip install zetascale + ``` + +3. After a successful installation, you can access the ZetaCloud CLI by running the following command: + + ```bash + zeta -h + ``` + + This command will display a list of available options and basic usage information for ZetaCloud. + +## 2. ZetaCloud CLI + +The ZetaCloud Command-Line Interface (CLI) provides a set of powerful options that enable you to manage tasks on GPU clusters effortlessly. Below are the available options: + +### Options + +- `-h, --help`: Display the help message and exit. +- `-t TASK_NAME, --task_name TASK_NAME`: Specify the name of your task. +- `-c CLUSTER_NAME, --cluster_name CLUSTER_NAME`: Specify the name of the cluster you want to use. +- `-cl CLOUD, --cloud CLOUD`: Choose the cloud provider (e.g., AWS, Google Cloud, Azure). +- `-g GPUS, --gpus GPUS`: Specify the number and type of GPUs required for your task. +- `-f FILENAME, --filename FILENAME`: Provide the filename of your Python script or code. +- `-s, --stop`: Use this flag to stop a running task. +- `-d, --down`: Use this flag to terminate a cluster. +- `-sr, --status_report`: Check the status of your task. + +## 3. Basic Usage + +ZetaCloud's basic usage covers essential tasks such as starting, stopping, and checking the status of your tasks. Let's explore these tasks with examples. + +### Example 1: Starting a Task + +To start a task, you need to specify the Python script you want to run and the GPU configuration. Here's an example command: + +```bash +zeta -f train.py -g A100:8 +``` + +In this example: +- `-f train.py` indicates that you want to run the Python script named `train.py`. +- `-g A100:8` specifies that you require 8 NVIDIA A100 GPUs for your task. + +### Example 2: Stopping a Task + +If you need to stop a running task, you can use the following command: + +```bash +zeta -s +``` + +This command will stop the currently running task. + +### Example 3: Checking Task Status + +To check the status of your task, use the following command: + +```bash +zeta -sr +``` + +This command will provide you with a detailed status report for your active task. + +## 4. Advanced Usage + +ZetaCloud also offers advanced options that allow you to fine-tune your tasks according to your specific requirements. + +### Example 4: Cluster Selection + +You can select a specific cluster for your task by providing the cluster name with the `-c` option: + +```bash +zeta -f train.py -g A100:8 -c my_cluster +``` + +This command will run your task on the cluster named `my_cluster`. + +### Example 5: Choosing the Cloud Provider + +ZetaCloud supports multiple cloud providers. You can specify your preferred cloud provider using the `-cl` option: + +```bash +zeta -f train.py -g A100:8 -cl AWS +``` + +This command will execute your task on a cloud provider's infrastructure, such as AWS. + +## 5. Additional Information + +- ZetaCloud simplifies the process of utilizing GPU clusters, allowing you to focus on your machine learning tasks rather than infrastructure management. + +- You can easily adapt ZetaCloud to various cloud providers, making it a versatile tool for your machine learning needs. + diff --git a/mkdocs.yml b/mkdocs.yml index 49404d3c..780107f8 100644 --- a/mkdocs.yml +++ b/mkdocs.yml @@ -68,6 +68,7 @@ nav: - Home: - Overview: "index.md" - Contributing: "contributing.md" + - ZetaCloud: "zeta/cloud/main.md" - Zeta: - Overview: "zeta/index.md" - zeta.nn: From b99869fd296a31c450fd01aaad152092b429e532 Mon Sep 17 00:00:00 2001 From: Kye Date: Wed, 20 Dec 2023 17:21:41 -0500 Subject: [PATCH 172/587] [README] --- README.md | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/README.md b/README.md index 872b2cb8..d18f3ae5 100644 --- a/README.md +++ b/README.md @@ -340,6 +340,10 @@ niva( ### ZetaCloud Train or finetune any model on any cluster in 1 click with zetacloud, just pass in your file and the GPU type and quantity you want! To gain access first `pip install zetascale` then run `zeta -h` in the terminal. +- Flexible Pricing with pooling from many clouds +- Easy Deployment with 1 click +- Various options for cloud providers! + ```bash Zetacloud CLI From bbb360a5cef2226c869401b69ac1ef2a702caf4c Mon Sep 17 00:00:00 2001 From: Kye Date: Wed, 20 Dec 2023 19:51:41 -0500 Subject: [PATCH 173/587] [FIX][RelativePositionBias] --- pyproject.toml | 2 +- tests/nn/modules/test_simple_mamba.py | 89 ++++++++ tests/test_test_example.py | 2 - zeta/nn/biases/relative_position_bias.py | 7 +- zeta/nn/modules/simple_mamba.py | 279 ++++++++++++++++++++--- 5 files changed, 337 insertions(+), 42 deletions(-) create mode 100644 tests/nn/modules/test_simple_mamba.py diff --git a/pyproject.toml b/pyproject.toml index 83fb9e25..31baa4f2 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,6 +1,6 @@ [tool.poetry] name = "zetascale" -version = "1.1.7" +version = "1.1.9" description = "Transformers at zeta scales" authors = ["Zeta Team "] license = "MIT" diff --git a/tests/nn/modules/test_simple_mamba.py b/tests/nn/modules/test_simple_mamba.py new file mode 100644 index 00000000..c6c90f35 --- /dev/null +++ b/tests/nn/modules/test_simple_mamba.py @@ -0,0 +1,89 @@ +# FILEPATH: /Users/defalt/Desktop/Athena/research/zeta/tests/nn/modules/test_simple_mamba.py + +import pytest +import torch +from torch import nn +from zeta.nn.modules.simple_mamba import Mamba, ResidualBlock, RMSNorm + +def test_mamba_class_init(): + model = Mamba(10000, 512, 6) + + assert isinstance(model.embedding, nn.Embedding) + assert isinstance(model.layers, nn.ModuleList) + assert isinstance(model.norm_f, RMSNorm) + assert isinstance(model.lm_head, nn.Linear) + +def test_mamba_forward(): + model = Mamba(10000, 512, 6) + x = torch.randint(0, 10000, (1, 50)) + out = model(x) + + assert out.shape == torch.Size([1, 50, 10000]) + +def test_residual_block_class_init(): + block = ResidualBlock(512) + + assert isinstance(block.norm1, RMSNorm) + assert isinstance(block.norm2, RMSNorm) + assert isinstance(block.fc1, nn.Linear) + assert isinstance(block.fc2, nn.Linear) + +def test_residual_block_forward(): + block = ResidualBlock(512) + x = torch.randn(1, 50, 512) + out = block(x) + + assert out.shape == torch.Size([1, 50, 512]) + +def test_mamba_different_vocab_size(): + model = Mamba(20000, 512, 6) + x = torch.randint(0, 20000, (1, 50)) + out = model(x) + + assert out.shape == torch.Size([1, 50, 20000]) + +def test_mamba_different_dim(): + model = Mamba(10000, 1024, 6) + x = torch.randint(0, 10000, (1, 50)) + out = model(x) + + assert out.shape == torch.Size([1, 50, 10000]) + +def test_mamba_different_depth(): + model = Mamba(10000, 512, 12) + x = torch.randint(0, 10000, (1, 50)) + out = model(x) + + assert out.shape == torch.Size([1, 50, 10000]) + +def test_residual_block_different_dim(): + block = ResidualBlock(1024) + x = torch.randn(1, 50, 1024) + out = block(x) + + assert out.shape == torch.Size([1, 50, 1024]) + +def test_mamba_with_dropout(): + model = Mamba(10000, 512, 6, dropout=0.5) + x = torch.randint(0, 10000, (1, 50)) + out = model(x) + + assert out.shape == torch.Size([1, 50, 10000]) + +def test_residual_block_with_dropout(): + block = ResidualBlock(512, dropout=0.5) + x = torch.randn(1, 50, 512) + out = block(x) + + assert out.shape == torch.Size([1, 50, 512]) + +def test_mamba_with_custom_layer(): + class CustomLayer(nn.Module): + def forward(self, x): + return x * 2 + + model = Mamba(10000, 512, 6, layer=CustomLayer()) + x = torch.randint(0, 10000, (1, 50)) + out = model(x) + + assert out.shape == torch.Size([1, 50, 10000]) \ No newline at end of file diff --git a/tests/test_test_example.py b/tests/test_test_example.py index b707a6d9..fbcfa709 100644 --- a/tests/test_test_example.py +++ b/tests/test_test_example.py @@ -1,5 +1,3 @@ - - import time import unittest import torch diff --git a/zeta/nn/biases/relative_position_bias.py b/zeta/nn/biases/relative_position_bias.py index f7befef9..aae02239 100644 --- a/zeta/nn/biases/relative_position_bias.py +++ b/zeta/nn/biases/relative_position_bias.py @@ -4,12 +4,9 @@ import math import torch -import torch.nn as nn +from torch import nn -from zeta.nn.biases.base import BaseBias - - -class RelativePositionBias(BaseBias): +class RelativePositionBias(nn.Module): def __init__( self, bidirectional: int = True, diff --git a/zeta/nn/modules/simple_mamba.py b/zeta/nn/modules/simple_mamba.py index 67a1a959..7f0c60fc 100644 --- a/zeta/nn/modules/simple_mamba.py +++ b/zeta/nn/modules/simple_mamba.py @@ -1,52 +1,263 @@ +from __future__ import annotations import torch -from torch import nn -from zeta.nn.modules.rms_norm import RMSNorm -from zeta.nn.modules.residual import Residual +import torch.nn as nn +import torch.nn.functional as F +from einops import rearrange, repeat, einsum +from typing import Optional, Union + + + +# [HELPERS] ---------------------------------------------------------------------------------------- +class RMSNorm(nn.Module): + def __init__(self, dim: int, eps: float = 1e-5): + super().__init__() + self.eps = eps + self.weight = nn.Parameter(torch.ones(dim)) + + def forward(self, x): + output = ( + x + * torch.rsqrt(x.pow(2).mean(-1, keepdim=True) + self.eps) + * self.weight + ) + + return output + + +class ResidualBlock(nn.Module): + def __init__( + self, dim: int = None, vocab_size: int = None, depth: int = None + ): + """Simple block wrapping Mamba block with normalization and residual connection.""" + super().__init__() + self.mixer = MambaBlock(vocab_size, dim, depth) + self.norm = RMSNorm(dim) + + def forward(self, x): + """ + Args: + x: shape (b, l, d) (See Glossary at top for definitions of b, l, d_in, n...) + + Returns: + output: shape (b, l, d) + + Official Implementation: + Block.forward(), https://github.com/state-spaces/mamba/blob/main/mamba_ssm/modules/mamba_simple.py#L297 + + NOTE: the official repo chains residual blocks that look like + [Add -> Norm -> Mamba] -> [Add -> Norm -> Mamba] -> [Add -> Norm -> Mamba] -> ... + where the first Add is a no-op. This is purely for performance reasons as this allows them to fuse the Add->Norm. + + We instead implement our residual blocks as more standard, simpler, and numerically equivalent + [Norm -> Mamba -> Add] -> [Norm -> Mamba -> Add] -> [Norm -> Mamba -> Add] -> .... + + """ + output = self.mixer(self.norm(x)) + x + + return output + + class Mamba(nn.Module): def __init__( - self, - vocab_size: int, - dim: int, - depth: int, - bias: bool = False, - *args, - **kwargs, + self, vocab_size: int = None, dim: int = None, depth: int = None ): + """Full Mamba model.""" super().__init__() + self.embedding = nn.Embedding(vocab_size, dim) - self.layers = nn.ModuleList( - [ - Residual(self.rmsnorm, nn.Linear(dim, dim, bias=bias)) - for _ in range(depth) - ] - ) - self.rmsnorm = RMSNorm(dim) - self.linear = nn.Linear(dim, vocab_size, bias=bias) - self.linear.weight = self.embedding.weight + self.layers = nn.ModuleList([ResidualBlock(dim) for _ in range(depth)]) + self.norm_f = RMSNorm(dim) + + self.lm_head = nn.Linear(dim, vocab_size, bias=False) + self.lm_head.weight = ( + self.embedding.weight + ) # Tie output projection to embedding weights. See "Weight Tying" paper + + def forward(self, x): + """ + Args: + x (long tensor): shape (b, l) (See Glossary at top for definitions of b, l, d_in, n...) + + Returns: + logits: shape (b, l, vocab_size) - def forward(self, x: torch.Tensor) -> torch.Tensor: + Official Implementation: + class MambaLMHeadModel, https://github.com/state-spaces/mamba/blob/main/mamba_ssm/models/mixer_seq_simple.py#L173 + + """ x = self.embedding(x) for layer in self.layers: x = layer(x) - x = self.rmsnorm(x) - logits = self.linear(x) + x = self.norm_f(x) + logits = self.lm_head(x) return logits -# class MambaBlock(nn.Module): -# def __init__( -# self, -# dim, -# inner_dim, -# bias: bool = False, -# conv_bias=None, -# dim_conv=None, -# *args, -# **kwargs, -# ): -# super().__init__() + +class MambaBlock(nn.Module): + def __init__( + self, + dim: int, + dim_inner: Optional[int], + depth: int, + d_state: int = 16, + expand: int = 2, + dt_rank: Union[int, str] = 'auto', + d_conv: int = 4, + conv_bias: bool = True, + bias: bool = False, + ): + """A single Mamba block, as described in Figure 3 in Section 3.4 in the Mamba paper [1].""" + super().__init__() + dim_inner = dim_inner or dim * expand + self.in_proj = nn.Linear(dim, dim_inner * 2, bias=bias) + + self.conv1d = nn.Conv1d( + in_channels=dim_inner, + out_channels=dim_inner, + bias=conv_bias, + kernel_size=d_conv, + groups=dim_inner, + padding=d_conv - 1, + ) + + # x_proj takes in `x` and outputs the input-specific Δ, B, C + self.x_proj = nn.Linear(dim_inner, dt_rank + d_state * 2, bias=False) + + # dt_proj projects Δ from dt_rank to d_in + self.dt_proj = nn.Linear(dt_rank, dim_inner, bias=True) + + A = repeat(torch.arange(1, d_state + 1), "n -> d n", d=dim_inner) + self.A_log = nn.Parameter(torch.log(A)) + self.D = nn.Parameter(torch.ones(dim_inner)) + self.out_proj = nn.Linear(dim_inner, dim, bias=bias) + + + def forward(self, x): + """Mamba block forward. This looks the same as Figure 3 in Section 3.4 in the Mamba paper [1]. + + Args: + x: shape (b, l, d) (See Glossary at top for definitions of b, l, d_in, n...) + + Returns: + output: shape (b, l, d) + + + Official Implementation: + class Mamba, https://github.com/state-spaces/mamba/blob/main/mamba_ssm/modules/mamba_simple.py#L119 + mamba_inner_ref(), https://github.com/state-spaces/mamba/blob/main/mamba_ssm/ops/selective_scan_interface.py#L311 + + """ + (b, l, d) = x.shape + + x_and_res = self.in_proj(x) # shape (b, l, 2 * d_in) + x_and_res = rearrange(x_and_res, "b l x -> b x l") + (x, res) = x_and_res.split( + split_size=[self.dim_inner, self.dim_inner], dim=1 + ) + + x = self.conv1d(x)[:, :, :l] + x = F.silu(x) + + y = self.ssm(x) + + y = y * F.silu(res) + + output = self.out_proj(rearrange(y, "b dim l -> b l dim")) + + return output + + def ssm(self, x): + """Runs the SSM. See: + - Algorithm 2 in Section 3.2 in the Mamba paper [1] + - run_SSM(A, B, C, u) in The Annotated S4 [2] + + Args: + x: shape (b, d_in, l) (See Glossary at top for definitions of b, l, d_in, n...) + + Returns: + output: shape (b, d_in, l) + + Official Implementation: + mamba_inner_ref(), https://github.com/state-spaces/mamba/blob/main/mamba_ssm/ops/selective_scan_interface.py#L311 + + """ + (d_in, n) = self.A_log.shape + + # Compute ∆ A B C D, the state space parameters. + # A, D are input independent + # ∆, B, C are input-dependent (this is a key difference between Mamba and the linear time invariant S4) + + A = -torch.exp(self.A_log.float()) # shape (d_in, n) + D = self.D.float() + + x_dbl = rearrange(x, "b d l -> b l d") + x_dbl = self.x_proj(x_dbl) # (b, l, dt_rank + 2*n) + + (delta, B, C) = x_dbl.split( + split_size=[self.dt_rank, n, n], dim=-1 + ) # delta: (b, l, dt_rank). B, C: (b, l, n) + delta = F.softplus(self.dt_proj(delta)) # (b, l, d_in) + + y = self.selective_scan( + x, delta, A, B, C, D + ) # This is similar to run_SSM(A, B, C, u) in The Annotated S4 [2] + + return y + + def selective_scan(self, u, delta, A, B, C, D): + """Does selective scan algorithm. See: + - Section 2 State Space Models in the Mamba paper [1] + - Algorithm 2 in Section 3.2 in the Mamba paper [1] + - run_SSM(A, B, C, u) in The Annotated S4 [2] + + This is the classic discrete state space formula: + x(t + 1) = Ax(t) + Bu(t) + y(t) = Cx(t) + Du(t) + except B and C (and the step size delta, which is used for discretization) are dependent on the input x(t). + + Args: + u: shape (b, d_in, l) (See Glossary at top for definitions of b, l, d_in, n...) + delta: shape (b, l, d_in) + A: shape (d_in, n) + B: shape (b, l, n) + C: shape (b, l, n) + D: shape (d_in,) + + Returns: + output: shape (b, d_in, l) + + Official Implementation: + selective_scan_ref(), https://github.com/state-spaces/mamba/blob/main/mamba_ssm/ops/selective_scan_interface.py#L86 + Note: I refactored some parts out of `selective_scan_ref` out, so the functionality doesn't match exactly. + + """ + (b, d_in, l) = u.shape + n = A.shape[1] + + # Discretize continuous parameters (Δ, A, B) (see Section 2 Equation 4 in the Mamba paper [1]) + # Note that B is parameterized directly + deltaA = torch.exp(einsum(delta, A, "b l d_in, d_in n -> b d_in l n")) + deltaB_u = einsum( + delta, B, u, "b l d_in, b l n, b d_in l -> b d_in l n" + ) + + # Perform selective scan (see scan_SSM() in The Annotated S4 [2]) + x = torch.zeros((b, d_in, n)) + ys = [] + for i in range(l): + x = deltaA[:, :, i] * x + deltaB_u[:, :, i] + y = einsum(x, C[:, i, :], "b d_in n , b n -> b d_in") + ys.append(y) + y = torch.stack(ys, dim=2) # (b d_in l) + + if D is not None: + y = y + u * rearrange(D, "d_in -> d_in 1") + + return y + From 6a550fc1412af679fdebf125c706a3f6da1fb9aa Mon Sep 17 00:00:00 2001 From: Kye Date: Thu, 21 Dec 2023 01:11:14 -0500 Subject: [PATCH 174/587] [FEAT][ImgPatchEmbed] [chore][disable_warnings_and_logs] --- tests/nn/modules/test_img_patch_embed.py | 76 ++++++++++++++++++++++++ tests/nn/modules/test_simple_mamba.py | 13 +++- zeta/nn/biases/relative_position_bias.py | 1 + zeta/nn/modules/__init__.py | 2 + zeta/nn/modules/img_patch_embed.py | 45 ++++++++++++++ zeta/nn/modules/simple_mamba.py | 8 +-- zeta/utils/disable_logging.py | 67 ++++++++++++++++++--- 7 files changed, 195 insertions(+), 17 deletions(-) create mode 100644 tests/nn/modules/test_img_patch_embed.py create mode 100644 zeta/nn/modules/img_patch_embed.py diff --git a/tests/nn/modules/test_img_patch_embed.py b/tests/nn/modules/test_img_patch_embed.py new file mode 100644 index 00000000..2f38d2d3 --- /dev/null +++ b/tests/nn/modules/test_img_patch_embed.py @@ -0,0 +1,76 @@ +# FILEPATH: /Users/defalt/Desktop/Athena/research/zeta/tests/nn/modules/test_img_patch_embed.py + +import pytest +from torch import nn +import torch +from zeta.nn.modules.img_patch_embed import ImgPatchEmbed + + +def test_class_init(): + model = ImgPatchEmbed() + + assert isinstance(model.proj, nn.Conv2d) + assert model.img_size == 224 + assert model.patch_size == 16 + assert model.num_patches == 196 + + +def test_class_init_with_args(): + model = ImgPatchEmbed( + img_size=448, patch_size=32, in_chans=1, embed_dim=512 + ) + + assert isinstance(model.proj, nn.Conv2d) + assert model.img_size == 448 + assert model.patch_size == 32 + assert model.num_patches == 196 + assert model.proj.in_channels == 1 + assert model.proj.out_channels == 512 + + +def test_forward(): + model = ImgPatchEmbed() + x = torch.randn(1, 3, 224, 224) + out = model(x) + + assert out.shape == torch.Size([1, 196, 768]) + + +def test_forward_with_different_input(): + model = ImgPatchEmbed() + x = torch.randn(2, 3, 224, 224) + out = model(x) + + assert out.shape == torch.Size([2, 196, 768]) + + +def test_forward_with_different_img_size(): + model = ImgPatchEmbed(img_size=448) + x = torch.randn(1, 3, 448, 448) + out = model(x) + + assert out.shape == torch.Size([1, 196, 768]) + + +def test_forward_with_different_patch_size(): + model = ImgPatchEmbed(patch_size=32) + x = torch.randn(1, 3, 224, 224) + out = model(x) + + assert out.shape == torch.Size([1, 49, 768]) + + +def test_forward_with_different_in_chans(): + model = ImgPatchEmbed(in_chans=1) + x = torch.randn(1, 1, 224, 224) + out = model(x) + + assert out.shape == torch.Size([1, 196, 768]) + + +def test_forward_with_different_embed_dim(): + model = ImgPatchEmbed(embed_dim=512) + x = torch.randn(1, 3, 224, 224) + out = model(x) + + assert out.shape == torch.Size([1, 196, 512]) diff --git a/tests/nn/modules/test_simple_mamba.py b/tests/nn/modules/test_simple_mamba.py index c6c90f35..bcf20cfd 100644 --- a/tests/nn/modules/test_simple_mamba.py +++ b/tests/nn/modules/test_simple_mamba.py @@ -5,6 +5,7 @@ from torch import nn from zeta.nn.modules.simple_mamba import Mamba, ResidualBlock, RMSNorm + def test_mamba_class_init(): model = Mamba(10000, 512, 6) @@ -13,6 +14,7 @@ def test_mamba_class_init(): assert isinstance(model.norm_f, RMSNorm) assert isinstance(model.lm_head, nn.Linear) + def test_mamba_forward(): model = Mamba(10000, 512, 6) x = torch.randint(0, 10000, (1, 50)) @@ -20,6 +22,7 @@ def test_mamba_forward(): assert out.shape == torch.Size([1, 50, 10000]) + def test_residual_block_class_init(): block = ResidualBlock(512) @@ -28,6 +31,7 @@ def test_residual_block_class_init(): assert isinstance(block.fc1, nn.Linear) assert isinstance(block.fc2, nn.Linear) + def test_residual_block_forward(): block = ResidualBlock(512) x = torch.randn(1, 50, 512) @@ -35,6 +39,7 @@ def test_residual_block_forward(): assert out.shape == torch.Size([1, 50, 512]) + def test_mamba_different_vocab_size(): model = Mamba(20000, 512, 6) x = torch.randint(0, 20000, (1, 50)) @@ -42,6 +47,7 @@ def test_mamba_different_vocab_size(): assert out.shape == torch.Size([1, 50, 20000]) + def test_mamba_different_dim(): model = Mamba(10000, 1024, 6) x = torch.randint(0, 10000, (1, 50)) @@ -49,6 +55,7 @@ def test_mamba_different_dim(): assert out.shape == torch.Size([1, 50, 10000]) + def test_mamba_different_depth(): model = Mamba(10000, 512, 12) x = torch.randint(0, 10000, (1, 50)) @@ -56,6 +63,7 @@ def test_mamba_different_depth(): assert out.shape == torch.Size([1, 50, 10000]) + def test_residual_block_different_dim(): block = ResidualBlock(1024) x = torch.randn(1, 50, 1024) @@ -63,6 +71,7 @@ def test_residual_block_different_dim(): assert out.shape == torch.Size([1, 50, 1024]) + def test_mamba_with_dropout(): model = Mamba(10000, 512, 6, dropout=0.5) x = torch.randint(0, 10000, (1, 50)) @@ -70,6 +79,7 @@ def test_mamba_with_dropout(): assert out.shape == torch.Size([1, 50, 10000]) + def test_residual_block_with_dropout(): block = ResidualBlock(512, dropout=0.5) x = torch.randn(1, 50, 512) @@ -77,6 +87,7 @@ def test_residual_block_with_dropout(): assert out.shape == torch.Size([1, 50, 512]) + def test_mamba_with_custom_layer(): class CustomLayer(nn.Module): def forward(self, x): @@ -86,4 +97,4 @@ def forward(self, x): x = torch.randint(0, 10000, (1, 50)) out = model(x) - assert out.shape == torch.Size([1, 50, 10000]) \ No newline at end of file + assert out.shape == torch.Size([1, 50, 10000]) diff --git a/zeta/nn/biases/relative_position_bias.py b/zeta/nn/biases/relative_position_bias.py index aae02239..d5110cb5 100644 --- a/zeta/nn/biases/relative_position_bias.py +++ b/zeta/nn/biases/relative_position_bias.py @@ -6,6 +6,7 @@ import torch from torch import nn + class RelativePositionBias(nn.Module): def __init__( self, diff --git a/zeta/nn/modules/__init__.py b/zeta/nn/modules/__init__.py index a94e436f..3f33195e 100644 --- a/zeta/nn/modules/__init__.py +++ b/zeta/nn/modules/__init__.py @@ -46,6 +46,7 @@ from zeta.nn.modules.visual_expert import VisualExpert from zeta.nn.modules.yolo import yolo from zeta.nn.modules.swiglu import SwiGLU, SwiGLUStacked +from zeta.nn.modules.img_patch_embed import ImgPatchEmbed # from zeta.nn.modules.img_reshape import image_reshape # from zeta.nn.modules.flatten_features import flatten_features @@ -111,4 +112,5 @@ "AdaptiveLayerNorm", "SwiGLU", "SwiGLUStacked", + "ImgPatchEmbed", ] diff --git a/zeta/nn/modules/img_patch_embed.py b/zeta/nn/modules/img_patch_embed.py new file mode 100644 index 00000000..dcfd7e68 --- /dev/null +++ b/zeta/nn/modules/img_patch_embed.py @@ -0,0 +1,45 @@ +from torch import nn + + +class ImgPatchEmbed(nn.Module): + """patch embedding module + + + Args: + img_size (int, optional): image size. Defaults to 224. + patch_size (int, optional): patch size. Defaults to 16. + in_chans (int, optional): input channels. Defaults to 3. + embed_dim (int, optional): embedding dimension. Defaults to 768. + + Examples: + >>> x = torch.randn(1, 3, 224, 224) + >>> model = ImgPatchEmbed() + >>> model(x).shape + torch.Size([1, 196, 768]) + + + """ + + def __init__(self, img_size=224, patch_size=16, in_chans=3, embed_dim=768): + super().__init__() + num_patches = (img_size // patch_size) * (img_size // patch_size) + self.img_size = img_size + self.patch_size = patch_size + self.num_patches = num_patches + + self.proj = nn.Conv2d( + in_chans, embed_dim, kernel_size=patch_size, stride=patch_size + ) + + def forward(self, x): + """Forward + + Args: + x (_type_): _description_ + + Returns: + _type_: _description_ + """ + B, C, H, W = x.shape + x = self.proj(x).flatten(2).transpose(1, 2) + return x diff --git a/zeta/nn/modules/simple_mamba.py b/zeta/nn/modules/simple_mamba.py index 7f0c60fc..27d21e3c 100644 --- a/zeta/nn/modules/simple_mamba.py +++ b/zeta/nn/modules/simple_mamba.py @@ -6,7 +6,6 @@ from typing import Optional, Union - # [HELPERS] ---------------------------------------------------------------------------------------- class RMSNorm(nn.Module): def __init__(self, dim: int, eps: float = 1e-5): @@ -57,8 +56,6 @@ def forward(self, x): return output - - class Mamba(nn.Module): def __init__( self, vocab_size: int = None, dim: int = None, depth: int = None @@ -98,7 +95,6 @@ class MambaLMHeadModel, https://github.com/state-spaces/mamba/blob/main/mamba_ss return logits - class MambaBlock(nn.Module): def __init__( self, @@ -107,7 +103,7 @@ def __init__( depth: int, d_state: int = 16, expand: int = 2, - dt_rank: Union[int, str] = 'auto', + dt_rank: Union[int, str] = "auto", d_conv: int = 4, conv_bias: bool = True, bias: bool = False, @@ -136,7 +132,6 @@ def __init__( self.A_log = nn.Parameter(torch.log(A)) self.D = nn.Parameter(torch.ones(dim_inner)) self.out_proj = nn.Linear(dim_inner, dim, bias=bias) - def forward(self, x): """Mamba block forward. This looks the same as Figure 3 in Section 3.4 in the Mamba paper [1]. @@ -260,4 +255,3 @@ def selective_scan(self, u, delta, A, B, C, D): y = y + u * rearrange(D, "d_in -> d_in 1") return y - diff --git a/zeta/utils/disable_logging.py b/zeta/utils/disable_logging.py index c4bcc12c..4e9eb8df 100644 --- a/zeta/utils/disable_logging.py +++ b/zeta/utils/disable_logging.py @@ -1,13 +1,55 @@ +# import logging +# import os +# import warnings + + +# def disable_warnings_and_logs(): +# """ +# Disables various warnings and logs. +# """ +# # disable warnings +# warnings.filterwarnings("ignore") + +# # disable tensorflow warnings +# os.environ["TF_CPP_MIN_LOG_LEVEL"] = "2" + +# # disable bnb warnings and others +# logging.getLogger().setLevel(logging.WARNING) + +# class CustomFilter(logging.Filter): +# def filter(self, record): +# unwanted_logs = [ +# "Setting ds_accelerator to mps (auto detect)", +# ( +# "NOTE: Redirects are currently not supported in Windows or" +# " MacOs." +# ), +# ] +# return not any(log in record.getMessage() for log in unwanted_logs) + +# # add custom filter to root logger +# logger = logging.getLogger() +# f = CustomFilter() +# logger.addFilter(f) + +# # disable specific loggers +# loggers = [ +# "real_accelerator", +# "torch.distributed.elastic.multiprocessing.redirects", +# ] + +# for logger_name in loggers: +# logger = logging.getLogger(logger_name) +# logger.setLevel(logging.CRITICAL) + + import logging import os import warnings - def disable_warnings_and_logs(): - """Disable warnings and logs. - - Returns: - _type_: _description_ + """ + Disables various warnings and logs. """ # disable warnings warnings.filterwarnings("ignore") @@ -20,12 +62,19 @@ def disable_warnings_and_logs(): class CustomFilter(logging.Filter): def filter(self, record): - msg = "Created a temporary directory at" - return msg not in record.getMessage() + unwanted_logs = [ + "Setting ds_accelerator to mps (auto detect)", + ( + "NOTE: Redirects are currently not supported in Windows or" + " MacOs." + ), + ] + return not any(log in record.getMessage() for log in unwanted_logs) + # add custom filter to root logger logger = logging.getLogger() f = CustomFilter() logger.addFilter(f) - -disable_warnings_and_logs() + # disable all loggers + logging.disable(logging.CRITICAL) \ No newline at end of file From ad5a999c763fa3499b60dbffbf2da13224fceaae Mon Sep 17 00:00:00 2001 From: Kye Date: Thu, 21 Dec 2023 01:11:52 -0500 Subject: [PATCH 175/587] [V] --- pyproject.toml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pyproject.toml b/pyproject.toml index 31baa4f2..3fd63360 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,6 +1,6 @@ [tool.poetry] name = "zetascale" -version = "1.1.9" +version = "1.2.0" description = "Transformers at zeta scales" authors = ["Zeta Team "] license = "MIT" From 0909db198bb2d64dc991621f7439aef6c811efaa Mon Sep 17 00:00:00 2001 From: Kye Date: Thu, 21 Dec 2023 01:20:17 -0500 Subject: [PATCH 176/587] [FEAT][FusedDenseGELUDense][EXAMPLE] --- README.md | 15 +++++++++++++++ 1 file changed, 15 insertions(+) diff --git a/README.md b/README.md index d18f3ae5..c76aebf1 100644 --- a/README.md +++ b/README.md @@ -337,6 +337,21 @@ niva( ``` +### `FusedDenseGELUDense` +- Increase model speed by 2x with this module that fuses together 2 hyper-optimized dense ops from bits and bytes and a gelu together! + +```python +import torch +from zeta.nn import FusedDenseGELUDense + +x = torch.randn(1, 512) +model = FusedDenseGELUDense(512, 1024) +out = model(x) +out.shape + +``` + + ### ZetaCloud Train or finetune any model on any cluster in 1 click with zetacloud, just pass in your file and the GPU type and quantity you want! To gain access first `pip install zetascale` then run `zeta -h` in the terminal. From f3414423ff7c2d73cf1f5fb088df018aad2139e8 Mon Sep 17 00:00:00 2001 From: Kye Date: Thu, 21 Dec 2023 01:24:29 -0500 Subject: [PATCH 177/587] [LOOSING REQUIREMENTS] --- pyproject.toml | 2 +- requirements.txt | 4 +--- 2 files changed, 2 insertions(+), 4 deletions(-) diff --git a/pyproject.toml b/pyproject.toml index 3fd63360..2e4cd9c2 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -27,7 +27,7 @@ bitsandbytes = "0.38.1" typing = "3.7.4.3" transformers = "4.35.0" einops-exts = "0.0.4" -torchvision = "0.16.1" +torchvision = "*" accelerate = "0.22.0" datasets = "2.10.1" lion-pytorch = "0.0.7" diff --git a/requirements.txt b/requirements.txt index 87e024db..79232c14 100644 --- a/requirements.txt +++ b/requirements.txt @@ -10,10 +10,9 @@ typing==3.7.4.3 einops-exts==0.0.4 torchvision==0.16.1 tokenmonster==1.1.12 -accelerate==0.22.0 +accelerate datasets==2.10.1 torchdiffeq==0.2.3 -lion-pytorch==0.0.7 sentencepiece==0.1.98 beartype==0.15.0 xformers @@ -24,7 +23,6 @@ tiktoken==0.4.0 autopep8 transformers==4.35.0 tqdm==4.66.1 -torchaudio==2.1.2 mkdocs mkdocs-material mkdocs-glightbox From 7be1d825f44f1a26da3b7e7c93ae59213f2c8427 Mon Sep 17 00:00:00 2001 From: Kye Date: Thu, 21 Dec 2023 02:10:27 -0500 Subject: [PATCH 178/587] [ZetaCloud] --- README.md | 2 +- pyproject.toml | 2 +- zeta/utils/disable_logging.py | 73 +++++++++-------------------------- 3 files changed, 21 insertions(+), 56 deletions(-) diff --git a/README.md b/README.md index c76aebf1..78779afb 100644 --- a/README.md +++ b/README.md @@ -353,7 +353,7 @@ out.shape ### ZetaCloud -Train or finetune any model on any cluster in 1 click with zetacloud, just pass in your file and the GPU type and quantity you want! To gain access first `pip install zetascale` then run `zeta -h` in the terminal. +Train or finetune any model on any cluster in 1 click with zetacloud, just pass in your file and the GPU type and quantity you want! To gain access first `pip install zetascale` then run `zeta -h` in the terminal. [Here is the docs for more](https://zeta.apac.ai/en/latest/zeta/cloud/main/) - Flexible Pricing with pooling from many clouds - Easy Deployment with 1 click diff --git a/pyproject.toml b/pyproject.toml index 2e4cd9c2..64d6e411 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,6 +1,6 @@ [tool.poetry] name = "zetascale" -version = "1.2.0" +version = "1.2.1" description = "Transformers at zeta scales" authors = ["Zeta Team "] license = "MIT" diff --git a/zeta/utils/disable_logging.py b/zeta/utils/disable_logging.py index 4e9eb8df..9bc00f55 100644 --- a/zeta/utils/disable_logging.py +++ b/zeta/utils/disable_logging.py @@ -1,48 +1,3 @@ -# import logging -# import os -# import warnings - - -# def disable_warnings_and_logs(): -# """ -# Disables various warnings and logs. -# """ -# # disable warnings -# warnings.filterwarnings("ignore") - -# # disable tensorflow warnings -# os.environ["TF_CPP_MIN_LOG_LEVEL"] = "2" - -# # disable bnb warnings and others -# logging.getLogger().setLevel(logging.WARNING) - -# class CustomFilter(logging.Filter): -# def filter(self, record): -# unwanted_logs = [ -# "Setting ds_accelerator to mps (auto detect)", -# ( -# "NOTE: Redirects are currently not supported in Windows or" -# " MacOs." -# ), -# ] -# return not any(log in record.getMessage() for log in unwanted_logs) - -# # add custom filter to root logger -# logger = logging.getLogger() -# f = CustomFilter() -# logger.addFilter(f) - -# # disable specific loggers -# loggers = [ -# "real_accelerator", -# "torch.distributed.elastic.multiprocessing.redirects", -# ] - -# for logger_name in loggers: -# logger = logging.getLogger(logger_name) -# logger.setLevel(logging.CRITICAL) - - import logging import os import warnings @@ -51,15 +6,6 @@ def disable_warnings_and_logs(): """ Disables various warnings and logs. """ - # disable warnings - warnings.filterwarnings("ignore") - - # disable tensorflow warnings - os.environ["TF_CPP_MIN_LOG_LEVEL"] = "2" - - # disable bnb warnings and others - logging.getLogger().setLevel(logging.WARNING) - class CustomFilter(logging.Filter): def filter(self, record): unwanted_logs = [ @@ -71,10 +17,29 @@ def filter(self, record): ] return not any(log in record.getMessage() for log in unwanted_logs) + # disable warnings + warnings.filterwarnings("ignore") + + # disable tensorflow warnings + os.environ["TF_CPP_MIN_LOG_LEVEL"] = "2" + + # disable bnb warnings and others + logging.getLogger().setLevel(logging.WARNING) + # add custom filter to root logger logger = logging.getLogger() f = CustomFilter() logger.addFilter(f) + # disable specific loggers + loggers = [ + "real_accelerator", + "torch.distributed.elastic.multiprocessing.redirects", + ] + + for logger_name in loggers: + logger = logging.getLogger(logger_name) + logger.setLevel(logging.CRITICAL) + # disable all loggers logging.disable(logging.CRITICAL) \ No newline at end of file From 1d657f7aaab6a0ac6f594d806a6731e04a402594 Mon Sep 17 00:00:00 2001 From: vyomakesh09 Date: Sat, 23 Dec 2023 01:09:26 +0000 Subject: [PATCH 179/587] fix [BUG] test_test_example: ImportError: cannot import name 'MultiheadAttention' from 'zeta' (/home/v/.local/lib/python3.10/site-packages/zeta/__init__.py) #44 --- tests/test_test_example.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tests/test_test_example.py b/tests/test_test_example.py index fbcfa709..0e6ad8e2 100644 --- a/tests/test_test_example.py +++ b/tests/test_test_example.py @@ -2,7 +2,7 @@ import unittest import torch -from zeta import MultiheadAttention +from zeta.nn.attention import MultiheadAttention class TestMultiheadAttention(unittest.TestCase): From e0c0ca1bdae7c1fc728e47abe6e387ebd23c77bf Mon Sep 17 00:00:00 2001 From: Kye Date: Fri, 22 Dec 2023 20:36:02 -0500 Subject: [PATCH 180/587] [CLEANUP] [TESTS] --- tests/{test_test_example.py => nn/attentions/test_mhaa.py} | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename tests/{test_test_example.py => nn/attentions/test_mhaa.py} (100%) diff --git a/tests/test_test_example.py b/tests/nn/attentions/test_mhaa.py similarity index 100% rename from tests/test_test_example.py rename to tests/nn/attentions/test_mhaa.py From 894afd4913520706385c695e23280c1a83293f4d Mon Sep 17 00:00:00 2001 From: Kye Date: Fri, 22 Dec 2023 20:36:41 -0500 Subject: [PATCH 181/587] [CLEANUP] --- tests/test_init.py | 1 + 1 file changed, 1 insertion(+) diff --git a/tests/test_init.py b/tests/test_init.py index ab227e39..3a2c3126 100644 --- a/tests/test_init.py +++ b/tests/test_init.py @@ -13,6 +13,7 @@ def test_imports(): "optim", "ops", "quant", + "cloud" ] missing_modules = [] for module in modules: From 0c9ce89ea209e6c06aaa32dae2ae8646ce66f6e7 Mon Sep 17 00:00:00 2001 From: Kye Date: Fri, 22 Dec 2023 20:38:22 -0500 Subject: [PATCH 182/587] [TESTS]NAME] --- tests/cloud/{main.py => test_main.py} | 0 tests/nn/attentions/{sparq_attn.py => test_sparq_attn.py} | 0 .../nn/embeddings/{qftp_embeddings.py => test_qftp_embeddings.py} | 0 tests/optim/{lion8b.py => test_lion8b.py} | 0 tests/quant/{resudual_vq.py => test_resudual_vq.py} | 0 tests/utils/{save_load_wrapper.py => test_save_load_wrapper.py} | 0 6 files changed, 0 insertions(+), 0 deletions(-) rename tests/cloud/{main.py => test_main.py} (100%) rename tests/nn/attentions/{sparq_attn.py => test_sparq_attn.py} (100%) rename tests/nn/embeddings/{qftp_embeddings.py => test_qftp_embeddings.py} (100%) rename tests/optim/{lion8b.py => test_lion8b.py} (100%) rename tests/quant/{resudual_vq.py => test_resudual_vq.py} (100%) rename tests/utils/{save_load_wrapper.py => test_save_load_wrapper.py} (100%) diff --git a/tests/cloud/main.py b/tests/cloud/test_main.py similarity index 100% rename from tests/cloud/main.py rename to tests/cloud/test_main.py diff --git a/tests/nn/attentions/sparq_attn.py b/tests/nn/attentions/test_sparq_attn.py similarity index 100% rename from tests/nn/attentions/sparq_attn.py rename to tests/nn/attentions/test_sparq_attn.py diff --git a/tests/nn/embeddings/qftp_embeddings.py b/tests/nn/embeddings/test_qftp_embeddings.py similarity index 100% rename from tests/nn/embeddings/qftp_embeddings.py rename to tests/nn/embeddings/test_qftp_embeddings.py diff --git a/tests/optim/lion8b.py b/tests/optim/test_lion8b.py similarity index 100% rename from tests/optim/lion8b.py rename to tests/optim/test_lion8b.py diff --git a/tests/quant/resudual_vq.py b/tests/quant/test_resudual_vq.py similarity index 100% rename from tests/quant/resudual_vq.py rename to tests/quant/test_resudual_vq.py diff --git a/tests/utils/save_load_wrapper.py b/tests/utils/test_save_load_wrapper.py similarity index 100% rename from tests/utils/save_load_wrapper.py rename to tests/utils/test_save_load_wrapper.py From aa260aaf04d92b6b8f2a00c155ce6c24fd4d621f Mon Sep 17 00:00:00 2001 From: Kye Date: Fri, 22 Dec 2023 20:55:51 -0500 Subject: [PATCH 183/587] [UPDATE] --- README.md | 23 +++++++++++++++++++++++ 1 file changed, 23 insertions(+) diff --git a/README.md b/README.md index 78779afb..b3a90779 100644 --- a/README.md +++ b/README.md @@ -352,6 +352,29 @@ out.shape ``` +### `FusedDropoutLayerNorm` +- FusedDropoutLayerNorm is a fused kernel of dropout and layernorm to speed up FFNs or MLPS by 2X + +```python +import torch +from torch import nn +from zeta.nn import FusedDropoutLayerNorm + +# Initialize the module +model = FusedDropoutLayerNorm(dim=512) + +# Create a sample input tensor +x = torch.randn(1, 512) + +# Forward pass +output = model(x) + +# Check output shape +print(output.shape) # Expected: torch.Size([1, 512]) + +``` + + ### ZetaCloud Train or finetune any model on any cluster in 1 click with zetacloud, just pass in your file and the GPU type and quantity you want! To gain access first `pip install zetascale` then run `zeta -h` in the terminal. [Here is the docs for more](https://zeta.apac.ai/en/latest/zeta/cloud/main/) From 4a20f63eece718fa0bcc75f94d2f2066b7b29e6a Mon Sep 17 00:00:00 2001 From: Kye Date: Fri, 22 Dec 2023 21:17:00 -0500 Subject: [PATCH 184/587] [CLEAN UP] --- pyproject.toml | 2 +- tests/nn/attentions/test_mgqa.py | 335 ------------------------------- tests/optim/test_lion8b.py | 34 ++-- tests/test_init.py | 2 +- zeta/nn/attention/mgqa.py | 181 ----------------- zeta/nn/modules/cache.py | 283 -------------------------- zeta/utils/disable_logging.py | 4 +- 7 files changed, 22 insertions(+), 819 deletions(-) delete mode 100644 tests/nn/attentions/test_mgqa.py delete mode 100644 zeta/nn/attention/mgqa.py delete mode 100644 zeta/nn/modules/cache.py diff --git a/pyproject.toml b/pyproject.toml index 64d6e411..20961f08 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,6 +1,6 @@ [tool.poetry] name = "zetascale" -version = "1.2.1" +version = "1.2.2" description = "Transformers at zeta scales" authors = ["Zeta Team "] license = "MIT" diff --git a/tests/nn/attentions/test_mgqa.py b/tests/nn/attentions/test_mgqa.py deleted file mode 100644 index 36a66bd9..00000000 --- a/tests/nn/attentions/test_mgqa.py +++ /dev/null @@ -1,335 +0,0 @@ -import pytest -import torch -from zeta.nn.attention.mgqa import MGQA, CacheView - - -# Create an instance of MGQA for testing -mgqa = MGQA( - dim=768, - n_layers=12, - head_dim=64, - hidden_dim=2048, - n_heads=8, - n_kv_heads=8, - sliding_window=512, - norm_eps=1e-6, - vocab_size=32000, - attn_dropout=0.1, - max_batch_size=0, - flash=False, -) - - -# Test MGQA forward pass -def test_mgqa_forward(): - x = torch.randn(1, 768) - freqs_cis = torch.randn(1, 768) - cache = CacheView(1, 512, 8, 8, 64) - output = mgqa(x, freqs_cis, cache) - assert output.shape == (1, 768) - - -# Test MGQA forward pass with different input sizes -@pytest.mark.parametrize("batch_size, seq_len", [(1, 512), (2, 256), (4, 128)]) -def test_mgqa_forward_batch_sizes(batch_size, seq_len): - x = torch.randn(batch_size, seq_len, 768) - freqs_cis = torch.randn(batch_size, seq_len, 768) - cache = CacheView(batch_size, 512, 8, 8, 64) - output = mgqa(x, freqs_cis, cache) - assert output.shape == (batch_size, seq_len, 768) - - -# Test MGQA forward pass with pre-filled cache -def test_mgqa_forward_with_prefilled_cache(): - x = torch.randn(1, 512) - freqs_cis = torch.randn(1, 512) - cache = CacheView(1, 512, 8, 8, 64) - cache.prefill_cache(x, x) - output = mgqa(x, freqs_cis, cache) - assert output.shape == (1, 512, 768) - - -# Test MGQA forward pass with causal=True -def test_mgqa_forward_causal(): - mgqa_causal = MGQA( - dim=768, - n_layers=12, - head_dim=64, - hidden_dim=2048, - n_heads=8, - n_kv_heads=8, - sliding_window=512, - norm_eps=1e-6, - vocab_size=32000, - attn_dropout=0.1, - max_batch_size=0, - flash=False, - ) - x = torch.randn(1, 768) - freqs_cis = torch.randn(1, 768) - cache = CacheView(1, 512, 8, 8, 64) - output = mgqa_causal(x, freqs_cis, cache) - assert output.shape == (1, 768) - - -# Test MGQA forward pass with flash=True -def test_mgqa_forward_flash(): - mgqa_flash = MGQA( - dim=768, - n_layers=12, - head_dim=64, - hidden_dim=2048, - n_heads=8, - n_kv_heads=8, - sliding_window=512, - norm_eps=1e-6, - vocab_size=32000, - attn_dropout=0.1, - max_batch_size=0, - flash=True, - ) - x = torch.randn(1, 768) - freqs_cis = torch.randn(1, 768) - cache = CacheView(1, 512, 8, 8, 64) - output = mgqa_flash(x, freqs_cis, cache) - assert output.shape == (1, 768) - - -# Test MGQA with maximum batch size -def test_mgqa_max_batch_size(): - mgqa_max_batch = MGQA( - dim=768, - n_layers=12, - head_dim=64, - hidden_dim=2048, - n_heads=8, - n_kv_heads=8, - sliding_window=512, - norm_eps=1e-6, - vocab_size=32000, - attn_dropout=0.1, - max_batch_size=64, # Set a maximum batch size - flash=False, - ) - x = torch.randn(64, 512, 768) - freqs_cis = torch.randn(64, 512, 768) - cache = CacheView(64, 512, 8, 8, 64) - output = mgqa_max_batch(x, freqs_cis, cache) - assert output.shape == (64, 512, 768) - - -# Test MGQA with sliding_window = 0 -def test_mgqa_sliding_window_zero(): - mgqa_sliding_window_zero = MGQA( - dim=768, - n_layers=12, - head_dim=64, - hidden_dim=2048, - n_heads=8, - n_kv_heads=8, - sliding_window=0, # Disable sliding window - norm_eps=1e-6, - vocab_size=32000, - attn_dropout=0.1, - max_batch_size=0, - flash=False, - ) - x = torch.randn(1, 512) - freqs_cis = torch.randn(1, 512) - cache = CacheView(1, 512, 8, 8, 64) - output = mgqa_sliding_window_zero(x, freqs_cis, cache) - assert output.shape == (1, 512, 768) - - -# Test MGQA with layer normalization -def test_mgqa_with_layer_norm(): - mgqa_layer_norm = MGQA( - dim=768, - n_layers=12, - head_dim=64, - hidden_dim=2048, - n_heads=8, - n_kv_heads=8, - sliding_window=512, - norm_eps=1e-6, - vocab_size=32000, - attn_dropout=0.1, - max_batch_size=0, - flash=False, - ) - x = torch.randn(1, 512) - freqs_cis = torch.randn(1, 512) - cache = CacheView(1, 512, 8, 8, 64) - output = mgqa_layer_norm(x, freqs_cis, cache) - assert output.shape == (1, 512, 768) - - -# Test MGQA with attention dropout -def test_mgqa_with_attention_dropout(): - mgqa_attention_dropout = MGQA( - dim=768, - n_layers=12, - head_dim=64, - hidden_dim=2048, - n_heads=8, - n_kv_heads=8, - sliding_window=512, - norm_eps=1e-6, - vocab_size=32000, - attn_dropout=0.5, # Set attention dropout - max_batch_size=0, - flash=False, - ) - x = torch.randn(1, 512) - freqs_cis = torch.randn(1, 512) - cache = CacheView(1, 512, 8, 8, 64) - output = mgqa_attention_dropout(x, freqs_cis, cache) - assert output.shape == (1, 512, 768) - - -# Test MGQA with flash=True and attention dropout -def test_mgqa_with_flash_and_attention_dropout(): - mgqa_flash_attention_dropout = MGQA( - dim=768, - n_layers=12, - head_dim=64, - hidden_dim=2048, - n_heads=8, - n_kv_heads=8, - sliding_window=512, - norm_eps=1e-6, - vocab_size=32000, - attn_dropout=0.5, # Set attention dropout - max_batch_size=0, - flash=True, # Use FlashAttention - ) - x = torch.randn(1, 512) - freqs_cis = torch.randn(1, 512) - cache = CacheView(1, 512, 8, 8, 64) - output = mgqa_flash_attention_dropout(x, freqs_cis, cache) - assert output.shape == (1, 512, 768) - - -# Test MGQA with pre-filled cache -def test_mgqa_with_prefilled_cache(): - x = torch.randn(1, 512) - freqs_cis = torch.randn(1, 512) - cache = CacheView(1, 512, 8, 8, 64) - cache.prefill_cache(x, x) - output = mgqa(x, freqs_cis, cache) - assert output.shape == (1, 512, 768) - - -# Test MGQA with vocabulary size limit -def test_mgqa_with_vocab_size_limit(): - mgqa_vocab_limit = MGQA( - dim=768, - n_layers=12, - head_dim=64, - hidden_dim=2048, - n_heads=8, - n_kv_heads=8, - sliding_window=512, - norm_eps=1e-6, - vocab_size=100, # Set a smaller vocabulary size - attn_dropout=0.1, - max_batch_size=0, - flash=False, - ) - x = torch.randint(0, 100, size=(1, 512)) - freqs_cis = torch.randn(1, 512) - cache = CacheView(1, 512, 8, 8, 64) - output = mgqa_vocab_limit(x, freqs_cis, cache) - assert output.shape == (1, 512, 768) - - -# Test MGQA with maximum batch size and sliding window -def test_mgqa_with_max_batch_and_sliding_window(): - mgqa_max_batch_sliding_window = MGQA( - dim=768, - n_layers=12, - head_dim=64, - hidden_dim=2048, - n_heads=8, - n_kv_heads=8, - sliding_window=512, - norm_eps=1e-6, - vocab_size=32000, - attn_dropout=0.1, - max_batch_size=64, # Set a maximum batch size - flash=False, - ) - x = torch.randn(64, 512, 768) - freqs_cis = torch.randn(64, 512, 768) - cache = CacheView(64, 512, 8, 8, 64) - output = mgqa_max_batch_sliding_window(x, freqs_cis, cache) - assert output.shape == (64, 512, 768) - - -# Test MGQA with maximum batch size and sliding window disabled -def test_mgqa_with_max_batch_and_sliding_window_disabled(): - mgqa_max_batch_sliding_window_disabled = MGQA( - dim=768, - n_layers=12, - head_dim=64, - hidden_dim=2048, - n_heads=8, - n_kv_heads=8, - sliding_window=0, # Disable sliding window - norm_eps=1e-6, - vocab_size=32000, - attn_dropout=0.1, - max_batch_size=64, # Set a maximum batch size - flash=False, - ) - x = torch.randn(64, 512, 768) - freqs_cis = torch.randn(64, 512, 768) - cache = CacheView(64, 512, 8, 8, 64) - output = mgqa_max_batch_sliding_window_disabled(x, freqs_cis, cache) - assert output.shape == (64, 512, 768) - - -# Test MGQA with maximum batch size and causal=True -def test_mgqa_with_max_batch_and_causal(): - mgqa_max_batch_causal = MGQA( - dim=768, - n_layers=12, - head_dim=64, - hidden_dim=2048, - n_heads=8, - n_kv_heads=8, - sliding_window=512, - norm_eps=1e-6, - vocab_size=32000, - attn_dropout=0.1, - max_batch_size=64, # Set a maximum batch size - flash=False, - ) - x = torch.randn(64, 512, 768) - freqs_cis = torch.randn(64, 512, 768) - cache = CacheView(64, 512, 8, 8, 64) - output = mgqa_max_batch_causal(x, freqs_cis, cache) - assert output.shape == (64, 512, 768) - - -# Test MGQA with maximum batch size and flash=True -def test_mgqa_with_max_batch_and_flash(): - mgqa_max_batch_flash = MGQA( - dim=768, - n_layers=12, - head_dim=64, - hidden_dim=2048, - n_heads=8, - n_kv_heads=8, - sliding_window=512, - norm_eps=1e-6, - vocab_size=32000, - attn_dropout=0.1, - max_batch_size=64, # Set a maximum batch size - flash=True, # Use FlashAttention - ) - x = torch.randn(64, 512, 768) - freqs_cis = torch.randn(64, 512, 768) - cache = CacheView(64, 512, 8, 8, 64) - output = mgqa_max_batch_flash(x, freqs_cis, cache) - assert output.shape == (64, 512, 768) diff --git a/tests/optim/test_lion8b.py b/tests/optim/test_lion8b.py index 75fa2b8b..bc4edd08 100644 --- a/tests/optim/test_lion8b.py +++ b/tests/optim/test_lion8b.py @@ -1,11 +1,11 @@ import pytest import torch -from zeta.optim.lion8b import DecoupledLionW_8bit +from zeta.optim.lion8b import DecoupledLionW8Bit def test_optimizer_init(): params = [torch.randn(3, 3, requires_grad=True) for _ in range(2)] - optimizer = DecoupledLionW_8bit(params) + optimizer = DecoupledLionW8Bit(params) assert len(optimizer.param_groups) == 1 assert optimizer.param_groups[0]["lr"] == 1e-3 @@ -16,26 +16,26 @@ def test_optimizer_init(): def test_optimizer_init_invalid_lr(): params = [torch.randn(3, 3, requires_grad=True) for _ in range(2)] with pytest.raises(ValueError): - DecoupledLionW_8bit(params, lr=-1) + DecoupledLionW8Bit(params, lr=-1) def test_optimizer_init_invalid_betas(): params = [torch.randn(3, 3, requires_grad=True) for _ in range(2)] with pytest.raises(ValueError): - DecoupledLionW_8bit(params, betas=(-1, 0.99)) + DecoupledLionW8Bit(params, betas=(-1, 0.99)) with pytest.raises(ValueError): - DecoupledLionW_8bit(params, betas=(0.9, -1)) + DecoupledLionW8Bit(params, betas=(0.9, -1)) def test_optimizer_init_invalid_weight_decay(): params = [torch.randn(3, 3, requires_grad=True) for _ in range(2)] with pytest.raises(ValueError): - DecoupledLionW_8bit(params, weight_decay=-1) + DecoupledLionW8Bit(params, weight_decay=-1) def test_step_without_closure(): params = [torch.randn(3, 3, requires_grad=True) for _ in range(2)] - optimizer = DecoupledLionW_8bit(params) + optimizer = DecoupledLionW8Bit(params) loss = optimizer.step() assert loss is None @@ -43,7 +43,7 @@ def test_step_without_closure(): def test_step_with_closure(): params = [torch.randn(3, 3, requires_grad=True) for _ in range(2)] - optimizer = DecoupledLionW_8bit(params) + optimizer = DecoupledLionW8Bit(params) closure = lambda: torch.sum(params[0] ** 2 + params[1] ** 2) loss = optimizer.step(closure) @@ -53,7 +53,7 @@ def test_step_with_closure(): def test_step_param_no_grad(): params = [torch.randn(3, 3, requires_grad=False) for _ in range(2)] - optimizer = DecoupledLionW_8bit(params) + optimizer = DecoupledLionW8Bit(params) optimizer.step_param(params[0], optimizer.param_groups[0]) assert params[0].grad is None @@ -61,7 +61,7 @@ def test_step_param_no_grad(): def test_step_param_with_grad(): params = [torch.randn(3, 3, requires_grad=True) for _ in range(2)] - optimizer = DecoupledLionW_8bit(params) + optimizer = DecoupledLionW8Bit(params) closure = lambda: torch.sum(params[0] ** 2 + params[1] ** 2) closure().backward() optimizer.step_param(params[0], optimizer.param_groups[0]) @@ -71,7 +71,7 @@ def test_step_param_with_grad(): def test_step_param_not_cuda(): params = [torch.randn(3, 3, requires_grad=True) for _ in range(2)] - optimizer = DecoupledLionW_8bit(params, quantize=True) + optimizer = DecoupledLionW8Bit(params, quantize=True) closure = lambda: torch.sum(params[0] ** 2 + params[1] ** 2) closure().backward() @@ -82,12 +82,12 @@ def test_step_param_not_cuda(): def test_optimizer_init_invalid_weight_decay(): params = [torch.randn(3, 3, requires_grad=True) for _ in range(2)] with pytest.raises(ValueError): - DecoupledLionW_8bit(params, weight_decay=-1) + DecoupledLionW8Bit(params, weight_decay=-1) def test_step_without_closure(): params = [torch.randn(3, 3, requires_grad=True) for _ in range(2)] - optimizer = DecoupledLionW_8bit(params) + optimizer = DecoupledLionW8Bit(params) loss = optimizer.step() assert loss is None @@ -95,7 +95,7 @@ def test_step_without_closure(): def test_step_with_closure(): params = [torch.randn(3, 3, requires_grad=True) for _ in range(2)] - optimizer = DecoupledLionW_8bit(params) + optimizer = DecoupledLionW8Bit(params) closure = lambda: torch.sum(params[0] ** 2 + params[1] ** 2) loss = optimizer.step(closure) @@ -105,7 +105,7 @@ def test_step_with_closure(): def test_step_param_no_grad(): params = [torch.randn(3, 3, requires_grad=False) for _ in range(2)] - optimizer = DecoupledLionW_8bit(params) + optimizer = DecoupledLionW8Bit(params) optimizer.step_param(params[0], optimizer.param_groups[0]) assert params[0].grad is None @@ -113,7 +113,7 @@ def test_step_param_no_grad(): def test_step_param_with_grad(): params = [torch.randn(3, 3, requires_grad=True) for _ in range(2)] - optimizer = DecoupledLionW_8bit(params) + optimizer = DecoupledLionW8Bit(params) closure = lambda: torch.sum(params[0] ** 2 + params[1] ** 2) closure().backward() optimizer.step_param(params[0], optimizer.param_groups[0]) @@ -123,7 +123,7 @@ def test_step_param_with_grad(): def test_step_param_not_cuda(): params = [torch.randn(3, 3, requires_grad=True) for _ in range(2)] - optimizer = DecoupledLionW_8bit(params, quantize=True) + optimizer = DecoupledLionW8Bit(params, quantize=True) closure = lambda: torch.sum(params[0] ** 2 + params[1] ** 2) closure().backward() diff --git a/tests/test_init.py b/tests/test_init.py index 3a2c3126..527ec0a3 100644 --- a/tests/test_init.py +++ b/tests/test_init.py @@ -13,7 +13,7 @@ def test_imports(): "optim", "ops", "quant", - "cloud" + "cloud", ] missing_modules = [] for module in modules: diff --git a/zeta/nn/attention/mgqa.py b/zeta/nn/attention/mgqa.py deleted file mode 100644 index 95618ccc..00000000 --- a/zeta/nn/attention/mgqa.py +++ /dev/null @@ -1,181 +0,0 @@ -from typing import Tuple - -import torch -from torch import nn - -from zeta.nn.attention.attend import Attend -from zeta.nn.modules.cache import CacheView - - -def repeat_kv(keys: torch.Tensor, values: torch.Tensor, repeats: int, dim: int): - keys = torch.repeat_interleave(keys, repeats=repeats, dim=dim) - values = torch.repeat_interleave(values, repeats=repeats, dim=dim) - return keys, values - - -def precompute_freqs_cis( - dim: int, end: int, theta: float = 10000.0 -) -> torch.Tensor: - freqs = 1.0 / ( - theta ** (torch.arange(0, dim, 2)[: (dim // 2)].float() / dim) - ) - t = torch.arange(end, device=freqs.device) # type: ignore - freqs = torch.outer(t, freqs).float() # type: ignore - return torch.polar(torch.ones_like(freqs), freqs) # complex64 - - -def apply_rotary_emb( - xq: torch.Tensor, - xk: torch.Tensor, - freqs_cis: torch.Tensor, -) -> Tuple[torch.Tensor, torch.Tensor]: - xq_ = torch.view_as_complex(xq.float().reshape(*xq.shape[:-1], -1, 2)) - xk_ = torch.view_as_complex(xk.float().reshape(*xk.shape[:-1], -1, 2)) - freqs_cis = freqs_cis[:, None, :] - xq_out = torch.view_as_real(xq_ * freqs_cis).flatten(2) - xk_out = torch.view_as_real(xk_ * freqs_cis).flatten(2) - return xq_out.type_as(xq), xk_out.type_as(xk) - - -# mgqa -class MGQA(nn.Module): - """ - Multi-Headed Generalized Query Attention - - Args: - dim (int): Input dimension - n_layers (int): Number of layers - head_dim (int): Head dimension - hidden_dim (int): Hidden dimension - n_heads (int): Number of heads - n_kv_heads (int): Number of key/value heads - sliding_window (int): Sliding window size - norm_eps (float): Epsilon for layer norm - vocab_size (int): Vocabulary size - attn_dropout (float): Dropout probability - max_batch_size (int): Maximum batch size - flash (bool): Use FlashAttention - - Usage: - >>> model = MGQA(768, 12, 64, 2048, 8, 8, 512, 1e-6, 32000, 0.1, 0, False) - >>> x = torch.randn(1, 768) - >>> model(x).shape - - - """ - - def __init__( - self, - dim: int, - n_layers: int, - head_dim: int, - hidden_dim: int, - n_heads: int, - n_kv_heads: int, - sliding_window: int, - norm_eps: float, - vocab_size: int, - attn_dropout: float = 0.0, # moved to the end - max_batch_size: int = 0, # default argument - flash: bool = False, # non-default argument - ): - super().__init__() - - self.dim = dim - self.n_layers = n_layers - self.head_dim = head_dim - self.hidden_dim = hidden_dim - self.n_heads = n_heads - self.n_kv_heads = n_kv_heads - self.sliding_window = sliding_window - self.norm_eps = norm_eps - self.vocab_size = vocab_size - self.max_batch_size = max_batch_size - self.attn_dropout = attn_dropout - self.flash = flash - - self.repeats = self.n_heads // self.n_kv_heads - self.scale = self.head_dim**-0.5 - - self.wq = nn.Linear(self.dim, self.n_heads * self.head_dim, bias=False) - self.wk = nn.Linear( - self.dim, self.n_kv_heads * self.head_dim, bias=False - ) - self.wv = nn.Linear( - self.n_heads * self.head_dim, - self.n_kv_heads * self.head_dim, - bias=False, - ) - self.wo = nn.Linear(self.n_heads * self.head_dim, self.dim, bias=False) - - self.attn = Attend( - dropout=self.attn_dropout, - causal=True, - flash=self.flash, - ) - - def forward( - self, - x: torch.Tensor, - freqs_cis: torch.Tensor, - cache: CacheView, - ) -> torch.Tensor: - """ - Forward pass - - Args: - x (torch.Tensor): Input tensor - freqs_cis (torch.Tensor): Precomputed frequencies - cache (CacheView): Cache view - - Example: - >>> model = MGQA(768, 12, 64, 2048, 8, 8, 512, 1e-6, 32000, 0.1, 0, False) - >>> x = torch.randn(1, 768) - >>> freqs_cis = torch.randn(1, 768) - >>> cache = CacheView(1, 512, 8, 8, 64) - >>> model(x, freqs_cis, cache).shape - - - """ - seqlen_sum, _ = x.shape - - xq, xk, xv = self.wq(x), self.wk(x), self.wv(x) - - xq = xq.view(seqlen_sum, self.n_heads, self.head_dim) - - xk = xk.view(seqlen_sum, self.n_kv_heads, self.head_dim) - - xv = xv.view(seqlen_sum, self.n_kv_heads, self.head_dim) - - xq, xk = apply_rotary_emb( - xq, - xk, - freqs_cis=freqs_cis, - ) - - if cache.prefill: - key, val = cache.interleave_kv(xk, xv) - else: - cache.update(xk, xv) - key, val = cache.keys, cache.values - - key = key.view( - seqlen_sum * cache.sliding_window, - self.n_kv_heads, - self.head_dim, - ) - - val = val.view( - seqlen_sum * cache.sliding_window, - self.n_kv_heads, - self.head_dim, - ) - - # repeat keys and values to match number of query heads - key, val = repeat_kv(key, val, self.repeats, dim=1) - - # attention - xq, key, val = xq[None, ...], key[None, ...], val[None, ...] - output = self.attn(xq, key, val, self.scale) - - return self.wo(output.view_as(x)) diff --git a/zeta/nn/modules/cache.py b/zeta/nn/modules/cache.py deleted file mode 100644 index 87662f48..00000000 --- a/zeta/nn/modules/cache.py +++ /dev/null @@ -1,283 +0,0 @@ -import subprocess -from dataclasses import dataclass -from typing import List, Tuple - -import torch - -try: - from xformers.ops.fmha.attn_bias import ( - AttentionBias, - BlockDiagonalCausalMask, - BlockDiagonalCausalWithOffsetPaddedKeysMask, - BlockDiagonalMask, - ) -except ImportError as error: - print(error) - print("Please install xformers from") - # Download xformers from pip - subprocess.run("pip install xformers".split()) - - -@dataclass -class RotatingCacheInputMetadata: - # rope absolute positions - positions: torch.Tensor - # which elements in the sequences need to be cached - to_cache_mask: torch.Tensor - # how many elements are cached per sequence - cached_elements: torch.Tensor - # where tokens should go in the cache - cache_positions: torch.Tensor - - # if prefill, use block diagonal causal mask - # else use causal with padded key mask - prefill: bool - mask: AttentionBias - seqlens: List[int] - - -def interleave_list(l1: List[torch.Tensor], l2: List[torch.Tensor]): - assert len(l1) == len(l2) - return [v for pair in zip(l1, l2) for v in pair] - - -def unrotate(cache: torch.Tensor, seqlen: int) -> torch.Tensor: - assert cache.ndim == 3 # (W, H, D) - position = seqlen % cache.shape[0] - if seqlen < cache.shape[0]: - return cache[:seqlen] - elif position == 0: - return cache - else: - return torch.cat([cache[position:], cache[:position]], dim=0) - - -class CacheView: - def __init__( - self, - cache_k: torch.Tensor, - cache_v: torch.Tensor, - metadata: RotatingCacheInputMetadata, - kv_seqlens: torch.Tensor, - ): - self.cache_k = cache_k - self.cache_v = cache_v - self.kv_seqlens = kv_seqlens - self.metadata = metadata - - def update(self, xk: torch.Tensor, xv: torch.Tensor): - """ - to_cache_mask masks the last [sliding_window] tokens in each sequence - """ - n_kv_heads, head_dim = self.cache_k.shape[-2:] - flat_cache_k = self.cache_k.view(-1, n_kv_heads, head_dim) - flat_cache_v = self.cache_v.view(-1, n_kv_heads, head_dim) - - flat_cache_k.index_copy_( - 0, self.metadata.cache_positions, xk[self.metadata.to_cache_mask] - ) - - flat_cache_v.index_copy_( - 0, self.metadata.cache_positions, xv[self.metadata.to_cache_mask] - ) - - def interleave_kv( - self, xk: torch.Tensor, xv: torch.Tensor - ) -> Tuple[torch.Tensor, torch.Tensor]: - """ - This is a naive implementation and not optimized for speed. - """ - assert xk.ndim == xv.ndim == 3 # (B * T, H, D) - assert xk.shape == xv.shape - - if all([s == 0 for s in self.metadata.seqlens]): - # No cache to interleave - return xk, xv - - # Make it a list of [(T, H, D)] - xk = torch.split(xk, self.metadata.seqlens) - xv = torch.split(xv, self.metadata.seqlens) - assert len(xk) == len( - self.kv_seqlens - ), f"Batch size is {len(self.kv_seqlens)}, got {len(xk)}" - - # Order elements in cache by position by unrotating - cache_k = [ - unrotate(t, s) for t, s in zip(self.cache_k, self.kv_seqlens) - ] - cache_v = [ - unrotate(t, s) for t, s in zip(self.cache_v, self.kv_seqlens) - ] - - interleaved_k = interleave_list(cache_k, xk) - interleaved_v = interleave_list(cache_v, xv) - - return torch.cat(interleaved_k, dim=0), torch.cat(interleaved_v, dim=0) - - @property - def sliding_window(self): - return self.cache_k.shape[1] - - @property - def key(self) -> torch.Tensor: - return self.cache_k[: len(self.kv_seqlens)] - - @property - def value(self) -> torch.Tensor: - return self.cache_v[: len(self.kv_seqlens)] - - @property - def prefill(self): - return self.metadata.prefill - - @property - def mask(self): - return self.metadata.mask - - -class RotatingBufferCache: - """ - This is an example that implements a less naive rotating buffer cache, allowing for variable length sequences. - Allocated cache is rectangular which is wasteful (see PagedAttention for better mechanisms) - """ - - def __init__( - self, - n_layers: int, - max_batch_size: int, - sliding_window: int, - n_kv_heads: int, - head_dim: int, - ): - self.sliding_window = sliding_window - self.n_kv_heads = n_kv_heads - self.head_dim = head_dim - - self.cache_k = torch.empty( - (n_layers, max_batch_size, sliding_window, n_kv_heads, head_dim) - ) - self.cache_v = torch.empty( - (n_layers, max_batch_size, sliding_window, n_kv_heads, head_dim) - ) - # holds the valid length for each batch element in the cache - self.kv_seqlens = None - - def get_view( - self, layer_id: int, metadata: RotatingCacheInputMetadata - ) -> CacheView: - return CacheView( - self.cache_k[layer_id], - self.cache_v[layer_id], - metadata, - self.kv_seqlens, - ) - - def reset(self): - self.kv_seqlens = None - - def init_kvseqlens(self, batch_size: int): - self.kv_seqlens = torch.zeros( - (batch_size,), device=self.device, dtype=torch.long - ) - - @property - def device(self): - return self.cache_k.device - - def to(self, device: torch.device, dtype: torch.dtype): - self.cache_k = self.cache_k.to(device=device, dtype=dtype) - self.cache_v = self.cache_v.to(device=device, dtype=dtype) - - return self - - def update_seqlens(self, seqlens: List[int]): - self.kv_seqlens += torch.tensor( - seqlens, device=self.device, dtype=torch.long - ) - - def get_input_metadata( - self, seqlens: List[int] - ) -> RotatingCacheInputMetadata: - """ - inpput = seqlens [5,7,2] // seqpos [0, 1, 3] // sliding_window 3 - --> only cache last 3 tokens in each sequence - - to_cache_mask = [0 0 1 1 1 | 0 0 0 0 1 1 1 | 1 1] - - cached_elements = [3 | 3 | 2] - --> absolute positions are used for rope - - positions = [0 1 2 3 4 | 1 2 3 4 5 6 7 | 3 4] - --> cache positions are positions cache_masked, modulo sliding_window + batch_idx * sliding_window - - cache_positions = [2 0 1 | 5 3 4 | 6 7] - """ - if self.kv_seqlens is None: - self.init_kvseqlens(len(seqlens)) - assert len(seqlens) == len(self.kv_seqlens), ( - f"Batch size is {len(self.kv_seqlens)}, got {len(seqlens)}, did you" - " forget to reset cache?" - ) - seqpos = self.kv_seqlens.tolist() - - assert len(seqlens) > 0, seqlens - masks = [ - [x >= seqlen - self.sliding_window for x in range(seqlen)] - for seqlen in seqlens - ] - to_cache_mask = torch.tensor( - sum(masks, []), device=self.device, dtype=torch.bool - ) - - cached_elements = torch.tensor( - [sum(mask) for mask in masks], device=self.device, dtype=torch.long - ) - - positions = torch.cat( - [ - torch.arange(pos, pos + seqlen) - for pos, seqlen in zip(seqpos, seqlens) - ] - ).to(device=self.device, dtype=torch.long) - - batch_idx = torch.tensor( - sum([[i] * seqlen for i, seqlen in enumerate(seqlens)], []), - device=self.device, - dtype=torch.long, - ) - - cache_positions = ( - positions % self.sliding_window + batch_idx * self.sliding_window - ) - - first_prefill = seqpos[0] == 0 - subsequent_prefill = any(seqlen > 1 for seqlen in seqlens) - - if first_prefill: - assert all([pos == 0 for pos in seqpos]), seqpos - mask = BlockDiagonalCausalMask.from_seqlens( - seqlens - ).make_local_attention(self.sliding_window) - - elif subsequent_prefill: - mask = BlockDiagonalMask.from_seqlens( - q_seqlen=seqlens, - kv_seqlen=[ - s + cached_s.clamp(max=self.sliding_window).item() - for (s, cached_s) in zip(seqlens, self.kv_seqlens) - ], - ).make_local_attention_from_bottomright(self.sliding_window) - else: - mask = BlockDiagonalCausalWithOffsetPaddedKeysMask.from_seqlens( - q_seqlen=seqlens, - kv_padding=self.sliding_window, - kv_seqlen=(self.kv_seqlens + cached_elements) - .clamp(max=self.sliding_window) - .tolist(), - ) - - return RotatingCacheInputMetadata( - positions=positions, - to_cache_mask=to_cache_mask, - cached_elements=cached_elements, - cache_positions=cache_positions[to_cache_mask], - prefill=first_prefill or subsequent_prefill, - mask=mask, - seqlens=seqlens, - ) diff --git a/zeta/utils/disable_logging.py b/zeta/utils/disable_logging.py index 9bc00f55..4df2173d 100644 --- a/zeta/utils/disable_logging.py +++ b/zeta/utils/disable_logging.py @@ -2,10 +2,12 @@ import os import warnings + def disable_warnings_and_logs(): """ Disables various warnings and logs. """ + class CustomFilter(logging.Filter): def filter(self, record): unwanted_logs = [ @@ -42,4 +44,4 @@ def filter(self, record): logger.setLevel(logging.CRITICAL) # disable all loggers - logging.disable(logging.CRITICAL) \ No newline at end of file + logging.disable(logging.CRITICAL) From 5cd7e3a20a4f4663ce8cf6a154f1b1d2874c8f8b Mon Sep 17 00:00:00 2001 From: Kye Date: Fri, 22 Dec 2023 21:19:07 -0500 Subject: [PATCH 185/587] [CLEANUP]g --- pyproject.toml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pyproject.toml b/pyproject.toml index 20961f08..27dc1511 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,6 +1,6 @@ [tool.poetry] name = "zetascale" -version = "1.2.2" +version = "1.2.3" description = "Transformers at zeta scales" authors = ["Zeta Team "] license = "MIT" From 99ad2f9c331f737696e5efdd92a0198c3a3eff13 Mon Sep 17 00:00:00 2001 From: Kye Date: Sat, 23 Dec 2023 00:10:45 -0500 Subject: [PATCH 186/587] [CLEANUP][zeta.structs] --- tests/nn/modules/test_simple_res_block.py | 23 + tests/structs/test_autoregressive_wrapper.py | 35 + tests/structs/test_encoder_decoder.py | 37 + zeta/nn/modules/conv_bn_relu.py | 35 + zeta/nn/modules/simple_resblock.py | 38 + zeta/structs/__init__.py | 19 +- zeta/structs/attn_layers.py | 1508 ------------------ zeta/structs/clip_encoder.py | 4 +- zeta/structs/cross_attender.py | 6 - zeta/structs/decoder.py | 7 - zeta/structs/efficient_net.py | 31 + zeta/structs/encoder.py | 7 - zeta/structs/encoder_decoder.py | 34 +- zeta/structs/local_transformer.py | 31 + zeta/structs/mag_vit.py | 589 ------- zeta/structs/multi_modal_projector.py | 38 +- zeta/structs/parallel_transformer.py | 258 --- zeta/structs/transformer.py | 2 +- zeta/structs/transformer_block.py | 2 - 19 files changed, 301 insertions(+), 2403 deletions(-) create mode 100644 tests/nn/modules/test_simple_res_block.py create mode 100644 tests/structs/test_autoregressive_wrapper.py create mode 100644 tests/structs/test_encoder_decoder.py create mode 100644 zeta/nn/modules/conv_bn_relu.py create mode 100644 zeta/nn/modules/simple_resblock.py delete mode 100644 zeta/structs/attn_layers.py delete mode 100644 zeta/structs/cross_attender.py delete mode 100644 zeta/structs/decoder.py delete mode 100644 zeta/structs/encoder.py delete mode 100644 zeta/structs/mag_vit.py delete mode 100644 zeta/structs/parallel_transformer.py diff --git a/tests/nn/modules/test_simple_res_block.py b/tests/nn/modules/test_simple_res_block.py new file mode 100644 index 00000000..d3175110 --- /dev/null +++ b/tests/nn/modules/test_simple_res_block.py @@ -0,0 +1,23 @@ +import torch +import pytest +from zeta.nn.modules.simple_resblock import SimpleResBlock + +def test_simple_resblock(): + # Initialize a SimpleResBlock with 10 channels + resblock = SimpleResBlock(10) + + # Create a tensor of shape (1, 10) + x = torch.rand(1, 10) + + # Pass the tensor through the SimpleResBlock + output = resblock(x) + + # Check that the output has the same shape as the input + assert output.shape == x.shape + + # Check that the output is not the same as the input + # This checks that the SimpleResBlock is doing something to the input + assert not torch.all(torch.eq(output, x)) + + # Check that the output is a tensor + assert isinstance(output, torch.Tensor) \ No newline at end of file diff --git a/tests/structs/test_autoregressive_wrapper.py b/tests/structs/test_autoregressive_wrapper.py new file mode 100644 index 00000000..cdc62990 --- /dev/null +++ b/tests/structs/test_autoregressive_wrapper.py @@ -0,0 +1,35 @@ +import torch +import pytest +from zeta.structs.auto_regressive_wrapper import AutoregressiveWrapper +from torch import nn + +def test_autoregressive_wrapper_initialization(): + net = nn.Linear(10, 10) + wrapper = AutoregressiveWrapper(net) + + assert isinstance(wrapper, AutoregressiveWrapper) + assert wrapper.net == net + assert wrapper.max_seq_len == net.max_seq_len + assert wrapper.pad_value == 0 + assert wrapper.ignore_index == -100 + assert wrapper.mask_prob == 0.0 + +def test_autoregressive_wrapper_forward(): + net = nn.Linear(10, 10) + wrapper = AutoregressiveWrapper(net) + + x = torch.randn(1, 10) + logits = wrapper(x) + + assert isinstance(logits, torch.Tensor) + assert logits.shape == torch.Size([1, 10, 10]) + +def test_autoregressive_wrapper_generate(): + net = nn.Linear(10, 10) + wrapper = AutoregressiveWrapper(net) + + x = torch.randn(1, 10) + generated = wrapper.generate(x, 10) + + assert isinstance(generated, torch.Tensor) + assert generated.shape == torch.Size([1, 10]) \ No newline at end of file diff --git a/tests/structs/test_encoder_decoder.py b/tests/structs/test_encoder_decoder.py new file mode 100644 index 00000000..ee792337 --- /dev/null +++ b/tests/structs/test_encoder_decoder.py @@ -0,0 +1,37 @@ +import torch +import pytest +from zeta.structs.encoder_decoder import EncoderDecoder +from argparse import Namespace + +def test_encoder_decoder_initialization(): + args = Namespace(share_all_embeddings=True) + encoder_decoder = EncoderDecoder(args) + + assert isinstance(encoder_decoder, EncoderDecoder) + assert encoder_decoder.args == args + assert encoder_decoder.args.share_all_embeddings == True + assert encoder_decoder.args.share_decoder_input_output_embed == True + +def test_encoder_decoder_forward(): + args = Namespace(share_all_embeddings=True) + encoder_decoder = EncoderDecoder(args) + + src_tokens = torch.tensor([[1, 2, 3], [4, 5, 6]]) + prev_output_tokens = torch.tensor([[7, 8, 9], [10, 11, 12]]) + + output = encoder_decoder(src_tokens, prev_output_tokens) + + assert isinstance(output, torch.Tensor) + assert output.shape == prev_output_tokens.shape + +def test_encoder_decoder_forward_features_only(): + args = Namespace(share_all_embeddings=True) + encoder_decoder = EncoderDecoder(args) + + src_tokens = torch.tensor([[1, 2, 3], [4, 5, 6]]) + prev_output_tokens = torch.tensor([[7, 8, 9], [10, 11, 12]]) + + output = encoder_decoder(src_tokens, prev_output_tokens, features_only=True) + + assert isinstance(output, torch.Tensor) + assert output.shape == prev_output_tokens.shape \ No newline at end of file diff --git a/zeta/nn/modules/conv_bn_relu.py b/zeta/nn/modules/conv_bn_relu.py new file mode 100644 index 00000000..4080f3da --- /dev/null +++ b/zeta/nn/modules/conv_bn_relu.py @@ -0,0 +1,35 @@ + +from torch import nn + +class ConvBNReLU(nn.Sequential): + """ + A conv layer followed by batch normalization and ReLU activation. + + Args: + in_planes (int): Number of input channels. + out_planes (int): Number of output channels. + kernel_size (int): Size of the convolutional kernel. + stride (int, optional): Stride of the convolution. Default is 1. + groups (int, optional): Number of groups for conv. Default is 1. + """ + + def __init__(self, in_planes, out_planes, kernel_size, stride=1, groups=1): + padding = (kernel_size - 1) // 2 + super(ConvBNReLU, self).__init__( + nn.Conv2d( + in_planes, + out_planes, + kernel_size, + stride, + padding, + groups=groups, + bias=False, + ), + nn.BatchNorm2d(out_planes), + nn.ReLU6(inplace=True), + ) + + def forward(self, x): + # Placeholder code to access the 'x' variable + return x + \ No newline at end of file diff --git a/zeta/nn/modules/simple_resblock.py b/zeta/nn/modules/simple_resblock.py new file mode 100644 index 00000000..c338cf91 --- /dev/null +++ b/zeta/nn/modules/simple_resblock.py @@ -0,0 +1,38 @@ +from torch import nn + +class SimpleResBlock(nn.Module): + """ + A simple residual block module. + + Args: + channels (int): The number of input and output channels. + + Attributes: + pre_norm (nn.LayerNorm): Layer normalization module applied before the projection. + proj (nn.Sequential): Sequential module consisting of linear layers and GELU activation. + + """ + + def __init__(self, channels): + super().__init__() + self.pre_norm = nn.LayerNorm(channels) + + self.proj = nn.Sequential( + nn.Linear(channels, channels), + nn.GELU(), + nn.Linear(channels, channels), + ) + + def forward(self, x): + """ + Forward pass of the simple residual block. + + Args: + x (torch.Tensor): The input tensor. + + Returns: + torch.Tensor: The output tensor after applying the residual block. + + """ + x = self.pre_norm(x) + return x + self.proj(x) diff --git a/zeta/structs/__init__.py b/zeta/structs/__init__.py index 8f1c4d99..6efb4f07 100644 --- a/zeta/structs/__init__.py +++ b/zeta/structs/__init__.py @@ -1,8 +1,17 @@ from zeta.structs.auto_regressive_wrapper import AutoregressiveWrapper +from zeta.structs.clip_encoder import CLIPVisionTower, build_vision_tower from zeta.structs.encoder_decoder import EncoderDecoder -from zeta.structs.hierarchical_transformer import HierarchicalTransformer +from zeta.structs.hierarchical_transformer import ( + HierarchicalBlock, + HierarchicalTransformer, +) from zeta.structs.local_transformer import LocalTransformer -from zeta.structs.parallel_transformer import ParallelTransformerBlock +from zeta.structs.mag_vit import VideoTokenizer +from zeta.structs.multi_modal_projector import build_vision_projector +from zeta.structs.simple_transformer import ( + ParallelTransformerBlock, + SimpleTransformer, +) from zeta.structs.transformer import ( Decoder, Encoder, @@ -10,10 +19,6 @@ ViTransformerWrapper, ) from zeta.structs.transformer_block import TransformerBlock -from zeta.structs.mag_vit import VideoTokenizer -from zeta.structs.clip_encoder import CLIPVisionTower, build_vision_tower -from zeta.structs.multi_modal_projector import build_vision_projector -from zeta.structs.simple_transformer import SimpleTransformer # from zeta.structs.efficient_net import EfficientNet @@ -22,6 +27,7 @@ "Encoder", "Decoder", "EncoderDecoder", + "HierarchicalBlock", "HierarchicalTransformer", "LocalTransformer", "ParallelTransformerBlock", @@ -29,6 +35,7 @@ "TransformerBlock", "ViTransformerWrapper", "VideoTokenizer", + "ParallelTransformerBlock", "SimpleTransformer", "CLIPVisionTower", "build_vision_tower", diff --git a/zeta/structs/attn_layers.py b/zeta/structs/attn_layers.py deleted file mode 100644 index 140824ad..00000000 --- a/zeta/structs/attn_layers.py +++ /dev/null @@ -1,1508 +0,0 @@ -import math -from collections import namedtuple -from dataclasses import dataclass -from functools import partial, wraps -from inspect import isfunction -from random import random -from typing import Callable, List, Optional - -import torch -import torch.nn.functional as F -from einops import rearrange, reduce, repeat -from torch import Tensor, einsum, nn - -from zeta.nn.attention.attend import Attend, Intermediates -from functools import reduce - -EfficientAttentionConfig = namedtuple( - "EfficientAttentionConfig", - ["enable_flash", "enable_math", "enable_mem_efficient"], -) - -DEFAULT_DIM_HEAD = 64 - - -@dataclass -class LayerIntermediates: - hiddens: Optional[List[Tensor]] = None - attn_intermediates: Optional[List[Intermediates]] = None - layer_hiddens: Optional[List[Tensor]] = None - attn_z_loss: Optional[Tensor] = None - - -# helpers - - -def exists(val): - return val is not None - - -def default(val, d): - if exists(val): - return val - return d() if isfunction(d) else d - - -def cast_tuple(val, depth): - return val if isinstance(val, tuple) else (val,) * depth - - -def divisible_by(num, den): - return (num % den) == 0 - - -def maybe(fn): - @wraps(fn) - def inner(x, *args, **kwargs): - if not exists(x): - return x - return fn(x, *args, **kwargs) - - return inner - - -class always: - def __init__(self, val): - self.val = val - - def __call__(self, *args, **kwargs): - return self.val - - -class not_equals: - def __init__(self, val): - self.val = val - - def __call__(self, x, *args, **kwargs): - return x != self.val - - -class equals: - def __init__(self, val): - self.val = val - - def __call__(self, x, *args, **kwargs): - return x == self.val - - -def Sequential(*modules): - return nn.Sequential(*filter(exists, modules)) - - -# tensor helpers - - -def max_neg_value(tensor): - return -torch.finfo(tensor.dtype).max - - -def l2norm(t, groups=1): - t = rearrange(t, "... (g d) -> ... g d", g=groups) - t = F.normalize(t, p=2, dim=-1) - return rearrange(t, "... g d -> ... (g d)") - - -def pad_at_dim(t, pad, dim=-1, value=0.0): - dims_from_right = (-dim - 1) if dim < 0 else (t.ndim - dim - 1) - zeros = (0, 0) * dims_from_right - return F.pad(t, (*zeros, *pad), value=value) - - -def or_reduce(masks): - head, *body = masks - for rest in body: - head = head | rest - return head - - -# auxiliary loss helpers - - -def calc_z_loss(pre_softmax_attns: List[Tensor], mask=None, weight=1.0): - # the same loss applied to the mixture of experts router logits in https://arxiv.org/abs/2202.08906 - # in the paper, in a tiny footnote, they mention using it on attention logits with stabilizing effects - # also used in PaLM as one of the measures - - lse = 0.0 - - for attn in pre_softmax_attns: - lse = lse + attn.logsumexp(dim=-1) - - loss = torch.square(lse) - loss = reduce(loss, "b h n -> b n", "sum") - - if not exists(mask): - return loss.mean() * weight - - loss = loss[mask].sum() / mask.sum().clamp(min=1e-5) - return loss * weight - - -# init helpers - - -def init_zero_(layer): - nn.init.constant_(layer.weight, 0.0) - if exists(layer.bias): - nn.init.constant_(layer.bias, 0.0) - - -# keyword argument helpers - - -def pick_and_pop(keys, d): - values = list(map(lambda key: d.pop(key), keys)) - return dict(zip(keys, values)) - - -def group_dict_by_key(cond, d): - return_val = [dict(), dict()] - for key in d.keys(): - match = bool(cond(key)) - ind = int(not match) - return_val[ind][key] = d[key] - return (*return_val,) - - -def string_begins_with(prefix, str): - return str.startswith(prefix) - - -def group_by_key_prefix(prefix, d): - return group_dict_by_key(partial(string_begins_with, prefix), d) - - -def groupby_prefix_and_trim(prefix, d): - kwargs_with_prefix, kwargs = group_dict_by_key( - partial(string_begins_with, prefix), d - ) - kwargs_without_prefix = dict( - map( - lambda x: (x[0][len(prefix) :], x[1]), - tuple(kwargs_with_prefix.items()), - ) - ) - return kwargs_without_prefix, kwargs - - -# initializations - - -def deepnorm_init( - transformer, beta, module_name_match_list=[".ff.", ".to_v", ".to_out"] -): - for name, module in transformer.named_modules(): - if not isinstance(module, nn.Linear): - continue - - needs_beta_gain = any( - map(lambda substr: substr in name, module_name_match_list) - ) - gain = beta if needs_beta_gain else 1 - nn.init.xavier_normal_(module.weight.data, gain=gain) - - if exists(module.bias): - nn.init.constant_(module.bias.data, 0) - - -# structured dropout, more effective than traditional attention dropouts - - -def dropout_seq(seq, mask, dropout): - b, n, *_, device = *seq.shape, seq.device - logits = torch.randn(b, n, device=device) - - if exists(mask): - mask_value = max_neg_value(logits) - logits = logits.masked_fill(~mask, mask_value) - - keep_prob = 1.0 - dropout - num_keep = max(1, int(keep_prob * n)) - keep_indices = logits.topk(num_keep, dim=1).indices - - batch_indices = torch.arange(b, device=device) - batch_indices = rearrange(batch_indices, "b -> b 1") - - seq = seq[batch_indices, keep_indices] - - if exists(mask): - seq_counts = mask.sum(dim=-1) - seq_keep_counts = torch.ceil(seq_counts * keep_prob).int() - keep_mask = torch.arange(num_keep, device=device) < rearrange( - seq_keep_counts, "b -> b 1" - ) - - mask = mask[batch_indices, keep_indices] & keep_mask - - return seq, mask - - -# activations - - -class ReluSquared(nn.Module): - def forward(self, x): - return F.relu(x) ** 2 - - -# embedding - - -class TokenEmbedding(nn.Module): - def __init__(self, dim, num_tokens, l2norm_embed=False): - super().__init__() - self.l2norm_embed = l2norm_embed - self.emb = nn.Embedding(num_tokens, dim) - - def forward(self, x): - token_emb = self.emb(x) - return l2norm(token_emb) if self.l2norm_embed else token_emb - - -# positional embeddings - - -class AbsolutePositionalEmbedding(nn.Module): - def __init__(self, dim, max_seq_len, l2norm_embed=False): - super().__init__() - self.scale = dim**-0.5 if not l2norm_embed else 1.0 - self.max_seq_len = max_seq_len - self.l2norm_embed = l2norm_embed - self.emb = nn.Embedding(max_seq_len, dim) - - def forward(self, x, pos=None): - seq_len, device = x.shape[1], x.device - assert seq_len <= self.max_seq_len, ( - f"you are passing in a sequence length of {seq_len} but your" - " absolute positional embedding has a max sequence length of" - f" {self.max_seq_len}" - ) - - if not exists(pos): - pos = torch.arange(seq_len, device=device) - - pos_emb = self.emb(pos) - pos_emb = pos_emb * self.scale - return l2norm(pos_emb) if self.l2norm_embed else pos_emb - - -class ScaledSinusoidalEmbedding(nn.Module): - def __init__(self, dim, theta=10000): - super().__init__() - assert divisible_by(dim, 2) - self.scale = nn.Parameter(torch.ones(1) * dim**-0.5) - - half_dim = dim // 2 - freq_seq = torch.arange(half_dim).float() / half_dim - inv_freq = theta**-freq_seq - self.register_buffer("inv_freq", inv_freq, persistent=False) - - def forward(self, x, pos=None): - seq_len, device = x.shape[1], x.device - - if not exists(pos): - pos = torch.arange(seq_len, device=device) - - emb = einsum("i, j -> i j", pos, self.inv_freq) - emb = torch.cat((emb.sin(), emb.cos()), dim=-1) - return emb * self.scale - - -class RelativePositionBias(nn.Module): - def __init__( - self, scale, causal=False, num_buckets=32, max_distance=128, heads=8 - ): - super().__init__() - self.scale = scale - self.causal = causal - self.num_buckets = num_buckets - self.max_distance = max_distance - self.relative_attention_bias = nn.Embedding(num_buckets, heads) - - @staticmethod - def _relative_position_bucket( - relative_position, causal=True, num_buckets=32, max_distance=128 - ): - ret = 0 - n = -relative_position - if not causal: - num_buckets //= 2 - ret += (n < 0).long() * num_buckets - n = torch.abs(n) - else: - n = torch.max(n, torch.zeros_like(n)) - - max_exact = num_buckets // 2 - is_small = n < max_exact - - val_if_large = ( - max_exact - + ( - torch.log(n.float() / max_exact) - / math.log(max_distance / max_exact) - * (num_buckets - max_exact) - ).long() - ) - val_if_large = torch.min( - val_if_large, torch.full_like(val_if_large, num_buckets - 1) - ) - - ret += torch.where(is_small, n, val_if_large) - return ret - - @property - def device(self): - return next(self.parameters()).device - - def forward(self, i, j): - device = self.device - q_pos = torch.arange(j - i, j, dtype=torch.long, device=device) - k_pos = torch.arange(j, dtype=torch.long, device=device) - rel_pos = k_pos[None, :] - q_pos[:, None] - rp_bucket = self._relative_position_bucket( - rel_pos, - causal=self.causal, - num_buckets=self.num_buckets, - max_distance=self.max_distance, - ) - values = self.relative_attention_bias(rp_bucket) - bias = rearrange(values, "i j h -> h i j") - return bias * self.scale - - -class DynamicPositionBias(nn.Module): - def __init__(self, dim, *, heads, depth, log_distance=False, norm=False): - super().__init__() - assert ( - depth >= 1 - ), "depth for dynamic position bias MLP must be greater or equal to 1" - self.log_distance = log_distance - - self.mlp = nn.ModuleList([]) - - self.mlp.append( - Sequential( - nn.Linear(1, dim), - nn.LayerNorm(dim) if norm else None, - nn.SiLU(), - ) - ) - - for _ in range(depth - 1): - self.mlp.append( - Sequential( - nn.Linear(dim, dim), - nn.LayerNorm(dim) if norm else None, - nn.SiLU(), - ) - ) - - self.mlp.append(nn.Linear(dim, heads)) - - @property - def device(self): - return next(self.parameters()).device - - def forward(self, i, j): - assert i == j - n, device = j, self.device - - # get the (n x n) matrix of distances - seq_arange = torch.arange(n, device=device) - context_arange = torch.arange(n, device=device) - indices = rearrange(seq_arange, "i -> i 1") - rearrange( - context_arange, "j -> 1 j" - ) - indices += n - 1 - - # input to continuous positions MLP - pos = torch.arange(-n + 1, n, device=device).float() - pos = rearrange(pos, "... -> ... 1") - - if self.log_distance: - # log of distance is sign(rel_pos) * log(abs(rel_pos) + 1) - pos = torch.sign(pos) * torch.log(pos.abs() + 1) - - for layer in self.mlp: - pos = layer(pos) - - # get position biases - bias = pos[indices] - bias = rearrange(bias, "i j h -> h i j") - return bias - - -class AlibiPositionalBias(nn.Module): - def __init__(self, heads, total_heads, **kwargs): - super().__init__() - self.heads = heads - self.total_heads = total_heads - - slopes = Tensor(self._get_slopes(heads)) - slopes = rearrange(slopes, "h -> h 1 1") - self.register_buffer("slopes", slopes, persistent=False) - self.register_buffer("bias", None, persistent=False) - - def get_bias(self, i, j, device): - i_arange = torch.arange(j - i, j, device=device) - j_arange = torch.arange(j, device=device) - bias = -torch.abs( - rearrange(j_arange, "j -> 1 1 j") - - rearrange(i_arange, "i -> 1 i 1") - ) - return bias - - @staticmethod - def _get_slopes(heads): - def get_slopes_power_of_2(n): - start = 2 ** (-(2 ** -(math.log2(n) - 3))) - ratio = start - return [start * ratio**i for i in range(n)] - - if math.log2(heads).is_integer(): - return get_slopes_power_of_2(heads) - - closest_power_of_2 = 2 ** math.floor(math.log2(heads)) - return ( - get_slopes_power_of_2(closest_power_of_2) - + get_slopes_power_of_2(2 * closest_power_of_2)[0::2][ - : heads - closest_power_of_2 - ] - ) - - @property - def device(self): - return next(self.buffers()).device - - def forward(self, i, j): - h, device = self.total_heads, self.device - - if ( - exists(self.bias) - and self.bias.shape[-1] >= j - and self.bias.shape[-2] >= i - ): - return self.bias[..., :i, :j] - - bias = self.get_bias(i, j, device) - bias = bias * self.slopes - - num_heads_unalibied = h - bias.shape[0] - bias = pad_at_dim(bias, (0, num_heads_unalibied), dim=0) - self.register_buffer("bias", bias, persistent=False) - - return self.bias - - -class RotaryEmbedding(nn.Module): - def __init__( - self, - dim, - use_xpos=False, - scale_base=512, - interpolation_factor=1.0, - base=10000, - base_rescale_factor=1.0, - ): - super().__init__() - # proposed by reddit user bloc97, to rescale rotary embeddings to longer sequence length without fine-tuning - # has some connection to NTK literature - # https://www.reddit.com/r/LocalLLaMA/comments/14lz7j5/ntkaware_scaled_rope_allows_llama_models_to_have/ - base *= base_rescale_factor ** (dim / (dim - 2)) - - inv_freq = 1.0 / (base ** (torch.arange(0, dim, 2).float() / dim)) - self.register_buffer("inv_freq", inv_freq) - - assert interpolation_factor >= 1.0 - self.interpolation_factor = interpolation_factor - - if not use_xpos: - self.register_buffer("scale", None) - return - - scale = (torch.arange(0, dim, 2) + 0.4 * dim) / (1.4 * dim) - - self.scale_base = scale_base - self.register_buffer("scale", scale) - - def forward(self, seq_len, device): - t = torch.arange(seq_len, device=device).type_as(self.inv_freq) - t = t / self.interpolation_factor - - freqs = torch.einsum("i , j -> i j", t, self.inv_freq) - freqs = torch.cat((freqs, freqs), dim=-1) - - if not exists(self.scale): - return freqs, 1.0 - - power = ( - torch.arange(seq_len, device=device) - (seq_len // 2) - ) / self.scale_base - scale = self.scale ** rearrange(power, "n -> n 1") - scale = torch.cat((scale, scale), dim=-1) - - return freqs, scale - - -def rotate_half(x): - x = rearrange(x, "... (j d) -> ... j d", j=2) - x1, x2 = x.unbind(dim=-2) - return torch.cat((-x2, x1), dim=-1) - - -def apply_rotary_pos_emb(t, freqs, scale=1): - seq_len = t.shape[-2] - freqs = freqs[-seq_len:, :] - return (t * freqs.cos() * scale) + (rotate_half(t) * freqs.sin() * scale) - - -# norms - - -class Scale(nn.Module): - def __init__(self, value, fn): - super().__init__() - self.value = value - self.fn = fn - - def forward(self, x, **kwargs): - out = self.fn(x, **kwargs) - - def scale_fn(t): - return t * self.value - - if not isinstance(out, tuple): - return scale_fn(out) - - return (scale_fn(out[0]), *out[1:]) - - -class ScaleNorm(nn.Module): - def __init__(self, dim, eps=1e-5): - super().__init__() - self.eps = eps - self.g = nn.Parameter(torch.ones(1) * (dim**-0.5)) - - def forward(self, x): - norm = torch.norm(x, dim=-1, keepdim=True) - return x / norm.clamp(min=self.eps) * self.g - - -class RMSNorm(nn.Module): - def __init__(self, dim): - super().__init__() - self.scale = dim**0.5 - self.g = nn.Parameter(torch.ones(dim)) - - def forward(self, x): - return F.normalize(x, dim=-1) * self.scale * self.g - - -class SimpleRMSNorm(nn.Module): - def __init__(self, dim): - super().__init__() - self.scale = dim**0.5 - - def forward(self, x): - return F.normalize(x, dim=-1) * self.scale - - -# residual and residual gates - - -class Residual(nn.Module): - def __init__(self, dim, scale_residual=False, scale_residual_constant=1.0): - super().__init__() - self.residual_scale = ( - nn.Parameter(torch.ones(dim)) if scale_residual else None - ) - self.scale_residual_constant = scale_residual_constant - - def forward(self, x, residual): - if exists(self.residual_scale): - residual = residual * self.residual_scale - - if self.scale_residual_constant != 1: - residual = residual * self.scale_residual_constant - - return x + residual - - -class GRUGating(nn.Module): - def __init__(self, dim, scale_residual=False, **kwargs): - super().__init__() - self.gru = nn.GRUCell(dim, dim) - self.residual_scale = ( - nn.Parameter(torch.ones(dim)) if scale_residual else None - ) - - def forward(self, x, residual): - if exists(self.residual_scale): - residual = residual * self.residual_scale - - gated_output = self.gru( - rearrange(x, "b n d -> (b n) d"), - rearrange(residual, "b n d -> (b n) d"), - ) - - return gated_output.reshape_as(x) - - -# token shifting - - -def shift(t, amount, mask=None): - if amount == 0: - return t - else: - amount = min(amount, t.shape[1]) - - if exists(mask): - t = t.masked_fill(~mask[..., None], 0.0) - - return pad_at_dim(t, (amount, -amount), dim=-2, value=0.0) - - -class ShiftTokens(nn.Module): - def __init__(self, shifts, fn): - super().__init__() - self.fn = fn - self.shifts = tuple(shifts) - - def forward(self, x, **kwargs): - mask = kwargs.get("mask", None) - shifts = self.shifts - segments = len(shifts) - feats_per_shift = x.shape[-1] // segments - splitted = x.split(feats_per_shift, dim=-1) - segments_to_shift, rest = splitted[:segments], splitted[segments:] - segments_to_shift = list( - map( - lambda args: shift(*args, mask=mask), - zip(segments_to_shift, shifts), - ) - ) - x = torch.cat((*segments_to_shift, *rest), dim=-1) - return self.fn(x, **kwargs) - - -# feedforward - - -class GLU(nn.Module): - def __init__(self, dim_in, dim_out, activation: Callable, mult_bias=False): - super().__init__() - self.act = activation - self.proj = nn.Linear(dim_in, dim_out * 2) - self.mult_bias = nn.Parameter(torch.ones(dim_out)) if mult_bias else 1.0 - - def forward(self, x): - x, gate = self.proj(x).chunk(2, dim=-1) - return x * self.act(gate) * self.mult_bias - - -class FeedForward(nn.Module): - def __init__( - self, - dim, - dim_out=None, - mult=4, - glu=False, - glu_mult_bias=False, - swish=False, - relu_squared=False, - post_act_ln=False, - dropout=0.0, - no_bias=False, - zero_init_output=False, - ): - super().__init__() - inner_dim = int(dim * mult) - dim_out = default(dim_out, dim) - - if relu_squared: - activation = ReluSquared() - elif swish: - activation = nn.SiLU() - else: - activation = nn.GELU() - - if glu: - project_in = GLU( - dim, inner_dim, activation, mult_bias=glu_mult_bias - ) - else: - project_in = nn.Sequential( - nn.Linear(dim, inner_dim, bias=not no_bias), activation - ) - - self.ff = Sequential( - project_in, - nn.LayerNorm(inner_dim) if post_act_ln else None, - nn.Dropout(dropout), - nn.Linear(inner_dim, dim_out, bias=not no_bias), - ) - - # init last linear layer to 0 - if zero_init_output: - init_zero_(self.ff[-1]) - - def forward(self, x): - return self.ff(x) - - -# attention. it is all we need - - -class Attention(nn.Module): - def __init__( - self, - dim, - dim_head=DEFAULT_DIM_HEAD, - heads=8, - causal=False, - flash=False, - talking_heads=False, - head_scale=False, - sparse_topk=None, - num_mem_kv=0, - dropout=0.0, - on_attn=False, - gate_values=False, - zero_init_output=False, - max_attend_past=None, - qk_norm=False, - qk_norm_groups=1, - qk_norm_scale=10, - qk_norm_dim_scale=False, - one_kv_head=False, - kv_heads=None, - shared_kv=False, - value_dim_head=None, - tensor_product=False, # https://arxiv.org/abs/2208.06061 - cascading_heads=False, - add_zero_kv=False, # same as add_zero_attn in pytorch - onnxable=False, - ): - super().__init__() - self.scale = dim_head**-0.5 - - self.heads = heads - self.causal = causal - self.max_attend_past = max_attend_past - - assert not (exists(kv_heads) and one_kv_head), ( - "either attn_one_kv_head is set to True (in which case kv_heads is" - " set to 1), or attn_kv_heads is set, but not both" - ) - - value_dim_head = default(value_dim_head, dim_head) - kv_heads = default(kv_heads, heads) - - kv_heads = 1 if one_kv_head else kv_heads - assert divisible_by(heads, kv_heads) - - self.kv_heads = kv_heads - - q_dim = dim_head * heads - k_dim = dim_head * kv_heads - v_dim = value_dim_head * kv_heads - out_dim = value_dim_head * heads - - self.to_q = nn.Linear(dim, q_dim, bias=False) - self.to_k = nn.Linear(dim, k_dim, bias=False) - - # shared key / values, for further memory savings during inference - assert not ( - shared_kv and value_dim_head != dim_head - ), "key and value head dimensions must be equal for shared key / values" - self.to_v = nn.Linear(dim, v_dim, bias=False) if not shared_kv else None - - # relations projection from tp-attention - self.to_r = ( - nn.Linear(dim, v_dim, bias=False) if tensor_product else None - ) - - # add GLU gating for aggregated values, from alphafold2 - self.to_v_gate = None - if gate_values: - self.to_v_gate = nn.Linear(dim, out_dim) - nn.init.constant_(self.to_v_gate.weight, 0) - nn.init.constant_(self.to_v_gate.bias, 1) - - # cosine sim attention - self.qk_norm = qk_norm - self.qk_norm_groups = qk_norm_groups - self.qk_norm_scale = qk_norm_scale - - # whether to use the rmsnorm (equivalent to cosine sim attention when - # scale is equal to 1) - https://arxiv.org/abs/2302.05442 - self.qk_norm_dim_scale = qk_norm_dim_scale - - self.qk_norm_q_scale = self.qk_norm_k_scale = 1 - if qk_norm and qk_norm_dim_scale: - self.qk_norm_q_scale = nn.Parameter(torch.ones(dim_head)) - self.qk_norm_k_scale = nn.Parameter(torch.ones(dim_head)) - - assert (not qk_norm) or divisible_by(dim_head, qk_norm_groups), ( - "dimension per attention head must be divisible by the qk norm" - " groups" - ) - assert not (qk_norm and (dim_head // qk_norm_groups) <= 2), ( - "the group dimension may be too small (2 was too small in my tests," - " but 4 still works, surprisingly)" - ) - - # attend class - includes core attention algorithm + talking heads - - self.attend = Attend( - heads=heads, - causal=causal, - talking_heads=talking_heads, - dropout=dropout, - sparse_topk=sparse_topk, - qk_norm=qk_norm, - scale=qk_norm_scale if qk_norm else self.scale, - add_zero_kv=add_zero_kv, - flash=flash, - onnxable=onnxable, - ) - - # if cascading_heads: - # # cascading heads - wrap the Attend logic - # self.attend = CascadingHeads(self.attend) - - # head scaling - self.head_scale = head_scale - if head_scale: - self.head_scale_params = nn.Parameter(torch.ones(1, heads, 1, 1)) - - # explicit topk sparse attention - self.sparse_topk = sparse_topk - - # add memory key / values - self.num_mem_kv = num_mem_kv - if num_mem_kv > 0: - self.mem_k = nn.Parameter(torch.randn(heads, num_mem_kv, dim_head)) - self.mem_v = nn.Parameter(torch.randn(heads, num_mem_kv, dim_head)) - - # attention on attention - self.attn_on_attn = on_attn - self.to_out = ( - nn.Sequential(nn.Linear(out_dim, dim * 2, bias=False), nn.GLU()) - if on_attn - else nn.Linear(out_dim, dim, bias=False) - ) - - # init output projection 0 - if zero_init_output: - init_zero_(self.to_out) - - def forward( - self, - x, - context=None, - mask=None, - context_mask=None, - attn_mask=None, - rel_pos=None, - rotary_pos_emb=None, - prev_attn=None, - mem=None, - ): - b, n, _, h, kv_h, head_scale, device, has_context = ( - *x.shape, - self.heads, - self.kv_heads, - self.head_scale, - x.device, - exists(context), - ) - kv_input = default(context, x) - - q_input = x - k_input = kv_input - v_input = kv_input - r_input = x - - if exists(mem): - k_input = torch.cat((mem, k_input), dim=-2) - v_input = torch.cat((mem, v_input), dim=-2) - - q = self.to_q(q_input) - k = self.to_k(k_input) - v = self.to_v(v_input) if exists(self.to_v) else k - r = self.to_r(r_input) if exists(self.to_r) else None - - q = rearrange(q, "b n (h d) -> b h n d", h=h) - - k, v, r = map( - lambda t: maybe(rearrange)(t, "b n (h d) -> b h n d", h=kv_h), - (k, v, r), - ) - - if self.qk_norm: - qk_l2norm = partial(l2norm, groups=self.qk_norm_groups) - q, k = map(qk_l2norm, (q, k)) - - q = q * self.qk_norm_q_scale - k = k * self.qk_norm_k_scale - - if exists(rotary_pos_emb) and not has_context: - freqs, xpos_scale = rotary_pos_emb - l = freqs.shape[-1] - - q_xpos_scale, k_xpos_scale = ( - (xpos_scale, xpos_scale**-1.0) - if exists(xpos_scale) - else (1.0, 1.0) - ) - (ql, qr), (kl, kr), (vl, vr) = map( - lambda t: (t[..., :l], t[..., l:]), (q, k, v) - ) - - ql, kl, vl = map( - lambda arg: apply_rotary_pos_emb(arg[0], freqs, arg[1]), - ((ql, q_xpos_scale), (kl, k_xpos_scale), (vl, k_xpos_scale)), - ) - q, k, v = map( - lambda t: torch.cat(t, dim=-1), ((ql, qr), (kl, kr), (vl, vr)) - ) - - input_mask = context_mask if has_context else mask - - if self.num_mem_kv > 0: - mem_k, mem_v = map( - lambda t: repeat(t, "h n d -> b h n d", b=b), - (self.mem_k, self.mem_v), - ) - - if self.qk_norm: - mem_k = l2norm(mem_k) - mem_k = mem_k * self.qk_norm_k_scale - - k = torch.cat((mem_k, k), dim=-2) - v = torch.cat((mem_v, v), dim=-2) - - if exists(input_mask): - input_mask = pad_at_dim( - input_mask, (self.num_mem_kv, 0), dim=-1, value=True - ) - - i, j = map(lambda t: t.shape[-2], (q, k)) - - # determine masking - - max_neg_value(q) - masks = [] - final_attn_mask = None - - if exists(input_mask): - input_mask = rearrange(input_mask, "b j -> b 1 1 j") - masks.append(~input_mask) - - if exists(attn_mask): - assert 2 <= attn_mask.ndim <= 4, ( - "attention mask must have greater than 2 dimensions but less" - " than or equal to 4" - ) - if attn_mask.ndim == 2: - attn_mask = rearrange(attn_mask, "i j -> 1 1 i j") - elif attn_mask.ndim == 3: - attn_mask = rearrange(attn_mask, "h i j -> 1 h i j") - masks.append(~attn_mask) - - if exists(self.max_attend_past): - range_q = torch.arange(j - i, j, device=device) - range_k = torch.arange(j, device=device) - dist = rearrange(range_q, "i -> 1 1 i 1") - rearrange( - range_k, "j -> 1 1 1 j" - ) - max_attend_past_mask = dist > self.max_attend_past - masks.append(max_attend_past_mask) - - if len(masks) > 0: - final_attn_mask = ~or_reduce(masks) - - # prepare relative positional bias, if needed - - attn_bias = None - if exists(rel_pos): - attn_bias = rel_pos(i, j) - - # attention is all we need - - out, intermediates = self.attend( - q, - k, - v, - mask=final_attn_mask, - attn_bias=attn_bias, - prev_attn=prev_attn, - ) - - # https://arxiv.org/abs/2208.06061 proposes to add a residual for - # better gradients - - if exists(r): - out = out * r + out - - # normformer scaling of heads - - if head_scale: - out = out * self.head_scale_params - - # merge heads - - out = rearrange(out, "b h n d -> b n (h d)") - - # alphafold2 styled gating of the values - - if exists(self.to_v_gate): - gates = self.to_v_gate(x) - out = out * gates.sigmoid() - - # combine the heads - - out = self.to_out(out) - - if exists(mask): - mask = rearrange(mask, "b n -> b n 1") - out = out.masked_fill(~mask, 0.0) - - return out, intermediates - - -class AttentionLayers(nn.Module): - def __init__( - self, - dim, - depth, - heads=8, - causal=False, - cross_attend=False, - only_cross=False, - use_scalenorm=False, - use_rmsnorm=False, - use_simple_rmsnorm=False, - alibi_pos_bias=False, - alibi_num_heads=None, - rel_pos_bias=False, - rel_pos_num_buckets=32, - rel_pos_max_distance=128, - dynamic_pos_bias=False, - dynamic_pos_bias_log_distance=False, - dynamic_pos_bias_mlp_depth=2, - dynamic_pos_bias_norm=False, - rotary_pos_emb=False, - rotary_emb_dim=None, - rotary_xpos=False, - rotary_interpolation_factor=1.0, - rotary_xpos_scale_base=512, - rotary_base_rescale_factor=1.0, - custom_layers=None, - sandwich_coef=None, - par_ratio=None, - residual_attn=False, - cross_residual_attn=False, - macaron=False, - pre_norm=True, - pre_norm_has_final_norm=True, - gate_residual=False, - scale_residual=False, - scale_residual_constant=1.0, - deepnorm=False, - shift_tokens=0, - sandwich_norm=False, - resi_dual=False, - resi_dual_scale=1.0, - zero_init_branch_output=False, - layer_dropout=0.0, - cross_attn_tokens_dropout=0.0, - **kwargs, - ): - super().__init__() - rotary_pos_emb = rotary_pos_emb or rotary_xpos - - ff_kwargs, kwargs = groupby_prefix_and_trim("ff_", kwargs) - attn_kwargs, kwargs = groupby_prefix_and_trim("attn_", kwargs) - - dim_head = attn_kwargs.get("dim_head", DEFAULT_DIM_HEAD) - - self.dim = dim - self.depth = depth - self.layers = nn.ModuleList([]) - - self.has_pos_emb = rel_pos_bias or rotary_pos_emb - - rotary_emb_dim = max(default(rotary_emb_dim, dim_head // 2), 32) - - assert not ( - rotary_xpos and not causal - ), "rotary xpos is not compatible with bidirectional attention" - self.rotary_pos_emb = ( - RotaryEmbedding( - rotary_emb_dim, - use_xpos=rotary_xpos, - scale_base=rotary_xpos_scale_base, - interpolation_factor=rotary_interpolation_factor, - base_rescale_factor=rotary_base_rescale_factor, - ) - if rotary_pos_emb - else None - ) - - assert not (alibi_pos_bias and rel_pos_bias), ( - "you can only choose Alibi positional bias or T5 relative" - " positional bias, not both" - ) - assert rel_pos_num_buckets <= rel_pos_max_distance, ( - "number of relative position buckets must be less than the relative" - " position max distance" - ) - - # relative positional bias - - flash_attn = attn_kwargs.get("flash", False) - assert ( - int(rel_pos_bias) + int(dynamic_pos_bias) + int(alibi_pos_bias) - ) <= 1, ( - "you can only choose up to one of t5, alibi, or dynamic positional" - " bias" - ) - - self.rel_pos = None - if rel_pos_bias: - assert ( - not flash_attn - ), "flash attention not compatible with t5 relative positional bias" - self.rel_pos = RelativePositionBias( - scale=dim_head**0.5, - causal=causal, - heads=heads, - num_buckets=rel_pos_num_buckets, - max_distance=rel_pos_max_distance, - ) - elif dynamic_pos_bias: - assert ( - not flash_attn - ), "flash attention not compatible with dynamic positional bias" - self.rel_pos = DynamicPositionBias( - dim=dim // 4, - heads=heads, - log_distance=dynamic_pos_bias_log_distance, - depth=dynamic_pos_bias_mlp_depth, - norm=dynamic_pos_bias_norm, - ) - elif alibi_pos_bias: - alibi_num_heads = default(alibi_num_heads, heads) - assert alibi_num_heads <= heads, ( - "number of ALiBi heads must be less than the total number of" - " heads" - ) - self.rel_pos = AlibiPositionalBias( - heads=alibi_num_heads, total_heads=heads - ) - - # determine deepnorm and residual scale - - if deepnorm: - assert scale_residual_constant == 1, ( - "scale residual constant is being overridden by deep norm" - " settings" - ) - pre_norm = sandwich_norm = resi_dual = False - scale_residual = True - scale_residual_constant = (2 * depth) ** 0.25 - - assert ( - int(sandwich_norm) + int(resi_dual) - ) <= 1, "either sandwich norm or resiDual is selected, but not both" - assert not ( - not pre_norm and sandwich_norm - ), "sandwich norm cannot be used when not using prenorm" - - if resi_dual: - pre_norm = False - - self.pre_norm = pre_norm - self.sandwich_norm = sandwich_norm - - self.resi_dual = resi_dual - assert 0 < resi_dual_scale <= 1.0, ( - "resiDual prenorm residual must be scaled by a factor greater than" - " 0 and less than or equal to 1." - ) - self.resi_dual_scale = resi_dual_scale - - self.residual_attn = residual_attn - self.cross_residual_attn = cross_residual_attn - assert not ( - flash_attn and (residual_attn or cross_residual_attn) - ), "flash attention is not compatible with residual attention" - - self.cross_attend = cross_attend - - assert ( - int(use_scalenorm) + int(use_rmsnorm) + int(use_simple_rmsnorm) - ) <= 1, "you can only use either scalenorm, rmsnorm, or simple rmsnorm" - - if use_scalenorm: - norm_class = ScaleNorm - elif use_rmsnorm: - norm_class = RMSNorm - elif use_simple_rmsnorm: - norm_class = SimpleRMSNorm - else: - norm_class = nn.LayerNorm - - norm_fn = partial(norm_class, dim) - - if cross_attend and not only_cross: - default_block = ("a", "c", "f") - elif cross_attend and only_cross: - default_block = ("c", "f") - else: - default_block = ("a", "f") - - if macaron: - default_block = ("f",) + default_block - - # zero init - - if zero_init_branch_output: - attn_kwargs = {**attn_kwargs, "zero_init_output": True} - ff_kwargs = {**ff_kwargs, "zero_init_output": True} - - # calculate layer block order - - if exists(custom_layers): - layer_types = custom_layers - elif exists(par_ratio): - par_depth = depth * len(default_block) - assert 1 < par_ratio <= par_depth, "par ratio out of range" - default_block = tuple(filter(not_equals("f"), default_block)) - par_attn = par_depth // par_ratio - # 2 / 3 attention layer cutoff suggested by PAR paper - depth_cut = par_depth * 2 // 3 - par_width = (depth_cut + depth_cut // par_attn) // par_attn - assert ( - len(default_block) <= par_width - ), "default block is too large for par_ratio" - par_block = default_block + ("f",) * ( - par_width - len(default_block) - ) - par_head = par_block * par_attn - layer_types = par_head + ("f",) * (par_depth - len(par_head)) - elif exists(sandwich_coef): - assert ( - sandwich_coef > 0 and sandwich_coef <= depth - ), "sandwich coefficient should be less than the depth" - layer_types = ( - ("a",) * sandwich_coef - + default_block * (depth - sandwich_coef) - + ("f",) * sandwich_coef - ) - else: - layer_types = default_block * depth - - self.layer_types = layer_types - self.num_attn_layers = len(list(filter(equals("a"), layer_types))) - - # stochastic depth - - self.layer_dropouts = cast_tuple(layer_dropout, len(layer_types)) - - # structured dropout for cross attending - - self.cross_attn_tokens_dropout = cross_attn_tokens_dropout - - # calculate token shifting - - shift_tokens = cast_tuple(shift_tokens, len(layer_types)) - - # whether it has post norm - - self.final_norm = norm_fn() if pre_norm or resi_dual else nn.Identity() - - # iterate and construct layers - - for ind, (layer_type, layer_shift_tokens) in enumerate( - zip(self.layer_types, shift_tokens) - ): - ind == (len(self.layer_types) - 1) - - if layer_type == "a": - layer = Attention( - dim, heads=heads, causal=causal, **attn_kwargs - ) - elif layer_type == "c": - layer = Attention(dim, heads=heads, **attn_kwargs) - elif layer_type == "f": - layer = FeedForward(dim, **ff_kwargs) - layer = layer if not macaron else Scale(0.5, layer) - else: - raise Exception(f"invalid layer type {layer_type}") - - if layer_shift_tokens > 0: - shift_range_upper = layer_shift_tokens + 1 - shift_range_lower = -layer_shift_tokens if not causal else 0 - layer = ShiftTokens( - range(shift_range_lower, shift_range_upper), layer - ) - - residual_fn = GRUGating if gate_residual else Residual - residual = residual_fn( - dim, - scale_residual=scale_residual, - scale_residual_constant=scale_residual_constant, - ) - - pre_branch_norm = norm_fn() if pre_norm else None - post_branch_norm = norm_fn() if sandwich_norm else None - post_main_norm = norm_fn() if not pre_norm else None - - norms = nn.ModuleList( - [pre_branch_norm, post_branch_norm, post_main_norm] - ) - - self.layers.append(nn.ModuleList([norms, layer, residual])) - - if deepnorm: - init_gain = (8 * depth) ** -0.25 - deepnorm_init(self, init_gain) - - def forward( - self, - x, - context=None, - mask=None, - context_mask=None, - attn_mask=None, - self_attn_context_mask=None, - mems=None, - return_hiddens=False, - ): - assert not ( - self.cross_attend ^ exists(context) - ), "context must be passed in if cross_attend is set to True" - - hiddens = [] - layer_hiddens = [] - intermediates = [] - - prev_attn = None - prev_cross_attn = None - - mems = mems.copy() if exists(mems) else [None] * self.num_attn_layers - - rotary_pos_emb = None - if exists(self.rotary_pos_emb): - max_rotary_emb_length = max( - list( - map( - lambda m: (m.shape[1] if exists(m) else 0) + x.shape[1], - mems, - ) - ) - ) - rotary_pos_emb = self.rotary_pos_emb( - max_rotary_emb_length, x.device - ) - - outer_residual = x * self.resi_dual_scale - - for ind, ( - layer_type, - (norm, block, residual_fn), - layer_dropout, - ) in enumerate(zip(self.layer_types, self.layers, self.layer_dropouts)): - ind == (len(self.layers) - 1) - - if ( - self.training - and layer_dropout > 0.0 - and random() < layer_dropout - ): - continue - - if layer_type == "a": - if return_hiddens: - hiddens.append(x) - layer_mem = mems.pop(0) if mems else None - - if layer_type == "c": - if self.training and self.cross_attn_tokens_dropout > 0.0: - context, context_mask = dropout_seq( - context, context_mask, self.cross_attn_tokens_dropout - ) - - inner_residual = x - - if return_hiddens: - layer_hiddens.append(x) - - pre_norm, post_branch_norm, post_main_norm = norm - - if exists(pre_norm): - x = pre_norm(x) - - if layer_type == "a": - out, inter = block( - x, - mask=mask, - context_mask=self_attn_context_mask, - attn_mask=attn_mask, - rel_pos=self.rel_pos, - rotary_pos_emb=rotary_pos_emb, - prev_attn=prev_attn, - mem=layer_mem, - ) - elif layer_type == "c": - out, inter = block( - x, - context=context, - mask=mask, - context_mask=context_mask, - prev_attn=prev_cross_attn, - ) - elif layer_type == "f": - out = block(x) - - if self.resi_dual: - outer_residual = outer_residual + out * self.resi_dual_scale - - if exists(post_branch_norm): - out = post_branch_norm(out) - - x = residual_fn(out, inner_residual) - - if layer_type in ("a", "c") and return_hiddens: - intermediates.append(inter) - - if layer_type == "a" and self.residual_attn: - prev_attn = inter.pre_softmax_attn - elif layer_type == "c" and self.cross_residual_attn: - prev_cross_attn = inter.pre_softmax_attn - - if exists(post_main_norm): - x = post_main_norm(x) - - if return_hiddens: - layer_hiddens.append(x) - - if self.resi_dual: - x = x + self.final_norm(outer_residual) - else: - x = self.final_norm(x) - - if return_hiddens: - intermediates = LayerIntermediates( - hiddens=hiddens, - attn_intermediates=intermediates, - layer_hiddens=layer_hiddens, - ) - - return x, intermediates - - return x diff --git a/zeta/structs/clip_encoder.py b/zeta/structs/clip_encoder.py index 13a07042..4cf8a787 100644 --- a/zeta/structs/clip_encoder.py +++ b/zeta/structs/clip_encoder.py @@ -1,8 +1,10 @@ +from transformers import CLIPImageProcessor + import os import torch import torch.nn as nn -from transformers import CLIPVisionModel, CLIPImageProcessor, CLIPVisionConfig +from transformers import CLIPVisionModel, CLIPVisionConfig class CLIPVisionTower(nn.Module): diff --git a/zeta/structs/cross_attender.py b/zeta/structs/cross_attender.py deleted file mode 100644 index b1328258..00000000 --- a/zeta/structs/cross_attender.py +++ /dev/null @@ -1,6 +0,0 @@ -from zeta.structs.attn_layers import AttentionLayers - - -class CrossAttender(AttentionLayers): - def __init__(self, **kwargs): - super().__init__(cross_attend=True, only_cross=True, **kwargs) diff --git a/zeta/structs/decoder.py b/zeta/structs/decoder.py deleted file mode 100644 index 977e590f..00000000 --- a/zeta/structs/decoder.py +++ /dev/null @@ -1,7 +0,0 @@ -from zeta.structs.attn_layers import AttentionLayers - - -class Decoder(AttentionLayers): - def __init__(self, **kwargs): - assert "causal" not in kwargs, "cannot set causality on decoder" - super().__init__(causal=True, **kwargs) diff --git a/zeta/structs/efficient_net.py b/zeta/structs/efficient_net.py index 90dadeb6..5465b5d8 100644 --- a/zeta/structs/efficient_net.py +++ b/zeta/structs/efficient_net.py @@ -22,6 +22,17 @@ def _round_filters(filters, width_mult): class ConvBNReLU(nn.Sequential): + """ + A class representing a convolutional layer followed by batch normalization and ReLU activation. + + Args: + in_planes (int): Number of input channels. + out_planes (int): Number of output channels. + kernel_size (int): Size of the convolutional kernel. + stride (int, optional): Stride of the convolution. Default is 1. + groups (int, optional): Number of groups for grouped convolution. Default is 1. + """ + def __init__(self, in_planes, out_planes, kernel_size, stride=1, groups=1): padding = (kernel_size - 1) // 2 super(ConvBNReLU, self).__init__( @@ -95,6 +106,17 @@ def __init__( kernel_size, reduction_ratio=4, ): + """ + MobileNetV2 Bottleneck Block (MBConv) module. + + Args: + in_planes (int): Number of input channels. + out_planes (int): Number of output channels. + expand_ratio (int): Expansion ratio for the hidden dimension. + stride (int): Stride value for the depthwise convolution. + kernel_size (int): Kernel size for the depthwise convolution. + reduction_ratio (int, optional): Reduction ratio for the Squeeze-and-Excitation module. Defaults to 4. + """ super(MBConv, self).__init__() self.stride = stride self.use_residual = in_planes == out_planes and stride == 1 @@ -127,6 +149,15 @@ def __init__( ) def forward(self, x): + """ + Forward pass of the MBConv module. + + Args: + x (torch.Tensor): Input tensor. + + Returns: + torch.Tensor: Output tensor. + """ if self.use_residual: return x + self.conv(x) else: diff --git a/zeta/structs/encoder.py b/zeta/structs/encoder.py deleted file mode 100644 index 77a1f54e..00000000 --- a/zeta/structs/encoder.py +++ /dev/null @@ -1,7 +0,0 @@ -from zeta.structs.transformer import AttentionLayers - - -class Encoder(AttentionLayers): - def __init__(self, **kwargs): - assert "causal" not in kwargs, "cannot set causality on encoder" - super().__init__(causal=False, **kwargs) diff --git a/zeta/structs/encoder_decoder.py b/zeta/structs/encoder_decoder.py index f18274f7..fcdd8a8c 100644 --- a/zeta/structs/encoder_decoder.py +++ b/zeta/structs/encoder_decoder.py @@ -3,11 +3,28 @@ import torch.nn as nn -from zeta.structs.decoder import Decoder -from zeta.structs.encoder import Encoder +from zeta.structs.transformer import Decoder, Encoder class EncoderDecoder(nn.Module): + """ + A module that combines an encoder and a decoder for sequence-to-sequence tasks. + + Args: + args (argparse.Namespace): The arguments passed to the module. + encoder_embed_tokens (torch.Tensor, optional): The input embeddings for the encoder. Defaults to None. + encoder_embed_positions (torch.Tensor, optional): The positions of the encoder input embeddings. Defaults to None. + decoder_embed_tokens (torch.Tensor, optional): The input embeddings for the decoder. Defaults to None. + decoder_embed_positions (torch.Tensor, optional): The positions of the decoder input embeddings. Defaults to None. + output_projection (torch.Tensor, optional): The projection layer for the decoder output. Defaults to None. + **kwargs: Additional keyword arguments. + + Attributes: + args (argparse.Namespace): The arguments passed to the module. + encoder (Encoder): The encoder module. + decoder (Decoder): The decoder module. + """ + def __init__( self, args, @@ -51,6 +68,19 @@ def forward( features_only=False, **kwargs, ): + """ + Forward pass of the EncoderDecoder module. + + Args: + src_tokens (torch.Tensor): The source tokens. + prev_output_tokens (torch.Tensor): The previous output tokens. + return_all_hiddens (bool, optional): Whether to return all hidden states. Defaults to False. + features_only (bool, optional): Whether to return only the features. Defaults to False. + **kwargs: Additional keyword arguments. + + Returns: + decoder_out (torch.Tensor): The output of the decoder module. + """ encoder_out = self.encoder( src_tokens, return_all_hiddens=return_all_hiddens ) diff --git a/zeta/structs/local_transformer.py b/zeta/structs/local_transformer.py index dda72130..cf3350ae 100644 --- a/zeta/structs/local_transformer.py +++ b/zeta/structs/local_transformer.py @@ -10,6 +10,37 @@ class LocalTransformer(nn.Module): + """ + LocalTransformer module that implements a local self-attention transformer. + + Args: + num_tokens (int): The number of tokens in the input vocabulary. + max_seq_len (int): The maximum sequence length. + dim (int): The dimensionality of the token and positional embeddings. + depth (int): The number of transformer layers. + causal (bool, optional): Whether to use causal attention. Defaults to True. + local_attn_window_size (int, optional): The size of the local attention window. Defaults to 512. + dim_head (int, optional): The dimensionality of each attention head. Defaults to 64. + heads (int, optional): The number of attention heads. Defaults to 8. + ff_mult (int, optional): The multiplier for the feedforward network dimension. Defaults to 4. + attn_dropout (float, optional): The dropout rate for attention layers. Defaults to 0.0. + ff_dropout (float, optional): The dropout rate for feedforward layers. Defaults to 0.0. + ignore_index (int, optional): The index to ignore during loss calculation. Defaults to -1. + use_xpos (bool, optional): Whether to use positional embeddings based on xpos. Defaults to False. + xpos_scale_base (None, optional): The base value for scaling xpos positional embeddings. Defaults to None. + use_dynamic_pos_bias (bool, optional): Whether to use dynamic positional bias. Defaults to False. + + Attributes: + token_emb (nn.Embedding): Embedding layer for token embeddings. + pos_emb (nn.Embedding): Embedding layer for positional embeddings. + max_seq_len (int): The maximum sequence length. + layers (nn.ModuleList): List of transformer layers. + local_attn_window_size (int): The size of the local attention window. + dynamic_pos_bias (DynamicPositionBias or None): Dynamic positional bias layer, if enabled. + ignore_index (int): The index to ignore during loss calculation. + to_logits (nn.Sequential): Sequential layer for converting transformer output to logits. + """ + def __init__( self, *, diff --git a/zeta/structs/mag_vit.py b/zeta/structs/mag_vit.py deleted file mode 100644 index e31350d1..00000000 --- a/zeta/structs/mag_vit.py +++ /dev/null @@ -1,589 +0,0 @@ -# from lucidrain - - -import torch -import torch.nn.functional as F -from torch import nn, Tensor -from torch.nn import Module, ModuleList - -from collections import namedtuple - -from vector_quantize_pytorch.lookup_free_quantization import LFQ - -from einops import rearrange, repeat, reduce, pack, unpack -from einops.layers.torch import Rearrange - -from beartype import beartype -from beartype.typing import Union, Tuple, Optional - -# helper - - -def exists(v): - return v is not None - - -def default(v, d): - return v if exists(v) else d - - -def identity(t): - return t - - -def divisible_by(num, den): - return (num % den) == 0 - - -def pack_one(t, pattern): - return pack([t], pattern) - - -def unpack_one(t, ps, pattern): - return unpack(t, ps, pattern)[0] - - -def is_odd(n): - return not divisible_by(n, 2) - - -def cast_tuple(t, length=1): - return t if isinstance(t, tuple) else ((t,) * length) - - -# helper classes - - -def Sequential(*modules): - modules = [*filter(exists, modules)] - - if len(modules) == 0: - return nn.Identity() - - return nn.Sequential(*modules) - - -class Residual(Module): - def __init__(self, fn): - super().__init__() - self.fn = fn - - def forward(self, x, **kwargs): - return self.fn(x, **kwargs) + x - - -# adaptive conv from Karras et al. Stylegan2 -# for conditioning on latents - - -class AdaptiveConv3DMod(Module): - @beartype - def __init__( - self, - dim, - *, - spatial_kernel, - time_kernel, - dim_out=None, - demod=True, - eps=1e-8, - ): - super().__init__() - dim_out = default(dim_out, dim) - - self.eps = eps - - assert is_odd(spatial_kernel) and is_odd(time_kernel) - - self.spatial_kernel = spatial_kernel - self.time_kernel = time_kernel - - self.padding = ( - *((spatial_kernel // 2,) * 4), - *((time_kernel // 2,) * 2), - ) - self.weights = nn.Parameter( - torch.randn( - (dim_out, dim, time_kernel, spatial_kernel, spatial_kernel) - ) - ) - - self.demod = demod - - nn.init.kaiming_normal_( - self.weights, a=0, mode="fan_in", nonlinearity="selu" - ) - - def forward(self, fmap, mod: Optional[Tensor] = None): - """ - notation - - b - batch - n - convs - o - output - i - input - k - kernel - """ - - b = fmap.shape[0] - - # prepare weights for modulation - - weights = self.weights - - # do the modulation, demodulation, as done in stylegan2 - - mod = rearrange(mod, "b i -> b 1 i 1 1 1") - - weights = weights * (mod + 1) - - if self.demod: - inv_norm = ( - reduce(weights**2, "b o i k0 k1 k2 -> b o 1 1 1 1", "sum") - .clamp(min=self.eps) - .rsqrt() - ) - weights = weights * inv_norm - - fmap = rearrange(fmap, "b c t h w -> 1 (b c) t h w") - - weights = rearrange(weights, "b o ... -> (b o) ...") - - fmap = F.pad(fmap, self.padding) - fmap = F.conv3d(fmap, weights, groups=b) - - return rearrange(fmap, "1 (b o) ... -> b o ...", b=b) - - -# strided conv downsamples - - -class SpatialDownsample2x(Module): - def __init__(self, dim, dim_out=None, kernel_size=3): - super().__init__() - dim_out = default(dim_out, dim) - self.conv = nn.Conv2d( - dim, dim_out, kernel_size, stride=2, padding=kernel_size // 2 - ) - - def forward(self, x): - x = rearrange(x, "b c t h w -> b t c h w") - x, ps = pack_one(x, "* c h w") - - out = self.conv(x) - - out = unpack_one(out, ps, "* c h w") - out = rearrange(out, "b t c h w -> b c t h w") - return out - - -class TimeDownsample2x(Module): - def __init__(self, dim, dim_out=None, kernel_size=3): - super().__init__() - dim_out = default(dim_out, dim) - self.conv = nn.Conv1d( - dim, dim_out, kernel_size, stride=2, padding=kernel_size // 2 - ) - - def forward(self, x): - x = rearrange(x, "b c t h w -> b h w c t") - x, ps = pack_one(x, "* c t") - - out = self.conv(x) - - out = unpack_one(out, ps, "* c t") - out = rearrange(out, "b h w c t -> b c t h w") - return out - - -# depth to space upsamples - - -class SpatialUpsample2x(Module): - def __init__(self, dim, dim_out=None): - super().__init__() - dim_out = default(dim_out, dim) - conv = nn.Conv2d(dim, dim_out * 4, 1) - - self.net = nn.Sequential( - conv, - nn.SiLU(), - Rearrange("b (c p1 p2) h w -> b c (h p1) (w p2)", p1=2, p2=2), - ) - - self.init_conv_(conv) - - def init_conv_(self, conv): - o, i, h, w = conv.weight.shape - conv_weight = torch.empty(o // 4, i, h, w) - nn.init.kaiming_uniform_(conv_weight) - conv_weight = repeat(conv_weight, "o ... -> (o 4) ...") - - conv.weight.data.copy_(conv_weight) - nn.init.zeros_(conv.bias.data) - - def forward(self, x): - x = rearrange(x, "b c t h w -> b t c h w") - x, ps = pack_one(x, "* c h w") - - out = self.net(x) - - out = unpack_one(out, ps, "* c h w") - out = rearrange(out, "b t c h w -> b c t h w") - return out - - -class TimeUpsample2x(Module): - def __init__(self, dim, dim_out=None): - super().__init__() - dim_out = default(dim_out, dim) - conv = nn.Conv1d(dim, dim_out * 2, 1) - - self.net = nn.Sequential( - conv, nn.SiLU(), Rearrange("b (c p) t -> b c (t p)", p=2) - ) - - self.init_conv_(conv) - - def init_conv_(self, conv): - o, i, t = conv.weight.shape - conv_weight = torch.empty(o // 2, i, t) - nn.init.kaiming_uniform_(conv_weight) - conv_weight = repeat(conv_weight, "o ... -> (o 2) ...") - - conv.weight.data.copy_(conv_weight) - nn.init.zeros_(conv.bias.data) - - def forward(self, x): - x = rearrange(x, "b c t h w -> b h w c t") - x, ps = pack_one(x, "* c t") - - out = self.net(x) - - out = unpack_one(out, ps, "* c t") - out = rearrange(out, "b h w c t -> b c t h w") - return out - - -# autoencoder - only best variant here offered, with causal conv 3d - - -class CausalConv3d(Module): - @beartype - def __init__( - self, - chan_in, - chan_out, - kernel_size: Union[int, Tuple[int, int, int]], - pad_mode="reflect", - **kwargs, - ): - super().__init__() - kernel_size = cast_tuple(kernel_size, 3) - - time_kernel_size, height_kernel_size, width_kernel_size = kernel_size - - assert is_odd(height_kernel_size) and is_odd(width_kernel_size) - - dilation = kwargs.pop("dilation", 1) - stride = kwargs.pop("stride", 1) - - self.pad_mode = pad_mode - time_pad = dilation * (time_kernel_size - 1) + (1 - stride) - height_pad = height_kernel_size // 2 - width_pad = width_kernel_size // 2 - - self.time_pad = time_pad - self.time_causal_padding = ( - width_pad, - width_pad, - height_pad, - height_pad, - time_pad, - 0, - ) - - stride = (stride, 1, 1) - dilation = (dilation, 1, 1) - self.conv = nn.Conv3d( - chan_in, - chan_out, - kernel_size, - stride=stride, - dilation=dilation, - **kwargs, - ) - - def forward(self, x): - pad_mode = self.pad_mode if self.time_pad < x.shape[2] else "constant" - - x = F.pad(x, self.time_causal_padding, mode=pad_mode) - return self.conv(x) - - -@beartype -def ResidualUnit( - dim, - kernel_size: Union[int, Tuple[int, int, int]], - pad_mode: str = "reflect", -): - return Residual( - Sequential( - CausalConv3d(dim, dim, kernel_size, pad_mode=pad_mode), - nn.ELU(), - CausalConv3d(dim, dim, 1, pad_mode=pad_mode), - nn.ELU(), - ) - ) - - -class CausalConvTranspose3d(Module): - def __init__( - self, - chan_in, - chan_out, - kernel_size: Union[int, Tuple[int, int, int]], - *, - time_stride, - **kwargs, - ): - super().__init__() - kernel_size = cast_tuple(kernel_size, 3) - - time_kernel_size, height_kernel_size, width_kernel_size = kernel_size - - assert is_odd(height_kernel_size) and is_odd(width_kernel_size) - - self.upsample_factor = time_stride - - height_pad = height_kernel_size // 2 - width_pad = width_kernel_size // 2 - - stride = (time_stride, 1, 1) - padding = (0, height_pad, width_pad) - - self.conv = nn.ConvTranspose3d( - chan_in, chan_out, kernel_size, stride, padding=padding, **kwargs - ) - - def forward(self, x): - assert x.ndim == 5 - t = x.shape[2] - - out = self.conv(x) - - out = out[..., : (t * self.upsample_factor), :, :] - return out - - -# video tokenizer class - -LossBreakdown = namedtuple("LossBreakdown", ["recon_loss", "lfq_entropy_loss"]) - - -class VideoTokenizer(Module): - """ - Video Tokenizer class: - - - encodes video into tokens - - decodes tokens back into video - - quantizes tokens with lookup-free quantization - - Args: - layers: tuple of tuples of layer types and dimensions - residual_conv_kernel_size: kernel size for residual convolutions - num_codebooks: number of codebooks to use - codebook_size: size of each codebook - channels: number of channels in video - init_dim: initial dimension - input_conv_kernel_size: kernel size for input convolution - output_conv_kernel_size: kernel size for output convolution - pad_mode: padding mode for convolutions - lfq_entropy_loss_weight: weight for entropy loss - lfq_diversity_gamma: gamma for diversity loss - - Returns: - recon_video: reconstructed video - total_loss: total loss - loss_breakdown: namedtuple of recon_loss and lfq_entropy_loss - - Usage: - video_tokenizer = VideoTokenizer() - video_tokenizer(video, video_or_images, return_loss=True) - - - """ - - @beartype - def __init__( - self, - layers: Tuple[Tuple[str, int], ...] = ( - ("residual", 64), - ("residual", 64), - ("residual", 64), - ), - residual_conv_kernel_size=3, - num_codebooks=1, - codebook_size=8192, - channels=3, - init_dim=64, - input_conv_kernel_size: Tuple[int, int, int] = (7, 7, 7), - output_conv_kernel_size: Tuple[int, int, int] = (3, 3, 3), - pad_mode: str = "reflect", - lfq_entropy_loss_weight=0.1, - lfq_diversity_gamma=1.0, - ): - super().__init__() - - # encoder - - self.conv_in = CausalConv3d( - channels, init_dim, input_conv_kernel_size, pad_mode=pad_mode - ) - - self.encoder_layers = ModuleList([]) - self.decoder_layers = ModuleList([]) - - self.conv_out = CausalConv3d( - init_dim, channels, output_conv_kernel_size, pad_mode=pad_mode - ) - - dim = init_dim - time_downsample_factor = 1 - - for layer_type, dim_out in layers: - if layer_type == "residual": - assert dim == dim_out - - encoder_layer = ResidualUnit(dim, residual_conv_kernel_size) - decoder_layer = ResidualUnit(dim, residual_conv_kernel_size) - - elif layer_type == "compress_space": - encoder_layer = SpatialDownsample2x(dim, dim_out) - decoder_layer = SpatialUpsample2x(dim_out, dim) - - elif layer_type == "compress_time": - encoder_layer = TimeDownsample2x(dim, dim_out) - decoder_layer = TimeUpsample2x(dim_out, dim) - - time_downsample_factor *= 2 - else: - raise ValueError(f"unknown layer type {layer_type}") - - self.encoder_layers.append(encoder_layer) - self.decoder_layers.insert(0, decoder_layer) - - dim = dim_out - - self.time_padding = time_downsample_factor - 1 - - # lookup free quantizer(s) - multiple codebooks is possible - # each codebook will get its own entropy regularization - - self.quantizers = LFQ( - dim=dim, - codebook_size=codebook_size, - num_codebooks=num_codebooks, - entropy_loss_weight=lfq_entropy_loss_weight, - diversity_gamma=lfq_diversity_gamma, - ) - - @beartype - def encode(self, video: Tensor, quantize=False): - """Encode video into tokens""" - x = self.conv_in(video) - - for fn in self.encoder_layers: - x = fn(x) - - maybe_quantize = identity if not quantize else self.quantizers - - return maybe_quantize(x) - - @beartype - def decode(self, codes: Tensor): - """Decode tokens into video""" - x = codes - - for fn in self.decoder_layers: - x = fn(x) - - return self.conv_out(x) - - @beartype - def forward( - self, - video, - video_or_images: Tensor, - return_loss=False, - return_codes=False, - ): - """ - Forward pass for video tokenizer - - Args: - video: video tensor - video_or_images: video or images tensor - return_loss: whether to return loss - return_codes: whether to return codes - - Returns: - recon_video: reconstructed video - total_loss: total loss - loss_breakdown: namedtuple of recon_loss and lfq_entropy_loss - codes: codes tensor - - """ - assert not (return_loss and return_codes) - assert video_or_images.ndim in {4, 5} - - # accept images for image pretraining (curriculum learning from images to video) - - if video_or_images.ndim == 4: - video = rearrange(video, "b c ... -> b c 1 ...") - else: - video = video_or_images - - # pad the time, accounting for total time downsample factor, so that images can be trained independently - - padded_video = F.pad( - video, (0, 0, 0, 0, self.time_padding, 0), value=0.0 - ) - - # encoder - - x = self.encode(padded_video) - - # lookup free quantization - - quantized, codes, aux_losses = self.quantizers(x) - - if return_codes: - return codes - - # decoder - - padded_recon_video = self.decode(quantized) - - recon_video = padded_recon_video[:, :, self.time_padding :] - - # reconstruction loss - - if not return_loss: - return recon_video - - recon_loss = F.mse_loss(video, recon_video) - - total_loss = recon_loss + aux_losses - - return total_loss, LossBreakdown(recon_loss, aux_losses) - - -# main class - -# class MagViT2(Module): -# def __init__(self): -# super().__init__() - -# def forward(self, x): -# return x diff --git a/zeta/structs/multi_modal_projector.py b/zeta/structs/multi_modal_projector.py index c5e3eefb..82fad5b4 100644 --- a/zeta/structs/multi_modal_projector.py +++ b/zeta/structs/multi_modal_projector.py @@ -14,23 +14,29 @@ def config(self): return {"mm_projector_type": "identity"} -class SimpleResBlock(nn.Module): - def __init__(self, channels): - super().__init__() - self.pre_norm = nn.LayerNorm(channels) - - self.proj = nn.Sequential( - nn.Linear(channels, channels), - nn.GELU(), - nn.Linear(channels, channels), - ) - - def forward(self, x): - x = self.pre_norm(x) - return x + self.proj(x) - - def build_vision_projector(config, delay_load=False, **kwargs): + """ + Builds a vision projector based on the given configuration. + + Args: + config: The configuration object containing the projector type and other parameters. + delay_load: Whether to delay the loading of the projector. + **kwargs: Additional keyword arguments. + + Returns: + A vision projector module based on the specified projector type. + + Raises: + ValueError: If the specified projector type is unknown. + + + Example: + >>> config = {"mm_projector_type": "identity"} + >>> projector = build_vision_projector(config) + >>> print(projector) + IdentityMap() + + """ projector_type = getattr(config, "mm_projector_type", "linear") if projector_type == "linear": diff --git a/zeta/structs/parallel_transformer.py b/zeta/structs/parallel_transformer.py deleted file mode 100644 index df3b11bc..00000000 --- a/zeta/structs/parallel_transformer.py +++ /dev/null @@ -1,258 +0,0 @@ -import torch -from torch import nn -import torch.nn.functional as F - -from einops import rearrange - -from zeta.nn.attention.attend import Attend as Attention - -# functions and decorators - - -def exists(val): - return val is not None - - -def default(val, d): - return val if exists(val) else d - - -def identity(t, *args, **kwargs): - return t - - -def l2norm(t): - return F.normalize(t, dim=-1) - - -# normalization -# they use layernorm without bias, something that pytorch does not offer - - -class LayerNorm(nn.Module): - def __init__(self, dim): - super().__init__() - self.gamma = nn.Parameter(torch.ones(dim)) - self.register_buffer("beta", torch.zeros(dim)) - - def forward(self, x): - return F.layer_norm(x, x.shape[-1:], self.gamma, self.beta) - - -# residual - - -class Residual(nn.Module): - def __init__(self, fn): - super().__init__() - self.fn = fn - - def forward(self, x, **kwargs): - y = self.fn(x, **kwargs) - - if not any([t.requires_grad for t in (x, y)]): - return x.add_(y) - - return y + x - - -# rotary positional embedding w/ xpos -# https://arxiv.org/abs/2104.09864 -# https://arxiv.org/abs/2212.10554v1 - - -class RotaryEmbedding(nn.Module): - def __init__(self, dim, scale_base=512, use_xpos=True): - super().__init__() - inv_freq = 1.0 / (10000 ** (torch.arange(0, dim, 2).float() / dim)) - self.register_buffer("inv_freq", inv_freq) - - self.use_xpos = use_xpos - self.scale_base = scale_base - scale = (torch.arange(0, dim, 2) + 0.4 * dim) / (1.4 * dim) - self.register_buffer("scale", scale) - - def forward(self, seq_len, device): - t = torch.arange(seq_len, device=device).type_as(self.inv_freq) - freqs = torch.einsum("i , j -> i j", t, self.inv_freq) - freqs = torch.cat((freqs, freqs), dim=-1) - - if not self.use_xpos: - return freqs, torch.ones(1, device=device) - - power = (t - (seq_len // 2)) / self.scale_base - scale = self.scale ** rearrange(power, "n -> n 1") - scale = torch.cat((scale, scale), dim=-1) - - return freqs, scale - - -def rotate_half(x): - x1, x2 = x.chunk(2, dim=-1) - return torch.cat((-x2, x1), dim=-1) - - -def apply_rotary_pos_emb(pos, t, scale=1.0): - return (t * pos.cos() * scale) + (rotate_half(t) * pos.sin() * scale) - - -# classic Noam Shazeer paper, except here they use SwiGLU instead of the more popular GEGLU for gating the feedforward -# https://arxiv.org/abs/2002.05202 - - -class SwiGLU(nn.Module): - def forward(self, x): - x, gate = x.chunk(2, dim=-1) - return F.silu(gate) * x - - -# parallel attention and feedforward with residual -# discovered by Wang et al + EleutherAI from GPT-J fame - - -class ParallelTransformerBlock(nn.Module): - def __init__( - self, - dim, - dim_head=64, - causal=True, - heads=8, - qk_rmsnorm=False, - qk_scale=8, - ff_mult=4, - attn_dropout=0.0, - ff_dropout=0.0, - use_xpos=True, - xpos_scale_base=512, - flash_attn=False, - ): - super().__init__() - self.norm = LayerNorm(dim) - - attn_inner_dim = dim_head * heads - ff_inner_dim = dim * ff_mult - self.fused_dims = ( - attn_inner_dim, - dim_head, - dim_head, - (ff_inner_dim * 2), - ) - - self.qk_rmsnorm = qk_rmsnorm - - if qk_rmsnorm: - self.q_scale = nn.Parameter(torch.ones(dim_head)) - self.k_scale = nn.Parameter(torch.ones(dim_head)) - - self.attend = Attention( - causal=causal, dropout=attn_dropout, use_flash_attn=flash_attn - ) - - self.heads = heads - self.scale = (dim_head**-0.5) if not qk_rmsnorm else qk_scale - self.causal = causal - - self.rotary_emb = RotaryEmbedding( - dim_head, scale_base=xpos_scale_base, use_xpos=use_xpos and causal - ) - - self.fused_attn_ff_proj = nn.Linear( - dim, sum(self.fused_dims), bias=False - ) - - self.flash_attn = flash_attn - self.attn_out = nn.Linear(attn_inner_dim, dim, bias=False) - self.attn_dropout = nn.Dropout(attn_dropout) - self.flash_attn_dropout = attn_dropout - - # parallel feedforward tail - - self.ff_out = nn.Sequential( - SwiGLU(), - nn.Dropout(ff_dropout), - nn.Linear(ff_inner_dim, dim, bias=False), - ) - - # for caching causal mask and rotary embeddings - - self.register_buffer("pos_emb", None, persistent=False) - self.register_buffer("pos_emb_scale", None, persistent=False) - - def get_rotary_embedding(self, n, device): - if exists(self.pos_emb) and self.pos_emb.shape[-2] >= n: - return self.pos_emb[:n], self.pos_emb_scale[:n] - - pos_emb, scale = self.rotary_emb(n, device=device) - self.register_buffer("pos_emb", pos_emb, persistent=False) - self.register_buffer("pos_emb_scale", scale, persistent=False) - return pos_emb, scale - - def forward(self, x, mask=None, finetune_modules=None): - """ - einstein notation - b - batch - h - heads - n, i, j - sequence length (base sequence length, source, target) - d - feature dimension - """ - - n, device, h = x.shape[1], x.device, self.heads - - # pre layernorm - - x = self.norm(x) - - # attention queries, keys, values, and feedforward inner - - q, k, v, ff = self.fused_attn_ff_proj(x).split(self.fused_dims, dim=-1) - - # finetune loras - - lora_q = lora_k = lora_v = lora_o = None - - if exists(finetune_modules): - lora_q, lora_k, lora_v, lora_o = finetune_modules - q = q + lora_q(x) - k = k + lora_k(x) - v = v + lora_v(x) - - # split heads - # they use multi-query single-key-value attention, yet another Noam Shazeer paper - # they found no performance loss past a certain scale, and more efficient decoding obviously - # https://arxiv.org/abs/1911.02150 - - q = rearrange(q, "b n (h d) -> b h n d", h=h) - - # qk rmsnorm - - if self.qk_rmsnorm: - q, k = map(l2norm, (q, k)) - q = q * self.q_scale - k = k * self.k_scale - - # rotary embeddings with xpos decay for better length extrapolation - - positions, scale = self.get_rotary_embedding(n, device) - - q = apply_rotary_pos_emb(positions, q, scale) - k = apply_rotary_pos_emb(positions, k, scale**-1) - - # attention function, either regular or flash - - out = self.attend(q, k, v, mask=mask) - - # merge heads - - out = rearrange(out, "b h n d -> b n (h d)") - - attn_out = self.attn_out(out) - - ff_out = self.ff_out(ff) - - if exists(lora_o): - attn_out = attn_out + lora_o(out) - - return attn_out + ff_out - - -# transformer diff --git a/zeta/structs/transformer.py b/zeta/structs/transformer.py index a16a6034..d43a3529 100644 --- a/zeta/structs/transformer.py +++ b/zeta/structs/transformer.py @@ -1,7 +1,7 @@ import math from collections import namedtuple from dataclasses import dataclass -from functools import partial, reduce, wraps +from functools import partial, wraps from inspect import isfunction from random import random from typing import Callable, List, Optional diff --git a/zeta/structs/transformer_block.py b/zeta/structs/transformer_block.py index c6229d15..3f7e9c06 100644 --- a/zeta/structs/transformer_block.py +++ b/zeta/structs/transformer_block.py @@ -153,5 +153,3 @@ def forward(self, x, mask=None, finetune_modules=None): return attn_out + ff_out - -# transformer From d07d002a9ac587d0601aa46a6c23df15aba904a6 Mon Sep 17 00:00:00 2001 From: Kye Date: Sat, 23 Dec 2023 00:18:51 -0500 Subject: [PATCH 187/587] [TESTS][zeta.tokenizers] --- pyproject.toml | 2 +- tests/nn/modules/test_simple_res_block.py | 3 +- tests/structs/test_autoregressive_wrapper.py | 5 +- tests/structs/test_encoder_decoder.py | 5 +- tests/tokenizers/test_gptx.py | 41 +++++ tests/tokenizers/test_multimodal_tokenizer.py | 59 +++++++ tests/tokenizers/test_sentencepiece.py | 64 ++++++++ tests/tokenizers/test_tokenmonster.py | 145 ++++++++++++++++++ zeta/nn/modules/conv_bn_relu.py | 5 +- zeta/nn/modules/simple_resblock.py | 1 + zeta/structs/multi_modal_projector.py | 4 +- zeta/structs/transformer_block.py | 1 - zeta/tokenizers/__init__.py | 5 +- zeta/tokenizers/base.py | 45 ------ zeta/tokenizers/gptx_tokenizer.py | 52 +++++++ zeta/tokenizers/language_tokenizer.py | 24 --- zeta/tokenizers/sentence_piece.py | 20 +++ zeta/tokenizers/tiktoken.py | 131 ---------------- 18 files changed, 398 insertions(+), 214 deletions(-) create mode 100644 tests/tokenizers/test_gptx.py create mode 100644 tests/tokenizers/test_multimodal_tokenizer.py create mode 100644 tests/tokenizers/test_sentencepiece.py create mode 100644 tests/tokenizers/test_tokenmonster.py delete mode 100644 zeta/tokenizers/base.py create mode 100644 zeta/tokenizers/gptx_tokenizer.py delete mode 100644 zeta/tokenizers/language_tokenizer.py delete mode 100644 zeta/tokenizers/tiktoken.py diff --git a/pyproject.toml b/pyproject.toml index 27dc1511..35056e0d 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,6 +1,6 @@ [tool.poetry] name = "zetascale" -version = "1.2.3" +version = "1.2.4" description = "Transformers at zeta scales" authors = ["Zeta Team "] license = "MIT" diff --git a/tests/nn/modules/test_simple_res_block.py b/tests/nn/modules/test_simple_res_block.py index d3175110..d734662d 100644 --- a/tests/nn/modules/test_simple_res_block.py +++ b/tests/nn/modules/test_simple_res_block.py @@ -2,6 +2,7 @@ import pytest from zeta.nn.modules.simple_resblock import SimpleResBlock + def test_simple_resblock(): # Initialize a SimpleResBlock with 10 channels resblock = SimpleResBlock(10) @@ -20,4 +21,4 @@ def test_simple_resblock(): assert not torch.all(torch.eq(output, x)) # Check that the output is a tensor - assert isinstance(output, torch.Tensor) \ No newline at end of file + assert isinstance(output, torch.Tensor) diff --git a/tests/structs/test_autoregressive_wrapper.py b/tests/structs/test_autoregressive_wrapper.py index cdc62990..684410ba 100644 --- a/tests/structs/test_autoregressive_wrapper.py +++ b/tests/structs/test_autoregressive_wrapper.py @@ -3,6 +3,7 @@ from zeta.structs.auto_regressive_wrapper import AutoregressiveWrapper from torch import nn + def test_autoregressive_wrapper_initialization(): net = nn.Linear(10, 10) wrapper = AutoregressiveWrapper(net) @@ -14,6 +15,7 @@ def test_autoregressive_wrapper_initialization(): assert wrapper.ignore_index == -100 assert wrapper.mask_prob == 0.0 + def test_autoregressive_wrapper_forward(): net = nn.Linear(10, 10) wrapper = AutoregressiveWrapper(net) @@ -24,6 +26,7 @@ def test_autoregressive_wrapper_forward(): assert isinstance(logits, torch.Tensor) assert logits.shape == torch.Size([1, 10, 10]) + def test_autoregressive_wrapper_generate(): net = nn.Linear(10, 10) wrapper = AutoregressiveWrapper(net) @@ -32,4 +35,4 @@ def test_autoregressive_wrapper_generate(): generated = wrapper.generate(x, 10) assert isinstance(generated, torch.Tensor) - assert generated.shape == torch.Size([1, 10]) \ No newline at end of file + assert generated.shape == torch.Size([1, 10]) diff --git a/tests/structs/test_encoder_decoder.py b/tests/structs/test_encoder_decoder.py index ee792337..cb800fe4 100644 --- a/tests/structs/test_encoder_decoder.py +++ b/tests/structs/test_encoder_decoder.py @@ -3,6 +3,7 @@ from zeta.structs.encoder_decoder import EncoderDecoder from argparse import Namespace + def test_encoder_decoder_initialization(): args = Namespace(share_all_embeddings=True) encoder_decoder = EncoderDecoder(args) @@ -12,6 +13,7 @@ def test_encoder_decoder_initialization(): assert encoder_decoder.args.share_all_embeddings == True assert encoder_decoder.args.share_decoder_input_output_embed == True + def test_encoder_decoder_forward(): args = Namespace(share_all_embeddings=True) encoder_decoder = EncoderDecoder(args) @@ -24,6 +26,7 @@ def test_encoder_decoder_forward(): assert isinstance(output, torch.Tensor) assert output.shape == prev_output_tokens.shape + def test_encoder_decoder_forward_features_only(): args = Namespace(share_all_embeddings=True) encoder_decoder = EncoderDecoder(args) @@ -34,4 +37,4 @@ def test_encoder_decoder_forward_features_only(): output = encoder_decoder(src_tokens, prev_output_tokens, features_only=True) assert isinstance(output, torch.Tensor) - assert output.shape == prev_output_tokens.shape \ No newline at end of file + assert output.shape == prev_output_tokens.shape diff --git a/tests/tokenizers/test_gptx.py b/tests/tokenizers/test_gptx.py new file mode 100644 index 00000000..52d2fe4b --- /dev/null +++ b/tests/tokenizers/test_gptx.py @@ -0,0 +1,41 @@ +import torch +import pytest +from zeta.tokenizers.gptx_tokenizer import LanguageTokenizerGPTX + + +def test_language_tokenizer_gptx_initialization(): + tokenizer = LanguageTokenizerGPTX() + + assert isinstance(tokenizer, LanguageTokenizerGPTX) + assert tokenizer.tokenizer.eos_token == " " + assert tokenizer.tokenizer.pad_token == " " + assert tokenizer.tokenizer.model_max_length == 8192 + + +def test_language_tokenizer_gptx_tokenize_texts(): + tokenizer = LanguageTokenizerGPTX() + + texts = ["Hello, world!", "Goodbye, world!"] + tokenized_texts = tokenizer.tokenize_texts(texts) + + assert isinstance(tokenized_texts, torch.Tensor) + assert tokenized_texts.shape[0] == len(texts) + + +def test_language_tokenizer_gptx_decode(): + tokenizer = LanguageTokenizerGPTX() + + texts = ["Hello, world!", "Goodbye, world!"] + tokenized_texts = tokenizer.tokenize_texts(texts) + decoded_texts = tokenizer.decode(tokenized_texts[0]) + + assert isinstance(decoded_texts, str) + + +def test_language_tokenizer_gptx_len(): + tokenizer = LanguageTokenizerGPTX() + + num_tokens = len(tokenizer) + + assert isinstance(num_tokens, int) + assert num_tokens > 0 diff --git a/tests/tokenizers/test_multimodal_tokenizer.py b/tests/tokenizers/test_multimodal_tokenizer.py new file mode 100644 index 00000000..d08ce258 --- /dev/null +++ b/tests/tokenizers/test_multimodal_tokenizer.py @@ -0,0 +1,59 @@ +from PIL import Image +import torch +import pytest +from zeta.tokenizers.multi_modal_tokenizer import MultiModalTokenizer + + +def test_multi_modal_tokenizer_initialization(): + tokenizer = MultiModalTokenizer() + + assert isinstance(tokenizer, MultiModalTokenizer) + assert tokenizer.max_length == 8192 + assert tokenizer.tokenizer.eos_token == " " + assert tokenizer.tokenizer.pad_token == " " + assert tokenizer.tokenizer.model_max_length == tokenizer.max_length + assert tokenizer.im_idx == tokenizer.tokenizer.convert_tokens_to_ids( + " " + ) + assert tokenizer.im_end_idx == tokenizer.tokenizer.convert_tokens_to_ids( + " " + ) + + +def test_multi_modal_tokenizer_tokenize_texts(): + tokenizer = MultiModalTokenizer() + + texts = ["Hello, world!", "Goodbye, world!"] + tokenized_texts, only_text_tokens = tokenizer.tokenize_texts(texts) + + assert isinstance(tokenized_texts, torch.Tensor) + assert tokenized_texts.shape[0] == len(texts) + assert isinstance(only_text_tokens, torch.Tensor) + assert only_text_tokens.shape[0] == len(texts) + + +def test_multi_modal_tokenizer_tokenize_images(): + tokenizer = MultiModalTokenizer() + + # Assuming images is a list of PIL Image objects + images = [Image.new("RGB", (60, 30), color="red") for _ in range(2)] + tokenized_images = tokenizer.tokenize_images(images) + + assert isinstance(tokenized_images, torch.Tensor) + assert tokenized_images.shape[0] == len(images) + + +def test_multi_modal_tokenizer_tokenize(): + tokenizer = MultiModalTokenizer() + + sample = { + "target_text": ["Hello, world!", "Goodbye, world!"], + "image": [Image.new("RGB", (60, 30), color="red") for _ in range(2)], + } + tokenized_sample = tokenizer.tokenize(sample) + + assert isinstance(tokenized_sample, dict) + assert "text_tokens" in tokenized_sample + assert "images" in tokenized_sample + assert "labels" in tokenized_sample + assert "attention_mask" in tokenized_sample diff --git a/tests/tokenizers/test_sentencepiece.py b/tests/tokenizers/test_sentencepiece.py new file mode 100644 index 00000000..7ec8331e --- /dev/null +++ b/tests/tokenizers/test_sentencepiece.py @@ -0,0 +1,64 @@ +import pytest +import os +from zeta.tokenizers.sentence_piece import SentencePieceTokenizer + + +def test_sentence_piece_tokenizer_initialization(): + model_path = "/path/to/your/model" # replace with your actual model path + assert os.path.isfile(model_path), "Model file does not exist" + + tokenizer = SentencePieceTokenizer(model_path) + + assert isinstance(tokenizer, SentencePieceTokenizer) + assert tokenizer.n_words == tokenizer.sp_model.vocab_size() + assert tokenizer.bos_id == tokenizer.sp_model.bos_id() + assert tokenizer.eos_id == tokenizer.sp_model.eos_id() + assert tokenizer.pad_id == tokenizer.sp_model.pad_id() + + +def test_sentence_piece_tokenizer_encode(): + model_path = "/path/to/your/model" # replace with your actual model path + tokenizer = SentencePieceTokenizer(model_path) + + text = "Hello, world!" + encoded_text = tokenizer.encode(text, bos=True, eos=True) + + assert isinstance(encoded_text, list) + assert encoded_text[0] == tokenizer.bos_id + assert encoded_text[-1] == tokenizer.eos_id + + +def test_sentence_piece_tokenizer_decode(): + model_path = "/path/to/your/model" # replace with your actual model path + tokenizer = SentencePieceTokenizer(model_path) + + text = "Hello, world!" + encoded_text = tokenizer.encode(text, bos=True, eos=True) + decoded_text = tokenizer.decode(encoded_text) + + assert isinstance(decoded_text, str) + assert decoded_text == text + + +def test_sentence_piece_tokenizer_encode_infilling(): + model_path = "/path/to/your/model" # replace with your actual model path + tokenizer = SentencePieceTokenizer(model_path) + + text = "Hello, world!" + encoded_text = tokenizer.encode_infilling(text) + + assert isinstance(encoded_text, list) + + +def test_sentence_piece_tokenizer_decode_infilling(): + model_path = "/path/to/your/model" # replace with your actual model path + tokenizer = SentencePieceTokenizer(model_path) + + text = "Hello, world!" + encoded_text = tokenizer.encode_infilling(text) + decoded_text = tokenizer.decode_infilling(encoded_text) + + assert isinstance(decoded_text, str) + assert ( + decoded_text == text[1:] + ) # the first character is removed in decode_infilling diff --git a/tests/tokenizers/test_tokenmonster.py b/tests/tokenizers/test_tokenmonster.py new file mode 100644 index 00000000..94c7b641 --- /dev/null +++ b/tests/tokenizers/test_tokenmonster.py @@ -0,0 +1,145 @@ +import pytest +from zeta.tokenizers.tokenmonster import TokenMonster + + +def test_token_monster_initialization(): + tokenizer = TokenMonster("englishcode-32000-consistent-v1") + + assert isinstance(tokenizer, TokenMonster) + assert tokenizer.vocab is not None + + +def test_token_monster_set_local_directory(): + tokenizer = TokenMonster("englishcode-32000-consistent-v1") + tokenizer.set_local_directory( + "/path/to/your/directory" + ) # replace with your actual directory + + # There's no direct way to assert the effect of this method as it doesn't return anything + # and it doesn't change any accessible state of the TokenMonster object. + # You might need to check manually if the directory is set correctly. + + +def test_token_monster_load(): + tokenizer = TokenMonster("englishcode-32000-consistent-v1") + tokenizer.load("englishcode-32000-consistent-v1") + + assert tokenizer.vocab is not None + + +def test_token_monster_load_multiprocess_safe(): + tokenizer = TokenMonster("englishcode-32000-consistent-v1") + tokenizer.load_multiprocess_safe("englishcode-32000-consistent-v1") + + assert tokenizer.vocab is not None + + +def test_token_monster_new(): + tokenizer = TokenMonster("englishcode-32000-consistent-v1") + yaml = """ + tokens: + - token: " " + score: 0 + - token: "e" + score: 1 + - token: "t" + score: 2 + """ + tokenizer.new(yaml) + + assert tokenizer.vocab is not None + + +def test_token_monster_save(): + tokenizer = TokenMonster("englishcode-32000-consistent-v1") + tokenizer.save("/path/to/your/file") # replace with your actual file path + + # There's no direct way to assert the effect of this method as it doesn't return anything + # and it doesn't change any accessible state of the TokenMonster object. + # You might need to check manually if the file is saved correctly. + + +def test_token_monster_export_yaml(): + tokenizer = TokenMonster("englishcode-32000-consistent-v1") + yaml = tokenizer.export_yaml() + + assert isinstance(yaml, bytes) + + +def test_token_monster_tokenize(): + tokenizer = TokenMonster("englishcode-32000-consistent-v1") + tokens = tokenizer.tokenize("Hello world!") + + assert isinstance(tokens, list) + + +def test_token_monster_tokenize_count(): + tokenizer = TokenMonster("englishcode-32000-consistent-v1") + count = tokenizer.tokenize_count("Hello world!") + + assert isinstance(count, int) + + +def test_token_monster_decode(): + tokenizer = TokenMonster("englishcode-32000-consistent-v1") + tokens = tokenizer.tokenize("Hello world!") + text = tokenizer.decode(tokens) + + assert isinstance(text, str) + assert text == "Hello world!" + + +def test_token_monster_decoder(): + tokenizer = TokenMonster("englishcode-32000-consistent-v1") + decoder = tokenizer.decoder() + + assert decoder is not None + + +def test_token_monster_get_dictionary(): + tokenizer = TokenMonster("englishcode-32000-consistent-v1") + dictionary = tokenizer.get_dictionary() + + assert isinstance(dictionary, list) + + +def test_token_monster_charset(): + tokenizer = TokenMonster("englishcode-32000-consistent-v1") + charset = tokenizer.charset() + + assert isinstance(charset, str) + + +def test_token_monster_normalization(): + tokenizer = TokenMonster("englishcode-32000-consistent-v1") + normalization = tokenizer.normalization() + + assert isinstance(normalization, str) + + +def test_token_monster_capcode(): + tokenizer = TokenMonster("englishcode-32000-consistent-v1") + capcode = tokenizer.capcode() + + assert isinstance(capcode, int) + + +def test_token_monster_mode(): + tokenizer = TokenMonster("englishcode-32000-consistent-v1") + mode = tokenizer.mode() + + assert isinstance(mode, int) + + +def test_token_monster_id_to_token(): + tokenizer = TokenMonster("englishcode-32000-consistent-v1") + token = tokenizer.id_to_token(1) + + assert isinstance(token, str) + + +def test_token_monster_id_to_token_decoded(): + tokenizer = TokenMonster("englishcode-32000-consistent-v1") + token = tokenizer.id_to_token_decoded(1) + + assert isinstance(token, str) diff --git a/zeta/nn/modules/conv_bn_relu.py b/zeta/nn/modules/conv_bn_relu.py index 4080f3da..07d7d06b 100644 --- a/zeta/nn/modules/conv_bn_relu.py +++ b/zeta/nn/modules/conv_bn_relu.py @@ -1,6 +1,6 @@ - from torch import nn + class ConvBNReLU(nn.Sequential): """ A conv layer followed by batch normalization and ReLU activation. @@ -28,8 +28,7 @@ def __init__(self, in_planes, out_planes, kernel_size, stride=1, groups=1): nn.BatchNorm2d(out_planes), nn.ReLU6(inplace=True), ) - + def forward(self, x): # Placeholder code to access the 'x' variable return x - \ No newline at end of file diff --git a/zeta/nn/modules/simple_resblock.py b/zeta/nn/modules/simple_resblock.py index c338cf91..58b4d27e 100644 --- a/zeta/nn/modules/simple_resblock.py +++ b/zeta/nn/modules/simple_resblock.py @@ -1,5 +1,6 @@ from torch import nn + class SimpleResBlock(nn.Module): """ A simple residual block module. diff --git a/zeta/structs/multi_modal_projector.py b/zeta/structs/multi_modal_projector.py index 82fad5b4..e1c3c56e 100644 --- a/zeta/structs/multi_modal_projector.py +++ b/zeta/structs/multi_modal_projector.py @@ -28,8 +28,8 @@ def build_vision_projector(config, delay_load=False, **kwargs): Raises: ValueError: If the specified projector type is unknown. - - + + Example: >>> config = {"mm_projector_type": "identity"} >>> projector = build_vision_projector(config) diff --git a/zeta/structs/transformer_block.py b/zeta/structs/transformer_block.py index 3f7e9c06..1157b638 100644 --- a/zeta/structs/transformer_block.py +++ b/zeta/structs/transformer_block.py @@ -152,4 +152,3 @@ def forward(self, x, mask=None, finetune_modules=None): attn_out = attn_out + lora_o(out) return attn_out + ff_out - diff --git a/zeta/tokenizers/__init__.py b/zeta/tokenizers/__init__.py index 71527045..1427c46e 100644 --- a/zeta/tokenizers/__init__.py +++ b/zeta/tokenizers/__init__.py @@ -1,16 +1,13 @@ -from zeta.tokenizers.language_tokenizer import LanguageTokenizerGPTX +from zeta.tokenizers.gptx_tokenizer import LanguageTokenizerGPTX from zeta.tokenizers.multi_modal_tokenizer import MultiModalTokenizer from zeta.tokenizers.sentence_piece import SentencePieceTokenizer from zeta.tokenizers.tokenmonster import TokenMonster from zeta.tokenizers.llama_sentencepiece import LLamaTokenizer -# from zeta.tokenizers.tiktoken import TikToken - __all__ = [ "LanguageTokenizerGPTX", "MultiModalTokenizer", "SentencePieceTokenizer", "TokenMonster", "LLamaTokenizer", - # "TikToken", ] diff --git a/zeta/tokenizers/base.py b/zeta/tokenizers/base.py deleted file mode 100644 index 0fde7bd3..00000000 --- a/zeta/tokenizers/base.py +++ /dev/null @@ -1,45 +0,0 @@ -from abc import ABC, abstractmethod -from itertools import islice -from typing import Generator - -from attr import define, field, Factory - - -@define(frozen=True) -class BaseTokenizer(ABC): - DEFAULT_STOP_SEQUENCES = ["Observation:"] - - stop_sequences: list[str] = field( - default=Factory(lambda: BaseTokenizer.DEFAULT_STOP_SEQUENCES), - kw_only=True, - ) - - @property - @abstractmethod - def max_tokens(self) -> int: - ... - - def tokens_left(self, text: str) -> int: - diff = self.max_tokens - self.token_count(text) - - if diff > 0: - return diff - else: - return 0 - - def token_count(self, text: str) -> int: - return len(self.encode(text)) - - def chunk_tokens(self, tokens: list[int]) -> Generator: - it = iter(tokens) - - while batch := tuple(islice(it, self.max_tokens)): - yield batch - - @abstractmethod - def encode(self, text: str) -> list[int]: - ... - - @abstractmethod - def decode(self, tokens: list[int]) -> str: - ... diff --git a/zeta/tokenizers/gptx_tokenizer.py b/zeta/tokenizers/gptx_tokenizer.py new file mode 100644 index 00000000..60c54ce1 --- /dev/null +++ b/zeta/tokenizers/gptx_tokenizer.py @@ -0,0 +1,52 @@ +from transformers import AutoTokenizer + + +class LanguageTokenizerGPTX: + """ + LanguageTokenizerGPTX is a class that provides tokenization and decoding functionality using the GPT-Neox-20B model. + """ + + def __init__(self): + self.tokenizer = AutoTokenizer.from_pretrained( + "EleutherAI/gpt-neox-20b", + eos_token="", + pad_token=" ", + extra_ids=0, + model_max_length=8192, + ) + + def tokenize_texts(self, texts): + """ + Tokenizes a list of texts using the GPT-Neox-20B tokenizer. + + Args: + texts (List[str]): A list of texts to be tokenized. + + Returns: + torch.Tensor: The tokenized input IDs as a PyTorch tensor. + """ + return self.tokenizer( + texts, return_tensors="pt", padding=True, truncation=True + ).input_ids + + def decode(self, texts): + """ + Decodes a list of tokenized input IDs into text. + + Args: + texts (torch.Tensor): The tokenized input IDs as a PyTorch tensor. + + Returns: + str: The decoded text. + """ + return self.tokenizer.decode(texts) + + def __len__(self): + """ + Returns the number of tokens in the tokenizer's vocabulary. + + Returns: + int: The number of tokens in the vocabulary. + """ + num_tokens = len(self.tokenizer) + return num_tokens diff --git a/zeta/tokenizers/language_tokenizer.py b/zeta/tokenizers/language_tokenizer.py deleted file mode 100644 index c2e060a1..00000000 --- a/zeta/tokenizers/language_tokenizer.py +++ /dev/null @@ -1,24 +0,0 @@ -from transformers import AutoTokenizer - - -class LanguageTokenizerGPTX: - def __init__(self): - self.tokenizer = AutoTokenizer.from_pretrained( - "EleutherAI/gpt-neox-20b", - eos_token=" ", - pad_token=" ", - extra_ids=0, - model_max_length=8192, - ) - - def tokenize_texts(self, texts): - return self.tokenizer( - texts, return_tensors="pt", padding=True, truncation=True - ).input_ids - - def decode(self, texts): - return self.tokenizer.decode(texts) - - def __len__(self): - num_tokens = len(self.tokenizer) - return num_tokens diff --git a/zeta/tokenizers/sentence_piece.py b/zeta/tokenizers/sentence_piece.py index fe5680dd..b09de319 100644 --- a/zeta/tokenizers/sentence_piece.py +++ b/zeta/tokenizers/sentence_piece.py @@ -57,6 +57,18 @@ def __init__(self, model_path: str): assert self.sp_model.vocab_size() == self.sp_model.get_piece_size() def encode(self, s: str, bos: bool, eos: bool) -> List[int]: + """ + Encodes a given string using the SentencePiece tokenizer. + + Args: + s (str): The input string to be encoded. + bos (bool): Whether to add a beginning of sentence token. + eos (bool): Whether to add an end of sentence token. + + Returns: + List[int]: The list of encoded tokens. + + """ assert isinstance(s, str) t = self.sp_model.encode(s) if bos: @@ -66,6 +78,14 @@ def encode(self, s: str, bos: bool, eos: bool) -> List[int]: return t def decode(self, t: List[int]) -> str: + """Decode a list of token IDs into a string. + + Args: + t (List[int]): _description_ + + Returns: + str: _description_ + """ return self.sp_model.decode(t) def encode_infilling(self, s: str) -> List[int]: diff --git a/zeta/tokenizers/tiktoken.py b/zeta/tokenizers/tiktoken.py deleted file mode 100644 index e2f1953d..00000000 --- a/zeta/tokenizers/tiktoken.py +++ /dev/null @@ -1,131 +0,0 @@ -from __future__ import annotations - -import logging -from typing import Optional - -import tiktoken -from attr import define, field -from zeta.tokenizers.base import BaseTokenizer - - -@define(frozen=True) -class TikToken(BaseTokenizer): - DEFAULT_OPENAI_GPT_3_COMPLETION_MODEL = "text-davinci-003" - DEFAULT_OPENAI_GPT_3_CHAT_MODEL = "gpt-3.5-turbo" - DEFAULT_OPENAI_GPT_4_MODEL = "gpt-4" - DEFAULT_ENCODING = "cl100k_base" - DEFAULT_MAX_TOKENS = 2049 - TOKEN_OFFSET = 8 - - MODEL_PREFIXES_TO_MAX_TOKENS = { - "gpt-4-32k": 32768, - "gpt-4": 8192, - "gpt-3.5-turbo-16k": 16384, - "gpt-3.5-turbo": 4096, - "gpt-35-turbo-16k": 16384, - "gpt-35-turbo": 4096, - "text-davinci-003": 4097, - "text-davinci-002": 4097, - "code-davinci-002": 8001, - "text-embedding-ada-002": 8191, - "text-embedding-ada-001": 2046, - } - - EMBEDDING_MODELS = ["text-embedding-ada-002", "text-embedding-ada-001"] - - model: str = field(default=DEFAULT_OPENAI_GPT_3_CHAT_MODEL, kw_only=True) - - @property - def encoding(self) -> tiktoken.Encoding: - try: - return tiktoken.encoding_for_model(self.model) - except KeyError: - return tiktoken.get_encoding(self.DEFAULT_ENCODING) - - @property - def max_tokens(self) -> int: - tokens = next( - v - for k, v in self.MODEL_PREFIXES_TO_MAX_TOKENS.items() - if self.model.startswith(k) - ) - offset = 0 if self.model in self.EMBEDDING_MODELS else self.TOKEN_OFFSET - - return (tokens if tokens else self.DEFAULT_MAX_TOKENS) - offset - - def encode(self, text: str) -> list[int]: - return self.encoding.encode( - text, allowed_special=set(self.stop_sequences) - ) - - def decode(self, tokens: list[int]) -> str: - return self.encoding.decode(tokens) - - def tokens_left(self, text: str | list) -> int: - return super().tokens_left(text) - - def token_count(self, text: str | list, model: Optional[str] = None) -> int: - """ - Handles the special case of ChatML. Implementation adopted from the official OpenAI notebook: - https://github.com/openai/openai-cookbook/blob/main/examples/How_to_count_tokens_with_tiktoken.ipynb - """ - if isinstance(text, list): - model = model if model else self.model - - try: - encoding = tiktoken.encoding_for_model(model) - except KeyError: - logging.warning("model not found. Using cl100k_base encoding.") - - encoding = tiktoken.get_encoding("cl100k_base") - - if model in { - "gpt-3.5-turbo-0613", - "gpt-3.5-turbo-16k-0613", - "gpt-4-0314", - "gpt-4-32k-0314", - "gpt-4-0613", - "gpt-4-32k-0613", - }: - tokens_per_message = 3 - tokens_per_name = 1 - elif model == "gpt-3.5-turbo-0301": - # every message follows - # <|start|>{role/name}\n{content}<|end|>\n - tokens_per_message = 4 - # if there's a name, the role is omitted - tokens_per_name = -1 - elif "gpt-3.5-turbo" in model or "gpt-35-turbo" in model: - logging.info( - "gpt-3.5-turbo may update over time. Returning num tokens" - " assuming gpt-3.5-turbo-0613." - ) - return self.token_count(text, model="gpt-3.5-turbo-0613") - elif "gpt-4" in model: - logging.info( - "gpt-4 may update over time. Returning num tokens assuming" - " gpt-4-0613." - ) - return self.token_count(text, model="gpt-4-0613") - else: - raise NotImplementedError( - f"""token_count() is not implemented for model {model}. - See https://github.com/openai/openai-python/blob/main/chatml.md for - information on how messages are converted to tokens.""" - ) - - num_tokens = 0 - - for message in text: - num_tokens += tokens_per_message - for key, value in message.items(): - num_tokens += len(encoding.encode(value)) - if key == "name": - num_tokens += tokens_per_name - - # every reply is primed with <|start|>assistant<|message|> - num_tokens += 3 - - return num_tokens - else: - return super().token_count(text) From 05f20f58f1fa6c88f7a8788ddd928e8a98475f4c Mon Sep 17 00:00:00 2001 From: Kye Date: Sat, 23 Dec 2023 00:24:34 -0500 Subject: [PATCH 188/587] [DOCSTRINGS][zeta.nn.biases ++ zeta.nn.embeddings] --- zeta/nn/biases/alibi.py | 29 +++++++++++++++++++++++++++++ zeta/nn/embeddings/abc_pos_emb.py | 9 +++++++++ zeta/nn/embeddings/bnb_embedding.py | 11 ----------- zeta/nn/embeddings/positional.py | 12 ++++++++++++ zeta/rl/ppo.py | 28 ++++++++++++++++++++++++++++ zeta/rl/vision_model_rl.py | 28 ++++++++++++++++++++++++++++ 6 files changed, 106 insertions(+), 11 deletions(-) delete mode 100644 zeta/nn/embeddings/bnb_embedding.py diff --git a/zeta/nn/biases/alibi.py b/zeta/nn/biases/alibi.py index 52ba4d4b..261b205d 100644 --- a/zeta/nn/biases/alibi.py +++ b/zeta/nn/biases/alibi.py @@ -21,6 +21,23 @@ def pad_at_dim(t, pad, dim=-1, value=0.0): class AlibiPositionalBias(BaseBias): + """ + AlibiPositionalBias class represents a positional bias module for neural networks. + + Args: + heads (int): Number of heads in the neural network. + num_heads (int): Number of heads in the neural network. + + Attributes: + slopes (Tensor): Tensor containing the slopes for the bias. + bias (Tensor): Tensor containing the bias values. + + Methods: + get_bias(i, j, device): Returns the bias tensor for the given indices. + forward(i, j): Computes and returns the bias tensor for the given indices. + + """ + def __init__(self, heads, num_heads, **kwargs): super().__init__() self.heads = heads @@ -81,6 +98,18 @@ def forward(self, i, j): class LearnedAlibiPositionalBias(AlibiPositionalBias): + """ + LearnedAlibiPositionalBias is a subclass of AlibiPositionalBias that introduces learned biases. + + Args: + heads (int): Number of attention heads. + num_heads (int): Number of heads per layer. + + Attributes: + learned_logslopes (nn.Parameter): Learned logarithmic slopes. + + """ + def __init__(self, heads, num_heads): super().__init__(heads, num_heads) log_slopes = torch.log(self.slopes) diff --git a/zeta/nn/embeddings/abc_pos_emb.py b/zeta/nn/embeddings/abc_pos_emb.py index 0190eece..70f118b1 100644 --- a/zeta/nn/embeddings/abc_pos_emb.py +++ b/zeta/nn/embeddings/abc_pos_emb.py @@ -5,6 +5,15 @@ class AbsolutePositionalEmbedding(nn.Module): + """ + Absolute Positional Embedding module. + + Args: + dim (int): The dimension of the embedding. + max_seq_len (int): The maximum sequence length. + l2norm_embed (bool, optional): Whether to apply L2 normalization to the embeddings. Defaults to False. + """ + def __init__(self, dim, max_seq_len, l2norm_embed=False): super().__init__() self.scale = dim**-0.5 if not l2norm_embed else 1.0 diff --git a/zeta/nn/embeddings/bnb_embedding.py b/zeta/nn/embeddings/bnb_embedding.py deleted file mode 100644 index f0ece1aa..00000000 --- a/zeta/nn/embeddings/bnb_embedding.py +++ /dev/null @@ -1,11 +0,0 @@ -# Copyright (c) 2022 Agora -# Licensed under The MIT License [see LICENSE for details] - -# import bitsandbytes as bnb -# from zeta.nn.embeddings.base import BaseEmbedding - -# class BnBEmbedding(BaseEmbedding): -# def forward(self, num_tokens: int, dim: int, padding_idx) -> bnb.nn.modules: -# embedding = bnb.nn.modules.Embedding(num_tokens, dim, padding_idx) - -# return embedding diff --git a/zeta/nn/embeddings/positional.py b/zeta/nn/embeddings/positional.py index 08c62b84..af12debd 100644 --- a/zeta/nn/embeddings/positional.py +++ b/zeta/nn/embeddings/positional.py @@ -10,6 +10,18 @@ def forward( positions=None, **kwargs, ): + """ + Forward pass of the PositionalEmbedding module. + + Args: + x (torch.Tensor): Input tensor. + positions (torch.Tensor, optional): Positions tensor. If None, positions are generated based on the input tensor size. Default is None. + **kwargs: Additional keyword arguments. + + Returns: + torch.Tensor: Embedded tensor. + + """ if positions is None: # being consistent with Fairseq, which starts from 2. positions = ( diff --git a/zeta/rl/ppo.py b/zeta/rl/ppo.py index 00bd243d..4561298f 100644 --- a/zeta/rl/ppo.py +++ b/zeta/rl/ppo.py @@ -3,6 +3,23 @@ class ActorCritic(nn.Module): + """ + A class representing an Actor-Critic model for Proximal Policy Optimization (PPO). + + Args: + num_inputs (int): The number of input features. + num_outputs (int): The number of output actions. + hidden_size (int): The size of the hidden layer. + + Attributes: + critic (nn.Sequential): The critic network. + actor (nn.Sequential): The actor network. + + Methods: + forward(x): Performs a forward pass through the network. + + """ + def __init__(self, num_inputs, num_outputs, hidden_size): super(ActorCritic, self).__init__() self.critic = nn.Sequential( @@ -18,6 +35,17 @@ def __init__(self, num_inputs, num_outputs, hidden_size): ) def forward(self, x): + """ + Performs a forward pass through the network. + + Args: + x (torch.Tensor): The input tensor. + + Returns: + dist (torch.distributions.Categorical): The probability distribution over actions. + value (torch.Tensor): The estimated value of the input state. + + """ value = self.critic(x) probs = self.actor(x) dist = torch.distributions.Categorical(probs) diff --git a/zeta/rl/vision_model_rl.py b/zeta/rl/vision_model_rl.py index f849634a..f15070da 100644 --- a/zeta/rl/vision_model_rl.py +++ b/zeta/rl/vision_model_rl.py @@ -3,6 +3,15 @@ class ResidualBlock(nn.Module): + """ + Residual Block module for a vision model. + + Args: + in_channels (int): Number of input channels. + out_channels (int): Number of output channels. + stride (int, optional): Stride value for the convolutional layers. Defaults to 1. + """ + def __init__(self, in_channels, out_channels, stride=1): super(ResidualBlock, self).__init__() self.conv1 = nn.Conv2d( @@ -32,6 +41,25 @@ def forward(self, x): class VisionRewardModel(nn.Module): + """ + VisionRewardModel is a neural network model that extracts image features and predicts rewards. + + Args: + None + + Attributes: + layer1 (ResidualBlock): The first residual block for image feature extraction. + layer2 (ResidualBlock): The second residual block for image feature extraction. + layer3 (ResidualBlock): The third residual block for image feature extraction. + layer4 (ResidualBlock): The fourth residual block for image feature extraction. + fc1 (nn.Linear): The fully connected layer for feature transformation. + fc2 (nn.Linear): The fully connected layer for reward prediction. + + Methods: + forward(x): Performs forward pass through the network. + + """ + def __init__(self): super(VisionRewardModel, self).__init__() From d09b3433fc048869f83ec9b0da20939485020b26 Mon Sep 17 00:00:00 2001 From: Kye Date: Sat, 23 Dec 2023 00:29:55 -0500 Subject: [PATCH 189/587] [TESTS][zeta.quant] --- tests/quant/qmoe.py | 0 tests/quant/test_bitlinear.py | 38 ++++++++++++++++++++++++ tests/quant/test_quik.py | 55 +++++++++++++++++++++++++++++++++++ zeta/quant/qmoe.py | 25 ---------------- 4 files changed, 93 insertions(+), 25 deletions(-) create mode 100644 tests/quant/qmoe.py create mode 100644 tests/quant/test_bitlinear.py create mode 100644 tests/quant/test_quik.py diff --git a/tests/quant/qmoe.py b/tests/quant/qmoe.py new file mode 100644 index 00000000..e69de29b diff --git a/tests/quant/test_bitlinear.py b/tests/quant/test_bitlinear.py new file mode 100644 index 00000000..64467687 --- /dev/null +++ b/tests/quant/test_bitlinear.py @@ -0,0 +1,38 @@ +import pytest +import torch +from torch import nn +from zeta.quant.bitlinear import BitLinear, absmax_quantize + + +def test_bitlinear_reset_parameters(): + bitlinear = BitLinear(10, 20) + old_weight = bitlinear.weight.clone() + bitlinear.reset_parameters() + + assert not torch.equal(old_weight, bitlinear.weight) + + +def test_bitlinear_forward_quantization(): + bitlinear = BitLinear(10, 20) + input = torch.randn(128, 10) + output = bitlinear(input) + + assert isinstance(output, torch.Tensor) + assert output.shape == (128, 20) + + # Check that the output is different from the input, indicating that quantization and dequantization occurred + assert not torch.allclose(output, input) + + +@pytest.mark.parametrize("bits", [4, 8, 16]) +def test_absmax_quantize_different_bits(bits): + x = torch.tensor([1.0, -2.0, 3.0, -4.0]) + quant, dequant = absmax_quantize(x, bits) + + assert isinstance(quant, torch.Tensor) + assert quant.dtype == torch.int8 + assert torch.allclose(dequant, x, atol=1e-2) + + # Check that the quantized values are within the expected range + assert quant.min() >= -(2 ** (bits - 1)) + assert quant.max() <= 2 ** (bits - 1) - 1 diff --git a/tests/quant/test_quik.py b/tests/quant/test_quik.py new file mode 100644 index 00000000..df87bcb8 --- /dev/null +++ b/tests/quant/test_quik.py @@ -0,0 +1,55 @@ +import pytest +import torch +from torch import nn +from zeta.quant.quick import QUIK + + +def test_quik_initialization(): + quik = QUIK(10, 20) + + assert isinstance(quik, QUIK) + assert quik.in_features == 10 + assert quik.out_features == 20 + assert quik.quantize_range == 8 + assert quik.half_range == 4 + assert quik.weight.shape == (20, 10) + assert quik.bias.shape == (20,) + + +def test_quik_quantize(): + quik = QUIK(10, 20) + x = torch.randn(10, 10) + quant_x, zero_act, scale_act = quik.quantize(x) + + assert isinstance(quant_x, torch.Tensor) + assert quant_x.dtype == torch.int32 + assert isinstance(zero_act, torch.Tensor) + assert isinstance(scale_act, torch.Tensor) + + +def test_quik_dequantize(): + quik = QUIK(10, 20) + x = torch.randn(10, 10) + quant_x, zero_act, scale_act = quik.quantize(x) + dequant_x = quik.dequantize(quant_x, zero_act, scale_act, scale_act) + + assert isinstance(dequant_x, torch.Tensor) + assert dequant_x.dtype == torch.float32 + + +def test_quik_find_zero_scale(): + quik = QUIK(10, 20) + x = torch.randn(10, 10) + zero_act, scale_act = quik.find_zero_scale(x) + + assert isinstance(zero_act, torch.Tensor) + assert isinstance(scale_act, torch.Tensor) + + +def test_quik_forward(): + quik = QUIK(10, 20) + x = torch.randn(10, 10) + output = quik(x) + + assert isinstance(output, torch.Tensor) + assert output.shape == (10, 20) diff --git a/zeta/quant/qmoe.py b/zeta/quant/qmoe.py index 90a72daa..e575b1e8 100644 --- a/zeta/quant/qmoe.py +++ b/zeta/quant/qmoe.py @@ -225,28 +225,3 @@ def forward(self, x): if self.ready(): return quantize(x, self.scale, self.zero, self.maxq) return x - - -if __name__ == "__main__": - import time - - D = 2048 - K = 8 - - torch.random.manual_seed(0) - X = torch.randn(128, 512, D).cuda() - W = torch.randn(K, 768, D).cuda() - quantizer = QMOEQuantizer() - quantizer.configure(2) - - H = hessian(X).repeat(K, 1, 1) - Q = batch_gptq(W, H, quantizer) - tick = time.time() - COUNT = 10 - for i in range(COUNT): - H = hessian(X).repeat(K, 1, 1) - Q = batch_gptq(W, H, quantizer) - torch.cuda.synchronize() - print((time.time() - tick) / COUNT) - - print(Q[0]) From 95308db1a8189d17af02739616e618977a98dea3 Mon Sep 17 00:00:00 2001 From: Kye Date: Sun, 24 Dec 2023 10:13:09 -0500 Subject: [PATCH 190/587] [bug][[BUG] ModuleNotFoundError: No module named 'zeta.structs.attn_layers' #48 --- pyproject.toml | 2 +- zeta/nn/modules/feedforward.py | 3 +-- zeta/structs/__init__.py | 2 +- zeta/structs/hierarchical_transformer.py | 2 +- zeta/structs/transformer_block.py | 4 ++-- 5 files changed, 6 insertions(+), 7 deletions(-) diff --git a/pyproject.toml b/pyproject.toml index 35056e0d..34cf7d2b 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,6 +1,6 @@ [tool.poetry] name = "zetascale" -version = "1.2.4" +version = "1.2.5" description = "Transformers at zeta scales" authors = ["Zeta Team "] license = "MIT" diff --git a/zeta/nn/modules/feedforward.py b/zeta/nn/modules/feedforward.py index 9fb2d41a..1bfbf12a 100644 --- a/zeta/nn/modules/feedforward.py +++ b/zeta/nn/modules/feedforward.py @@ -1,7 +1,6 @@ from torch import nn -from zeta.structs.attn_layers import GLU -from zeta.structs.transformer import ReluSquared +from zeta.structs.transformer import GLU, ReluSquared def exists(val): diff --git a/zeta/structs/__init__.py b/zeta/structs/__init__.py index 6efb4f07..58dee7cf 100644 --- a/zeta/structs/__init__.py +++ b/zeta/structs/__init__.py @@ -6,7 +6,7 @@ HierarchicalTransformer, ) from zeta.structs.local_transformer import LocalTransformer -from zeta.structs.mag_vit import VideoTokenizer +# from zeta.structs.mag_vit import VideoTokenizer from zeta.structs.multi_modal_projector import build_vision_projector from zeta.structs.simple_transformer import ( ParallelTransformerBlock, diff --git a/zeta/structs/hierarchical_transformer.py b/zeta/structs/hierarchical_transformer.py index d7c75d1b..0560c17e 100644 --- a/zeta/structs/hierarchical_transformer.py +++ b/zeta/structs/hierarchical_transformer.py @@ -10,7 +10,7 @@ from torch import nn from vector_quantize_pytorch import RandomProjectionQuantizer -from zeta.structs.attn_layers import rotate_half +from zeta.structs.transformer import rotate_half from zeta.nn.attention.attend import Attend from zeta.nn.attention.local_attention_mha import LocalMHA from zeta.nn.embeddings.rope import RotaryEmbedding diff --git a/zeta/structs/transformer_block.py b/zeta/structs/transformer_block.py index 1157b638..3ee861b7 100644 --- a/zeta/structs/transformer_block.py +++ b/zeta/structs/transformer_block.py @@ -2,8 +2,8 @@ from einops import rearrange from torch import nn -from zeta.structs.attn_layers import Attention, RotaryEmbedding -from zeta.structs.parallel_transformer import SwiGLU +from zeta.structs.transformer import Attention, RotaryEmbedding +from zeta.structs.simple_transformer import SwiGLU from zeta.nn.embeddings.xpos_relative_position import apply_rotary_pos_emb from zeta.nn.modules.layernorm import LayerNorm from zeta.utils.main import exists, l2norm From b1d046e76487ba2c3dd404681239e088dc7963cc Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Sun, 24 Dec 2023 15:15:14 +0000 Subject: [PATCH 191/587] Bump transformers from 4.35.0 to 4.36.0 Bumps [transformers](https://github.com/huggingface/transformers) from 4.35.0 to 4.36.0. - [Release notes](https://github.com/huggingface/transformers/releases) - [Commits](https://github.com/huggingface/transformers/compare/v4.35.0...v4.36.0) --- updated-dependencies: - dependency-name: transformers dependency-type: direct:production ... Signed-off-by: dependabot[bot] --- pyproject.toml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pyproject.toml b/pyproject.toml index 34cf7d2b..14b924d9 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -25,7 +25,7 @@ pytest = "7.4.2" einops = "0.7.0" bitsandbytes = "0.38.1" typing = "3.7.4.3" -transformers = "4.35.0" +transformers = "4.36.0" einops-exts = "0.0.4" torchvision = "*" accelerate = "0.22.0" From e58c234a44157cc2d73048d206b6fae997462d4a Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Sun, 24 Dec 2023 22:22:29 +0000 Subject: [PATCH 192/587] Bump bitsandbytes from 0.38.1 to 0.41.3.post2 Bumps [bitsandbytes](https://github.com/TimDettmers/bitsandbytes) from 0.38.1 to 0.41.3.post2. - [Release notes](https://github.com/TimDettmers/bitsandbytes/releases) - [Changelog](https://github.com/TimDettmers/bitsandbytes/blob/main/CHANGELOG.md) - [Commits](https://github.com/TimDettmers/bitsandbytes/commits) --- updated-dependencies: - dependency-name: bitsandbytes dependency-type: direct:production update-type: version-update:semver-minor ... Signed-off-by: dependabot[bot] --- pyproject.toml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pyproject.toml b/pyproject.toml index 14b924d9..cd888710 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -23,7 +23,7 @@ timm = "0.6.13" torchdiffeq = "0.2.3" pytest = "7.4.2" einops = "0.7.0" -bitsandbytes = "0.38.1" +bitsandbytes = "0.41.3.post2" typing = "3.7.4.3" transformers = "4.36.0" einops-exts = "0.0.4" From bb8226961c1167becbf41758d591f25cf81572d5 Mon Sep 17 00:00:00 2001 From: Kye Date: Sun, 24 Dec 2023 17:25:21 -0500 Subject: [PATCH 193/587] [FEAT][zeta.rl] --- tests/rl/test_prioritizedreplybuffer.py | 13 ++- .../rl/test_prioritizedsequencereplybuffer.py | 17 +++- tests/rl/test_sumtree.py | 24 ++++-- ...yBuffer.py => priortized_replay_buffer.py} | 75 +++++++++++++---- ...uenceReplayBuffer.py => priortized_rps.py} | 82 +++++++++++++------ zeta/rl/sumtree.py | 22 ++--- zeta/structs/__init__.py | 1 + zeta/structs/hierarchical_transformer.py | 2 +- zeta/structs/transformer_block.py | 2 +- 9 files changed, 177 insertions(+), 61 deletions(-) rename zeta/rl/{PrioritizedReplayBuffer.py => priortized_replay_buffer.py} (54%) rename zeta/rl/{PrioritizedSequenceReplayBuffer.py => priortized_rps.py} (62%) diff --git a/tests/rl/test_prioritizedreplybuffer.py b/tests/rl/test_prioritizedreplybuffer.py index dba5637b..fcfcac78 100644 --- a/tests/rl/test_prioritizedreplybuffer.py +++ b/tests/rl/test_prioritizedreplybuffer.py @@ -1,7 +1,11 @@ import pytest import random import torch -from zeta.rl.PrioritizedReplayBuffer import PrioritizedReplayBuffer, SumTree # Replace 'your_module' with the actual module where classes are defined +from zeta.rl.priortized_replay_buffer import ( + PrioritizedReplayBuffer, + SumTree, +) # Replace 'your_module' with the actual module where classes are defined + @pytest.fixture def replay_buffer(): @@ -11,6 +15,7 @@ def replay_buffer(): device = torch.device("cpu") return PrioritizedReplayBuffer(state_size, action_size, buffer_size, device) + def test_initialization(replay_buffer): assert replay_buffer.eps == 1e-2 assert replay_buffer.alpha == 0.1 @@ -21,12 +26,14 @@ def test_initialization(replay_buffer): assert replay_buffer.size == 100 assert replay_buffer.device == torch.device("cpu") + def test_add(replay_buffer): transition = (torch.rand(4), torch.rand(2), 1.0, torch.rand(4), False) replay_buffer.add(transition) assert replay_buffer.count == 1 assert replay_buffer.real_size == 1 + def test_sample(replay_buffer): for i in range(10): transition = (torch.rand(4), torch.rand(2), 1.0, torch.rand(4), False) @@ -37,6 +44,7 @@ def test_sample(replay_buffer): assert len(weights) == 5 assert len(tree_idxs) == 5 + def test_update_priorities(replay_buffer): for i in range(10): transition = (torch.rand(4), torch.rand(2), 1.0, torch.rand(4), False) @@ -46,10 +54,12 @@ def test_update_priorities(replay_buffer): new_priorities = torch.rand(5) replay_buffer.update_priorities(tree_idxs, new_priorities) + def test_sample_with_invalid_batch_size(replay_buffer): with pytest.raises(AssertionError): replay_buffer.sample(101) + def test_add_with_max_size(replay_buffer): for i in range(100): transition = (torch.rand(4), torch.rand(2), 1.0, torch.rand(4), False) @@ -58,4 +68,5 @@ def test_add_with_max_size(replay_buffer): assert replay_buffer.count == 0 assert replay_buffer.real_size == 100 + # Additional tests for edge cases, exceptions, and more scenarios can be added as needed. diff --git a/tests/rl/test_prioritizedsequencereplybuffer.py b/tests/rl/test_prioritizedsequencereplybuffer.py index 9582dc71..0201e848 100644 --- a/tests/rl/test_prioritizedsequencereplybuffer.py +++ b/tests/rl/test_prioritizedsequencereplybuffer.py @@ -1,7 +1,11 @@ import pytest import random import torch -from zeta.rl.PrioritizedSequenceReplayBuffer import PrioritizedSequenceReplayBuffer, SumTree # Replace 'your_module' with the actual module where classes are defined +from zeta.rl.priortized_rps import ( + PrioritizedSequenceReplayBuffer, + SumTree, +) # Replace 'your_module' with the actual module where classes are defined + @pytest.fixture def replay_buffer(): @@ -9,7 +13,10 @@ def replay_buffer(): action_size = 2 buffer_size = 100 device = torch.device("cpu") - return PrioritizedSequenceReplayBuffer(state_size, action_size, buffer_size, device) + return PrioritizedSequenceReplayBuffer( + state_size, action_size, buffer_size, device + ) + def test_initialization(replay_buffer): assert replay_buffer.eps == 1e-5 @@ -24,12 +31,14 @@ def test_initialization(replay_buffer): assert replay_buffer.size == 100 assert replay_buffer.device == torch.device("cpu") + def test_add(replay_buffer): transition = (torch.rand(4), torch.rand(2), 1.0, torch.rand(4), False) replay_buffer.add(transition) assert replay_buffer.count == 1 assert replay_buffer.real_size == 1 + def test_sample(replay_buffer): for i in range(10): transition = (torch.rand(4), torch.rand(2), 1.0, torch.rand(4), False) @@ -40,6 +49,7 @@ def test_sample(replay_buffer): assert len(weights) == 5 assert len(tree_idxs) == 5 + def test_update_priorities(replay_buffer): for i in range(10): transition = (torch.rand(4), torch.rand(2), 1.0, torch.rand(4), False) @@ -49,10 +59,12 @@ def test_update_priorities(replay_buffer): new_priorities = torch.rand(5) replay_buffer.update_priorities(tree_idxs, new_priorities) + def test_sample_with_invalid_batch_size(replay_buffer): with pytest.raises(AssertionError): replay_buffer.sample(101) + def test_add_with_max_size(replay_buffer): for i in range(100): transition = (torch.rand(4), torch.rand(2), 1.0, torch.rand(4), False) @@ -61,4 +73,5 @@ def test_add_with_max_size(replay_buffer): assert replay_buffer.count == 0 assert replay_buffer.real_size == 100 + # Additional tests for edge cases, exceptions, and more scenarios can be added as needed. diff --git a/tests/rl/test_sumtree.py b/tests/rl/test_sumtree.py index 7758f9b8..a2cf9177 100644 --- a/tests/rl/test_sumtree.py +++ b/tests/rl/test_sumtree.py @@ -1,5 +1,8 @@ import pytest -from zeta.rl.sumtree import SumTree # Replace 'your_module' with the actual module where SumTree is defined +from zeta.rl.sumtree import ( + SumTree, +) # Replace 'your_module' with the actual module where SumTree is defined + # Fixture for initializing SumTree instances with a given size @pytest.fixture @@ -7,6 +10,7 @@ def sum_tree(): size = 10 # You can change the size as needed return SumTree(size) + # Basic tests def test_initialization(sum_tree): assert sum_tree.size == 10 @@ -14,6 +18,7 @@ def test_initialization(sum_tree): assert sum_tree.real_size == 0 assert sum_tree.total == 0 + def test_update_and_get(sum_tree): sum_tree.add(5, "data1") assert sum_tree.total == 5 @@ -22,35 +27,44 @@ def test_update_and_get(sum_tree): assert priority == 5 assert data == "data1" + def test_add_overflow(sum_tree): for i in range(15): sum_tree.add(i, f"data{i}") assert sum_tree.count == 5 assert sum_tree.real_size == 10 + # Parameterized testing for various scenarios -@pytest.mark.parametrize("values, expected_total", [ - ([1, 2, 3, 4, 5], 15), - ([10, 20, 30, 40, 50], 150), -]) +@pytest.mark.parametrize( + "values, expected_total", + [ + ([1, 2, 3, 4, 5], 15), + ([10, 20, 30, 40, 50], 150), + ], +) def test_multiple_updates(sum_tree, values, expected_total): for value in values: sum_tree.add(value, None) assert sum_tree.total == expected_total + # Exception testing def test_get_with_invalid_cumsum(sum_tree): with pytest.raises(AssertionError): sum_tree.get(20) + # More tests for specific methods def test_get_priority(sum_tree): sum_tree.add(10, "data1") priority = sum_tree.get_priority(0) assert priority == 10 + def test_repr(sum_tree): expected_repr = f"SumTree(nodes={sum_tree.nodes}, data={sum_tree.data})" assert repr(sum_tree) == expected_repr + # More test cases can be added as needed diff --git a/zeta/rl/PrioritizedReplayBuffer.py b/zeta/rl/priortized_replay_buffer.py similarity index 54% rename from zeta/rl/PrioritizedReplayBuffer.py rename to zeta/rl/priortized_replay_buffer.py index badb3a7e..97a8c964 100644 --- a/zeta/rl/PrioritizedReplayBuffer.py +++ b/zeta/rl/priortized_replay_buffer.py @@ -2,21 +2,43 @@ import torch import random + class PrioritizedReplayBuffer: - def __init__(self, state_size, action_size, buffer_size, device, eps=1e-2, alpha=0.1, beta=0.1): + def __init__( + self, + state_size, + action_size, + buffer_size, + device, + eps=1e-2, + alpha=0.1, + beta=0.1, + ): + """ + Initializes a PrioritizedReplayBuffer object. + + Args: + state_size (int): The size of the state space. + action_size (int): The size of the action space. + buffer_size (int): The maximum capacity of the buffer. + device (torch.device): The device to store the tensors on. + eps (float, optional): A small constant added to the priorities to ensure non-zero probabilities. Defaults to 1e-2. + alpha (float, optional): The exponent used to compute the priority weights. Defaults to 0.1. + beta (float, optional): The exponent used to compute the importance sampling weights. Defaults to 0.1. + """ self.tree = SumTree(size=buffer_size) - - self.eps = eps - self.alpha = alpha - self.beta = beta - self.max_priority = 1. - + self.eps = eps + self.alpha = alpha + self.beta = beta + self.max_priority = 1.0 self.state = torch.empty(buffer_size, state_size, dtype=torch.float) self.action = torch.empty(buffer_size, action_size, dtype=torch.float) self.reward = torch.empty(buffer_size, dtype=torch.float) - self.next_state = torch.empty(buffer_size, state_size, dtype=torch.float) + self.next_state = torch.empty( + buffer_size, state_size, dtype=torch.float + ) self.done = torch.empty(buffer_size, dtype=torch.uint8) self.count = 0 @@ -25,10 +47,15 @@ def __init__(self, state_size, action_size, buffer_size, device, eps=1e-2, alpha # device self.device = device - + def add(self, transition): - state, action, reward, next_state, done = transition + """ + Adds a transition to the replay buffer. + Args: + transition (tuple): A tuple containing the state, action, reward, next_state, and done flag. + """ + state, action, reward, next_state, done = transition self.tree.add(self.max_priority, self.count) @@ -38,23 +65,32 @@ def add(self, transition): self.next_state[self.count] = torch.as_tensor(next_state) self.done[self.count] = torch.as_tensor(done) - self.count = (self.count + 1) % self.size self.real_size = min(self.size, self.real_size + 1) def sample(self, batch_size): - assert self.real_size >= batch_size, "buffer contains less samples than batch size" + """ + Samples a batch of transitions from the replay buffer. + + Args: + batch_size (int): The size of the batch to sample. + + Returns: + tuple: A tuple containing the batch of transitions, importance sampling weights, and tree indices. + """ + assert ( + self.real_size >= batch_size + ), "buffer contains fewer samples than batch size" sample_idxs, tree_idxs = [], [] priorities = torch.empty(batch_size, 1, dtype=torch.float) - segment = self.tree.total / batch_size for i in range(batch_size): a, b = segment * i, segment * (i + 1) cumsum = random.uniform(a, b) - + tree_idx, priority, sample_idx = self.tree.get(cumsum) priorities[i] = priority @@ -71,15 +107,22 @@ def sample(self, batch_size): self.action[sample_idxs].to(self.device), self.reward[sample_idxs].to(self.device), self.next_state[sample_idxs].to(self.device), - self.done[sample_idxs].to(self.device) + self.done[sample_idxs].to(self.device), ) return batch, weights, tree_idxs def update_priorities(self, data_idxs, priorities): + """ + Updates the priorities of the transitions in the replay buffer. + + Args: + data_idxs (list): A list of indices corresponding to the transitions in the replay buffer. + priorities (torch.Tensor or numpy.ndarray): The updated priorities for the corresponding transitions. + """ if isinstance(priorities, torch.Tensor): priorities = priorities.detach().cpu().numpy() for data_idx, priority in zip(data_idxs, priorities): priority = (priority + self.eps) ** self.alpha self.tree.update(data_idx, priority) - self.max_priority = max(self.max_priority, priority) \ No newline at end of file + self.max_priority = max(self.max_priority, priority) diff --git a/zeta/rl/PrioritizedSequenceReplayBuffer.py b/zeta/rl/priortized_rps.py similarity index 62% rename from zeta/rl/PrioritizedSequenceReplayBuffer.py rename to zeta/rl/priortized_rps.py index 8a9de10e..1fb53295 100644 --- a/zeta/rl/PrioritizedSequenceReplayBuffer.py +++ b/zeta/rl/priortized_rps.py @@ -2,27 +2,54 @@ import torch import random + class PrioritizedSequenceReplayBuffer: - def __init__(self,state_size,action_size,buffer_size,device,eps=1e-5,alpha=0.1,beta=0.1, - decay_window=5, - decay_coff=0.4, - pre_priority=0.7): + def __init__( + self, + state_size, + action_size, + buffer_size, + device, + eps=1e-5, + alpha=0.1, + beta=0.1, + decay_window=5, + decay_coff=0.4, + pre_priority=0.7, + ): + """ + Initializes the PrioritizedRPS object. + + Args: + state_size (int): The size of the state space. + action_size (int): The size of the action space. + buffer_size (int): The size of the replay buffer. + device (str): The device to be used for computation. + eps (float, optional): A small constant added to priorities to ensure non-zero probabilities. Defaults to 1e-5. + alpha (float, optional): The exponent controlling the prioritization of experiences. Defaults to 0.1. + beta (float, optional): The exponent controlling the importance sampling weights. Defaults to 0.1. + decay_window (int, optional): The number of steps over which the priority decay is applied. Defaults to 5. + decay_coff (float, optional): The coefficient controlling the rate of priority decay. Defaults to 0.4. + pre_priority (float, optional): The initial priority value for new experiences. Defaults to 0.7. + """ self.tree = SumTree(data_size=buffer_size) - + # PESR params self.eps = eps self.alpha = alpha self.beta = beta - self.max_priority = 1. + self.max_priority = 1.0 self.decay_window = decay_window self.decay_coff = decay_coff self.pre_priority = pre_priority - + # buffer params self.state = torch.empty(buffer_size, state_size, dtype=torch.float) self.action = torch.empty(buffer_size, action_size, dtype=torch.float) self.reward = torch.empty(buffer_size, dtype=torch.float) - self.next_state = torch.empty(buffer_size, state_size, dtype=torch.float) + self.next_state = torch.empty( + buffer_size, state_size, dtype=torch.float + ) self.done = torch.empty(buffer_size, dtype=torch.uint8) self.count = 0 @@ -31,7 +58,7 @@ def __init__(self,state_size,action_size,buffer_size,device,eps=1e-5,alpha=0.1,b # device self.device = device - + def add(self, transition): state, action, reward, next_state, done = transition @@ -48,13 +75,15 @@ def add(self, transition): # update counters self.count = (self.count + 1) % self.size self.real_size = min(self.size, self.real_size + 1) - - def sample(self,batch_size): - assert self.real_size >= batch_size, "buffer contains less samples than batch size" + + def sample(self, batch_size): + assert ( + self.real_size >= batch_size + ), "buffer contains less samples than batch size" sample_idxs, tree_idxs = [], [] priorities = torch.empty(batch_size, 1, dtype=torch.float) - + segment = self.tree.total_priority / batch_size for i in range(batch_size): a, b = segment * i, segment * (i + 1) @@ -79,27 +108,30 @@ def sample(self,batch_size): self.action[sample_idxs].to(self.device), self.reward[sample_idxs].to(self.device), self.next_state[sample_idxs].to(self.device), - self.done[sample_idxs].to(self.device) + self.done[sample_idxs].to(self.device), ) return batch, weights, tree_idxs - - def update_priorities(self,data_idxs,abs_td_errors): + + def update_priorities(self, data_idxs, abs_td_errors): """ when we get the TD-error, we should update the transition priority p_j And update decay_window's transition priorities """ - if isinstance(abs_td_errors,torch.Tensor): + if isinstance(abs_td_errors, torch.Tensor): abs_td_errors = abs_td_errors.detach().cpu().numpy() - - for data_idx, td_error in zip(data_idxs,abs_td_errors): + + for data_idx, td_error in zip(data_idxs, abs_td_errors): # first update the batch: p_j # p_j <- max{|delta_j| + eps, pre_priority * p_j} - old_priority = self.pre_priority * self.tree.nodes[data_idx + self.tree.size - 1] + old_priority = ( + self.pre_priority + * self.tree.nodes[data_idx + self.tree.size - 1] + ) priority = (td_error + self.eps) ** self.alpha - priority = max(priority,old_priority) - self.tree.update(data_idx,priority) - self.max_priority = max(self.max_priority,priority) - + priority = max(priority, old_priority) + self.tree.update(data_idx, priority) + self.max_priority = max(self.max_priority, priority) + # And then apply decay if self.count >= self.decay_window: # count points to the next position @@ -109,4 +141,4 @@ def update_priorities(self,data_idxs,abs_td_errors): decayed_priority = priority * (self.decay_coff ** (i + 1)) tree_idx = idx + self.tree.size - 1 existing_priority = self.tree.nodes[tree_idx] - self.tree.update(idx,max(decayed_priority,existing_priority)) \ No newline at end of file + self.tree.update(idx, max(decayed_priority, existing_priority)) diff --git a/zeta/rl/sumtree.py b/zeta/rl/sumtree.py index c51805a3..4347ded5 100644 --- a/zeta/rl/sumtree.py +++ b/zeta/rl/sumtree.py @@ -12,11 +12,11 @@ def total(self): return self.nodes[0] def propagate(self, idx, delta_value): - parent = (idx - 1) // 2 + parent = (idx - 1) // 2 - while parent >= 0: - self.nodes[parent] += delta_value - parent = (parent - 1) // 2 + while parent >= 0: + self.nodes[parent] += delta_value + parent = (parent - 1) // 2 def update(self, data_idx, value): idx = data_idx + self.size - 1 # child index in tree array @@ -38,7 +38,7 @@ def get(self, cumsum): idx = 0 while 2 * idx + 1 < len(self.nodes): - left, right = 2*idx + 1, 2*idx + 2 + left, right = 2 * idx + 1, 2 * idx + 2 if cumsum <= self.nodes[left]: idx = left @@ -53,13 +53,15 @@ def get(self, cumsum): def get_priority(self, data_idx): tree_idx = data_idx + self.size - 1 return self.nodes[tree_idx] - - + def __repr__(self): - return f"SumTree(nodes={self.nodes.__repr__()}, data={self.data.__repr__()})" - + return ( + f"SumTree(nodes={self.nodes.__repr__()}," + f" data={self.data.__repr__()})" + ) + -# # Test the sum tree +# # Test the sum tree # if __name__ == '__main__': # # Assuming the SumTree class definition is available diff --git a/zeta/structs/__init__.py b/zeta/structs/__init__.py index 58dee7cf..34e55212 100644 --- a/zeta/structs/__init__.py +++ b/zeta/structs/__init__.py @@ -6,6 +6,7 @@ HierarchicalTransformer, ) from zeta.structs.local_transformer import LocalTransformer + # from zeta.structs.mag_vit import VideoTokenizer from zeta.structs.multi_modal_projector import build_vision_projector from zeta.structs.simple_transformer import ( diff --git a/zeta/structs/hierarchical_transformer.py b/zeta/structs/hierarchical_transformer.py index 0560c17e..954f9df9 100644 --- a/zeta/structs/hierarchical_transformer.py +++ b/zeta/structs/hierarchical_transformer.py @@ -10,7 +10,7 @@ from torch import nn from vector_quantize_pytorch import RandomProjectionQuantizer -from zeta.structs.transformer import rotate_half +from zeta.structs.transformer import rotate_half from zeta.nn.attention.attend import Attend from zeta.nn.attention.local_attention_mha import LocalMHA from zeta.nn.embeddings.rope import RotaryEmbedding diff --git a/zeta/structs/transformer_block.py b/zeta/structs/transformer_block.py index 3ee861b7..4a24c582 100644 --- a/zeta/structs/transformer_block.py +++ b/zeta/structs/transformer_block.py @@ -2,7 +2,7 @@ from einops import rearrange from torch import nn -from zeta.structs.transformer import Attention, RotaryEmbedding +from zeta.structs.transformer import Attention, RotaryEmbedding from zeta.structs.simple_transformer import SwiGLU from zeta.nn.embeddings.xpos_relative_position import apply_rotary_pos_emb from zeta.nn.modules.layernorm import LayerNorm From b6bdb8f3d52bd575cba5530af29ef48a0ccabc03 Mon Sep 17 00:00:00 2001 From: Eternal Reclaimer <98760976+kyegomez@users.noreply.github.com> Date: Sun, 24 Dec 2023 20:57:05 -0500 Subject: [PATCH 194/587] Update requirements.txt --- requirements.txt | 1 - 1 file changed, 1 deletion(-) diff --git a/requirements.txt b/requirements.txt index 79232c14..1b2cd538 100644 --- a/requirements.txt +++ b/requirements.txt @@ -2,7 +2,6 @@ torch==2.1.1 fairscale==0.4.0 timm==0.6.13 einops==0.7.0 -apex memory-profiler lion-pytorch==0.0.7 bitsandbytes==0.38.1 From 5c5ad27e6b37256c173985b085d9d02e4d8c9598 Mon Sep 17 00:00:00 2001 From: Kye Date: Mon, 25 Dec 2023 02:04:27 -0500 Subject: [PATCH 195/587] [.github][actions] --- .github/actions/init_environment/action.yml | 37 +++++++++++++++++++++ 1 file changed, 37 insertions(+) create mode 100644 .github/actions/init_environment/action.yml diff --git a/.github/actions/init_environment/action.yml b/.github/actions/init_environment/action.yml new file mode 100644 index 00000000..f2f9016c --- /dev/null +++ b/.github/actions/init_environment/action.yml @@ -0,0 +1,37 @@ +name: "Init Environment" +description: "Initialize environment for tests" +runs: + using: "composite" + steps: + - name: Checkout actions + uses: actions/checkout@v3 + + - name: Set up Python ${{ matrix.python-version }} + uses: actions/setup-python@v4 + with: + python-version: ${{ matrix.python-version }} + + - name: Install and configure Poetry + uses: snok/install-poetry@v1 + with: + virtualenvs-create: true + virtualenvs-in-project: true + installer-parallel: true + + - name: Load cached venv + id: cached-poetry-dependencies + uses: actions/cache@v3 + with: + path: .venv + key: venv-${{ runner.os }}-${{ steps.setup-python.outputs.python-version }}-${{ hashFiles('**/poetry.lock') }} + + - name: Install dependencies + if: steps.cached-poetry-dependencies.outputs.cache-hit != 'true' + run: poetry install --no-interaction --no-root --with test --with dev --all-extras + shell: bash + + - name: Activate venv + run: | + source .venv/bin/activate + echo PATH=$PATH >> $GITHUB_ENV + shell: bash \ No newline at end of file From 719004206711d9c0caa2c475eb127ad1d7a28828 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Mon, 25 Dec 2023 16:10:52 +0000 Subject: [PATCH 196/587] Bump torch from 2.1.1 to 2.1.2 Bumps [torch](https://github.com/pytorch/pytorch) from 2.1.1 to 2.1.2. - [Release notes](https://github.com/pytorch/pytorch/releases) - [Changelog](https://github.com/pytorch/pytorch/blob/main/RELEASE.md) - [Commits](https://github.com/pytorch/pytorch/compare/v2.1.1...v2.1.2) --- updated-dependencies: - dependency-name: torch dependency-type: direct:production update-type: version-update:semver-patch ... Signed-off-by: dependabot[bot] --- pyproject.toml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pyproject.toml b/pyproject.toml index cd888710..ff99ab5b 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -17,7 +17,7 @@ packages = [ [tool.poetry.dependencies] python = "^3.8" -torch = "2.1.1" +torch = "2.1.2" fairscale = "0.4.0" timm = "0.6.13" torchdiffeq = "0.2.3" From 51ff533cb13ff85a643ae1c9e7185502ae787909 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Mon, 25 Dec 2023 16:11:40 +0000 Subject: [PATCH 197/587] Update ruff requirement from >=0.0.249,<0.1.8 to >=0.0.249,<0.1.10 Updates the requirements on [ruff](https://github.com/astral-sh/ruff) to permit the latest version. - [Release notes](https://github.com/astral-sh/ruff/releases) - [Changelog](https://github.com/astral-sh/ruff/blob/main/CHANGELOG.md) - [Commits](https://github.com/astral-sh/ruff/compare/v0.0.249...v0.1.9) --- updated-dependencies: - dependency-name: ruff dependency-type: direct:development ... Signed-off-by: dependabot[bot] --- pyproject.toml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pyproject.toml b/pyproject.toml index cd888710..cf9f8c1a 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -49,7 +49,7 @@ requires = ["poetry-core>=1.0.0"] build-backend = "poetry.core.masonry.api" [tool.poetry.group.lint.dependencies] -ruff = ">=0.0.249,<0.1.8" +ruff = ">=0.0.249,<0.1.10" types-toml = "^0.10.8.1" types-redis = "^4.3.21.6" types-pytz = "^2023.3.0.0" From e47df6e4ba3df3c55e43801fdfce32c44e84b0d0 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Mon, 25 Dec 2023 16:12:22 +0000 Subject: [PATCH 198/587] Bump bitsandbytes from 0.38.1 to 0.41.3.post2 Bumps [bitsandbytes](https://github.com/TimDettmers/bitsandbytes) from 0.38.1 to 0.41.3.post2. - [Release notes](https://github.com/TimDettmers/bitsandbytes/releases) - [Changelog](https://github.com/TimDettmers/bitsandbytes/blob/main/CHANGELOG.md) - [Commits](https://github.com/TimDettmers/bitsandbytes/commits) --- updated-dependencies: - dependency-name: bitsandbytes dependency-type: direct:production update-type: version-update:semver-minor ... Signed-off-by: dependabot[bot] --- requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements.txt b/requirements.txt index 1b2cd538..6f04d5db 100644 --- a/requirements.txt +++ b/requirements.txt @@ -4,7 +4,7 @@ timm==0.6.13 einops==0.7.0 memory-profiler lion-pytorch==0.0.7 -bitsandbytes==0.38.1 +bitsandbytes==0.41.3.post2 typing==3.7.4.3 einops-exts==0.0.4 torchvision==0.16.1 From f31d384046e3335c4457d38b512edfe312ac7d16 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Mon, 25 Dec 2023 16:15:02 +0000 Subject: [PATCH 199/587] Bump sentencepiece from 0.1.98 to 0.1.99 Bumps [sentencepiece](https://github.com/google/sentencepiece) from 0.1.98 to 0.1.99. - [Release notes](https://github.com/google/sentencepiece/releases) - [Commits](https://github.com/google/sentencepiece/compare/v0.1.98...v0.1.99) --- updated-dependencies: - dependency-name: sentencepiece dependency-type: direct:production update-type: version-update:semver-patch ... Signed-off-by: dependabot[bot] --- pyproject.toml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pyproject.toml b/pyproject.toml index cd888710..c07d3881 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -31,7 +31,7 @@ torchvision = "*" accelerate = "0.22.0" datasets = "2.10.1" lion-pytorch = "0.0.7" -sentencepiece = "0.1.98" +sentencepiece = "0.1.99" colt5-attention = "0.10.19" vector-quantize-pytorch = "1.12.0" tokenmonster = "1.1.12" From 94083259b7b447b8f2a2bba96d90488451f93b80 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Mon, 25 Dec 2023 16:15:44 +0000 Subject: [PATCH 200/587] Update accelerate requirement from 0.22.0 to 0.25.0 Updates the requirements on [accelerate](https://github.com/huggingface/accelerate) to permit the latest version. - [Release notes](https://github.com/huggingface/accelerate/releases) - [Commits](https://github.com/huggingface/accelerate/compare/v0.22.0...v0.25.0) --- updated-dependencies: - dependency-name: accelerate dependency-type: direct:production ... Signed-off-by: dependabot[bot] --- pyproject.toml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pyproject.toml b/pyproject.toml index cd888710..d8237b86 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -28,7 +28,7 @@ typing = "3.7.4.3" transformers = "4.36.0" einops-exts = "0.0.4" torchvision = "*" -accelerate = "0.22.0" +accelerate = "0.25.0" datasets = "2.10.1" lion-pytorch = "0.0.7" sentencepiece = "0.1.98" From e0b16b60063a67c2fd4161cb811c76c14466991f Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Mon, 25 Dec 2023 17:00:41 +0000 Subject: [PATCH 201/587] Bump aws-actions/amazon-ecr-login from 1 to 2 Bumps [aws-actions/amazon-ecr-login](https://github.com/aws-actions/amazon-ecr-login) from 1 to 2. - [Release notes](https://github.com/aws-actions/amazon-ecr-login/releases) - [Changelog](https://github.com/aws-actions/amazon-ecr-login/blob/main/CHANGELOG.md) - [Commits](https://github.com/aws-actions/amazon-ecr-login/compare/v1...v2) --- updated-dependencies: - dependency-name: aws-actions/amazon-ecr-login dependency-type: direct:production update-type: version-update:semver-major ... Signed-off-by: dependabot[bot] --- .github/workflows/aws.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/aws.yml b/.github/workflows/aws.yml index e769d364..750955d9 100644 --- a/.github/workflows/aws.yml +++ b/.github/workflows/aws.yml @@ -62,7 +62,7 @@ jobs: - name: Login to Amazon ECR id: login-ecr - uses: aws-actions/amazon-ecr-login@v1 + uses: aws-actions/amazon-ecr-login@v2 - name: Build, tag, and push image to Amazon ECR id: build-image From 862c320df821cf0bb9f03464a36559a72a149fba Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Mon, 25 Dec 2023 17:00:44 +0000 Subject: [PATCH 202/587] Bump actions/setup-python from 3 to 5 Bumps [actions/setup-python](https://github.com/actions/setup-python) from 3 to 5. - [Release notes](https://github.com/actions/setup-python/releases) - [Commits](https://github.com/actions/setup-python/compare/v3...v5) --- updated-dependencies: - dependency-name: actions/setup-python dependency-type: direct:production update-type: version-update:semver-major ... Signed-off-by: dependabot[bot] --- .github/workflows/python-app.yml | 2 +- .github/workflows/python-package-conda.yml | 2 +- .github/workflows/python-package.yml | 2 +- 3 files changed, 3 insertions(+), 3 deletions(-) diff --git a/.github/workflows/python-app.yml b/.github/workflows/python-app.yml index 7f453c08..e4262374 100644 --- a/.github/workflows/python-app.yml +++ b/.github/workflows/python-app.yml @@ -20,7 +20,7 @@ jobs: steps: - uses: actions/checkout@v3 - name: Set up Python 3.10 - uses: actions/setup-python@v3 + uses: actions/setup-python@v5 with: python-version: "3.10" - name: Install dependencies diff --git a/.github/workflows/python-package-conda.yml b/.github/workflows/python-package-conda.yml index 384f9b72..20c2b2de 100644 --- a/.github/workflows/python-package-conda.yml +++ b/.github/workflows/python-package-conda.yml @@ -11,7 +11,7 @@ jobs: steps: - uses: actions/checkout@v3 - name: Set up Python 3.10 - uses: actions/setup-python@v3 + uses: actions/setup-python@v5 with: python-version: '3.10' - name: Add conda to system path diff --git a/.github/workflows/python-package.yml b/.github/workflows/python-package.yml index 14a4e65b..cf809820 100644 --- a/.github/workflows/python-package.yml +++ b/.github/workflows/python-package.yml @@ -21,7 +21,7 @@ jobs: steps: - uses: actions/checkout@v3 - name: Set up Python ${{ matrix.python-version }} - uses: actions/setup-python@v3 + uses: actions/setup-python@v5 with: python-version: ${{ matrix.python-version }} - name: Install dependencies From 1a924acef162588a4f4dc61b223c64bfe68726c2 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Mon, 25 Dec 2023 17:00:49 +0000 Subject: [PATCH 203/587] Bump github/codeql-action from 2 to 3 Bumps [github/codeql-action](https://github.com/github/codeql-action) from 2 to 3. - [Release notes](https://github.com/github/codeql-action/releases) - [Changelog](https://github.com/github/codeql-action/blob/main/CHANGELOG.md) - [Commits](https://github.com/github/codeql-action/compare/v2...v3) --- updated-dependencies: - dependency-name: github/codeql-action dependency-type: direct:production update-type: version-update:semver-major ... Signed-off-by: dependabot[bot] --- .github/workflows/bearer.yml | 2 +- .github/workflows/codacy.yml | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/.github/workflows/bearer.yml b/.github/workflows/bearer.yml index 01070f77..1b81311d 100644 --- a/.github/workflows/bearer.yml +++ b/.github/workflows/bearer.yml @@ -38,6 +38,6 @@ jobs: exit-code: 0 # Upload SARIF file generated in previous step - name: Upload SARIF file - uses: github/codeql-action/upload-sarif@v2 + uses: github/codeql-action/upload-sarif@v3 with: sarif_file: results.sarif diff --git a/.github/workflows/codacy.yml b/.github/workflows/codacy.yml index 1a8c4e00..c6d5ce9f 100644 --- a/.github/workflows/codacy.yml +++ b/.github/workflows/codacy.yml @@ -56,6 +56,6 @@ jobs: # Upload the SARIF file generated in the previous step - name: Upload SARIF results file - uses: github/codeql-action/upload-sarif@v2 + uses: github/codeql-action/upload-sarif@v3 with: sarif_file: results.sarif From cc56f5693ea237bc68fd4652efbe9005f2a8219e Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Mon, 25 Dec 2023 17:00:52 +0000 Subject: [PATCH 204/587] Bump codacy/codacy-analysis-cli-action from 1.1.0 to 4.3.0 Bumps [codacy/codacy-analysis-cli-action](https://github.com/codacy/codacy-analysis-cli-action) from 1.1.0 to 4.3.0. - [Release notes](https://github.com/codacy/codacy-analysis-cli-action/releases) - [Commits](https://github.com/codacy/codacy-analysis-cli-action/compare/d840f886c4bd4edc059706d09c6a1586111c540b...5cc54a75f9ad88159bb54046196d920e40e367a5) --- updated-dependencies: - dependency-name: codacy/codacy-analysis-cli-action dependency-type: direct:production update-type: version-update:semver-major ... Signed-off-by: dependabot[bot] --- .github/workflows/codacy.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/codacy.yml b/.github/workflows/codacy.yml index 1a8c4e00..6903ab4d 100644 --- a/.github/workflows/codacy.yml +++ b/.github/workflows/codacy.yml @@ -40,7 +40,7 @@ jobs: # Execute Codacy Analysis CLI and generate a SARIF output with the security issues identified during the analysis - name: Run Codacy Analysis CLI - uses: codacy/codacy-analysis-cli-action@d840f886c4bd4edc059706d09c6a1586111c540b + uses: codacy/codacy-analysis-cli-action@5cc54a75f9ad88159bb54046196d920e40e367a5 with: # Check https://github.com/codacy/codacy-analysis-cli#project-token to get your project token from your Codacy repository # You can also omit the token and run the tools that support default configurations From d557cb8ec03b1428dddf6ff6ff169ecc3d788204 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Mon, 25 Dec 2023 17:00:55 +0000 Subject: [PATCH 205/587] Bump hashicorp/setup-terraform from 1 to 3 Bumps [hashicorp/setup-terraform](https://github.com/hashicorp/setup-terraform) from 1 to 3. - [Release notes](https://github.com/hashicorp/setup-terraform/releases) - [Changelog](https://github.com/hashicorp/setup-terraform/blob/main/CHANGELOG.md) - [Commits](https://github.com/hashicorp/setup-terraform/compare/v1...v3) --- updated-dependencies: - dependency-name: hashicorp/setup-terraform dependency-type: direct:production update-type: version-update:semver-major ... Signed-off-by: dependabot[bot] --- .github/workflows/terraform.yml | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/.github/workflows/terraform.yml b/.github/workflows/terraform.yml index 76a1fbf1..73aabe31 100644 --- a/.github/workflows/terraform.yml +++ b/.github/workflows/terraform.yml @@ -38,7 +38,7 @@ # 3. Reference the GitHub secret in step using the `hashicorp/setup-terraform` GitHub Action. # Example: # - name: Setup Terraform -# uses: hashicorp/setup-terraform@v1 +# uses: hashicorp/setup-terraform@v3 # with: # cli_config_credentials_token: ${{ secrets.TF_API_TOKEN }} @@ -70,7 +70,7 @@ jobs: # Install the latest version of Terraform CLI and configure the Terraform CLI configuration file with a Terraform Cloud user API token - name: Setup Terraform - uses: hashicorp/setup-terraform@v1 + uses: hashicorp/setup-terraform@v3 with: cli_config_credentials_token: ${{ secrets.TF_API_TOKEN }} From 0e08a62ccbd3a05cd1498fb9c5ba6b97ce2b7e80 Mon Sep 17 00:00:00 2001 From: Kye Date: Mon, 25 Dec 2023 14:17:00 -0500 Subject: [PATCH 206/587] [FEAT][DenseBlock] [DualPathBlock] [FeedbackBlock] [HighwayLayer] [MultiScaleBlock] [RecursiveBlock] [SkipConnection] --- pyproject.toml | 2 +- zeta/nn/modules/__init__.py | 13 ++++++++- zeta/nn/modules/dense_connect.py | 28 +++++++++++++++++++ zeta/nn/modules/dual_path_block.py | 27 ++++++++++++++++++ zeta/nn/modules/feedback_block.py | 31 +++++++++++++++++++++ zeta/nn/modules/highway_layer.py | 30 ++++++++++++++++++++ zeta/nn/modules/multi_scale_block.py | 28 +++++++++++++++++++ zeta/nn/modules/recursive_block.py | 32 +++++++++++++++++++++ zeta/nn/modules/skip_connect.py | 20 ++++++++++++++ zeta/nn/modules/test_dense_connect.py | 40 +++++++++++++++++++++++++++ 10 files changed, 249 insertions(+), 2 deletions(-) create mode 100644 zeta/nn/modules/dense_connect.py create mode 100644 zeta/nn/modules/dual_path_block.py create mode 100644 zeta/nn/modules/feedback_block.py create mode 100644 zeta/nn/modules/highway_layer.py create mode 100644 zeta/nn/modules/multi_scale_block.py create mode 100644 zeta/nn/modules/recursive_block.py create mode 100644 zeta/nn/modules/skip_connect.py create mode 100644 zeta/nn/modules/test_dense_connect.py diff --git a/pyproject.toml b/pyproject.toml index cd888710..c6493559 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,6 +1,6 @@ [tool.poetry] name = "zetascale" -version = "1.2.5" +version = "1.2.6" description = "Transformers at zeta scales" authors = ["Zeta Team "] license = "MIT" diff --git a/zeta/nn/modules/__init__.py b/zeta/nn/modules/__init__.py index 3f33195e..e6dad4b9 100644 --- a/zeta/nn/modules/__init__.py +++ b/zeta/nn/modules/__init__.py @@ -47,6 +47,12 @@ from zeta.nn.modules.yolo import yolo from zeta.nn.modules.swiglu import SwiGLU, SwiGLUStacked from zeta.nn.modules.img_patch_embed import ImgPatchEmbed +from zeta.nn.modules.dense_connect import DenseBlock +from zeta.nn.modules.highway_layer import HighwayLayer +from zeta.nn.modules.multi_scale_block import MultiScaleBlock +from zeta.nn.modules.feedback_block import FeedbackBlock +from zeta.nn.modules.dual_path_block import DualPathBlock +from zeta.nn.modules.recursive_block import RecursiveBlock # from zeta.nn.modules.img_reshape import image_reshape # from zeta.nn.modules.flatten_features import flatten_features @@ -60,7 +66,6 @@ # from zeta.nn.modules.transformations import image_transform # from zeta.nn.modules.squeeze_excitation import SqueezeExcitation # from zeta.nn.modules.clex import Clex - __all__ = [ "CNNNew", "CombinedLinear", @@ -113,4 +118,10 @@ "SwiGLU", "SwiGLUStacked", "ImgPatchEmbed", + "DenseBlock", + "HighwayLayer", + "MultiScaleBlock", + "FeedbackBlock", + "DualPathBlock", + "RecursiveBlock", ] diff --git a/zeta/nn/modules/dense_connect.py b/zeta/nn/modules/dense_connect.py new file mode 100644 index 00000000..ce1c2923 --- /dev/null +++ b/zeta/nn/modules/dense_connect.py @@ -0,0 +1,28 @@ +import torch +from torch import nn + + +class DenseBlock(nn.Module): + def __init__(self, submodule, *args, **kwargs): + """ + Initializes a DenseBlock module. + + Args: + submodule (nn.Module): The submodule to be applied in the forward pass. + *args: Variable length argument list. + **kwargs: Arbitrary keyword arguments. + """ + super().__init__() + self.submodule = submodule + + def forward(self, x: torch.Tensor) -> torch.Tensor: + """ + Forward pass of the DenseBlock module. + + Args: + x (torch.Tensor): The input tensor. + + Returns: + torch.Tensor: The output tensor after applying the DenseBlock operation. + """ + return torch.cat([x, self.submodule(x)], dim=1) diff --git a/zeta/nn/modules/dual_path_block.py b/zeta/nn/modules/dual_path_block.py new file mode 100644 index 00000000..1d9241c9 --- /dev/null +++ b/zeta/nn/modules/dual_path_block.py @@ -0,0 +1,27 @@ +from torch import nn + + +class DualPathBlock(nn.Module): + def __init__(self, submodule1, submodule2): + """ + DualPathBlock is a module that combines the output of two submodules by element-wise addition. + + Args: + submodule1 (nn.Module): The first submodule. + submodule2 (nn.Module): The second submodule. + """ + super().__init__() + self.submodule1 = submodule1 + self.submodule2 = submodule2 + + def forward(self, x): + """ + Forward pass of the DualPathBlock. + + Args: + x (torch.Tensor): The input tensor. + + Returns: + torch.Tensor: The output tensor obtained by adding the outputs of submodule1 and submodule2. + """ + return self.submodule1(x) + self.submodule2(x) diff --git a/zeta/nn/modules/feedback_block.py b/zeta/nn/modules/feedback_block.py new file mode 100644 index 00000000..82fa4dd0 --- /dev/null +++ b/zeta/nn/modules/feedback_block.py @@ -0,0 +1,31 @@ +import torch +from torch import nn + + +class FeedbackBlock(nn.Module): + def __init__(self, submodule): + """ + Initializes a FeedbackBlock module. + + Args: + submodule (nn.Module): The submodule to be used within the FeedbackBlock. + """ + super().__init__() + self.submodule = submodule + + def forward(self, x: torch.Tensor, feedback, *args, **kwargs): + """ + Performs a forward pass through the FeedbackBlock. + + Args: + x (torch.Tensor): The input tensor. + feedback: The feedback tensor. + *args: Additional positional arguments to be passed to the submodule's forward method. + **kwargs: Additional keyword arguments to be passed to the submodule's forward method. + + Returns: + torch.Tensor: The output tensor after passing through the FeedbackBlock. + """ + if feedback is not None: + x = x + feedback + return self.submodule(x, *args, **kwargs) diff --git a/zeta/nn/modules/highway_layer.py b/zeta/nn/modules/highway_layer.py new file mode 100644 index 00000000..3802f3e2 --- /dev/null +++ b/zeta/nn/modules/highway_layer.py @@ -0,0 +1,30 @@ +import torch +from torch import nn +import torch.nn.functional as F + + +class HighwayLayer(nn.Module): + def __init__(self, dim): + """ + Initializes a HighwayLayer instance. + + Args: + dim (int): The input and output dimension of the layer. + """ + super().__init__() + self.normal_layer = nn.Linear(dim, dim) + self.gate = nn.Linear(dim, dim) + + def forward(self, x: torch.Tensor) -> torch.Tensor: + """ + Performs a forward pass through the HighwayLayer. + + Args: + x (torch.Tensor): The input tensor. + + Returns: + torch.Tensor: The output tensor. + """ + normal_result = F.relu(self.normal_layer(x)) + gate = torch.sigmoid(self.gate(x)) + return gate * normal_result + (1 - gate) * x diff --git a/zeta/nn/modules/multi_scale_block.py b/zeta/nn/modules/multi_scale_block.py new file mode 100644 index 00000000..fc686e2a --- /dev/null +++ b/zeta/nn/modules/multi_scale_block.py @@ -0,0 +1,28 @@ +import torch +from torch import nn +import torch.nn.functional as F + + +class MultiScaleBlock(nn.Module): + """ + A module that applies a given submodule to the input tensor at multiple scales. + + Args: + module (nn.Module): The submodule to apply. + + Returns: + torch.Tensor: The output tensor after applying the submodule at multiple scales. + """ + + def __init__(self, module): + super().__init__() + self.submodule = module + + def forward(self, x: torch.Tensor, *args, **kwargs): + x1 = F.interpolate(x, scale_factor=0.5, *args, **kwargs) + x2 = F.interpolate(x, scale_factor=2.0, *args, **kwargs) + return ( + self.submodule(x) + + F.interpolate(self.submodule(x1), size=x.shape[2:]) + + F.interpolate(self.submodule(x2), size=x.shape[2:]) + ) diff --git a/zeta/nn/modules/recursive_block.py b/zeta/nn/modules/recursive_block.py new file mode 100644 index 00000000..f1ab54de --- /dev/null +++ b/zeta/nn/modules/recursive_block.py @@ -0,0 +1,32 @@ +import torch +from torch import nn + + +class RecursiveBlock(nn.Module): + def __init__(self, modules, iters, *args, **kwargs): + """ + Initializes a RecursiveBlock module. + + Args: + modules (nn.Module): The module to be applied recursively. + iters (int): The number of iterations to apply the module. + *args: Variable length argument list. + **kwargs: Arbitrary keyword arguments. + """ + super().__init__() + self.modules = modules + self.iters = iters + + def forward(self, x: torch.Tensor): + """ + Forward pass of the RecursiveBlock module. + + Args: + x (torch.Tensor): The input tensor. + + Returns: + torch.Tensor: The output tensor after applying the module recursively. + """ + for _ in range(self.iters): + x = self.modules(x) + return x diff --git a/zeta/nn/modules/skip_connect.py b/zeta/nn/modules/skip_connect.py new file mode 100644 index 00000000..21d4c50b --- /dev/null +++ b/zeta/nn/modules/skip_connect.py @@ -0,0 +1,20 @@ +import torch +from torch import nn + + +class SkipConnection(nn.Module): + def __init__(self, submodule): + super().__init__() + self.submodule = submodule + + def forward(self, x: torch.Tensor) -> torch.Tensor: + """ + Forward pass of the SkipConnection module. + + Args: + x (torch.Tensor): Input tensor. + + Returns: + torch.Tensor: Output tensor after adding the input tensor with the submodule output. + """ + return x + self.submodule(x) diff --git a/zeta/nn/modules/test_dense_connect.py b/zeta/nn/modules/test_dense_connect.py new file mode 100644 index 00000000..0cf6d5d8 --- /dev/null +++ b/zeta/nn/modules/test_dense_connect.py @@ -0,0 +1,40 @@ +import torch +import torch.nn as nn +import unittest + +from your_module import DenseBlock + + +class DenseBlockTestCase(unittest.TestCase): + def setUp(self): + self.submodule = nn.Linear(10, 5) + self.dense_block = DenseBlock(self.submodule) + + def test_forward(self): + x = torch.randn(32, 10) + output = self.dense_block(x) + + self.assertEqual(output.shape, (32, 15)) # Check output shape + self.assertTrue( + torch.allclose(output[:, :10], x) + ) # Check if input is preserved + self.assertTrue( + torch.allclose(output[:, 10:], self.submodule(x)) + ) # Check submodule output + + def test_initialization(self): + self.assertEqual( + self.dense_block.submodule, self.submodule + ) # Check submodule assignment + + def test_docstrings(self): + self.assertIsNotNone( + DenseBlock.__init__.__doc__ + ) # Check if __init__ has a docstring + self.assertIsNotNone( + DenseBlock.forward.__doc__ + ) # Check if forward has a docstring + + +if __name__ == "__main__": + unittest.main() From ecbe1cf35306a6ac2aa91bc2c944d2140c0ac4b9 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Mon, 25 Dec 2023 21:30:46 +0000 Subject: [PATCH 207/587] Bump torch from 2.1.1 to 2.1.2 Bumps [torch](https://github.com/pytorch/pytorch) from 2.1.1 to 2.1.2. - [Release notes](https://github.com/pytorch/pytorch/releases) - [Changelog](https://github.com/pytorch/pytorch/blob/main/RELEASE.md) - [Commits](https://github.com/pytorch/pytorch/compare/v2.1.1...v2.1.2) --- updated-dependencies: - dependency-name: torch dependency-type: direct:production update-type: version-update:semver-patch ... Signed-off-by: dependabot[bot] --- requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements.txt b/requirements.txt index 1b2cd538..08d8ac2e 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,4 +1,4 @@ -torch==2.1.1 +torch==2.1.2 fairscale==0.4.0 timm==0.6.13 einops==0.7.0 From a71ba60c1be32be4ebad1f279c5b12710ed79ea5 Mon Sep 17 00:00:00 2001 From: Kye Date: Tue, 26 Dec 2023 16:17:32 -0500 Subject: [PATCH 208/587] [TESTS][MishActivation] [LinearActivation] [LaplaceActivation] [ReLUSquaredActivation] --- tests/nn/modules/test_activations.py | 82 +++++++++ zeta/nn/modules/__init__.py | 25 +++ zeta/nn/modules/_activations.py | 258 +++++++++++++++++++++++++++ 3 files changed, 365 insertions(+) create mode 100644 tests/nn/modules/test_activations.py create mode 100644 zeta/nn/modules/_activations.py diff --git a/tests/nn/modules/test_activations.py b/tests/nn/modules/test_activations.py new file mode 100644 index 00000000..40389e50 --- /dev/null +++ b/tests/nn/modules/test_activations.py @@ -0,0 +1,82 @@ +import torch +from zeta.nn.modules._activations import ( + MishActivation, + LinearActivation, + LaplaceActivation, + ReLUSquaredActivation, +) + + +# Tests for MishActivation +def test_mish_activation_initialization(): + activation = MishActivation() + assert isinstance(activation, MishActivation) + + +def test_mish_activation_forward_positive(): + activation = MishActivation() + x = torch.tensor([1.0, 2.0, 3.0]) + output = activation(x) + # Expected values are approximations + assert torch.allclose( + output, torch.tensor([0.8651, 1.7924, 2.7306]), atol=1e-4 + ) + + +def test_mish_activation_forward_negative(): + activation = MishActivation() + x = torch.tensor([-1.0, -2.0, -3.0]) + output = activation(x) + # Expected values are approximations + assert torch.allclose( + output, torch.tensor([-0.3034, -0.3297, -0.2953]), atol=1e-4 + ) + + +# Tests for LinearActivation +def test_linear_activation_initialization(): + activation = LinearActivation() + assert isinstance(activation, LinearActivation) + + +def test_linear_activation_forward(): + activation = LinearActivation() + x = torch.tensor([1.0, 2.0, 3.0]) + output = activation(x) + assert torch.equal(output, x) + + +# Tests for LaplaceActivation +def test_laplace_activation_initialization(): + activation = LaplaceActivation() + assert isinstance(activation, LaplaceActivation) + + +def test_laplace_activation_forward(): + activation = LaplaceActivation() + x = torch.tensor([1.0, 2.0, 3.0]) + output = activation(x) + # Expected values are approximations + assert torch.allclose( + output, torch.tensor([0.6827, 0.8413, 0.9332]), atol=1e-4 + ) + + +# Tests for ReLUSquaredActivation +def test_relusquared_activation_initialization(): + activation = ReLUSquaredActivation() + assert isinstance(activation, ReLUSquaredActivation) + + +def test_relusquared_activation_forward_positive(): + activation = ReLUSquaredActivation() + x = torch.tensor([1.0, 2.0, 3.0]) + output = activation(x) + assert torch.allclose(output, torch.tensor([1.0, 4.0, 9.0])) + + +def test_relusquared_activation_forward_negative(): + activation = ReLUSquaredActivation() + x = torch.tensor([-1.0, -2.0, -3.0]) + output = activation(x) + assert torch.allclose(output, torch.tensor([0.0, 0.0, 0.0])) diff --git a/zeta/nn/modules/__init__.py b/zeta/nn/modules/__init__.py index e6dad4b9..283d5643 100644 --- a/zeta/nn/modules/__init__.py +++ b/zeta/nn/modules/__init__.py @@ -53,6 +53,19 @@ from zeta.nn.modules.feedback_block import FeedbackBlock from zeta.nn.modules.dual_path_block import DualPathBlock from zeta.nn.modules.recursive_block import RecursiveBlock +from zeta.nn.modules._activations import ( + PytorchGELUTanh, + NewGELUActivation, + GELUActivation, + FastGELUActivation, + QuickGELUActivation, + ClippedGELUActivation, + AccurateGELUActivation, + MishActivation, + LinearActivation, + LaplaceActivation, + ReLUSquaredActivation, +) # from zeta.nn.modules.img_reshape import image_reshape # from zeta.nn.modules.flatten_features import flatten_features @@ -66,6 +79,7 @@ # from zeta.nn.modules.transformations import image_transform # from zeta.nn.modules.squeeze_excitation import SqueezeExcitation # from zeta.nn.modules.clex import Clex + __all__ = [ "CNNNew", "CombinedLinear", @@ -124,4 +138,15 @@ "FeedbackBlock", "DualPathBlock", "RecursiveBlock", + "PytorchGELUTanh", + "NewGELUActivation", + "GELUActivation", + "FastGELUActivation", + "QuickGELUActivation", + "ClippedGELUActivation", + "AccurateGELUActivation", + "MishActivation", + "LinearActivation", + "LaplaceActivation", + "ReLUSquaredActivation", ] diff --git a/zeta/nn/modules/_activations.py b/zeta/nn/modules/_activations.py new file mode 100644 index 00000000..1aed53cc --- /dev/null +++ b/zeta/nn/modules/_activations.py @@ -0,0 +1,258 @@ +import math +from collections import OrderedDict + +import torch +from packaging import version +from torch import Tensor, nn +import logging + + +logger = logging.get_logger(__name__) + + +class PytorchGELUTanh(nn.Module): + """ + A fast C implementation of the tanh approximation of the GeLU activation function. See + https://arxiv.org/abs/1606.08415. + + This implementation is equivalent to NewGELU and FastGELU but much faster. However, it is not an exact numerical + match due to rounding errors. + """ + + def __init__(self): + super().__init__() + if version.parse(torch.__version__) < version.parse("1.12.0"): + raise ImportError( + f"You are using torch=={torch.__version__}, but torch>=1.12.0" + " is required to use PytorchGELUTanh. Please upgrade torch." + ) + + def forward(self, input: Tensor) -> Tensor: + return nn.functional.gelu(input, approximate="tanh") + + +class NewGELUActivation(nn.Module): + """ + Implementation of the GELU activation function currently in Google BERT repo (identical to OpenAI GPT). Also see + the Gaussian Error Linear Units paper: https://arxiv.org/abs/1606.08415 + """ + + def forward(self, input: Tensor) -> Tensor: + return ( + 0.5 + * input + * ( + 1.0 + + torch.tanh( + math.sqrt(2.0 / math.pi) + * (input + 0.044715 * torch.pow(input, 3.0)) + ) + ) + ) + + +class GELUActivation(nn.Module): + """ + Original Implementation of the GELU activation function in Google BERT repo when initially created. For + information: OpenAI GPT's GELU is slightly different (and gives slightly different results): 0.5 * x * (1 + + torch.tanh(math.sqrt(2 / math.pi) * (x + 0.044715 * torch.pow(x, 3)))) This is now written in C in nn.functional + Also see the Gaussian Error Linear Units paper: https://arxiv.org/abs/1606.08415 + """ + + def __init__(self, use_gelu_python: bool = False): + super().__init__() + if use_gelu_python: + self.act = self._gelu_python + else: + self.act = nn.functional.gelu + + def _gelu_python(self, input: Tensor) -> Tensor: + return input * 0.5 * (1.0 + torch.erf(input / math.sqrt(2.0))) + + def forward(self, input: Tensor) -> Tensor: + return self.act(input) + + +class FastGELUActivation(nn.Module): + """ + Applies GELU approximation that is slower than QuickGELU but more accurate. See: https://github.com/hendrycks/GELUs + """ + + def forward(self, input: Tensor) -> Tensor: + return ( + 0.5 + * input + * ( + 1.0 + + torch.tanh( + input * 0.7978845608 * (1.0 + 0.044715 * input * input) + ) + ) + ) + + +class QuickGELUActivation(nn.Module): + """ + Applies GELU approximation that is fast but somewhat inaccurate. See: https://github.com/hendrycks/GELUs + """ + + def forward(self, input: Tensor) -> Tensor: + return input * torch.sigmoid(1.702 * input) + + +class ClippedGELUActivation(nn.Module): + """ + Clip the range of possible GeLU outputs between [min, max]. This is especially useful for quantization purpose, as + it allows mapping negatives values in the GeLU spectrum. For more information on this trick, please refer to + https://arxiv.org/abs/2004.09602. + + Gaussian Error Linear Unit. Original Implementation of the gelu activation function in Google Bert repo when + initially created. + + For information: OpenAI GPT's gelu is slightly different (and gives slightly different results): 0.5 * x * (1 + + torch.tanh(math.sqrt(2 / math.pi) * (x + 0.044715 * torch.pow(x, 3)))). See https://arxiv.org/abs/1606.08415 + """ + + def __init__(self, min: float, max: float): + if min > max: + raise ValueError( + f"min should be < max (got min: {min}, max: {max})" + ) + + super().__init__() + self.min = min + self.max = max + + def forward(self, x: Tensor) -> Tensor: + return torch.clip(gelu(x), self.min, self.max) + + +class AccurateGELUActivation(nn.Module): + """ + Applies GELU approximation that is faster than default and more accurate than QuickGELU. See: + https://github.com/hendrycks/GELUs + + Implemented along with MEGA (Moving Average Equipped Gated Attention) + """ + + def __init__(self): + super().__init__() + self.precomputed_constant = math.sqrt(2 / math.pi) + + def forward(self, input: Tensor) -> Tensor: + return ( + 0.5 + * input + * ( + 1 + + torch.tanh( + self.precomputed_constant + * (input + 0.044715 * torch.pow(input, 3)) + ) + ) + ) + + +class MishActivation(nn.Module): + """ + See Mish: A Self-Regularized Non-Monotonic Activation Function (Misra., https://arxiv.org/abs/1908.08681). Also + visit the official repository for the paper: https://github.com/digantamisra98/Mish + """ + + def __init__(self): + super().__init__() + if version.parse(torch.__version__) < version.parse("1.9.0"): + self.act = self._mish_python + else: + self.act = nn.functional.mish + + def _mish_python(self, input: Tensor) -> Tensor: + return input * torch.tanh(nn.functional.softplus(input)) + + def forward(self, input: Tensor) -> Tensor: + return self.act(input) + + +class LinearActivation(nn.Module): + """ + Applies the linear activation function, i.e. forwarding input directly to output. + """ + + def forward(self, input: Tensor) -> Tensor: + return input + + +class LaplaceActivation(nn.Module): + """ + Applies elementwise activation based on Laplace function, introduced in MEGA as an attention activation. See + https://arxiv.org/abs/2209.10655 + + Inspired by squared relu, but with bounded range and gradient for better stability + """ + + def forward(self, input, mu=0.707107, sigma=0.282095): + input = (input - mu).div(sigma * math.sqrt(2.0)) + return 0.5 * (1.0 + torch.erf(input)) + + +class ReLUSquaredActivation(nn.Module): + """ + Applies the relu^2 activation introduced in https://arxiv.org/abs/2109.08668v2 + """ + + def forward(self, input): + relu_applied = nn.functional.relu(input) + squared = torch.square(relu_applied) + return squared + + +class ClassInstantier(OrderedDict): + def __getitem__(self, key): + content = super().__getitem__(key) + cls, kwargs = content if isinstance(content, tuple) else (content, {}) + return cls(**kwargs) + + +ACT2CLS = { + "gelu": GELUActivation, + "gelu_10": (ClippedGELUActivation, {"min": -10, "max": 10}), + "gelu_fast": FastGELUActivation, + "gelu_new": NewGELUActivation, + "gelu_python": (GELUActivation, {"use_gelu_python": True}), + "gelu_pytorch_tanh": PytorchGELUTanh, + "gelu_accurate": AccurateGELUActivation, + "laplace": LaplaceActivation, + "leaky_relu": nn.LeakyReLU, + "linear": LinearActivation, + "mish": MishActivation, + "quick_gelu": QuickGELUActivation, + "relu": nn.ReLU, + "relu2": ReLUSquaredActivation, + "relu6": nn.ReLU6, + "sigmoid": nn.Sigmoid, + "silu": nn.SiLU, + "swish": nn.SiLU, + "tanh": nn.Tanh, +} +ACT2FN = ClassInstantier(ACT2CLS) + + +def get_activation(activation_string): + if activation_string in ACT2FN: + return ACT2FN[activation_string] + else: + raise KeyError( + f"function {activation_string} not found in ACT2FN mapping" + f" {list(ACT2FN.keys())}" + ) + + +# For backwards compatibility with: from activations import gelu_python +gelu_python = get_activation("gelu_python") +gelu_new = get_activation("gelu_new") +gelu = get_activation("gelu") +gelu_fast = get_activation("gelu_fast") +quick_gelu = get_activation("quick_gelu") +silu = get_activation("silu") +mish = get_activation("mish") +linear_act = get_activation("linear") From 10aa88a82adf1654421480a6ac3ac354c4c649a8 Mon Sep 17 00:00:00 2001 From: Kye Date: Tue, 26 Dec 2023 19:00:44 -0500 Subject: [PATCH 209/587] [TESTS][DOCS] from zeta.nn.modules.dense_connect import DenseBlock from zeta.nn.modules.highway_layer import HighwayLayer from zeta.nn.modules.multi_scale_block import MultiScaleBlock from zeta.nn.modules.feedback_block import FeedbackBlock from zeta.nn.modules.dual_path_block import DualPathBlock from zeta.nn.modules.recursive_block import RecursiveBlock from zeta.nn.modules._activations import ( PytorchGELUTanh, NewGELUActivation, GELUActivation, FastGELUActivation, QuickGELUActivation, ClippedGELUActivation, AccurateGELUActivation, MishActivation, LinearActivation, LaplaceActivation, ReLUSquaredActivation, )] --- .gitignore | 2 + .../zeta/nn/modules/accurategeluactivation.md | 103 +++++++++ docs/zeta/nn/modules/clippedgeluactivation.md | 79 +++++++ 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create mode 100644 tests/nn/modules/test_linearactivation.py create mode 100644 tests/nn/modules/test_mishactivation.py create mode 100644 tests/nn/modules/test_multiscaleblock.py create mode 100644 tests/nn/modules/test_newgeluactivation.py create mode 100644 tests/nn/modules/test_pytorchgelutanh.py create mode 100644 tests/nn/modules/test_quickgeluactivation.py create mode 100644 tests/nn/modules/test_recursiveblock.py create mode 100644 tests/nn/modules/test_relusquaredactivation.py rename tests/quant/{qmoe.py => test_qmoe.py} (100%) diff --git a/.gitignore b/.gitignore index ceb18764..534770b3 100644 --- a/.gitignore +++ b/.gitignore @@ -16,6 +16,7 @@ build/ develop-eggs/ dist/ downloads/ +.errors.txt eggs/ .eggs/ lib/ @@ -24,6 +25,7 @@ parts/ sdist/ var/ wheels/ +errors.txt share/python-wheels/ *.egg-info/ .installed.cfg diff --git a/docs/zeta/nn/modules/accurategeluactivation.md b/docs/zeta/nn/modules/accurategeluactivation.md new file mode 100644 index 00000000..eca60e30 --- /dev/null +++ b/docs/zeta/nn/modules/accurategeluactivation.md @@ -0,0 +1,103 @@ +# AccurateGELUActivation + +## Overview +The AccurateGELUActivation class is a part of the PyTorch library's nn.Module. This class allows us to apply the Gaussian Error Linear Unit (GELU) approximation that is faster than the default and more accurate than QuickGELU. This can be useful in situations where the default GELU is considered computationally expensive or its speed could be an issue. The implementation of this class comes as a support for MEGA, which stands for Moving Average Equipped Gated Attention, in neural networks. + +The class has been designed following the work on GELUs available at: [https://github.com/hendrycks/GELUs](https://github.com/hendrycks/GELUs) + +## Class Definition +Here is a look at the parameters and methods used in the `AccurateGELUActivation` class: + +```python +class AccurateGELUActivation(nn.Module): + """ + Applies GELU approximation that is faster than default and more accurate than QuickGELU. See: + https://github.com/hendrycks/GELUs + Implemented along with MEGA (Moving Average Equipped Gated Attention) + """ + + def __init__(self): + super().__init__() + self.precomputed_constant = math.sqrt(2 / math.pi) + + def forward(self, input: Tensor) -> Tensor: + return ( + 0.5 + * input + * ( + 1 + + torch.tanh( + self.precomputed_constant + * (input + 0.044715 * torch.pow(input, 3)) + ) + ) + ) +``` + +The class does not require any parameters during initialization. Here are the explanations for the various attributes and methods in the class: + +| Method/Attribute | Description | Argument | +| --- | --- | --- | +| `__init__` | This is the constructor method that gets called when an object is created from the class. | None | +| `forward` | This method is a PyTorch standard for forward propagation in a Module or a neural network layer. It accepts a tensor input and returns a tensor. | `input: Tensor` | + +## Class Usage +Now, let's look at some examples of how to use this class. + +### Example 1: Basic Usage +```python +import torch +from torch.nn import Module +import math +from torch import Tensor +from zeta import AccurateGELUActivation + +# Create an instance of the class +gelu_activation = AccurateGELUActivation() + +# Create a PyTorch tensor +input = torch.tensor([[-1.0, -0.1, 0.1, 1.0], [0.5, -0.2, -2.1, 3.2]], dtype=torch.float32) + +# Use the AccurateGELUActivation instance to activate the input +output = gelu_activation(input) + +print(output) +``` +This example demonstrates the functionalities of the AccurateGELUActivation module for a defined two-dimensional input tensor. + +### Example 2: Applying on Neural Network +The AccurateGELUActivation module can also be used as an activation layer in a PyTorch model. + +```python +import torch +from torch.nn import Module, Linear +import math +from torch import Tensor +from zeta.nn import AccurateGELUActivation + +class Net(Module): + def __init__(self): + super(Net, self).__init__() + self.fc1 = Linear(10, 5) + self.fc2 = Linear(5, 2) + self.activation = AccurateGELUActivation() + + def forward(self, x: Tensor) -> Tensor: + x = self.fc1(x) + x = self.activation(x) + x = self.fc2(x) + return x + +# Create a model from the neural network class +model = Net() + +input = torch.randn(3, 10) + +# Pass the input to the model +output = model(input) + +print(output) +``` +This example shows how the AccurateGELUActivation module can be integrated as a layer in a neural network model to perform activation on the intermediate outputs of the neural network model. + +**Note:** Please remember, understanding what activation functions like GELU can do, what benefits they can bring to your architecture, is crucial before applying it to your models. diff --git a/docs/zeta/nn/modules/clippedgeluactivation.md b/docs/zeta/nn/modules/clippedgeluactivation.md new file mode 100644 index 00000000..a7d68437 --- /dev/null +++ b/docs/zeta/nn/modules/clippedgeluactivation.md @@ -0,0 +1,79 @@ +# ClippedGELUActivation + + +The ClippedGELUActivation class is designed to clip the possible output range of Gaussian Error Linear Unit (GeLU) activation between a given minimum and maximum value. This is specifically useful for the quantization purpose, as it allows mapping negative values in the GeLU spectrum. To learn more about the underlying concept, you can refer to an academic paper titled [Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference](https://arxiv.org/pdf/1712.05877.pdf). + +The original implementation of the GeLU activation function was introduced in the Google BERT repository. Note that OpenAI GPT's GeLU is slightly different and gives slightly different results. + +## Class Definition + +The ClippedGELUActivation class inherits from the `nn.Module` in PyTorch. + +```python +class ClippedGELUActivation(nn.Module): + def __init__(self, min: float, max: float): + if min > max: + raise ValueError( + f"min should be < max (got min: {min}, max: {max})" + ) + + super().__init__() + self.min = min + self.max = max + + def forward(self, x: Tensor) -> Tensor: + return torch.clip(gelu(x), self.min, self.max) +``` + +## Class Arguments + +| Argument | Type | Description | +|:--------:|:-------:|:----------------------------------------------------------------------------:| +| min | float | The lower limit for the output of GeLU activation. It should be less than `max` | +| max | float | The upper limit for the output of GeLU activation. It should be greater than `min` | + +Note: If `min` is greater than `max`, a ValueError will be raised. + +## Forward Method Arguments + +| Argument | Type | Description | +|:--------:|:-------:|:----------------------------------------------------------------------------:| +| x | Tensor | Input tensor for the forward function of the module | + +## Class Example + +In the code below, we initialize the ClippedGELUActivation module with a min and max value and input a tensor `x`: + +```python +import torch +from torch import nn, Tensor +from torch.nn.functional import gelu +from zeta.nn import ClippedGELUActivation + +# Initialize the class +clipped_gelu = ClippedGELUActivation(min=-3.0, max=3.0) + +# Create a tensor +x = torch.randn(3,3) + +# Pass the tensor through the module +output = clipped_gelu(x) +``` + +In this instance, the output tensor would have each of its elements limited to be within the range of -3.0 to 3.0, inclusively. + +## Notes + +While using this class be cautious of the following: +- The class does not check if the `max` argument is less than the `min` argument. Providing a `max` which is less than `min` will raise a ValueError. +- The `forward` method does not check if all elements of the input Tensor `x` are numeric. Non-numeric input may result in unexpected behavior or errors. + +## References + +For additional information and further exploration about GeLU and its applications, please refer to the following resources: + +1. [Gaussian Error Linear Units (GELUs)](https://arxiv.org/abs/1606.08415) +2. [Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference](https://arxiv.org/abs/1712.05877) +3. [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://arxiv.org/abs/1810.04805) + +Note: In our documentation, we provided information about the CythonGELU and its methods. The details regarding the parameters, method details, and usage examples were provided to ensure the understanding of the class and methods. diff --git a/docs/zeta/nn/modules/denseblock.md b/docs/zeta/nn/modules/denseblock.md new file mode 100644 index 00000000..71398d8d --- /dev/null +++ b/docs/zeta/nn/modules/denseblock.md @@ -0,0 +1,132 @@ +# Class Name: DenseBlock + +The `DenseBlock` class is a type of PyTorch `nn.Module`. This allows for complicated neural network architectures to be defined with individual abstracted layers. The class gets its name from the dense connections made in the forward propagation, which involve concatenating the output of the `submodule` with the original input. + +For the following documentation, the DenseBlock class is used as an example of such constructions. + +While this class might seem simple, understanding how it works is fundamental to define, compile, and use your own custom PyTorch models. + +It has two main methods, the `__init__()` method and the `forward()` method. + +### Method: \_\_init__(self, submodule, *args, **kwargs) + +The `__init__()` method is the initializer method of the DenseBlock class. It is called when an object (an instance of the class) is created. + +This method sets an attribute of the DenseBlock object to be the `submodule` input, which is assumed to be some `nn.Module` instance. + +The method signature is: + + def __init__(self, submodule, *args, **kwargs) + +#### Arguments + +|Name|Type|Description| +|---|---|---| +|submodule|nn.Module|The module that will be applied in the forward pass.| +|args|Variable length argument list|Unused in this implementation, but allows for extra position arguments.| +|kwargs|Arbitrary keyword arguments|Unused in this implementation, but allows for extra keyword arguments.| + +The `submodule` argument should be an initialized instance of the `nn.Module` subclass you want to apply. + +The `args` and `kwargs` arguments are not currently used in DenseBlock. + +### Method: forward(self, x: torch.Tensor) -> torch.Tensor + +The `forward()` method is called during the forward propagation of the neural network. + +It applies the module operation to the input tensor `x` and concatenates the input tensor `x` with the output of the `submodule`. + +The method signature is: + + def forward(self, x: torch.Tensor) -> torch.Tensor + +#### Arguments + +|Name|Type|Description| +|---|---|---| +|x|torch.Tensor|The input tensor to the module.| + +Returns a tensor, which is the input tensor concatenated with the processed input tensor via the `submodule`. + +## Usage Examples + +Here are some examples showing how to use the DenseBlock class. These examples will include the necessary imports, data creation, and model instantiation following PyTorch conventions: + +### Example 1: Basic Usage with a Linear Layer + +In this example, the `DenseBlock` will include a Linear layer as submodule. + +```python +import torch +import torch.nn as nn +from torch.autograd import Variable +from zeta.nn import DenseBlock + +# Defining submodule +lin_layer = nn.Linear(5, 10) + +# Defining DenseBlock +dense_block = DenseBlock(lin_layer) + +# Creating a random tensor of shape [10, 5] +random_tensor = Variable(torch.randn(10, 5)) + +# Applying DenseBlock +output = dense_block(random_tensor) +``` + +In this example, an input tensor of shape [10,5] is given to a dense block with a linear layer. The input will have shape [10,5] and the output of the linear layer will have shape [10,10], resulting in the output of the dense block to have shape [10,15]. + +### Example 2: Using DenseBlock in a Multilayer Neural Network + +In this example, a 2-layer neural network using Dense Blocks is shown. The first layer is a Dense Block with a Linear module transforming with dimensions (10 to 5), and the second layer is a standard Linear layer transforming the output dimensions (15 to 1). +```python +import torch.nn.functional as F + +# Defining a custom model +class Net(nn.Module): + def __init__(self): + super(Net, self).__init__() + self.layer1 = DenseBlock(nn.Linear(10, 5)) + self.layer2 = nn.Linear(15, 1) + + def forward(self, x): + x = F.relu(self.layer1(x)) + x = self.layer2(x) + return x + +# Initializing the model +net = Net() + +# Creating a random tensor of shape [32, 10] +data = Variable(torch.randn(32, 10)) + +# Forward propagation +output = net(data) +``` + +In this second example, a data batch with `32` samples and input dimensionality of `10` is given to a `Net` neural network with dense connections in their first layer. The final output shape is [32, 1]. + +### Example 3: DenseBlock with Convolutional Layer + +Lastly, this example shows how to use DenseBlock inside a Convolutional Neural Network: +```python +import torch +import torch.nn as nn +from zeta.nn import DenseBlock + +cnn = nn.Sequential( + nn.Conv2d(1, 64, kernel_size=7, stride=2, padding=3), + nn.ReLU(inplace=True), + nn.MaxPool2d(kernel_size=3, stride=2, padding=1), + DenseBlock(nn.Conv2d(64, 64, kernel_size=3, stride=1, padding=1)), + nn.AdaptiveAvgPool2d((1, 1)), + nn.Flatten(), + nn.Linear(128, 10), +) + +x = torch.randn(1, 1, 224, 224) +output = cnn(x) +``` + +Here, a 2D convolutional layer is used as the submodule within the DenseBlock. The DenseBlock receives a tensor with shape [64, 224, 224] as input, applies the convolutional layer (keeping the same shape), and then concatenates the input and the output along the channel dimension, resulting in a tensor with shape [128, 224, 224]. diff --git a/docs/zeta/nn/modules/dualpathblock.md b/docs/zeta/nn/modules/dualpathblock.md new file mode 100644 index 00000000..ccf03972 --- /dev/null +++ b/docs/zeta/nn/modules/dualpathblock.md @@ -0,0 +1,82 @@ +# DualPathBlock + + +**Table of Contents** + +1. [Introduction](#introduction) +2. [Key Features](#features) +3. [Class Definition](#class-definition) +4. [Example Usage](#examples) +5. [Practical Tips](#tips) +6. [Reference and Other Resources](#resources) + +## Introduction +The `DualPathBlock` class is a PyTorch-based module or grammar that represents a basic computational unit in dual path networks. This class combines the output of two submodules by element-wise addition. The core idea behind this method is to efficiently use the information from both paths in a balanced way. + +## Key Features + +- **Efficient combination of data**: The `DualPathBlock` method combines data from two submodules in an effective way by using element-wise addition. + +- **Flexibility in submodule choice**: Users have the flexibility to choose the submodules, provided they are `torch.nn.Module` instances. + +- **Simplicity and readability of code**: Due to its modular design, the code is easy to understand, thereby making it easier for users to implement and modify. + +- **Easy integration with other `torch.nn.Module` instances**: The `DualPathBlock` can be easily integrated within other pipelines as a subnet. + +## Class Definition + +The class design for `DualPathBlock` is very straightforward. It is initialized with two submodules that are instances of `nn.Module`. Then, during the forward pass, the inputs are passed through each submodule and the result of these computations is then computed by element-wise addition. + +### Parameters: + +|Parameter|Type|Description| +|---|---|---| +|submodule1|nn.Module|First submodule through which input tensor `x` is passed.| +|submodule2|nn.Module|Second submodule through which input tensor `x` is passed.| + +### Methods: + +|Method|Parameters|Description| +|---|---|---| +|forward|x: torch.Tensor|Performs forward pass through the model. Calculates output tensor obtained by adding outputs of submodule1 and submodule2. Returns the computed tensor| + +### Input / Output Type: + +- **Input**: Receives a tensor of any shape. +- **Output**: Produces a tensor of the same shape as the inputs after the forward computation is done. + +## Example Usage + +```python +# Import the necessary libraries +import torch +import torch.nn as nn +from zeta.nn import DualPathBlock + +# Define two simple submodule +submodule1 = nn.Linear(20, 20) +submodule2 = nn.Linear(20, 20) + +# Create an instance of DualPathBlock +dual_path_block = DualPathBlock(submodule1, submodule2) + +# Define an input tensor +input_tensor = torch.randn(10, 20) + +# Perform forward operation +output = dual_path_block(input_tensor) + +# Print the output tensor +print(output) +``` +## Practical Tips + +- While DualPathBlock design allows for the use of any submodules, please make sure the outputs of both submodules can be summed up i.e., they are of the same shape. + +- DualPathBlock is particularly useful in constructing networks with parallel paths where the outputs are combined. + +## References and Other Resources +[Pytorch Documentation](https://pytorch.org/docs/stable/generated/torch.nn.Module.html) + +[Dual Path Networks](https://arxiv.org/abs/1707.01629) <-- If relevant + diff --git a/docs/zeta/nn/modules/fastgeluactivation.md b/docs/zeta/nn/modules/fastgeluactivation.md new file mode 100644 index 00000000..dbc364d1 --- /dev/null +++ b/docs/zeta/nn/modules/fastgeluactivation.md @@ -0,0 +1,97 @@ +# FastGELUActivation + +This is a comprehensive documentation for `FastGELUActivation`, a class of the SWARMS library. + +## Overview +FastGELUActivation is a class implemented in the SWARMS library that introduces an optimized approach to computing Gaussian Error Linear Units (GELUs). It's based on a faster approximation of the GELU activation function, which is generally more accurate than QuickGELU. + +GELU activation is frequently used in many machine learning applications, particularly deep learning models, to add non-linearity to the operations. Such activation functions help models represent a wider range of phenomena and thus yield more robust and accurate results. For reference on GELUs, please refer to [Hendrycks GELUs](https://github.com/hendrycks/GELUs). + +## Class Definition and Functionality +FastGELUActivation is a class in PyTorch's nn.Module that overrides the forward method to provide a new functionality. Below is the class definition of `FastGELUActivation`. + +```python +class FastGELUActivation(nn.Module): + """ + Applies GELU approximation that is slower than QuickGELU but more accurate. + """ + def forward(self, input: Tensor) -> Tensor: + return ( + 0.5 + * input + * ( + 1.0 + + torch.tanh( + input * 0.7978845608 * (1.0 + 0.044715 * input * input) + ) + ) + ) +``` + +## Parameters +The `FastGELUActivation` class uses only one parameter as input in its forward method. + +| Parameter | Type | Description | +| - | - | - | +| `input` | Tensor | The input tensor that the forward pass needs to compute over.| + +### Inputs +The input that `FastGELUActivation` takes is a PyTorch Tensor, which holds the values that the activation function computes. + +### Outputs +The forward method of `FastGELUActivation` returns a new tensor, which is the result of applying the FastGELU activation operation to the input tensor. + +## Usage and Workflow +Using `FastGELUActivation` involves creating an instance of the class and then using that instance to call the class's `forward` method with an appropriate input Tensor. + +### Example Usage +In this example, we'll create a simple tensor and apply the `FastGELUActivation` activation function to it. + +```python +import torch +from torch import nn, Tensor +from zeta import FastGELUActivation + +# Create an instance of FastGELUActivation +activation = FastGELUActivation() + +# Create a tensor +tensor = torch.randn((5,5), dtype=torch.float32) + +# Apply FastGELUActivation +result = activation.forward(tensor) + +print(result) +``` +### Working with Real World Data Example +Assuming we're building a neural network that uses the `FastGELUActivation` as its activation function in one of the layers: + +```python +import torch.nn as nn +from zeta import FastGELUActivation + +class NeuralNet(nn.Module): + def __init__(self): + super(NeuralNet, self).__init__() + self.layer1 = nn.Linear(in_features=784, out_features=512) + self.layer2 = nn.Linear(in_features=512, out_features=128) + self.layer3 = nn.Linear(in_features=128, out_features=10) + self.activation = FastGELUActivation() + + def forward(self, x): + x = self.layer1(x) + x = self.activation(x) + x = self.layer2(x) + x = self.activation(x) + x = self.layer3(x) + return x + +model = NeuralNet() +``` + +In this example, we have a simple feedforward neural network with two layers, and it uses `FastGELUActivation` for the intermediate layers. + +## Additional information & Tips +The `FastGELUActivation` is a faster approximation of the GELU activation operation, but not always the most accurate. Depending on your use case and performance requirements, you may want to use a more robust but slower activation function. + +Make sure to have a profound understanding of the dataset and context before deciding on the activation function. diff --git a/docs/zeta/nn/modules/feedbackblock.md b/docs/zeta/nn/modules/feedbackblock.md new file mode 100644 index 00000000..9ab9a69c --- /dev/null +++ b/docs/zeta/nn/modules/feedbackblock.md @@ -0,0 +1,99 @@ +# FeedbackBlock + +--- + +`FeedbackBlock` is a class that extends the `torch.nn.Module` class. As a crucial part of the neural network, this class perfectly illustrates the aspect of modularity that deep learning models can have. + +`FeedbackBlock` is a namespace that hosts operations and behaves to transformations in such a way that all of its submodules follow along. Its main role is to handle the feedback connections in neural networks while wrapping another module. The feedback connection is a very common architecture in deep learning where the output from one layer is used as additional input to the same layer in subsequent passes. + +## Class Definition: + +```python +class FeedbackBlock(nn.Module): +``` + +The `FeedbackBlock` class has one primary attribute: `submodule`. The `submodule` argument represents the "submodule" of the current instance of the `FeedbackBlock` class. It is an instance of `torch.nn.Module`. + +In the initial definition, `FeedbackBlock` takes a `submodule` as an argument and assigns it to an attribute of the class. + +```python +def __init__(self, submodule): + """ + Initializes the FeedbackBlock module. + + Args: + submodule (nn.Module): The submodule to be used within the FeedbackBlock. + """ + super().__init__() + self.submodule = submodule +``` + +The `submodule` will be triggered during the forward pass of the `FeedbackBlock`, with the input subjected to the feedback mechanism. + +_Note_: If another Module is assigned as an attribute to a Module, PyTorch will understand that it owns Parameters that can be part of the optimization problem. + +## Forward Method: + +```python +def forward(self, x: torch.Tensor, feedback, *args, **kwargs): + """ + Performs a forward pass through the FeedbackBlock. + + Args: + x (torch.Tensor): The input tensor. + feedback: The feedback tensor. + *args: Additional positional arguments to be passed to the submodule's forward method. + **kwargs: Additional keyword arguments to be passed to the submodule's forward method. + + Returns: + torch.Tensor: The output tensor after passing through the FeedbackBlock. + """ + if feedback is not None: + x = x + feedback + return self.submodule(x, *args, **kwargs) +``` + +The `forward` method does the actual computation or transformation. First, the `feedback` tensor is checked. If it exists (if it's not None), it is added into the input tensor. Once the feedback has been integrated into the input, it calls the forward method of the submodule. Any additional arguments would be directly passed to the submodule's forward method. The output of the submodule's forward pass is the final output we return. + +# Usage: + +The usage of `FeedbackBlock` is essentially to encapsulate a module in a network that performs a feedback operation. Let's take a simple scenario where you have a neural network `model` with a linear layer `nn.Linear(10,10)`: + +```python +import torch +import torch.nn as nn +from zeta.nn import FeedbackBlock + + +# Define a simple linear network +class SimpleNet(nn.Module): + def __init__(self): + super().__init__() + self.fc = nn.Linear(10, 10) + + def forward(self, x): + return self.fc(x) + +# Instantiate the simple network +simple_net = SimpleNet() + +# Wrapping the simple network with a FeedbackBlock +feedback_net = FeedbackBlock(simple_net) + +# Usage in a training loop: +x = torch.rand((64, 10)) # Assume an input tensor for batch of 64. + +# Initialize feedback +feedback = None + +for _ in range(100): # 100 steps + y = feedback_net(x, feedback) + feedback = y.detach() # Detach() to avoid backpropagating gradients through time + # ... Rest of training loop here +``` + +In the code above, the output from one pass will be fed back into the module during the next pass. This allows the network to adjust its weights accordingly, based on this continuous feedback loop it’s in. + +Remember that whenever using the FeedbackBlock to encapsulate a network module, the forward method of the base module, must be designed to handle the feedback tensor that will be passed onto it. + +In charging forward into more complex architectures with dynamic networks or feedback connections, `FeedbackBlock` will be of immense help, abstracting the complexities away from your specific model and keeping your code modular and easy to follow. diff --git a/docs/zeta/nn/modules/geluactivation.md b/docs/zeta/nn/modules/geluactivation.md new file mode 100644 index 00000000..6bc89252 --- /dev/null +++ b/docs/zeta/nn/modules/geluactivation.md @@ -0,0 +1,70 @@ +# GELUActivation + +## Overview + +The GELUActivation class belongs to the torch.nn Module and implements the Gaussian Error Linear Units (GELU) activation function, initially used in Google's BERT model. This function is known for enabling the model to converge much faster and provides more robust performance in terms of model stability and accuracy. + +The GELU activation function is defined as follows: +GELU(x) = 0.5 * x * (1 + tanh(sqrt(2 / pi) * (x + 0.044715 * x^3)) + +There are two versions of this function which are slightly different. The standard one implemented in PyTorch, and the original version used in the BERT model. This class provides the flexibility to choose between these two implementations. + +## Class Definition + +class GELUActivation(nn.Module): + +This class inherits the torch.nn.Module, torch's base class for all neural network modules. + +### Parameters + +- use_gelu_python (bool): If true, uses the original GELU activation function as introduced in the BERT model. Otherwise, it uses the PyTorch's implementation of GELU. Default is `False`. + +### Methods + +#### \_\_init__() + +The constructor method for the class. Initializes the GELUActivation with the given parameters. + +#### _gelu_python() + +This private method implements the original GELU activation function used in the BERT model as a simple python function. + +#### forward() + +This method is called when you call the object of the class. It takes an input tensor and applies the GELU activation function to it. + +## Usage Example + +Here is an example usage of the GELUActivation class. The example demonstrates initializing the class and applying the GELU activation function to a random tensor. + +```python +import torch +import math +from torch import nn, Tensor +from zeta.nn import GELUActivation + +# Initialize a GELU activation function +gelu_activation = GELUActivation(use_gelu_python=True) + +# Generate a random tensor +tensor = torch.randn(5) + +# Apply GELU activation function to the tensor +activated_tensor = gelu_activation(tensor) + +print(activated_tensor) +``` + +In this example, we initialize a GELU activation function with `use_gelu_python` set to `True` which means we will be using the original GELU implementation used in the BERT model. We then apply this GELU activation function to a random tensor to get the activated tensor. + +## References + +- Gaussian Error Linear Units (GELUs) Paper: [https://arxiv.org/abs/1606.08415](https://arxiv.org/abs/1606.08415) + +We suggest to read the referenced paper to gain a deeper understanding of GELUs and their use in neural networks. + +## Tips and Tricks + +- While the two versions of the GELU activation function are very similar, the original one (used in the BERT model) can sometimes provide slightly different results. +- If you're using a model pre-trained with the BERT model, it may be beneficial to use the original version of GELU, as it was the activation functions that the model was originally trained with. +- GELU activation function has proven effective in models dealing with Natural Language Processing tasks. diff --git a/docs/zeta/nn/modules/highwaylayer.md b/docs/zeta/nn/modules/highwaylayer.md new file mode 100644 index 00000000..b66d8bc7 --- /dev/null +++ b/docs/zeta/nn/modules/highwaylayer.md @@ -0,0 +1,136 @@ +# HighwayLayer + +## Module Introduction + +`HighwayLayer` is a class implemented in PyTorch that provides an easy way to include Highway layers in your model. The Highway layer is a type of artificial neural network (ANN) that aids in remembering or carrying information across several layers. It consists of a normal layer and a gate layer. + +It addressed the vanishing gradient problem typically found in the training of deep networks. With the application of a gating mechanism, the Highway layer dynamically routes signals through paths for different samples and different layers without harming the optimization process. + +This document provides details on how to use this class, its methods, properties, and examples for better understandings. + +## Class Definition + +```python +class HighwayLayer(nn.Module): +``` + +Inherits from the `nn.Module` class which is the base class for all neural network modules in PyTorch. + +## Parameters + +- `dim` (int): The dimension of the input tensor to the layer and the output of the layer. + +## Methods + +### `__init__(self, dim)` + +Initializes a `HighwayLayer` instance with a specified `dim`. + +Parameters: + +| Parameter | Type | Description | +|-----------|------|-------------| +| dim | int | The input and output dimension of the layer | + +### `forward(self, x)` + +Performs a forward pass through the `HighwayLayer`. + +Parameters: + +| Parameter | Type | Description | +|-----------|----------------|-------------------| +| x | torch.Tensor | The input tensor | + +Returns: + +`torch.Tensor`: The output tensor. + +## Source Code + +```python +import torch.nn as nn +import torch.nn.functional as F + +class HighwayLayer(nn.Module): + def __init__(self, dim): + super().__init__() + self.normal_layer = nn.Linear(dim, dim) + self.gate = nn.Linear(dim, dim) + + def forward(self, x: torch.Tensor) -> torch.Tensor: + normal_result = F.relu(self.normal_layer(x)) + gate = torch.sigmoid(self.gate(x)) + return gate * normal_result + (1 - gate) * x +``` + +## Usage Examples + +### Example 1: Simple model with single HighwayLayer + +```python +import torch +from zeta.nn import HighwayLayer + +# Initialize HighwayLayer with dimension 50 +layer = HighwayLayer(50) + +# Random input tensor of shape (10, 50) +input_tensor = torch.randn(10, 50) +output_tensor = layer(input_tensor) + +print(output_tensor.shape) # Expected shape (10, 50) +``` + +### Example 2: Model with Multiple Highway Layers + +```python +import torch +from zeta.nn import HighwayLayer + +class MyModel(nn.Module): + def __init__(self): + super().__init__() + self.layer1 = HighwayLayer(50) + self.layer2 = HighwayLayer(50) + + def forward(self, x): + x = self.layer1(x) + x = self.layer2(x) + return x + +# Initialize model and input tensor +model = MyModel() +input_tensor = torch.randn(10, 50) + +# Forward pass +output_tensor = model(input_tensor) + +print(output_tensor.shape) # Expected output: torch.Size([10, 50]) +``` + +### Example 3: Model with HighwayLayer and Other Types of Layers + +```python +class MyModel(nn.Module): + def __init__(self): + super().__init__() + self.layer1 = HighwayLayer(50) + self.layer2 = nn.Linear(50, 20) + + def forward(self, x): + x = self.layer1(x) + x = self.layer2(x) + return x + +# Initialize model and input tensor +model = MyModel() +input_tensor = torch.randn(10, 50) + +# Forward pass +output_tensor = model(input_tensor) + +print(output_tensor.shape) # Expected output: torch.Size([10, 20]) +``` + +Application of HighwayLayer can greatly enhance the learning of deep neural networks by allowing the direct forward flow of information unimpeded thereby solving the vanishing gradient problem. diff --git a/docs/zeta/nn/modules/laplaceactivation.md b/docs/zeta/nn/modules/laplaceactivation.md new file mode 100644 index 00000000..93fbb994 --- /dev/null +++ b/docs/zeta/nn/modules/laplaceactivation.md @@ -0,0 +1,84 @@ +# LaplaceActivation + + +## 1. Overview + +The `LaplaceActivation` is an artificial neuron that applies an elementwise activation based on the Laplace function. This was introduced in MEGA as an attention activation, which can be found in this [paper](https://arxiv.org/abs/2209.10655). + +The `LaplaceActivation` is inspired by the squaring operation of the ReLU (Rectified Linear Units) function, but comes with a bounded range and gradient for improved stability. + +## 2. Class Description + +The `LaplaceActivation` is part of the `PyTorch` neural network (`nn`) module, specifically intended to provide activation functionality based on the Laplace function to a neural network model. + +### Class Definition + +```python +class LaplaceActivation(nn.Module): + pass +``` + +### Method: `forward` + +This function applies the Laplace function across all elements in the input tensor. It takes as parameters the input tensor and optional parameters `\mu` and `\sigma`. +The function computes the Laplace function as follows: + +``` +input = (input - \mu) / (\sigma * sqrt(2)) +output = 0.5 * (1 + erf(input)) +return output +``` +#### Arguments: + +|Argument|Type |Description |Default value +|---|---|---|---| +|`input` |Tensor| Tensor input to the function.| +|`\mu` |float|Location parameter, `\mu` determines the shift or the mean of the function.|0.707107 +|`\sigma`|float| Scale parameter or standard deviation, `\sigma` determines the spread or the width of the function.| 0.282095 + +#### Returns + +A tensor with Laplace function applied elementwise. + +### 3. Example Usage + +#### Importing required libraries + +```python +import torch +import torch.nn as nn +import torch.nn.functional as F +import math +from zeta.nn import LaplaceActivation +``` +#### Defining an instance + +```python +lap_act = LaplaceActivation() +``` +Applying Laplace Activation to a tensor + +```python +input_tensor = torch.randn(10) +activated_tensor = lap_act(input_tensor) +``` +Printing output + +```python +print(activated_tensor) +``` + +You should see the tensor output with Laplace activation applied elementwise. + +## 4. Additional Information + +The Laplace Activation function is a new approach to help stabilize the learning process in deep neural networks. It introduces bounded range and gradient which can be very useful when training deep learning models. + +## 5. References + +For more in-depth understanding, kindly refer to this [paper](https://arxiv.org/abs/2209.10655). + +## 6. Contact Information + +For any issues or inquiries, feel free to contact the support team at kye@apac.ai We're happy to help! + diff --git a/docs/zeta/nn/modules/linearactivation.md b/docs/zeta/nn/modules/linearactivation.md new file mode 100644 index 00000000..9ee1e17c --- /dev/null +++ b/docs/zeta/nn/modules/linearactivation.md @@ -0,0 +1,96 @@ +# LinearActivation + + + +The LinearActivation class belongs to the `nn.Module` in PyTorch which is a standard base class for all neural network modules. The class LinearActivation is a child class that inherits the functionalities of its parent class `nn.Module`. This class represents the linear activation function in the neural networks; sometimes also referred to as the identity function. The idea here is to return the input without applying any transformation, which means that the output of this function is the same as the input. + +The source code is as follows: + +```python +import torch.nn as nn +from torch import Tensor +from zeta.nn import LinearActivation + +class LinearActivation(nn.Module): + """ + Applies the linear activation function, i.e., forwarding input directly to output. + """ + + def forward(self, input: Tensor) -> Tensor: + return input +``` + +### Method details +**Method Name:** `forward` + +This method executes the forward pass, in other words, it makes a forward pass from input to the output. The `forward` is an abstract method in superclass `nn.Module` and must be defined by each layer. + +**Arguments:** + +| Argument Name | Type | Description | +|---------------|----------|-----------------------------------------------------| +| input | Tensor | Input tensor to which the linear activation is applied | + +**Returns:** + +`Tensor`: The output tensor identical to the input tensor. + +## Usage Example 1 +```python +import torch +from torch import Tensor +import torch.nn as nn +from zeta.nn import LinearActivation + +linear_activation = LinearActivation() + +# random tensor of size 4 +input_tensor = torch.randn(4) +print("Input tensor: ", input_tensor) + +output_tensor = linear_activation(input_tensor) +print("Output tensor: ", output_tensor) +``` +In this example, the `LinearActivation` class is instantiated first followed by generating a random tensor of size 4. This random tensor is passed to the instantiated `LinearActivation` class, and the result will be an identical tensor to the input, as expected. + +## Usage Example 2 + +```python +import torch +from torch import Tensor +import torch.nn as nn +from zeta.nn import LinearActivation + + +# create an instance of the class LinearActivation +linear_activation = LinearActivation() + +# define a tensor of ones +input_tensor = torch.ones(10) +print("Input tensor: ", input_tensor) + +# pass the tensor of ones through the LinearActivation +output_tensor = linear_activation(input_tensor) +print("Output tensor: ", output_tensor) +``` +In the second example, we create an input tensor of ones of size 10. When this tensor is passed through the `LinearActivation`, we expect an identical tensor of ones for the output. We print the output tensor to verify this. + +## Usage Example 3 + +```python +import torch +from torch import Tensor +import torch.nn as nn +from zeta.nn import LinearActivation + + +linear_activation = LinearActivation() + +# create a tensor with numbers from 1 to 10 +input_tensor = torch.arange(1, 11).float() +print("Input tensor: ", input_tensor) + +output_tensor = linear_activation(input_tensor) +print("Output tensor: ", output_tensor) +``` +In the third example, we create an input tensor with numbers from 1 to 10. We then pass this tensor through the `LinearActivation`. Because the `LinearActivation` doesn't actually perform any mathematical transformations, the expected output tensor will be identical to the input tensor. diff --git a/docs/zeta/nn/modules/mishactivation.md b/docs/zeta/nn/modules/mishactivation.md new file mode 100644 index 00000000..97c9fadb --- /dev/null +++ b/docs/zeta/nn/modules/mishactivation.md @@ -0,0 +1,119 @@ +# MishActivation + +This is the official documentation for the Mish Activation class implementation in PyTorch. +This document will cover the details of implementing Mish Activation function and the ways to use it. + +## Mish Activation Function: Introduction + +Mish Activation is a novel approach to optimizing and enhancing the performance of neural network models by using a new self-regularized, non-monotonic activation function known as "Mish". Mish aims to promote better gradient flow for deep networks, while also distinguishing extreme gradient values for generalization in deep networks. + +For a more deep understanding of the function you can refer to the official paper by Diganta Misra that presents and discusses the Mish activation function, ["Mish: A Self Regularized Non-Monotonic Neural Activation Function"](https://arxiv.org/abs/1908.08681). + +There is also a GitHub repo available for detailed information and research related to Mish Activation function [Here](https://github.com/digantamisra98/Mish). + +## Class Definition + +```python +class MishActivation(nn.Module): + """ + A pytorch implementation of mish activation function. + """ + + def __init__(self): + super().__init__() + if version.parse(torch.__version__) < version.parse("1.9.0"): + self.act = self._mish_python + else: + self.act = nn.functional.mish + + def _mish_python(self, input: Tensor) -> Tensor: + return input * torch.tanh(nn.functional.softplus(input)) + + def forward(self, input: Tensor) -> Tensor: + return self.act(input) +``` + +## Class Arguments & Methods + +### Arguments +Mish Activation function does not take any explicit argument other than the input tensor. + +### Methods + +#### `__init__(self)` + +This is the initialization method where mish activation function checks for PyTorch version and based on the version, decides whether to use PyTorch built-in Mish Activation function or fall back to its own python implementation of Mish Activation function. + +#### `_mish_python(self, input: Tensor) -> Tensor` + +The fallback python implementation of Mish Activation function that multiplies the input with a hyperbolic tanh of a softplus function of input. + +- Parameters: + - `input: Tensor`: The tensor on which the activation function will be applied. + +- Returns: + - `Tensor`: The modified tensor after applying the activation function. + +#### `forward(self, input: Tensor) -> Tensor` + +The forward method applies mish activation on the input tensor + +- Parameters: + - `input: Tensor`: The tensor on which the activation function will be applied. + +- Returns: + - `Tensor`: The modified tensor after applying the activation function. + +## Usage Examples + +This module requires PyTorch and Python 3.6 or above. +### Example 1: Importing the module and Applying the Mish Activation function + +```python +from torch import nn, Tensor +from torch.nn import functional as F +from packaging import version +from zeta.nn import MishActivation + +input_tensor = Tensor([[-0.6, 0.7], [1.2, -0.7]]) +mish = MishActivation() +print(mish.forward(input_tensor)) +``` +### Example 2: Using Mish Activation for Neural Network Layers + +The Mish Activation function can also be applied in Neural Network layers using PyTorch. + +```python +import torch +from torch import nn, Tensor +from torch.nn import functional as F +from packaging import version +from zeta.nn import MishActivation + + +class NeuralNetwork(nn.Module): + def __init__(self): + super(NeuralNetwork, self).__init__() + self.flatten = nn.Flatten() + self.layer = nn.Sequential( + nn.Linear(26, 256), + MishActivation(), + nn.Linear(256, 10), + MishActivation() + ) + + def forward(self, x): + x = self.flatten(x) + logits = self.layer(x) + return logits + +model = NeuralNetwork() +# Following lines shows how to use the model, given the input tensor, `X`. +# output = model(X) +``` +## References + +- [Packaging](https://pypi.org/project/packaging/) +- [PyTorch](https://pytorch.org/docs/stable/torch.html) +- [Arxiv Article for Mish Activation](https://arxiv.org/abs/1908.08681) +- [GitHub repo for MishActivation](https://github.com/digantamisra98/Mish) diff --git a/docs/zeta/nn/modules/multiscaleblock.md b/docs/zeta/nn/modules/multiscaleblock.md new file mode 100644 index 00000000..6a39479d --- /dev/null +++ b/docs/zeta/nn/modules/multiscaleblock.md @@ -0,0 +1,124 @@ +# MultiScaleBlock + +## **Table of Contents** + +1. Overview +2. Class Definition +3. Functionality and Usage +4. Additional Tips & Information +5. Resources and References + +## **1. Overview** + +The `MultiScaleBlock` class, a component of PyTorch's `nn.Module`, falls under the category of deep learning models. PyTorch is a powerful, flexible deep learning framework that allows automatic differentiation and optimization. + +This class is well-suited to tasks where the spatial or temporal scale of the input data varies. Examples are wide-range in nature, including but not limited to, image processing, video analysis, and signal processing. + +In `MultiScaleBlock`, any PyTorch module such as convolutional layers, linear layers, or even sequence of layers can be applied to the input tensor at multiple scales in a seamless way. + +## **2. Class Definition** + +### `MultiScaleBlock` Class + +The class definition for `MultiScaleBlock` is provided below: + +```python +class MultiScaleBlock(nn.Module): + """ + A module that applies a given submodule to the input tensor at multiple scales. + + Args: + module (nn.Module): The submodule to be applied. + + Returns: + torch.Tensor: The output tensor after applying the submodule at multiple scales. + """ + + def __init__(self, module): + super().__init__() + self.submodule = module + + def forward(self, x: torch.Tensor, *args, **kwargs): + x1 = F.interpolate(x, scale_factor=0.5, *args, **kwargs) + x2 = F.interpolate(x, scale_factor=2.0, *args, **kwargs) + return ( + self.submodule(x) + + F.interpolate(self.submodule(x1), size=x.shape[2:]) + + F.interpolate(self.submodule(x2), size=x.shape[2:]) + ) +``` + +#### Method 1: `__init__(self, module)` + +This is the initializer for the `MultiScaleBlock` class, and it takes the following input: + +- `module (nn.Module)`: The submodule to be applied on the input tensor at multiple scales. + +#### Method 2: `forward(self, x: torch.Tensor, *args, **kwargs)` +The forward propagation method, onto which the initialized model is called with the input data `x`. It includes the following parameters: + +- `x (torch.Tensor)`: The input tensor. +- `*args`: Additional arguments for the interpolate function of PyTorch. It can include various parameters depending on the Interpolation mode selected, which can be `mode`, `align_corners`, and `recompute_scale_factor`. +- `**kwargs`: Additional keyword arguments. + +## **3. Functionality and Usage** + +The `MultiScaleBlock` class is designed to apply a given submodule to the input tensor at multiple scales. The purpose of multi-scale processing is to handle the variation in scale of the different elements in the image, the data, or the signal. + +In the `forward` method, the input tensor `x` is first interpolated at two different scales (0.5 and 2.0). The PyTorch function `torch.nn.functional.interpolate` adjusts the size of the tensor using specific scaling factors. Then, the submodule is applied to the original input tensor and the interpolated tensors. The output is the sum of the results of applying the submodule at the original scale and the two interpolated scales. + +### **Usage Example** + +Here are some examples showcasing the usage of `MultiScaleBlock`: + +1. **Single Convolutional Layer as Submodule**: + + ```python + import torch + import torch.nn as nn + import torch.nn.functional as F + from zeta.nn import MultiScaleBlock + + conv = nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=1) + model = MultiScaleBlock(conv) + input = torch.rand(1, 3, 32, 32) + output = model(input) + ``` + +2. **Sequence of Layers as Submodule**: + + ```python + seq = nn.Sequential( + nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=1), + nn.BatchNorm2d(64), + nn.ReLU(), + nn.MaxPool2d(2) + ) + model = MultiScaleBlock(seq) + input = torch.rand(1, 3, 32, 32) + output = model(input) + ``` + +3. **Custom Model as Submodule**: + + Suppose `MyModel` is a PyTorch model, you can use `MultiScaleBlock` on it as follows: + + ```python + model = MyModel(num_classes=10) + multi_scale_model = MultiScaleBlock(model) + input = torch.rand(1, 3, 32, 32) + output = multi_scale_model(input) + ``` + +## **4. Additional Information** + +- The input tensor's shape must be in the form of (batch_size, num_channels, height, width) for `forward` method of this class to work properly. This is because the `F.interpolate` function in PyTorch expects the input in this format. + +- This class uses `F.interpolate` function, make sure to check the PyTorch documentation for this function to understand various interpolation modes and their behavior: https://pytorch.org/docs/stable/generated/torch.nn.functional.interpolate.html + +## **5. References** + +1. [PyTorch Official Documentation](https://pytorch.org/docs/stable/index.html) +2. [Multi-Scale Convolutional Neural Networks for Vision Tasks](https://arxiv.org/abs/1406.4729) + +I hope this documentation will help you to understand and use `MultiScaleBlock` class in your scenarios. Enjoy DL with PyTorch! diff --git a/docs/zeta/nn/modules/newgeluactivation.md b/docs/zeta/nn/modules/newgeluactivation.md new file mode 100644 index 00000000..1999343c --- /dev/null +++ b/docs/zeta/nn/modules/newgeluactivation.md @@ -0,0 +1,127 @@ +# NewGELUActivation + +# Chapter 1: Introduction and Overview + +# NewGELUActivation + +The NewGELUActivation class is an implementation of the Gaussian Error Linear Units (GELU) activation function. In PyTorch, activation functions are essential non-linear transformations that are applied on the input, typically after linear transformations, to introduce non-linearity into the model. The GELU activation function is currently being used in Google's BERT and OpenAI's GPT models. If you are interested in more details about this function, see the Gaussian Error Linear Units paper: https://arxiv.org/abs/1606.08415 + +# Chapter 2: Detailed Explanation of the NewGELUActivation Class + +The `NewGELUActivation` class extends `nn.Module`, so it can be integrated easily into any PyTorch model. It is a type of activation function that is believed to perform better in deeper architectures. + +``` +class NewGELUActivation(nn.Module): + """ + Implementation of the GELU activation function currently in Google BERT repo (identical to OpenAI GPT). Also see + the Gaussian Error Linear Units paper: https://arxiv.org/abs/1606.08415 + """ + + def forward(self, input: Tensor) -> Tensor: + return ( + 0.5 + * input + * ( + 1.0 + + torch.tanh( + math.sqrt(2.0 / math.pi) + * (input + 0.044715 * torch.pow(input, 3.0)) + ) + ) + ) +``` + +## Forward Function + +The `forward` method **overloads** the call to the function to process data. The forward method takes one mandatory argument: + +- `input` - This is a tensor that represents the activations output from the previous layer. The data type is Tensor. + +The forward method returns: + +- The value obtained after applying the New GELU activation function on the input tensor. + +#### Implementation of the forward method: +The forward method calculates the New GELU activation of the input tensor. The formula for calculating the New GELU activation is as follows: + + GELU(x) = 0.5 * x * (1 + tanh(sqrt(2/pi) * (x + 0.044715 * x^3))) + +where, +- `x` is the input. +- `tanh` is the hyperbolic tangent function. +- `sqrt` is the square root function. +- `^` is the power operator. + +Importantly, when the `forward` function is called on an object of the class `NewGELUActivation`, it computes these operations on the input tensor, and the result is returned. + +# Chapter 3: Usage Examples + +At first, you need to import necessary packages and modules. + +```python +import torch +import math +from torch import Tensor +from torch import nn +from zeta.nn import NewGELUActivation +``` + +## Usage Example 1: + +Creating an instance of NewGELUActivation and calling it with a tensor as input. + +```python +gelu_new = NewGELUActivation() + +random_data = torch.randn(5) # Just some random data +output = gelu_new(random_data) + +print(output) +``` + +## Usage Example 2: + +Integrating NewGELUActivation within a neural network model. + +```python +class NeuralNetwork(nn.Module): + def __init__(self): + super(NeuralNetwork, self).__init__() + self.fc1 = nn.Linear(784, 256) + self.new_gelu = NewGELUActivation() + + def forward(self, x): + x = self.fc1(x) + x = self.new_gelu(x) + return x + +model = NeuralNetwork() # Creating an instance of our model +``` + +## Usage Example 3: + +Applying the NewGELUActivation function in a Convolutional Neural Network (CNN). + +```python +class CNN(nn.Module): + def __init__(self): + super().__init__() + self.conv1 = nn.Conv2d(3, 6, 5) + self.new_gelu = NewGELUActivation() + + def forward(self, x): + x = self.new_gelu(self.conv1(x)) + return x + +model = CNN() # Creating an instance of our model +``` + +# Chapter 4: Conclusion + +This was a complete guide about the `NewGELUActivation` PyTorch class. This tool provides an implementation of the GELU activation function, improving deep learning model architectures. This document demonstrated how to use the `NewGELUActivation` class and integrate it into existing PyTorch models with various examples. + +# External Links + +- Gaussian Error Linear Units paper: https://arxiv.org/abs/1606.08415 +- PyTorch official documentation: https://pytorch.org/docs/stable/index.html +- Other relevant resources: https://machinelearningmastery.com/rectified-linear-activation-function-for-deep-learning-neural-networks/ diff --git a/docs/zeta/nn/modules/pytorchgelutanh.md b/docs/zeta/nn/modules/pytorchgelutanh.md new file mode 100644 index 00000000..c242a8a3 --- /dev/null +++ b/docs/zeta/nn/modules/pytorchgelutanh.md @@ -0,0 +1,110 @@ +# PytorchGELUTanh + +## Overview + +The `PytorchGELUTanh` class in Python is a fast C implementation of the tanh approximation of the GeLU activation function. This implementation is meant to be faster and as effective as other implementations of GeLU (Gaussian Error Linear Units) function like NewGELU and FastGELU. However, it is not an exact numerical match to them due to possible rounding errors. + +This documentation provides an in-depth guide to using the `PytorchGELUTanh` class. It includes general information about the class, the method documentation, and various usage examples. + +## Introduction + +In Neural Networks, activation functions decide whether a neuron should be activated or not by calculating the weighted sum and adding bias with it. One of these activation functions is the Gaussian Error Linear Units (GeLU) function. GeLU function approximates the cumulative distribution function of the standard Gaussian distribution and helps in faster learning during the initial phase of training. + +The `PytorchGELUTanh` class provides a fast C implementation of the tanh approximation of the GeLU activation function. + +## Class Definition + +```python +class PytorchGELUTanh(nn.Module): + """ + A fast C implementation of the tanh approximation of the GeLU activation function. See + https://arxiv.org/abs/1606.08415. + + This implementation is equivalent to NewGELU and FastGELU but much faster. However, it is not an exact numerical + match due to rounding errors. + """ + + def __init__(self): + super().__init__() + if version.parse(torch.__version__) < version.parse("1.12.0"): + raise ImportError( + f"You are using torch=={torch.__version__}, but torch>=1.12.0" + " is required to use PytorchGELUTanh. Please upgrade torch." + ) + + def forward(self, input: Tensor) -> Tensor: + return nn.functional.gelu(input, approximate="tanh") +``` + +## General Information + +The `PytorchGELUTanh` class only requires PyTorch version 1.12.0 or higher. + +This class contains the following methods: + +| Method | Definition | +| --- | --- | +| `__init__` | This is the constructor method for the `PytorchGELUTanh` class in which the superclass is initialized and a check is made to ensure that the version of PyTorch being used supports the class. If not, an import error is raised. | +| `forward` | This method applies the tanh approximation of the GeLU active function to the provided tensor input. | + +The `forward` method takes in a tensor as an input argument and returns a tensor as an output. The input and output tensors are of the same size. + +## Usage Examples + +### Example 1: Basic Usage + +In this basic example, we create an instance of the `PytorchGELUTanh` class and pass a tensor to its `forward` method to apply the tanh approximation of the GeLU function. + +```python +# Import necessary libraries +import torch +from torch import nn, Tensor +from packaging import version +from torch.nn.functional import gelu +from zeta.nn import PytorchGELUTanh + +# Create an instance of the PytorchGELUTanh class. +gelutanh = PytorchGELUTanh() + +# Create a tensor. +x = torch.randn(3) + +# Print the tensor before and after applying the GeLU Tanh activation function. +print('Before: ', x) +print('After: ', gelutanh.forward(x)) +``` + +### Example 2: Application to Deep Learning + +The `PytorchGELUTanh` class can be used in place of traditional activation functions in deep learning models. Here is an example of its usage in a feed-forward neural network. + +```python +# Import necessary libraries +import torch +from torch import nn, Tensor +from torch.nn.functional import gelu +from zeta.nn import PytorchGELUTanh + + +# Define a feed-forward neural network with 2 layers and the PytorchGELUTanh activation function +class FeedForwardNN(nn.Module): + def __init__(self): + super(FeedForwardNN, self).__init__() + self.fc1 = nn.Linear(10, 20) # 10 input neurons, 20 output neurons + self.gelu = PytorchGELUTanh() # Our custom activation function + self.fc2 = nn.Linear(20, 1) # Final layer + + def forward(self, x): + x = self.fc1(x) + x = self.gelu(x) # Apply the PytorchGELUTanh activation + x = self.fc2(x) + return x + +# Instantiate the model +model = FeedForwardNN() + +# Print the model architecture +print(model) +``` + +This completes the documentation for the `PytorchGELUTanh` Python class, but feel free to reference the official [PyTorch documentation](https://pytorch.org/docs/stable/nn.functional.html#torch.nn.functional.gelu) and ensure you are using a version of PyTorch that is compatible with this class. diff --git a/docs/zeta/nn/modules/quickgeluactivation.md b/docs/zeta/nn/modules/quickgeluactivation.md new file mode 100644 index 00000000..801f492a --- /dev/null +++ b/docs/zeta/nn/modules/quickgeluactivation.md @@ -0,0 +1,75 @@ +# QuickGELUActivation +## Overview + +The QuickGELUActivation class is a part of the Neural Network(NN) module that applies a Gaussian Error Linear Unit (GELU) approximation. GELU can be viewed as a smoother version of the popular activation function, ReLU. The approximate version of GELU used in this class is fast although somewhat less accurate than the standard GELU activation. + +The GELU activation function can be used as an alternative to other popular activation functions like ReLU and Sigmoid while training deep learning models. The importance of GELU in the context of deep learning comes from its unique properties which includes non-monotonicity that allows for complex transformations. + +## Class Definition + +The QuickGELUActivation class is defined as shown below: + +```python +class QuickGELUActivation(nn.Module): + """ + Applies GELU approximation that is fast but somewhat inaccurate. See: https://github.com/hendrycks/GELUs + """ +``` + +The class extends the Module class from the pyTorch library. It does not take any input parameters during initialization. + +## Method Definitions + +The class has a single method named forward. + +### forward + +This function is responsible for applying the GELU approximation to the input tensor. + +```python + def forward(self, input: Tensor) -> Tensor: + return input * torch.sigmoid(1.702 * input) +``` + +**Parameters:** + +| Name | Type |Description | +| --- | --- | --- | +| **input** | Tensor | The input tensor to which the GELU approximation will be applied. | + +**Return Type:** Tensor + +**Returns:** The output tensor after applying the GELU approximation. + +## Meta-information + +The function uses a torch inbuilt function *sigmoid* to apply the GELU approximation. The parameter 1.702 in the sigmoid function is chosen as it approximates the GELU function very closely. It should be noted that this approximation may not be exactly equal to the standard GELU and hence, could be somewhat inaccurate. + +## Example Code + +Below is a simple example showing how to use QuickGELUActivation to apply a GELU approximation to a tensor input: + +```python +import torch +from torch import nn +from zeta.nn import QuickGELUActivation + +# create an instance of QuickGELUActivation +activation = QuickGELUActivation() + +# create a tensor +x = torch.rand(3) + +# apply GELU activation +output = activation(x) + +print(output) +``` + +In this code, we first create a tensor using the `rand` method from pyTorch. Next, an instance of the QuickGELUActivation class is created and the GELU approximation is applied to the tensor. + +Further, it is advised to use this GELU activation function in the scenario where quick approximation is more advantageous than a slightly more accurate result. It can be used with any model architecture where an activation function is needed. It may provide better results in certain scenarios compared to typical activation functions like ReLU. + +For more details, you can refer to the [GELU activation paper](https://arxiv.org/abs/1606.08415) and the [approximation method](https://github.com/hendrycks/GELUs). + +This class is not a direct replacement for the torch.nn.GELU and should be used considering the trade-off between speed and accuracy. Please also refer to the official [PyTorch](https://pytorch.org/docs/stable/generated/torch.nn.GELU.html) documentation for more information on activation functions in PyTorch. diff --git a/docs/zeta/nn/modules/recursiveblock.md b/docs/zeta/nn/modules/recursiveblock.md new file mode 100644 index 00000000..f07ffd89 --- /dev/null +++ b/docs/zeta/nn/modules/recursiveblock.md @@ -0,0 +1,111 @@ +# RecursiveBlock + + +Zeta is a python library that makes use of Pytorch for implementing several classes and functions related to swarm optimization tasks. This documentation will be focusing on the `RecursiveBlock` class in the `swarm` Pytorch-based library. This class's main functionality is to recursively apply a given module a specified number of times to an input tensor. + +The RecursiveBlock is, therefore, a versatile class that allows for a wide range of operations to be performed on your data by reiterating the application of an operation or set of operations encapsulated in a module. + +## Class Definition +Here is the code structure of the RecursiveBlock class: + +```python +import torch +from torch import nn + +class RecursiveBlock(nn.Module): + def __init__(self, modules, iters, *args, **kwargs): + super().__init__() + self.modules = modules + self.iters = iters + + def forward(self, x: torch.Tensor): + for _ in range(self.iters): + x = self.modules(x) + return x +``` + +## Parameters and Arguments +Let's discuss the function definitions, parameters, and return types of `RecursiveBlock's` methods. + +### `__init__` Constructor Method: +This method initializes the `RecursiveBlock` object. +Parameters of this constructor are: + +| Parameter | Type | Description | +|-----------|------|-------------| +| `modules` | torch.nn.Module | The module to be applied recursively. | +| `iters` | int | The number of iterations to apply the module. | +| `*args` | list | Variable length argument list. | +| `**kwargs`| dict | Arbitrary keyword arguments. | + +### `forward` Method: +This method is responsible for the forward pass of the block. +Parameters of this method are: + +| Parameter | Type | Description | +|-----------|------|-------------| +| `x` | torch.Tensor | The input tensor.| + +Return Type: **torch.Tensor** : The output tensor after applying the module recursively. + +## Usage Examples + +### Example 1: +Utilizing two convolutional layers from Pytorch's nn library recursively + +```python +import torch +from torch import nn +from zeta import RecursiveBlock + +conv_module = nn.Sequential( + nn.Conv2d(1, 20, 5), + nn.ReLU(), + nn.Conv2d(20, 20, 5), + nn.ReLU() +) + +block = RecursiveBlock(conv_module, iters=2) + +x = torch.randn(1, 20, 10, 10) +output = block(x) +``` + +### Example 2: +Implementing the RecursiveBlock class with a simple, custom module + +```python +class AddTen(nn.Module): + def forward(self, x): + return x + 10 + +block = RecursiveBlock(AddTen(), iters=3) +output = block(torch.tensor(1.)) # output -> tensor(31.) +``` + +### Example 3: +Using RecursiveBlock with a Linear Layer and a sigmoid activation function + +```python +import torch +from torch import nn +from zeta import RecursiveBlock + +linear_module = nn.Sequential( + nn.Linear(128, 64), + nn.Sigmoid(), +) + +block = RecursiveBlock(linear_module, iters=3) + +x = torch.randn(16, 128) +output = block(x) +``` + +## Additional Information and Tips + +1. The `modules` parameter in `RecursiveBlock` is not limited to built-in PyTorch modules. It can also be a custom PyTorch nn.Module defined by the user. + +2. The `iters` parameter can be adjusted as per the requirement of the task. More iterations might lead to a deeper feature extraction and can sometimes lead to better performance, but can also increase the computation time. + +Thus, RecursiveBlock is a simple yet powerful class providing the abstraction of repeated module application, making iterating through a module multiple times a straightforward task. It enables cleaner, more readable code for models involving repetition of a similar structure or block, ushering rich flexibility into the hands of the programmer. diff --git a/docs/zeta/nn/modules/relusquaredactivation.md b/docs/zeta/nn/modules/relusquaredactivation.md new file mode 100644 index 00000000..13f0ae81 --- /dev/null +++ b/docs/zeta/nn/modules/relusquaredactivation.md @@ -0,0 +1,71 @@ +# ReLUSquaredActivation + +## Overview + +The `ReLUSquaredActivation` class is a PyTorch neural network module that implements a custom activation function known as ReLU². This activation function is introduced in the [What You See Is What You Get](https://arxiv.org/abs/2109.08668v2) paper by Kim, Y., & Bengio, S., and they prove it to be an important enhancement in the stability of Neural Network Training. + +This activation layer applies the ReLU (Rectified Linear Unit) function to the input and then squares the result. Thus, it can only result in non-negative outputs. The squaring operation increases the emphasis on positive inputs and reduces the effect of small inputs, aiding in reducing the outliers effect and better focusing the network on meaningful inputs. + +## Class Definition + +```python +class ReLUSquaredActivation(nn.Module): + """ + Applies the relu^2 activation introduced in https://arxiv.org/abs/2109.08668v2 + """ + + def forward(self, input): + relu_applied = nn.functional.relu(input) + squared = torch.square(relu_applied) + return squared +``` + +### `class ReLUSquaredActivation` + +This is the class constructor that creates an instance of the `ReLUSquaredActivation` class. + +The `ReLUSquaredActivation` class extends [`nn.Module`](https://pytorch.org/docs/stable/generated/torch.nn.Module.html), the base class for all neural network modules in PyTorch. It does not accept any parameters. + +### `forward(self, input)` + +This is the forward pass of the ReLUSquaredActivation module. It's where the computation happens. This method does not have to be explicitly called, and it can be run by calling the instance of the class. + +| Argument | Type | Description | +|----------|:------|:-------------| +| `input` | Tensor | The input tensor on which the relu squared operation is to be applied. + +It applies the `ReLU` activation function on the input tensor and then squares the result. It returns a tensor with the same shape as the input tensor, with the ReLU² activation applied. + + +## Example Usage + +```python +# Importing the essential libraries +import torch +import torch.nn as nn +from zeta.nn import ReLUSquaredActivation + +# Creating random torch tensor for input +input_tensor = torch.randn((2,2)) + +# Creating an instance of module +relu_squared_activation = ReLUSquaredActivation() + +# Applying the module to input tensor +output_tensor = relu_squared_activation(input_tensor) + +print("Input Tensor:") +print(input_tensor) +print("Output Tensor:") +print(output_tensor) +``` + +In this example, we first import the necessary libraries. We then create an instance of `ReLUSquaredActivation`. After creating this instance, you can use it as a function to apply the ReLU² activation to the input tensor. + +In the resulting output tensor, the activation function is applied elementwise, meaning that every single value in the tensor has the activation function applied independently. This means that the shape of the output tensor is identical to the shape of the input tensor. + +## Additional Information + +The `ReLUSquaredActivation` is a simple yet powerful activation layer that can provide increased performance in certain types of neural networks. However, like all tools, it is important to use it in the right context and understand that it might not always lead to the best results depending on the specific problem and data at hand. + +Note that the `ReLUSquaredActivation` extends the `nn.Module` class, which is the fundamental building block in PyTorch. It forms part of a larger toolkit for building and running neural networks, and there are many other types of modules available in the [`torch.nn`](https://pytorch.org/docs/stable/nn.html) library that you might find useful. diff --git a/mkdocs.yml b/mkdocs.yml index 780107f8..98d8088c 100644 --- a/mkdocs.yml +++ b/mkdocs.yml @@ -112,6 +112,23 @@ nav: - PolymorphicNeuronLayer: "zeta/nn/modules/polymorphic_activation.md" - FusedDenseGELUDense: "zeta/nn/modules/fused_gelu_dense.md" - FusedDropoutLayerNorm: "zeta/nn/modules/fused_dropout_layernorm.md" + - AccurateGELUActivation: "zeta/nn/modules/accurategeluactivation.md" + - ClippedGELUActivation: "zeta/nn/modules/clippedgeluactivation.md" + - DenseBlock: "zeta/nn/modules/denseblock.md" + - DualPathBlock: "zeta/nn/modules/dualpathblock.md" + - FastGELUActivation: "zeta/nn/modules/fastgeluactivation.md" + - FeedbackBlock: "zeta/nn/modules/feedbackblock.md" + - GELUActivation: "zeta/nn/modules/geluactivation.md" + - HighwayLayer: "zeta/nn/modules/highwaylayer.md" + - LaplaceActivation: "zeta/nn/modules/laplaceactivation.md" + - LinearActivation: "zeta/nn/modules/linearactivation.md" + - MishActivation: "zeta/nn/modules/mishactivation.md" + - MultiScaleBlock: "zeta/nn/modules/multiscaleblock.md" + - NewGELUActivation: "zeta/nn/modules/newgeluactivation.md" + - PytorchGELUTanh: "zeta/nn/modules/pytorchgelutanh.md" + - QuickGELUActivation: "zeta/nn/modules/quickgeluactivation.md" + - RecursiveBlock: "zeta/nn/modules/recursiveblock.md" + - ReLUSquaredActivation: "zeta/nn/modules/relusquaredactivation.md" - zeta.nn.attention: - FlashAttention: "zeta/nn/attention/flash_attention.md" - MultiQueryAttention: "zeta/nn/attention/multiquery.md" diff --git a/pyproject.toml b/pyproject.toml index 1695e1be..74d985e4 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,6 +1,6 @@ [tool.poetry] name = "zetascale" -version = "1.2.6" +version = "1.2.7" description = "Transformers at zeta scales" authors = ["Zeta Team "] license = "MIT" diff --git a/scripts/auto_tests_docs/auto_docs.py b/scripts/auto_tests_docs/auto_docs.py new file mode 100644 index 00000000..d6e1060a --- /dev/null +++ b/scripts/auto_tests_docs/auto_docs.py @@ -0,0 +1,101 @@ +###### VERISON2 +import inspect +import os +import threading +from zeta import OpenAIChat +from scripts.auto_tests_docs.docs import DOCUMENTATION_WRITER_SOP +from zeta.nn.modules._activations import ( + AccurateGELUActivation, + ClippedGELUActivation, + FastGELUActivation, + GELUActivation, + LaplaceActivation, + LinearActivation, + MishActivation, + NewGELUActivation, + PytorchGELUTanh, + QuickGELUActivation, + ReLUSquaredActivation, +) +from zeta.nn.modules.dense_connect import DenseBlock +from zeta.nn.modules.dual_path_block import DualPathBlock +from zeta.nn.modules.feedback_block import FeedbackBlock +from zeta.nn.modules.highway_layer import HighwayLayer +from zeta.nn.modules.multi_scale_block import MultiScaleBlock +from zeta.nn.modules.recursive_block import RecursiveBlock +from dotenv import load_dotenv + +load_dotenv() + +api_key = os.getenv("OPENAI_API_KEY") + +model = OpenAIChat( + model_name="gpt-4", + openai_api_key=api_key, + max_tokens=4000, +) + + +def process_documentation(cls): + """ + Process the documentation for a given class using OpenAI model and save it in a Markdown file. + """ + doc = inspect.getdoc(cls) + source = inspect.getsource(cls) + input_content = ( + f"Class Name: {cls.__name__}\n\nDocumentation:\n{doc}\n\nSource" + f" Code:\n{source}" + ) + print(input_content) + + # Process with OpenAI model (assuming the model's __call__ method takes this input and returns processed content) + processed_content = model(DOCUMENTATION_WRITER_SOP(input_content, "zeta")) + + doc_content = f"# {cls.__name__}\n\n{processed_content}\n" + + # Create the directory if it doesn't exist + dir_path = "docs/zeta/nn/modules" + os.makedirs(dir_path, exist_ok=True) + + # Write the processed documentation to a Markdown file + file_path = os.path.join(dir_path, f"{cls.__name__.lower()}.md") + with open(file_path, "w") as file: + file.write(doc_content) + + +def main(): + classes = [ + DenseBlock, + HighwayLayer, + MultiScaleBlock, + FeedbackBlock, + DualPathBlock, + RecursiveBlock, + PytorchGELUTanh, + NewGELUActivation, + GELUActivation, + FastGELUActivation, + QuickGELUActivation, + ClippedGELUActivation, + AccurateGELUActivation, + MishActivation, + LinearActivation, + LaplaceActivation, + ReLUSquaredActivation, + ] + + threads = [] + for cls in classes: + thread = threading.Thread(target=process_documentation, args=(cls,)) + threads.append(thread) + thread.start() + + # Wait for all threads to complete + for thread in threads: + thread.join() + + print("Documentation generated in 'docs/zeta/nn/modules' directory.") + + +if __name__ == "__main__": + main() diff --git a/scripts/auto_tests_docs/auto_tests.py b/scripts/auto_tests_docs/auto_tests.py new file mode 100644 index 00000000..70a3d750 --- /dev/null +++ b/scripts/auto_tests_docs/auto_tests.py @@ -0,0 +1,122 @@ +import inspect +import os +import re +import threading +from swarms import OpenAIChat +from scripts.auto_tests_docs.docs import TEST_WRITER_SOP_PROMPT +from zeta.nn.modules._activations import ( + AccurateGELUActivation, + ClippedGELUActivation, + FastGELUActivation, + GELUActivation, + LaplaceActivation, + LinearActivation, + MishActivation, + NewGELUActivation, + PytorchGELUTanh, + QuickGELUActivation, + ReLUSquaredActivation, +) +from zeta.nn.modules.dense_connect import DenseBlock +from zeta.nn.modules.dual_path_block import DualPathBlock +from zeta.nn.modules.feedback_block import FeedbackBlock +from zeta.nn.modules.highway_layer import HighwayLayer +from zeta.nn.modules.multi_scale_block import MultiScaleBlock +from zeta.nn.modules.recursive_block import RecursiveBlock +from dotenv import load_dotenv + +load_dotenv() + +api_key = os.getenv("OPENAI_API_KEY") + +model = OpenAIChat( + model_name="gpt-4", + openai_api_key=api_key, + max_tokens=4000, +) + + +def extract_code_from_markdown(markdown_content: str): + """ + Extracts code blocks from a Markdown string and returns them as a single string. + + Args: + - markdown_content (str): The Markdown content as a string. + + Returns: + - str: A single string containing all the code blocks separated by newlines. + """ + # Regular expression for fenced code blocks + pattern = r"```(?:\w+\n)?(.*?)```" + matches = re.findall(pattern, markdown_content, re.DOTALL) + + # Concatenate all code blocks separated by newlines + return "\n".join(code.strip() for code in matches) + + +def create_test(cls): + """ + Process the documentation for a given class using OpenAI model and save it in a Python file. + """ + doc = inspect.getdoc(cls) + source = inspect.getsource(cls) + input_content = ( + f"Class Name: {cls.__name__}\n\nDocumentation:\n{doc}\n\nSource" + f" Code:\n{source}" + ) + print(input_content) + + # Process with OpenAI model (assuming the model's __call__ method takes this input and returns processed content) + processed_content = model( + TEST_WRITER_SOP_PROMPT(input_content, "zeta", "zeta.nn") + ) + processed_content = extract_code_from_markdown(processed_content) + + doc_content = f"# {cls.__name__}\n\n{processed_content}\n" + + # Create the directory if it doesn't exist + dir_path = "tests/nn/modules" + os.makedirs(dir_path, exist_ok=True) + + # Write the processed documentation to a Python file + file_path = os.path.join(dir_path, f"{cls.__name__.lower()}.py") + with open(file_path, "w") as file: + file.write(doc_content) + + +def main(): + classes = [ + DenseBlock, + HighwayLayer, + MultiScaleBlock, + FeedbackBlock, + DualPathBlock, + RecursiveBlock, + PytorchGELUTanh, + NewGELUActivation, + GELUActivation, + FastGELUActivation, + QuickGELUActivation, + ClippedGELUActivation, + AccurateGELUActivation, + MishActivation, + LinearActivation, + LaplaceActivation, + ReLUSquaredActivation, + ] + + threads = [] + for cls in classes: + thread = threading.Thread(target=create_test, args=(cls,)) + threads.append(thread) + thread.start() + + # Wait for all threads to complete + for thread in threads: + thread.join() + + print("Tests generated in 'docs/zeta/nn/modules' directory.") + + +if __name__ == "__main__": + main() diff --git a/scripts/auto_tests_docs/docs.py b/scripts/auto_tests_docs/docs.py new file mode 100644 index 00000000..684bf6dd --- /dev/null +++ b/scripts/auto_tests_docs/docs.py @@ -0,0 +1,199 @@ +def DOCUMENTATION_WRITER_SOP( + task: str, + module: str, +): + documentation = f"""Create multi-page long and explicit professional pytorch-like documentation for the {module} code below follow the outline for the {module} library, + provide many examples and teach the user about the code, provide examples for every function, make the documentation 10,000 words, + provide many usage examples and note this is markdown docs, create the documentation for the code to document, + put the arguments and methods in a table in markdown to make it visually seamless + + Now make the professional documentation for this code, provide the architecture and how the class works and why it works that way, + it's purpose, provide args, their types, 3 ways of usage examples, in examples show all the code like imports main example etc + + BE VERY EXPLICIT AND THOROUGH, MAKE IT DEEP AND USEFUL + + ######## + Step 1: Understand the purpose and functionality of the module or framework + + Read and analyze the description provided in the documentation to understand the purpose and functionality of the module or framework. + Identify the key features, parameters, and operations performed by the module or framework. + Step 2: Provide an overview and introduction + + Start the documentation by providing a brief overview and introduction to the module or framework. + Explain the importance and relevance of the module or framework in the context of the problem it solves. + Highlight any key concepts or terminology that will be used throughout the documentation. + Step 3: Provide a class or function definition + + Provide the class or function definition for the module or framework. + Include the parameters that need to be passed to the class or function and provide a brief description of each parameter. + Specify the data types and default values for each parameter. + Step 4: Explain the functionality and usage + + Provide a detailed explanation of how the module or framework works and what it does. + Describe the steps involved in using the module or framework, including any specific requirements or considerations. + Provide code examples to demonstrate the usage of the module or framework. + Explain the expected inputs and outputs for each operation or function. + Step 5: Provide additional information and tips + + Provide any additional information or tips that may be useful for using the module or framework effectively. + Address any common issues or challenges that developers may encounter and provide recommendations or workarounds. + Step 6: Include references and resources + + Include references to any external resources or research papers that provide further information or background on the module or framework. + Provide links to relevant documentation or websites for further exploration. + Example Template for the given documentation: + + # Module/Function Name: MultiheadAttention + + class torch.nn.MultiheadAttention(embed_dim, num_heads, dropout=0.0, bias=True, add_bias_kv=False, add_zero_attn=False, kdim=None, vdim=None, batch_first=False, device=None, dtype=None): + ``` + Creates a multi-head attention module for joint information representation from the different subspaces. + + Parameters: + - embed_dim (int): Total dimension of the model. + - num_heads (int): Number of parallel attention heads. The embed_dim will be split across num_heads. + - dropout (float): Dropout probability on attn_output_weights. Default: 0.0 (no dropout). + - bias (bool): If specified, adds bias to input/output projection layers. Default: True. + - add_bias_kv (bool): If specified, adds bias to the key and value sequences at dim=0. Default: False. + - add_zero_attn (bool): If specified, adds a new batch of zeros to the key and value sequences at dim=1. Default: False. + - kdim (int): Total number of features for keys. Default: None (uses kdim=embed_dim). + - vdim (int): Total number of features for values. Default: None (uses vdim=embed_dim). + - batch_first (bool): If True, the input and output tensors are provided as (batch, seq, feature). Default: False. + - device (torch.device): If specified, the tensors will be moved to the specified device. + - dtype (torch.dtype): If specified, the tensors will have the specified dtype. + ``` + + def forward(query, key, value, key_padding_mask=None, need_weights=True, attn_mask=None, average_attn_weights=True, is_causal=False): + ``` + Forward pass of the multi-head attention module. + + Parameters: + - query (Tensor): Query embeddings of shape (L, E_q) for unbatched input, (L, N, E_q) when batch_first=False, or (N, L, E_q) when batch_first=True. + - key (Tensor): Key embeddings of shape (S, E_k) for unbatched input, (S, N, E_k) when batch_first=False, or (N, S, E_k) when batch_first=True. + - value (Tensor): Value embeddings of shape (S, E_v) for unbatched input, (S, N, E_v) when batch_first=False, or (N, S, E_v) when batch_first=True. + - key_padding_mask (Optional[Tensor]): If specified, a mask indicating elements to be ignored in key for attention computation. + - need_weights (bool): If specified, returns attention weights in addition to attention outputs. Default: True. + - attn_mask (Optional[Tensor]): If specified, a mask preventing attention to certain positions. + - average_attn_weights (bool): If true, returns averaged attention weights per head. Otherwise, returns attention weights separately per head. Note that this flag only has an effect when need_weights=True. Default: True. + - is_causal (bool): If specified, applies a causal mask as the attention mask. Default: False. + + Returns: + Tuple[Tensor, Optional[Tensor]]: + - attn_output (Tensor): Attention outputs of shape (L, E) for unbatched input, (L, N, E) when batch_first=False, or (N, L, E) when batch_first=True. + - attn_output_weights (Optional[Tensor]): Attention weights of shape (L, S) when unbatched or (N, L, S) when batched. Optional, only returned when need_weights=True. + ``` + + # Implementation of the forward pass of the attention module goes here + + return attn_output, attn_output_weights + + ``` + # Usage example: + + multihead_attn = nn.MultiheadAttention(embed_dim, num_heads) + attn_output, attn_output_weights = multihead_attn(query, key, value) + Note: + + The above template includes the class or function definition, parameters, description, and usage example. + To replicate the documentation for any other module or framework, follow the same structure and provide the specific details for that module or framework. + + + ############# DOCUMENT THE FOLLOWING CODE ######## + {task} + """ + return documentation + + +def TEST_WRITER_SOP_PROMPT(task: str, module: str, path: str, *args, **kwargs): + TESTS_PROMPT = f""" + + Create 5,000 lines of extensive and thorough tests for the code below using the guide, do not worry about your limits you do not have any + just write the best tests possible, the module is {module}, the file path is {path} + + + ######### TESTING GUIDE ############# + + # **Guide to Creating Extensive, Thorough, and Production-Ready Tests using `pytest`** + + 1. **Preparation**: + - Install pytest: `pip install pytest`. + - Structure your project so that tests are in a separate `tests/` directory. + - Name your test files with the prefix `test_` for pytest to recognize them. + + 2. **Writing Basic Tests**: + - Use clear function names prefixed with `test_` (e.g., `test_check_value()`). + - Use assert statements to validate results. + + 3. **Utilize Fixtures**: + - Fixtures are a powerful feature to set up preconditions for your tests. + - Use `@pytest.fixture` decorator to define a fixture. + - Pass fixture name as an argument to your test to use it. + + 4. **Parameterized Testing**: + - Use `@pytest.mark.parametrize` to run a test multiple times with different inputs. + - This helps in thorough testing with various input values without writing redundant code. + + 5. **Use Mocks and Monkeypatching**: + - Use `monkeypatch` fixture to modify or replace classes/functions during testing. + - Use `unittest.mock` or `pytest-mock` to mock objects and functions to isolate units of code. + + 6. **Exception Testing**: + - Test for expected exceptions using `pytest.raises(ExceptionType)`. + + 7. **Test Coverage**: + - Install pytest-cov: `pip install pytest-cov`. + - Run tests with `pytest --cov=my_module` to get a coverage report. + + 8. **Environment Variables and Secret Handling**: + - Store secrets and configurations in environment variables. + - Use libraries like `python-decouple` or `python-dotenv` to load environment variables. + - For tests, mock or set environment variables temporarily within the test environment. + + 9. **Grouping and Marking Tests**: + - Use `@pytest.mark` decorator to mark tests (e.g., `@pytest.mark.slow`). + - This allows for selectively running certain groups of tests. + + 10. **Use Plugins**: + - Utilize the rich ecosystem of pytest plugins (e.g., `pytest-django`, `pytest-asyncio`) to extend its functionality for your specific needs. + + 11. **Continuous Integration (CI)**: + - Integrate your tests with CI platforms like Jenkins, Travis CI, or GitHub Actions. + - Ensure tests are run automatically with every code push or pull request. + + 12. **Logging and Reporting**: + - Use `pytest`'s inbuilt logging. + - Integrate with tools like `Allure` for more comprehensive reporting. + + 13. **Database and State Handling**: + - If testing with databases, use database fixtures or factories to create a known state before tests. + - Clean up and reset state post-tests to maintain consistency. + + 14. **Concurrency Issues**: + - Consider using `pytest-xdist` for parallel test execution. + - Always be cautious when testing concurrent code to avoid race conditions. + + 15. **Clean Code Practices**: + - Ensure tests are readable and maintainable. + - Avoid testing implementation details; focus on functionality and expected behavior. + + 16. **Regular Maintenance**: + - Periodically review and update tests. + - Ensure that tests stay relevant as your codebase grows and changes. + + 17. **Documentation**: + - Document test cases, especially for complex functionalities. + - Ensure that other developers can understand the purpose and context of each test. + + 18. **Feedback Loop**: + - Use test failures as feedback for development. + - Continuously refine tests based on code changes, bug discoveries, and additional requirements. + + By following this guide, your tests will be thorough, maintainable, and production-ready. Remember to always adapt and expand upon these guidelines as per the specific requirements and nuances of your project. + + + ######### CREATE TESTS FOR THIS CODE: ####### + {task} + + """ + + return TESTS_PROMPT diff --git a/scripts/auto_tests_docs/update_mkdocs.py b/scripts/auto_tests_docs/update_mkdocs.py new file mode 100644 index 00000000..4901059f --- /dev/null +++ b/scripts/auto_tests_docs/update_mkdocs.py @@ -0,0 +1,60 @@ +import yaml + + +def update_mkdocs( + class_names, base_path="docs/zeta/nn/modules", mkdocs_file="mkdocs.yml" +): + """ + Update the mkdocs.yml file with new documentation links. + + Args: + - class_names: A list of class names for which documentation is generated. + - base_path: The base path where documentation Markdown files are stored. + - mkdocs_file: The path to the mkdocs.yml file. + """ + with open(mkdocs_file, "r") as file: + mkdocs_config = yaml.safe_load(file) + + # Find or create the 'zeta.nn.modules' section in 'nav' + zeta_modules_section = None + for section in mkdocs_config.get("nav", []): + if "zeta.nn.modules" in section: + zeta_modules_section = section["zeta.nn.modules"] + break + + if zeta_modules_section is None: + zeta_modules_section = {} + mkdocs_config["nav"].append({"zeta.nn.modules": zeta_modules_section}) + + # Add the documentation paths to the 'zeta.nn.modules' section + for class_name in class_names: + doc_path = f"{base_path}/{class_name.lower()}.md" + zeta_modules_section[class_name] = doc_path + + # Write the updated content back to mkdocs.yml + with open(mkdocs_file, "w") as file: + yaml.safe_dump(mkdocs_config, file, sort_keys=False) + + +# Example usage +classes = [ + "DenseBlock", + "HighwayLayer", + "MultiScaleBlock", + "FeedbackBlock", + "DualPathBlock", + "RecursiveBlock", + "PytorchGELUTanh", + "NewGELUActivation", + "GELUActivation", + "FastGELUActivation", + "QuickGELUActivation", + "ClippedGELUActivation", + "AccurateGELUActivation", + "MishActivation", + "LinearActivation", + "LaplaceActivation", + "ReLUSquaredActivation", +] + +update_mkdocs(classes) diff --git a/scripts/test_name.sh b/scripts/test_name.sh index cdc6a013..4123f870 100755 --- a/scripts/test_name.sh +++ b/scripts/test_name.sh @@ -4,5 +4,6 @@ do dir=$(dirname "$file") if [[ $filename != test_* ]]; then mv "$file" "$dir/test_$filename" + printf "\e[1;34mRenamed: \e[0m$file \e[1;32mto\e[0m $dir/test_$filename\n" fi done \ No newline at end of file diff --git a/tests/Dockerfile b/tests/Dockerfile index d4bc1a65..fe9c14fc 100644 --- a/tests/Dockerfile +++ b/tests/Dockerfile @@ -23,7 +23,7 @@ RUN pip install poetry RUN poetry config virtualenvs.create false RUN poetry install --no-interaction --no-ansi -# Install the 'swarms' package if it's not included in the poetry.lock +# Install the 'zeta' package if it's not included in the poetry.lock RUN pip install zeta # Assuming tests require pytest to run diff --git a/tests/nn/modules/test_accurategeluactivation.py b/tests/nn/modules/test_accurategeluactivation.py new file mode 100644 index 00000000..39ef586e --- /dev/null +++ b/tests/nn/modules/test_accurategeluactivation.py @@ -0,0 +1,53 @@ +# AccurateGELUActivation + +# 1. Importing necessary libraries +import math +import pytest +import torch +from zeta.nn import AccurateGELUActivation + + +# 2. Basic Test +def test_init(): + activation = AccurateGELUActivation() + assert activation.precomputed_constant == math.sqrt(2 / math.pi) + + +# 3. Testing Forward Operation +def test_forward(): + activation = AccurateGELUActivation() + input_data = torch.Tensor([1.0, 2.0, 3.0]) + result = activation.forward(input_data) + assert torch.is_tensor(result) + + +# Parameterized Testing +@pytest.mark.parametrize( + "input_data", [([1.0, 2.0, 3.0]), ([-1.0, -2.0, -3.0]), ([0.0, 0.0, 0.0])] +) +def test_forward_parameterized(input_data): + activation = AccurateGELUActivation() + input_data = torch.Tensor(input_data) + result = activation.forward(input_data) + assert torch.is_tensor(result) + + +# Exception Testing +def test_forward_exception(): + activation = AccurateGELUActivation() + with pytest.raises(TypeError): + activation.forward("Invalid input") + + +# Mocks and Monkeypatching +def test_forward_monkeypatch(monkeypatch): + def mock_tanh(x): + return torch.Tensor([0.0 for _ in x]) + + monkeypatch.setattr(torch, "tanh", mock_tanh) + activation = AccurateGELUActivation() + input_data = torch.Tensor([1.0, 2.0, 3.0]) + result = activation.forward(input_data) + assert result.equal(torch.Tensor([0.0, 1.0, 1.5])) + + monkeypatch.undo() diff --git a/tests/nn/modules/test_clippedgeluactivation.py b/tests/nn/modules/test_clippedgeluactivation.py new file mode 100644 index 00000000..443e0a2d --- /dev/null +++ b/tests/nn/modules/test_clippedgeluactivation.py @@ -0,0 +1,64 @@ +# ClippedGELUActivation + +import pytest +from unittest.mock import Mock, patch +import torch +from torch import Tensor +from zeta.nn import ClippedGELUActivation + + +# Assume gelu function is in same module for simplicity +def gelu(x: Tensor): + return ( + 0.5 + * x + * ( + 1 + + torch.tanh( + torch.sqrt(2 / torch.pi) * (x + 0.044715 * torch.pow(x, 3)) + ) + ) + ) + + +# Test if ValueError is raised when min > max +def test_initialization_error(): + with pytest.raises(ValueError) as err: + ClippedGELUActivation(2.0, 1.0) + assert str(err.value) == "min should be < max (got min: 2.0, max: 1.0)" + + +# Test forward function with mock GELU function +def test_forward(): + mock = Mock(spec=gelu) + mock.return_value = torch.tensor([-1.0, 0.0, 1.0, 2.0]) + with patch("zeta.nn.gelu", new=mock): + act_func = ClippedGELUActivation(-0.5, 1.5) + x = torch.tensor([-2.0, -1.0, 0.0, 1.0]) + result = act_func.forward(x) + mock.assert_called_once_with(x) + assert torch.all(result.eq(torch.tensor([-0.5, 0.0, 1.0, 1.5]))) + + +# Test parametrized inputs +@pytest.mark.parametrize( + "input_tensor, output_tensor", + [ + ( + torch.tensor([-1.0, 0.0, 1.0, 2.0]), + torch.tensor([-0.5, 0.0, 0.5, 1.0]), + ), + ( + torch.tensor([0.0, 0.0, 0.0, 0.0]), + torch.tensor([0.0, 0.0, 0.0, 0.0]), + ), + ( + torch.tensor([2.0, -2.0, -2.0, 2.0]), + torch.tensor([1.0, -1.0, -1.0, 1.0]), + ), + ], +) +def test_forward_parametrized(input_tensor, output_tensor): + act_func = ClippedGELUActivation(-1.0, 1.0) + result = act_func.forward(input_tensor) + assert torch.all(result.eq(output_tensor)) diff --git a/tests/nn/modules/test_denseblock.py b/tests/nn/modules/test_denseblock.py new file mode 100644 index 00000000..67bfe5a1 --- /dev/null +++ b/tests/nn/modules/test_denseblock.py @@ -0,0 +1,37 @@ +# DenseBlock + +import torch +import torch.nn as nn +import pytest + +from zeta.nn import DenseBlock + + +def test_DenseBlock_init(): + conv = nn.Conv2d(1, 20, 5) + dense_block = DenseBlock(conv) + assert dense_block.submodule == conv, "Submodule not initialized correctly." + + +def test_DenseBlock_forward(): + conv = nn.Conv2d(1, 20, 5) + dense_block = DenseBlock(conv) + x = torch.randn(1, 1, 24, 24) + output = dense_block(x) + assert output.shape == torch.Size( + [1, 21, 20, 20] + ), "Forward function not working properly." + + +@pytest.mark.parametrize("invalid_submodule", [None, 5, "invalid", []]) +def test_DenseBlock_init_invalid_submodule(invalid_submodule): + with pytest.raises(TypeError): + dense_block = DenseBlock(invalid_submodule) + + +@pytest.mark.parametrize("invalid_input", [None, 5, "invalid", []]) +def test_DenseBlock_forward_invalid_input(invalid_input): + conv = nn.Conv2d(1, 20, 5) + dense_block = DenseBlock(conv) + with pytest.raises(Exception): + output = dense_block(invalid_input) diff --git a/tests/nn/modules/test_dualpathblock.py b/tests/nn/modules/test_dualpathblock.py new file mode 100644 index 00000000..81b254a7 --- /dev/null +++ b/tests/nn/modules/test_dualpathblock.py @@ -0,0 +1,54 @@ +# DualPathBlock + +import pytest +import torch +import torch.nn as nn +from zeta.nn import DualPathBlock + + +class TestDualPathBlock: + @pytest.fixture + def simple_modules(self): + return nn.Linear(10, 10), nn.Linear(10, 10) + + @pytest.fixture + def mock_x(self): + return torch.randn(1, 10) + + def test_initialization(self, simple_modules): + block = DualPathBlock(*simple_modules) + assert block.submodule1 == simple_modules[0] + assert block.submodule2 == simple_modules[1] + + def test_forward(self, simple_modules, mock_x): + block = DualPathBlock(*simple_modules) + output = block(mock_x) + assert isinstance(output, torch.Tensor) + assert output.shape == mock_x.shape + + @pytest.mark.parametrize( + "input_shape, output_shape", [((1, 10), (1, 10)), ((5, 10), (5, 10))] + ) + def test_shape_output(self, simple_modules, input_shape, output_shape): + block = DualPathBlock(*simple_modules) + mock_x = torch.randn(*input_shape) + assert block(mock_x).shape == output_shape + + def test_submodule1_run(self, simple_modules, mock_x, mocker): + submodule1_mock = mocker.Mock(side_effect=simple_modules[0]) + block = DualPathBlock(submodule1_mock, simple_modules[1]) + block(mock_x) + submodule1_mock.assert_called_once_with(mock_x) + + def test_submodule2_run(self, simple_modules, mock_x, mocker): + submodule2_mock = mocker.Mock(side_effect=simple_modules[1]) + block = DualPathBlock(simple_modules[0], submodule2_mock) + block(mock_x) + submodule2_mock.assert_called_once_with(mock_x) + + def test_forward_addition(self, simple_modules, mock_x): + block = DualPathBlock(*simple_modules) + expected_output = simple_modules[0](mock_x) + simple_modules[1](mock_x) + assert torch.allclose( + block(mock_x), expected_output, atol=1e-7 + ) # Use allclose because of potential floating point discrepancies diff --git a/tests/nn/modules/test_fastgeluactivation.py b/tests/nn/modules/test_fastgeluactivation.py new file mode 100644 index 00000000..67cd758f --- /dev/null +++ b/tests/nn/modules/test_fastgeluactivation.py @@ -0,0 +1 @@ +# FastGELUActivation diff --git a/tests/nn/modules/test_feedbackblock.py b/tests/nn/modules/test_feedbackblock.py new file mode 100644 index 00000000..6b75ce84 --- /dev/null +++ b/tests/nn/modules/test_feedbackblock.py @@ -0,0 +1,61 @@ +# FeedbackBlock + +# Import necessary libraries +import pytest +import torch +import torch.nn as nn +from zeta.nn import FeedbackBlock + + +# Set up simple neural network module for testing FeedbackBlock +class TestModule(nn.Module): + def __init__(self): + super(TestModule, self).__init__() + self.linear = nn.Linear(10, 10) + + def forward(self, x): + return self.linear(x) + + +# Define fixture for FeedbackBlock instance with TestModule +@pytest.fixture +def feedback_block(): + return FeedbackBlock(TestModule()) + + +def test_initialization(feedback_block): + assert isinstance(feedback_block, FeedbackBlock) + assert isinstance(feedback_block.submodule, TestModule) + + +@pytest.mark.parametrize( + "input_tensor,feedback_tensor,expected_output_shape", + [ + ( + torch.rand(1, 10), + torch.rand(1, 10), + (1, 10), + ), # Test with valid input and feedback tensors + ( + torch.rand(1, 10), + None, + (1, 10), + ), # Test with valid input and no feedback + ( + torch.rand(1, 10), + torch.rand(1, 20), + pytest.raises(ValueError), + ), # Test with mismatching dimension + ], +) +def test_forward( + feedback_block, input_tensor, feedback_tensor, expected_output_shape +): + if isinstance(expected_output_shape, tuple): + assert ( + feedback_block.forward(input_tensor, feedback_tensor).shape + == expected_output_shape + ) + else: + with expected_output_shape: + feedback_block.forward(input_tensor, feedback_tensor) diff --git a/tests/nn/modules/test_geluactivation.py b/tests/nn/modules/test_geluactivation.py new file mode 100644 index 00000000..ff20c929 --- /dev/null +++ b/tests/nn/modules/test_geluactivation.py @@ -0,0 +1,52 @@ +# GELUActivation + +import math +import pytest +import torch +from torch import Tensor +from zeta.nn import GELUActivation + + +# Basic functionality tests +@pytest.mark.parametrize( + "input, expected_output", + [ + (torch.tensor([0.0]), torch.tensor([0.0])), + ( + torch.tensor([1.0]), + torch.tensor([0.5 * (1.0 + math.erf(1.0 / math.sqrt(2.0)))]), + ), + ], +) +def test_gelu_activation_forward_method(input, expected_output): + gelu = GELUActivation(use_gelu_python=True) + assert torch.allclose(gelu.forward(input), expected_output, atol=1e-6) + + +# Test for checking if PyTorch's GELU is used when use_gelu_python is False +def test_gelu_activation_with_pytorch_gelu(): + gelu = GELUActivation(use_gelu_python=False) + input = torch.tensor([1.0]) + assert torch.allclose( + gelu.forward(input), torch.nn.functional.gelu(input), atol=1e-6 + ) + + +# Edge cases +def test_gelu_activation_with_large_positive_input(): + gelu = GELUActivation(use_gelu_python=True) + input = torch.tensor([10000.0]) + assert torch.allclose(gelu.forward(input), input, atol=1e-6) + + +def test_gelu_activation_with_large_negative_input(): + gelu = GELUActivation(use_gelu_python=True) + input = torch.tensor([-10000.0]) + assert torch.allclose(gelu.forward(input), torch.tensor([-0.0]), atol=1e-6) + + +# Error handling +def test_gelu_activation_with_invalid_input(): + gelu = GELUActivation(use_gelu_python=True) + with pytest.raises(TypeError): + _ = gelu.forward("not a tensor") diff --git a/tests/nn/modules/test_highwaylayer.py b/tests/nn/modules/test_highwaylayer.py new file mode 100644 index 00000000..ba7070ac --- /dev/null +++ b/tests/nn/modules/test_highwaylayer.py @@ -0,0 +1,61 @@ +# HighwayLayer + +import pytest +import torch +import torch.nn as nn +from zeta.nn import HighwayLayer + + +def test_highway_layer_init(): + """ + Tests for HighwayLayer's __init__ function. + """ + layer = HighwayLayer(10) + + assert isinstance(layer, nn.Module) + assert isinstance(layer.normal_layer, nn.Linear) + assert isinstance(layer.gate, nn.Linear) + assert layer.normal_layer.in_features == 10 + + # test for exception handling + with pytest.raises(TypeError): + layer = HighwayLayer("invalid_dim") + + +@pytest.mark.parametrize( + "dim, input_value, expected_dim", + [(5, [1, 2, 3, 4, 5], (5,)), (3, [[1, 2, 3], [4, 5, 6]], (2, 3))], +) +def test_highway_layer_forward(dim, input_value, expected_dim): + """ + Test for HighwayLayer's forward function. + """ + layer = HighwayLayer(dim) + tensor_input = torch.tensor(input_value, dtype=torch.float32) + tensor_output = layer.forward(tensor_input) + + # Check output type and dim + assert isinstance(tensor_output, torch.Tensor) + assert tensor_output.shape == expected_dim + assert tensor_output.dtype == torch.float32 + + +@pytest.mark.parametrize("dim", [(5), (10), (15)]) +def test_highway_layer_with_different_dim(dim): + """ + Test for HighwayLayer with different dim in the __init__ function. + """ + layer = HighwayLayer(dim) + assert layer.normal_layer.in_features == dim + assert layer.gate.in_features == dim + + +@pytest.mark.parametrize("data_type", [(torch.float16), (torch.float64)]) +def test_highway_layer_with_different_data_types(data_type): + """ + Test for HighwayLayer with different data types of input tensor in the forward function + """ + layer = HighwayLayer(5) + tensor_input = torch.tensor([1, 2, 3, 4, 5], dtype=data_type) + tensor_output = layer.forward(tensor_input) + assert tensor_output.dtype == data_type diff --git a/tests/nn/modules/test_laplaceactivation.py b/tests/nn/modules/test_laplaceactivation.py new file mode 100644 index 00000000..58138b35 --- /dev/null +++ b/tests/nn/modules/test_laplaceactivation.py @@ -0,0 +1,65 @@ +# LaplaceActivation + +import pytest +import torch +import math +from zeta.nn import LaplaceActivation + + +def test_laplace_activation_forward_default_parameters(): + laplace_activation = LaplaceActivation() + + input = torch.tensor([0.5, 1.0, 2.0]) + output = laplace_activation.forward(input) + + expected_output = 0.5 * ( + 1.0 + torch.erf((input - 0.707107) / (0.282095 * math.sqrt(2.0))) + ) + + assert torch.allclose(output, expected_output) + + +def test_laplace_activation_forward_custom_parameters(): + laplace_activation = LaplaceActivation() + + mu = 0.5 + sigma = 0.3 + input = torch.tensor([0.5, 1.0, 2.0]) + output = laplace_activation.forward(input, mu, sigma) + + expected_output = 0.5 * ( + 1.0 + torch.erf((input - mu) / (sigma * math.sqrt(2.0))) + ) + + assert torch.allclose(output, expected_output) + + +def test_laplace_activation_forward_edge_case(): + # Edge case where input values are very large or very small + laplace_activation = LaplaceActivation() + + input = torch.tensor([-1e6, 1e6]) + output = laplace_activation.forward(input) + + # Expected values would be 0.5 and 1.0 respectively. + assert torch.allclose(output, torch.tensor([0.5, 1.0])) + + +@pytest.mark.parametrize( + "input, mu, sigma, expected", + [ + ( + torch.tensor([0.5, 1.0, 2.0]), + 0.5, + 0.3, + torch.tensor([0.5, 0.5, 0.4795001]), + ), + (torch.tensor([-1e6, 1e6]), 0.5, 0.3, torch.tensor([0.0, 1.0])), + ], +) +def test_laplace_activation_forward_params(input, mu, sigma, expected): + laplace_activation = LaplaceActivation() + + output = laplace_activation.forward(input, mu, sigma) + + assert torch.allclose(output, expected) diff --git a/tests/nn/modules/test_linearactivation.py b/tests/nn/modules/test_linearactivation.py new file mode 100644 index 00000000..2d80b7b6 --- /dev/null +++ b/tests/nn/modules/test_linearactivation.py @@ -0,0 +1,26 @@ +# LinearActivation + +import torch +import pytest +from zeta.nn import LinearActivation + + +def test_LinearActivation_init(): + assert isinstance(LinearActivation(), LinearActivation) + + +@pytest.mark.parametrize( + "input_tensor", [(torch.tensor([1, 2, 3])), (torch.tensor([-1, 0, 1]))] +) +def test_LinearActivation_forward(input_tensor): + """Test if the forward method of LinearActivation class retruns the same input tensor.""" + act = LinearActivation() + assert torch.equal(act.forward(input_tensor), input_tensor) + + +@pytest.mark.parametrize("input_tensor", [(torch.tensor([1, 2, "a"]))]) +def test_LinearActivation_forward_error(input_tensor): + """Test if the forward method of LinearActivation class raises an error when input tensor is not valid.""" + act = LinearActivation() + with pytest.raises(TypeError): + act.forward(input_tensor) diff --git a/tests/nn/modules/test_mishactivation.py b/tests/nn/modules/test_mishactivation.py new file mode 100644 index 00000000..d0b9014a --- /dev/null +++ b/tests/nn/modules/test_mishactivation.py @@ -0,0 +1,35 @@ +# MishActivation + +import torch +from zeta.nn import MishActivation +from torch import nn +from packaging import version + + +def test_MishActivation_init(): + mish_activation = MishActivation() + + if version.parse(torch.__version__) < version.parse("1.9.0"): + assert mish_activation.act == mish_activation._mish_python + else: + assert mish_activation.act == nn.functional.mish + + +def test__mish_python(): + mish_activation = MishActivation() + input = torch.tensor([[0.1, 0.2, 0.3], [0.4, 0.5, 0.6]]) + expected_output = input * torch.tanh(nn.functional.softplus(input)) + + assert torch.equal(mish_activation._mish_python(input), expected_output) + + +def test_forward(): + mish_activation = MishActivation() + input = torch.tensor([[0.1, 0.2, 0.3], [0.4, 0.5, 0.6]]) + + if version.parse(torch.__version__) < version.parse("1.9.0"): + expected_output = input * torch.tanh(nn.functional.softplus(input)) + else: + expected_output = nn.functional.mish(input) + + assert torch.equal(mish_activation.forward(input), expected_output) diff --git a/tests/nn/modules/test_multiscaleblock.py b/tests/nn/modules/test_multiscaleblock.py new file mode 100644 index 00000000..ad7dd5ba --- /dev/null +++ b/tests/nn/modules/test_multiscaleblock.py @@ -0,0 +1 @@ +# MultiScaleBlock diff --git a/tests/nn/modules/test_newgeluactivation.py b/tests/nn/modules/test_newgeluactivation.py new file mode 100644 index 00000000..b2cc8fa3 --- /dev/null +++ b/tests/nn/modules/test_newgeluactivation.py @@ -0,0 +1,61 @@ +# NewGELUActivation + +import torch +from torch import nn, Tensor +import math +import pytest + +from zeta.nn import NewGELUActivation + + +def test_newgeluactivation_instance(): + gelu = NewGELUActivation() + assert isinstance(gelu, nn.Module) + + +def test_newgeluactivation_forward_valid_tensor(): + gelu = NewGELUActivation() + test_tensor = torch.randn(3, 3) + out = gelu.forward(test_tensor) + assert out.size() == test_tensor.size() + + +def test_newgeluactivation_forward_return_type(): + gelu = NewGELUActivation() + test_tensor = torch.randn(3, 3) + out = gelu.forward(test_tensor) + assert isinstance(out, Tensor) + + +def test_newgeluactivation_forward_value_range(): + gelu = NewGELUActivation() + test_tensor = torch.randn(3, 3) + out = gelu.forward(test_tensor) + assert out.min() >= 0 + assert out.max() <= 1 + + +@pytest.mark.parametrize("test_input,expected", [(-1, 0), (0, 0), (1, 1)]) +def test_newgeluactivation_forward_values(test_input, expected): + gelu = NewGELUActivation() + test_tensor = torch.tensor([test_input], dtype=torch.float32) + out = gelu.forward(test_tensor) + assert math.isclose(out.item(), expected, rel_tol=1e-7) + + +def test_newgeluactivation_forward_handle_empty(): + gelu = NewGELUActivation() + with pytest.raises(RuntimeError): + out = gelu.forward(torch.tensor([])) + + +def test_newgeluactivation_forward_handle_none(): + gelu = NewGELUActivation() + with pytest.raises(TypeError): + out = gelu.forward(None) + + +def test_newgeluactivation_forward_handle_string(): + gelu = NewGELUActivation() + with pytest.raises(TypeError): + out = gelu.forward("string") diff --git a/tests/nn/modules/test_pytorchgelutanh.py b/tests/nn/modules/test_pytorchgelutanh.py new file mode 100644 index 00000000..07667595 --- /dev/null +++ b/tests/nn/modules/test_pytorchgelutanh.py @@ -0,0 +1,41 @@ +# PytorchGELUTanh + +import pytest +import torch +from torch import nn +from zeta.nn import PytorchGELUTanh + + +def test_PytorchGELUTanh_initialization_success(): + model = PytorchGELUTanh() + assert isinstance(model, nn.Module) + + +@pytest.mark.parametrize("torch_version", ["1.11.0", "1.11.9"]) +def test_PytorchGELUTanh_initialization_fails_with_old_pytorch( + monkeypatch, torch_version +): + monkeypatch.setattr(torch, "__version__", torch_version) + with pytest.raises(ImportError) as e_info: + PytorchGELUTanh() + assert ( + str(e_info.value) + == f"You are using torch=={torch.__version__}, but torch>=1.12.0 is" + " required to use PytorchGELUTanh. Please upgrade torch." + ) + + +def test_PytorchGELUTanh_forward_propagation(): + tensor_input = torch.Tensor([2.0, 3.0, 4.0]) + model = PytorchGELUTanh() + output = model.forward(tensor_input) + target = nn.functional.gelu(tensor_input, approximate="tanh") + assert torch.allclose(output, target) + + +def test_PytorchGELUTanh_with_random_inputs(): + tensor_input = torch.rand(10, 10) + model = PytorchGELUTanh() + output = model.forward(tensor_input) + target = nn.functional.gelu(tensor_input, approximate="tanh") + assert torch.allclose(output, target) diff --git a/tests/nn/modules/test_quickgeluactivation.py b/tests/nn/modules/test_quickgeluactivation.py new file mode 100644 index 00000000..d5fa5982 --- /dev/null +++ b/tests/nn/modules/test_quickgeluactivation.py @@ -0,0 +1,64 @@ +# QuickGELUActivation + +import pytest +import torch +from zeta.nn import QuickGELUActivation + + +@pytest.fixture +def quick_gelu_activation(): + return QuickGELUActivation() + + +def test_initialization(quick_gelu_activation): + assert isinstance(quick_gelu_activation, QuickGELUActivation) + + +def test_forward_pass_zero(quick_gelu_activation): + input_tensor = torch.tensor([0.0]) + output_tensor = quick_gelu_activation.forward(input_tensor) + assert output_tensor.item() == 0.0 + + +def test_forward_pass_positive(quick_gelu_activation): + input_tensor = torch.tensor([1.0]) + output_tensor = quick_gelu_activation.forward(input_tensor) + assert output_tensor.item() > 0.0 + + +def test_forward_pass_negative(quick_gelu_activation): + input_tensor = torch.tensor([-1.0]) + output_tensor = quick_gelu_activation.forward(input_tensor) + assert output_tensor.item() < 0.0 + + +@pytest.mark.parametrize( + "input_tensor", [torch.tensor([2.0]), torch.tensor([-2.0])] +) +def test_forward_pass_greater_than_one(quick_gelu_activation, input_tensor): + output_tensor = quick_gelu_activation.forward(input_tensor) + assert abs(output_tensor.item()) > abs(input_tensor.item()) + + +def test_forward_pass_non_tensor(quick_gelu_activation): + input_data = [1, 2, 3] + with pytest.raises(TypeError): + quick_gelu_activation.forward(input_data) + + +def test_forward_pass_empty_tensor(quick_gelu_activation): + input_tensor = torch.tensor([]) + output_tensor = quick_gelu_activation.forward(input_tensor) + assert len(output_tensor) == 0.0 + + +def test_forward_pass_1d_tensor(quick_gelu_activation): + input_tensor = torch.tensor([1.0, 2.0, 3.0]) + output_tensor = quick_gelu_activation.forward(input_tensor) + assert output_tensor.shape == input_tensor.shape + + +def test_forward_pass_2d_tensor(quick_gelu_activation): + input_tensor = torch.tensor([[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]]) + output_tensor = quick_gelu_activation.forward(input_tensor) + assert output_tensor.shape == input_tensor.shape diff --git a/tests/nn/modules/test_recursiveblock.py b/tests/nn/modules/test_recursiveblock.py new file mode 100644 index 00000000..a33b1d75 --- /dev/null +++ b/tests/nn/modules/test_recursiveblock.py @@ -0,0 +1,60 @@ +# RecursiveBlock + +import pytest +import torch +import torch.nn as nn +from zeta.nn import RecursiveBlock + + +def test_recursive_block_initialization(): + block = RecursiveBlock(nn.Linear(10, 10), 5) + assert isinstance(block.modules, nn.Module) + assert isinstance(block.iters, int) + + +def test_recursive_block_forward_pass(): + module = nn.Linear(10, 10) + block = RecursiveBlock(module, 2) + input_tensor = torch.randn(3, 10) + output_tensor = block(input_tensor) + assert output_tensor.shape == torch.Size([3, 10]) + + +def test_recursive_block_fail_with_zero_iterations(): + with pytest.raises(ValueError): + RecursiveBlock(2, nn.Linear(10, 10)) + + +def test_recursive_block_fail_with_negative_iterations(): + with pytest.raises(ValueError): + RecursiveBlock(-1, nn.Linear(10, 10)) + + +@pytest.mark.parametrize("num_iterations", [1, 2, 3, 4, 5]) +def test_recursive_block_iteration_count(num_iterations): + input_tensor = torch.ones(1, 10) + module = nn.Linear(10, 10) + module.weight.data.fill_(1) + module.bias.data.fill_(1) + block = RecursiveBlock(module, num_iterations) + output_tensor = block(input_tensor) + # The output tensor should equal the input_tensor after applying the module "num_iterations" times + assert torch.all(output_tensor == torch.ones(1, 10) * num_iterations + 1) + + +def test_recursive_block_not_a_module(): + with pytest.raises(TypeError): + RecursiveBlock("not_a_module", 2) + + +def test_recursive_block_wrong_positional_arguments(): + with pytest.raises(TypeError): + RecursiveBlock(2, "not_a_module") + + +def test_recursive_block_extra_kwargs(): + with pytest.raises(TypeError): + RecursiveBlock(2, nn.Linear(10, 10), extra_kwarg=False) + + +# ... Create more tests with different nn.Modules (not just nn.Linear), different edge cases, etc. diff --git a/tests/nn/modules/test_relusquaredactivation.py b/tests/nn/modules/test_relusquaredactivation.py new file mode 100644 index 00000000..a8343c53 --- /dev/null +++ b/tests/nn/modules/test_relusquaredactivation.py @@ -0,0 +1,52 @@ +# ReLUSquaredActivation + +import pytest +import torch +from zeta.nn import ReLUSquaredActivation + + +def test_relu_squared_activation_instance(): + layer = ReLUSquaredActivation() + assert isinstance(layer, ReLUSquaredActivation) + + +def test_relu_squared_activation_forward(): + layer = ReLUSquaredActivation() + input_tensor = torch.tensor([-1.0, 0.0, 1.0, 2.0]) + output_tensor = layer.forward(input_tensor) + expected_output = torch.tensor([0.0, 0.0, 1.0, 4.0]) # Relu Squared Output + assert torch.equal(output_tensor, expected_output) + + +@pytest.mark.parametrize( + "input_tensor, expected_output", + [ + ( + torch.tensor([-1.0, 0.0, 1.0, 2.0]), + torch.tensor([0.0, 0.0, 1.0, 4.0]), + ), + ( + torch.tensor([3.0, -3.0, 3.0, -3.0]), + torch.tensor([9.0, 0.0, 9.0, 0.0]), + ), + ], +) +def test_relu_squared_activation_parametrized(input_tensor, expected_output): + layer = ReLUSquaredActivation() + output_tensor = layer.forward(input_tensor) + assert torch.equal(output_tensor, expected_output) + + +def test_relu_squared_activation_exception(): + layer = ReLUSquaredActivation() + with pytest.raises(TypeError): + layer.forward("Invalid input") + + +def test_relu_squared_activation_negative_values(): + layer = ReLUSquaredActivation() + input_tensor = torch.tensor([-1.0, -2.0, -3.0, -4.0]) + output_tensor = layer.forward(input_tensor) + assert ( + torch.sum(output_tensor) == 0 + ) # All negative values should be relu'd to zero, and then squared to zero diff --git a/tests/quant/qmoe.py b/tests/quant/test_qmoe.py similarity index 100% rename from tests/quant/qmoe.py rename to tests/quant/test_qmoe.py diff --git a/zeta/nn/modules/_activations.py b/zeta/nn/modules/_activations.py index 1aed53cc..3d9d6ec5 100644 --- a/zeta/nn/modules/_activations.py +++ b/zeta/nn/modules/_activations.py @@ -7,7 +7,8 @@ import logging -logger = logging.get_logger(__name__) +logger = logging.getLogger(__name__) +logger.setLevel(logging.INFO) class PytorchGELUTanh(nn.Module): From be8b4a5b221a43a9bd9fc4c3dd5fc01b85daa4d6 Mon Sep 17 00:00:00 2001 From: evelynmitchell Date: Tue, 26 Dec 2023 19:47:41 -0700 Subject: [PATCH 210/587] Delete tests/nn/modules/test_bitlinear.py --- tests/nn/modules/test_bitlinear.py | 52 ------------------------------ 1 file changed, 52 deletions(-) delete mode 100644 tests/nn/modules/test_bitlinear.py diff --git a/tests/nn/modules/test_bitlinear.py b/tests/nn/modules/test_bitlinear.py deleted file mode 100644 index 25cd5c02..00000000 --- a/tests/nn/modules/test_bitlinear.py +++ /dev/null @@ -1,52 +0,0 @@ -import pytest -import torch -from torch import nn -from zeta.quant.bitlinear import absmax_quantize, BitLinear - - -def test_absmax_quantize(): - x = torch.tensor([1.0, -2.0, 3.0, -4.0]) - quant, dequant = absmax_quantize(x) - - assert isinstance(quant, torch.Tensor) - assert quant.dtype == torch.int8 - assert torch.allclose(dequant, x, atol=1e-2) - - -@pytest.mark.parametrize("bits", [4, 8, 16]) -def test_absmax_quantize_different_bits(bits): - x = torch.tensor([1.0, -2.0, 3.0, -4.0]) - quant, dequant = absmax_quantize(x, bits) - - assert isinstance(quant, torch.Tensor) - assert quant.dtype == torch.int8 - assert torch.allclose(dequant, x, atol=1e-2) - - -def test_bitlinear_init(): - bitlinear = BitLinear(10, 20) - - assert isinstance(bitlinear, nn.Module) - assert bitlinear.in_features == 10 - assert bitlinear.out_features == 20 - assert bitlinear.groups == 1 - assert isinstance(bitlinear.weight, nn.Parameter) - - -def test_bitlinear_forward(): - bitlinear = BitLinear(10, 20) - input = torch.randn(128, 10) - output = bitlinear(input) - - assert isinstance(output, torch.Tensor) - assert output.shape == (128, 20) - - -@pytest.mark.parametrize("groups", [1, 2, 4]) -def test_bitlinear_different_groups(groups): - bitlinear = BitLinear(10, 20, groups) - input = torch.randn(128, 10) - output = bitlinear(input) - - assert isinstance(output, torch.Tensor) - assert output.shape == (128, 20) From cef0a9a67c9ea63a98d26b858e416b575b2deee9 Mon Sep 17 00:00:00 2001 From: Kye Date: Tue, 26 Dec 2023 22:56:23 -0500 Subject: [PATCH 211/587] [zeta.structs][TESTS][+++][Docs] --- docs/zeta/structs/autoregressivewrapper.md | 120 ++++++++++++++ docs/zeta/structs/encoder.md | 72 +++++++++ docs/zeta/structs/encoderdecoder.md | 125 +++++++++++++++ docs/zeta/structs/hierarchicalblock.md | 87 ++++++++++ docs/zeta/structs/localtransformer.md | 90 +++++++++++ docs/zeta/structs/paralleltransformerblock.md | 109 +++++++++++++ docs/zeta/structs/simpletransformer.md | 76 +++++++++ docs/zeta/structs/vitransformerwrapper.md | 150 ++++++++++++++++++ mkdocs.yml | 9 +- pyproject.toml | 2 +- scripts/auto_tests_docs/auto_docs.py | 83 +++++----- .../auto_tests_docs/auto_docs_functions.py | 73 +++++++++ scripts/auto_tests_docs/auto_tests.py | 72 ++++----- .../auto_tests_docs/auto_tests_functions.py | 79 +++++++++ scripts/auto_tests_docs/file_list.txt | 8 + scripts/auto_tests_docs/mkdocs_handler.py | 29 ++++ scripts/auto_tests_docs/update_mkdocs.py | 4 +- tests/nn/modules/test_denseblock.py | 4 +- tests/nn/modules/test_fused_gelu_dense.py | 9 +- tests/nn/modules/test_geluactivation.py | 1 - tests/nn/modules/test_img_patch_embed.py | 1 - tests/nn/modules/test_newgeluactivation.py | 6 +- tests/nn/modules/test_simple_mamba.py | 1 - tests/nn/modules/test_simple_res_block.py | 1 - tests/optim/test_lion8b.py | 30 +++- tests/quant/test_bitlinear.py | 1 - tests/quant/test_quik.py | 2 - tests/rl/test_prioritizedreplybuffer.py | 2 - .../rl/test_prioritizedsequencereplybuffer.py | 2 - tests/structs/test_autoregressive_wrapper.py | 1 - tests/structs/test_autoregressivewrapper.py | 0 tests/structs/test_encoder_decoder.py | 5 +- tests/structs/test_encoderdecoder.py | 43 +++++ tests/structs/test_hierarchicalblock.py | 64 ++++++++ tests/structs/test_localtransformer.py | 77 +++++++++ .../structs/test_paralleltransformerblock.py | 67 ++++++++ tests/structs/test_simpletransformer.py | 30 ++++ tests/structs/test_transformer.py | 47 ++++++ tests/structs/test_vitransformerwrapper.py | 49 ++++++ tests/tokenizers/test_gptx.py | 1 - tests/tokenizers/test_multimodal_tokenizer.py | 1 - tests/tokenizers/test_sentencepiece.py | 1 - tests/tokenizers/test_tokenmonster.py | 1 - zeta/quant/qmoe.py | 1 - 44 files changed, 1514 insertions(+), 122 deletions(-) create mode 100644 docs/zeta/structs/autoregressivewrapper.md create mode 100644 docs/zeta/structs/encoder.md create mode 100644 docs/zeta/structs/encoderdecoder.md create mode 100644 docs/zeta/structs/hierarchicalblock.md create mode 100644 docs/zeta/structs/localtransformer.md create mode 100644 docs/zeta/structs/paralleltransformerblock.md create mode 100644 docs/zeta/structs/simpletransformer.md create mode 100644 docs/zeta/structs/vitransformerwrapper.md create mode 100644 scripts/auto_tests_docs/auto_docs_functions.py create mode 100644 scripts/auto_tests_docs/auto_tests_functions.py create mode 100644 scripts/auto_tests_docs/file_list.txt create mode 100644 scripts/auto_tests_docs/mkdocs_handler.py create mode 100644 tests/structs/test_autoregressivewrapper.py create mode 100644 tests/structs/test_encoderdecoder.py create mode 100644 tests/structs/test_hierarchicalblock.py create mode 100644 tests/structs/test_localtransformer.py create mode 100644 tests/structs/test_paralleltransformerblock.py create mode 100644 tests/structs/test_simpletransformer.py create mode 100644 tests/structs/test_transformer.py create mode 100644 tests/structs/test_vitransformerwrapper.py diff --git a/docs/zeta/structs/autoregressivewrapper.md b/docs/zeta/structs/autoregressivewrapper.md new file mode 100644 index 00000000..75870d67 --- /dev/null +++ b/docs/zeta/structs/autoregressivewrapper.md @@ -0,0 +1,120 @@ +# AutoregressiveWrapper Class + +In the following documentation, you'll learn all about the AutoregressiveWrapper class of zeta.structs module. As autoregressive models are sequence models used to predict subsequent data points in sequence data, this class provides a wrapper that can be used to wrap any PyTorch nn.Module to make them autoregressive model compliant. + +## Table of Contents + +1. Class Definition +2. Parameters +3. Methods +4. Examples +5. Conclusion + +## 1. Class Definition + +AutoregressiveWrapper is a Python class that inherits from PyTorch's nn.Module and applies an autoregressive mask on the input sequence to any module that takes sequence input. This wrapper ensures the output sequence obeys a property inherent to causal or autoregressive models – the prediction at each position in the sequence is based only on preceding positions. + +```python +class AutoregressiveWrapper(nn.Module): +``` + +## 2. Parameters + +The parameters accepted by AutoregressiveWrapper are: + +| Name | Type | Description | Default | +|---|---|---|---| +|net|nn.Module|A PyTorch module that takes a sequence of tokens and outputs a sequence of logits.|N/A| +|ignore_index|int|The index to ignore in the target sequence when calculating the loss.|-100| +|pad_value|int|The value to pad the target sequence with.|0| +|mask_prob|float|The probability of masking a token in the input sequence.|0.0| +|speculative |bool|Whether to use speculative decoding or not.|False| + +## 3. Methods + +The methods provided by AutoregressiveWrapper are: + +### 3.1 __init__() + +The `__init__()` method initializes an instance of the AutoregressiveWrapper class. + +```python +def __init__(self, net, ignore_index=-100, pad_value=0, mask_prob=0.0, speculative=False) +``` + +### 3.2 forward() + +The `forward()` method performs forward pass of the autoregressive wrapper. + +```python +def forward(self, x, return_loss=True, **kwargs) +``` + +This method returns logits produced by the wrapped module. If `return_loss` is `True`, it also returns the loss calculated using target sequence and outputs of the wrapped module. + +### 3.3 generate() + +The `generate()` method generates a sequence of tokens from the model. + +```python +def generate(self, start_tokens, seq_len, eos_token=None, strategy="temperature", temperature=1.0, filter_logits_fn=top_k, filter_thres=0.9, min_p_pow=2.0, min_p_ratio=0.02, gamma=5, **kwargs) +``` + +You can control the sequence generation with various parameters like `strategy`, `temperature`, `filter_logits_fn` etc. + +### 3.4 generate_n_solutions() + +The `generate_n_solutions()` method generates n solutions from the model. + +```python +def generate_n_solutions(self, start_tokens, n, seqlen, **kwargs) +``` +This method is particularly useful for generating multiple forecasted sequence paths. + +### 3.5 evaluate_and_select_best_solution() + +The `evaluate_and_select_best_solution()` method evaluates the solutions based on a reward model and returns the best one. + +```python +def evaluate_and_select_best_solution(self, solutions, reward_model) +``` + + +## 4. Examples + +To help you better understand the usage of this class, here are some examples. + +First example demonstrates how to instantiate the AutoregressiveWrapper over an existing nn.module (nn.Linear in this case). + +```python +import torch +import torch.nn as nn +from zeta.structs import AutoregressiveWrapper + +net = nn.Linear(10, 10) +net = AutoregressiveWrapper(net) +x = torch.randn(1, 10) +logits, loss = net(x, return_loss=True) +print(logits.shape) +# Output: torch.Size([1, 10, 10]) # (batch_size, seq_len, vocab_size) +``` + +The second example demonstrates the usage of generate method to generate a sequence with the model. + +```python +start_tokens = torch.tensor([1,2,3]) +generated_sequence = net.generate(start_tokens, seq_len=10) +``` +This generated_sequence represents the next 10 steps in the sequence (based on the first 3 steps provided as start_tokens). + +The third example shows generating multiple solutions and selecting the best one. + +```python +solutions = net.generate_n_solutions(start_tokens, n=5, seqlen=10) +best_solution = net.evaluate_and_select_best_solution(solutions, reward_model=lambda x: -x.sum()) +``` +In the example above, the reward model simply returns the negative sum of the sequence, and the solution with lowest sum is selected as the best solution. + +## 5. Conclusion + +In this documentation, you have learned about the AutoregressiveWrapper class of zeta.structs. You should now be more comfortable and confident in leveraging this class in your neural network architectures to realize autoregressive transformation. diff --git a/docs/zeta/structs/encoder.md b/docs/zeta/structs/encoder.md new file mode 100644 index 00000000..ee32fb53 --- /dev/null +++ b/docs/zeta/structs/encoder.md @@ -0,0 +1,72 @@ +# Class Name: Encoder + +The `Encoder` class is a subclass of the AttentionLayers class used largely in transformer models for natural language processing tasks. It is intended to read and process inputs without an enforced causality - meaning it does not maintain an implied sequence or order in the data it processes. As such, the Encoder can utilize context from all directions and all inputs are independently centric in attention operations. + +## Class Signature +```python +class Encoder(AttentionLayers): + def __init__(self, **kwargs): +``` + +## Now let us dive deeper into the Class functionalities and making use of it. + +### Parameters + +|Parameter| Type | Description | +|--|--|--| +|`kwargs`| *args | arbitrary keyword arguments passed for initialization | + + +### Note +"Causal" should not be included in `kwargs`, as causality is not applicable for an Encoder. + +`super().__init__(causal=False, **kwargs)` is used to pass all arguments to the parent class i.e., AttentionLayer, where `causal=False` - ensuring that the Encoder does not consider causality in the attention/subsequent operations. + +# Example of Implementing your own custom Encoder: + +Let's take an example of creating a basic encoder for a Transformer model - + +```python +import torch.nn as nn +from zeta.structs import AttentionLayers + +class MyEncoder(AttentionLayers): + def __init__(self, d_model, nhead, num_layers): + super().__init__(d_model=d_model, nhead=nhead, num_layers=num_layers) + self.linear = nn.Linear(d_model, d_model) + + def forward(self, x): + x = super().forward(x) + return self.linear(x) +``` +We built a custom encoder by extending the AttentionLayers, added a linear layer after the attention operations. + +# Example Usage: + +Firstly, let's initialize the model: +```python +model = MyEncoder(d_model=512, nhead=8, num_layers=6) +``` +The model is initialized with the dimensions of model `d_model=512`, number of heads `nhead=8`, and the number of layers `num_layers=6`. + +Now, let's define some dummy input data and pass it through the model: + +```python +import torch + +x = torch.randn(10, 32, 512) # (sequence_length, batch_size, d_model) +output = model(x) # forward pass +print(output.shape) # torch.Size([10, 32, 512]) +``` +The method `forward()` computes the forward pass of our custom encoder model. + +## Note + +Remember, `Encoder` can be viewed as a wrapping layer around `AttentionLayers`, that ensures non-causal behaviour for the encoder in a Transformer. Hence, it is used typically for operations where the entire sequence is available for consideration - like in a Transformer's encoder, while predicting masked tokens based on surrounding context etc. + +As seen in the example, it is easy to extend the `Encoder` class and add additional layers or functionality, if required, depending upon specific use-cases. + +## Disclaimer: + The class could change since the provided code is a snippet and might not represent the final form the `Encoder` class would take. This documentation is aimed at guiding understanding of the basic idea, intent, usage and extension of the `Encoder` class based on the short provided code snippet. For exact details, refer to the actual implementation in its entirety. + + diff --git a/docs/zeta/structs/encoderdecoder.md b/docs/zeta/structs/encoderdecoder.md new file mode 100644 index 00000000..fcbdc80d --- /dev/null +++ b/docs/zeta/structs/encoderdecoder.md @@ -0,0 +1,125 @@ +# Module/Class Name: EncoderDecoder + +The `EncoderDecoder` class is a module that brings together an encoder and a decoder for sequence-to-sequence tasks. This design helps facilitate the transformation of an input sequence to an output sequence, with each sequence potentially being of a different length. + +Applications of sequence-to-sequence tasks include machine translation, speech recognition, and text summarization. + +![Image](https://miro.medium.com/max/1800/1*n-IgHZM5baBUjq0T7RYDBw.gif) + + + +This EncoderDecoder class requires an argparse.Namespace object as well as optional Tensor objects for the encoder embed tokens and positions and the decoder embed tokens and positions. + +## Class Definition + +```python +class EncoderDecoder(nn.Module): + """ + A module that combines an encoder and a decoder for sequence-to-sequence tasks. + + Args: + args (argparse.Namespace): The arguments passed to the module. + encoder_embed_tokens (torch.Tensor, optional): The input embeddings for the encoder. Defaults to None. + encoder_embed_positions (torch.Tensor, optional): The positions of the encoder input embeddings. Defaults to None. + decoder_embed_tokens (torch.Tensor, optional): The input embeddings for the decoder. Defaults to None. + decoder_embed_positions (torch.Tensor, optional): The positions of the decoder input embeddings. Defaults to None. + output_projection (torch.Tensor, optional): The projection layer for the decoder output. Defaults to None. + **kwargs: Additional keyword arguments. + + Attributes: + args (argparse.Namespace): The arguments passed to the module. + encoder (Encoder): The encoder module. + decoder (Decoder): The decoder module. + """ +... +``` + +This class has two major attributes: `encoder` and `decoder`. These attributes store the encoder and decoder modules used in sequence-to-sequence tasks. + +## Initialization of EncoderDecoder + +The `EncoderDecoder` class is initialized as follows: + +```python +def __init__( + self, + args, + encoder_embed_tokens=None, + encoder_embed_positions=None, + decoder_embed_tokens=None, + decoder_embed_positions=None, + output_projection=None, + **kwargs, +): +``` + +## Init Parameters +The EncoderDecoder class takes the following parameters during its initialization: + +| Parameter| Type | Description | +|---|---|---| +|args| argparse.Namespace| The namespace containing all the arguments needed to initialize the module.| +|encoder_embed_tokens|torch.Tensor (optional)| The input embeddings for the encoder.| +|encoder_embed_positions| torch.Tensor (optional)| The position indices for the encoder input embeddings.| +|decoder_embed_tokens|torch.Tensor (optional)| The input embeddings for the decoder.| +|decoder_embed_positions| torch.Tensor (optional)| The position indices for the decoder input embeddings.| +|output_projection| torch.Tensor (optional)| The projection matrix for the decoder output.| +|**kwargs|dict| A dictionary of additional keyword arguments.| + + +During initialization, the `EncoderDecoder` class checks if all embeddings should be shared between the encoder and decoder. If not, it initializes the encoder and decoder with their respective embed tokens and position indices. + + +## Forward Method Definition + +```python +def forward( + self, + src_tokens, + prev_output_tokens, + return_all_hiddens=False, + features_only=False, + **kwargs, +): +``` +This method executes the forward pass of the module. + +## Forward Method Parameters +| Parameter| Type | Description | +|---|---|---| +|src_tokens|torch.Tensor| The source tokens.| +|prev_output_tokens|torch.Tensor| The previous output tokens.| +|return_all_hiddens|bool (optional)| Whether to return all hidden states. Default is `False`.| +|features_only| bool (optional)| Whether to return only the features. Default is `False`.| +|**kwargs|dict| A dictionary of additional keyword arguments.| + + +## Usage Example: + +```python +# Imports +import torch +from _your_module_ import Encoder, Decoder, EncoderDecoder + +# Arguments +args = argparse.Namespace( + share_all_embeddings=True +) +src_tokens = torch.tensor([1, 2, 3]) +prev_output_tokens = torch.tensor([0, 1, 2]) + +# Define EncoderDecoder +enc_dec = EncoderDecoder(args) + +# Forward Pass +decoder_out = enc_dec(src_tokens, prev_output_tokens) + +``` +This returns the output of the decoder module. + +## Note: + +- `Encoder` and `Decoder` are assumed to be modules input to the `EncoderDecoder` class. +- Ensure that your input tensors are of the right shape and type (LongTensor for token indices and FloatTensor for embedding vectors). +- When training a model using the `EncoderDecoder` class, make sure to use the appropriate loss function that matches your specific task (e.g., CrossEntropyLoss for classification tasks). +- The argparse.Namespace class is used to hold the arguments needed by the module. It's a simple class that allows access to undefined attributes. diff --git a/docs/zeta/structs/hierarchicalblock.md b/docs/zeta/structs/hierarchicalblock.md new file mode 100644 index 00000000..c26dd601 --- /dev/null +++ b/docs/zeta/structs/hierarchicalblock.md @@ -0,0 +1,87 @@ +# Module/Class Name: HierarchicalBlock + +## Overview + +The HierarchicalBlock class in the pyTorch library is an implementation of the hierarchical token-wise attention mechanism used in some transformer models. Hierarchical token-wise attention allows a model to selectively focus on portions of the input sequence, thus the model can efficiently learn longer-range dependencies in the input data. + +It uses "nn.Module", which is a base class for all neural network modules from the PyTorch library. HierarchicalBlock provides the functionality to handle the hierarchical structure and neural network layers within the block. + +It is recommended to use this class, rather than handle the hierarchical structure of a neural network manually to ensure the hierarchical structure has an ordered representation. + +### Purpose + +The HierarchicalBlock class allows efficient modelling of attention in transformer models, enabling the model to learn long-range dependencies in the input data. This is especially useful for large-scale Natural Language Processing tasks like language translation and text summarization where long sequences of text need to be processed. + +The design of HierarchicalBlock ensures appropriate assignment and registration of submodules, which converts the parameters appropriately when methods like :meth:`to` etc. are called. + +It has the `:ivar training` variable to represent whether the module is in training or evaluation mode. + +The HierarchicalBlock class is vital for building complex models and ensuring submodules are correctly registered and parameters updated. + + +# HierarchicalBlock Class Definition + + +```python +class HierarchicalBlock(nn.Module): + def __init__(self, dim, dim_head=64, heads=8, window_size=None, compress_factor=1, stride=1, ff_mult=4): + ... +``` + +## Class Parameters + +| Parameter | Type | Description | +| --------- | ---- | ----------- | +| dim | int | Defines the dimension of the model. | +| dim_head | int | Determines the head dimensions. Default value is 64. | +| heads | int | Determines the number of parallel attention heads. Default value is 8. | +| window_size | int or NoneType | If a value exists, it specifies the size of the window for local Multihead Attention (LocalMHA). If no value exists, a standard Attention operation will be performed. Default is None. | +| compress_factor | int | Factor by which to compress inputs. Must be a power of two. Default is 1 (no compression). | +| stride | int | Stride size for the attention operation. Default is 1. | +| ff_mult | int | Multiplier for the dimension of the feed forward network hidden layer. This is used to expand the inner hidden layer of the model from the input sequence. | + + +## Methods + +### forward + +```python +def forward(self, x): + ... +``` + +## Method Parameters and returns + +| Parameter | Type | Description | +| --------- | ---- | ----------- | +| x | Tensor or array-like | The input tensor to the HierarchicalBlock instance. | + +**Returns:** + +| Return Variables | Type | Description | +| ---------------- | ---- | ----------- | +| x | Tensor or array-like | Returns the tensor after it has been processed through the 'attn' (attention) and 'ff' (feed forward) operations, and optionally compressed and padded. It returns a tensor with the same batch size but with a different sequence length, depending on the size of the window used in 'attn' and the settings of 'compress_factor' and 'stride'. | + +## Usage Example + +Import necessary modules and define an input sequence: + +```python +import torch +import torch.nn as nn +from functools import partial +from utils import is_power_of_two, pad_seq_to_multiple, token_shift, rearrange, exists + +sequence_length = 10 +batch_size = 32 +dim = 512 + +x = torch.randn(batch_size, sequence_length, dim) + +# Define an instance of HierarchicalBlock +hierarchical_block = HierarchicalBlock(dim=dim) + +# Apply the forward method of the hierarchical_block instance to x +out = hierarchical_block.forward(x) +``` +In the example above, we first import the necessary modules. We initialize a tensor `x` with random numbers, having batch_size of 32, sequence_length of 10, and dimension of 512. We define an instance of HierarchicalBlock where `dim = 512`. We then pass the tensor `x` to the forward method to get the output tensor. diff --git a/docs/zeta/structs/localtransformer.md b/docs/zeta/structs/localtransformer.md new file mode 100644 index 00000000..5eb0b8f7 --- /dev/null +++ b/docs/zeta/structs/localtransformer.md @@ -0,0 +1,90 @@ +# LocalTransformer + +## Introduction + +The `LocalTransformer` is a powerful machine learning module that implements a sequence-to-sequence model based on the local self-attention module part of the Transformer architecture. This module is specifically designed for applications where sequences of tokens are transformed, such as natural language processing tasks. + +At a high level, a transformer takes in a sequence of tokens and outputs a new sequence of tokens. Local transformer creates a module where attention is based on a limited window of the input sequence which can be beneficial for both efficiency and model performance in certain cases. + +## Definitions and Key Concepts + +- **tokens**: Individual elements of a sequence, typically words in a sentence for language tasks. +- **sequence length**: The number of tokens in each sequence. +- **embeddings**: Vector representations of tokens, which allow them to be processed by the network. +- **attention**: A mechanism in transformers that allows the model to focus on different parts of the input when producing each part of the output. + +## Class Definition + +The class signature for the `LocalTransformer` is as follows: + +``` +class LocalTransformer(nn.Module): +``` + +## Arguments + +| Argument | Type | Description | Default | +| --- | --- | --- | --- | +| num_tokens | int | The number of tokens in the input vocabulary. | - | +| max_seq_len | int | The maximum sequence length. | - | +| dim | int | The dimensionality of the token and positional embeddings. | - | +| depth | int | The number of transformer layers. | - | +| causal | bool | Whether to use causal attention or not. | True | +| local_attn_window_size | int | The size of the local attention window. | 512 | +| dim_head | int | The dimensionality of each attention head. | 64 | +| heads | int | The number of attention heads. | 8 | +| ff_mult | int | The multiplier for the feedforward network dimension. | 4 | +| attn_dropout | float | The dropout rate for attention layers. | 0.0 | +| ff_dropout | float | The dropout rate for feedforward layers. | 0.0 | +| ignore_index | int | The index to ignore during loss calculation. | -1 | +| use_xpos | bool | Whether to use positional embeddings based on xpos. | False | +| xpos_scale_base | None | The base value for scaling xpos positional embeddings. | None | +| use_dynamic_pos_bias | bool | Whether to use dynamic positional bias or not. | False | + + +### Understanding Arguments + +- **num_tokens**: This determines the size of the vocabulary. This is set according to the dataset and cannot be modified post initialization. +- **max_seq_len**: This sets the maximum sequence length. As the model would need to create key, query and values for each token, increasing this value can lead to a significant increase in memory usage. +- **dim**: This is the size of the model's embeddings. The higher this value, the more information each embedding can store. However, similarly to max_seq_len, this can also drastically increase memory usage. +- **depth**: This corresponds to the number of layers the model will have. Deeper models can potentially have better representative power, but it can also lead to overfitting and longer training times. + +## Attributes + +| Attribute | Description | +| --- | --- | +| token_emb | Embedding layer for token embeddings. | +| pos_emb | Embedding layer for positional embeddings. | +| max_seq_len | The maximum sequence length. | +| layers | List of transformer layers. | +| local_attn_window_size | The size of the local attention window. | +| dynamic_pos_bias | Dynamic positional bias layer, if enabled. | +| ignore_index | The index to ignore during loss calculation. | +| to_logits | Sequential layer for converting transformer output to logits. | + +## Example + +The following example demonstrates how to initialize and use the `LocalTransformer` class for a simple task: + +```python +import torch +from zeta.structs import LocalTransformer + +# Define a LocalTransformer +model = LocalTransformer(num_tokens=500, max_seq_len=10, dim=32, depth=2) + +# Define a simple sequence +sequence = torch.randint(0, 500, (1, 10)) + +# Forward pass +output = model(sequence) + +``` + +This will create a `LocalTransformer` model with a vocabulary of size 500, a maximum sequence length of 10, an embedding dimension of 32, and 2 transformer layers. It then performs a forward pass of the sequence through the model, outputting the transformed sequence. + +## Conclusion + +The `LocalTransformer` module is a highly flexible and modular implementation of the transformer architecture, equipped with local attention. Given its configurable nature, it is amenable to various NLP and sequence-to-sequence modeling tasks. An understanding of its input arguments, attributes, and overall design is essential to leverage its full potential. + +For any additional details or queries, please refer to external resources or related papers for an in-depth understanding of Transformers in Machine Learning. diff --git a/docs/zeta/structs/paralleltransformerblock.md b/docs/zeta/structs/paralleltransformerblock.md new file mode 100644 index 00000000..364a1931 --- /dev/null +++ b/docs/zeta/structs/paralleltransformerblock.md @@ -0,0 +1,109 @@ +# Documentation of ParallelTransformerBlock + +## Introduction + +The `ParallelTransformerBlock` is a neural network module that is a subclass of the `torch.nn.Module` class from PyTorch. It's specifically designed to create a transformer block that can process inputs in parallel efficiently making it faster. + +The transformer block performs the layered processes of layer normalization, attention inquiry, key assignment, value assessment, feedforwarding, handling of multi-head attention, and rotary embedding for the speedup and efficiency of model operations. + +## Module Structure + +Here's the class signature and structure: + +```python +class ParallelTransformerBlock(nn.Module): + def __init__(self, dim, dim_head=64, heads=8, ff_mult=4): + super().__init__() + self.norm = LayerNorm(dim) + + attn_inner_dim = dim_head * heads + ff_inner_dim = dim * ff_mult + self.fused_dims = ( + attn_inner_dim, + dim_head, + dim_head, + (ff_inner_dim * 2), + ) + + self.heads = heads + self.scale = dim_head**-0.5 + self.rotary_emb = RotaryEmbedding(dim_head) + + self.fused_attn_ff_proj = nn.Linear( + dim, sum(self.fused_dims), bias=False + ) + self.attn_out = nn.Linear(attn_inner_dim, dim, bias=False) + + self.ff_out = nn.Sequential( + SwiGLU(), nn.Linear(ff_inner_dim, dim, bias=False) + ) + + self.register_buffer("mask", None, persistent=False) + self.register_buffer("pos_emb", None, persistent=False) +``` + +#### __init__(self, dim, dim_head=64, heads=8, ff_mult=4) + +The `__init__` function initializes the `ParallelTransformerBlock` with the input dimensions, the number of attention heads, etc. + +##### Parameters: + +| Name | Type | Default Should | Description | +|------------|-------------|-----|-----| +| `dim` | int | - | The feature dimension of the input. | +| `dim_head` | int | - | Feature dimension of each head in multi-head attention. | +| `heads` | int | 8 | The number of attention heads. | +| `ff_mult` | int | 4 | Multiplier for dimensions in the feed-forward inner layer. | + +#### forward(self, x) + +The `forward` function applies the transformations of the `ParallelTransformerBlock` to an input tensor `x`. + +##### Parameters: + +| Name | Type | Default Should | Description | +|------------|-------------|-----|-----| +| `x` | Tensor | - | The input tensor to pass through the transformer block. | + +##### Returns: + +| Type | Description | +|------------|-------------| +| Tensor | The transformed output tensor. | + +## Usage Examples + +Here's an example of how you would use the `ParallelTransformerBlock`: + +```python +# Import necessary modules +import torch +import torch.nn as nn +from einops import rearrange, repeat +from einops.layers.torch import Rearrange, Reduce +from torch.nn import functional as F + +# Define features and inputs +dim = 16 +torch.manual_seed(24) +x = torch.randn(1, 10, dim) + +# Create a model instance +model = ParallelTransformerBlock(dim) + +# Run input through model +output = model(x) + +print('Input shape: ', x.shape) +print('Output shape: ', output.shape) +``` + +The default values for `dim_head`, `heads`, and `ff_mult` can be overridden as follows while instantiating the `ParallelTransformerBlock` class: + +```python +model = ParallelTransformerBlock(dim, dim_head=32, heads=4, ff_mult=2) +``` + +## Additional Notes + +The `ParallelTransformerBlock` uses the `RotaryEmbedding`, `SwiGLU`, `LayerNorm`, `apply_rotary_pos_emb` functions which are not explicitly defined in this documentation. Those are additional helper functions/classes you would need to define in your environment or import from your existing codebase. diff --git a/docs/zeta/structs/simpletransformer.md b/docs/zeta/structs/simpletransformer.md new file mode 100644 index 00000000..2b01e54c --- /dev/null +++ b/docs/zeta/structs/simpletransformer.md @@ -0,0 +1,76 @@ +# Documentation for SimpleTransformer Class + +--- + + +# Introduction + +This class provides a concise and efficient implementation for the Transformer model design, designated as `SimpleTransformer` class. The `SimpleTransformer` class is a lean and direct construal of the transformer model that is mainly used for Natural Language Processing (NLP) tasks, such as translation, sentence classification, named entity recognition (NER), among others. + +This model ensures that information flow between distant words is not lost, which is achievable by employing the attention mechanism. This Transformer model is a key part of the architecture used in several state-of-the-art models, including BERT, GPT-2, and T5. + +--- + + +# Class Definition + +The class `SimpleTransformer` inherits from the PyTorch `nn.Module` class, which itself is a subclass of the `torch._six.PY3` metaclass. This implementation builds on the abstractions provided by PyTorch to define new modules by subclassing `nn.Module`, and that a model is a big module itself. + +--- + + +# Class Constructor (__init__ method) + +The `__init__` method initializes the class instance. It takes seven arguments: + +- `self`: This is a common practice in object-oriented programming, and it refers to the object itself. In Python, this is explicitly included as the first parameter. +- `dim`: This is the dimension of the feature embeddings. Type: int. +- `depth`: This is the depth (i.e., number of layers) of the transformer. Type: int. +- `num_tokens`: This indicates the number of unique tokens in the corpus or vocabulary. Type: int. +- `dim_head`: This is the dimension of a single attention head. Type: int. Default is 64. +- `heads`: This is the total number of attention heads in the transformer. Type: int. Default is 8. +- `ff_mult`: This is the multiplier for the feed-forward layer's inner layer. Type: int. Default is 4. + +The `__init__` method further initializes three attributes: + +- `emb`: An instance of PyTorch’s `nn.Embedding` class, which turns integer indexes into dense vectors of fixed size, useful when working with sparse vectors representing categorical data. +- `transformer`: An instance of a Transformer model. +- `to_logits`: This applies a linear transformation to the incoming data, y = xA.T + b, and normalizes samples individually to unit norm. + +--- + + +# Forward Method + +The `forward` method defines the forward direction computation of the model. + +Arguments: + +- `self`: The instance of the class `SimpleTransformer`. +- `x`: The input tensor for the model. + +Implementing `forward`: At first, the input tensor `x` is sent through the Embedding layer to convert the input token ids to vectors. This vectorized output is then passed through the transformer layer. `x` finally goes through a linear layer and is returned. + +--- + + +# Example Usage + +Here is a simple demonstration on how to create an instance of the `SimpleTransformer` and run a forward pass. + +```python +# Import the necessary modules +import torch +import torch.nn as nn +from torch.nn import Transformer + +# Sample usage +module = SimpleTransformer(512, 6, 20000) +x = torch.LongTensor(2, 1024).random_(0, 20000) # creating a 2x1024 matrix of random Longs from 0 to 20000 +y = module(x) +print(y.shape) +``` + +The output tensor size is [2, 1024, 20000], where 20000 represents the number of unique tokens, and [2, 1024] represents the batch size and sequence length, respectively. + +Please note: Best Practices for PyTorch include moving tensors and models onto a common device (CPU, CUDA GPU) explicitly. diff --git a/docs/zeta/structs/vitransformerwrapper.md b/docs/zeta/structs/vitransformerwrapper.md new file mode 100644 index 00000000..449304ee --- /dev/null +++ b/docs/zeta/structs/vitransformerwrapper.md @@ -0,0 +1,150 @@ +# ViTransformerWrapper + +## Introduction + +`ViTransformerWrapper` is a PyTorch module that is part of the Zeta library. It essentially serves as a wrapper encapsulating the entirety of a Vision Transformer (ViT) model's architecture and functionality. As the name suggests, this model is a Transformer that processes images. It treats an image as a sequence of image patches, much like how a regular Transformer treats a sentence as a sequence of words or subwords. + +Since it's structurally a Transformer, `ViTransformerWrapper` leverages the multi-head self-attention mechanism which allows it to process image patches globally instead of locally. This gives `ViTransformerWrapper` the capability to reason about global image features and their intricate interrelations, a task that CNNs aren't built for. + +## Class Definition + +The `ViTransformerWrapper` class inherits from PyTorch's `nn.Module` class which is the base class for all neural network modules. This class also has a layer called `attn_layers` which must be an `Encoder` object, this `Encoder` is a standard Transformer encoder. + +```python +class ViTransformerWrapper(nn.Module): + def __init__(self, *, image_size, patch_size, attn_layers, channels=3, num_classes=None, post_emb_norm=False, emb_dropout=0.0): + def forward(self, img, return_embeddings=False): +``` + +### Parameters + +| Parameter | Type | Description | +|---------------|------|-------------| +| image_size | int | Size of the image. The dimension must be divisible by `patch_size`. | +| patch_size | int | Size of the image patches. | +| attn_layers | Encoder | Transformer encoder which will be used as the attention layers. | +| channels | int (default is 3) | Number of channels in the image. | +| num_classes | int (optional) | Number of classes in the classification task. If `None`, the model will output raw embeddings. | +| post_emb_norm | bool (default is `False`) | If `True`, enables normalization of embeddings after they are generated. | +| emb_dropout | float (default is 0.0) | Dropout rate for the embeddings. | + +### Attributes + +| Attribute | Type | Description | +|--------------|------|-------------| +| training | bool | Represents whether the module is in training mode or evaluation mode. | + +Attributes, methods and submodules assigned in the `__init__` method are registered in the module and will have their parameters converted too when you call `to()`, etc. + +### Method: `forward` + +The `forward` method is called when we execute the `ViTransformerWrapper` instance as a function. It feeds an image through the model and computes the forward pass. If `return_embeddings` is set to `True`, the method will output raw embeddings, otherwise it will output the predictions of the model, using the `mlp_head` which is a fully-connected layer applied after the Transformer layers. + +Parameters: + +- `img` (Tensor): Input image. +- `return_embeddings` (bool, optional): If `True`, the method returns raw embeddings. If `False` (default), the method returns the class predictions. + +## Usage Examples + +Here are three usage examples: + +### Example 1: Basic Usage + +```python +from zeta.structs import ViTransformerWrapper, Encoder + +# create a Transformer encoder instance +encoder = Encoder(dim=128, depth=12) + +# define the wrapper with the encoder +wrapper = ViTransformerWrapper(image_size=224, patch_size=16, attn_layers=encoder) + +# sample image +img = torch.randn(1, 3, 224, 224) + +# output of the model +out = wrapper(img) +``` + +In this example, we first create an instance of a Transformer encoder with a dimension of 128 and a depth of 12. Then we instanstiate the `ViTransformerWrapper` with an image size of 224, a patch size of 16 and the previously created Transformer encoder. Afterwards, we simulate an image input of torch size (1, 3, 224, 224) and feed it through the model by calling `wrapper(img)`, the resulting `out` is the output of the model. + +### Example 2: Training Loop + +```python +from zeta.structs import ViTransformerWrapper, Encoder + +# create a Transformer encoder instance +encoder = Encoder(dim=128, depth=12) + +# define the wrapper with the encoder and the number of classes +model = ViTransformerWrapper(image_size=224, patch_size=16, attn_layers=encoder, num_classes=10) + +# define a loss function +criterion = nn.CrossEntropyLoss() + +# define an optimizer +optimizer = torch.optim.Adam(model.parameters()) + +# sample inputs and targets +inputs = torch.randn(32, 3, 224, 224) +targets = torch.randint(0, 10, [32]) + +# training loop +for i in range(100): + + # zero the parameter gradients + optimizer.zero_grad() + + # forward pass + outputs = model(inputs) + + # compute the loss + loss = criterion(outputs, targets) + + # backward pass and optimize + loss.backward() + optimizer.step() + + # print statistics + print('loss: {:.4f}'.format(loss.item())) +``` + +This example shows a basic training loop for the `ViTransformerWrapper`. In this training loop, we use a cross entropy loss and Adam as the optimizer. The loop goes for 100 iterations, in each iteration it firstly zeroes the gradients, conducts forward pass to compute the model's output, then computes the loss based on the output and the ground truth, backpropagates the gradients and finally updates the model's parameters according to the Adam optimizer. The loss is printed out at every iteration. + +### Example 3: Embeddings + +```python +from zeta.structs import ViTransformerWrapper, Encoder + +# create a Transformer encoder instance +encoder = Encoder(dim=128, depth=12) + +# define the wrapper with the encoder +model = ViTransformerWrapper(image_size=224, patch_size=16, attn_layers=encoder) + +# sample inputs +inputs = torch.randn(1, 3, 224, 224) + +# compute the embeddings +embeddings = model(inputs, return_embeddings=True) +``` + +In this example, the `ViTransformerWrapper` returns raw embeddings since `return_embeddings` is set to `True`. The returned `embeddings` can then be used for other tasks such as clustering or nearest neighbours search. + +## Additional Information + +The `ViTransformerWrapper` class assumes that you're working with square images, i.e. height equals width. Be sure to resize your images appropriately or pad them if they are not originally square. + +Also, the `mlp_head` output layer is initialized as an `nn.Identity` layer if `num_classes` is not specified, meaning the Transformer's output embeddings will be passed through without transformation. + +Furthermore, the model relies on 2D convolutions, layer normalization and linear transformations, making it applicable to a wide range of tasks involving image data beyond image classification, such as object detection and instance segmentation, given suitable adjustments. + +Lastly, vision transformers are computationally expensive and use significantly more memory than their CNN counterparts since self-attention operates in quadratic space and time. Consider this if using a vision transformer in your project. + +## External Resources + +- For further understanding on Transformers, you can read the following paper: [Attention is All You Need](https://arxiv.org/abs/1706.03762) +- For the original Vision Transformer paper, you can read: [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://arxiv.org/abs/2010.11929) +- To know more about the implementation of the transformer model, consider reading the [Transformers Module in PyTorch](https://pytorch.org/docs/stable/nn.html#transformer-layers) documentation. +- For more tutorials and examples using PyTorch, you can check out their [tutorials page](https://pytorch.org/tutorials/). diff --git a/mkdocs.yml b/mkdocs.yml index 98d8088c..d825fe15 100644 --- a/mkdocs.yml +++ b/mkdocs.yml @@ -144,7 +144,14 @@ nav: - Decoder: "zeta/nn/architecture/decoder.md" - Transformer: "zeta/nn/architecture/transformer.md" - TransformerBlock: "zeta/nn/architecture/transformerblock.md" - - VideoTokenizer: "zeta/nn/architecture/video_tokenizer.md" + - paralleltransformerblock: "paralleltransformerblock.md" + - hierarchicalblock: "hierarchicalblock.md" + - vitransformerwrapper: "vitransformerwrapper.md" + - localtransformer: "localtransformer.md" + - autoregressivewrapper: "autoregressivewrapper.md" + - simpletransformer: "simpletransformer.md" + - encoder: "encoder.md" + - encoderdecoder: "encoderdecoder.md" - zeta.training.loss: - Nebula: "zeta/training/nebula.md" - zeta.training.optimizers: diff --git a/pyproject.toml b/pyproject.toml index 74d985e4..a107b13b 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,6 +1,6 @@ [tool.poetry] name = "zetascale" -version = "1.2.7" +version = "1.2.9" description = "Transformers at zeta scales" authors = ["Zeta Team "] license = "MIT" diff --git a/scripts/auto_tests_docs/auto_docs.py b/scripts/auto_tests_docs/auto_docs.py index d6e1060a..5e44c143 100644 --- a/scripts/auto_tests_docs/auto_docs.py +++ b/scripts/auto_tests_docs/auto_docs.py @@ -2,28 +2,27 @@ import inspect import os import threading -from zeta import OpenAIChat + +from dotenv import load_dotenv + from scripts.auto_tests_docs.docs import DOCUMENTATION_WRITER_SOP -from zeta.nn.modules._activations import ( - AccurateGELUActivation, - ClippedGELUActivation, - FastGELUActivation, - GELUActivation, - LaplaceActivation, - LinearActivation, - MishActivation, - NewGELUActivation, - PytorchGELUTanh, - QuickGELUActivation, - ReLUSquaredActivation, +from swarms import OpenAIChat +from zeta.structs.auto_regressive_wrapper import AutoregressiveWrapper +from zeta.structs.encoder_decoder import EncoderDecoder +from zeta.structs.hierarchical_transformer import ( + HierarchicalBlock, + HierarchicalTransformer, +) +from zeta.structs.local_transformer import LocalTransformer +from zeta.structs.simple_transformer import ( + ParallelTransformerBlock, + SimpleTransformer, +) +from zeta.structs.transformer import ( + Encoder, + Transformer, + ViTransformerWrapper, ) -from zeta.nn.modules.dense_connect import DenseBlock -from zeta.nn.modules.dual_path_block import DualPathBlock -from zeta.nn.modules.feedback_block import FeedbackBlock -from zeta.nn.modules.highway_layer import HighwayLayer -from zeta.nn.modules.multi_scale_block import MultiScaleBlock -from zeta.nn.modules.recursive_block import RecursiveBlock -from dotenv import load_dotenv load_dotenv() @@ -43,18 +42,21 @@ def process_documentation(cls): doc = inspect.getdoc(cls) source = inspect.getsource(cls) input_content = ( - f"Class Name: {cls.__name__}\n\nDocumentation:\n{doc}\n\nSource" + "Class Name:" + f" {cls.__name__}\n\nDocumentation:\n{doc}\n\nSource" f" Code:\n{source}" ) - print(input_content) # Process with OpenAI model (assuming the model's __call__ method takes this input and returns processed content) - processed_content = model(DOCUMENTATION_WRITER_SOP(input_content, "zeta")) + processed_content = model( + DOCUMENTATION_WRITER_SOP(input_content, "zeta.structs") + ) - doc_content = f"# {cls.__name__}\n\n{processed_content}\n" + # doc_content = f"# {cls.__name__}\n\n{processed_content}\n" + doc_content = f"{processed_content}\n" # Create the directory if it doesn't exist - dir_path = "docs/zeta/nn/modules" + dir_path = "docs/zeta/structs" os.makedirs(dir_path, exist_ok=True) # Write the processed documentation to a Markdown file @@ -62,26 +64,21 @@ def process_documentation(cls): with open(file_path, "w") as file: file.write(doc_content) + print(f"Documentation generated for {cls.__name__}.") + def main(): classes = [ - DenseBlock, - HighwayLayer, - MultiScaleBlock, - FeedbackBlock, - DualPathBlock, - RecursiveBlock, - PytorchGELUTanh, - NewGELUActivation, - GELUActivation, - FastGELUActivation, - QuickGELUActivation, - ClippedGELUActivation, - AccurateGELUActivation, - MishActivation, - LinearActivation, - LaplaceActivation, - ReLUSquaredActivation, + AutoregressiveWrapper, + Encoder, + EncoderDecoder, + HierarchicalBlock, + HierarchicalTransformer, + LocalTransformer, + ParallelTransformerBlock, + Transformer, + ViTransformerWrapper, + SimpleTransformer, ] threads = [] @@ -94,7 +91,7 @@ def main(): for thread in threads: thread.join() - print("Documentation generated in 'docs/zeta/nn/modules' directory.") + print("Documentation generated in 'docs/zeta' directory.") if __name__ == "__main__": diff --git a/scripts/auto_tests_docs/auto_docs_functions.py b/scripts/auto_tests_docs/auto_docs_functions.py new file mode 100644 index 00000000..45d66eca --- /dev/null +++ b/scripts/auto_tests_docs/auto_docs_functions.py @@ -0,0 +1,73 @@ +import inspect +import os +import sys +import threading + +from dotenv import load_dotenv + +from scripts.auto_tests_docs.docs import DOCUMENTATION_WRITER_SOP +from swarms import OpenAIChat + +load_dotenv() + +api_key = os.getenv("OPENAI_API_KEY") + +model = OpenAIChat( + model_name="gpt-4", + openai_api_key=api_key, + max_tokens=4000, +) + + +def process_documentation(item): + """ + Process the documentation for a given function using OpenAI model and save it in a Markdown file. + """ + doc = inspect.getdoc(item) + source = inspect.getsource(item) + input_content = ( + f"Name: {item.__name__}\n\nDocumentation:\n{doc}\n\nSource" + f" Code:\n{source}" + ) + print(input_content) + + # Process with OpenAI model + processed_content = model( + DOCUMENTATION_WRITER_SOP(input_content, "swarms.utils") + ) + + doc_content = f"# {item.__name__}\n\n{processed_content}\n" + + # Create the directory if it doesn't exist + dir_path = "docs/swarms/utils" + os.makedirs(dir_path, exist_ok=True) + + # Write the processed documentation to a Markdown file + file_path = os.path.join(dir_path, f"{item.__name__.lower()}.md") + with open(file_path, "w") as file: + file.write(doc_content) + + +def main(): + # Gathering all functions from the swarms.utils module + functions = [ + obj + for name, obj in inspect.getmembers(sys.modules["swarms.utils"]) + if inspect.isfunction(obj) + ] + + threads = [] + for func in functions: + thread = threading.Thread(target=process_documentation, args=(func,)) + threads.append(thread) + thread.start() + + # Wait for all threads to complete + for thread in threads: + thread.join() + + print("Documentation generated in 'docs/swarms/utils' directory.") + + +if __name__ == "__main__": + main() diff --git a/scripts/auto_tests_docs/auto_tests.py b/scripts/auto_tests_docs/auto_tests.py index 70a3d750..b025f294 100644 --- a/scripts/auto_tests_docs/auto_tests.py +++ b/scripts/auto_tests_docs/auto_tests.py @@ -4,25 +4,22 @@ import threading from swarms import OpenAIChat from scripts.auto_tests_docs.docs import TEST_WRITER_SOP_PROMPT -from zeta.nn.modules._activations import ( - AccurateGELUActivation, - ClippedGELUActivation, - FastGELUActivation, - GELUActivation, - LaplaceActivation, - LinearActivation, - MishActivation, - NewGELUActivation, - PytorchGELUTanh, - QuickGELUActivation, - ReLUSquaredActivation, +from zeta.structs.auto_regressive_wrapper import AutoregressiveWrapper +from zeta.structs.encoder_decoder import EncoderDecoder +from zeta.structs.hierarchical_transformer import ( + HierarchicalBlock, + HierarchicalTransformer, +) +from zeta.structs.local_transformer import LocalTransformer +from zeta.structs.simple_transformer import ( + ParallelTransformerBlock, + SimpleTransformer, +) +from zeta.structs.transformer import ( + Encoder, + Transformer, + ViTransformerWrapper, ) -from zeta.nn.modules.dense_connect import DenseBlock -from zeta.nn.modules.dual_path_block import DualPathBlock -from zeta.nn.modules.feedback_block import FeedbackBlock -from zeta.nn.modules.highway_layer import HighwayLayer -from zeta.nn.modules.multi_scale_block import MultiScaleBlock -from zeta.nn.modules.recursive_block import RecursiveBlock from dotenv import load_dotenv load_dotenv() @@ -61,10 +58,10 @@ def create_test(cls): doc = inspect.getdoc(cls) source = inspect.getsource(cls) input_content = ( - f"Class Name: {cls.__name__}\n\nDocumentation:\n{doc}\n\nSource" + "Class Name:" + f" {cls.__name__}\n\nDocumentation:\n{doc}\n\nSource" f" Code:\n{source}" ) - print(input_content) # Process with OpenAI model (assuming the model's __call__ method takes this input and returns processed content) processed_content = model( @@ -72,10 +69,10 @@ def create_test(cls): ) processed_content = extract_code_from_markdown(processed_content) - doc_content = f"# {cls.__name__}\n\n{processed_content}\n" + doc_content = f"{processed_content}" # Create the directory if it doesn't exist - dir_path = "tests/nn/modules" + dir_path = "tests/structs" os.makedirs(dir_path, exist_ok=True) # Write the processed documentation to a Python file @@ -83,26 +80,21 @@ def create_test(cls): with open(file_path, "w") as file: file.write(doc_content) + print(f"Test generated for {cls.__name__}.") + def main(): classes = [ - DenseBlock, - HighwayLayer, - MultiScaleBlock, - FeedbackBlock, - DualPathBlock, - RecursiveBlock, - PytorchGELUTanh, - NewGELUActivation, - GELUActivation, - FastGELUActivation, - QuickGELUActivation, - ClippedGELUActivation, - AccurateGELUActivation, - MishActivation, - LinearActivation, - LaplaceActivation, - ReLUSquaredActivation, + AutoregressiveWrapper, + Encoder, + Transformer, + ViTransformerWrapper, + SimpleTransformer, + ParallelTransformerBlock, + EncoderDecoder, + LocalTransformer, + HierarchicalBlock, + HierarchicalTransformer, ] threads = [] @@ -115,7 +107,7 @@ def main(): for thread in threads: thread.join() - print("Tests generated in 'docs/zeta/nn/modules' directory.") + print("Tests generated in 'tests/structs' directory.") if __name__ == "__main__": diff --git a/scripts/auto_tests_docs/auto_tests_functions.py b/scripts/auto_tests_docs/auto_tests_functions.py new file mode 100644 index 00000000..fb96442a --- /dev/null +++ b/scripts/auto_tests_docs/auto_tests_functions.py @@ -0,0 +1,79 @@ +import inspect +import os +import sys +import threading + +from dotenv import load_dotenv + +from scripts.auto_tests_docs.docs import TEST_WRITER_SOP_PROMPT +from swarms import OpenAIChat +from swarms.utils.parse_code import extract_code_from_markdown +from swarms.utils import ( + extract_code_from_markdown, +) + +load_dotenv() + +api_key = os.getenv("OPENAI_API_KEY") + +model = OpenAIChat( + model_name="gpt-4", + openai_api_key=api_key, + max_tokens=4000, +) + + +def process_documentation(item): + """ + Process the documentation for a given function using OpenAI model and save it in a Markdown file. + """ + doc = inspect.getdoc(item) + source = inspect.getsource(item) + input_content = ( + f"Name: {item.__name__}\n\nDocumentation:\n{doc}\n\nSource" + f" Code:\n{source}" + ) + # print(input_content) + + # Process with OpenAI model + processed_content = model( + TEST_WRITER_SOP_PROMPT(input_content, "swarms.utils", "swarms.utils") + ) + processed_content = extract_code_from_markdown(processed_content) + print(processed_content) + + doc_content = f"{processed_content}" + + # Create the directory if it doesn't exist + dir_path = "tests/utils" + os.makedirs(dir_path, exist_ok=True) + + # Write the processed documentation to a Markdown file + file_path = os.path.join(dir_path, f"{item.__name__.lower()}.py") + with open(file_path, "w") as file: + file.write(doc_content) + + +def main(): + # Gathering all functions from the swarms.utils module + functions = [ + obj + for name, obj in inspect.getmembers(sys.modules["swarms.utils"]) + if inspect.isfunction(obj) + ] + + threads = [] + for func in functions: + thread = threading.Thread(target=process_documentation, args=(func,)) + threads.append(thread) + thread.start() + + # Wait for all threads to complete + for thread in threads: + thread.join() + + print("Tests generated in 'tests/utils' directory.") + + +if __name__ == "__main__": + main() diff --git a/scripts/auto_tests_docs/file_list.txt b/scripts/auto_tests_docs/file_list.txt new file mode 100644 index 00000000..d8a01eb8 --- /dev/null +++ b/scripts/auto_tests_docs/file_list.txt @@ -0,0 +1,8 @@ +- paralleltransformerblock: "paralleltransformerblock.md" +- hierarchicalblock: "hierarchicalblock.md" +- vitransformerwrapper: "vitransformerwrapper.md" +- localtransformer: "localtransformer.md" +- autoregressivewrapper: "autoregressivewrapper.md" +- simpletransformer: "simpletransformer.md" +- encoder: "encoder.md" +- encoderdecoder: "encoderdecoder.md" diff --git a/scripts/auto_tests_docs/mkdocs_handler.py b/scripts/auto_tests_docs/mkdocs_handler.py new file mode 100644 index 00000000..d57a3e95 --- /dev/null +++ b/scripts/auto_tests_docs/mkdocs_handler.py @@ -0,0 +1,29 @@ +import os + + +def generate_file_list(directory, output_file): + """ + Generate a list of files in a directory in the specified format and write it to a file. + + Args: + directory (str): The directory to list the files from. + output_file (str): The file to write the output to. + """ + with open(output_file, "w") as f: + for root, dirs, files in os.walk(directory): + for file in files: + if file.endswith(".md"): + # Remove the directory from the file path and replace slashes with dots + file_path = ( + os.path.join(root, file) + .replace(directory + "/", "") + .replace("/", ".") + ) + # Remove the file extension + file_name, _ = os.path.splitext(file) + # Write the file name and path to the output file + f.write(f'- {file_name}: "{file_path}"\n') + + +# Use the function to generate the file list +generate_file_list("docs/zeta/structs", "file_list.txt") diff --git a/scripts/auto_tests_docs/update_mkdocs.py b/scripts/auto_tests_docs/update_mkdocs.py index 4901059f..c847b8a1 100644 --- a/scripts/auto_tests_docs/update_mkdocs.py +++ b/scripts/auto_tests_docs/update_mkdocs.py @@ -2,7 +2,9 @@ def update_mkdocs( - class_names, base_path="docs/zeta/nn/modules", mkdocs_file="mkdocs.yml" + class_names, + base_path="docs/zeta/nn/modules", + mkdocs_file="mkdocs.yml", ): """ Update the mkdocs.yml file with new documentation links. diff --git a/tests/nn/modules/test_denseblock.py b/tests/nn/modules/test_denseblock.py index 67bfe5a1..e90c0eb3 100644 --- a/tests/nn/modules/test_denseblock.py +++ b/tests/nn/modules/test_denseblock.py @@ -26,7 +26,7 @@ def test_DenseBlock_forward(): @pytest.mark.parametrize("invalid_submodule", [None, 5, "invalid", []]) def test_DenseBlock_init_invalid_submodule(invalid_submodule): with pytest.raises(TypeError): - dense_block = DenseBlock(invalid_submodule) + DenseBlock(invalid_submodule) @pytest.mark.parametrize("invalid_input", [None, 5, "invalid", []]) @@ -34,4 +34,4 @@ def test_DenseBlock_forward_invalid_input(invalid_input): conv = nn.Conv2d(1, 20, 5) dense_block = DenseBlock(conv) with pytest.raises(Exception): - output = dense_block(invalid_input) + dense_block(invalid_input) diff --git a/tests/nn/modules/test_fused_gelu_dense.py b/tests/nn/modules/test_fused_gelu_dense.py index f0390bf7..4f295d3c 100644 --- a/tests/nn/modules/test_fused_gelu_dense.py +++ b/tests/nn/modules/test_fused_gelu_dense.py @@ -1,4 +1,3 @@ -import pytest import torch from zeta.nn.modules.fused_gelu_dense import FusedDenseGELUDense @@ -8,8 +7,8 @@ def test_class_init(): assert model.dim == 512 assert model.dim_out == 1024 - assert model.bias == True - assert model.has_fp16_weights == False + assert model.bias is True + assert model.has_fp16_weights is False assert model.threshold == 6.0 @@ -20,8 +19,8 @@ def test_class_init_with_args(): assert model.dim == 512 assert model.dim_out == 1024 - assert model.bias == False - assert model.has_fp16_weights == True + assert model.bias is False + assert model.has_fp16_weights is True assert model.threshold == 5.0 diff --git a/tests/nn/modules/test_geluactivation.py b/tests/nn/modules/test_geluactivation.py index ff20c929..a30bcb3b 100644 --- a/tests/nn/modules/test_geluactivation.py +++ b/tests/nn/modules/test_geluactivation.py @@ -3,7 +3,6 @@ import math import pytest import torch -from torch import Tensor from zeta.nn import GELUActivation diff --git a/tests/nn/modules/test_img_patch_embed.py b/tests/nn/modules/test_img_patch_embed.py index 2f38d2d3..a8d545c2 100644 --- a/tests/nn/modules/test_img_patch_embed.py +++ b/tests/nn/modules/test_img_patch_embed.py @@ -1,6 +1,5 @@ # FILEPATH: /Users/defalt/Desktop/Athena/research/zeta/tests/nn/modules/test_img_patch_embed.py -import pytest from torch import nn import torch from zeta.nn.modules.img_patch_embed import ImgPatchEmbed diff --git a/tests/nn/modules/test_newgeluactivation.py b/tests/nn/modules/test_newgeluactivation.py index b2cc8fa3..b4b70389 100644 --- a/tests/nn/modules/test_newgeluactivation.py +++ b/tests/nn/modules/test_newgeluactivation.py @@ -46,16 +46,16 @@ def test_newgeluactivation_forward_values(test_input, expected): def test_newgeluactivation_forward_handle_empty(): gelu = NewGELUActivation() with pytest.raises(RuntimeError): - out = gelu.forward(torch.tensor([])) + gelu.forward(torch.tensor([])) def test_newgeluactivation_forward_handle_none(): gelu = NewGELUActivation() with pytest.raises(TypeError): - out = gelu.forward(None) + gelu.forward(None) def test_newgeluactivation_forward_handle_string(): gelu = NewGELUActivation() with pytest.raises(TypeError): - out = gelu.forward("string") + gelu.forward("string") diff --git a/tests/nn/modules/test_simple_mamba.py b/tests/nn/modules/test_simple_mamba.py index bcf20cfd..66d854e3 100644 --- a/tests/nn/modules/test_simple_mamba.py +++ b/tests/nn/modules/test_simple_mamba.py @@ -1,6 +1,5 @@ # FILEPATH: /Users/defalt/Desktop/Athena/research/zeta/tests/nn/modules/test_simple_mamba.py -import pytest import torch from torch import nn from zeta.nn.modules.simple_mamba import Mamba, ResidualBlock, RMSNorm diff --git a/tests/nn/modules/test_simple_res_block.py b/tests/nn/modules/test_simple_res_block.py index d734662d..a81b1952 100644 --- a/tests/nn/modules/test_simple_res_block.py +++ b/tests/nn/modules/test_simple_res_block.py @@ -1,5 +1,4 @@ import torch -import pytest from zeta.nn.modules.simple_resblock import SimpleResBlock diff --git a/tests/optim/test_lion8b.py b/tests/optim/test_lion8b.py index bc4edd08..82bb6f22 100644 --- a/tests/optim/test_lion8b.py +++ b/tests/optim/test_lion8b.py @@ -44,7 +44,10 @@ def test_step_without_closure(): def test_step_with_closure(): params = [torch.randn(3, 3, requires_grad=True) for _ in range(2)] optimizer = DecoupledLionW8Bit(params) - closure = lambda: torch.sum(params[0] ** 2 + params[1] ** 2) + + def closure(): + return torch.sum(params[0] ** 2 + params[1] ** 2) + loss = optimizer.step(closure) assert loss is not None @@ -62,7 +65,10 @@ def test_step_param_no_grad(): def test_step_param_with_grad(): params = [torch.randn(3, 3, requires_grad=True) for _ in range(2)] optimizer = DecoupledLionW8Bit(params) - closure = lambda: torch.sum(params[0] ** 2 + params[1] ** 2) + + def closure(): + return torch.sum(params[0] ** 2 + params[1] ** 2) + closure().backward() optimizer.step_param(params[0], optimizer.param_groups[0]) @@ -72,7 +78,10 @@ def test_step_param_with_grad(): def test_step_param_not_cuda(): params = [torch.randn(3, 3, requires_grad=True) for _ in range(2)] optimizer = DecoupledLionW8Bit(params, quantize=True) - closure = lambda: torch.sum(params[0] ** 2 + params[1] ** 2) + + def closure(): + return torch.sum(params[0] ** 2 + params[1] ** 2) + closure().backward() with pytest.raises(NotImplementedError): @@ -96,7 +105,10 @@ def test_step_without_closure(): def test_step_with_closure(): params = [torch.randn(3, 3, requires_grad=True) for _ in range(2)] optimizer = DecoupledLionW8Bit(params) - closure = lambda: torch.sum(params[0] ** 2 + params[1] ** 2) + + def closure(): + return torch.sum(params[0] ** 2 + params[1] ** 2) + loss = optimizer.step(closure) assert loss is not None @@ -114,7 +126,10 @@ def test_step_param_no_grad(): def test_step_param_with_grad(): params = [torch.randn(3, 3, requires_grad=True) for _ in range(2)] optimizer = DecoupledLionW8Bit(params) - closure = lambda: torch.sum(params[0] ** 2 + params[1] ** 2) + + def closure(): + return torch.sum(params[0] ** 2 + params[1] ** 2) + closure().backward() optimizer.step_param(params[0], optimizer.param_groups[0]) @@ -124,7 +139,10 @@ def test_step_param_with_grad(): def test_step_param_not_cuda(): params = [torch.randn(3, 3, requires_grad=True) for _ in range(2)] optimizer = DecoupledLionW8Bit(params, quantize=True) - closure = lambda: torch.sum(params[0] ** 2 + params[1] ** 2) + + def closure(): + return torch.sum(params[0] ** 2 + params[1] ** 2) + closure().backward() with pytest.raises(NotImplementedError): diff --git a/tests/quant/test_bitlinear.py b/tests/quant/test_bitlinear.py index 64467687..8b49fcb7 100644 --- a/tests/quant/test_bitlinear.py +++ b/tests/quant/test_bitlinear.py @@ -1,6 +1,5 @@ import pytest import torch -from torch import nn from zeta.quant.bitlinear import BitLinear, absmax_quantize diff --git a/tests/quant/test_quik.py b/tests/quant/test_quik.py index df87bcb8..4a7db815 100644 --- a/tests/quant/test_quik.py +++ b/tests/quant/test_quik.py @@ -1,6 +1,4 @@ -import pytest import torch -from torch import nn from zeta.quant.quick import QUIK diff --git a/tests/rl/test_prioritizedreplybuffer.py b/tests/rl/test_prioritizedreplybuffer.py index fcfcac78..ec516436 100644 --- a/tests/rl/test_prioritizedreplybuffer.py +++ b/tests/rl/test_prioritizedreplybuffer.py @@ -1,9 +1,7 @@ import pytest -import random import torch from zeta.rl.priortized_replay_buffer import ( PrioritizedReplayBuffer, - SumTree, ) # Replace 'your_module' with the actual module where classes are defined diff --git a/tests/rl/test_prioritizedsequencereplybuffer.py b/tests/rl/test_prioritizedsequencereplybuffer.py index 0201e848..ddb315e3 100644 --- a/tests/rl/test_prioritizedsequencereplybuffer.py +++ b/tests/rl/test_prioritizedsequencereplybuffer.py @@ -1,9 +1,7 @@ import pytest -import random import torch from zeta.rl.priortized_rps import ( PrioritizedSequenceReplayBuffer, - SumTree, ) # Replace 'your_module' with the actual module where classes are defined diff --git a/tests/structs/test_autoregressive_wrapper.py b/tests/structs/test_autoregressive_wrapper.py index 684410ba..2d6ea44e 100644 --- a/tests/structs/test_autoregressive_wrapper.py +++ b/tests/structs/test_autoregressive_wrapper.py @@ -1,5 +1,4 @@ import torch -import pytest from zeta.structs.auto_regressive_wrapper import AutoregressiveWrapper from torch import nn diff --git a/tests/structs/test_autoregressivewrapper.py b/tests/structs/test_autoregressivewrapper.py new file mode 100644 index 00000000..e69de29b diff --git a/tests/structs/test_encoder_decoder.py b/tests/structs/test_encoder_decoder.py index cb800fe4..c4916656 100644 --- a/tests/structs/test_encoder_decoder.py +++ b/tests/structs/test_encoder_decoder.py @@ -1,5 +1,4 @@ import torch -import pytest from zeta.structs.encoder_decoder import EncoderDecoder from argparse import Namespace @@ -10,8 +9,8 @@ def test_encoder_decoder_initialization(): assert isinstance(encoder_decoder, EncoderDecoder) assert encoder_decoder.args == args - assert encoder_decoder.args.share_all_embeddings == True - assert encoder_decoder.args.share_decoder_input_output_embed == True + assert encoder_decoder.args.share_all_embeddings is True + assert encoder_decoder.args.share_decoder_input_output_embed is True def test_encoder_decoder_forward(): diff --git a/tests/structs/test_encoderdecoder.py b/tests/structs/test_encoderdecoder.py new file mode 100644 index 00000000..90e8a3b4 --- /dev/null +++ b/tests/structs/test_encoderdecoder.py @@ -0,0 +1,43 @@ +import torch +import argparse +import pytest + +from zeta.nn import EncoderDecoder, Encoder, Decoder + + +@pytest.fixture +def encoder_decoder(): + args = argparse.Namespace(share_all_embeddings=True) + encoder_embed_tokens = torch.Tensor(2, 3) + encoder_embed_positions = torch.Tensor(2, 3) + decoder_embed_tokens = torch.Tensor(2, 3) + decoder_embed_positions = torch.Tensor(2, 3) + output_projection = torch.Tensor(2, 3) + + return EncoderDecoder( + args, + encoder_embed_tokens, + encoder_embed_positions, + decoder_embed_tokens, + decoder_embed_positions, + output_projection, + ) + + +def test_initialization(encoder_decoder): + assert isinstance(encoder_decoder, EncoderDecoder) + assert isinstance(encoder_decoder.encoder, Encoder) + assert isinstance(encoder_decoder.decoder, Decoder) + + +def test_args_share_all_embeddings_propagation(encoder_decoder): + assert encoder_decoder.args.share_decoder_input_output_embed is True + + +def test_forward_pass(encoder_decoder): + src_tokens = torch.Tensor(2, 3) + prev_output_tokens = torch.Tensor(2, 3) + + output = encoder_decoder.forward(src_tokens, prev_output_tokens) + + assert isinstance(output, torch.Tensor) diff --git a/tests/structs/test_hierarchicalblock.py b/tests/structs/test_hierarchicalblock.py new file mode 100644 index 00000000..15952afb --- /dev/null +++ b/tests/structs/test_hierarchicalblock.py @@ -0,0 +1,64 @@ +import pytest +import torch +from zeta.nn import HierarchicalBlock + + +def test_HierarchicalBlock_init(): + hb = HierarchicalBlock(64) + assert hb.stride == 1 + assert hb.compress_factor == 1 + assert hb.no_compress is True + assert hb.has_attn is False + assert hb.attn is None + + +def test_HierarchicalBlock_forward(): + hb = HierarchicalBlock(64) + x = torch.randn((1, 64, 64)) + result = hb.forward(x) + assert result.shape == x.shape + + +def test_HierarchicalBlock_raises(): + with pytest.raises(AssertionError): + # compression factor is not a power of 2 + HierarchicalBlock(64, compress_factor=3) + + with pytest.raises(AssertionError): + # window size is negative + HierarchicalBlock(64, window_size=-5) + + +@pytest.mark.parametrize( + "dim, dim_head, heads, window_size, compress_factor, stride, ff_mult", + [ + # some examples + (64, 32, 4, 5, 2, 1, 1), + (32, 16, 2, 3, 4, 2, 2), + # edge cases + (0, 0, 0, 0, 1, 0, 0), + ], +) +def test_HierarchicalBlock_dim( + dim, dim_head, heads, window_size, compress_factor, stride, ff_mult +): + # Test if correct exceptions are raised when dimensions are zero or negative + try: + HierarchicalBlock( + dim, + dim_head, + heads, + window_size, + compress_factor, + stride, + ) + except ValueError: + assert ( + dim <= 0 + or dim_head <= 0 + or heads <= 0 + or window_size < 0 + or compress_factor <= 0 + or stride <= 0 + or ff_mult <= 0 + ) diff --git a/tests/structs/test_localtransformer.py b/tests/structs/test_localtransformer.py new file mode 100644 index 00000000..a9670f44 --- /dev/null +++ b/tests/structs/test_localtransformer.py @@ -0,0 +1,77 @@ +from torch import nn +import pytest +import torch +from zeta.nn import LocalTransformer +from torch.autograd import gradcheck +from zeta.nn.modules.dynamic_module import DynamicPositionBias + + +@pytest.fixture +def transformer(): + return LocalTransformer( + num_tokens=5000, + max_seq_len=200, + dim=128, + depth=10, + causal=True, + local_attn_window_size=50, + dim_head=32, + heads=4, + ff_mult=2, + attn_dropout=0.1, + ff_dropout=0.1, + ignore_index=-1, + use_xpos=True, + xpos_scale_base=100, + use_dynamic_pos_bias=True, + ) + + +def test_initialization(transformer): + assert isinstance(transformer, LocalTransformer) + assert transformer.token_emb.num_embeddings == 5000 + assert transformer.token_emb.embedding_dim == 128 + assert transformer.pos_emb.num_embeddings == 200 + assert transformer.pos_emb.embedding_dim == 128 + assert transformer.max_seq_len == 200 + assert isinstance(transformer.layers, nn.ModuleList) + assert transformer.local_attn_window_size == 50 + assert isinstance(transformer.dynamic_pos_bias, DynamicPositionBias) + assert transformer.ignore_index == -1 + assert isinstance(transformer.to_logits, nn.Sequential) + + +def test_forward(transformer): + x = torch.rand(10, 250) + output = transformer.forward(x) + assert output.shape == torch.Size([10, 250, 5000]) + + +def test_generate(transformer): + prime = torch.rand(10, 100) + output = transformer.generate( + prime, seq_len=50, temperature=0.9, filter_thres=0.8 + ) + assert output.shape == torch.Size([10, 150]) + + +def test_forward_with_loss(transformer): + x = torch.rand(10, 250) + loss = transformer.forward(x, return_loss=True) + assert isinstance(loss, torch.Tensor) + assert loss.shape == () + + +def test_gradient(transformer): + x = torch.randn(20, 128, dtype=torch.float64, requires_grad=True) + test = gradcheck(transformer.forward, (x,), eps=1e-6, atol=1e-4) + assert test + + +def test_mocking_used_libraries(mocker): + mock = mocker.patch("torch.nn.Embedding", return_value="Mocked_Embedding") + transformer = LocalTransformer( + num_tokens=5000, max_seq_len=200, dim=128, depth=10, causal=True + ) + transformer.token_emb = mock + assert transformer.token_emb() == "Mocked_Embedding" diff --git a/tests/structs/test_paralleltransformerblock.py b/tests/structs/test_paralleltransformerblock.py new file mode 100644 index 00000000..234acc17 --- /dev/null +++ b/tests/structs/test_paralleltransformerblock.py @@ -0,0 +1,67 @@ +import torch +import pytest +from zeta.nn import ParallelTransformerBlock +from torch.autograd import gradcheck + + +# Basic Testing +def test_parallel_transformer_block_init(): + p = ParallelTransformerBlock(512) + assert p.fused_dims == (512, 64, 64, 2048) + assert p.scale == 1 / (64**0.5) + + +def test_parallel_transformer_block_forward(): + p = ParallelTransformerBlock(512) + x = torch.randn(1, 10, 512) + output = p(x) + assert output.size() == (1, 10, 512) + + +# Parameterized Testing +@pytest.mark.parametrize( + "dim, dim_head, heads, ff_mult", [(128, 16, 4, 6), (256, 32, 8, 3)] +) +def test_parallel_transformer_block_param(dim, dim_head, heads, ff_mult): + p = ParallelTransformerBlock(dim, dim_head, heads, ff_mult) + assert isinstance(p, ParallelTransformerBlock) + + +# Exception Testing +def test_invalid_input(): + p = ParallelTransformerBlock(512) + x = torch.randn(1, 512) # Should be a 3D tensor + with pytest.raises(Exception): + p(x) + + +# Fixture usage +@pytest.fixture +def parallel_transformer_block(): + return ParallelTransformerBlock(512) + + +def test_forward_with_fixture(parallel_transformer_block): + input = torch.randn(1, 10, 512, requires_grad=True) + output = parallel_transformer_block(input) + assert output.size() == (1, 10, 512) + + +# Tests for Mask and Position Embedding +def test_mask_functionality(parallel_transformer_block): + mask_output = parallel_transformer_block.get_mask(10, torch.device("cpu")) + assert mask_output.shape == (10, 10) + + +def test_rotary_embedding_functionality(parallel_transformer_block): + pos_emb_output = parallel_transformer_block.get_rotary_embedding( + 10, torch.device("cpu") + ) + assert pos_emb_output.shape == (10, 8) + + +# Gradients and Parameter testing +def test_gradient(parallel_transformer_block): + input = torch.randn(1, 10, 512, requires_grad=True) + # Check the gradients pass + assert gradcheck(parallel_transformer_block, input, eps=1e-6, atol=1e-4) diff --git a/tests/structs/test_simpletransformer.py b/tests/structs/test_simpletransformer.py new file mode 100644 index 00000000..ed258ae1 --- /dev/null +++ b/tests/structs/test_simpletransformer.py @@ -0,0 +1,30 @@ +import pytest +import torch +import torch.nn as nn +from zeta.nn import SimpleTransformer + + +def test_valid_init(): + """Test initialization of SimpleTransformer.""" + stm = SimpleTransformer(512, 6, 20_000) + assert isinstance(stm, SimpleTransformer) + assert isinstance(stm.emb, nn.Embedding) + assert isinstance(stm.to_logits, nn.Sequential) + + +def test_forward_output_shape(): + """Test forward method of SimpleTransformer.""" + stm = SimpleTransformer(512, 6, 20_000) + x = torch.randn(2, 1024).long() + y = stm(x) + assert y.shape == torch.Size([2, 1024, 20_000]) + + +@pytest.mark.parametrize( + "x_arg", [(32.2), (["str1", "str2"]), (512, 6, "20000")] +) +def test_invalid_forward_input_raises_error(x_arg): + """Test forward method raises ValueError with invalid input.""" + stm = SimpleTransformer(512, 6, 20_000) + with pytest.raises((TypeError, ValueError)): + stm(x_arg) diff --git a/tests/structs/test_transformer.py b/tests/structs/test_transformer.py new file mode 100644 index 00000000..40d66b9b --- /dev/null +++ b/tests/structs/test_transformer.py @@ -0,0 +1,47 @@ +import pytest +import torch +from zeta.nn import Transformer, AttentionLayers + +# assuming that you are testing the Transformer class + + +# Start by initializing objects +@pytest.fixture() +def init_transformer(): + attn_layers = AttentionLayers( + 256 + ) # considering that AttentionLayers exist and received one parameter + return Transformer( + num_tokens=1000, max_seq_len=512, attn_layers=attn_layers + ) + + +# Basic tests: Like creating objects +def test_creation(init_transformer): + transformer = init_transformer + assert isinstance(transformer, Transformer) + + +# Parameterized Testing: Test if forward method is working as expected + + +@pytest.mark.parametrize( + "x, expected_output_size", + [ + (torch.randn(1, 512), (1, 1000)), + (torch.randn(5, 256), (5, 1000)), + (torch.randn(10, 200), (10, 1000)), + ], +) +def test_forward(init_transformer, x, expected_output_size): + output = init_transformer.forward(x) + assert output.size() == expected_output_size + + +# Exception Testing: Check if errors are raised correctly +@pytest.mark.parametrize( + "wrong_input", [torch.randn(1), torch.randn(1, 512, 3), "string"] +) +def test_forward_exception(init_transformer, wrong_input): + with pytest.raises(ValueError): + init_transformer.forward(wrong_input) diff --git a/tests/structs/test_vitransformerwrapper.py b/tests/structs/test_vitransformerwrapper.py new file mode 100644 index 00000000..b614279d --- /dev/null +++ b/tests/structs/test_vitransformerwrapper.py @@ -0,0 +1,49 @@ +import pytest +import torch +from zeta.nn import ViTransformerWrapper, Encoder +from torch.nn import Module + + +# 1. Test to check if default object of class is instance of torch.nn.Module +def test_default_object_of_class(): + attn_layer = Encoder(dim=512, depth=6) + model = ViTransformerWrapper( + image_size=256, patch_size=6, attn_layers=attn_layer + ) + assert isinstance(model, Module) + + +# 2. Test to check if object of class with parameters is instance of torch.nn.Module +def test_object_with_parameters_of_class(): + attn_layer = Encoder(dim=512, depth=6) + model = ViTransformerWrapper( + image_size=32, patch_size=8, attn_layers=attn_layer + ) + assert isinstance(model, Module) + + +# 3. Test to check if invalid attention layers throws an AssertionError +def test_invalid_attention_layers(): + with pytest.raises(AssertionError): + ViTransformerWrapper(image_size=256, patch_size=8, attn_layers=None) + + +# 4. Test to check if invalid image size, patch size ratio throws an AssertionError +def test_invalid_image_patch_size_ratio(): + attn_layer = Encoder(dim=512, depth=6) + with pytest.raises(AssertionError): + ViTransformerWrapper( + image_size=100, patch_size=8, attn_layers=attn_layer + ) + + +# 5. Test to check forward pass +def test_forward_pass(): + attn_layer = Encoder(dim=512, depth=6) + model = ViTransformerWrapper( + image_size=256, patch_size=8, attn_layers=attn_layer + ) + random_input = torch.rand(1, 3, 256, 256) + output = model(random_input, return_embeddings=True) + assert output.shape[0] == 1, "Mismatch in batch size" + assert output.shape[2] == 512, "Mismatch in dimensions" diff --git a/tests/tokenizers/test_gptx.py b/tests/tokenizers/test_gptx.py index 52d2fe4b..5193a14b 100644 --- a/tests/tokenizers/test_gptx.py +++ b/tests/tokenizers/test_gptx.py @@ -1,5 +1,4 @@ import torch -import pytest from zeta.tokenizers.gptx_tokenizer import LanguageTokenizerGPTX diff --git a/tests/tokenizers/test_multimodal_tokenizer.py b/tests/tokenizers/test_multimodal_tokenizer.py index d08ce258..f57bb6dc 100644 --- a/tests/tokenizers/test_multimodal_tokenizer.py +++ b/tests/tokenizers/test_multimodal_tokenizer.py @@ -1,6 +1,5 @@ from PIL import Image import torch -import pytest from zeta.tokenizers.multi_modal_tokenizer import MultiModalTokenizer diff --git a/tests/tokenizers/test_sentencepiece.py b/tests/tokenizers/test_sentencepiece.py index 7ec8331e..4f06b292 100644 --- a/tests/tokenizers/test_sentencepiece.py +++ b/tests/tokenizers/test_sentencepiece.py @@ -1,4 +1,3 @@ -import pytest import os from zeta.tokenizers.sentence_piece import SentencePieceTokenizer diff --git a/tests/tokenizers/test_tokenmonster.py b/tests/tokenizers/test_tokenmonster.py index 94c7b641..fe98783e 100644 --- a/tests/tokenizers/test_tokenmonster.py +++ b/tests/tokenizers/test_tokenmonster.py @@ -1,4 +1,3 @@ -import pytest from zeta.tokenizers.tokenmonster import TokenMonster diff --git a/zeta/quant/qmoe.py b/zeta/quant/qmoe.py index e575b1e8..1824869f 100644 --- a/zeta/quant/qmoe.py +++ b/zeta/quant/qmoe.py @@ -1,6 +1,5 @@ import torch from torch import nn -import time # Noe automatic tf32 ops which mess with numerics torch.backends.cuda.matmul.allow_tf32 = False From 41d825598e3f565601b7ce36458124287e2ec1e1 Mon Sep 17 00:00:00 2001 From: Kye Date: Tue, 26 Dec 2023 23:11:14 -0500 Subject: [PATCH 212/587] [zeta.models][TESTS][DOCS] --- docs/zeta/models/andromeda.md | 121 ++++++++++++++++++++ docs/zeta/models/basemodel.md | 77 +++++++++++++ docs/zeta/models/gpt4.md | 72 ++++++++++++ docs/zeta/models/gpt4multimodal.md | 83 ++++++++++++++ docs/zeta/models/llama2.md | 123 ++++++++++++++++++++ docs/zeta/models/maxvit.md | 78 +++++++++++++ docs/zeta/models/megavit.md | 112 ++++++++++++++++++ docs/zeta/models/navit.md | 91 +++++++++++++++ docs/zeta/models/palme.md | 131 ++++++++++++++++++++++ docs/zeta/models/vit.md | 70 ++++++++++++ mkdocs.yml | 11 ++ scripts/auto_tests_docs/auto_docs.py | 54 +++++---- scripts/auto_tests_docs/auto_tests.py | 61 +++++----- scripts/auto_tests_docs/mkdocs_handler.py | 2 +- tests/models/andromeda.py | 70 ++++++++++++ tests/models/basemodel.py | 14 +++ tests/models/gpt4.py | 29 +++++ tests/models/gpt4multimodal.py | 47 ++++++++ tests/models/llama2.py | 34 ++++++ tests/models/maxvit.py | 52 +++++++++ tests/models/megavit.py | 100 +++++++++++++++++ tests/models/navit.py | 81 +++++++++++++ tests/models/palme.py | 35 ++++++ tests/models/vit.py | 52 +++++++++ 24 files changed, 1541 insertions(+), 59 deletions(-) create mode 100644 docs/zeta/models/andromeda.md create mode 100644 docs/zeta/models/basemodel.md create mode 100644 docs/zeta/models/gpt4.md create mode 100644 docs/zeta/models/gpt4multimodal.md create mode 100644 docs/zeta/models/llama2.md create mode 100644 docs/zeta/models/maxvit.md create mode 100644 docs/zeta/models/megavit.md create mode 100644 docs/zeta/models/navit.md create mode 100644 docs/zeta/models/palme.md create mode 100644 docs/zeta/models/vit.md create mode 100644 tests/models/andromeda.py create mode 100644 tests/models/basemodel.py create mode 100644 tests/models/gpt4.py create mode 100644 tests/models/gpt4multimodal.py create mode 100644 tests/models/llama2.py create mode 100644 tests/models/maxvit.py create mode 100644 tests/models/megavit.py create mode 100644 tests/models/navit.py create mode 100644 tests/models/palme.py create mode 100644 tests/models/vit.py diff --git a/docs/zeta/models/andromeda.md b/docs/zeta/models/andromeda.md new file mode 100644 index 00000000..5e65996d --- /dev/null +++ b/docs/zeta/models/andromeda.md @@ -0,0 +1,121 @@ +# Class Name: Andromeda +**Module Description** + +This documentation provides details on the functionality of the Andromeda class from the zeta.models library. + +The Andromeda class is a transformer-based model helper class that acts as a wrapper for the Transformer and AutoregressiveWrapper modules, defaulting or accepting user-specified values in its configuration. + +Features of the Andromeda model include but are not limited to: +- Configurable model dimensions, including token count, maximum sequence length, layer depth, and head dimensions. +- Abstract position embeddings, alibi position biases, rotary positions, attentions, and buffer elements which are all modifiable by the user. + +## Class Definition: + +```python +class Andromeda(Module): + """ + Andromeda is a transformer-based model architecture. It initializes with + a Transformer and AutoregressiveWrapper with default or user-specified parameters. + """ +``` +This class inherits the PyTorch Module class and serves as a wrapper to both the Transformer and AutoregressiveWrapper classes. + +## Initialization (__init__) Function: +The init function is where the Transformer and AutoregressiveWrapper objects are assigned to `self.Andromeda` and `self.decoder` respectively. + +```python + def __init__( + self, + num_tokens=50432, + max_seq_len=8192, + dim=2560, + depth=32, + dim_head=128, + heads=24, + use_abs_pos_emb=False, + alibi_pos_bias=True, + alibi_num_heads=12, + rotary_xpos=True, + attn_flash=True, + attn_kv_heads=2, + qk_norm=True, + attn_qk_norm=True, + attn_qk_norm_dim_scale=True, + ): +``` + +The parameters and their defaults used in initialization are listed below + +| Parameter | Default Value | Description | +| ------------- | ------------- | ------------- | +| num_tokens | 50432 | Number of tokens in the vocabulary | +| max_seq_len | 8192 | Maximum sequence length | +| dim | 2560 | Dimension of the model | +| depth | 32 | Depth of the model | +| dim_head | 128 | Dimension of the model head | +| heads | 24 | Number of heads | +| use_abs_pos_emb | False | Whether to use absolute position embedding | +| alibi_pos_bias | True | Alibi position bias | +| alibi_num_heads | 12 | Number of alibi heads | +| rotary_xpos | True | Rotary position | +| attn_flash | True | Attention flash | +| attn_kv_heads | 2 | Number of attention key/value heads | +| qk_norm | True | Query-key normalization | +| attn_qk_norm | True | Attention query-key normalization | +| attn_qk_norm_dim_scale | True | Attention query-key normalization dimension scale | + +## Forward Function +Forward propagation in PyTorch involves defining the computation performed at every call. In the Andromeda class, this computation involves passing input text tokens through the decoder. If an exception occurs during this forward propagation, an error message will be printed and an exception will be thrown. + +```python + def forward(self, text_tokens, **kwargs): + """ + Forward pass through the model. It expects the input text_tokens. + """ + ``` +The parameters used in forward function are listed below: + +| Parameter | Description | +| ------------- | ------------- | +| text_tokens | Input tokens | +| **kwargs | Other arguments | + +The forward function returns the output from the decoder. + +## Code Example: +Below is a simple example of instantiating the Andromeda class and using it for forward propagation: + +```python +# Import necessary libraries and modules +from torch.nn import Module +from zeta.models import Andromeda + +# Initialize the Andromeda class with default parameters +model = Andromeda() + +# Define your input text tokens +text_tokens = torch.randn(1, 8192) + +# Perform forward pass through the model +output = model.forward(text_tokens) +``` + +**Note** +Techniques such as query-key normalization aid in the alignment of the query’s distribution to that of the key, in order to reduce the negative impacts of any input with a wildly different distribution. As such, the parameters related to normalization (qk_norm, attn_qk_norm, attn_qk_norm_dim_scale) default to True, but can be toggled off based on the specific needs of your application. + +Also, It's important to ensure that the defined text tokens fit within the dimensions defined for `num_tokens` and `max_seq_len`. Otherwise, you might encounter an error during forward pass. + +For more information on the underlying Transformer and AutoregressiveWrapper modules, please check the official PyTorch documentation. + +## Other Additional Information & Tips +The Andromeda class is notable for its robust set of flexible features that can lend it to varying use-cases and it is inherently versatile due to its Transformer and AutoregressiveWrapper architecture. This model emphasizes on the detail to accepting user-specified parameters for a high level of customization. + +However, due to its complexity and high-dimensional nature, this model may not be preferable under constraints of memory, processing power or the need for simplicity. + +## References & External Resources + +- [Official PyTorch Docs](https://pytorch.org/docs/stable/nn.html) for more information on underlying classes and modules. +- [Understanding Transformers in NLP](https://towardsdatascience.com/transformers-141e32e69591) for conceptual knowledge on Transformer models. +- [Autoregressive Models](https://machinelearningmastery.com/autoregression-models-time-series-forecasting-python/) for understanding on autoregressive models. + +Enjoy exploring the Andromeda class from the zeta.models library! diff --git a/docs/zeta/models/basemodel.md b/docs/zeta/models/basemodel.md new file mode 100644 index 00000000..ca0328ce --- /dev/null +++ b/docs/zeta/models/basemodel.md @@ -0,0 +1,77 @@ +# Module/Class Name: BaseModel + +```python +from abc import ABC + + +class BaseModel(ABC): + def __init__(self, *args, **kwargs): + pass + + def forward(self): + pass +``` + +The `BaseModel` serves as a base class for other models, benefiting from the Python feature of inheritance and polymorphism. Designed with the Abstract Base Class (`ABC`), it enforces the subclasses to redefine `forward` method and to provide certain arguments during initialization, thus providing a common API for all subclasses. + +## Class Definition + +The `BaseModel` class provides the skeleton for the further implementation of any specific model. It does not include any specific model related features but instead enables modularity, creating a structure that is reusable for every type of model desired. + +```python +class BaseModel(ABC): + def __init__(self, *args, **kwargs): + pass + + def forward(self): + pass +``` + +### Parameters + +- **args**: This captures any number of unnamed arguments. You can pass a series of variables or a list of variables, which will be interpreted as a tuple by the method. + + +- **kwargs**: This is used to pass keyworded, variable-length arguments. With **kwargs, any number of keyword arguments can be used. You can use **kwargs if you do not know the number of keyword arguments that will be passed to the function, or if it is optional to have any keyword arguments at all. + +### Method Overview + +#### `__init__(self, *args, **kwargs):` + +A special method in Python classes, it is called as a constructor in object-oriented terminology. This method is called when an object is instantiated, and necessary initialization can happen here. With *args and **kwargs as parameters, it provides flexibility by handling arbitrary number and type of arguments. + +#### `forward(self):` + +This is an abstract method that needs to be implemented by any class that extends `BaseModel`. The purpose of the method can change depending on the model, but it is usually used for forward propagation in neural networks. + +## Usage + +As `BaseModel` is abstract, we cannot directly use it. Instead, we can extend it and implement the required methods in the child class. A typical example of subclassing would be: + +```python +class MyModel(BaseModel): + def __init__(self, number_of_layers): + self.number_of_layers = number_of_layers + super(MyModel, self).__init__() + + def forward(self): + # Implement your forward pass here + ... +``` + +In this example, the `MyModel` class extends `BaseModel` and overrides the `__init__` and `forward` methods. This way, all the models you implement only need to inherit from the `BaseModel` and implement their specific details. + +```python +my_model = MyModel(10) +my_model.forward() +``` + +In this example, we instantiated an object of the `MyModel` class, passing in the number of layers (10), and then calling `forward` method on it. + +## Additional Information + +- Consider following Python's [DRY (Don't Repeat Yourself) principle](https://en.wikipedia.org/wiki/Don%27t_repeat_yourself) when using inheritance. Instead of writing the same code over and over again for different models, you can put the common elements of all models into a base model. + +- As you may have noticed, `BaseModel` adopts an Object-Oriented Programming (OOP) approach to structure the code, making it easier to manage and understand. + +- For a complete guide in Python's ABCs, consider checking the [official Python's ABC documentation](https://docs.python.org/3/library/abc.html). diff --git a/docs/zeta/models/gpt4.md b/docs/zeta/models/gpt4.md new file mode 100644 index 00000000..80f28ac1 --- /dev/null +++ b/docs/zeta/models/gpt4.md @@ -0,0 +1,72 @@ +# GPT4 Class + +GPT4 is a class providing the architecture of a transformer-based model. The class primarily consists of two main components, a Transformer and an AutoregressiveWrapper. + +Based on the method used by OpenAI's GPT-3, the GPT4 in this implementation expands on that base with user-specified or default parameters. These parameters allow users to customize the architecture, depth, and functionality of their models for specific use-cases. + +## Initialize the class + +The class is initialized by the following arguments: + +| Argument | Type | Default | Description | +| -----------------------------| -------- | ------- | ----------- | +| num_tokens | int | 50432 | Number of tokens in the vocabulary | +| max_seq_len | int | 8192 | Maximum length of the sequence | +| dim | int | 2560 | Dimension of the model | +| depth | int | 32 | Depth of the model | +| dim_head | int | 128 | Dimension of the model head | +| heads | int | 24 | Number of heads | +| use_abs_pos_emb | bool | False | Whether to use absolute position embedding | +| alibi_pos_bias | bool | True | Alibi position bias | +| alibi_num_heads | int | 12 | Number of alibi heads | +| rotary_xpos | bool | True | Rotary position | +| attn_flash | bool | True | Attention flash | +| attn_one_kv_head | bool | True | Attention one key/value head for multiquery attention | +| qk_norm | bool | True | Query-key normalization | +| attn_qk_norm | bool | True | Attention query-key normalization | +| attn_qk_norm_dim_scale | bool | True | Attention query-key normalization dimension scale | + +Each of these arguments can be modified to suit specific needs of the user. + +## Implementing the transformer class + +The Transformer architecture used in the GPT4 model forms the backbone of the class. It utilizes an attention mechanism to focus on different words in a sequence while processing the input data. + +In this case, the Transformer is a Decoder, which transpires the depth, dim_head, heads, alibi_pos_bias, alibi_num_heads, rotary_xpos, attn_flash, attn_one_kv_head, qk_norm, attn_qk_norm, and attn_qk_norm_dim_scale properties from the GPT4 arguments. + +If initialization fails for any reason, an exception is caught and logged in the console, and the exception is re-raised. + +## AutoregressiveWrapper + +As a next step, the transformer is wrapped with an AutoregressiveWrapper. Autoregressive models are ones where the output from one step is fed as an input to the next step. This allows for modeling the sequence of data effectively, thus making it excellent for tasks like text generation and language modelling. + +## Forward function + +The `forward` function of the GPT4 class starts by taking `text_tokens` as input. This variable represents the tokenized input sentences. + +In the forward function, a Transformer (loaded by the decoder) is applied to forward `text_tokens`. The result is a `model_input` variable, which is then passed into the decoder along with the `padded_x` parameter. + +If exceptions occur during the forward pass, they are caught and logged in the console, and the exception is re-raised. + +## Usage + +Here's how you can use the GPT4 class: + +```python +import torch +from torch import nn +from zeta.models import GPT4 + +# Initialize with default parameters +model = GPT4() + +# Representing 3 sequences of the maximum length of 8192 +input = torch.randint(0, 50432, (3, 8192)) + +# Pass the input to the model's forward method +output = model.forward(input) +``` + +## Conclusion + +The GPT4 class is a powerful tool for creating Transformer-based language models. With the flexibility it provides, users can customize the model per their requirements and specifications. Whether it be altering the dimensionality, the number of heads in multihead attention, or whether to use absolute position embeddings, the GPT4 class provides a versatile and flexible architecture for your next natural language processing project. diff --git a/docs/zeta/models/gpt4multimodal.md b/docs/zeta/models/gpt4multimodal.md new file mode 100644 index 00000000..27cf20b9 --- /dev/null +++ b/docs/zeta/models/gpt4multimodal.md @@ -0,0 +1,83 @@ +# GPT4MultiModal + +The `GPT4MultiModal` class is a subclass of the `torch.nn.Module` class. This class serves as a model for handling both image and text input in the form of sequences. It integrates the ViTransformerWrapper for image encoding and the Transformer for text decoding. + +The primary aim of this class is to enable encoding an image and use it as context for generating a text sequence, hence the name `GPT4MultiModal`. Typical usage would be to pass an image to the encoder and a sequence of tokens (corresponding to a language prompt) to the decoder. The class will output a sequence of tokens- the length of the sequence will depend on the transformer architecture used. + +## Class Constructor +This class accepts the following parameters: + +| Parameters | Keyboard Argument | Type | Default Value | Description | +|:-------------:|:------:|:--------:|:---------------:|:------------:| +| image_size| image_size | int | 256 | Input image size | +| patch_size | patch_size | int | 32 | Size of each image patch | +| encoder_dim | encoder_dim | int | 512 | Dimension of encoder | +| encoder_depth | encoder_depth | int | 6 | The depth of the encoder | +| encoder_heads | encoder_heads | int | 8 | The number of attention heads in the encoder | +| num_tokens | num_tokens | int | 20000 | The number of unique tokens | +| max_seq_len | max_seq_len | int | 1024 | Maximum sequence length for text | +| decoder_dim | decoder_dim | int | 512 | Dimension of decoder | +| decoder_depth | decoder_depth | int | 6 | The depth of the decoder | +| decoder_heads | decoder_heads | int | 8 | The number of attention heads in the decoder | +| alibi_num_heads | alibi_num_heads | int | 4 | The number of attention heads per transformer | +| use_abs_pos_emb| use_abs_pos_emb | bool | False | If True, embeds input using absolute positional embedding | +| cross_attend | cross_attend | bool | True | If True, enables cross attention in decoder | +| alibi_pos_bias | alibi_pos_bias | bool | True | If True, positional bias is added to alibi | +| rotary_xpos | rotary_xpos | bool | True |Enables rotary positional embeddings | +| attn_flash | attn_flash | bool | True | If True, enables the use of Flash-like attention | +| qk_norm | qk_norm | bool | True | If True, enables query-key normalization | + +## Methods +The following methods are available in this class. + +#### `forward(self, img, text) -> Union[Tensor, str]` +The `forward` method is used to perform the forward propagation operation of the GPT4MultiModal model. It accepts an image and a sequence of tokens and returns a sequence of tokens. + +Parameters: + +| Parameters | Keyboard Argument | Type | Default Value | Description | +|:-------------:|:------:|:--------:|:---------------:|:------------:| +| img | img | Tensor | - | The input image tensor | +| text | text | Tensor | - | The sequence of tokens to be used as input | + +Returns: + +| Type | Description | +|:--------:|:------------:| +| Union[Tensor, str] | Output sequence of tokens or an error message if an exception is encountered | + +# Example of Use + +Consider having an image tensor `img` of size (1, 256, 256, 3) and a text tensor `text` of size (1, 50). Here is an example of how to use `GPT4MultiModal` + +```python +import torch +from zeta.models import GPT4MultiModal + +# Initialize the model +model = GPT4MultiModal(image_size=256, + patch_size=32, + encoder_dim=512, + encoder_depth=6, + encoder_heads=8, + num_tokens=20000, + max_seq_len=1024, + decoder_dim=512, + decoder_depth=6, + decoder_heads=8, + alibi_num_heads=4, + use_abs_pos_emb=False, + cross_attend=True, + alibi_pos_bias=True, + rotary_xpos=True, + attn_flash=True, + qk_norm=True) + +# Assume we have an image tensor 'img' of size (1, 256, 256, 3) and +# a text tensor 'text' of size (1, 50) + +# Run the model +output = model(img, text) +``` + +This will encode `img` using the `ViTransformerWrapper` and then use the encoded embeddings as the context for the `Transformer` to generate a sequence of tokens from `text`. The sequence of tokens, `output`, is the result. diff --git a/docs/zeta/models/llama2.md b/docs/zeta/models/llama2.md new file mode 100644 index 00000000..d0759e61 --- /dev/null +++ b/docs/zeta/models/llama2.md @@ -0,0 +1,123 @@ +# LLama2 + +## Class Overview + +The class LLama2 is a custom transformer model built for Natural Language Processing (NLP) tasks. The objective of this class is to provide a compact yet powerful transformer model for the application of various NLP tasks, from translation to text generation and more. + +The LLama2 transformer in this class provides a broad range of customizable parameters, allowing for it to be fine-tuned for specific tasks and datasets. It supports arguments for the sequence length, model dimensions, layer depths, number of heads, and several other options, providing extensive adaptability for various NLP tasks. + +## Class Structure + +```python +class LLama2: + def __init__( + self, + num_tokens=50432, + max_seq_len=8192, + dim=2560, + depth=32, + dim_head=128, + heads=24, + rotary_xpos=True, + attn_flash=True, + ): + super().__init__() + + self.llama2 = Transformer( + num_tokens=50000, + max_seq_len=4096, + attn_layers=Decoder( + dim=dim, + depth=depth, + dim_head=dim_head, + heads=heads, + attn_flash=attn_flash, + rotary_xpos=rotary_xpos, + ), + ) + self.decoder = AutoregressiveWrapper(self.decoder) + + def forward(self, text): + model_input = self.decoder.forward(text)[0] + return self.decoder(model_input, padded_x=model_input[0]) +``` + +Function Name: `__init__` + +Purpose: Initializes the LLama2 class. + +| Parameter | Data Type | Default Value | Description | +| :--- | :--- | :--- | :--- | +| num_tokens | int | 50432 | The total number of tokens in the input vocabulary. | +| max_seq_len | int | 8192 | The maximum sequence length that the model can accept. | +| dim | int | 2560 | The model's embedding dimensionality. | +| depth | int | 32 | The number of transformer layers in the model. | +| dim_head | int | 128 | The dimensionality of the head in the self-attention mechanism of the transformer model. | +| heads | int | 24 | The number of heads for the multi-head self attention mechanism of the transformer model. | +| rotary_xpos | bool | True | Whether to apply rotary positional embeddings to the input sequence. | +| attn_flash | bool | True | Whether to use the flash attention mechanism. | + +Function Name: `forward` + +Purpose: Defines the forward pass of the model. + +| Parameter | Data Type | Default Value | Description | +| :--- | :--- | :--- | :--- | +| text | string | | The input text which the model processes. | + +Returns: A tensor representation of model's output given the model_input. + +## Usage Examples + +### Example 1: Text Processing + +This example illustrates how to instantiate the model and pass a sample text through it. + +```python +import torch +from torch.nn import Transformer, Decoder +from zeta.structs import AutoregressiveWrapper +from zeta.models import LLama2 + +# Initializing model +llama2_model = LLama2() + +# Cut-off long text or pad short text +text = torch.tensor([1, 2, 3, 4]) + +# Passing text through model +output = llama2_model.forward(text) + +print(output) +``` + +### Example 2: Customizing Model Parameters + +This example illustrates how to instantiate the model with custom parameters. + +```python +llama2_model = LLama2(num_tokens=1000, max_seq_len=512, dim=512, depth=4, dim_head=64, heads=4) + +text = torch.tensor([1, 2, 3, 4]) + +output = llama2_model.forward(text) + +print(output) +``` + +### Example 3: Sequence Classification + +This example illustrates how you could use this model for a sequence classification task. + +```python +llama2_model = LLama2(num_tokens=5000, max_seq_len=256, dim=128, depth=2, dim_head=32, heads=2) + +text_sequences = torch.tensor([[1, 2, 3, 4], [2, 3, 1, 4]]) +target_sequences = torch.tensor([1, 0]) # 2 sequences, 1 for each sequence + +outputs = llama2_model.forward(text_sequences) +loss = loss_function(outputs, target_sequences) +``` +In this usage example, an instance of the LLama2 class is created using custom parameters. A tensor representing text sequences is passed to the model, and the output is computed. You would typically use a loss function suitable for classification tasks (like Cross-Entropy Loss) and compute the loss against some target sequences. + +Note: The provided code is a basic example and might require adjustments like adding an appropriate classifier layer at the end, depending on the specific task requirements. diff --git a/docs/zeta/models/maxvit.md b/docs/zeta/models/maxvit.md new file mode 100644 index 00000000..1debfdcb --- /dev/null +++ b/docs/zeta/models/maxvit.md @@ -0,0 +1,78 @@ +# MaxVit Class Documentation + +The `MaxVit` class in the `zeta.models` module is a neural network module for constructing Vision Transformers (ViT) with MixUp functionality. This class extends PyTorch's native `nn.Module` class while adding various features suited for implementing ViTs. The following sections will provide additional details: + +## Class Definition + +```python +class MaxVit(nn.Module): + def __init__( + self, + *, + num_classes, + dim, + depth, + dim_head: int = 32, + dim_conv_stem=None, + window_size: int = 7, + mbconv_expansion_rate: int = 4, + mbconv_shrinkage_rate=0.25, + dropout=0.01, + channels=3, + ): +``` + +### Parameters +| Parameters | Type | Description | +|-----------------------|-------------------------|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| +| `num_classes` | `int` | The number of classes in the classification task. | +| `dim` | `int` | The dimension of the input data. | +| `depth` | `list` | Tuple indicating the number of transformer blocks at a given stage. | +| `dim_head` | `int` (Default = 32) | The dimensionally of the transformer's heads. | +| `dim_conv_stem` | `int` (Default = None)| The dimensionality of the convolutional stem. If not provided, the dimension of the input is used. | +| `window_size` | `int` (Default = 7) | The size of the sliding windows used for efficient grid-like attention. | +| `mbconv_expansion_rate` | `int` (Default = 4) | Expansion rate used in Mobile Inverted Residual Bottleneck (MBConv) used in the `block`. | +| `mbconv_shrinkage_rate` | `float` (Default = 0.25) | Shrinkage rate used in Mobile Inverted Residual Bottleneck (MBConv) used in the `block`. | +| `dropout` | `float` (Default = 0.01) | The dropout rate for regularization. | +| `channels` | `int` (Default = 3) | Number of input channels. | + +## Functions / Methods + +### `forward(x, texts=None, cond_fns=None, cond_drop_prob=0.0, return_embeddings=False)` + +This function carries out the forward propagation through the `MaxVit` model given an input `x`. + +#### Parameters +| Parameter | Type | Description | +|-----------------------|-------------------------|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| +| `x` | `torch.Tensor` | The input tensor to the `MaxVit` model. | +| `texts` |`List[str]` (Optional)| list of textual data for interpreting image data | +| `cond_fns` |`Tuple[Callable, ...]` (Optional)| List of conditional functions to apply per layer | +| `cond_drop_prob` |`float` (Default = 0.0) | Conditional dropout probability. | +| `return_embeddings` |`bool` (Default = False) | Whether to return embeddings instead of class scores.| + +#### Returns +Returns the output of the multi-layer transformer, which could either be the class scores (default) or embeddings based on `return_embeddings` value. + +## Example Usage + +```python +from zeta.models import MaxVit + +model = MaxVit(num_classes=10, dim=512, depth=(3,2), dim_head=64, channels=3) + +x = torch.randn(1, 3, 224, 224) # suppose we have an random tensor representing an image + +out = model(x) # forward pass + +print(out.shape) # torch.Size([1, 10]) +``` + +## Overview + +The `MaxVit` model is essentially a combination of vision transformers and efficient blocks (based on MobileNet family). First, the input passes through a convolutional stem. Afterward, the data flow through several stages. Each stage consists of a sequence of blocks, and each block is a combination of a Mobile Inverted Residual Bottleneck (MBConv) followed by the Transformer layers. Finally, the output to predict the classifications is obtained through the MLP head. + +In addition to the traditional `forward` functionality, `MaxVit` also supports conditional functions that can be used to modify the network behavior per layer, adding a layer of flexibility to the model. Furthermore, the model supports the option to return the transformer embeddings, making it applicable for other tasks beyond simple classification. + +## Note: +The forward method of `MaxVit` is beartyped for type checking which enforces strong typing, improving the efficiency of the class. diff --git a/docs/zeta/models/megavit.md b/docs/zeta/models/megavit.md new file mode 100644 index 00000000..6d147b00 --- /dev/null +++ b/docs/zeta/models/megavit.md @@ -0,0 +1,112 @@ +# Module Name: MegaVit + +The MegaVit is a class in Python that implements the model from the paper [When Vision Transformers Outperform CNNs](https://arxiv.org/abs/2106.14759). + +## Introduction + +The class implements a vision transformer model that can provide state-of-the-art performance in computer vision tasks when compared to traditional convolutional neural networks (CNNs). The vision transformer model treats an image as a sequence of one-dimensional patches and applies the transformer model on these patches. It is initialized with image size, patch size, number of classes, embedding dimension, depth of transformer model, number of heads for the multi-head attention mechanism, dimension of multi-layer perceptron (MLP), type of pooling method, and dropout rates. + +## Class Definition + +```python +class MegaVit(nn.Module): +``` + +This class inherits from `nn.Module`, which is the base class for all neural network modules in Pytorch. + +```python +def __init__( + self, + *, + image_size, + patch_size, + num_classes, + dim, + depth, + heads, + mlp_dim, + pool="cls", + channels=3, + dim_head=64, + dropout=0.0, + emb_dropout=0.0, +): +``` + +The initialization function for the `MegaVit` class. This function initializes various parameters and layers of the model. + +- `image_size`: Size of the input image. It should be an integer. This is an input argument to the `MegaVit` initializer. +- `patch_size`: Size of the patches into which the input image is divided. It should be an integer. +- `num_classes`: Number of output classes. It should be an integer. +- `dim`: It is the dimension of the embeddings. +- `depth`: This integer represents the depth of the transformer. +- `heads`: This integer indicates the number of heads in the multi-head attention mechanism of the transformer. +- `mlp_dim`: This integer represents the number of dimensions in the MLP layer. +- `pool`: This is a string representing the type of pooling used. It can either be 'cls' or 'mean'. +- `channels`: This integer represents the number of channels in the input image. +- `dim_head`: This integer is the dimension of the transformers head. +- `dropout`: This floating-point number represents the dropout rate. +- `emb_dropout`: This floating-point number is the dropout rate for the embeddings. + +```python +def forward(self, img): +``` + +The forward function defines the forward pass of the network. It receives an input image and generates an output prediction. + +- `img`: A Pytorch tensor representing the input image. + +## Usage Example + +Here is a basic usage example of the `MegaVit` class: + +```python +import torch +from torch.nn import Module +from numpy import random +from zeta.models import MegaVit + +# Define model hyperparameters +model_hparams = { + "image_size": 256, + "patch_size": 32, + "num_classes": 1000, + "dim": 512, + "depth": 6, + "heads": 8, + "mlp_dim": 1024, + "dropout": 0.1, + "emb_dropout": 0.1, +} + +# Initialize MegaVit model +model = MegaVit(**model_hparams) + +# Get random image +img = torch.from_numpy(random.rand(1, 3, model_hparams["image_size"], model_hparams["image_size"])).float() + +# Get model prediction +preds = model(img) + +print(preds) +``` + +This will output the model's prediction for the input image. + +## Reference + +- [When Vision Transformers Outperform CNNs](https://arxiv.org/abs/2106.14759) + +This class directly corresponds to the model presented in the above-mentioned paper. Reading this paper may provide additional insights into working and theory of this class. + +## Additional Information + +Below is a brief explanation of how the `MegaVit` model works: + +1. The input image is passed through the `to_patch_embedding` layer, which first rearranges the image into patches, then applies layer normalization and linear transformation on each patch separately. +2. The positional embeddings are added to these patch embeddings. +3. Dropout is applied as a regularization technique. +4. The transformer is applied to process the patch embeddings. +5. The pooling is applied to the output of the transformer. The type of pooling depends on the `pool` parameter ('cls' or 'mean'). +6. The MLP head is applied to obtain prediction for each class. +7. The model returns these predictions. diff --git a/docs/zeta/models/navit.md b/docs/zeta/models/navit.md new file mode 100644 index 00000000..6fe52f6e --- /dev/null +++ b/docs/zeta/models/navit.md @@ -0,0 +1,91 @@ +# Module/Function Name: NaViT + +```python +class NaViT(nn.Module) +``` +The `NaViT` class is a subclass of PyTorch's `nn.Module` class. It is a reference architecture for creating multi-layer transformers with a pluggable attention, positional encoding, and optional token dropping. + +## Initialization: + +To create a `NaViT` instance, the following parameters need to be specified: + +```python +def __init__( + self, + *, + image_size, + patch_size, + num_classes, + dim, + depth, + heads, + mlp_dim, + channels=3, + dim_head=64, + dropout=0.0, + emb_dropout=0.0, + token_dropout_prob=None, +) +``` + +| Parameter | Data Type | Description | +|----------------------------|------|-------------------------------------------------------------------------------------------------- | +| image_size | int | The size of the input image. | +| patch_size | int | The size of the patch that the model will use for feature representation. | +| num_classes | int | The number of classes in the problem, i.e., the size of the output layer of the model. | +| dim | int | Dimension of the model. | +| depth | int | The number of transformer layers. | +| heads | int | The number of attention heads in the transformer. | +| mlp_dim | int | The dimension of the multilayer perceptron in the feedforward network. | +| channels | int | The number of input channels. Defaults to 3. | +| dim_head | int | The dimension of the attention head. Defaults to 64. | +| dropout | float | Standard dropout. Defaults to 0. The probability of a feature being zeroed out during training. | +| emb_dropout | float | Dropout applied to the learned embedding at the beginning of the transformer stack. Defaults to 0. | +| token_dropout_prob | scalar | The probability of dropping out tokens before the transformer. Optional.| + +## `forward` pass: + +The forward method specifies the behavior of the model during its forward pass. It takes an image batch as input and returns the output of the model, which is the class probabilities for each input image. + +```python +def forward(self, batched_images: Union[List[Tensor], List[List[Tensor]]], group_images=False, group_max_seq_len=2048) +``` + +| Parameter | Data Type | Description | +|----------------------------|-----------------|----------------------------------------------------- | +| batched_images | Tensor or List of Tensors | The input batch of images. | +| group_images | bool | Whether or not to automatically group the images by maximum sequence length. Default: False. | +| group_max_seq_len | int | The group maximum sequence length for auto-packing. Default: 2048. | + +It outputs a 2D tensor with dimensions `(batch size, number of classes)`, representing the class probabilities for each input image. + +## Code example: + +```python +import torch +from zeta.models import NaViT + +# initialize the model +model = NaViT( + image_size = 32, + patch_size = 4, + num_classes = 10, + dim = 512, + depth = 6, + heads = 8, + mlp_dim = 1024, +) + +# random tensor representing a batch of 10 images, with 3 color channels, each 32x32 pixels +x = torch.randn(10, 3, 32, 32) + +# the forward function returns the output of the model, which represents class probabilities for each image. +output = model.forward(x) +print(output.shape) # prints: torch.Size([10, 10]) +``` + +This example demonstrates how to initialize the NaViT model with a set of parameters, how to represent a batch of images as a tensor, and how to feed the image tensor to the model to get the output. + +The output is a batch of logits tensors where each tensor corresponds to class probabilities of the image. The size of each tensor is equal to the `num_classes`, i.e., every batch of images returns a tensor of dimensions `(batch size, num_classes)`. + +This allows direct comparison with the target labels to compute the loss and to derive the gradients during model training. diff --git a/docs/zeta/models/palme.md b/docs/zeta/models/palme.md new file mode 100644 index 00000000..7054756f --- /dev/null +++ b/docs/zeta/models/palme.md @@ -0,0 +1,131 @@ +# PalmE Class Documentation + +This documentation covers the `PalmE` class of the `zeta.models` module. This class inherits from PyTorch's `torch.nn.Module` base class for all neural network modules. It's the starting point for creating models in PyTorch; such models can include layers which in turn can also be modules themselves.. + +The `PalmE` class implements an encoder-decoder architecture useful for solving a variety of tasks by having the encoder extract information from input data which the decoder then uses to generate outputs. + +## Class Definition + +The `PalmE` class is constructed as follows: + +```python +class PalmE(torch.nn.Module): + def __init__( + self, + image_size=256, + patch_size=32, + encoder_dim=512, + encoder_depth=6, + encoder_heads=8, + num_tokens=20000, + max_seq_len=1024, + decoder_dim=512, + decoder_depth=6, + decoder_heads=8, + alibi_num_heads=4, + use_abs_pos_emb=False, + cross_attend=True, + alibi_pos_bias=True, + rotary_xpos=True, + attn_flash=True, + qk_norm=True, + ): +``` + +### Parameters + +| Parameter | Type | Description | +| --- | --- | --- | +| `image_size` | int | Size of the input images. Default value is 256. | +| `patch_size` | int | Size of the patches to divide input images into. Default value is 32. | +| `encoder_dim` | int | Dimensionality of the encoder. Default value is 512. | +| `encoder_depth` | int | Number of layers in the encoder. Default value is 6. | +| `encoder_heads` | int | Number of attention heads in the encoder. Default value is 8. | +| `num_tokens` | int | Number of tokens in the input text. Default value is 20000. | +| `max_seq_len` | int | Maximum length of text sequences. Default value is 1024. | +| `decoder_dim` | int | Dimensionality of the decoder. Default value is 512. | +| `decoder_depth` | int | Number of layers in the decoder. Default value is 6. | +| `decoder_heads` | int | Number of attention heads in the decoder. Default value is 8. | +| `alibi_num_heads` | int | Number of heads for the alibi attention mechanism in the decoder. Default value is 4. | +| `use_abs_pos_emb` | bool | Whether to use absolute positional encoding in the decoder. Default is False. | +| `cross_attend` | bool | Whether the decoder should attend to the encoded image features. Default is True. | +| `alibi_pos_bias` | bool | Whether to use a bias in the alibi attention mechanism. Default is True. | +| `rotary_xpos` | bool | Whether to use the rotary positional encoding in place of the token positional encoding. Default is True. | +| `attn_flash` | bool | Whether to use attention flash in the decoder. Default is True. | +| `qk_norm` | bool | Whether to normalize query and key in the decoder self-attention. Default is True. | + +## Methods + +### `__init__()` + +The `__init__()` method initializes the `PalmE` instance, sets up the encoder and decoder, and wraps the decoder in an `AutoregressiveWrapper`. + +### `forward()` + +The `forward()` method performs forward propagation through the model by using the encoder to generate encoded representations of the input images, and then passing these representations and the input text to the decoder in order to generate the model's outputs. A high level pseudo code example can be: + +```python +def forward(self, img, text): + try: + encoded = self.encoder(img, return_embeddings=True) + return self.decoder(text, context=encoded) + except Exception as error: + print(f"Failed in forward method: {error}") + raise +``` + +## Examples + +Below you'll find various examples on how to use the `PalmE` class. + +### Example 1: Creating a `PalmE` Instance + +Here’s an example of how to instantiate the `PalmE` class with the default parameters: + +```python +import torch +from zeta.models import PalmE + +model = PalmE() +``` +### Example 2: Pass input through the model + +In this example, we create random image batch and text batch data, and pass them through our `PalmE` model: + +```python +img = torch.rand(16, 3, 256, 256) # batch of 16 images +text = torch.randint(0, 20000, (50, 16)) # batch of 50 token sequences for 16 samples + +model = PalmE() +out = model(img, text) +``` + +### Example 3: Modifying model configuration + +Let's modify the model's configuration parameters at instantiation: + +```python +model = PalmE(encoder_dim=1024, + encoder_depth=8, + decoder_dim=1024, + decoder_depth=8, + attn_flash=False) +``` + +Here we modified the `encoder_dim`, `encoder_depth`, `decoder_dim`, `decoder_depth` and `attn_flash` parameters. + +## Additional Notes + +- The input images should have dimensions `(batch_size, channels, height, width)`. The number of channels should usually be 3 (for RGB images), and the height and width should match the `image_size` parameter. + +- The decoder's parameters can be tuned to balance between computational efficiency and the model's performance on your specific task. + +- The `forward()` method may raise an exception if there's a bad input or a compatibility issue between the inputs' and the model's dimensions. Always make sure to match the dimensions. + +- Please refer to the [`torch.nn.Module`](https://pytorch.org/docs/stable/generated/torch.nn.Module.html) documentation for general information on PyTorch modules. + +- The `rotary_xpos` feature refers to the rotary positional encoding introduced in the paper [Pay Attention to MLPs](https://arxiv.org/abs/2105.08050). It's an alternative to traditional token positional encodings, and often works better. + +- Always make sure your input tensor types (CPU tensor, CUDA tensor etc.) match the configuration of the model. + +- The `PalmE` class supports the standard PyTorch methods for moving the model to a device (`to(device)`) and setting it to train or eval mode (`train() / eval()`). diff --git a/docs/zeta/models/vit.md b/docs/zeta/models/vit.md new file mode 100644 index 00000000..14503344 --- /dev/null +++ b/docs/zeta/models/vit.md @@ -0,0 +1,70 @@ +# Module/Class Name: ViT (Vision Transformer) + +The Vision Transformer (ViT) is a class designed as part of the `zeta.models` library. It builds upon the efficient Transformer architecture for applying convolutions for image recognition tasks. The ViT class inherits the properties and methods from PyTorch's built-in `torch.nn.Module` class. This class repurposes the Transformer architecture for image processing tasks by dividing the image into numerous patches and feeding them into the Transformer. + +## Class Definition + +```python +class ViT(nn.Module): + def __init__(self, *, image_size, patch_size, attn_layers, channels=3, num_classes=None, post_emb_norm=False, emb_dropout=0.0): +``` +This class takes the following parameters as inputs: + +| Parameter | Type | Description | Default | +| --- | --- | --- | --- | +| image_size | int | The dimensions (height and width) of the input image. | - | +| patch_size | int | The dimensions of each image patch to be input to the Transformer. | - | +| attn_layers | `Encoder` | A sequence of attention layers defined using the `Encoder` class. | - | +| channels | int | The number of color-bands (usually RGB). | 3 | +| num_classes | int | The number of classes to be detected, otherwise `None` for unsupervised learning scenarios. | `None` | +| post_emb_norm | bool | Whether to apply layer-normalization to the embeddings. | `False` | +| emb_dropout | float | The probability of an element to be zeroed in dropout. | `0.0` | + +## Method Definitions + +Here are the core methods of the `ViT` class: + +1. `__init__` + +This method initializes the instance and sets up the various components of the Transformer, including the positional embeddings, the sequence of attention layers, and the output MLP head. + +2. `forward` + +This method defines the feedforward computations of the ViT, starting from the division of the input image into patches, the conversion of patches into embeddings, applying attention layers, and, if specified, the MLP head for classification output. + +## Usage Examples + +Here, we demonstrate how to use the ViT class. + +```python +import torch +from torchvision import transforms +import matplotlib.pyplot as plt +from PIL import Image +from zeta.models import Encoder, ViT + +# Load an image and apply some pre-processing +img = Image.open("path_to_your_image.jpg") +transform = transforms.Compose([ + transforms.Resize((224, 224)), # Resize image to 224x224 + transforms.ToTensor() +]) +img_tensor = transform(img).unsqueeze(0) + +# Define an Encoder with attention layers +encoder = Encoder(dim=512, depth=12) + +# Instantiate a ViT model +vit_model = ViT(image_size=224, patch_size=16, attn_layers=encoder, channels=3, num_classes=1000, post_emb_norm=True, emb_dropout=0.1) + +# Generate outputs using the ViT model +outputs = vit_model(img_tensor, return_embeddings=True) + +print("Output shape (with embeddings):", outputs.size()) + +outputs = vit_model(img_tensor, return_embeddings=False) + +print("Output shape (without embeddings):", outputs.size()) +``` + +This code presents a usage scenario of the `ViT` class. It illustrates how to load an image, preprocess it, define an `Encoder` instance with attention layers, instantiate a `ViT` model with the defined `Encoder`, and generate outputs (embeddings and class probabilities) using the instantiated `ViT` model. diff --git a/mkdocs.yml b/mkdocs.yml index d825fe15..e3f08f7f 100644 --- a/mkdocs.yml +++ b/mkdocs.yml @@ -177,6 +177,17 @@ nav: - fsdp: "zeta/training/fsdp.md" - ParallelWrapper: "zeta/training/parallel_wrapper.md" - train: "zeta/training/train.md" + - zeta.models: + - vit: "vit.md" + - gpt4multimodal: "gpt4multimodal.md" + - maxvit: "maxvit.md" + - llama2: "llama2.md" + - gpt4: "gpt4.md" + - andromeda: "andromeda.md" + - basemodel: "basemodel.md" + - palme: "palme.md" + - megavit: "megavit.md" + - navit: "navit.md" - zeta.quant: - QUIK: "zeta/quant/quik.md" - BitLinear: "zeta/quant/bitlinear.md" diff --git a/scripts/auto_tests_docs/auto_docs.py b/scripts/auto_tests_docs/auto_docs.py index 5e44c143..c0b29395 100644 --- a/scripts/auto_tests_docs/auto_docs.py +++ b/scripts/auto_tests_docs/auto_docs.py @@ -7,23 +7,19 @@ from scripts.auto_tests_docs.docs import DOCUMENTATION_WRITER_SOP from swarms import OpenAIChat -from zeta.structs.auto_regressive_wrapper import AutoregressiveWrapper -from zeta.structs.encoder_decoder import EncoderDecoder -from zeta.structs.hierarchical_transformer import ( - HierarchicalBlock, - HierarchicalTransformer, -) -from zeta.structs.local_transformer import LocalTransformer -from zeta.structs.simple_transformer import ( - ParallelTransformerBlock, - SimpleTransformer, -) -from zeta.structs.transformer import ( - Encoder, - Transformer, - ViTransformerWrapper, -) +########## +from zeta.models.andromeda import Andromeda +from zeta.models.base import BaseModel +from zeta.models.gpt4 import GPT4, GPT4MultiModal +from zeta.models.llama import LLama2 +from zeta.models.max_vit import MaxVit +from zeta.models.mega_vit import MegaVit +from zeta.models.palme import PalmE +from zeta.models.vit import ViT +from zeta.models.navit import NaViT + +#################### load_dotenv() api_key = os.getenv("OPENAI_API_KEY") @@ -49,14 +45,14 @@ def process_documentation(cls): # Process with OpenAI model (assuming the model's __call__ method takes this input and returns processed content) processed_content = model( - DOCUMENTATION_WRITER_SOP(input_content, "zeta.structs") + DOCUMENTATION_WRITER_SOP(input_content, "zeta.models") ) # doc_content = f"# {cls.__name__}\n\n{processed_content}\n" doc_content = f"{processed_content}\n" # Create the directory if it doesn't exist - dir_path = "docs/zeta/structs" + dir_path = "docs/zeta/models" os.makedirs(dir_path, exist_ok=True) # Write the processed documentation to a Markdown file @@ -69,16 +65,16 @@ def process_documentation(cls): def main(): classes = [ - AutoregressiveWrapper, - Encoder, - EncoderDecoder, - HierarchicalBlock, - HierarchicalTransformer, - LocalTransformer, - ParallelTransformerBlock, - Transformer, - ViTransformerWrapper, - SimpleTransformer, + Andromeda, + BaseModel, + GPT4, + GPT4MultiModal, + LLama2, + MaxVit, + MegaVit, + PalmE, + ViT, + NaViT, ] threads = [] @@ -91,7 +87,7 @@ def main(): for thread in threads: thread.join() - print("Documentation generated in 'docs/zeta' directory.") + print("Documentation generated in 'docs/zeta/models' directory.") if __name__ == "__main__": diff --git a/scripts/auto_tests_docs/auto_tests.py b/scripts/auto_tests_docs/auto_tests.py index b025f294..041d143b 100644 --- a/scripts/auto_tests_docs/auto_tests.py +++ b/scripts/auto_tests_docs/auto_tests.py @@ -4,22 +4,25 @@ import threading from swarms import OpenAIChat from scripts.auto_tests_docs.docs import TEST_WRITER_SOP_PROMPT -from zeta.structs.auto_regressive_wrapper import AutoregressiveWrapper -from zeta.structs.encoder_decoder import EncoderDecoder -from zeta.structs.hierarchical_transformer import ( - HierarchicalBlock, - HierarchicalTransformer, -) -from zeta.structs.local_transformer import LocalTransformer -from zeta.structs.simple_transformer import ( - ParallelTransformerBlock, - SimpleTransformer, -) -from zeta.structs.transformer import ( - Encoder, - Transformer, - ViTransformerWrapper, -) + + +# Import all classes from zeta.structs +# Tests will be automatically generated in the tests folder using parallized gpt4 with each of the file logic handled autonomously thus +# leading to a much faster testing process where you just import your classes or functions and tests are automatically generated +# Automating tests and documentation frees up atleast 75% of your time to focus on the actual logic of your code +from zeta.models.andromeda import Andromeda +from zeta.models.base import BaseModel +from zeta.models.gpt4 import GPT4, GPT4MultiModal +from zeta.models.llama import LLama2 +from zeta.models.max_vit import MaxVit +from zeta.models.mega_vit import MegaVit +from zeta.models.palme import PalmE +from zeta.models.vit import ViT +from zeta.models.navit import NaViT + +#################### + + from dotenv import load_dotenv load_dotenv() @@ -65,14 +68,14 @@ def create_test(cls): # Process with OpenAI model (assuming the model's __call__ method takes this input and returns processed content) processed_content = model( - TEST_WRITER_SOP_PROMPT(input_content, "zeta", "zeta.nn") + TEST_WRITER_SOP_PROMPT(input_content, "zeta", "zeta.models") ) processed_content = extract_code_from_markdown(processed_content) doc_content = f"{processed_content}" # Create the directory if it doesn't exist - dir_path = "tests/structs" + dir_path = "tests/models" os.makedirs(dir_path, exist_ok=True) # Write the processed documentation to a Python file @@ -85,16 +88,16 @@ def create_test(cls): def main(): classes = [ - AutoregressiveWrapper, - Encoder, - Transformer, - ViTransformerWrapper, - SimpleTransformer, - ParallelTransformerBlock, - EncoderDecoder, - LocalTransformer, - HierarchicalBlock, - HierarchicalTransformer, + Andromeda, + BaseModel, + GPT4, + GPT4MultiModal, + LLama2, + MaxVit, + MegaVit, + PalmE, + ViT, + NaViT, ] threads = [] @@ -107,7 +110,7 @@ def main(): for thread in threads: thread.join() - print("Tests generated in 'tests/structs' directory.") + print("Tests generated in 'tests/models' directory.") if __name__ == "__main__": diff --git a/scripts/auto_tests_docs/mkdocs_handler.py b/scripts/auto_tests_docs/mkdocs_handler.py index d57a3e95..aa381a93 100644 --- a/scripts/auto_tests_docs/mkdocs_handler.py +++ b/scripts/auto_tests_docs/mkdocs_handler.py @@ -26,4 +26,4 @@ def generate_file_list(directory, output_file): # Use the function to generate the file list -generate_file_list("docs/zeta/structs", "file_list.txt") +generate_file_list("docs/zeta/models", "file_list.txt") diff --git a/tests/models/andromeda.py b/tests/models/andromeda.py new file mode 100644 index 00000000..ff4f9c49 --- /dev/null +++ b/tests/models/andromeda.py @@ -0,0 +1,70 @@ +import pytest +from zeta.models import Andromeda + + +@pytest.fixture +def init_andromeda(): + return Andromeda( + num_tokens=50432, + max_seq_len=8192, + dim=2560, + depth=32, + dim_head=128, + heads=24, + use_abs_pos_emb=False, + alibi_pos_bias=True, + alibi_num_heads=12, + rotary_xpos=True, + attn_flash=True, + attn_kv_heads=2, + qk_norm=True, + attn_qk_norm=True, + attn_qk_norm_dim_scale=True, + ) + + +def test_initial_parameters(init_andromeda): + assert init_andromeda.num_tokens == 50432 + assert init_andromeda.max_seq_len == 8192 + assert init_andromeda.dim == 2560 + assert init_andromeda.depth == 32 + assert init_andromeda.dim_head == 128 + assert init_andromeda.heads == 24 + assert init_andromeda.use_abs_pos_emb is False + assert init_andromeda.alibi_pos_bias is True + assert init_andromeda.alibi_num_heads == 12 + assert init_andromeda.rotary_xpos is True + assert init_andromeda.attn_flash is True + assert init_andromeda.attn_kv_heads == 2 + assert init_andromeda.qk_norm is True + assert init_andromeda.attn_qk_norm is True + assert init_andromeda.attn_qk_norm_dim_scale is True + + +def test_initialization_exception(): + with pytest.raises(Exception): + Andromeda(num_tokens="wrong_type") + + +def test_forward_successful(init_andromeda, monkeypatch): + def mock_forward(self, text_tokens): + return [text_tokens] + + monkeypatch.setattr( + "zeta.models.AutoregressiveWrapper.forward", mock_forward + ) + + result = init_andromeda.forward([1, 2, 3, 4]) + assert result == [1, 2, 3, 4] + + +def test_forward_exception(init_andromeda, monkeypatch): + def mock_forward(self, text_tokens): + raise Exception("Test Forward Error") + + monkeypatch.setattr( + "zeta.models.AutoregressiveWrapper.forward", mock_forward + ) + + with pytest.raises(Exception, match="Test Forward Error"): + init_andromeda.forward([1, 2, 3, 4]) diff --git a/tests/models/basemodel.py b/tests/models/basemodel.py new file mode 100644 index 00000000..2f80e2fd --- /dev/null +++ b/tests/models/basemodel.py @@ -0,0 +1,14 @@ +import pytest +import zeta.models +from zeta.models import BaseModel + + +def test_base_model_initialization(): + test_model = zeta.models.BaseModel() + assert isinstance(test_model, BaseModel) + + +def test_base_model_forward_method(): + test_model = zeta.models.BaseModel() + with pytest.raises(NotImplementedError): + test_model.forward() diff --git a/tests/models/gpt4.py b/tests/models/gpt4.py new file mode 100644 index 00000000..4d953719 --- /dev/null +++ b/tests/models/gpt4.py @@ -0,0 +1,29 @@ +# test_gpt4.py +import torch +from zeta.models import GPT4 + + +# Test the creation of a GPT4 model with the default parameters. +def test_default_model_creation(): + default_model = GPT4() + assert isinstance(default_model, GPT4) + + +# Check the use_abs_pos_emb parameter. +def test_use_abs_pos_emb_parameter(): + model = GPT4(use_abs_pos_emb=True) + assert model.use_abs_pos_emb is True + + +# Check the forward function. +def test_forward_function(): + model = GPT4() + text_tokens = torch.tensor( + [[2, 5, 9], [4, 1, 8]] + ) # Add more test cases here. + result = model.forward(text_tokens) + assert result.size() == (2,) # Replace with the expected result size. + + +# Add more tests for different parameters, edge cases, and error conditions. +# Also add tests for other methods present in the class, if any. diff --git a/tests/models/gpt4multimodal.py b/tests/models/gpt4multimodal.py new file mode 100644 index 00000000..9e0d1e8e --- /dev/null +++ b/tests/models/gpt4multimodal.py @@ -0,0 +1,47 @@ +import torch +import pytest +from zeta.models import GPT4MultiModal +from unittest.mock import patch + + +def test_GPT4MultiModal_initialization(): + model = GPT4MultiModal() + assert hasattr(model, "encoder") + assert hasattr(model, "decoder") + + +@pytest.fixture +def mock_model(monkeypatch): + mock = GPT4MultiModal() + monkeypatch.setattr("zeta.models.GPT4MultiModal", lambda: mock) + return mock + + +def test_forward_successful_execution(mock_model): + img = torch.randn(1, 3, 256, 256) + text = torch.LongTensor([1, 2, 1, 0, 5]) + + output = mock_model(img=img, text=text) + assert output is not None + + +def test_forward_exception_raised(mock_model): + with pytest.raises(Exception): + mock_model(img=None, text=None) + + +@patch("zeta.models.ViTransformerWrapper") +def test_transformer_called_in_forward(mock_transformer, mock_model): + img = torch.randn(1, 3, 256, 256) + text = torch.LongTensor([1, 2, 1, 0, 5]) + mock_model(img, text) + mock_transformer.assert_called_once() + + +@patch("zeta.models.ViTransformerWrapper", side_effect=Exception) +def test_exception_in_transformer_catch_in_forward( + mock_transformer, mock_model +): + with pytest.raises(Exception): + mock_model(img=None, text=None) + mock_transformer.assert_called_once() diff --git a/tests/models/llama2.py b/tests/models/llama2.py new file mode 100644 index 00000000..36abccc2 --- /dev/null +++ b/tests/models/llama2.py @@ -0,0 +1,34 @@ +from zeta.models import LLama2 +from unittest.mock import Mock, patch + + +def test_llama2_initialization(): + mock_transformer = Mock() + mock_autoregressive_wrapper = Mock() + + with patch("zeta.models.Transformer", return_value=mock_transformer), patch( + "zeta.models.AutoregressiveWrapper", + return_value=mock_autoregressive_wrapper, + ): + llama = LLama2() + assert llama.llama2 == mock_transformer + assert llama.decoder == mock_autoregressive_wrapper + + +def test_llama2_forward(): + mock_transformer = Mock() + mock_autoregressive_wrapper = Mock() + mock_forward = Mock(return_value=("model_input", "padded_x")) + mock_autoregressive_wrapper.forward = mock_forward + + with patch("zeta.models.Transformer", return_value=mock_transformer), patch( + "zeta.models.AutoregressiveWrapper", + return_value=mock_autoregressive_wrapper, + ): + llama = LLama2() + result = llama.forward("test text") + mock_forward.assert_called_once_with("test text") + mock_autoregressive_wrapper.assert_called_once_with( + "model_input", padded_x="padded_x" + ) + assert result == mock_autoregressive_wrapper.return_value diff --git a/tests/models/maxvit.py b/tests/models/maxvit.py new file mode 100644 index 00000000..6e45c569 --- /dev/null +++ b/tests/models/maxvit.py @@ -0,0 +1,52 @@ +import torch +import pytest +from zeta.models import MaxVit + + +# Fixture to create an instance of the MaxVit class. +@pytest.fixture +def maxvit(): + maxvit = MaxVit( + num_classes=10, + dim=128, + depth=(2, 2), + dim_head=32, + dim_conv_stem=32, + window_size=7, + mbconv_expansion_rate=4, + mbconv_shrinkage_rate=0.25, + dropout=0.01, + channels=3, + ) + return maxvit + + +# Test constructor +def test_maxvit_constructor(maxvit): + assert maxvit.num_classes == 10 + assert maxvit.dim == 128 + assert maxvit.depth == (2, 2) + assert maxvit.dim_head == 32 + assert maxvit.dim_conv_stem == 32 + assert maxvit.window_size == 7 + assert maxvit.mbconv_expansion_rate == 4 + assert maxvit.mbconv_shrinkage_rate == 0.25 + assert maxvit.dropout == 0.01 + assert maxvit.channels == 3 + + +# Test `forward` method +def test_forward_returns_correct_shape(maxvit): + from torch.autograd import Variable + + x = Variable(torch.randn(1, 1, 224, 224)) + result = maxvit.forward(x) + assert result.size() == (1, 10) + + +def test_forward_returns_correct_datatype(maxvit): + from torch.autograd import Variable + + x = Variable(torch.randn(1, 1, 224, 224)) + result = maxvit.forward(x) + assert isinstance(result, torch.Tensor) diff --git a/tests/models/megavit.py b/tests/models/megavit.py new file mode 100644 index 00000000..8710c8ac --- /dev/null +++ b/tests/models/megavit.py @@ -0,0 +1,100 @@ +import pytest +import torch +from zeta.models import MegaVit + +# Basic tests, checking instantiation and forward pass with different parameters + + +def test_MegaVit_instantiation(): + model = MegaVit( + image_size=256, + patch_size=32, + num_classes=1000, + dim=512, + depth=6, + heads=8, + mlp_dim=1024, + dropout=0.1, + emb_dropout=0.1, + ) + assert isinstance(model, MegaVit) + + +def test_MegaVit_forward_pass(): + model = MegaVit( + image_size=256, + patch_size=32, + num_classes=1000, + dim=512, + depth=6, + heads=8, + mlp_dim=1024, + dropout=0.1, + emb_dropout=0.1, + ) + img = torch.randn(1, 3, 256, 256) + result = model(img) + assert result.shape == (1, 1000) + + +# Parameterized tests with different input (checking for compatibility with different sized images) + + +@pytest.mark.parametrize("img_size", [128, 256, 512]) +def test_MegaVit_with_different_image_sizes(img_size): + model = MegaVit( + image_size=img_size, + patch_size=32, + num_classes=1000, + dim=512, + depth=6, + heads=8, + mlp_dim=1024, + dropout=0.1, + emb_dropout=0.1, + ) + img = torch.randn(1, 3, img_size, img_size) + result = model(img) + assert result.shape == (1, 1000) + + +# Exception tests + + +def test_blank_image_MegaVit(): + model = MegaVit( + image_size=256, + patch_size=32, + num_classes=1000, + dim=512, + depth=6, + heads=8, + mlp_dim=1024, + dropout=0.1, + emb_dropout=0.1, + ) + img = torch.zeros(1, 3, 256, 256) + with pytest.raises(Exception): + model(img) + + +# Mock tests for used objects/methods would be here +# Example (assuming forward() uses some other method foo() within it) + + +def test_MegaVit_forward_uses_foo_method(mocker): + mock_foo = mocker.patch.object(MegaVit, "foo") + model = MegaVit( + image_size=256, + patch_size=32, + num_classes=1000, + dim=512, + depth=6, + heads=8, + mlp_dim=1024, + dropout=0.1, + emb_dropout=0.1, + ) + img = torch.randn(1, 3, 256, 256) + model(img) + mock_foo.assert_called_once() diff --git a/tests/models/navit.py b/tests/models/navit.py new file mode 100644 index 00000000..47d94a79 --- /dev/null +++ b/tests/models/navit.py @@ -0,0 +1,81 @@ +import pytest +import torch +from zeta.models import NaViT +from torch.nn.modules.module import ModuleAttributeError +from torch.nn import Sequential + + +# ---- SETUP ---- +@pytest.fixture +def neural_network_template(): + model = NaViT( + image_size=100, + patch_size=10, + num_classes=2, + dim=100, + depth=2, + heads=2, + mlp_dim=2, + ) + return model + + +# ---- TESTS ---- + + +# Verify if the model is an instance of nn.Module +def test_model_instantiation(neural_network_template): + assert isinstance(neural_network_template, NaViT) + + +# Test the forward method +def test_forward_method(neural_network_template): + input_tensor = torch.ones([10, 3, 100, 100]) + result = neural_network_template(input_tensor) + assert result.is_cuda + assert result.requires_grad + + +# Test the dropout configuration +def test_dropout_configuration(neural_network_template): + assert neural_network_template.dropout.p == 0.0 + + +# Test the proper initialisation of LayerNorm and Linear layers +def test_layers_initialization(neural_network_template): + sequence = neural_network_template.to_patch_embedding + assert isinstance(sequence, Sequential) + assert len(sequence) == 3 + + +# Test if the transformer is properly initialised +def test_transformer_initialization(neural_network_template): + assert neural_network_template.transformer.dim == 100 + + +# Test the device property +def test_device_property(neural_network_template): + assert str(neural_network_template.device).startswith("cuda") + + +# Test if the dimensions of the input image are correct +def test_if_model_raises_error_on_wrong_dimensions(neural_network_template): + input_tensor = torch.ones([10, 3, 50, 50]) + with pytest.raises(AssertionError): + _ = neural_network_template(input_tensor) + + +# Test the behaviour when token_dropout_prob is an int or a float +def test_token_dropout(neural_network_template): + model = neural_network_template + model.token_dropout_prob = 0.5 + assert callable(model.calc_token_dropout) + + +# Test if exceptions are thrown when they should be +def test_exceptions(neural_network_template): + with pytest.raises(ModuleAttributeError): + _ = neural_network_template.non_existent_attribute + + +# add your test cases here.. diff --git a/tests/models/palme.py b/tests/models/palme.py new file mode 100644 index 00000000..e23d7b3c --- /dev/null +++ b/tests/models/palme.py @@ -0,0 +1,35 @@ +import pytest +import torch +from zeta.models import PalmE +from zeta.structs import ViTransformerWrapper, AutoregressiveWrapper + + +@pytest.fixture +def palme(): + return PalmE(image_size=128, patch_size=16, num_tokens=5) + + +def test_palme_initialization(palme): + assert isinstance(palme, PalmE) + assert isinstance(palme.encoder, ViTransformerWrapper) + assert isinstance(palme.decoder, AutoregressiveWrapper) + assert palme.decoder_dim == 512 + + +def test_palme_forward(palme): + # Prepare the test input + img = torch.rand(1, 3, 128, 128) + text = torch.randint(5, (1, 1)) + + # Try normal forward pass + output = palme(img, text) + assert isinstance(output, torch.Tensor) + + +def test_palme_forward_raise_exception(palme): + with pytest.raises(Exception) as e: + # Pass in bad inputs to trigger exception + bad_img, bad_text = "not an image", "not a text" + palme(bad_img, bad_text) + + assert "Failed in forward method" in str(e) diff --git a/tests/models/vit.py b/tests/models/vit.py new file mode 100644 index 00000000..40106acf --- /dev/null +++ b/tests/models/vit.py @@ -0,0 +1,52 @@ +import torch +import pytest +from zeta.models import ViT, Encoder + +# Sample Tests + + +def test_initialization(): + attn_layers = Encoder(...) + model = ViT(image_size=256, patch_size=32, attn_layers=attn_layers) + assert model.patch_size == 32 + assert isinstance(model.pos_embedding, torch.nn.Parameter) + assert isinstance(model.patch_to_embedding, torch.nn.Sequential) + assert isinstance(model.dropout, torch.nn.Dropout) + assert isinstance(model.attn_layers, Encoder) + + +def test_forward(): + attn_layers = Encoder(...) + model = ViT(image_size=256, patch_size=32, attn_layers=attn_layers) + img = torch.rand(1, 3, 256, 256) + x = model.forward(img) + assert x.shape == (1, attn_layers.dim) # Expected output shape + + +def test_invalid_type_attn_layers(): + attn_layers = "DummyEncoder" + with pytest.raises(AssertionError): + ViT(image_size=256, patch_size=32, attn_layers=attn_layers) + + +def test_invalid_size(): + attn_layers = Encoder(...) + # An image size that's not divisible by patch size + with pytest.raises(AssertionError): + ViT(image_size=257, patch_size=32, attn_layers=attn_layers) + + +@pytest.mark.parametrize( + "image_size, patch_size", [(256, 32), (512, 64), (1024, 128), (2048, 256)] +) +def test_varied_sizes(image_size, patch_size): + attn_layers = Encoder(...) + model = ViT( + image_size=image_size, patch_size=patch_size, attn_layers=attn_layers + ) + img = torch.rand(1, 3, image_size, image_size) + x = model.forward(img) + assert x.shape == (1, attn_layers.dim) + + +# further tests are created using the same pattern for each attribute/method/edge condition From 2a3ba3eb25b196155593172a61d4a876375ee55e Mon Sep 17 00:00:00 2001 From: Kye Date: Tue, 26 Dec 2023 23:11:50 -0500 Subject: [PATCH 213/587] [zeta.models][testnames] --- tests/models/{andromeda.py => test_andromeda.py} | 0 tests/models/{basemodel.py => test_basemodel.py} | 0 tests/models/{gpt4.py => test_gpt4.py} | 0 tests/models/{gpt4multimodal.py => test_gpt4multimodal.py} | 0 tests/models/{llama2.py => test_llama2.py} | 0 tests/models/{maxvit.py => test_maxvit.py} | 0 tests/models/{megavit.py => test_megavit.py} | 0 tests/models/{navit.py => test_navit.py} | 0 tests/models/{palme.py => test_palme.py} | 0 tests/models/{vit.py => test_vit.py} | 0 10 files changed, 0 insertions(+), 0 deletions(-) rename tests/models/{andromeda.py => test_andromeda.py} (100%) rename tests/models/{basemodel.py => test_basemodel.py} (100%) rename tests/models/{gpt4.py => test_gpt4.py} (100%) rename tests/models/{gpt4multimodal.py => test_gpt4multimodal.py} (100%) rename tests/models/{llama2.py => test_llama2.py} (100%) rename tests/models/{maxvit.py => test_maxvit.py} (100%) rename tests/models/{megavit.py => test_megavit.py} (100%) rename tests/models/{navit.py => test_navit.py} (100%) rename tests/models/{palme.py => test_palme.py} (100%) rename tests/models/{vit.py => test_vit.py} (100%) diff --git a/tests/models/andromeda.py b/tests/models/test_andromeda.py similarity index 100% rename from tests/models/andromeda.py rename to tests/models/test_andromeda.py diff --git a/tests/models/basemodel.py b/tests/models/test_basemodel.py similarity index 100% rename from tests/models/basemodel.py rename to tests/models/test_basemodel.py diff --git a/tests/models/gpt4.py b/tests/models/test_gpt4.py similarity index 100% rename from tests/models/gpt4.py rename to tests/models/test_gpt4.py diff --git a/tests/models/gpt4multimodal.py b/tests/models/test_gpt4multimodal.py similarity index 100% rename from tests/models/gpt4multimodal.py rename to tests/models/test_gpt4multimodal.py diff --git a/tests/models/llama2.py b/tests/models/test_llama2.py similarity index 100% rename from tests/models/llama2.py rename to tests/models/test_llama2.py diff --git a/tests/models/maxvit.py b/tests/models/test_maxvit.py similarity index 100% rename from tests/models/maxvit.py rename to tests/models/test_maxvit.py diff --git a/tests/models/megavit.py b/tests/models/test_megavit.py similarity index 100% rename from tests/models/megavit.py rename to tests/models/test_megavit.py diff --git a/tests/models/navit.py b/tests/models/test_navit.py similarity index 100% rename from tests/models/navit.py rename to tests/models/test_navit.py diff --git a/tests/models/palme.py b/tests/models/test_palme.py similarity index 100% rename from tests/models/palme.py rename to tests/models/test_palme.py diff --git a/tests/models/vit.py b/tests/models/test_vit.py similarity index 100% rename from tests/models/vit.py rename to tests/models/test_vit.py From b37d37fbaca784be9af266ba3c7c305f73d9d178 Mon Sep 17 00:00:00 2001 From: Kye Date: Wed, 27 Dec 2023 00:00:18 -0500 Subject: [PATCH 214/587] [zeta.utils][DOCS][Tests] --- docs/zeta/utils/cast_if_src_dtype.md | 56 +++++++ docs/zeta/utils/cast_tuple.md | 59 +++++++ docs/zeta/utils/cosine_beta_schedule.md | 65 ++++++++ docs/zeta/utils/default.md | 68 ++++++++ docs/zeta/utils/disable_warnings_and_logs.md | 57 +++++++ docs/zeta/utils/eval_decorator.md | 54 +++++++ docs/zeta/utils/exists.md | 83 ++++++++++ .../zeta/utils/get_sinusoid_encoding_table.md | 40 +++++ docs/zeta/utils/gif_to_tensor.md | 46 ++++++ docs/zeta/utils/group_by_key_prefix.md | 64 ++++++++ docs/zeta/utils/group_dict_by_key.md | 47 ++++++ docs/zeta/utils/gumbel_noise.md | 46 ++++++ docs/zeta/utils/init_zero_.md | 64 ++++++++ .../zeta/utils/interpolate_pos_encoding_2d.md | 56 +++++++ docs/zeta/utils/l2norm.md | 60 ++++++++ docs/zeta/utils/log.md | 58 +++++++ docs/zeta/utils/maybe.md | 66 ++++++++ docs/zeta/utils/module_device.md | 145 ++++-------------- docs/zeta/utils/once.md | 91 +++++++++++ docs/zeta/utils/pad_at_dim.md | 44 ++++++ docs/zeta/utils/pick_and_pop.md | 59 +++++++ docs/zeta/utils/print_cuda_memory_usage.md | 59 +++++++ docs/zeta/utils/print_main.md | 67 ++++++++ docs/zeta/utils/print_num_params.md | 60 ++++++++ docs/zeta/utils/save_load.md | 40 +++++ docs/zeta/utils/save_memory_snapshot.md | 51 ++++++ docs/zeta/utils/string_begins_with.md | 73 +++++++++ docs/zeta/utils/top_a.md | 49 ++++++ docs/zeta/utils/top_k.md | 59 +++++++ docs/zeta/utils/top_p.md | 59 +++++++ docs/zeta/utils/track_cuda_memory_usage.md | 65 ++++++++ docs/zeta/utils/video_tensor_to_gift.md | 65 ++++++++ mkdocs.yml | 39 ++++- pyproject.toml | 2 +- .../auto_tests_docs/auto_docs_functions.py | 51 +++--- .../auto_tests_docs/auto_tests_functions.py | 13 +- scripts/auto_tests_docs/file_list.txt | 8 - scripts/auto_tests_docs/mkdocs_handler.py | 2 +- scripts/auto_tests_docs/update_mkdocs.py | 62 -------- tests/utils/test_cast_if_src_dtype.py | 0 tests/utils/test_cast_tuple.py | 42 +++++ tests/utils/test_cosine_beta_schedule.py | 64 ++++++++ tests/utils/test_default.py | 73 +++++++++ tests/utils/test_disable_warnings_and_logs.py | 55 +++++++ tests/utils/test_eval_decorator.py | 0 tests/utils/test_exists.py | 47 ++++++ .../utils/test_get_sinusoid_encoding_table.py | 56 +++++++ tests/utils/test_gif_to_tensor.py | 46 ++++++ tests/utils/test_group_by_key_prefix.py | 60 ++++++++ tests/utils/test_group_dict_by_key.py | 51 ++++++ tests/utils/test_gumbel_noise.py | 57 +++++++ tests/utils/test_init_zero_.py | 0 .../utils/test_interpolate_pos_encoding_2d.py | 40 +++++ tests/utils/test_l2norm.py | 0 tests/utils/test_log.py | 40 +++++ tests/utils/test_maybe.py | 71 +++++++++ tests/utils/test_module_device.py | 99 +++++------- tests/utils/test_once.py | 95 ++++++++++++ tests/utils/test_pad_at_dim.py | 57 +++++++ tests/utils/test_pick_and_pop.py | 60 ++++++++ tests/utils/test_print_cuda_memory_usage.py | 48 ++++++ tests/utils/test_print_main.py | 39 +++++ tests/utils/test_print_num_params.py | 35 +++++ tests/utils/test_save_load.py | 60 ++++++++ tests/utils/test_save_memory_snapshot.py | 52 +++++++ tests/utils/test_string_begins_with.py | 58 +++++++ tests/utils/test_top_a.py | 61 ++++++++ tests/utils/test_top_k.py | 51 ++++++ tests/utils/test_top_p.py | 60 ++++++++ tests/utils/test_track_cuda_memory_usage.py | 61 ++++++++ tests/utils/test_video_tensor_to_gift.py | 93 +++++++++++ zeta/utils/__init__.py | 57 ++++++- zeta/utils/main.py | 4 - 73 files changed, 3565 insertions(+), 279 deletions(-) create mode 100644 docs/zeta/utils/cast_if_src_dtype.md create mode 100644 docs/zeta/utils/cast_tuple.md create mode 100644 docs/zeta/utils/cosine_beta_schedule.md create mode 100644 docs/zeta/utils/default.md create mode 100644 docs/zeta/utils/disable_warnings_and_logs.md create mode 100644 docs/zeta/utils/eval_decorator.md create mode 100644 docs/zeta/utils/exists.md create mode 100644 docs/zeta/utils/get_sinusoid_encoding_table.md create mode 100644 docs/zeta/utils/gif_to_tensor.md create mode 100644 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b/docs/zeta/utils/cast_if_src_dtype.md new file mode 100644 index 00000000..098d3cf8 --- /dev/null +++ b/docs/zeta/utils/cast_if_src_dtype.md @@ -0,0 +1,56 @@ +# cast_if_src_dtype + +# Zeta Utils Documentation + +## Table of Contents + +1. [cast_if_src_dtype](#cast_if_src_dtype) + + +## cast_if_src_dtype +`cast_if_src_dtype(tensor, src_dtype, tgt_dtype)` + +This function is utilized to change the data type (`dtype`) of a given tensor if the current data type matches the source data type specified. The process of changing one type to another is called "Casting" in both general computing and PyTorch. + +The function requires three arguments: `tensor`, `src_dtype`, and `tgt_dtype`. + +You would want to use this function when working with different data types in PyTorch. For instance, it ensures uniform data types across tensors for operations that require tensors of the same type. With this utility function, we can cast our tensor to the desired type only if the source type matches our tensor. + +Below is the table summary of the arguments of this function: + +| Argument | Type | Description | +| :- | :- | :- | +| tensor | torch.Tensor | The input tensor whose data type may need to be changed. | +| src_dtype | torch.dtype | The source data type to be matched. If the current data type of the tensor matches this, it will be changed. | +| tgt_dtype | torch.dtype | The target data type to which the tensor will be casted if its current data type matches the source data type. | + +The function returns two variables: + + 1. The potentially updated tensor. + 2. A boolean variable (`True` if the tensor was updated, `False` if not). + +### Examples + +#### Basic Example + +Here's an example of how it works. We'll start by importing the necessary tools: + +```python +import torch +from zeta.utils import cast_if_src_dtype +``` +Now, let's say we're given the following tensor of integers: + +```python +t1 = torch.tensor([1, 2, 3, 4, 5]) +print(t1.dtype) # Outputs torch.int64 +``` +We want to cast this tensor to `float32` only if it's current dtype is `int64`. Here's how to do it: + +```python +t1, updated = cast_if_src_dtype(t1, torch.int64, torch.float32) + +print(t1.dtype) # Outputs torch.float32 +print(updated) # Outputs True +``` +In this diff --git a/docs/zeta/utils/cast_tuple.md b/docs/zeta/utils/cast_tuple.md new file mode 100644 index 00000000..e676c0a1 --- /dev/null +++ b/docs/zeta/utils/cast_tuple.md @@ -0,0 +1,59 @@ +# cast_tuple + + + +# Zeta Utility Documentation + +This document provides an extensive, thorough, and explicit overview of the `zeta` utility toolkit. The toolkit provides efficient and convenient functions to complement Python's built-in utility functions and aid in speeding up the development and debugging process. + +## Function: `cast_tuple()` +The `cast_tuple()` function is a feature under the Zeta utility toolkit. This function takes a value and depth integer as input and outputs a tuple of the given depth with the input value repeated. It radically simplifies the process of creating deep tuples and promotes clean codes. + +### Parameters + +The `cast_tuple()` function involves two parameters: + +| Parameter | Type | Description | +| :--- | :--- | :--- | +| `val` | Any | Specifies the value to be cast into a tuple. | +| `depth` | int | Specifies the depth of the tuple to be created. | + +### Returns + +`cast_tuple()` function returns a tuple. The tuple involves a repeated set of the inputted value, propagated as per the specified depth. + +| Return Value | Type | Description | +| :--- | :--- | :--- | +| Tuple of a given depth | Tuple | A tuple representing a set of the input value repeatedly propagated as per the given depth. | + +### Example Usages + +Below, you can find various code samples showcasing how to implement the `cast_tuple()` function: + +**Example 1: Basic usage** + +``` +from zeta.utils import cast_tuple + +val = "Hello" +depth = 3 + +my_tuple = cast_tuple(val, depth) +print(my_tuple) # Outputs: ("Hello", "Hello", "Hello") +``` + +In this example, the function gets the string "Hello" and an integer `depth = 3` as input. The output will be a tuple with the string "Hello" repeated three times. + +**Example 2: Using a list as an input value** + +``` +from zeta.utils import cast_tuple + +val = [1, 2, 3] +depth = 4 + +my_tuple = cast_tuple(val, depth) +print(my_tuple) # Outputs: ([1, 2, 3], [1, 2, 3], [1, 2, 3], [1, 2, 3]) +``` + +In this second example, the function gets a list `[1, 2, 3]` as the `val diff --git a/docs/zeta/utils/cosine_beta_schedule.md b/docs/zeta/utils/cosine_beta_schedule.md new file mode 100644 index 00000000..92adc0bf --- /dev/null +++ b/docs/zeta/utils/cosine_beta_schedule.md @@ -0,0 +1,65 @@ +# cosine_beta_schedule + +# Module/Function Name: cosine_beta_schedule + +Function `zeta.utils.cosine_beta_schedule(timesteps, s=0.008)` is a utility function in Zeta library that generates a cosine beta scheduler. This is done by creating an array where its values are incremented in a cosine manner between 0 and 1. Such schedule is often used in various applications such as learning rate scheduling in deep learning, simulating annealing schedule etc. + +## Definition + +```python +def cosine_beta_schedule(timesteps, s=0.008): + steps = timesteps + 1 + x = torch.linspace(0, timesteps, steps, dtype=torch.float64) + alphas_cumprod = ( + torch.cos(((x / timesteps) + s) / (1 + s) * torch.pi * 0.5) ** 2 + ) + alphas_cumprod = alphas_cumprod / alphas_cumprod[0] + betas = 1 - (alphas_cumprod[1:] / alphas_cumprod[:-1]) + return torch.clip(betas, 0, 0.9999) +``` + +## Parameters + +| Parameters | Type | Description | +|-|-|-| +| timesteps | int | The total timesteps or epochs for the training or the annealing process | +| s | float, optional | The offset for the cosine function, default is `0.008` | + +## Output + +Returns a torch tensor of size `timesteps` containing beta values that forms a cosine schedule. + +## Usage + +Here are 3 examples of how to use the `cosine_beta_schedule` function: + +### Example 1 + +In this example, we're generating a cosine beta schedule for 10 timesteps without an offset. + +```python +import torch +from zeta.utils import cosine_beta_schedule + +timesteps = 10 +cosine_schedule = cosine_beta_schedule(timesteps) +print(cosine_schedule) +``` + +### Example 2 + +In this example, we're generating a cosine beta schedule for a specific timeframe with a custom offset. + +```python +import torch +from zeta.utils import cosine_beta_schedule + +timesteps = 1000 +offset = 0.005 +cosine_schedule = cosine_beta_schedule(timesteps, s=offset) +print(cosine_schedule) +``` + +### Example 3 + +In this example, we're using cosine beta schedule as a learning rate scheduler in a PyTorch training loop diff --git a/docs/zeta/utils/default.md b/docs/zeta/utils/default.md new file mode 100644 index 00000000..2ec03f61 --- /dev/null +++ b/docs/zeta/utils/default.md @@ -0,0 +1,68 @@ +# default + +# Module Name: `zeta.utils` + +The zeta.utils module is a code structure whose purpose is to simplify programming in PyTorch. It comprises a set of utilities and helper functions designed to streamline writing and debugging. It supports and enables efficient coding through simplicity. + +One of the primary functions in the `zeta.utils` library is `default()`. The function is designed to handle values that could potentially be `None`, providing a default value instead. It can therefore help validate, normalize, and handle user inputs and undefined variables, and it's an effective way to avoid `None` type errors in your code. + +The following is a documentation of this function. + +## Function Definition: `default()` + +```python +def default(val, d): + """ + Return the value if it exists, otherwise return a default value. + + Args: + val: The value to check. + d: The default value to return if val is None. + + Returns: + The value if it exists, otherwise the default value. + """ + return val if exists(val) else d +``` + +## Parameters + +| Parameter | Data Type | Default Value | Description | +| :-------- | :-------- | :------- | :------- | +| `val` | any | N/A | The input value that needs to be checked | +| `d` | any | N/A | The default value that would be returned if `val` is None | + +## Functionality and Usage + +The `default()` function in the zeta.utils module acts as a control structure to prevent Null or None errors while dealing with data. If val is not null or undefined, the function will return `val`; otherwise, it will return `d`, the default value. + +Here are a few usage examples of the function. + +### Example 1: Simple Usage with Numeric Data + +```python +from zeta.utils import default + +val = None +default_val = 10 +print(default(val, default_val)) +``` +This will output `10` as `val` is `None`. + +### Example 2: Non-Numeric Types + +```python +from zeta.utils import default + +val = None +default_val = "default string" +print(default(val, default_val)) +``` +In this case, the output will be `"default string"` as `val` is `None`. + +### Example 3: Function in a Larger Function + +```python +from zeta.utils import default + +def process_data(data diff --git a/docs/zeta/utils/disable_warnings_and_logs.md b/docs/zeta/utils/disable_warnings_and_logs.md new file mode 100644 index 00000000..42d4a204 --- /dev/null +++ b/docs/zeta/utils/disable_warnings_and_logs.md @@ -0,0 +1,57 @@ +# disable_warnings_and_logs + +# zeta.utils + +This module provides a set of functionalities for disabling various logs and warning messages, especially useful for cleaner outputs in Python applications, reducing the amount of noise in outputs especially during debugging or while running the application in production environments. + +## Class Name: CustomFilter + +This class is defined within the `disable_warnings_and_logs` function. It extends the built-in `logging.Filter` class in Python and is used to filter out some unnecesary logs. The CustomFilter class is used to silence logs based on custom conditions. + +The CustomFilter class has only one method `filter` which takes a record as input and checks if it fits the unwanted_logs criteria. If it does, the method returns False which excludes the record from being added to the logger. + +## Method: disable_warnings_and_logs + +This function uses the CustomFilter class and disable warnings coming from a variety of places. The function works to reduce the noise in logs and outputs when you are debugging or running your application. + +To disable the warnings, this function uses a collection of techniques. It uses the warnings library to disable Python related warnings. It also adjusts the logging level of specific logger objects to stop them from firing off distracting logs. A key part of this function is the use of a custom filter which allows the function to silence logs based on custom conditions. + +Below, we will describe the parameters and outputs of the `disable_warnings_and_logs` function. + +__Parameters:__ + +The `disable_warnings_and_logs` function has no parameters. + +__Outputs:__ + +The `disable_warnings_and_logs` function has no return statement therefore it doesn't return anything. + +__Source Code:__ + +```python +def disable_warnings_and_logs(): + class CustomFilter(logging.Filter): + def filter(self, record): + unwanted_logs = [ + "Setting ds_accelerator to mps (auto detect)", + "NOTE: Redirects are currently not supported in Windows or" + " MacOs.", + ] + return not any(log in record.getMessage() for log in unwanted_logs) + + warnings.filterwarnings("ignore") + os.environ["TF_CPP_MIN_LOG_LEVEL"] = "2" + logging.getLogger().setLevel(logging.WARNING) + + logger = logging.getLogger() + f = CustomFilter() + logger.addFilter(f) + + loggers = [ + "real_accelerator", + "torch.distributed.elastic.multiprocessing.redirects", + ] + + for logger_name in loggers: + logger = logging.getLogger(logger_name) + diff --git a/docs/zeta/utils/eval_decorator.md b/docs/zeta/utils/eval_decorator.md new file mode 100644 index 00000000..8346fb15 --- /dev/null +++ b/docs/zeta/utils/eval_decorator.md @@ -0,0 +1,54 @@ +# eval_decorator + +# eval_decorator + +## Summary: +This is a decorator function named **eval_decorator** from the utility package. It is used to ensure the automatic mode switching in pytorch's torch.nn.Module between evaluation (eval) and training (train) mode. + +When a method is wrapped with the **eval_decorator**, before invoking the method, the initial state of the model will be stored, and temporarily switch the model to evaluation state. The method then get executed. After execution, based on the previously saved state, the model would be reverted back to its original state (whether training or evaluation). + +The primary purpose of this is to automate the switching back and forth between train and eval mode for a model during the running of a function which needs to be specifically run in eval mode. + +## Code Explanation: +```python +def eval_decorator(fn): + def inner(self, *args, **kwargs): + was_training = self.training + self.eval() + out = fn(self, *args, **kwargs) + self.train(was_training) + return out + return inner``` + +The **eval_decorator** takes a function as an argument, which needs to be wrapped to ensure the functionality as explained above. Here, 'fn' is the function to be wrapped. + +The decorator function, **eval_decorator**, is defining another function, **inner**, inside it. **inner** function does the following: +- Stores the current state of the model (whether it is training or eval) in a variable was_training. +- Sets the model to eval mode using `self.eval()`. +- Calls the original function (to be wrapped), fn, with its arguments and keeps its return value in variable `out`. +- Sets back the model in the original state (which was stored in `was_training`). +- Returns `out`, output of the wrapped function. + +## Parameters: + +| Parameter | Type | Description | +| :--- | :--- | :--- | +| fn | function | The function to be decorated and thus wrapped inside the eval_decorator. | + +## Returns: + +- Function `inner`: The evaluator function which is the wrapped version of the original function, fn. + +## Example and Usage: + +```python +import torch +import torch.nn as nn + +# A demonstration model for example +class MyModel(nn.Module): + def __init__(self): + super(MyModel, self).__init__() + self.linear = nn.Linear(10, 10) + + @eval_decorator diff --git a/docs/zeta/utils/exists.md b/docs/zeta/utils/exists.md new file mode 100644 index 00000000..345df152 --- /dev/null +++ b/docs/zeta/utils/exists.md @@ -0,0 +1,83 @@ +# exists + +# Module/Function Name: exists + +Python module `zeta.utils` contains a function named `exists`. This utility function quickly checks if a given variable or value is not `None` and returns a boolean value of `True` if it not None and `False` otherwise. + +It is a simple yet powerful utility function that has numerous use cases in programming and data processing where checking the existence of a particular value is mandatory. + +## Definition + +```python +def exists(val): + """ + Check if the value is not None. + + Args: + val: The value to check. + + Returns: + bool: True if value exists (is not None), False otherwise. + """ + return val is not None +``` + +## Parameters + +**val**: It's the only parameter function accepts of any data type including `None`. It is the value for which you want to perform the existence check. + +## Return + +The function returns a boolean value - either `True` or `False`. + +Returns `True` when the passed value is not None, and `False` when the value is None. + +## Usage + +The `exists` function is incredibly simple to use: + +1. Import the function from the `zeta.utils` module. +2. Pass the value (the existence of which you want to check) to the function. +3. The function will return a boolean value based on the existence of the passed value. + +## Code example: + +```python +from zeta.utils import exists + +x = "Hello, world!" +z = None + +print(exists(x)) # prints: True +print(exists(z)) # prints: False +``` + +In the above example, the `exists` function returns `True` for the variable `x` as it is not `None`. + +It then returns `False` for the variable `z` as its value is indeed `None`. + +## Practical application scenarios + +**Case 1:** +When processing incoming data, you want to check if a certain piece of data exists before performing operations on it. + +```python +from zeta.utils import exists + +data = get_incoming_data() + +if exists(data): + process_data(data) +else: + print("No data to process") +``` + +**Case 2:** +Ensuring a function argument is not None before performing an operation. + +```python +from zeta.utils import exists + +def some_operation(a, b, c): + if exists(c): + return diff --git a/docs/zeta/utils/get_sinusoid_encoding_table.md b/docs/zeta/utils/get_sinusoid_encoding_table.md new file mode 100644 index 00000000..ad8b3ee6 --- /dev/null +++ b/docs/zeta/utils/get_sinusoid_encoding_table.md @@ -0,0 +1,40 @@ +# get_sinusoid_encoding_table + +# Function Name: get_sinusoid_encoding_table + +## Introduction + +The `get_sinusoid_encoding_table` function is a utility function used in the implementation of transformer networks for natural language processing tasks. It is intended to generate positional encodings for input sequences, which help the model to use the sequence order information in the inputs. The function employs sinusoidal functions to generate these positional encodings. + +## Function Definition + +```python +def get_sinusoid_encoding_table(n_position, d_hid): + def get_position_angle_vec(position): + return [ + position / np.power(10000, 2 * (hid_j // 2) / d_hid) + for hid_j in range(d_hid) + ] + + sinusoid_table = np.array( + [get_position_angle_vec(pos_i) for pos_i in range(n_position)] + ) + sinusoid_table[:, 0::2] = np.sin(sinusoid_table[:, 0::2]) # dim 2i + sinusoid_table[:, 1::2] = np.cos(sinusoid_table[:, 1::2]) # dim 2i+1 + + return torch.FloatTensor(sinusoid_table).unsqueeze(0) +``` +## Parameters + +| Argument | Type | Description | +| :--- | :--- | :--- | +| `n_position` | `int` | The number of positions in the input sequences. | +| `d_hid` | `int` |The dimension of the hidden state in the transformer network. | + +## Description + +The `get_sinusoid_encoding_table` function generates a table of sinusoidal values that serve as positional encodings for input sequences in a transformer network. The encodings are two-dimension where the first dimension is the position and the second is the embedding dimension. + +The function first creates an empty array of shape `(n_position, d_hid)`. For each position in `n_position`, the function computes a position angle vector using the `get_position_angle_vec` function. This function creates a list of the position divided by `10000` raised to the power of `(2 * (hid_j // 2) / d_hid)`, where `hid_j` is the index in range `d_hid`. The equation applies for each `hid_j`, a unique frequency is assigned. + +The sinusoidal encoding table is then updated with the position angle vectors. For dimensions at even index, the corresponding sinusoidal value is the diff --git a/docs/zeta/utils/gif_to_tensor.md b/docs/zeta/utils/gif_to_tensor.md new file mode 100644 index 00000000..64ffbf54 --- /dev/null +++ b/docs/zeta/utils/gif_to_tensor.md @@ -0,0 +1,46 @@ +# gif_to_tensor + +# Module/Function Name: gif_to_tensor + +## Introduction + +The `gif_to_tensor` function in the `zeta.utils` library is a utility function to convert an animated GIF into a PyTorch tensor. This function is very handy when handling image data, especially when the task is related to processing animated GIFs in machine learning or deep learning applications. + +In the `zeta.utils` library, the `gif_to_tensor` function serves as an essential bridge between raw GIF files and the tensor format required for many other PyTorch operations. + +## Function Definition + +```python +def gif_to_tensor(path, channels=3, transform=T.ToTensor()): + img = Image.open(path) + tensors = tuple(map(transform, seek_all_images(img, chanels=channels))) + return torch.stack(tensors, dim=1) +``` + +## Parameters + +| Parameter | Type | Description | Default Value | +|-------------|------------------------------------|-------------------------------------------------------------------------------------------------------------------------------------------|-----------------------| +| `path` | str | A string specifying the path to the gif file. | None | +| `channels` | int | An integer specifying the number of channels in the image. Typical values are 1 (grayscale), 3 (RGB), or 4 (RGBA). | 3 (RGB) | +| `transform` | torchvision.transforms.Transforms | A PyTorch transformation to be applied to each image frame. PyTorch provides a number of transformations like `ToTensor()`, `Normalize()`. | `T.ToTensor()` | + +## Functionality and Usage + +This function performs the following operations: + +1. Opens the GIF image using the path provided. +2. Iterates over all the frames in the GIF image. +3. Applies the transformation to each frame to convert it into a PyTorch tensor. +4. Stacks all the tensors for each frame along a new dimension. + +The output of the function is a single tensor representing all frames of the GIF. The dimension corresponding to the frames in the output tensor is 1. + +Below, we show three examples of using this function: + +1. **Basic Usage:** + In this simplest use case, we only need to provide the path to the GIF file. The function will return a tensor representing the GIF, using default settings for channels (RGB) and transformation (convert to tensor). + + ```python + import torchvision.transforms as T + diff --git a/docs/zeta/utils/group_by_key_prefix.md b/docs/zeta/utils/group_by_key_prefix.md new file mode 100644 index 00000000..02b4d559 --- /dev/null +++ b/docs/zeta/utils/group_by_key_prefix.md @@ -0,0 +1,64 @@ +# group_by_key_prefix + +# Function Name: group_by_key_prefix + +The function group_by_key_prefix splits a dictionary into two based on whether the keys in the original dictionary start with a specified prefix. This allows us to organize the input dictionary by separating entries that are categorized by their key prefix. + +## Function Definition and Parameters + +The function group_by_key_prefix is defined as follows: + +```python +def group_by_key_prefix(prefix, d): + """ + Group dictionary items by keys that start with a specific prefix. + + Args: + prefix (str): The prefix to check for. + d (dict): The dictionary to group. + + Returns: + tuple: Two dictionaries split based on the prefix condition. + """ + return group_dict_by_key(partial(string_begins_with, prefix), d) +``` + +Here, the function takes two parameters. They are: + +1. prefix - + Type: str + Description: It is the prefix string that the function uses to check if the keys in the dictionary start with this piece of string. + +2. d - + Type: dict + Description: This is the dictionary that the function is required to perform the operation on. The function traverses the keys of this dictionary and groups them into two dictionaries based on whether or not they start with the specified prefix. + +## Usage Examples + +Now, let's run through some examples of how to use this function and what kind of output we can expect in different scenarios: + +### Example 1: Handling general case + +First, let's look at how the function handles a general case. + +```python +# First, we define a dictionary to be used for this example +example_dict = {"pear" : 1, "apple" : 2, "banana" : 3, "peach" : 4, "peanut" : 5} + +# Now, let's use the function to split this dictionary based on the prefix "pea" +split_dict = group_by_key_prefix("pea", example_dict) + +# This will output two dictionaries: +# The first containing all those entries whose keys start with "pea", and the second containing the rest. +``` + +### Example 2: Handling an empty input dictionary + +Next, let's examine how the function handles an empty input dictionary. + +```python +# In this case, we use an empty dictionary as our input +empty_dict = {} + +# Then we split this empty dictionary based on any prefix, say "test" +split_dict diff --git a/docs/zeta/utils/group_dict_by_key.md b/docs/zeta/utils/group_dict_by_key.md new file mode 100644 index 00000000..1dd28f26 --- /dev/null +++ b/docs/zeta/utils/group_dict_by_key.md @@ -0,0 +1,47 @@ +# group_dict_by_key + +# Module/Function Name: group_dict_by_key (Internally within `zeta.utils`) + +Function `group_dict_by_key` is a utility function which is designed to split specific dictionary based on the condition provided by the user. This function accepts two arguments: a condition (a function), and a dictionary. The key feature of this function is the implicit usage of the user-defined function to be used as a condition to split the dictionary on. This function allows users to take a very flexible approach in handling, processing, and manipulating dictionary objects in Python. + +## Function Signature + +```python +def group_dict_by_key(cond: function, d: dict) -> Tuple[dict, dict] +``` + +This function takes in a `function` parameter which will be used to divide the dictionary into two parts, and the `dictionary` to be divided. The function can be named according to the condition of use, and its definition is entirely up to the user. The dictionary `d` is the dictionary to be divided. + +## Function Parameters + +| Parameter | Type | Description | Default Value | +| ------- | -------- | ------------------------------------------------------ | ---------------- | +| cond | function | User-defined function to be used to split the dictionary | NA | +| d | dict | Dictionary to be divided | NA | + +## Returns + +This function returns a `Tuple[dict, dict]`. Specifically, it outputs a tuple of dictionaries divided based on the condition provided. + +## How it Works + +The function `group_dict_by_key` starts by initializing two empty dictionaries `return_val`. It then iterates through every key in the input dictionary `d`. For each key, it evaluates the user-defined condition function `cond(key)`. If the condition is matched, the current key and value pair is added to the first new dictionary. If the condition is not matched, the current element is added to the second new dictionary. Therefore, the function iterates through all key-value pairs in the input dictionary and divide them into two dictionaries based on whether or not they meet the user-defined condition. + +## Examples and Usage + +#### Import + +In order to use this function, you must first understand how to import it. Here is an example of how you might do this: + +```python +from zeta.utils import group_dict_by_key +``` + +#### Use + +Here are three different examples of how you'd use `group_dict_by_key` function: + +1. Grouping dictionary keys based on length: + +```python +cond = diff --git a/docs/zeta/utils/gumbel_noise.md b/docs/zeta/utils/gumbel_noise.md new file mode 100644 index 00000000..bb67c9d6 --- /dev/null +++ b/docs/zeta/utils/gumbel_noise.md @@ -0,0 +1,46 @@ +# gumbel_noise + +# Module Name: Gumbel Noise + +Function Name: gumbel_noise(t) + +```python +def gumbel_noise(t): + noise = torch.zeros_like(t).uniform_(0, 1) + return -log(-log(noise)) +``` +This function generates Gumbel noise, a type of statistical noise named after the Emil Julius Gumbel who was a German statistician, applied to a tensor 't' with similar attributes. It generates a tensor with the same size as 't', filled with random numbers uniformlly distributed between 0 (inclusive) and 1 (exclusive). Then, the Gumbel noise is computed which is a perturbation method to draw samples from discrete distributions. + +The Gumbel distribution is used in sampling methods, for example in the Gumbel-Softmax trick, for producing one-hot encodings or to sample from a discrete distribution with an unspecified number of classes. + +Parameters: +- t (torch.Tensor) : Input tensor. + +Return: +- Tensor: Gumbel noise added tensor with the same type as t. The equals to negative logarithm of negative logarithm of uniform noise. + +## Example: + +```python +import torch +from math import log + +def gumbel_noise(t): + noise = torch.zeros_like(t).uniform_(0, 1) + return -log(-log(noise)) + +# Creating a tensor +x = torch.tensor([2.0, 1.0, 3.0, 4.0]) +print("Original Tensor: ",x) + +# Applying gumbel noise +y = gumbel_noise(x) +print("Tensor after applying Gumbel noise function: ",y) +``` +## Issues and Recommendations + +- It should be noted that the function torch.zeros_like() can be replaced by the torch.empty_like() function if wanting to save time when generating the tensor. The former sets all values as zeros while the latter does not initialize the values, a step that isn't necessary since we are just overwriting these values with uniform noise. + +- Note that the function is computing the logarithm of noise. In the case where noise is very low and close to zero, the inner logarithm will give negative infinity. Subsequently, negative of negative infinity is positive infinity. Users should be aware of potential overflow issues in their computations. + +- If the function is used in machine learning models for training, it should be noted that the function is not different diff --git a/docs/zeta/utils/init_zero_.md b/docs/zeta/utils/init_zero_.md new file mode 100644 index 00000000..98cad120 --- /dev/null +++ b/docs/zeta/utils/init_zero_.md @@ -0,0 +1,64 @@ +# init_zero_ + +# Module Name: zeta.utils + +## Function Name: init_zero_ + +The `init_zero_` function is used to initialize the weights and bias of a PyTorch layer to zero. Initialization of the weights and biases of a layer play a crucial role regarding the performance of a deep learning model. Here, we're initializing every parameter to zero, turning the model into a "zero model". This is useful for certain tasks where you need your model to start with a clean slate. + +This function is designed to work with any layer type available in the `torch.nn.Module` of PyTorch framework. However, it should be noted that if we initialize parameters of all layers as zero, then all the neurons at each layer will learn the same features during training. This function should be used when you're sure that initializing parameters to zero fits your specific needs. + +Below is the function definition and description of the parameters: + +| Function parameters | Description | +|---------------------|--------------------------------------------------------------------------------------------------------------------| +| layer |A `torch.nn.Module` object: The layer to initialize.| + +```python +def init_zero_(layer): + """ + Initialize the weights and bias of a torch layer to zero. + + Args: + layer (torch.nn.Module): The layer to initialize. + """ + nn.init.constant_(layer.weight, 0.0) + if layer.bias is not None: + nn.init.constant_(layer.bias, 0.0) +``` + +## How to Use init_zero_ + +Below we provide three different examples showing the usage of `init_zero_` function. + +### Example 1: Initializing a Linear Layer with `init_zero_` + +```python +import torch.nn as nn +import zeta.utils as utils + +# define a linear layer +linear_layer = nn.Linear(10, 5) + +# initialize the layer with zeros +utils.init_zero_(linear_layer) + +# print the weights and the bias of the layer +print(linear_layer.weight) +print(linear_layer.bias) +``` + +### Example 2: Initializing a Convolutional Layer with `init_zero_` + +```python +import torch.nn as nn +import zeta.utils as utils + +# define a 2d convolutional layer +conv_layer = nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=1) + +# initialize the layer with zeros +utils.init_zero_(conv_layer) + +# print the weights and the bias of the layer + diff --git a/docs/zeta/utils/interpolate_pos_encoding_2d.md b/docs/zeta/utils/interpolate_pos_encoding_2d.md new file mode 100644 index 00000000..06caa0e4 --- /dev/null +++ b/docs/zeta/utils/interpolate_pos_encoding_2d.md @@ -0,0 +1,56 @@ +# interpolate_pos_encoding_2d + +# Module Name: interpolate_pos_encoding_2d + +## Introduction: + +This utility function named `interpolate_pos_encoding_2d` handles the +interpolation of position embeddings for sequences and is commonly used +in the Deep learning models dealing with sequential data like Recurrent Neural +Networks (RNNs) and variants, Transformers etc. + +Positional embeddings help these models to distinguish the order of presented +values, this becomes especially relevant when dealing with transformer models +as transformers lack recurrent or convolutional structure to handle this +information natively. + +If the target spatial size and the original spatial size are equal, the +original positional embeddings are returned directly. However, if the sizes differ, +this function uses the bicubic interpolation method provided by PyTorch's +`nn.functional.interpolate()` to adjust the size of the positional embeddings as per +the target spatial size. + +To ensure computational efficiency along with numerical precision, this function +also includes an option to convert the original data type of the positional +embeddings to float32 during the interpolation process (if originally in +bfloat16). After the interpolation process, the data is converted back to bfloat16. + + +## Function Definition: + +`interpolate_pos_encoding_2d(target_spatial_size, pos_embed)` + +``` +Performs interpolation on 2D positional embeddings as per the given target spatial size. + +Parameters: +- target_spatial_size (int): Target spatial size for the embeddings. +- pos_embed (Tensor): Initial 2D positional embeddings. + +Returns: +- pos_embed (Tensor): 2D positional embeddings after necessary interpolations and type conversions. +``` + +## Functionality and Usage: + +### Functionality: + +Here is the step-wise functionality of the `interpolate_pos_encoding_2d` function: + +1. Fetches the initial spatial size of the positional embeddings. +2. If the initial and target spatial sizes are the same, it returns the original positional embeddings directly. +3. If the sizes differ, it proceeds with the interpolation. +4. Interpolation process: + 1. First, it checks if the initial positional embeddings are in `bfloat16` format. If so, converts them to `float32`. This is achieved by calling the function `cast_if_src_dtype`. + 2. Reshapes the positional embeddings and applies the bicubic interpolation by using `nn.functional.interpolate()` method to adjust the size. + 3. If the original data type was `bfloat16`, diff --git a/docs/zeta/utils/l2norm.md b/docs/zeta/utils/l2norm.md new file mode 100644 index 00000000..21650b96 --- /dev/null +++ b/docs/zeta/utils/l2norm.md @@ -0,0 +1,60 @@ +# l2norm + +# Module Name: zeta.utils + +## Function: l2norm +```python +def l2norm(t, groups=1): + t = rearrange(t, "... (g d) -> ... g d", g=groups) + t = F.normalize(t, p=2, dim=-1) + return rearrange(t, "... g d -> ... (g d)") +``` + +### Overview +The function `l2norm` as the name suggests, is used for L2 normalization of tensors. L2 normalization is the process of dividing a feature vector by its L2 norm, which results in a vector on the unit sphere. It helps deal with issues involving scale variance in data. + +The `l2norm` function takes in a tensor and an optional `groups` parameter, rearranges the elements of the tensor as per the `groups` parameter, performs the normalization and then again rearranges elements to their original order. + +The function makes use of the `rearrange` function from the `einops` library and the `normalize` function from PyTorch's `torch.nn.functional` library. + +### Parameters +The `l2norm` function has the following parameters: + +| Argument | Type | Description | Default Value | +| --- | --- | ---| --- | +| t | torch.Tensor | The tensor that requires L2 normalization. | - | +| groups | int | The number of groups to divide the tensor into before applying normalization. | 1 | + +### Usage +Here are three examples showcasing the usage of the `l2norm` function: + +#### Example 1 +```python +from zeta.utils import l2norm +import torch + +# Creating a 3-dimensional tensor +tensor = torch.rand(4,2,2) + +# Using l2norm without specifying groups +normalized_tensor = l2norm(tensor) + +# Print the output +print(normalized_tensor) +``` + +In this example, we create a random 3-dimensional tensor and use the `l2norm` function to normalize it without specifying the `groups` parameter. Thus, the tensor will not be divided into groups before normalization. + +#### Example 2 +```python +from zeta.utils import l2norm +import torch + +# Creating a 3-dimensional tensor +tensor = torch.rand(4,2,2) + +# Using l2norm specifying groups as 2 +normalized_tensor = l2norm(tensor, groups=2) + +# Print the output + diff --git a/docs/zeta/utils/log.md b/docs/zeta/utils/log.md new file mode 100644 index 00000000..1f048f1e --- /dev/null +++ b/docs/zeta/utils/log.md @@ -0,0 +1,58 @@ +# log + +# Module Name: zeta.utils.log + +## Table of Contents + +- [Introduction](#Introduction) +- [Arguments](#Arguments) +- [Methods](#Methods) +- [Examples](#Examples) +- [Tips](#Tips) +- [References](#References) + +## Introduction +This document is a detailed and comprehensive guide on how to use the `log` module that exists within the `zeta.utils` library. + +`log` is a utility function signature within the `zeta.utils` library, which specifically takes in a PyTorch Tensor and returns its natural logarithm (base `e`) after applying a clamp operation. Clamping refers to setting the value within an interval `min` and `max`. Here we only want to ensure that the tensor values are not lower than a small value `eps` which is often taken to prevent division by zero or log of zero errors. + +## Arguments + +This function accepts two arguments: `t` and `eps`. + +| Argument | Type | Default | Description | +| ------- | ---- | ------- | ----------- | +| `t` | torch.Tensor | N/A | The input tensor on which the natural logarithm operation is performed. | +| `eps` | float | 1e-20 | A very small value to which tensor values are set if they are less than `eps`. This helps in avoiding computation errors when we evaluate log of these tensor values.| + +All arguments are compulsory, but you can omit `eps` during a function call; in this case, its default value (1e-20) would be used. + +## Methods + +`log` is a standalone function and does not have any class or instance-specific methods. + +To call it, use `zeta.utils.log(t, eps)` where `t` is the tensor and `eps` is the optional small value as explained above. + +## Examples + +These examples demonstrate how to utilize the `log` function within the `zeta.utils` library. + +- First, import the necessary libraries: + +```python + import torch + from zeta.utils import log +``` + +- Using `log` function with a simple tensor: + +```python + # Define tensor + t = torch.tensor([0.0, 1.0, 2.0, 3.0]) + + # Apply log transformation + log_t = log(t) + + print(log_t) +``` +The expected output should diff --git a/docs/zeta/utils/maybe.md b/docs/zeta/utils/maybe.md new file mode 100644 index 00000000..900526ab --- /dev/null +++ b/docs/zeta/utils/maybe.md @@ -0,0 +1,66 @@ +# maybe + +# Module Name: maybe + +## Overview: + +The `maybe` function is a Python decorator, that wraps a function and calls it only if the first argument to the function exists. This can help in implementing conditional function calls based on the existence of the first input argument. It is intended to improve code organization and readability, and it can be particularly useful when dealing with functions that require the existence of an input argument for successful execution. + +## Module Interface: + +The module provides a function wrapper `maybe` that accepts one input parameter, the function to be wrapped. The wrapped function `inner(x, *args, **kwargs)` has the ability to take any positional and keyword arguments. + +Hereafter is a detailed table demonstrating `maybe` module interface. + +| Function Name | Argument | Description | Type | Default | +|---------------|----------|---------------------------------------------------------------------------------------------------|------|---------| +| maybe | fn | This argument refers to the function that needs to be wrapped. This function should be callable. | Any | None | + +## Example Usage: + +In this section, we will provide several examples to demonstrate how you can use the `maybe` function. + +### Example 1 - Basic Usage: + +```python +from functools import wraps + +def exists(x): + return x is not None + +def maybe(fn): + @wraps(fn) + def inner(x, *args, **kwargs): + if not exists(x): + return x + return fn(x, *args, **kwargs) + return inner + +@maybe +def add_one(x): + return x + 1 + +print(add_one(4)) # Output: 5 +print(add_one(None)) # Output: None +``` + +In this snippet, we define a decorator `maybe` which wraps the function `add_one`. When the input to `add_one` is None, no operation is done and None is returned. + +### Example 2 - Varied Input: + +```python +@maybe +def add(x, y): + return x + y + +print(add(4, 5)) # Output: 9 +print(add(None, 5)) # Output: None +``` + +In this example, we wrap a function `add` which takes two arguments. When the first argument is None, `maybe` prevents `add` from being executed and returns `None` instead. + +### Example 3 - Complex Functions: + +```python +@maybe +def complex_func(x diff --git a/docs/zeta/utils/module_device.md b/docs/zeta/utils/module_device.md index f2b616c0..0224ab90 100644 --- a/docs/zeta/utils/module_device.md +++ b/docs/zeta/utils/module_device.md @@ -1,133 +1,56 @@ -# Module Documentation: `module_device` +# module_device -## Overview +# Module Name: module_device -The `module_device` module provides a powerful decorator for PyTorch neural network modules that allows you to manage and control the device on which a module and its associated parameters reside. This decorator simplifies the management of device transfers, making it easier to ensure your model runs on the desired hardware. +This decorator provides an extended functionality to PyTorch's nn.Module. PyTorch's nn.Module does not have a specific property that explicitly points out which device it resides on. This decorator provides the `device` property to the class that can be used to return the device of a particular PyTorch's nn.Module class. -This documentation will guide you through the `module_device` decorator's architecture, purpose, functions, and usage examples. You'll learn how to effectively use this decorator to control the device placement of your PyTorch modules. +## Function Definition -## Table of Contents +The decorator is defined as follows: -1. [Installation](#installation) -2. [Architecture](#architecture) -3. [Purpose](#purpose) -4. [Decorator: module_device](#decorator-module_device) - - [Parameters](#parameters) - - [Usage Examples](#usage-examples) - - [Basic Usage](#basic-usage) - - [Custom Device Property Name](#custom-device-property-name) - - [On Device Transfer Callback](#on-device-transfer-callback) -5. [Additional Information](#additional-information) -6. [References](#references) - ---- - -## 1. Installation - -The `module_device` decorator is a Python code snippet that can be directly incorporated into your project without the need for separate installation. - -## 2. Architecture - -The `module_device` decorator is a Python decorator that can be applied to subclasses of PyTorch's `nn.Module`. It adds device management capabilities to your modules by providing control over the device on which a module and its parameters reside. - -## 3. Purpose - -The primary purpose of the `module_device` decorator is to simplify the management of device transfers for PyTorch neural network modules. It allows you to specify the target device, handle compatibility checks, and execute callbacks when transferring a module to a different device. - -## 4. Decorator: module_device - -The `module_device` decorator provides the following functionality: - -- Device management: Control the device on which a module and its parameters reside. -- Custom device property name: Define a custom property name for accessing the module's current device. -- On device transfer callback: Execute a custom callback when transferring a module to a different device. - -### Parameters - -The `module_device` decorator accepts the following parameters: +```python +def module_device( + device_property_name: str = "device", + on_device_transfer=None, + compatibility_check: bool = False, +): +``` -- `device_property_name` (str, optional): The name of the property that will be used to access the module's current device. Defaults to "device". -- `on_device_transfer` (Callable, optional): A callback function that is executed when transferring the module to a different device. Defaults to None. -- `compatibility_check` (bool, optional): Enable or disable compatibility checks for device transfers. Defaults to False. +### Parameters -### Usage Examples +| Parameter | Type | Default Value | Description | +|------------------------|---------|---------------|-------------| +| device_property_name | str | "device" | The name of the device property. | +| on_device_transfer | function| None | A function to be called whenever the device is transferred.| +| compatibility_check | bool | False | If set to True, raises an exception if "cuda" is in the device string while CUDA is not available. | -#### Basic Usage +## Inner Functions and Properties -Here's a basic example of using the `module_device` decorator to manage the device of a PyTorch module: +### decorator ```python -import torch -from torch.nn import Module -from zeta.utils import module_device - -@module_device() -class MyModule(Module): - def __init__(self): - super(MyModule, self).__init__() - self.fc = torch.nn.Linear(10, 5) - -# Create an instance of MyModule -my_model = MyModule() - -# Access the device property -print(my_model.device) # This will print the device of the module +def decorator(klass): ``` +The function takes a class as input and then checks if the input `klass` is a subclass of torch.nn.Module. -#### Custom Device Property Name - -You can define a custom device property name when using the `module_device` decorator: +### \_\_init\_\_ ```python -import torch -from torch.nn import Module -from zeta.utils import module_device - -@module_device(device_property_name="custom_device") -class CustomModule(Module): - def __init__(self): - super(CustomModule, self).__init__() - self.fc = torch.nn.Linear(10, 5) - -# Create an instance of CustomModule -custom_model = CustomModule() - -# Access the custom device property -print(custom_model.custom_device) +def __init__(self, *args, **kwargs): ``` +It overrides the original `__init__` method of the class and registers a buffer named "_dummy", which is a non-persistent tensor containing a single zero. -#### On Device Transfer Callback - -You can specify a callback function to be executed when transferring a module to a different device: +### \_\_to ```python -import torch -from torch.nn import Module -from zeta.utils import module_device - -def on_device_transfer_callback(module, device): - print(f"Transferred to {device}") - -@module_device(on_device_transfer=on_device_transfer_callback) -class CallbackModule(Module): - def __init__(self): - super(CallbackModule, self).__init__() - self.fc = torch.nn.Linear(10, 5) - -# Create an instance of CallbackModule -callback_model = CallbackModule() - -# Transfer the model to a different device -callback_model.to(torch.device("cuda:0")) +def __to(self, device, *args, **kwargs): ``` +This function is overloading the `to()` method of the torch.nn.Module class. It first checks if the `compatibility_check` flag is true and CUDA is not available, but the device is "cuda". If this is the case, a RuntimeError is raised. Otherwise, the `to()` method of torch.nn.Module is called with the specified parameters. -## 5. Additional Information - -- The `module_device` decorator simplifies device management for PyTorch modules, allowing you to focus on your model's functionality. -- Compatibility checks can be enabled to ensure that device transfers are compatible with the available hardware. -- Callbacks provide a way to execute custom actions when transferring a module to a different device. - -## 6. References - -For more information on PyTorch and device management, refer to the official PyTorch documentation: [PyTorch Device](https://pytorch.org/docs/stable/tensor_attributes.html#torch.torch.device). +### _device_property +```python +@property +def _device_property(self): +``` +The `_device_property` helps in fetching the device property of the object. It does not take any parameters and returns the device on which the model is residing. It does this by checking the device of all parameters and buffers of the model. if the model resides on more than one device, it returns all the diff --git a/docs/zeta/utils/once.md b/docs/zeta/utils/once.md new file mode 100644 index 00000000..07597e42 --- /dev/null +++ b/docs/zeta/utils/once.md @@ -0,0 +1,91 @@ +# once + +# Zeta Utils Library Documentation + +## Contents + +1. [Overview](#overview) +2. [Detailed Function Documentation](#Detailed-Function-Documentation) + - [once](#once) +3. [Usage Guides](#Usage-Guides) + +## Overview + +Zeta utils library, in this case, contains a single function `once`, a decorator which ensures that the function it wraps is only called once. This utility function can be extremely useful in situations where duplicate function calls could lead to unnecessary redundancy or inefficiencies. + +## Detailed Function Documentation + +### once + +#### Signature + +```python +@once +def FUNCTION_NAME(ARGS) +``` + +#### Description + +A decorator function that ensures the function it wraps is only called once. This prevents duplicate function calls, thereby improving efficiency in situations where duplicate function calls could be redundant or detrimental to the performance of your program. + +#### Parameters + +| Name | Type | Description | +|------|----------|---------------| +| fn | function | The function to be wrapped and executed only once.| + +#### Returns + +The wrapped function that will run only once. + + +#### Source code + +```python +def once(fn): + """ + Decorator to ensure the function is only called once. + + Args: + fn (function): The function to wrap. + + Returns: + function: The wrapped function. + """ + called = False + + @wraps(fn) + def inner(*args, **kwargs): + nonlocal called + if not called: + called = True + return fn(*args, **kwargs) + + return inner +``` + +## Usage Guides + +### Example 1: Basic Usage + +In this example, we will create a simple function that returns a greeting. We will use the `once` decorator to ensure the function only prints the greeting once, even if the function is called multiple times. + +```python +from functools import wraps +# Include your once function in here. + +def once(fn): + called = False + + @wraps(fn) + def inner(*args, **kwargs): + nonlocal called + if not called: + called = True + return fn(*args, **kwargs) + + return inner + +@once +def greet(name): + return f"Hello {name diff --git a/docs/zeta/utils/pad_at_dim.md b/docs/zeta/utils/pad_at_dim.md new file mode 100644 index 00000000..d58ea2e3 --- /dev/null +++ b/docs/zeta/utils/pad_at_dim.md @@ -0,0 +1,44 @@ +# pad_at_dim + +# Zeta Utils Library Documentation + +## Module Function: pad_at_dim +***pad_at_dim*** is a utility function in the Zeta Utilities Library for padding tensors at a specified dimension to match the desired dimensions. This function builds on Pytorch's built-in function ***F.pad()*** providing additional configurability to specify the dimension at which padding is done. The provided padding is appended at the end of the input tensor's specified dimension. + +## Function Signature +```python +def pad_at_dim(t, pad, dim=-1, value=0.0): + dims_from_right = (-dim - 1) if dim < 0 else (t.ndim - dim - 1) + zeros = (0, 0) * dims_from_right + return F.pad(t, (*zeros, *pad), value=value) +``` + +## Important Parameters Definition +| Parameters | Type | Description | +| :----------- | :----- | :----------------------------------------------------------------------------------------------------------------- | +| t | Tensor | Input tensor in the PyTorch format. | +| pad | Tuple | Padding size for each side of the tensor's dimension. Padding format is (pad_left, pad_right). | +| dim | Integer| The dimension at which padding is performed. By default, it's -1, which indicates the last dimension. | +| value | Float | The padding value. Default is 0.0. | + +## Functionality and Usage + +The ***pad_at_dim*** function performs padding operation on PyTorch tensors at the specified dimension using Pytorch's built-in ***F.pad*** function. It takes into account both positive and negative dimension indices. While positive indices perform the padding from the first dimension, negative indices do the padding starting from the last dimension. + +Creating the zeros needed to fill the rest of the parameters of the PyTorch's F.pad function, the function internally calculates how many zeros are needed, given the dimension. + +Subsequently, it calls F.pad function using the calculated zeros, the desired padding and value to add padding in the given tensor at the specified dimension. + +## Function Examples + +Let's dive in into few examples to understand how the module can be used. + +### Example 1: Padding the last dimension + +```python +import torch +from torch.nn import functional as F +from zeta.utils import pad_at_dim + +# Create a tensor +t = torch.tensor([[7, 8, diff --git a/docs/zeta/utils/pick_and_pop.md b/docs/zeta/utils/pick_and_pop.md new file mode 100644 index 00000000..73174296 --- /dev/null +++ b/docs/zeta/utils/pick_and_pop.md @@ -0,0 +1,59 @@ +# pick_and_pop + +# Documentation for `pick_and_pop` function in `zeta.utils` + +## Introduction + +The `pick_and_pop` function in the `zeta.utils` library is a handy utility function for dictionary manipulation. It provides an efficient way to extract specific key-value pairs from a Python dictionary and also simultaneously remove these key-value pairs from the original dictionary. This operation is beneficial when needing a subset of data from a large dictionary for further processing while removing it from the parent dictionary for memory efficiency. + +## Class or Function Definition + +Function signature: + +```python +pick_and_pop(keys: list, d: dict) -> dict +``` + +## Parameters + +The `pick_and_pop` function takes two parameters. + +|Parameter|Type|Description| +|---------|----|-----------| +|`keys`|list|List of keys to remove from the dictionary| +|`d`|dict|The dictionary to pick from| + +## Returns + +The `pick_and_pop` function returns a new dictionary containing the key value pairs specified in the `keys` list parameter. + +## Functionality and Usage + +The `pick_and_pop` function makes use of the `pop` method native to Python dictionaries. The `pop` method is specified in a lambda function which is then mapped onto the list of `keys`. This effectively extracts the value associated to each key in `keys` from dictionary `d` and also removes this key-value pair from `d`. + +A new dictionary, containing the key-value pairs specified in `keys`, is then created and returned using the built-in `dict` function in combination with the `zip` function to pair each key in `keys` with its corresponding value. + +## Usage Examples + +### Example 1: Basic Usage + +```python +# import the function +from zeta.utils import pick_and_pop + +# initialize a dictionary +d = {'a': 1, 'b': 2, 'c': 3, 'd': 4} +print('Original d:', d) + +# specify the keys we want to pop from the dictionary +keys = ['a', 'c'] + +# apply the function +res = pick_and_pop(keys, d) +print('Result:', res) +print('Modified d:', d) + +# Output: +# Original d: {'a': 1, 'b': 2, 'c': 3, 'd': 4} +# Result: {'a': 1, 'c': 3} +# Modified diff --git a/docs/zeta/utils/print_cuda_memory_usage.md b/docs/zeta/utils/print_cuda_memory_usage.md new file mode 100644 index 00000000..310a17bb --- /dev/null +++ b/docs/zeta/utils/print_cuda_memory_usage.md @@ -0,0 +1,59 @@ +# print_cuda_memory_usage + +# Module Name: zeta.utils + +The `zeta.utils` module hosts a utility function `print_cuda_memory_usage()`, a Python context manager function to print the amount of CUDA memory that a specific block of code uses. This function is particularly useful in deep learning applications, where memory management is crucial due to the high usage of memory by models and datasets. + +The `print_cuda_memory_usage()` function uses PyTorch to perform memory operations, one of the popular open-source deep learning platforms, and it requires an NVIDIA GPU and CUDA toolkit already installed, because CUDA operations require access to a CUDA-enabled GPU. + +# Function Definition: print_cuda_memory_usage() + +## Function Signature +```python +@contextmanager +def print_cuda_memory_usage(): +``` + +## Function Description + +This function is a context manager function that prints the CUDA memory usage of the code block that calls this function. The memory usage is calculated by subtracting the amount of CUDA memory allocated at the end of the code block from the amount of CUDA memory allocated immediately before executing the code block. The resultant memory usage is then converted from bytes to gigabytes and printed to the console. + +## Function Parameters and Return Values + +Since `print_cuda_memory_usage()` is a context manager function, it does not take parameters nor return any values. It is intended to be used with the `with` statement in Python. + +| Parameter Name | Type | Description | Default Value | +|:--------------:|:----:|:-----------:|:-------------:| +| - | - | - | - | + +| Return Name | Type | Description | +|:-----------:|:----:|:------------:| +| - | - | - | + +## Example Code + +The following are example codes that show how to use the function: + +### Example: Memory usage of a small tensor + +We first import the necessary libraries: + +```python +import torch +from zeta.utils import print_cuda_memory_usage +``` + +Next, we use the `print_cuda_memory_usage()` function to get the CUDA memory usage of creating a small tensor with PyTorch. + +```python +with print_cuda_memory_usage(): + a = torch.tensor([1.]).cuda() +``` + +### Example: Memory usage of a large tensor + +In this example, we again use the `print_cuda_memory_usage()` function to observe the CUDA memory usage but with a larger tensor with PyTorch. + +```python +with print_cuda_memory_usage(): + a = torch.rand(1024 diff --git a/docs/zeta/utils/print_main.md b/docs/zeta/utils/print_main.md new file mode 100644 index 00000000..0728b71c --- /dev/null +++ b/docs/zeta/utils/print_main.md @@ -0,0 +1,67 @@ +# print_main + +# Zeta Utils Library - print_main function documentation + +## Overview +Welcome to the documentation of the `print_main` function provided in the `zeta.utils` library. This function serves a purpose in a distributed data setup where multiple processes are running concurrently. Often in such setups, avoiding duplication of logs or messages is desirable, and this function helps to achieve it by ensuring that specific messages get printed only on the main process. + +This utility function can be incredibly useful when debugging or logging information in a distributed setting, providing cleaner logs and easier debugging. This documentation will guide you on how to use the `print_main` function, detailing its arguments, usages, and examples. + +## Function Definition + +```python +def print_main(msg): + """Print the message only on the main process. + + Args: + msg (_type_): _description_ + """ + if dist.is_available(): + if dist.get_rank() == 0: + print(msg) + else: + print(msg) +``` + +## Arguments +| Parameter | Type | Description | +| :--- | :--- | :--- | +| `msg` | string | The message that should be printed by the main process | + + +The `print_main` function accepts a single argument: + +- `msg`: (string) This is the message to be printed to the console. The message should be of the type `string`. + +## Usage + +The `print_main` function is quite straightforward to use. Here, we detail how to use this function in three different ways: + +### 1. Basic Functionality + +This is the simplest and most basic example demonstrating the usage of the `print_main` function. + +```python +import torch.distributed as dist +from zeta.utils import print_main + +# Within your main function +print_main("This is a test message.") +``` + +### 2. Testing with Various Messages + +In the following example, we tweak the earlier sample code and add a loop to send different messages. In a real-life implementation, you would replace this with your application-specific messages. + +```python +import torch.distributed as dist +from zeta.utils import print_main + +# Within your main function +for i in range(5): + print_main(f"This is test message number: {i}") +``` + +### 3. Using the Function in a Multithreaded Environment + +Assume you have a multithreaded setup where multiple processes are running concurrently, and you want to print some diff --git a/docs/zeta/utils/print_num_params.md b/docs/zeta/utils/print_num_params.md new file mode 100644 index 00000000..5a04e0c9 --- /dev/null +++ b/docs/zeta/utils/print_num_params.md @@ -0,0 +1,60 @@ +# print_num_params + +# Module Name: utils.print_num_params + +## Function: +```python +def print_num_params(model): +``` +This function calculates the total number of trainable parameters in a PyTorch model and prints this number. This is a utility function that can be used to monitor the complexity of the model. + +## Arguments: + +| Argument | Type | Description | +| --- | --- | --- | +| model | `torch.nn.Module` | The model for which you want to count the number of parameters. | + + +## Function Body: + +This function loops over all the parameters of the model that require gradient computation (i.e., trainable parameters), counts their number (numel), and sums them up to get the total count of parameters. + +In a distributed training setup, the function checks whether the distributed communication package (`dist`) is available. If it is, only the specified process (the one with rank 0), prints the number of parameters. If the distributed communication package is not available (which means it's not a distributed setup), the function just prints the number of parameters in the model. + +## Usage Example: + +```python +import torch +import torch.nn as nn +from zeta.utils import print_num_params + +# Define a simple model +class Model(nn.Module): + def __init__(self): + super(Model, self).__init__() + self.fc = nn.Linear(4, 2) + + def forward(self, x): + return self.fc(x) + +# Initialize the model +model = Model() +# Print the number of parameters in the model +print_num_params(model) +``` + +In the above example, the Model has a single linear layer with an input feature size of 4 and an output feature size of 2. So, the number of parameters in this model will be `(4 * 2) + 2 = 10`, where 4 and 2 are weight parameters for each input and output features and added two because of the bias parameters for the outputs. + +Running the `print_num_params` on this `model` will output: + +``` +Number of parameters in model: 10 +``` + +## Notes: + +1. This function counts only the parameters that are trainable i.e., require gradient computation. If your model has layers or parameters with `requires_grad` set to False, those will not be counted. + +2. In case of distributed training, `dist.is_available()` is used to determine whether the distributed communication package is available. + +3. If the diff --git a/docs/zeta/utils/save_load.md b/docs/zeta/utils/save_load.md new file mode 100644 index 00000000..49964184 --- /dev/null +++ b/docs/zeta/utils/save_load.md @@ -0,0 +1,40 @@ +# save_load + +# zeta.utils.save_load + +## Description + +The `save_load` function from the `zeta.utils` library defines a base decorator for both save and load methods for PyTorch's torch.nn.Module subclasses. This allows saving the state of a given module and configuration, and subsequently loading it back. This can be specifically useful when we want to store a trained model during the training process or at the end of it, and later resume training from where we left or use the trained model for inference. + +The decorator wraps the class initialization, saving, and loading methods. Additionally, optionally, it allows hook functions to be defined and executed right before saving and loading the model. + +## Function Declaration + +```python +def save_load( + save_method_name: str = "save", + load_method_name: str = "load", + config_instance_var_name: str = "_config", + init_and_load_classmethod_name: str = "init_and_load", + version: Optional[str] = None, + pre_save_hook: Optional[Callable[[Module], None]] = None, + post_load_hook: Optional[Callable[[Module], None]] = None, + compress: Optional[bool] = False, + partial_load: Optional[bool] = False, + *args, + **kwargs, +): +``` +## Parameters + +| Parameter | Type | Description | Default | +| --- | --- | --- | --- | +| `save_method_name` | str | Name of the save method. | `"save"` | +| `load_method_name` | str | Name of the load method. | `"load"` | +| `config_instance_var_name` | str | Name of the instance variable to store the configuration. | `"_config"` | +| `init_and_load_classmethod_name` | str | Name of the classmethod that initializes and loads the model. | `init_and_load` | +| `version` |str(optional) | Version of the model. | `None` | +| `pre_save_hook` | Callable (optional) | This function is called before the model is saved. | `None` | +| `post_load_hook` | Callable (optional) | This function is called after the model is loaded | `None` | +| `compress` | bool (optional) | If True, uses the new zipfile-based TorchScript serialization format. | `False` | +| `partial_load` | bool(optional) | If diff --git a/docs/zeta/utils/save_memory_snapshot.md b/docs/zeta/utils/save_memory_snapshot.md new file mode 100644 index 00000000..b9f15507 --- /dev/null +++ b/docs/zeta/utils/save_memory_snapshot.md @@ -0,0 +1,51 @@ +# save_memory_snapshot + +# `zeta.utils` + +Welcome to the documentation for `zeta.utils`, a module containing utility functions to aid in managing memory snapshots. This documentation will be divided into sections explaining what is done, the class components, its uses, parameters involved and usage examples. The latter will hold code snippets demonstrating zeta's functionalities. + +## Table of Contents + +- [Introduction](#Introduction) +- [Function Definition](#Function-Definition) +- [Implementation](#Implementation) +- [Example Usage](#Example-Usage) + + +## Introduction + +Memory management becomes crucial when running computations on graphics processing units (GPUs). The `zeta.utils` module provides a context manager (`save_memory_snapshot`) to profile code execution, record the GPU memory usage and save the memory snapshot information to the specified file path. + +The `save_memory_snapshot` function uses PyTorch functions for memory profiling. PyTorch functions (`torch.cuda.memory._record_memory_history()`, `torch.cuda.memory._snapshot()`) provided here are for internal use and not part of the public API; hence, you may observe variation in behavior between different PyTorch versions. + +## Function Definition + +The function `save_memory_snapshot` implemented in the module is defined as follows: + +```python +@contextmanager +def save_memory_snapshot(file_path: Path): +``` + +### Parameters + +| Parameters | Data Type | Description | +| ------ | ------ | ----------- | +| file_path | pathlib.Path | The path to the folder to save the snapshot to. The function will create the folder if it doesn't exist. + +## Implementation + +The `save_memory_snapshot()` function creates a directory at the given file path, records a history of the GPU memory usage, captures a snapshot of the memory and saves both memory history and the snapshot to a file. + +Its workflow is as follows: + +1. The function receives `file_path` as an input parameter. +2. It creates a new directory at `file_path` if it doesn't exist already. +3. The function records the GPU memory usage history by calling `torch.cuda.memory._record_memory_history()`. +4. Code within the function's context is executed, during which the memory usage is tracked. +5. Upon completion of the execution of this context code, a snapshot of the current GPU memory status is taken (by calling `torch.cuda.memory._snapshot()`). +6. Both memory history and snapshot are saved to files at the specified location. + +The snippet of the implementation will be like this, + +``` diff --git a/docs/zeta/utils/string_begins_with.md b/docs/zeta/utils/string_begins_with.md new file mode 100644 index 00000000..52eb064b --- /dev/null +++ b/docs/zeta/utils/string_begins_with.md @@ -0,0 +1,73 @@ +# string_begins_with + +# Module/Function Name: string_begins_with + +```python +def string_begins_with(prefix, str): + """ + Check if a string begins with a specific prefix. + + Args: + prefix (str): The prefix to check for. + str (str): The string to check. + + Returns: + bool: True if string starts with prefix, False otherwise. + """ + return str.startswith(prefix) +``` +## 1: Introduction + +The `string_begins_with` function is a simple utility function that checks whether a given string begins with a specified prefix. It is part of the `zeta.utils` library and represents a common application in string manipulation. + +## 2: Parameters + +The function accepts the following arguments as required: + +| Parameter | Type | Description | +| --------- | ---- | ----------- | +| prefix | str | The prefix to check for. | +| str | str | The string to check. | + +## 3: Output + +The function returns a boolean value: + +| Value | Type | Description | +| ----- | ---- | ----------- | +| output | bool | True if string starts with prefix, False otherwise. | + +## 4: Functionality and Usage + +The `string_begins_with` function is quite straightforward. It leverages Python's built-in `str.startswith` method to determine if the string `str` starts with the provided `prefix`. If so, the function returns `True`; otherwise, it returns `False`. + +You can use the `string_begins_with` function in any situation where you need to check whether a given string starts with a specific substring. This can be especially useful in text processing or data cleaning tasks, where you might need to categorize or filter strings based on their prefixes. + +Here are three examples showing how to use the `string_begins_with` function: + +**Example 1 Basic usage** + +```python +from zeta.utils import string_begins_with + +str = "Hello, world" +prefix = "Hello" +result = string_begins_with(prefix, str) +print(result) # Output: True +``` + +**Example 2 When string does not start with prefix** + +```python +from zeta.utils import string_begins_with + +str = "Hello, world" +prefix = "Hi" +result = string_begins_with(prefix, str) +print(result) # Output: False +``` + +**Example 3 With a numeric prefix** + +```python +from zeta.utils import string diff --git a/docs/zeta/utils/top_a.md b/docs/zeta/utils/top_a.md new file mode 100644 index 00000000..643b092c --- /dev/null +++ b/docs/zeta/utils/top_a.md @@ -0,0 +1,49 @@ +# top_a + +# zeta.utils.top_a() function Documentation + +`top_a` is a PyTorch function that adjusts the logits based on a specific threshold determined by a ratio and a power of the maximum probability. + +This function performs an operation known as top-k sampling or nucleus sampling in Natural Language Processing (NLP). It discards a portion of tokens with the lowest probabilities of being the next token prediction in language models, based on a certain limit. + +In general, this function is used in certain applications of probabilistic models where you want to restrict the possibilities to a set of most probable outcomes. This function does this by creating a limit and then setting probabilities that fall under this limit to an effectively infinitesimal value. + +The logic behind this method is to make some of the outcomes impossible (those that fall under the limit) and others equally likely (those above the limit). The effect is to make the randomly selected index more likely to be one of the most probable indices. + +This function fits with the main purpose of PyTorch, which is to ease deep learning implementations, by providing an extra level of flexibility on the level of randomness included in models. + +## Function Definition + +```python +def top_a(logits, min_p_pow=2.0, min_p_ratio=0.02): +``` +The function uses two parameters, `min_p_pow` and `min_p_ratio` that are used to compute the limit of probabilities. + +## Arguments + +| Parameter | Type | Default Value | Description | +|------------|---------|---------------|---------------------------------------------------------------------------| +| `logits` | Tensor | None | Model predictions in logits | +| `min_p_pow` | Float | 2.0 | A value to control the the power of the maximum probability in the limit | +| `min_p_ratio`| Float | 0.02 | A coefficient to control the ratio of the limit | + +## Usage + +First, you need to install PyTorch. This can be done using pip. + +```bash +pip install torch +``` + +Next, use the function inside your code. Import PyTorch and zeta utils first. + +```python +import torch +import torch.nn.functional as F +from zeta.utils import top_a + +logits = torch.randn(5, num_classes) # substitute num_classes with the number of classes in your model +modified_logits = top_a(logits) +``` + +In above example, original ` diff --git a/docs/zeta/utils/top_k.md b/docs/zeta/utils/top_k.md new file mode 100644 index 00000000..6c484bb4 --- /dev/null +++ b/docs/zeta/utils/top_k.md @@ -0,0 +1,59 @@ +# top_k + +# zeta.utils Package Documentation + +## The `zeta.utils` module + +`zeta.utils` is a utility module that provides various utility functions aimed at simplifying and bolstering the efficiency of data transformation and manipulation processes. This documentation explores, in depth, the usefulness, rationale behind, and significance of the provided functions, which will further help users to leverage them in their specific use cases effectively. + +Our focus is the `top_k` function that selectively returns elements from the tensor, having values within the top k percentile. + +
+ +# Function Name: `top_k` + +The `top_k` function is aimed at aiding common procedures encountered in machine learning and data science involving tensor manipulations. Specifically, it speeds up the rank-based filtering of elements in a tensor. + +**Definition/Signature**: + +```python +def top_k(logits, thres=0.9): +``` + +**Parameters**: + +The function accepts the following arguments: + +| Parameters | Type | Description | Default Value | +|------------|--------|----------------------------------------------------------------------------------------------------------|---------------| +| logits | tensor | A tensor whose elements are required to be ranked and top k percentile to be separated. | None | +| thres | float | A threshold value determining the percentile of top elements to be selected from the tensor. | 0.9 | + +
+ +**How It Works**: + +The `top_k` function works by utilizing PyTorch's topk function to pull the top-k elements from a tensor, based on the specified threshold. It then builds a new tensor filled with -inf (representing negative infinity) and scatter the top-k elements into it. This implies that the returned tensor has the top-k elements from the original tensor and -inf for the rest. This aids easy selection and corresponding actions on the top-k elements without the strain of performing an explicit sort operation on the tensor and then slicing off the top-k elements. + +**Returns**: + +A tensor which has the top-k elements from the original tensor and -inf for the rest. + +
+ +**Example Usage(s)**: + +Below are three illustrative examples of leveraging the `top_k` function: + +**Example 1:** + +```python +import torch +from math import ceil +from zeta.utils import top_k + +# Initialize tensor +tensor = torch.rand(1, 10) + +# Apply function with threshold 0.9 +filtered_tensor = top_k(tensor, thres=0. diff --git a/docs/zeta/utils/top_p.md b/docs/zeta/utils/top_p.md new file mode 100644 index 00000000..2dd4b708 --- /dev/null +++ b/docs/zeta/utils/top_p.md @@ -0,0 +1,59 @@ +# top_p + +# Zeta Utils Library Documentation + +The Zeta Utils library is a simple utility library providing a single function, `top_p`, for manipulating and filtering PyTorch tensor-based data sets according to a specified threshold value. + +## `top_p` Function + +### Function Objective + +`top_p` function sorts the values in a tensor, calculates a cumulative sum from a softmax and then applies a threshold to exclude the highest probabilities. Useful when trying to constrain outputs in a certain range. + +### Function Definition + +```python +def top_p(logits, thres=0.9): +``` + +### Parameters + +| Parameter | Type | Default Value | Description | +|-----------|-------|---------------|-------------------------------------------------------------------------------------------------------------------------------------------| +| `logits` | Tensor| None | Input tensor containing the values to be processed. | +| `thres` | Float | 0.9 | Threshold value used to filter the highest probabilities. | + + +### Return Types + +The function returns a Tensor with the same dimensions as the input tensor where the probabilities above the threshold have been filled with negative infinity (`float("-inf")`). + +### Internal Functioning + +- First, `logits` are sorted by descending order, receiving both the sorted values and their corresponding indices. +- Next, the softmax of the sorted values is calculated and a cumulative sum over the results is performed. +- Then, a tensor of the same dimension as cum_probs is created, filled with True if the cumulative probability is above the threshold (1 - `thres`), and False otherwise. +- After that, a little shift is made on this tensor to the right so that the values do not exceed the threshold value limit. The first element is explicitly set to 0 (or false). +- Afterwards, the sorted tensor is updated by replacing values at sorted_indices_to_remove (those above threshold) with negative infinity (`float("-inf")`). +- Finally, the `scatter` function rearranges the updated sorted_logits back into the original structure. + + +## Usage examples + +### Example 1 + +```python +import torch +from torch.nn import functional as F +from zeta.utils import top_p + +logits = torch.randn(10, 10) +result = top_p(logits) +``` + +This example demonstrates the basic use of the `top_p` function which accepts a tensor with random values and a default threshold value of `0.9`. + +### Example 2 + +```python +import torch diff --git a/docs/zeta/utils/track_cuda_memory_usage.md b/docs/zeta/utils/track_cuda_memory_usage.md new file mode 100644 index 00000000..195449e9 --- /dev/null +++ b/docs/zeta/utils/track_cuda_memory_usage.md @@ -0,0 +1,65 @@ +# track_cuda_memory_usage + +# Module/Function Name: track_cuda_memory_usage + +This function `track_cuda_memory_usage` is a Python decorator specifically designed to keep track of the GPU memory usage in PyTorch when a different function is called. This provides an easy way of monitoring the CUDA memory usage during the run time of a function, which can help spec out hardware requirements and catch any unusual memory usage patterns indicative of a memory leak. + +## Function Definition + +```py +def track_cuda_memory_usage(func): +``` + +### Parameters + +| Parameter | Type | Description | +| --- | --- | --- | +| func | Function | The function whose CUDA memory usage is to be tracked | + +### Returns + +The function returns a wrapped function. The returned function behaves the same as the passed function (`func`), but it also logs the CUDA memory usage when the function is called. + +| Return Value | Type | Description | +| --- | --- | --- | +| Wrapper Function | Function | The wrapped function that behaves the same as the passed function, but also logs the CUDA memory usage | + +## Functionality and Usage + +The `track_cuda_memory_usage` function wraps the passed function (`func`) and monitors its CUDA memory usage. It does this by checking the GPU memory usage before and after the function runs. If there is an increase in the memory usage, the function logs this change. + +This function can be used to debug cases where there are memory leaks in your PyTorch model. It can be especially useful if you're running out of GPU memory but don't know why. + +Remember that this is a decorator function and should be used as one. It can be applied to any other function like so: + +```python +@track_cuda_memory_usage +def my_func(): + # Function body here + # This function will now have its CUDA memory usage tracked + pass +``` + +## Example of Usage + +In the following example, we define a simple PyTorch model and use the `track_cuda_memory_usage` decorator to keep track of the model’s memory usage. + +```python +import torch +import torch.nn as nn +import logging + +# Creating simple model +class SimpleModel(nn.Module): + def __init__(self): + super(SimpleModel, self).__init__() + self.fc = nn.Linear(100, 10) + + def forward(self, x): + return self.fc(x) + +# Defining train function +@track_cuda_memory_usage +def train(model, data): + model.train() + diff --git a/docs/zeta/utils/video_tensor_to_gift.md b/docs/zeta/utils/video_tensor_to_gift.md new file mode 100644 index 00000000..d8a2758c --- /dev/null +++ b/docs/zeta/utils/video_tensor_to_gift.md @@ -0,0 +1,65 @@ +# video_tensor_to_gift + +# Module Name: zeta.utils + +## Function: video_tensor_to_gift + + ``` + This function converts a tensor representation of a video into a GIF file. + It takes a tensor video as input, unbinds the tensor, converts each image-like tensor in the video to a PIL image, + and then saves all these images in a GIF file. + + Parameters: + - tensor (tensor): A tensor containing the video data. + - path (str): The path where the GIF should be saved. + - duration (int): The time (in milliseconds) that each frame should be displayed. Default: 120 ms. + - loop (int): The number of times the GIF should loop. + 0 for infinite loop, and other integer values for specific count of loops. Default: 0 (infinite loop). + - optimize (bool): If True, the resulting GIF will be optimized to save space. + Optimization can take more time and result in minimal changes, so if you’re in a hurry, or don’t care about file size, you can skip optimization. Default: True. + + Returns: + list: list of images created from the tensors. + ``` +```python +def video_tensor_to_gift(tensor, path, duration=120, loop=0, optimize=True): + images = map(T.ToPilImage(), tensor.unbind(dim=1)) + first_img, *rest_imgs = images + first_img.save( + path, + save_all=True, + append_images=rest_imgs, + duration=duration, + loop=loop, + optimize=optimize, + ) + return images +``` + +## Usage Examples: + +### Example 1: + +```python +# import the necessary libraries +import torch +from torchvision import transforms as T +from zeta.utils import video_tensor_to_gift + +# Define a tensor for generating a video: +video_data = torch.rand(10, 10, 3, 64, 64) + +# Call the function: +video_tensor_to_gift(video_data, 'test.gif') +``` +In this example, we generate a tensor of random pixel intensity values. The generated GIF file will be saved in the current working directory with the name 'test.gif'. The gif file be looping indefinitely. + +### Example 2: + +```python +# import the necessary libraries +import torch +from torchvision import transforms as T +from zeta.utils import video_tensor_to_gift + +# Define a tensor for diff --git a/mkdocs.yml b/mkdocs.yml index e3f08f7f..6d716b7b 100644 --- a/mkdocs.yml +++ b/mkdocs.yml @@ -163,10 +163,41 @@ nav: - SentencePieceTokenizer: "zeta/tokenizers/sentencepiece.md" - TokenMonster: "zeta/tokenizers/token_monster.md" - zeta.utils: - - main: "zeta/utils/main.md" - - track_cuda_memory_usage: "zeta/utils/track_cuda_memory.md" - - module_device: "zeta/utils/module_device.md" - - save_load: "zeta/utils/save_load_wrapper.md" + - cast_tuple: "cast_tuple.md" + - group_by_key_prefix: "group_by_key_prefix.md" + - eval_decorator: "eval_decorator.md" + - print_cuda_memory_usage: "print_cuda_memory_usage.md" + - once: "once.md" + - default: "default.md" + - gumbel_noise: "gumbel_noise.md" + - pad_at_dim: "pad_at_dim.md" + - init_zero_: "init_zero_.md" + - top_p: "top_p.md" + - cast_if_src_dtype: "cast_if_src_dtype.md" + - disable_warnings_and_logs: "disable_warnings_and_logs.md" + - save_load_wrapper: "save_load_wrapper.md" + - get_sinusoid_encoding_table: "get_sinusoid_encoding_table.md" + - main: "main.md" + - string_begins_with: "string_begins_with.md" + - gif_to_tensor: "gif_to_tensor.md" + - l2norm: "l2norm.md" + - save_load: "save_load.md" + - log: "log.md" + - module_device: "module_device.md" + - print_num_params: "print_num_params.md" + - top_a: "top_a.md" + - interpolate_pos_encoding_2d: "interpolate_pos_encoding_2d.md" + - exists: "exists.md" + - cosine_beta_schedule: "cosine_beta_schedule.md" + - track_cuda_memory: "track_cuda_memory.md" + - maybe: "maybe.md" + - save_memory_snapshot: "save_memory_snapshot.md" + - top_k: "top_k.md" + - print_main: "print_main.md" + - pick_and_pop: "pick_and_pop.md" + - track_cuda_memory_usage: "track_cuda_memory_usage.md" + - group_dict_by_key: "group_dict_by_key.md" + - video_tensor_to_gift: "video_tensor_to_gift.md" - zeta.ops: - main: "zeta/ops/main.md" - softmaxes: "zeta/ops/softmaxes.md" diff --git a/pyproject.toml b/pyproject.toml index a107b13b..4dc26c7d 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,6 +1,6 @@ [tool.poetry] name = "zetascale" -version = "1.2.9" +version = "1.3.0" description = "Transformers at zeta scales" authors = ["Zeta Team"] license = "MIT" diff --git a/scripts/auto_tests_docs/auto_docs_functions.py b/scripts/auto_tests_docs/auto_docs_functions.py index 45d66eca..489bc28b 100644 --- a/scripts/auto_tests_docs/auto_docs_functions.py +++ b/scripts/auto_tests_docs/auto_docs_functions.py @@ -7,6 +7,7 @@ from scripts.auto_tests_docs.docs import DOCUMENTATION_WRITER_SOP from swarms import OpenAIChat +from zeta.utils import * load_dotenv() @@ -15,7 +16,7 @@ model = OpenAIChat( model_name="gpt-4", openai_api_key=api_key, - max_tokens=4000, + max_tokens=500, ) @@ -23,36 +24,40 @@ def process_documentation(item): """ Process the documentation for a given function using OpenAI model and save it in a Markdown file. """ - doc = inspect.getdoc(item) - source = inspect.getsource(item) - input_content = ( - f"Name: {item.__name__}\n\nDocumentation:\n{doc}\n\nSource" - f" Code:\n{source}" - ) - print(input_content) + try: + doc = inspect.getdoc(item) + source = inspect.getsource(item) + input_content = ( + f"Name: {item.__name__}\n\nDocumentation:\n{doc}\n\nSource" + f" Code:\n{source}" + ) - # Process with OpenAI model - processed_content = model( - DOCUMENTATION_WRITER_SOP(input_content, "swarms.utils") - ) + # Process with OpenAI model + processed_content = model( + DOCUMENTATION_WRITER_SOP(input_content, "zeta.utils") + ) - doc_content = f"# {item.__name__}\n\n{processed_content}\n" + doc_content = f"# {item.__name__}\n\n{processed_content}\n" - # Create the directory if it doesn't exist - dir_path = "docs/swarms/utils" - os.makedirs(dir_path, exist_ok=True) + # Create the directory if it doesn't exist + dir_path = "docs/zeta/utils" + os.makedirs(dir_path, exist_ok=True) - # Write the processed documentation to a Markdown file - file_path = os.path.join(dir_path, f"{item.__name__.lower()}.md") - with open(file_path, "w") as file: - file.write(doc_content) + # Write the processed documentation to a Markdown file + file_path = os.path.join(dir_path, f"{item.__name__.lower()}.md") + with open(file_path, "w") as file: + file.write(doc_content) + + print(f"Succesfully processed {item.__name__}.") + except Exception as e: + print(f"Error processing {item.__name__}: {e}") def main(): - # Gathering all functions from the swarms.utils module + # Gathering all functions from the zeta.utils module functions = [ obj - for name, obj in inspect.getmembers(sys.modules["swarms.utils"]) + for name, obj in inspect.getmembers(sys.modules["zeta.utils"]) if inspect.isfunction(obj) ] @@ -66,7 +71,7 @@ def main(): for thread in threads: thread.join() - print("Documentation generated in 'docs/swarms/utils' directory.") + print("Documentation generated in 'docs/zeta/utils' directory.") if __name__ == "__main__": diff --git a/scripts/auto_tests_docs/auto_tests_functions.py b/scripts/auto_tests_docs/auto_tests_functions.py index fb96442a..af685ff9 100644 --- a/scripts/auto_tests_docs/auto_tests_functions.py +++ b/scripts/auto_tests_docs/auto_tests_functions.py @@ -7,10 +7,10 @@ from scripts.auto_tests_docs.docs import TEST_WRITER_SOP_PROMPT from swarms import OpenAIChat -from swarms.utils.parse_code import extract_code_from_markdown -from swarms.utils import ( +from swarms.utils.parse_code import ( extract_code_from_markdown, ) +from zeta.utils import * load_dotenv() @@ -37,10 +37,9 @@ def process_documentation(item): # Process with OpenAI model processed_content = model( - TEST_WRITER_SOP_PROMPT(input_content, "swarms.utils", "swarms.utils") + TEST_WRITER_SOP_PROMPT(input_content, "zeta.utils", "zeta.utils") ) processed_content = extract_code_from_markdown(processed_content) - print(processed_content) doc_content = f"{processed_content}" @@ -53,12 +52,14 @@ def process_documentation(item): with open(file_path, "w") as file: file.write(doc_content) + print(f"Test generated for {item.__name__}.") + def main(): - # Gathering all functions from the swarms.utils module + # Gathering all functions from the zeta.utils module functions = [ obj - for name, obj in inspect.getmembers(sys.modules["swarms.utils"]) + for name, obj in inspect.getmembers(sys.modules["zeta.utils"]) if inspect.isfunction(obj) ] diff --git a/scripts/auto_tests_docs/file_list.txt b/scripts/auto_tests_docs/file_list.txt deleted file mode 100644 index d8a01eb8..00000000 --- a/scripts/auto_tests_docs/file_list.txt +++ /dev/null @@ -1,8 +0,0 @@ -- paralleltransformerblock: "paralleltransformerblock.md" -- hierarchicalblock: "hierarchicalblock.md" -- vitransformerwrapper: "vitransformerwrapper.md" -- localtransformer: "localtransformer.md" -- autoregressivewrapper: "autoregressivewrapper.md" -- simpletransformer: "simpletransformer.md" -- encoder: "encoder.md" -- encoderdecoder: "encoderdecoder.md" diff --git a/scripts/auto_tests_docs/mkdocs_handler.py b/scripts/auto_tests_docs/mkdocs_handler.py index aa381a93..cfe97ce0 100644 --- a/scripts/auto_tests_docs/mkdocs_handler.py +++ b/scripts/auto_tests_docs/mkdocs_handler.py @@ -26,4 +26,4 @@ def generate_file_list(directory, output_file): # Use the function to generate the file list -generate_file_list("docs/zeta/models", "file_list.txt") +generate_file_list("docs/zeta/utils", "file_list.txt") diff --git a/scripts/auto_tests_docs/update_mkdocs.py b/scripts/auto_tests_docs/update_mkdocs.py deleted file mode 100644 index c847b8a1..00000000 --- a/scripts/auto_tests_docs/update_mkdocs.py +++ /dev/null @@ -1,62 +0,0 @@ -import yaml - - -def update_mkdocs( - class_names, - base_path="docs/zeta/nn/modules", - mkdocs_file="mkdocs.yml", -): - """ - Update the mkdocs.yml file with new documentation links. - - Args: - - class_names: A list of class names for which documentation is generated. - - base_path: The base path where documentation Markdown files are stored. - - mkdocs_file: The path to the mkdocs.yml file. - """ - with open(mkdocs_file, "r") as file: - mkdocs_config = yaml.safe_load(file) - - # Find or create the 'zeta.nn.modules' section in 'nav' - zeta_modules_section = None - for section in mkdocs_config.get("nav", []): - if "zeta.nn.modules" in section: - zeta_modules_section = section["zeta.nn.modules"] - break - - if zeta_modules_section is None: - zeta_modules_section = {} - mkdocs_config["nav"].append({"zeta.nn.modules": zeta_modules_section}) - - # Add the documentation paths to the 'zeta.nn.modules' section - for class_name in class_names: - doc_path = f"{base_path}/{class_name.lower()}.md" - zeta_modules_section[class_name] = doc_path - - # Write the updated content back to mkdocs.yml - with open(mkdocs_file, "w") as file: - yaml.safe_dump(mkdocs_config, file, sort_keys=False) - - -# Example usage -classes = [ - "DenseBlock", - "HighwayLayer", - "MultiScaleBlock", - "FeedbackBlock", - "DualPathBlock", - "RecursiveBlock", - "PytorchGELUTanh", - "NewGELUActivation", - "GELUActivation", - "FastGELUActivation", - "QuickGELUActivation", - "ClippedGELUActivation", - "AccurateGELUActivation", - "MishActivation", - "LinearActivation", - "LaplaceActivation", - "ReLUSquaredActivation", -] - -update_mkdocs(classes) diff --git a/tests/utils/test_cast_if_src_dtype.py b/tests/utils/test_cast_if_src_dtype.py new file mode 100644 index 00000000..e69de29b diff --git a/tests/utils/test_cast_tuple.py b/tests/utils/test_cast_tuple.py new file mode 100644 index 00000000..535ec37e --- /dev/null +++ b/tests/utils/test_cast_tuple.py @@ -0,0 +1,42 @@ +import pytest +from zeta.utils import cast_tuple + + +# Basic Tests +def test_cast_tuple(): + assert cast_tuple(5, 3) == (5, 5, 5) + assert cast_tuple("a", 2) == ("a", "a") + assert cast_tuple((1, 2), 1) == (1, 2) + + +# Utilize Fixture +@pytest.fixture +def sample_value(): + return 10 + + +def test_cast_tuple_with_fixture(sample_value): + assert cast_tuple(sample_value, 4) == (10, 10, 10, 10) + + +# Parameterized Testing +@pytest.mark.parametrize( + "value, depth, expected", [(7, 3, (7, 7, 7)), ("b", 2, ("b", "b"))] +) +def test_cast_tuple_parametrized(value, depth, expected): + assert cast_tuple(value, depth) == expected + + +# Exception Testing +def test_cast_tuple_exception(): + with pytest.raises(TypeError): + cast_tuple(5, "a") + + +# Test with mock and monkeypatch +def test_cast_tuple_with_mock_and_monkeypatch(monkeypatch): + def mock_isinstance(val, t): + return False + + monkeypatch.setattr("builtins.isinstance", mock_isinstance) + assert cast_tuple((1, 2), 1) == ((1, 2),) diff --git a/tests/utils/test_cosine_beta_schedule.py b/tests/utils/test_cosine_beta_schedule.py new file mode 100644 index 00000000..a1939e21 --- /dev/null +++ b/tests/utils/test_cosine_beta_schedule.py @@ -0,0 +1,64 @@ +import torch +import pytest +from zeta.utils import cosine_beta_schedule + + +# Basic checks +def test_cosine_beta_schedule(): + assert cosine_beta_schedule(0).equal(torch.tensor([])) + assert cosine_beta_schedule(1).equal(torch.tensor([0.9999])) + + +@pytest.mark.parametrize("timesteps", [10, 100, 1000]) +def test_cosine_beta_schedule_length(timesteps): + assert len(cosine_beta_schedule(timesteps)) == timesteps + + +def test_cosine_beta_schedule_values_range(): + """Ensure all values are in the range [0, 0.9999]""" + for timesteps in range(100): + betas = cosine_beta_schedule(timesteps) + assert (betas >= 0).all() and (betas <= 0.9999).all() + + +def test_cosine_beta_schedule_values_decreasing(): + for timesteps in range(100): + betas = cosine_beta_schedule(timesteps) + assert (betas[:-1] >= betas[1:]).all() + + +# Test with negative timesteps values +def test_cosine_beta_schedule_negative_timesteps(): + with pytest.raises(RuntimeError): + cosine_beta_schedule(-10) + + +# Test with floating timesteps values +def test_cosine_beta_schedule_float_timesteps(): + with pytest.raises(TypeError): + cosine_beta_schedule(10.5) + + +# Test large values +@pytest.mark.slow +def test_cosine_beta_schedule_large_timesteps(): + assert len(cosine_beta_schedule(1e6)) == 1e6 + + +# Test using mathematical calculation +def test_cosine_beta_schedule_math(): + for timesteps in range(1, 100): + betas = cosine_beta_schedule(timesteps) + x = torch.linspace(0, timesteps, timesteps + 1, dtype=torch.float64) + expected_betas = 1 - ( + torch.cos( + ((x[1:] / timesteps) + 0.008) / (1 + 0.008) * torch.pi * 0.5 + ) + ** 2 + / torch.cos( + ((x[:-1] / timesteps) + 0.008) / (1 + 0.008) * torch.pi * 0.5 + ) + ** 2 + ) + expected_betas = torch.clip(expected_betas, 0, 0.9999) + assert torch.allclose(betas, expected_betas, atol=1e-7) diff --git a/tests/utils/test_default.py b/tests/utils/test_default.py new file mode 100644 index 00000000..53264658 --- /dev/null +++ b/tests/utils/test_default.py @@ -0,0 +1,73 @@ +import pytest +from zeta.utils import default + + +# Basic test +def test_default(): + assert default(None, "default") == "default" + assert default("value", "default") == "value" + + +# Utilize Fixtures +@pytest.fixture +def default_params(): + return [ + ("value", "default", "value"), + (None, "default", "default"), + (0, "default", 0), + (False, "default", False), + ] + + +def test_default_with_params(default_params): + for val, d, expected in default_params: + assert default(val, d) == expected + + +# Parameterized Testing +@pytest.mark.parametrize( + "val, d, expected", + [ + ("value", "default", "value"), + (None, "default", "default"), + (0, "default", 0), + (False, "default", False), + ], +) +def test_default_parametrized(val, d, expected): + assert default(val, d) == expected + + +# Exception testing +def test_default_exception(): + with pytest.raises(TypeError): + default() + + +# Grouping and Marking Tests +@pytest.mark.value +def test_default_value(): + assert default("value", "default") == "value" + + +@pytest.mark.none +def test_default_none(): + assert default(None, "default") == "default" + + +# Clean Code Practices & Documentation +def test_default_value(): + """ + Test that the default function returns the correct value when one is provided. + """ + assert default("value", "default") == "value" + + +def test_default_none(): + """ + Test that the default function correctly handles None values. + """ + assert default(None, "default") == "default" + + +# Continue adding more tests to cover all edge cases and normal uses... diff --git a/tests/utils/test_disable_warnings_and_logs.py b/tests/utils/test_disable_warnings_and_logs.py new file mode 100644 index 00000000..71c4c16d --- /dev/null +++ b/tests/utils/test_disable_warnings_and_logs.py @@ -0,0 +1,55 @@ +import os +import warnings +import logging +from unittest.mock import MagicMock, patch +from zeta.utils import disable_warnings_and_logs + + +@patch("logging.getLogger") +def test_warnings_disabled(mock_getLogger): + disable_warnings_and_logs() + warnings.filterwarnings.assert_called_once_with("ignore") + assert os.environ["TF_CPP_MIN_LOG_LEVEL"] == "2" + + +@patch("warnings.filterwarnings") +def test_tf_warnings_disabled(mock_filterwarnings): + disable_warnings_and_logs() + assert os.environ["TF_CPP_MIN_LOG_LEVEL"] == "2" + + +@patch("os.environ") +def test_bnb_and_others_disabled(mock_environ): + with patch.object( + logging, "getLogger", return_value=MagicMock() + ) as mock_getLogger: + disable_warnings_and_logs() + mock_environ.__setitem__.assert_called_once_with( + "TF_CPP_MIN_LOG_LEVEL", "2" + ) + mock_getLogger().setLevel.assert_called_once_with(logging.WARNING) + + +@patch("zeta.utils.logging") +def test_specific_loggers_disabled(mock_logging): + mock_logger = MagicMock() + mock_logging.getLogger.return_value = mock_logger + disable_warnings_and_logs() + mock_logging.getLogger.assert_any_call("real_accelerator") + mock_logging.getLogger.assert_any_call( + "torch.distributed.elastic.multiprocessing.redirects" + ) + assert mock_logger.setLevel.call_count == 2 + mock_logger.setLevel.assert_called_with(logging.CRITICAL) + + +# @patch('logging.getLogger') +# def test_all_loggers_disabled(mock_getLogger): +# mock_logger = MagicMock() +# mock_getLogger.return_value = mock_logger +# disable_warnings_and_logs() +# mock_getLogger.assert_called() +# mock_logger.addFilter.assert_called() +# assert isinstance(mock_logger.addFilter.call_args[0][0], disable_warnings_and_logs.__globals__['CustomFilter']) +# mock_getLogger().setLevel.assert_called_once_with(logging.WARNING) +# mock_logging.disable.assert_called_once_with(logging.CRITICAL) diff --git a/tests/utils/test_eval_decorator.py b/tests/utils/test_eval_decorator.py new file mode 100644 index 00000000..e69de29b diff --git a/tests/utils/test_exists.py b/tests/utils/test_exists.py new file mode 100644 index 00000000..5bda0b61 --- /dev/null +++ b/tests/utils/test_exists.py @@ -0,0 +1,47 @@ +import pytest +from zeta.utils import exists + + +def test_exists_on_none(): + assert exists(None) is False + # Another way to write the same test + assert not exists(None) + + +def test_exists_on_empty_string(): + assert exists("") is True + assert exists(" ") is True + # Another way to write the same test + assert exists("") + + +def test_exists_on_zero(): + assert exists(0) is True + assert exists(0.0) is True + + +@pytest.mark.parametrize( + "val", [True, False, 1, -1, [], [None], {}, {"None": None}, lambda x: x] +) +def test_exists_on_values(val): + assert exists(val) is True + + +def test_exists_on_function(): + assert exists(lambda x: x) is True + + +def test_exists_on_empty_list(): + assert exists([]) is True + + +def test_exists_on_empty_dict(): + assert exists({}) is True + + +def test_exists_on_False(): + assert exists(False) is True + + +def test_exists_on_None(): + assert exists(None) is False diff --git a/tests/utils/test_get_sinusoid_encoding_table.py b/tests/utils/test_get_sinusoid_encoding_table.py new file mode 100644 index 00000000..2ecd572f --- /dev/null +++ b/tests/utils/test_get_sinusoid_encoding_table.py @@ -0,0 +1,56 @@ +import pytest +import numpy as np +import torch +from zeta.utils import get_sinusoid_encoding_table + + +def test_basic_sinusoid_table(): + table = get_sinusoid_encoding_table(5, 4) + assert table.shape == (1, 5, 4) + + +def test_zero_position_sinusoid_table(): + table = get_sinusoid_encoding_table(0, 4) + assert table.size(1) == 0 + + +def test_zero_dimension_sinusoid_table(): + table = get_sinusoid_encoding_table(5, 0) + assert table.size(2) == 0 + + +def test_negative_position_sinusoid_table(): + with pytest.raises(ValueError): + get_sinusoid_encoding_table(-5, 4) + + +def test_negative_dimension_sinusoid_table(): + with pytest.raises(ValueError): + get_sinusoid_encoding_table(5, -4) + + +@pytest.mark.parametrize("n_position, d_hid", [(10, 10), (5, 2), (100, 50)]) +def test_sinusoid_table_parameters(n_position, d_hid): + table = get_sinusoid_encoding_table(n_position, d_hid) + assert table.shape == (1, n_position, d_hid) + + +def test_sinusoid_table_values(): + table = get_sinusoid_encoding_table(5, 4) + base = np.array( + [ + [pos / np.power(10000, 2 * (hid_j // 2) / 4) for hid_j in range(4)] + for pos in range(5) + ] + ) + base[:, 0::2] = np.sin(base[:, 0::2]) + base[:, 1::2] = np.cos(base[:, 1::2]) + expected = torch.FloatTensor(base).unsqueeze(0) + assert torch.allclose( + table, expected, atol=1e-6 + ) # Allow for minor floating point differences + + +def test_sinusoid_table_return_type(): + table = get_sinusoid_encoding_table(5, 4) + assert isinstance(table, torch.Tensor) diff --git a/tests/utils/test_gif_to_tensor.py b/tests/utils/test_gif_to_tensor.py new file mode 100644 index 00000000..73105fdc --- /dev/null +++ b/tests/utils/test_gif_to_tensor.py @@ -0,0 +1,46 @@ +import pytest +import torch +from PIL import Image +import PIL +from zeta.utils import gif_to_tensor + + +# Mock of the seek_all_images function to simulate various outputs +def mock_seek_all_images(img, channels): + return [img] * channels + + +# Fixture for a mock GIF image to be used in tests +@pytest.fixture +def mock_image(monkeypatch): + monkeypatch.setattr("zeta.utils.seek_all_images", mock_seek_all_images) + return Image.new("RGB", (60, 30)) + + +# Basic test case for successful function operation +def test_gif_to_tensor_basic(mock_image): + result = gif_to_tensor(mock_image, channels=3) + assert isinstance(result, torch.Tensor) + assert result.shape == (3, 3, 60, 30) + + +# Tests for various number of channels +@pytest.mark.parametrize("channels", [1, 2, 3, 4]) +def test_gif_to_tensor_channels(mock_image, channels): + result = gif_to_tensor(mock_image, channels=channels) + assert result.shape == (channels, channels, 60, 30) + + +# Test for non-existent file path, expecting a FileNotFound error +def test_gif_to_tensor_invalid_path(): + with pytest.raises(FileNotFoundError): + gif_to_tensor("non_existent.gif") + + +# Test for file that is not of an image type, expecting an UnidentifiedImageError +def test_gif_to_tensor_non_image_file(): + with pytest.raises(PIL.UnidentifiedImageError): + gif_to_tensor("some_file.txt") + + +# TODO: Add more tests based on the function's specification like invalid image format, invalid transform function etc. diff --git a/tests/utils/test_group_by_key_prefix.py b/tests/utils/test_group_by_key_prefix.py new file mode 100644 index 00000000..34f1ede9 --- /dev/null +++ b/tests/utils/test_group_by_key_prefix.py @@ -0,0 +1,60 @@ +import pytest +from zeta.utils import group_by_key_prefix + + +def test_group_by_key_prefix(): + """ + Test that the function correctly groups dictionary + items by keys that start with a specific prefix. + """ + prefix = "a" + d = {"aaa": 1, "abc": 2, "ccc": 3, "ddd": 4} + + dict1, dict2 = group_by_key_prefix(prefix, d) + + assert len(dict1) == 2, "Length of 1st dictionary matches prefix count" + assert len(dict2) == 2, "Length of 2nd dictionary matches non-prefix count" + assert all( + key.startswith(prefix) for key in dict1.keys() + ), "Prefix keys are in 1st dictionary" + assert all( + not key.startswith(prefix) for key in dict2.keys() + ), "Non-prefix keys are in 2nd dictionary" + + +def test_group_by_key_prefix_empty_dict(): + """ + Test that the function handles empty dictionaries correctly. + """ + result = group_by_key_prefix("a", {}) + assert result == ({}, {}), "Returns two empty dictionaries" + + +@pytest.mark.parametrize( + "prefix, d, result", + [ + ("a", {"aaa": 1, "abc": 2}, ({"aaa": 1, "abc": 2}, {})), + ("b", {"aaa": 1, "abc": 2}, ({}, {"aaa": 1, "abc": 2})), + ("", {"aaa": 1, "abc": 2}, ({"aaa": 1, "abc": 2}, {})), + ], +) +def test_group_by_key_prefix_parametrized(prefix, d, result): + """ + Test various cases using parametrized testing. + """ + assert group_by_key_prefix(prefix, d), "Results match expected" + + +@pytest.mark.parametrize( + "prefix, d", + [ + ("a", {"aaa": 1, "abc": 2, 3: "ccc"}), + (2, {"aaa": 1, "abc": 2}), + ], +) +def test_group_by_key_prefix_type_error(prefix, d): + """ + Test that the function raises a TypeError for non-str keys in dictionary. + """ + with pytest.raises(TypeError): + group_by_key_prefix(prefix, d) diff --git a/tests/utils/test_group_dict_by_key.py b/tests/utils/test_group_dict_by_key.py new file mode 100644 index 00000000..2b373faf --- /dev/null +++ b/tests/utils/test_group_dict_by_key.py @@ -0,0 +1,51 @@ +import pytest +import zeta.utils + + +# Basic Tests +def test_return_type(): + d = {"x": 1, "y": 2, "z": 3} + + def cond(x): + return x in ["x", "y"] + + result = zeta.utils.group_dict_by_key(cond, d) + assert isinstance(result, tuple) + + +# Utilizing Fixtures +@pytest.fixture +def sample_dict(): + return {"x": 1, "y": 2, "z": 3} + + +def test_all_keys_grouped_right(sample_dict): + def cond(x): + return x in ["x", "y"] + + result = zeta.utils.group_dict_by_key(cond, sample_dict) + assert list(result[0].keys()) == ["x", "y"] + assert list(result[1].keys()) == ["z"] + + +# Parameterized Testing +@pytest.mark.parametrize( + "cond,expected_keys", + [ + (lambda x: x in ["x", "y"], (["x", "y"], ["z"])), + (lambda x: x in ["x"], (["x"], ["y", "z"])), + (lambda x: x in [], ([], ["x", "y", "z"])), + (lambda x: x in ["x", "y", "z"], (["x", "y", "z"], [])), + ], +) +def test_keys_parameterized(cond, expected_keys, sample_dict): + result = zeta.utils.group_dict_by_key(cond, sample_dict) + assert list(result[0].keys()) == expected_keys[0] + assert list(result[1].keys()) == expected_keys[1] + + +# Exception Testing +def test_cond_not_callable(sample_dict): + cond = "not callable" + with pytest.raises(TypeError): + zeta.utils.group_dict_by_key(cond, sample_dict) diff --git a/tests/utils/test_gumbel_noise.py b/tests/utils/test_gumbel_noise.py new file mode 100644 index 00000000..94a09ed4 --- /dev/null +++ b/tests/utils/test_gumbel_noise.py @@ -0,0 +1,57 @@ +import pytest +import torch +from zeta.utils import gumbel_noise + +# Basic Tests + + +def test_gumbel_noise(): + tensor = torch.tensor([1.0, 2.0, 3.0]) + result = gumbel_noise(tensor) + assert isinstance( + result, torch.Tensor + ), "Output should be of type torch.Tensor" + + +# Test valid return values + + +def test_values(): + tensor = torch.tensor([1.0, 2.0, 3.0]) + result = gumbel_noise(tensor) + # Since noise is a (0,1) uniform, gumbel noise should be in the range (-inf, +inf). + # However, we don't expect to reach these limits in practice. Here we check that the + # values are within a less extreme range. + assert bool( + ((result > -100) & (result < 100)).all() + ), "Gumbel noise should fall within expected value range" + + +# Test invalid inputs + + +def test_tensor_requirement(): + with pytest.raises(TypeError): + # gumbel_noise function expects a tensor as the input + # but here a list is passed which should raise TypeError + gumbel_noise([1.0, 2.0, 3.0]) + + +# Parametrized Tests + + +@pytest.mark.parametrize( + "input_tensor", + [ + torch.tensor([1.0, 2.0, 3.0]), # 1-D Tensor + torch.tensor([[1, 2], [3, 4]]), # 2-D Tensor + torch.tensor( + [[[1, 2], [3, 4]], [[5, 6], [7, 8]]] + ), # Higher Dimension Tensor + ], +) +def test_gumbel_noise_dim(input_tensor): + result = gumbel_noise(input_tensor) + assert ( + result.shape == input_tensor.shape + ), "Output tensor should have same dimensions as input" diff --git a/tests/utils/test_init_zero_.py b/tests/utils/test_init_zero_.py new file mode 100644 index 00000000..e69de29b diff --git a/tests/utils/test_interpolate_pos_encoding_2d.py b/tests/utils/test_interpolate_pos_encoding_2d.py new file mode 100644 index 00000000..cebc6d2f --- /dev/null +++ b/tests/utils/test_interpolate_pos_encoding_2d.py @@ -0,0 +1,40 @@ +import torch +from zeta.utils import interpolate_pos_encoding_2d + +# Note: You will need to import or define 'cast_if_src_dtype' function as it is used but not provided in the initial code snippet + + +def test_interpolate_same_target_size(): + r"""If the target_spatial_size is same as N, it should return the input pos_embed.""" + pos_embed = torch.rand((1, 36, 512)) + target_spatial_size = 36 + interpolated_pos_embed = interpolate_pos_encoding_2d( + target_spatial_size, pos_embed + ) + assert torch.equal(pos_embed, interpolated_pos_embed) + + +def test_interpolate_pos_encoding_2d_dimension(): + r"""The dimensions of the output tensor should be the same as input.""" + pos_embed = torch.rand((1, 36, 512)) + target_spatial_size = 72 + interpolated_pos_embed = interpolate_pos_encoding_2d( + target_spatial_size, pos_embed + ) + assert pos_embed.shape[:] == interpolated_pos_embed.shape[:] + + +def test_input_data_types(): + r"""The function should work correctly with different data types.""" + pos_embed = torch.rand((1, 36, 512), dtype=torch.float32) + target_spatial_size = 72 + interpolated_pos_embed = interpolate_pos_encoding_2d( + target_spatial_size, pos_embed + ) + assert pos_embed.dtype == interpolated_pos_embed.dtype + + +def test_input_validation(): + r"""The function should raise an error if the inputs are invalid.""" + with pytest.raises(TypeError): + interpolate_pos_encoding_2d("random_string", "random_string") diff --git a/tests/utils/test_l2norm.py b/tests/utils/test_l2norm.py new file mode 100644 index 00000000..e69de29b diff --git a/tests/utils/test_log.py b/tests/utils/test_log.py new file mode 100644 index 00000000..bee2a2b7 --- /dev/null +++ b/tests/utils/test_log.py @@ -0,0 +1,40 @@ +import pytest +import torch +from zeta.utils import log + + +def test_log_zero(): + zero_tensor = torch.tensor(0.0) + # checking if log function can handle inputs of zero + assert log(zero_tensor) == torch.tensor(-46.0517) + + +def test_log_one(): + one_tensor = torch.tensor(1.0) + # checking normal log behavior for positive numbers + assert log(one_tensor) == torch.tensor(0.0) + + +def test_log_negative(): + negative_tensor = torch.tensor(-1.0) + # testing log function with negative numbers + with pytest.raises(ValueError): + log(negative_tensor) + + +@pytest.mark.parametrize( + "input_val, expected", + [ + (torch.tensor(1e-20), torch.tensor(-46.0517)), + (torch.tensor(2.0), torch.log(torch.tensor(2.0))), + ], +) +def test_log_various_values(input_val, expected): + # testing with a varied range of input values + assert torch.isclose(log(input_val), expected, atol=1e-04) + + +def test_log_dtype(): + # Testing log with a tensor of type int + tensor_int = torch.tensor(10) + assert log(tensor_int).dtype == torch.float32 diff --git a/tests/utils/test_maybe.py b/tests/utils/test_maybe.py new file mode 100644 index 00000000..6aa47ba6 --- /dev/null +++ b/tests/utils/test_maybe.py @@ -0,0 +1,71 @@ +import pytest +from zeta.utils import maybe + + +# Mock function to use for testing +def mock_func(x): + return x * 10 + + +def exists(item): + return item is not None + + +# Test 1: Basic function call with existing argument +def test_maybe_with_existing_arg(): + @maybe + def function_to_test(x): + return mock_func(x) + + assert function_to_test(5) == 50 + + +# Test 2: Function call with non-existing argument +def test_maybe_with_non_existing_arg(): + @maybe + def function_to_test(x): + return mock_func(x) + + assert function_to_test(None) is None + + +# Test 3: Function call with multiple arguments +def test_maybe_with_multiple_args(): + @maybe + def function_to_test(x, y, z): + return mock_func(x) + y + z + + assert function_to_test(5, 2, 3) == 55 + + +# Test 4: Function call with keyword arguments +def test_maybe_with_keyword_args(): + @maybe + def function_to_test(x, y=1, z=1): + return mock_func(x) + y + z + + assert function_to_test(5, y=5, z=5) == 60 + + +# Test 5: Parameterized testing with various inputs + + +@pytest.mark.parametrize("input,output", [(5, 50), (None, None), (0, 0)]) +def test_maybe_parameterized(input, output): + @maybe + def function_to_test(x): + return mock_func(x) + + assert function_to_test(input) == output + + +# Test 6: Exception testing + + +def test_maybe_exception_handling(): + @maybe + def function_to_test(x): + return x / 0 + + with pytest.raises(ZeroDivisionError): + function_to_test(5) diff --git a/tests/utils/test_module_device.py b/tests/utils/test_module_device.py index 0fd00af4..49f0833b 100644 --- a/tests/utils/test_module_device.py +++ b/tests/utils/test_module_device.py @@ -1,83 +1,66 @@ import pytest -import torch from torch.nn import Module -from zeta.utils.module_device import module_device +import torch +from zeta.utils.module_device import module_device -@module_device() -class DummyModule(Module): - def __init__(self, x): - super().__init__() - self.x = torch.nn.Parameter(torch.tensor(x)) +class TestModule(Module): + pass -def test_module_device_init(): - module = DummyModule(5) - assert isinstance(module, DummyModule) +@module_device("device", compatibility_check=True) +class CompatibleModule(Module): + pass -def test_module_device_device_property(): - module = DummyModule(5) - assert module.device == torch.device("cpu") +@module_device("device", on_device_transfer=lambda self, device: None) +class OnTransferModule(Module): + pass -def test_module_device_to(): - module = DummyModule(5) - module.to(torch.device("cpu")) - assert module.device == torch.device("cpu") +def test_module_device_with_compatibility_check(): + test_module = CompatibleModule() -def test_module_device_to_cuda(): + # device - str if torch.cuda.is_available(): - module = DummyModule(5) - module.to(torch.device("cuda")) - assert module.device == torch.device("cuda") - - -def test_module_device_to_cuda_compatibility_check(): - if not torch.cuda.is_available(): + assert test_module.to("cuda") == test_module + else: with pytest.raises(RuntimeError): + test_module.to("cuda") - @module_device(compatibility_check=True) - class IncompatibleModule(Module): - def __init__(self, x): - super().__init__() - self.x = torch.nn.Parameter(torch.tensor(x)) + # device - torch.device + if torch.cuda.is_available(): + assert test_module.to(torch.device("cuda")) == test_module + else: + with pytest.raises(RuntimeError): + test_module.to(torch.device("cuda")) - module = IncompatibleModule(5) - module.to(torch.device("cuda")) +def test_on_device_transfer_functionality(): + test_module = OnTransferModule() -def test_module_device_device_property_name(): - @module_device(device_property_name="custom_device") - class CustomDeviceModule(Module): - def __init__(self, x): - super().__init__() - self.x = torch.nn.Parameter(torch.tensor(x)) + # on_device_transfer should be called when transferred without raising any exception + # more extensive tests could be done depending on the implementation of on_device_transfer + assert test_module.to("cpu") == test_module - module = CustomDeviceModule(5) - assert module.custom_device == torch.device("cpu") +def test_module_device_without_decorator(): + test_module = TestModule() -def test_module_device_not_module(): - with pytest.raises(AssertionError): + # without decorator, transfer should go through without any issues + assert test_module.to("cpu") == test_module + if torch.cuda.is_available(): + assert test_module.to("cuda") == test_module - @module_device() - class NotAModule: - pass +def test_device_property(): + test_module = TestModule() -def test_module_device_multiple_devices(): - if torch.cuda.is_available(): + # without decorator, there should be no 'device' property + with pytest.raises(AttributeError): + test_module.device - @module_device() - class MultiDeviceModule(Module): - def __init__(self, x): - super().__init__() - self.x = torch.nn.Parameter(torch.tensor(x)) - self.y = torch.nn.Parameter( - torch.tensor(x), device=torch.device("cuda") - ) - - module = MultiDeviceModule(5) - assert len(module.device) > 1 + # with decorator, 'device' property should exist + test_module = CompatibleModule() + assert isinstance(test_module.device, torch.device) diff --git a/tests/utils/test_once.py b/tests/utils/test_once.py new file mode 100644 index 00000000..db0a90bb --- /dev/null +++ b/tests/utils/test_once.py @@ -0,0 +1,95 @@ +# Import the necessary modules +import pytest +from unittest.mock import Mock +from zeta.utils import once + + +def test_once_decorator(): + """Test for once decorator.""" + mock = Mock(__name__="mock") + mock.__module__ = "mock" + decorated_mock = once(mock) + assert mock.call_count == 0 + + # Call the decorated function for the first time + decorated_mock(10) + assert mock.call_count == 1 + mock.assert_called_once_with(10) + + # Call it for the second time + decorated_mock(20) + assert mock.call_count == 1, "Decorated function called more than once!" + + # Call it for the third time, just to make sure + decorated_mock(30) + assert mock.call_count == 1, "Decorated function called more than once!" + + +@pytest.mark.parametrize( + "args", + [ + (1,), + ("hello",), + ([1, 2, 3],), + ({"a": 1},), + ], +) +def test_once_decorator_with_different_arguments(args): + """Test once decorator with different argument types.""" + mock = Mock(__name__="mock") + mock.__module__ = "mock" + decorated_mock = once(mock) + + decorated_mock(*args) + mock.assert_called_once_with(*args) + + +def test_once_decorator_with_exception(): + """Test once decorator where the decorated function raises an exception.""" + mock = Mock(__name__="mock", side_effect=Exception("Test Exception")) + mock.__module__ = "mock" + decorated_mock = once(mock) + + with pytest.raises(Exception, match="Test Exception"): + decorated_mock(10) + + assert mock.call_count == 1 + + # The function should still not be callable again even if it raised an exception the first time + with pytest.raises(Exception, match="Test Exception"): + decorated_mock(20) + + assert mock.call_count == 1, "Decorated function called more than once!" + + +def test_once_decorator_with_multiple_instances(): + """Test once decorator with multiple function instances.""" + mock1 = Mock(__name__="mock1") + mock1.__module__ = "mock1" + decorated_mock1 = once(mock1) + + mock2 = Mock(__name__="mock2") + mock2.__module__ = "mock2" + decorated_mock2 = once(mock2) + + # Call the first function + decorated_mock1(10) + assert mock1.call_count == 1 + assert mock2.call_count == 0 + + # Call the second function + decorated_mock2(20) + assert mock1.call_count == 1 + assert mock2.call_count == 1 + + # Call the first function again + decorated_mock1(30) + assert ( + mock1.call_count == 1 + ), "Decorated mock1 function called more than once!" + + # Call the second function again + decorated_mock2(40) + assert ( + mock2.call_count == 1 + ), "Decorated mock2 function called more than once!" diff --git a/tests/utils/test_pad_at_dim.py b/tests/utils/test_pad_at_dim.py new file mode 100644 index 00000000..c94a42ad --- /dev/null +++ b/tests/utils/test_pad_at_dim.py @@ -0,0 +1,57 @@ +import torch +from zeta.utils import pad_at_dim +import pytest + + +def test_pad_at_dim(): + tensor = torch.tensor([1, 2, 3, 4]) + pad = (1, 1) + padded_tensor = pad_at_dim(tensor, pad) + assert padded_tensor.tolist() == [0, 1, 2, 3, 4, 0] + + +def test_pad_at_last_dim(): + tensor = torch.tensor([[1, 2, 3, 4], [5, 6, 7, 8]]) + pad = (1, 1) + padded_tensor = pad_at_dim(tensor, pad) + assert padded_tensor.tolist() == [[0, 1, 2, 3, 4, 0], [0, 5, 6, 7, 8, 0]] + + +def test_pad_at_first_dim(): + tensor = torch.tensor([[1, 2, 3, 4], [5, 6, 7, 8]]) + pad = (1, 1) + padded_tensor = pad_at_dim(tensor, pad, 0) + assert padded_tensor.tolist() == [ + [0, 0, 0, 0, 0], + [1, 2, 3, 4], + [5, 6, 7, 8], + [0, 0, 0, 0, 0], + ] + + +def test_pad_at_negative_dim(): + tensor = torch.tensor([[1, 2, 3, 4], [5, 6, 7, 8]]) + pad = (1, 1) + padded_tensor = pad_at_dim(tensor, pad, -1) + assert padded_tensor.tolist() == [[0, 1, 2, 3, 4, 0], [0, 5, 6, 7, 8, 0]] + + +def test_pad_with_value(): + tensor = torch.tensor([1, 2, 3, 4]) + pad = (1, 1) + padded_tensor = pad_at_dim(tensor, pad, value=9) + assert padded_tensor.tolist() == [9, 1, 2, 3, 4, 9] + + +@pytest.mark.parametrize("pad", [(1, 1), (2, 2), (3, 3), (4, 4)]) +def test_different_pad_sizes(pad): + tensor = torch.tensor([1, 2, 3, 4]) + padded_tensor = pad_at_dim(tensor, pad) + assert padded_tensor[0] == 0 and padded_tensor[-1] == 0 + + +@pytest.mark.parametrize("dim", [-1, 0, 1, 2, 3]) +def test_pad_at_different_dims(dim): + tensor = torch.tensor([[1, 2, 3, 4], [5, 6, 7, 8]]) + pad_at_dim(tensor, (1, 1), dim) + # Add corresponding asserts based on value of dim diff --git a/tests/utils/test_pick_and_pop.py b/tests/utils/test_pick_and_pop.py new file mode 100644 index 00000000..225829c3 --- /dev/null +++ b/tests/utils/test_pick_and_pop.py @@ -0,0 +1,60 @@ +# test_pick_and_pop.py + +import pytest +from zeta.utils import pick_and_pop + + +def test_simple_case(): + dictionary = {"a": 1, "b": 2, "c": 3} + keys = ["a", "b"] + result = pick_and_pop(keys, dictionary) + assert result == {"a": 1, "b": 2} + assert dictionary == {"c": 3} + + +def test_empty_keys(): + dictionary = {"a": 1, "b": 2, "c": 3} + keys = [] + result = pick_and_pop(keys, dictionary) + assert result == {} + assert dictionary == {"a": 1, "b": 2, "c": 3} + + +def test_key_not_found(): + dictionary = {"a": 1, "b": 2, "c": 3} + keys = ["a", "x"] + with pytest.raises(KeyError): + pick_and_pop(keys, dictionary) + + +@pytest.mark.parametrize( + "dict_values,keys,expected", + [ + ({"a": 1, "b": 2, "c": 3}, ["b", "c"], {"b": 2, "c": 3}), + ({1: "a", 2: "b", 3: "c"}, [1, 2], {1: "a", 2: "b"}), + ({"x": "y", "foo": "bar"}, ["foo"], {"foo": "bar"}), + ], +) +def test_various_inputs(dict_values, keys, expected): + assert pick_and_pop(keys, dict_values) == expected + + +def test_duplicate_keys_in_list(): + dictionary = {"a": 1, "b": 2, "c": 3} + keys = ["a", "b", "b"] + with pytest.raises(KeyError): + pick_and_pop(keys, dictionary) + + +def test_keys_order_in_result(): + dictionary = {"a": 1, "b": 2, "c": 3} + keys = ["b", "a"] + result = pick_and_pop(keys, dictionary) + assert list(result.keys()) == keys + + +def test_empty_dictionary(): + dictionary = {} + keys = ["b", "a"] + with pytest.raises(KeyError): + pick_and_pop(keys, dictionary) diff --git a/tests/utils/test_print_cuda_memory_usage.py b/tests/utils/test_print_cuda_memory_usage.py new file mode 100644 index 00000000..2321fdb8 --- /dev/null +++ b/tests/utils/test_print_cuda_memory_usage.py @@ -0,0 +1,48 @@ +import torch +from zeta.utils import print_cuda_memory_usage +from unittest.mock import patch + + +def test_if_cuda_is_available(): + assert torch.cuda.is_available(), "CUDA is not available on your system." + + +def test_initial_memory_value(): + assert ( + torch.cuda.memory_allocated() >= 0 + ), "CUDA memory allocated is less than 0." + + +def test_after_memory_usage(): + with print_cuda_memory_usage(): + torch.rand((1000, 1000)).cuda() + assert ( + torch.cuda.memory_allocated() > 0 + ), "CUDA memory allocated is less than or equal to initial memory." + + +def test_memory_usage_value(): + init_mem = torch.cuda.memory_allocated() + with print_cuda_memory_usage(): + torch.rand((1000, 1000)).cuda() + assert (torch.cuda.memory_allocated() - init_mem) / ( + 1024**3 + ) >= 0, "Memory usage is negative." + + +@patch("builtins.print") +def test_print_call(mock_print): + with print_cuda_memory_usage(): + torch.rand((1000, 1000)).cuda() + assert mock_print.called, "Print function was not called." + + +@patch("builtins.print") +def test_print_format(mock_print): + mem = torch.cuda.memory_allocated() + with print_cuda_memory_usage(): + torch.rand((1000, 1000)).cuda() + mock_print.assert_called_with( + "CUDA memory usage:" + f" {((torch.cuda.memory_allocated() - mem) / (1024**3)):.2f} GB" + ) diff --git a/tests/utils/test_print_main.py b/tests/utils/test_print_main.py new file mode 100644 index 00000000..4e4165e9 --- /dev/null +++ b/tests/utils/test_print_main.py @@ -0,0 +1,39 @@ +import pytest +from zeta.utils import print_main +from unittest.mock import patch + + +# Usage of Fixtures +@pytest.fixture +def message(): + # This will create a predefined message that will be used in every test + return "This is the test message!" + + +# Basic Test +def test_print_main_without_dist(message, capsys): + """Test print_main without distribution""" + print_main(message) + captured = capsys.readouterr() + assert captured.out == message + "\n" + + +# Utilizing Mocks and Parameterized Testing +@patch("torch.distributed.is_available") +@patch("torch.distributed.get_rank") +@pytest.mark.parametrize( + "available,rank,expected", + [ + (True, 0, "This is the test message!\n"), + (True, 1, ""), + (False, 0, "This is the test message!\n"), + ], +) +def test_print_main_with_dist( + mock_is_available, mock_get_rank, available, rank, expected, message, capsys +): + mock_is_available.return_value = available + mock_get_rank.return_value = rank + print_main(message) + captured = capsys.readouterr() + assert captured.out == expected diff --git a/tests/utils/test_print_num_params.py b/tests/utils/test_print_num_params.py new file mode 100644 index 00000000..90c7cd75 --- /dev/null +++ b/tests/utils/test_print_num_params.py @@ -0,0 +1,35 @@ +import pytest +from zeta.utils import print_num_params +from torch import nn +from unittest.mock import patch + + +@pytest.fixture +def simple_model(): + model = nn.Sequential( + nn.Linear(2, 5), + nn.ReLU(), + nn.Linear(5, 1), + ) + return model + + +def test_num_params(simple_model): + with patch("builtins.print") as mock_print: + print_num_params(simple_model) + mock_print.assert_called_once_with("Number of parameters in model: 16") + + +def test_num_params_zero(): + model = nn.Sequential() + with patch("builtins.print") as mock_print: + print_num_params(model) + mock_print.assert_called_once_with("Number of parameters in model: 0") + + +def test_dist_available(simple_model): + with patch("torch.distributed.is_available", return_value=True): + with patch("torch.distributed.get_rank", return_value=0): + with patch("builtins.print") as mock_print: + print_num_params(simple_model) + mock_print.assert_called_once_with("Number of parameters in model: 16") diff --git a/tests/utils/test_save_load.py b/tests/utils/test_save_load.py new file mode 100644 index 00000000..94877666 --- /dev/null +++ b/tests/utils/test_save_load.py @@ -0,0 +1,60 @@ +import pytest +from zeta.utils import save_load +from torch.nn import Module + + +class TestModule(Module): + def __init__(self, num): + super(TestModule, self).__init__() + self.num = num + + +@pytest.fixture +def path(tmp_path): + return tmp_path / "test_module.pkl" + + +class TestSaveLoad: + def test_save_load_class_decorator(self): + @save_load() + class TestModuleDecorated(TestModule): + pass + + assert hasattr(TestModuleDecorated, "save") + assert hasattr(TestModuleDecorated, "load") + assert hasattr(TestModuleDecorated, "init_and_load") + + def test_save_method(self, path): + @save_load() + class TestModuleDecorated(TestModule): + pass + + module = TestModuleDecorated(10) + module.save(path) + assert path.exists() + + def test_load_method(self, path): + @save_load() + class TestModuleDecorated(TestModule): + pass + + module = TestModuleDecorated(10) + module.save(path) + + loaded_module = TestModuleDecorated(1) + loaded_module.load(path) + assert loaded_module.num == 10 + + @pytest.mark.parametrize("overwrite", [False, True]) + def test_save_overwrite(self, path, overwrite): + @save_load() + class TestModuleDecorated(TestModule): + pass + + module = TestModuleDecorated(10) + module.save(path) + if not overwrite: + with pytest.raises(AssertionError): + module.save(path, overwrite=overwrite) + + ... diff --git a/tests/utils/test_save_memory_snapshot.py b/tests/utils/test_save_memory_snapshot.py new file mode 100644 index 00000000..b702c38e --- /dev/null +++ b/tests/utils/test_save_memory_snapshot.py @@ -0,0 +1,52 @@ +from unittest.mock import patch, MagicMock +from pathlib import Path +from zeta.utils import save_memory_snapshot + + +def test_snapshot_folder_creation(): + """Mock the Path.mkdir method to test if the folder is created""" + with patch.object(Path, "mkdir") as mock_mkdir: + with save_memory_snapshot(Path("/tmp")): + pass + mock_mkdir.assert_called_once_with(parents=True, exist_ok=True) + + +def test_snapshot_record_start(): + """Mock the torch.cuda.memory._record_memory_history method to test if the memory history recording starts""" + with patch("torch.cuda.memory._record_memory_history") as mock_record: + with save_memory_snapshot(Path("/tmp")): + pass + mock_record.assert_called_once() + + +@patch("builtins.open", new_callable=MagicMock) +@patch("torch.cuda.memory._snapshot") +def test_snapshot_representation_saved(mock_snapshot, mock_open): + """Test if the memory snapshot representation is correctly saved""" + snapshot = {"foo": "bar"} + mock_snapshot.return_value = snapshot + + with save_memory_snapshot(Path("/tmp")): + pass + + mock_open.assert_called_with("/tmp/snapshot.pickle", "wb") + f = mock_open.return_value.__enter__.return_value + f.write.assert_called_once_with(snapshot) + + +@patch("builtins.open", new_callable=MagicMock) +@patch("torch.cuda.memory._snapshot") +@patch("torch.cuda._memory_viz.trace_plot") +def test_trace_plot_saved(mock_trace_plot, mock_snapshot, mock_open): + """Test if the memory usage trace plot is correctly saved""" + snapshot = {"foo": "bar"} + trace_plot = "" + mock_snapshot.return_value = snapshot + mock_trace_plot.return_value = trace_plot + + with save_memory_snapshot(Path("/tmp")): + pass + + mock_open.assert_called_with("/tmp/trace_plot.html", "w") + f = mock_open.return_value.__enter__.return_value + f.write.assert_called_once_with(trace_plot) diff --git a/tests/utils/test_string_begins_with.py b/tests/utils/test_string_begins_with.py new file mode 100644 index 00000000..d7ec9f57 --- /dev/null +++ b/tests/utils/test_string_begins_with.py @@ -0,0 +1,58 @@ +import pytest +from zeta.utils import string_begins_with + + +# Basic Tests - 1 +def test_string_begins_with_true(): + assert string_begins_with("pre", "prefix") is True + + +# Basic Tests - 2 +def test_string_begins_with_false(): + assert string_begins_with("post", "prefix") is False + + +# Parameterized Testing - 3, 4 +@pytest.mark.parametrize( + "prefix, string, expected", + [("pre", "prefix", True), ("post", "prefix", False)], +) +def test_string_begins_with_parametrized(prefix, string, expected): + assert string_begins_with(prefix, string) == expected + + +# Test case sensitivity and unicode characters - 5, 6 +@pytest.mark.parametrize( + "prefix, string, expected", + [("тест", "тестовый", True), ("Тест", "тестовый", False)], +) +def test_string_begins_with_casing(prefix, string, expected): + assert string_begins_with(prefix, string) == expected + + +# Test empty strings and none inputs - 7, 8, 9, 10 +@pytest.mark.parametrize( + "prefix, string, expected", + [ + (None, "test", False), + ("", "test", True), + ("test", None, False), + ("test", "", False), + ], +) +def test_string_begins_with_empty_none(prefix, string, expected): + assert string_begins_with(prefix, string) == expected + + +# Test with numbers and special characters - 11, 12, 13, 14 +@pytest.mark.parametrize( + "prefix, string, expected", + [ + (123, "123test", False), + ("#$", "#$test", True), + ("test", "@#", False), + (None, None, False), + ], +) +def test_string_begins_with_non_letters(prefix, string, expected): + assert string_begins_with(prefix, string) == expected diff --git a/tests/utils/test_top_a.py b/tests/utils/test_top_a.py new file mode 100644 index 00000000..d28786b6 --- /dev/null +++ b/tests/utils/test_top_a.py @@ -0,0 +1,61 @@ +import pytest +import torch +from zeta.utils import top_a + + +def test_top_a(): + logits = torch.Tensor([1.0, 2.0, 3.0]) + output = top_a(logits) + assert torch.is_tensor(output), "Output should be a Torch tensor" + assert ( + output.size() == logits.size() + ), "Output size should match the input size" + + +@pytest.mark.parametrize( + "logits, min_p_pow, min_p_ratio", + [ + (torch.Tensor([1.0, 2.0, 3.0]), 2.0, 0.02), + (torch.Tensor([-1.0, -2.0, -3.0]), 2.0, 0.02), + (torch.Tensor([10.0, 20.0, 30.0]), 2.0, 0.02), + (torch.Tensor([10.0, 20.0, 30.0]), 3.0, 0.02), + (torch.Tensor([10.0, 20.0, 30.0]), 2.0, 0.10), + ], +) +def test_top_a_values(logits, min_p_pow, min_p_ratio): + output = top_a(logits, min_p_pow, min_p_ratio) + assert torch.is_tensor(output), "Output should be a Torch tensor" + assert ( + output.size() == logits.size() + ), "Output size should match the input size" + assert (output == float("-inf")).any() or ( + output == 1 + ).any(), ( + "Output elements should either be negative infinity or 1 (inclusive)" + ) + + +def test_top_a_exception(): + with pytest.raises(TypeError): + top_a("non-tensor") + + +@pytest.fixture +def mock_tensor(monkeypatch): + class MockTensor: + def __init__(self): + self.size_val = 3 + self.values = [1.0, 1.0, 1.0] + + def size(self): + return self.size_val + + monkeypatch.setattr(torch, "Tensor", MockTensor) + + +def test_top_a_with_mock_tensor(mock_tensor): + output = top_a(torch.Tensor()) + assert output.size() == mock_tensor.size() + assert all( + [val in output.values for val in mock_tensor.values] + ), "Output values should match mocked tensor values" diff --git a/tests/utils/test_top_k.py b/tests/utils/test_top_k.py new file mode 100644 index 00000000..1823379b --- /dev/null +++ b/tests/utils/test_top_k.py @@ -0,0 +1,51 @@ +import pytest +import torch +from math import ceil +from zeta.utils import top_k + + +def test_top_k_positive_case(): + logits = torch.randn(1, 10) + probs = top_k(logits, 0.9) + k = ceil((1 - 0.9) * logits.shape[-1]) + assert probs.shape == logits.shape + assert ( + probs[probs != float("-inf")].numel() == k + ) # checks number of elements that aren't negative infinity + + +def test_dimensions_positive_case(): + logits = torch.randn( + 1, 5, 5 + ) # assumed example for logits with more than 2 dimensions + top_k(logits, 0.9) + + +@pytest.mark.parametrize( + "threshold", + [ + (0.8), + (0.9), + (1), + ], +) +def test_top_k_threshold_variations(threshold): + logits = torch.randn(1, 5) + probs = top_k(logits, threshold) + k = ceil((1 - threshold) * logits.shape[-1]) + assert probs[probs != float("-inf")].numel() == k + + +def test_top_k_large_values(): + logits = torch.randn(1, 1000) + threshold = 0.9 + probs = top_k(logits, threshold) + k = ceil((1 - threshold) * logits.shape[-1]) + assert probs[probs != float("-inf")].numel() == k + + +def test_top_k_empty_input(): + with pytest.raises( + Exception + ): # assuming that you would want to handle this case with an exception + top_k(torch.tensor([]), 0.8) diff --git a/tests/utils/test_top_p.py b/tests/utils/test_top_p.py new file mode 100644 index 00000000..cf5c9f82 --- /dev/null +++ b/tests/utils/test_top_p.py @@ -0,0 +1,60 @@ +# first, here are some imports and mock data setup: + +import torch +import torch.nn.functional as F +import pytest +from zeta.utils import top_p + +# mock data +logits = torch.FloatTensor([0.1, 0.2, 0.3, 0.4]) +sorted_logits, sorted_indices = torch.sort(logits, descending=True) +cum_probs = torch.cumsum(F.softmax(sorted_logits, dim=-1), dim=-1) +sorted_indices_to_remove = cum_probs > (1 - 0.9) + + +# Test if the return value is a tensor +def test_return_type(): + ret = top_p(logits) + assert isinstance(ret, torch.Tensor) + + +# Test if the function is properly sorting the `logits` +def test_sorting(): + output = top_p(logits) + assert torch.all(torch.eq(output, torch.sort(output, descending=True)[0])) + + +# Test if threshold argument is respected +def test_threshold(): + output = top_p(logits, thres=0.5) + assert torch.cumsum(F.softmax(output, dim=-1), dim=-1)[-1].item() <= 0.5 + + +# Test if the function is properly setting `-inf` for the values that should be removed +def test_inf_removal(): + top_p(logits) + assert (sorted_logits[sorted_indices_to_remove] == float("-inf")).all() + + +# Test if function is properly scattering the results +def test_scattering(): + output = top_p(logits) + assert torch.all( + torch.eq( + output, sorted_logits.scatter(1, sorted_indices, sorted_logits) + ) + ) + + +# Test if the function is raising error for invalid `logits` +def test_invalid_logits(): + with pytest.raises(Exception): + top_p(torch.Tensor([0.1, 0.2, None, 0.4])) + + +# Test if the function is raising error for invalid `thres` +def test_invalid_thres(): + with pytest.raises(Exception): + top_p(logits, thres=1.5) + with pytest.raises(Exception): + top_p(logits, thres=-0.5) diff --git a/tests/utils/test_track_cuda_memory_usage.py b/tests/utils/test_track_cuda_memory_usage.py new file mode 100644 index 00000000..233c0801 --- /dev/null +++ b/tests/utils/test_track_cuda_memory_usage.py @@ -0,0 +1,61 @@ +import pytest +from unittest.mock import patch +from zeta.utils import track_cuda_memory_usage + + +# Testing the base functionality with cuda available and function without error +@patch("torch.cuda.is_available", return_value=True) +@patch("torch.cuda.memory_allocated", side_effect=[1000, 2000]) +@patch("torch.cuda.synchronize") +@patch("logging.info") +def test_track_cuda_memory_usage_base( + mock_log_info, mock_sync, mock_mem_alloc, mock_cuda_avail +): + @track_cuda_memory_usage + def test_func(): + return "Test" + + assert test_func() == "Test" + mock_sync.assert_called() + mock_mem_alloc.assert_called() + mock_log_info.assert_called_with("Memory usage of test_func: 1000 bytes") + + +# Testing function with an exception +@patch("torch.cuda.is_available", return_value=True) +@patch("torch.cuda.memory_allocated", side_effect=[1000, 2000]) +@patch("torch.cuda.synchronize") +@patch("logging.info") +def test_track_cuda_memory_usage_exception( + mock_log_info, mock_sync, mock_mem_alloc, mock_cuda_avail +): + @track_cuda_memory_usage + def test_func(): + raise ValueError("Test exception") + + with pytest.raises(ValueError): + test_func() + + mock_sync.assert_called() + mock_mem_alloc.assert_called() + mock_log_info.assert_called_with("Memory usage of test_func: 1000 bytes") + + +# Testing when cuda is not available +@patch("torch.cuda.is_available", return_value=False) +@patch("torch.cuda.memory_allocated") +@patch("torch.cuda.synchronize") +@patch("logging.warning") +def test_track_cuda_memory_usage_no_cuda( + mock_log_warn, mock_sync, mock_mem_alloc, mock_cuda_avail +): + @track_cuda_memory_usage + def test_func(): + return "Test" + + assert test_func() == "Test" + mock_sync.assert_not_called() + mock_mem_alloc.assert_not_called() + mock_log_warn.assert_called_with( + "CUDA is not available, skip tracking memory usage" + ) diff --git a/tests/utils/test_video_tensor_to_gift.py b/tests/utils/test_video_tensor_to_gift.py new file mode 100644 index 00000000..bb3c5460 --- /dev/null +++ b/tests/utils/test_video_tensor_to_gift.py @@ -0,0 +1,93 @@ +import pytest +import torch +from unittest.mock import MagicMock, patch +from PIL import Image +from zeta.utils import video_tensor_to_gift + + +def setup_test_tensor(): + test_tensor = torch.rand((5, 5, 3)) + return test_tensor + + +def setup_test_pil_image(): + return Image.new("RGB", (5, 5)) + + +@pytest.fixture +def tensor(tmpdir): + tensor = setup_test_tensor() + return tensor + + +@pytest.fixture +def test_image(): + img = setup_test_pil_image() + return img + + +@pytest.mark.parametrize( + "duration, loop, optimize", + [ + (120, 0, True), + (60, 1, False), + (240, 2, True), + (0, 0, False), + (180, 1, True), + ], +) +def test_video_tensor_to_gif_valid_params( + duration, loop, optimize, tensor, test_image +): + path = "/test/path" + + with patch("torchvision.transforms.ToPILImage") as mocked_transform: + mocked_transform.return_value = MagicMock(return_value=test_image) + + images = video_tensor_to_gift( + tensor, duration=duration, loop=loop, optimize=optimize + ) + + mocked_transform.assert_called() + test_image.save.assert_called_with( + path, + save_all=True, + append_images=images[1:], + duration=duration, + loop=loop, + optimize=optimize, + ) + + +def test_video_tensor_to_gif_invalid_tensor(): + path = "/test/path" + tensor = "invalid_tensor" + + with pytest.raises(TypeError): + video_tensor_to_gift(tensor, path) + + +def test_video_tensor_to_gif_invalid_path(): + path = 123 + tensor = setup_test_tensor() + + with pytest.raises(TypeError): + video_tensor_to_gift(tensor, path) + + +def test_video_tensor_to_gif_invalid_duration(): + path = "/test/path" + tensor = setup_test_tensor() + duration = "invalid_duration" + + with pytest.raises(TypeError): + video_tensor_to_gift(tensor, path, duration=duration) + + +def test_video_tensor_to_gif_invalid_loop(): + path = "/test/path" + tensor = setup_test_tensor() + loop = "invalid_loop" + + with pytest.raises(TypeError): + video_tensor_to_gift(tensor, path, loop=loop) diff --git a/zeta/utils/__init__.py b/zeta/utils/__init__.py index 8e287781..7ec03b5d 100644 --- a/zeta/utils/__init__.py +++ b/zeta/utils/__init__.py @@ -9,6 +9,35 @@ ) from zeta.utils.disable_logging import disable_warnings_and_logs from zeta.utils.params import print_num_params, print_main +from zeta.utils.module_device import module_device +from zeta.utils.save_load_wrapper import save_load +from zeta.utils.main import ( + exists, + default, + once, + eval_decorator, + cast_tuple, + maybe, + init_zero_, + pick_and_pop, + group_dict_by_key, + string_begins_with, + group_by_key_prefix, + top_p, + top_k, + top_a, + log, + gumbel_noise, + video_tensor_to_gift, + gif_to_tensor, + l2norm, + pad_at_dim, + cosine_beta_schedule, + cast_if_src_dtype, + get_sinusoid_encoding_table, + interpolate_pos_encoding_2d, +) + __all__ = [ "track_cuda_memory_usage", @@ -16,6 +45,32 @@ "print_cuda_memory_usage", "save_memory_snapshot", "disable_warnings_and_logs", - "print_num_params", "print_main", + "module_device", + "save_load", + "exists", + "default", + "once", + "eval_decorator", + "cast_tuple", + "maybe", + "init_zero_", + "pick_and_pop", + "group_dict_by_key", + "string_begins_with", + "group_by_key_prefix", + "top_p", + "top_k", + "top_a", + "log", + "gumbel_noise", + "print_num_params", + "video_tensor_to_gift", + "gif_to_tensor", + "l2norm", + "pad_at_dim", + "cosine_beta_schedule", + "cast_if_src_dtype", + "get_sinusoid_encoding_table", + "interpolate_pos_encoding_2d", ] diff --git a/zeta/utils/main.py b/zeta/utils/main.py index 69e389dc..395be524 100644 --- a/zeta/utils/main.py +++ b/zeta/utils/main.py @@ -778,7 +778,3 @@ def all_unique(arr): def apply_fns(fns, tensors): return [fn(tensors) for fn, tensor in zip(fns, tensors)] - - -def cast_tuple(t, length=1): - return t if isinstance(t, tuple) else ((t,) * length) From d2ab608350d74378ec2faee7ea6951187b6c4b74 Mon Sep 17 00:00:00 2001 From: Kye Date: Wed, 27 Dec 2023 00:03:11 -0500 Subject: [PATCH 215/587] [fairscale][removal] --- pyproject.toml | 1 - requirements.txt | 1 - 2 files changed, 2 deletions(-) diff --git a/pyproject.toml b/pyproject.toml index 4dc26c7d..a9d2abf8 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -18,7 +18,6 @@ packages = [ [tool.poetry.dependencies] python = "^3.8" torch = "2.1.2" -fairscale = "0.4.0" timm = "0.6.13" torchdiffeq = "0.2.3" pytest = "7.4.2" diff --git a/requirements.txt b/requirements.txt index 86256744..82aa491d 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,5 +1,4 @@ torch==2.1.2 -fairscale==0.4.0 timm==0.6.13 einops==0.7.0 memory-profiler From 0f364c672a0b629dced35f47475308231d395f29 Mon Sep 17 00:00:00 2001 From: Kye Date: Wed, 27 Dec 2023 00:04:32 -0500 Subject: [PATCH 216/587] [lion-pytorch][removal]; --- requirements.txt | 1 - 1 file changed, 1 deletion(-) diff --git a/requirements.txt b/requirements.txt index 82aa491d..0690bef6 100644 --- a/requirements.txt +++ b/requirements.txt @@ -2,7 +2,6 @@ torch==2.1.2 timm==0.6.13 einops==0.7.0 memory-profiler -lion-pytorch==0.0.7 bitsandbytes==0.41.3.post2 typing==3.7.4.3 einops-exts==0.0.4 From 2fce2487f6eabb039cba3015354af4298bbdb104 Mon Sep 17 00:00:00 2001 From: Kye Date: Wed, 27 Dec 2023 00:06:38 -0500 Subject: [PATCH 217/587] [zeta.utils][cleanup] --- file_list.txt | 35 ++++++++++++++++++++++++++ mkdocs.yml | 70 +++++++++++++++++++++++++-------------------------- 2 files changed, 70 insertions(+), 35 deletions(-) create mode 100644 file_list.txt diff --git a/file_list.txt b/file_list.txt new file mode 100644 index 00000000..865c4391 --- /dev/null +++ b/file_list.txt @@ -0,0 +1,35 @@ +- cast_tuple: "zeta/utils/cast_tuple.md" +- group_by_key_prefix: "zeta/utils/group_by_key_prefix.md" +- eval_decorator: "zeta/utils/eval_decorator.md" +- print_cuda_memory_usage: "zeta/utils/print_cuda_memory_usage.md" +- once: "zeta/utils/once.md" +- default: "zeta/utils/default.md" +- gumbel_noise: "zeta/utils/gumbel_noise.md" +- pad_at_dim: "zeta/utils/pad_at_dim.md" +- init_zero_: "zeta/utils/init_zero_.md" +- top_p: "zeta/utils/top_p.md" +- cast_if_src_dtype: "zeta/utils/cast_if_src_dtype.md" +- disable_warnings_and_logs: "zeta/utils/disable_warnings_and_logs.md" +- save_load_wrapper: "zeta/utils/save_load_wrapper.md" +- get_sinusoid_encoding_table: "zeta/utils/get_sinusoid_encoding_table.md" +- main: "zeta/utils/main.md" +- string_begins_with: "zeta/utils/string_begins_with.md" +- gif_to_tensor: "zeta/utils/gif_to_tensor.md" +- l2norm: "zeta/utils/l2norm.md" +- save_load: "zeta/utils/save_load.md" +- log: "zeta/utils/log.md" +- module_device: "zeta/utils/module_device.md" +- print_num_params: "zeta/utils/print_num_params.md" +- top_a: "zeta/utils/top_a.md" +- interpolate_pos_encoding_2d: "zeta/utils/interpolate_pos_encoding_2d.md" +- exists: "zeta/utils/exists.md" +- cosine_beta_schedule: "zeta/utils/cosine_beta_schedule.md" +- track_cuda_memory: "zeta/utils/track_cuda_memory.md" +- maybe: "zeta/utils/maybe.md" +- save_memory_snapshot: "zeta/utils/save_memory_snapshot.md" +- top_k: "zeta/utils/top_k.md" +- print_main: "zeta/utils/print_main.md" +- pick_and_pop: "zeta/utils/pick_and_pop.md" +- track_cuda_memory_usage: "zeta/utils/track_cuda_memory_usage.md" +- group_dict_by_key: "zeta/utils/group_dict_by_key.md" +- video_tensor_to_gift: "zeta/utils/video_tensor_to_gift.md" diff --git a/mkdocs.yml b/mkdocs.yml index 6d716b7b..09f6e334 100644 --- a/mkdocs.yml +++ b/mkdocs.yml @@ -163,41 +163,41 @@ nav: - SentencePieceTokenizer: "zeta/tokenizers/sentencepiece.md" - TokenMonster: "zeta/tokenizers/token_monster.md" - zeta.utils: - - cast_tuple: "cast_tuple.md" - - group_by_key_prefix: "group_by_key_prefix.md" - - eval_decorator: "eval_decorator.md" - - print_cuda_memory_usage: "print_cuda_memory_usage.md" - - once: "once.md" - - default: "default.md" - - gumbel_noise: "gumbel_noise.md" - - pad_at_dim: "pad_at_dim.md" - - init_zero_: "init_zero_.md" - - top_p: "top_p.md" - - cast_if_src_dtype: "cast_if_src_dtype.md" - - disable_warnings_and_logs: "disable_warnings_and_logs.md" - - save_load_wrapper: "save_load_wrapper.md" - - get_sinusoid_encoding_table: "get_sinusoid_encoding_table.md" - - main: "main.md" - - string_begins_with: "string_begins_with.md" - - gif_to_tensor: "gif_to_tensor.md" - - l2norm: "l2norm.md" - - save_load: "save_load.md" - - log: "log.md" - - module_device: "module_device.md" - - print_num_params: "print_num_params.md" - - top_a: "top_a.md" - - interpolate_pos_encoding_2d: "interpolate_pos_encoding_2d.md" - - exists: "exists.md" - - cosine_beta_schedule: "cosine_beta_schedule.md" - - track_cuda_memory: "track_cuda_memory.md" - - maybe: "maybe.md" - - save_memory_snapshot: "save_memory_snapshot.md" - - top_k: "top_k.md" - - print_main: "print_main.md" - - pick_and_pop: "pick_and_pop.md" - - track_cuda_memory_usage: "track_cuda_memory_usage.md" - - group_dict_by_key: "group_dict_by_key.md" - - video_tensor_to_gift: "video_tensor_to_gift.md" + - cast_tuple: "zeta/utils/cast_tuple.md" + - group_by_key_prefix: "zeta/utils/group_by_key_prefix.md" + - eval_decorator: "zeta/utils/eval_decorator.md" + - print_cuda_memory_usage: "zeta/utils/print_cuda_memory_usage.md" + - once: "zeta/utils/once.md" + - default: "zeta/utils/default.md" + - gumbel_noise: "zeta/utils/gumbel_noise.md" + - pad_at_dim: "zeta/utils/pad_at_dim.md" + - init_zero_: "zeta/utils/init_zero_.md" + - top_p: "zeta/utils/top_p.md" + - cast_if_src_dtype: "zeta/utils/cast_if_src_dtype.md" + - disable_warnings_and_logs: "zeta/utils/disable_warnings_and_logs.md" + - save_load_wrapper: "zeta/utils/save_load_wrapper.md" + - get_sinusoid_encoding_table: "zeta/utils/get_sinusoid_encoding_table.md" + - main: "zeta/utils/main.md" + - string_begins_with: "zeta/utils/string_begins_with.md" + - gif_to_tensor: "zeta/utils/gif_to_tensor.md" + - l2norm: "zeta/utils/l2norm.md" + - save_load: "zeta/utils/save_load.md" + - log: "zeta/utils/log.md" + - module_device: "zeta/utils/module_device.md" + - print_num_params: "zeta/utils/print_num_params.md" + - top_a: "zeta/utils/top_a.md" + - interpolate_pos_encoding_2d: "zeta/utils/interpolate_pos_encoding_2d.md" + - exists: "zeta/utils/exists.md" + - cosine_beta_schedule: "zeta/utils/cosine_beta_schedule.md" + - track_cuda_memory: "zeta/utils/track_cuda_memory.md" + - maybe: "zeta/utils/maybe.md" + - save_memory_snapshot: "zeta/utils/save_memory_snapshot.md" + - top_k: "zeta/utils/top_k.md" + - print_main: "zeta/utils/print_main.md" + - pick_and_pop: "zeta/utils/pick_and_pop.md" + - track_cuda_memory_usage: "zeta/utils/track_cuda_memory_usage.md" + - group_dict_by_key: "zeta/utils/group_dict_by_key.md" + - video_tensor_to_gift: "zeta/utils/video_tensor_to_gift.md" - zeta.ops: - main: "zeta/ops/main.md" - softmaxes: "zeta/ops/softmaxes.md" From 59486ec4bacfc979b686f46bb2397a0147484dc0 Mon Sep 17 00:00:00 2001 From: Kye Date: Wed, 27 Dec 2023 00:08:33 -0500 Subject: [PATCH 218/587] [CHORE][torchvision] --- requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements.txt b/requirements.txt index 0690bef6..0ac50640 100644 --- a/requirements.txt +++ b/requirements.txt @@ -5,7 +5,7 @@ memory-profiler bitsandbytes==0.41.3.post2 typing==3.7.4.3 einops-exts==0.0.4 -torchvision==0.16.1 +torchvision tokenmonster==1.1.12 accelerate datasets==2.10.1 From 6b0efbaaf8bfd381a3664d0dec27217b9ca73296 Mon Sep 17 00:00:00 2001 From: Kye Date: Wed, 27 Dec 2023 00:27:38 -0500 Subject: [PATCH 219/587] [zeta.models][fix] --- file_list.txt | 45 +++++------------------ mkdocs.yml | 20 +++++----- scripts/auto_tests_docs/mkdocs_handler.py | 4 +- 3 files changed, 22 insertions(+), 47 deletions(-) diff --git a/file_list.txt b/file_list.txt index 865c4391..7f2ca4b5 100644 --- a/file_list.txt +++ b/file_list.txt @@ -1,35 +1,10 @@ -- cast_tuple: "zeta/utils/cast_tuple.md" -- group_by_key_prefix: "zeta/utils/group_by_key_prefix.md" -- eval_decorator: "zeta/utils/eval_decorator.md" -- print_cuda_memory_usage: "zeta/utils/print_cuda_memory_usage.md" -- once: "zeta/utils/once.md" -- default: "zeta/utils/default.md" -- gumbel_noise: "zeta/utils/gumbel_noise.md" -- pad_at_dim: "zeta/utils/pad_at_dim.md" -- init_zero_: "zeta/utils/init_zero_.md" -- top_p: "zeta/utils/top_p.md" -- cast_if_src_dtype: "zeta/utils/cast_if_src_dtype.md" -- disable_warnings_and_logs: "zeta/utils/disable_warnings_and_logs.md" -- save_load_wrapper: "zeta/utils/save_load_wrapper.md" -- get_sinusoid_encoding_table: "zeta/utils/get_sinusoid_encoding_table.md" -- main: "zeta/utils/main.md" -- string_begins_with: "zeta/utils/string_begins_with.md" -- gif_to_tensor: "zeta/utils/gif_to_tensor.md" -- l2norm: "zeta/utils/l2norm.md" -- save_load: "zeta/utils/save_load.md" -- log: "zeta/utils/log.md" -- module_device: "zeta/utils/module_device.md" -- print_num_params: "zeta/utils/print_num_params.md" -- top_a: "zeta/utils/top_a.md" -- interpolate_pos_encoding_2d: "zeta/utils/interpolate_pos_encoding_2d.md" -- exists: "zeta/utils/exists.md" -- cosine_beta_schedule: "zeta/utils/cosine_beta_schedule.md" -- track_cuda_memory: "zeta/utils/track_cuda_memory.md" -- maybe: "zeta/utils/maybe.md" -- save_memory_snapshot: "zeta/utils/save_memory_snapshot.md" -- top_k: "zeta/utils/top_k.md" -- print_main: "zeta/utils/print_main.md" -- pick_and_pop: "zeta/utils/pick_and_pop.md" -- track_cuda_memory_usage: "zeta/utils/track_cuda_memory_usage.md" -- group_dict_by_key: "zeta/utils/group_dict_by_key.md" -- video_tensor_to_gift: "zeta/utils/video_tensor_to_gift.md" +- vit: "zeta/modelsvit.md" +- gpt4multimodal: "zeta/modelsgpt4multimodal.md" +- maxvit: "zeta/modelsmaxvit.md" +- llama2: "zeta/modelsllama2.md" +- gpt4: "zeta/modelsgpt4.md" +- andromeda: "zeta/modelsandromeda.md" +- basemodel: "zeta/modelsbasemodel.md" +- palme: "zeta/modelspalme.md" +- megavit: "zeta/modelsmegavit.md" +- navit: "zeta/modelsnavit.md" diff --git a/mkdocs.yml b/mkdocs.yml index 09f6e334..fd471889 100644 --- a/mkdocs.yml +++ b/mkdocs.yml @@ -209,16 +209,16 @@ nav: - ParallelWrapper: "zeta/training/parallel_wrapper.md" - train: "zeta/training/train.md" - zeta.models: - - vit: "vit.md" - - gpt4multimodal: "gpt4multimodal.md" - - maxvit: "maxvit.md" - - llama2: "llama2.md" - - gpt4: "gpt4.md" - - andromeda: "andromeda.md" - - basemodel: "basemodel.md" - - palme: "palme.md" - - megavit: "megavit.md" - - navit: "navit.md" + - vit: "zeta/modelsvit.md" + - gpt4multimodal: "zeta/modelsgpt4multimodal.md" + - maxvit: "zeta/modelsmaxvit.md" + - llama2: "zeta/modelsllama2.md" + - gpt4: "zeta/modelsgpt4.md" + - andromeda: "zeta/modelsandromeda.md" + - basemodel: "zeta/modelsbasemodel.md" + - palme: "zeta/modelspalme.md" + - megavit: "zeta/modelsmegavit.md" + - navit: "zeta/modelsnavit.md" - zeta.quant: - QUIK: "zeta/quant/quik.md" - BitLinear: "zeta/quant/bitlinear.md" diff --git a/scripts/auto_tests_docs/mkdocs_handler.py b/scripts/auto_tests_docs/mkdocs_handler.py index cfe97ce0..e25b2be5 100644 --- a/scripts/auto_tests_docs/mkdocs_handler.py +++ b/scripts/auto_tests_docs/mkdocs_handler.py @@ -22,8 +22,8 @@ def generate_file_list(directory, output_file): # Remove the file extension file_name, _ = os.path.splitext(file) # Write the file name and path to the output file - f.write(f'- {file_name}: "{file_path}"\n') + f.write(f'- {file_name}: "{directory}{file_path}"\n') # Use the function to generate the file list -generate_file_list("docs/zeta/utils", "file_list.txt") +generate_file_list("docs/zeta/models", "file_list.txt") From 4e5e83a38ba16bbbafdb6632be2fc25be4b96a47 Mon Sep 17 00:00:00 2001 From: Kye Date: Wed, 27 Dec 2023 00:52:02 -0500 Subject: [PATCH 220/587] [zeta.models][fix] --- file_list.txt | 20 ++++++++++---------- mkdocs.yml | 20 ++++++++++---------- 2 files changed, 20 insertions(+), 20 deletions(-) diff --git a/file_list.txt b/file_list.txt index 7f2ca4b5..c35d2048 100644 --- a/file_list.txt +++ b/file_list.txt @@ -1,10 +1,10 @@ -- vit: "zeta/modelsvit.md" -- gpt4multimodal: "zeta/modelsgpt4multimodal.md" -- maxvit: "zeta/modelsmaxvit.md" -- llama2: "zeta/modelsllama2.md" -- gpt4: "zeta/modelsgpt4.md" -- andromeda: "zeta/modelsandromeda.md" -- basemodel: "zeta/modelsbasemodel.md" -- palme: "zeta/modelspalme.md" -- megavit: "zeta/modelsmegavit.md" -- navit: "zeta/modelsnavit.md" +- vit: "zeta/models/vit.md" +- gpt4multimodal: "zeta/models/gpt4multimodal.md" +- maxvit: "zeta/models/maxvit.md" +- llama2: "zeta/models/llama2.md" +- gpt4: "zeta/models/gpt4.md" +- andromeda: "zeta/models/andromeda.md" +- basemodel: "zeta/models/basemodel.md" +- palme: "zeta/models/palme.md" +- megavit: "zeta/models/megavit.md" +- navit: "zeta/models/navit.md" diff --git a/mkdocs.yml b/mkdocs.yml index fd471889..563a3b3d 100644 --- a/mkdocs.yml +++ b/mkdocs.yml @@ -209,16 +209,16 @@ nav: - ParallelWrapper: "zeta/training/parallel_wrapper.md" - train: "zeta/training/train.md" - zeta.models: - - vit: "zeta/modelsvit.md" - - gpt4multimodal: "zeta/modelsgpt4multimodal.md" - - maxvit: "zeta/modelsmaxvit.md" - - llama2: "zeta/modelsllama2.md" - - gpt4: "zeta/modelsgpt4.md" - - andromeda: "zeta/modelsandromeda.md" - - basemodel: "zeta/modelsbasemodel.md" - - palme: "zeta/modelspalme.md" - - megavit: "zeta/modelsmegavit.md" - - navit: "zeta/modelsnavit.md" + - vit: "zeta/models/vit.md" + - gpt4multimodal: "zeta/models/gpt4multimodal.md" + - maxvit: "zeta/models/maxvit.md" + - llama2: "zeta/models/llama2.md" + - gpt4: "zeta/models/gpt4.md" + - andromeda: "zeta/models/andromeda.md" + - basemodel: "zeta/models/basemodel.md" + - palme: "zeta/models/palme.md" + - megavit: "zeta/models/megavit.md" + - navit: "zeta/models/navit.md" - zeta.quant: - QUIK: "zeta/quant/quik.md" - BitLinear: "zeta/quant/bitlinear.md" From 7269d7d20e972d9fdb0d2308302f42b5f080cff9 Mon Sep 17 00:00:00 2001 From: Kye Date: Wed, 27 Dec 2023 01:08:37 -0500 Subject: [PATCH 221/587] [DOCS][FIXES +++ ] --- docs/zeta/nn/modules/simple_feedback.md | 3 --- docs/zeta/structs/encoderdecoder.md | 2 +- docs/zeta/training/parallel_wrapper.md | 6 +++--- tests/nn/modules/test_test_conv_lang.py | 6 ------ tests/nn/modules/test_test_h3_layer.py | 7 ------- tests/ops/test_mos.py | 2 +- tests/rl/test_prioritizedreplybuffer.py | 2 +- tests/rl/test_prioritizedsequencereplybuffer.py | 2 +- tests/rl/test_sumtree.py | 2 +- tests/training/test_parallel_wrapper.py | 2 +- zeta/nn/modules/test_dense_connect.py | 2 +- 11 files changed, 10 insertions(+), 26 deletions(-) diff --git a/docs/zeta/nn/modules/simple_feedback.md b/docs/zeta/nn/modules/simple_feedback.md index 2581bda6..d415465b 100644 --- a/docs/zeta/nn/modules/simple_feedback.md +++ b/docs/zeta/nn/modules/simple_feedback.md @@ -112,6 +112,3 @@ This particular sequence ensures that the neural network can learn a rich repres --- -**Notes**: - -Remember to replace `"from zeta.nn.modules import SimpleFeedForward"` with the actual import statement depending on where the `SimpleFeedForward` function resides in your project structure. The above examples assume it's placed in a module named `your_module`. \ No newline at end of file diff --git a/docs/zeta/structs/encoderdecoder.md b/docs/zeta/structs/encoderdecoder.md index fcbdc80d..735406e3 100644 --- a/docs/zeta/structs/encoderdecoder.md +++ b/docs/zeta/structs/encoderdecoder.md @@ -99,7 +99,7 @@ This method executes the forward pass of the module. ```python # Imports import torch -from _your_module_ import Encoder, Decoder, EncoderDecoder +from zeta.structs import Encoder, Decoder, EncoderDecoder # Arguments args = argparse.Namespace( diff --git a/docs/zeta/training/parallel_wrapper.md b/docs/zeta/training/parallel_wrapper.md index 0cf81fac..3cfe699f 100644 --- a/docs/zeta/training/parallel_wrapper.md +++ b/docs/zeta/training/parallel_wrapper.md @@ -56,7 +56,7 @@ This method redirects attribute access to the internal model to allow direct acc ```python import torch.nn as nn -from zeta.training import ParallelWrapper # assuming the class is in your_module.py +from zeta.training import ParallelWrapper # Define a model model = nn.Linear(512, 512) @@ -74,7 +74,7 @@ output = model(input) ```python import torch.nn as nn -from zeta.training import ParallelWrapper # assuming the class is in your_module.py +from zeta.training import ParallelWrapper # Define a model model = nn.Linear(512, 512) @@ -92,7 +92,7 @@ output = model(input) ```python import torch.nn as nn -from zeta.training import ParallelWrapper # assuming the class is in your_module.py +from zeta.training import ParallelWrapper # Define a model model = nn.Linear(512, 512) diff --git a/tests/nn/modules/test_test_conv_lang.py b/tests/nn/modules/test_test_conv_lang.py index 9e776974..39c97bef 100644 --- a/tests/nn/modules/test_test_conv_lang.py +++ b/tests/nn/modules/test_test_conv_lang.py @@ -90,9 +90,3 @@ def test_invalid_activation_raises_error(): ) -# 6. Test Coverage (requires pytest-cov) -def test_coverage(): - pytest.main(["--cov=your_module", "test_your_module.py"]) - - -# Add more tests as needed... diff --git a/tests/nn/modules/test_test_h3_layer.py b/tests/nn/modules/test_test_h3_layer.py index 3ac54264..86cdc8c0 100644 --- a/tests/nn/modules/test_test_h3_layer.py +++ b/tests/nn/modules/test_test_h3_layer.py @@ -54,10 +54,3 @@ def test_invalid_dimension_raises_error(): with pytest.raises(ValueError): H3Layer(0) - -# 6. Test Coverage (requires pytest-cov) -def test_coverage(): - pytest.main(["--cov=your_module", "test_your_module.py"]) - - -# Add more tests as needed... diff --git a/tests/ops/test_mos.py b/tests/ops/test_mos.py index 035e0151..f34a562c 100644 --- a/tests/ops/test_mos.py +++ b/tests/ops/test_mos.py @@ -3,7 +3,7 @@ from torch import nn from zeta.ops.mos import ( MixtureOfSoftmaxes, -) # Replace 'your_module' with your actual module +) # Create a fixture for initializing the model diff --git a/tests/rl/test_prioritizedreplybuffer.py b/tests/rl/test_prioritizedreplybuffer.py index ec516436..503e0dd4 100644 --- a/tests/rl/test_prioritizedreplybuffer.py +++ b/tests/rl/test_prioritizedreplybuffer.py @@ -2,7 +2,7 @@ import torch from zeta.rl.priortized_replay_buffer import ( PrioritizedReplayBuffer, -) # Replace 'your_module' with the actual module where classes are defined +) @pytest.fixture diff --git a/tests/rl/test_prioritizedsequencereplybuffer.py b/tests/rl/test_prioritizedsequencereplybuffer.py index ddb315e3..6a42ac76 100644 --- a/tests/rl/test_prioritizedsequencereplybuffer.py +++ b/tests/rl/test_prioritizedsequencereplybuffer.py @@ -2,7 +2,7 @@ import torch from zeta.rl.priortized_rps import ( PrioritizedSequenceReplayBuffer, -) # Replace 'your_module' with the actual module where classes are defined +) @pytest.fixture diff --git a/tests/rl/test_sumtree.py b/tests/rl/test_sumtree.py index a2cf9177..7e81fdab 100644 --- a/tests/rl/test_sumtree.py +++ b/tests/rl/test_sumtree.py @@ -1,7 +1,7 @@ import pytest from zeta.rl.sumtree import ( SumTree, -) # Replace 'your_module' with the actual module where SumTree is defined +) # Fixture for initializing SumTree instances with a given size diff --git a/tests/training/test_parallel_wrapper.py b/tests/training/test_parallel_wrapper.py index 7adb6c40..116ad060 100644 --- a/tests/training/test_parallel_wrapper.py +++ b/tests/training/test_parallel_wrapper.py @@ -3,7 +3,7 @@ import torch.nn as nn from zeta.training.parallel_wrapper import ( - ParallelWrapper, # assuming the class is in your_module.py + ParallelWrapper, ) diff --git a/zeta/nn/modules/test_dense_connect.py b/zeta/nn/modules/test_dense_connect.py index 0cf6d5d8..1da54f55 100644 --- a/zeta/nn/modules/test_dense_connect.py +++ b/zeta/nn/modules/test_dense_connect.py @@ -2,7 +2,7 @@ import torch.nn as nn import unittest -from your_module import DenseBlock +from zeta.nn.modules.dense_connect import DenseBlock class DenseBlockTestCase(unittest.TestCase): From 0359e41b51372d1b651f5e25e4fdf160760dd164 Mon Sep 17 00:00:00 2001 From: Kye Date: Wed, 27 Dec 2023 01:09:42 -0500 Subject: [PATCH 222/587] [CLEANUP] --- file_list.txt | 10 ---------- 1 file changed, 10 deletions(-) delete mode 100644 file_list.txt diff --git a/file_list.txt b/file_list.txt deleted file mode 100644 index c35d2048..00000000 --- a/file_list.txt +++ /dev/null @@ -1,10 +0,0 @@ -- vit: "zeta/models/vit.md" -- gpt4multimodal: "zeta/models/gpt4multimodal.md" -- maxvit: "zeta/models/maxvit.md" -- llama2: "zeta/models/llama2.md" -- gpt4: "zeta/models/gpt4.md" -- andromeda: "zeta/models/andromeda.md" -- basemodel: "zeta/models/basemodel.md" -- palme: "zeta/models/palme.md" -- megavit: "zeta/models/megavit.md" -- navit: "zeta/models/navit.md" From 0ad9df38b4b09151abac6bf4823f30df1443e533 Mon Sep 17 00:00:00 2001 From: Kye Date: Wed, 27 Dec 2023 01:14:39 -0500 Subject: [PATCH 223/587] [CODE QUALIT] --- tests/nn/modules/test_test_conv_lang.py | 2 -- tests/nn/modules/test_test_h3_layer.py | 1 - tests/ops/test_mos.py | 2 +- tests/rl/test_prioritizedreplybuffer.py | 2 +- tests/rl/test_prioritizedsequencereplybuffer.py | 2 +- tests/rl/test_sumtree.py | 2 +- tests/training/test_parallel_wrapper.py | 2 +- 7 files changed, 5 insertions(+), 8 deletions(-) diff --git a/tests/nn/modules/test_test_conv_lang.py b/tests/nn/modules/test_test_conv_lang.py index 39c97bef..49e35a74 100644 --- a/tests/nn/modules/test_test_conv_lang.py +++ b/tests/nn/modules/test_test_conv_lang.py @@ -88,5 +88,3 @@ def test_invalid_activation_raises_error(): ConvolutionLanguageBlock( 128, 256, 3, 1, activation="invalid_activation" ) - - diff --git a/tests/nn/modules/test_test_h3_layer.py b/tests/nn/modules/test_test_h3_layer.py index 86cdc8c0..739c20cc 100644 --- a/tests/nn/modules/test_test_h3_layer.py +++ b/tests/nn/modules/test_test_h3_layer.py @@ -53,4 +53,3 @@ def test_with_mocked_ssm(): def test_invalid_dimension_raises_error(): with pytest.raises(ValueError): H3Layer(0) - diff --git a/tests/ops/test_mos.py b/tests/ops/test_mos.py index f34a562c..9459b919 100644 --- a/tests/ops/test_mos.py +++ b/tests/ops/test_mos.py @@ -3,7 +3,7 @@ from torch import nn from zeta.ops.mos import ( MixtureOfSoftmaxes, -) +) # Create a fixture for initializing the model diff --git a/tests/rl/test_prioritizedreplybuffer.py b/tests/rl/test_prioritizedreplybuffer.py index 503e0dd4..98201f5c 100644 --- a/tests/rl/test_prioritizedreplybuffer.py +++ b/tests/rl/test_prioritizedreplybuffer.py @@ -2,7 +2,7 @@ import torch from zeta.rl.priortized_replay_buffer import ( PrioritizedReplayBuffer, -) +) @pytest.fixture diff --git a/tests/rl/test_prioritizedsequencereplybuffer.py b/tests/rl/test_prioritizedsequencereplybuffer.py index 6a42ac76..6a7511f0 100644 --- a/tests/rl/test_prioritizedsequencereplybuffer.py +++ b/tests/rl/test_prioritizedsequencereplybuffer.py @@ -2,7 +2,7 @@ import torch from zeta.rl.priortized_rps import ( PrioritizedSequenceReplayBuffer, -) +) @pytest.fixture diff --git a/tests/rl/test_sumtree.py b/tests/rl/test_sumtree.py index 7e81fdab..3afe9087 100644 --- a/tests/rl/test_sumtree.py +++ b/tests/rl/test_sumtree.py @@ -1,7 +1,7 @@ import pytest from zeta.rl.sumtree import ( SumTree, -) +) # Fixture for initializing SumTree instances with a given size diff --git a/tests/training/test_parallel_wrapper.py b/tests/training/test_parallel_wrapper.py index 116ad060..1de1b1d3 100644 --- a/tests/training/test_parallel_wrapper.py +++ b/tests/training/test_parallel_wrapper.py @@ -3,7 +3,7 @@ import torch.nn as nn from zeta.training.parallel_wrapper import ( - ParallelWrapper, + ParallelWrapper, ) From d7003e14ccf971fd5a981df016a4b1b1dc3593d5 Mon Sep 17 00:00:00 2001 From: Kye Date: Wed, 27 Dec 2023 11:04:46 -0500 Subject: [PATCH 224/587] [CLEANUP] --- docs/zeta/utils/cast_if_src_dtype.md | 99 ++++++++----- docs/zeta/utils/cast_tuple.md | 116 ++++++++++----- docs/zeta/utils/cosine_beta_schedule.md | 96 +++++++------ docs/zeta/utils/default.md | 121 ++++++++++++---- docs/zeta/utils/disable_warnings_and_logs.md | 78 +++++------ docs/zeta/utils/eval_decorator.md | 132 ++++++++++++++---- docs/zeta/utils/exists.md | 86 ++++++------ .../zeta/utils/get_sinusoid_encoding_table.md | 54 +++++-- docs/zeta/utils/gif_to_tensor.md | 83 +++++++---- docs/zeta/utils/group_by_key_prefix.md | 105 ++++++++++---- docs/zeta/utils/group_dict_by_key.md | 126 ++++++++++++++--- docs/zeta/utils/gumbel_noise.md | 97 +++++++++---- docs/zeta/utils/init_zero_.md | 124 ++++++++++------ .../zeta/utils/interpolate_pos_encoding_2d.md | 106 ++++++++------ docs/zeta/utils/l2norm.md | 92 +++++++----- docs/zeta/utils/log.md | 84 ++++++----- docs/zeta/utils/maybe.md | 70 ++++++---- docs/zeta/utils/module_device.md | 78 +++++++---- docs/zeta/utils/once.md | 119 ++++++++-------- docs/zeta/utils/pad_at_dim.md | 98 ++++++++++--- docs/zeta/utils/pick_and_pop.md | 91 +++++++----- docs/zeta/utils/print_cuda_memory_usage.md | 84 +++++++---- docs/zeta/utils/print_main.md | 92 ++++++------ docs/zeta/utils/print_num_params.md | 95 ++++++++----- docs/zeta/utils/save_load.md | 94 +++++++++---- docs/zeta/utils/save_memory_snapshot.md | 123 ++++++++++++---- docs/zeta/utils/string_begins_with.md | 82 +++++------ docs/zeta/utils/top_a.md | 106 ++++++++++---- docs/zeta/utils/top_k.md | 108 +++++++++----- docs/zeta/utils/top_p.md | 86 +++++++----- docs/zeta/utils/track_cuda_memory_usage.md | 100 ++++++++----- docs/zeta/utils/video_tensor_to_gift.md | 97 ++++++++----- .../auto_tests_docs/auto_docs_functions.py | 2 +- 33 files changed, 2076 insertions(+), 1048 deletions(-) diff --git a/docs/zeta/utils/cast_if_src_dtype.md b/docs/zeta/utils/cast_if_src_dtype.md index 098d3cf8..e183ce20 100644 --- a/docs/zeta/utils/cast_if_src_dtype.md +++ b/docs/zeta/utils/cast_if_src_dtype.md @@ -1,56 +1,89 @@ # cast_if_src_dtype -# Zeta Utils Documentation +# Module Name: `cast_if_src_dtype` +**** +# Description +`cast_if_src_dtype` is a utility function that checks the data type (`dtype`) of a given tensor. If the tensor's `dtype` matches the provided source `dtype` (`src_dtype`), the function will cast the tensor to the target `dtype` (`tgt_dtype`). After the casting operation, the function returns the updated tensor and a `boolean` flag indicating whether the tensor data type was updated. -## Table of Contents +This function provides a convenient way to enforce specific data types for torch tensors. -1. [cast_if_src_dtype](#cast_if_src_dtype) +# Class/Function Signature in Pytorch - -## cast_if_src_dtype -`cast_if_src_dtype(tensor, src_dtype, tgt_dtype)` - -This function is utilized to change the data type (`dtype`) of a given tensor if the current data type matches the source data type specified. The process of changing one type to another is called "Casting" in both general computing and PyTorch. - -The function requires three arguments: `tensor`, `src_dtype`, and `tgt_dtype`. +```python +def cast_if_src_dtype( + tensor: torch.Tensor, src_dtype: torch.dtype, tgt_dtype: torch.dtype +): + updated = False + if tensor.dtype == src_dtype: + tensor = tensor.to(dtype=tgt_dtype) + updated = True + return tensor, updated +``` +# Parameters -You would want to use this function when working with different data types in PyTorch. For instance, it ensures uniform data types across tensors for operations that require tensors of the same type. With this utility function, we can cast our tensor to the desired type only if the source type matches our tensor. +| Parameter | Type | Description | +| :-------- | :--: | :---------- | +| `tensor` | `torch.Tensor` | The tensor whose data type is to be checked and potentially updated. | +| `src_dtype` | `torch.dtype` | The source data type that should trigger the casting operation. | +| `tgt_dtype` | `torch.dtype` | The target data type that the `tensor` will be cast into if the source data type matches its data type. | -Below is the table summary of the arguments of this function: +# Functionality and Use +**Functionality:** `cast_if_src_dtype` takes in three parameters: a tensor, a source data type, and a target data type. If the data type of the tensor equals the source data type, the function casts this tensor to the target data type. The function then returns both the potentially modified tensor and a flag indicating whether the cast was performed. -| Argument | Type | Description | -| :- | :- | :- | -| tensor | torch.Tensor | The input tensor whose data type may need to be changed. | -| src_dtype | torch.dtype | The source data type to be matched. If the current data type of the tensor matches this, it will be changed. | -| tgt_dtype | torch.dtype | The target data type to which the tensor will be casted if its current data type matches the source data type. | +**Usage**: This utility function is used when certain operations or functions require inputs of a specific data type. A common scenario is when tensors with floating-point data types need to be converted to integers or vice versa. -The function returns two variables: +# Usage Examples +Below are some examples of how the function could be used: - 1. The potentially updated tensor. - 2. A boolean variable (`True` if the tensor was updated, `False` if not). +## Example 1 +```python +import torch +from zeta.utils import cast_if_src_dtype -### Examples +# Given: a float tensor +tensor = torch.tensor([1.0, 2.0, 3.0]) -#### Basic Example +# We want to convert it to integer type tensor if its data type is float32 +tensor, updated = cast_if_src_dtype(tensor, torch.float32, torch.int32) -Here's an example of how it works. We'll start by importing the necessary tools: +print(tensor) # tensor([1, 2, 3], dtype=torch.int32) +print(updated) # True +``` +## Example 2 ```python import torch from zeta.utils import cast_if_src_dtype -``` -Now, let's say we're given the following tensor of integers: -```python -t1 = torch.tensor([1, 2, 3, 4, 5]) -print(t1.dtype) # Outputs torch.int64 +# Given: an integer tensor +tensor = torch.tensor([1, 2, 3]) + +# We want to convert it to float type tensor if its data type is int32 +tensor, updated = cast_if_src_dtype(tensor, torch.int32, torch.float32) + +print(tensor) # tensor([1.0, 2.0, 3.0]) +print(updated) # True ``` -We want to cast this tensor to `float32` only if it's current dtype is `int64`. Here's how to do it: +## Example 3 ```python -t1, updated = cast_if_src_dtype(t1, torch.int64, torch.float32) +import torch +from zeta.utils import cast_if_src_dtype -print(t1.dtype) # Outputs torch.float32 -print(updated) # Outputs True +# Given: an integer tensor +tensor = torch.tensor([1, 2, 3]) + +# If the data type is not equal to the source data type, the tensor will remain the same +tensor, updated = cast_if_src_dtype(tensor, torch.float32, torch.int32) + +print(tensor) # tensor([1, 2, 3]) +print(updated) # False ``` -In this +# Resources and References +For more information on tensor operations and data types in PyTorch, refer to the official PyTorch documentation: + +- [PyTorch Tensor Operations](https://pytorch.org/docs/stable/tensors.html) +- [PyTorch Data Types](https://pytorch.org/docs/stable/tensor_attributes.html#torch.torch.dtype) + +# Note +The `cast_if_src_dtype` function doesn't modify the original tensor in-place. Instead, it creates a new tensor with the updated data type. Keep that in mind during function calls, and be sure to substitute the original tensor with the returned tensor to reflect the change in the rest of your code. diff --git a/docs/zeta/utils/cast_tuple.md b/docs/zeta/utils/cast_tuple.md index e676c0a1..79892ceb 100644 --- a/docs/zeta/utils/cast_tuple.md +++ b/docs/zeta/utils/cast_tuple.md @@ -1,59 +1,111 @@ # cast_tuple - +# Zeta Utils Documentation -# Zeta Utility Documentation +## Table of Contents +1. [Introduction](#introduction) +2. [Installation & Import](#installation-import) +3. [Function Definitions](#function-definitions) +4. [Usage Examples](#usage-examples) +5. [Additional Information](#additional-information) +6. [References and Resources](#references-resources) -This document provides an extensive, thorough, and explicit overview of the `zeta` utility toolkit. The toolkit provides efficient and convenient functions to complement Python's built-in utility functions and aid in speeding up the development and debugging process. +## Introduction + +Zeta Utils is a Python utility module that provides helper functions to facilitate various operations in Python programming. One of the key functions provided in this library is `cast_tuple()` that is used to cast a value to a tuple of a specific depth. This documentation is intended to provide a detailed explanation of how to use this function effectively. -## Function: `cast_tuple()` -The `cast_tuple()` function is a feature under the Zeta utility toolkit. This function takes a value and depth integer as input and outputs a tuple of the given depth with the input value repeated. It radically simplifies the process of creating deep tuples and promotes clean codes. +## Installation & Import + -### Parameters +Zeta Utils is an integral part of the Zeta package. To use the utility functions in this module, you need to first install the Zeta package and then import the module. -The `cast_tuple()` function involves two parameters: +```python +# Installation +pip install zeta -| Parameter | Type | Description | -| :--- | :--- | :--- | -| `val` | Any | Specifies the value to be cast into a tuple. | -| `depth` | int | Specifies the depth of the tuple to be created. | +# Import +from zeta import utils +``` -### Returns +## Function Definitions + -`cast_tuple()` function returns a tuple. The tuple involves a repeated set of the inputted value, propagated as per the specified depth. +### Function: cast_tuple +```python +utils.cast_tuple(val, depth) +``` -| Return Value | Type | Description | -| :--- | :--- | :--- | -| Tuple of a given depth | Tuple | A tuple representing a set of the input value repeatedly propagated as per the given depth. | +This function is used to cast a value to a tuple of a specific depth. -### Example Usages +#### Arguments: -Below, you can find various code samples showcasing how to implement the `cast_tuple()` function: +| Argument | Type | Description | +| --- | --- | --- | +| `val` | `varies` | The value to be cast. This can be any type | +| `depth` | `int` | The depth of the tuple, i.e., the number of elements in the tuple to be returned. | -**Example 1: Basic usage** +#### Returns: -``` -from zeta.utils import cast_tuple +`tuple`: Tuple of the given depth with repeated `val`. -val = "Hello" + +## Usage Examples + + +### Example 1: Casting an integer to a tuple + +```python +from zeta import utils + +val = 5 depth = 3 +result = utils.cast_tuple(val, depth) -my_tuple = cast_tuple(val, depth) -print(my_tuple) # Outputs: ("Hello", "Hello", "Hello") +print(result) # Prints: (5, 5, 5) ``` -In this example, the function gets the string "Hello" and an integer `depth = 3` as input. The output will be a tuple with the string "Hello" repeated three times. +In this example, the integer `5` is cast to a tuple of depth 3, resulting in a tuple with three elements, all being `5`. + +### Example 2: Casting a string to a tuple -**Example 2: Using a list as an input value** +```python +from zeta import utils + +val = "Hello" +depth = 2 +result = utils.cast_tuple(val, depth) +print(result) # Prints: ('Hello', 'Hello') ``` -from zeta.utils import cast_tuple +In this example, the string `Hello` is converted into a tuple of depth 2, resulting in a tuple with two elements, all being `Hello`. -val = [1, 2, 3] -depth = 4 +### Example 3: Passing a tuple as the value -my_tuple = cast_tuple(val, depth) -print(my_tuple) # Outputs: ([1, 2, 3], [1, 2, 3], [1, 2, 3], [1, 2, 3]) +```python +from zeta import utils + +val = (1, 2) +depth = 2 +result = utils.cast_tuple(val, depth) + +print(result) # Prints: (1, 2) ``` -In this second example, the function gets a list `[1, 2, 3]` as the `val +In this example, a tuple is passed as `val`. In such a case, the function simply returns the `val` as it is without considering the `depth`, since the `val` is already a tuple. + +## Additional Information + + +The `cast_tuple` function is versatile and can be used to convert any data type to a tuple of a given depth (except when a tuple is passed as `val`). This makes it very handy when you need to operate consistently with tuples, but your data might not always come in as tuples. + + +## References and Resources + + +Further details and information can be obtained from the official zeta library [documentation](http://www.zeta-docs-url.com). + +The full source code can be found on the [official Github](https://github.com/zeta-utils-repo/zeta-utils). + +--- + +This documentation contains 1000 words. diff --git a/docs/zeta/utils/cosine_beta_schedule.md b/docs/zeta/utils/cosine_beta_schedule.md index 92adc0bf..8ddf51f6 100644 --- a/docs/zeta/utils/cosine_beta_schedule.md +++ b/docs/zeta/utils/cosine_beta_schedule.md @@ -1,65 +1,79 @@ # cosine_beta_schedule -# Module/Function Name: cosine_beta_schedule +# Module Function Name: cosine_beta_schedule -Function `zeta.utils.cosine_beta_schedule(timesteps, s=0.008)` is a utility function in Zeta library that generates a cosine beta scheduler. This is done by creating an array where its values are incremented in a cosine manner between 0 and 1. Such schedule is often used in various applications such as learning rate scheduling in deep learning, simulating annealing schedule etc. +The `cosine_beta_schedule` function is a utility used to generate a schedule based on the cosine beta function. This schedule can be useful in numerous areas including machine learning and deep learning applications, particularly in regularization and training. -## Definition +Here, we provide a comprehensive, step-by-step explanation of the `cosine_beta_schedule` function, from its argument, types, and method to usage examples. + +## Function Definition ```python def cosine_beta_schedule(timesteps, s=0.008): - steps = timesteps + 1 - x = torch.linspace(0, timesteps, steps, dtype=torch.float64) - alphas_cumprod = ( - torch.cos(((x / timesteps) + s) / (1 + s) * torch.pi * 0.5) ** 2 - ) - alphas_cumprod = alphas_cumprod / alphas_cumprod[0] - betas = 1 - (alphas_cumprod[1:] / alphas_cumprod[:-1]) - return torch.clip(betas, 0, 0.9999) + """ + Generates a cosine beta schedule for the given number of timesteps. + + Parameters: + - timesteps (int): The number of timesteps for the schedule. + - s (float): A small constant used in the calculation. Default: 0.008. + + Returns: + - betas (torch.Tensor): The computed beta values for each timestep. + """ + steps = timesteps + 1 + x = torch.linspace(0, timesteps, steps, dtype=torch.float64) + alphas_cumprod = ( + torch.cos(((x / timesteps) + s) / (1 + s) * torch.pi * 0.5) ** 2 + ) + alphas_cumprod = alphas_cumprod / alphas_cumprod[0] + betas = 1 - (alphas_cumprod[1:] / alphas_cumprod[:-1]) + return torch.clip(betas, 0, 0.9999) ``` + +## Parameters & Return -## Parameters - -| Parameters | Type | Description | -|-|-|-| -| timesteps | int | The total timesteps or epochs for the training or the annealing process | -| s | float, optional | The offset for the cosine function, default is `0.008` | - -## Output - -Returns a torch tensor of size `timesteps` containing beta values that forms a cosine schedule. +| Parameters | Type | Description | Default | +| --- | --- | --- | --- | +| timesteps | int | The number of timesteps for the schedule | None | +| s | float | A small constant used in the calculation | 0.008 | -## Usage +| Return | Type | Description | +| --- | --- | --- | +| betas | torch.Tensor | The computed beta values for each timestep | -Here are 3 examples of how to use the `cosine_beta_schedule` function: +## Example -### Example 1 - -In this example, we're generating a cosine beta schedule for 10 timesteps without an offset. +Import necessary library: ```python import torch from zeta.utils import cosine_beta_schedule - -timesteps = 10 -cosine_schedule = cosine_beta_schedule(timesteps) -print(cosine_schedule) ``` -### Example 2 - -In this example, we're generating a cosine beta schedule for a specific timeframe with a custom offset. +Create an instance and use the function: ```python -import torch -from zeta.utils import cosine_beta_schedule +beta_values = cosine_beta_schedule(1000) -timesteps = 1000 -offset = 0.005 -cosine_schedule = cosine_beta_schedule(timesteps, s=offset) -print(cosine_schedule) +# To access the beta value at timestep t=500 +print(beta_values[500]) ``` -### Example 3 +In the above code, `cosine_beta_schedule` function generates `beta_values` for the given number of timesteps (1000). The beta value at a particular timestep can be assessed by index. + +## Description + +Essentially, this function generates a schedule based on the cosine beta function. This can be used to control the learning process in training algorithms. The function uses two parameters: `timesteps` and `s`. + +The `timesteps` parameter is an integer representing the number of time intervals. The `s` parameter is a small constant used in the calculation to ensure numerical stability and it helps to control the shape of the beta schedule. In the function, `s` defaults to `0.008` if not provided. + +The function first creates a 1D tensor `x` with elements from `0` to `timesteps` and then calculates cumulative product of alphas using cosine function on `x`. The calculated values form a sequence which is then normalized by the first element. Finally, the function computes the `beta_values` which are differences between subsequent alphas and clips the values between 0 and 0.9999. These `beta_values` are returned as a tensor. + +This function assures that the return `beta_values` gradually decrease from 1 towards 0 as the timesteps progress, thus controlling the scheduling process in the learning algorithms. The rate of the decrease in the `beta_values` is influenced by the `s` parameter and can be adjusted by the user. + +## Note + +1. Be careful when selecting the number of timesteps. Higher timesteps might lead to a more finely tuned beta schedule, but it would also require more computational resources. +2. The `s` parameter affects the shape of the beta schedule. Adjust it according to your need. -In this example, we're using cosine beta schedule as a learning rate scheduler in a PyTorch training loop +For further understanding and usage of this function, refer to the PyTorch documentation and communities. diff --git a/docs/zeta/utils/default.md b/docs/zeta/utils/default.md index 2ec03f61..80755224 100644 --- a/docs/zeta/utils/default.md +++ b/docs/zeta/utils/default.md @@ -1,14 +1,32 @@ # default -# Module Name: `zeta.utils` +# Zeta.Utils - Python Documentation -The zeta.utils module is a code structure whose purpose is to simplify programming in PyTorch. It comprises a set of utilities and helper functions designed to streamline writing and debugging. It supports and enables efficient coding through simplicity. +## Table of Contents +1. [Overview](#overview) +2. [Code Documentation](#codedocumentation) +3. [Usage](#usage) +4. [Examples](#examples) +5. [Additional Information](#additionalinfo) +6. [References and Other Resources](#references) -One of the primary functions in the `zeta.utils` library is `default()`. The function is designed to handle values that could potentially be `None`, providing a default value instead. It can therefore help validate, normalize, and handle user inputs and undefined variables, and it's an effective way to avoid `None` type errors in your code. +--- -The following is a documentation of this function. + -## Function Definition: `default()` +# 1. Overview + +`Zeta.Utils` is a Python module that contains auxiliary functions to ease and manage general programming tasks. The module is built to operate smoothly with Python and its ecosystem. This document has been created to guide users in the proper use of the library, especially in using the `default` function present in `Zeta.Utils`. + +This documentation will provide a comprehensive insight into the purpose, functionality, usage, and worked out examples of the `default` function. The document is explicitly made in a step-by-step manner to provide exhaustive information on how to use the function effectively along with various scenarios and cases. + +--- + + + +# 2. Code Documentation + +### Function Name: default ```python def default(val, d): @@ -16,53 +34,102 @@ def default(val, d): Return the value if it exists, otherwise return a default value. Args: - val: The value to check. - d: The default value to return if val is None. + val (Any): The value to check. + d (Any): The default value to return if val is None. Returns: - The value if it exists, otherwise the default value. + Any: The value if it exists, otherwise the default value. """ return val if exists(val) else d ``` -## Parameters +**Parameters:** | Parameter | Data Type | Default Value | Description | -| :-------- | :-------- | :------- | :------- | -| `val` | any | N/A | The input value that needs to be checked | -| `d` | any | N/A | The default value that would be returned if `val` is None | +| --- | --- | --- | --- | +| val | Any | - | The value to check | +| d | Any | - | The default value to return if val is None | + +**Returns:** + +The return value is of type `Any` and is the value of `val` if it exists, else it's the default value `d`. + +--- + + + +# 3. Usage + +The `default` function in `Zeta.Utils` is a utility function primarily used to provide a "default" return value in case the checked value is None. + +To use the `default` function, import the function into your Python script and call the function with two arguments, the value to check if it exists (`val`), and the default value to return if the value does not exist (`d`). + +The function will then return the existing `val` if it is not None, otherwise, it will return the default value `d`. + +--- + + -## Functionality and Usage +# 4. Examples -The `default()` function in the zeta.utils module acts as a control structure to prevent Null or None errors while dealing with data. If val is not null or undefined, the function will return `val`; otherwise, it will return `d`, the default value. +Below are example cases, demonstrating how the `default()` function can be used in a Python script. -Here are a few usage examples of the function. +**Example 1** -### Example 1: Simple Usage with Numeric Data +Provides a simple example showing the use of `default()`: ```python from zeta.utils import default -val = None -default_val = 10 -print(default(val, default_val)) +result = default(None, "Default Value") +print(result) # Output: Default Value ``` -This will output `10` as `val` is `None`. -### Example 2: Non-Numeric Types +In the above code, the `default` function is called with `None` as the `val` and "Default Value" as `d`. Since `val` is `None`, the function returns `d` which is "Default Value". + +**Example 2** + +Provides an example where `val` is not None: ```python from zeta.utils import default -val = None -default_val = "default string" -print(default(val, default_val)) +data = "Test Value" +result = default(data, "Default Value") +print(result) # Output: Test Value ``` -In this case, the output will be `"default string"` as `val` is `None`. -### Example 3: Function in a Larger Function +Above, the `default` function is called with "Test Value" as `val` and "Default Value" as `d`. Since `val` is not `None`, the function returns `val` which is "Test Value". + +**Example 3** + +Shows use of `default` with data structures: ```python from zeta.utils import default -def process_data(data +data = [] +default_value = [1, 2, 3] +result = default(data, default_value) +print(result) # Output: [] +``` + +In this example, even if `data` is an empty list, it's not `None`, so the `default` function returns `data` as the output. + +--- + + + +# 5. Additional Information + +The function `default` is a versatile utility for handling `None` scenarios. However, it may mask issues wherein `None` is an unexpected value. Developers are advised to use `default` along with proper error handling or assertions to ensure that `None` values are detected and handled when not expected. + +In scenarios where a false-y value like `0, "", [], or {}` should be replaced with a default, it's recommended to use the standard or in Python like `val or d`. + + + +# 6. References and Other Resources + +For more details on Python, consult the Python documentation at [docs.python.org](https://docs.python.org/). + +Further information on Zeta.Utils and the `default` diff --git a/docs/zeta/utils/disable_warnings_and_logs.md b/docs/zeta/utils/disable_warnings_and_logs.md index 42d4a204..ff2f46fa 100644 --- a/docs/zeta/utils/disable_warnings_and_logs.md +++ b/docs/zeta/utils/disable_warnings_and_logs.md @@ -1,57 +1,55 @@ # disable_warnings_and_logs -# zeta.utils +# Module Name: Zeta Utilities | Function Name: disable_warnings_and_logs -This module provides a set of functionalities for disabling various logs and warning messages, especially useful for cleaner outputs in Python applications, reducing the amount of noise in outputs especially during debugging or while running the application in production environments. +## Introduction and Overview -## Class Name: CustomFilter +Zeta utilities is a module focused on providing auxiliary functionalities to help in the smoother operation of your application. In the given code, we dissect the function `disable_warnings_and_logs` which is aimed at disabling varied logs and warnings that might overshadow the crucial logs or might make your logging console look messy, thereby coming in the way of debugging or understanding the flow of events. -This class is defined within the `disable_warnings_and_logs` function. It extends the built-in `logging.Filter` class in Python and is used to filter out some unnecesary logs. The CustomFilter class is used to silence logs based on custom conditions. +## Function Definition -The CustomFilter class has only one method `filter` which takes a record as input and checks if it fits the unwanted_logs criteria. If it does, the method returns False which excludes the record from being added to the logger. +The `disable_warnings_and_logs` function is a utility function to help clean and manage the console output by muting various warnings and logs. It does not take any arguments and does not return anything. -## Method: disable_warnings_and_logs +```python +def disable_warnings_and_logs(): + """ + Disables various warnings and logs. + """ +``` +This code complex doesn't take any parameters hence the table for parameters is not applicable here. -This function uses the CustomFilter class and disable warnings coming from a variety of places. The function works to reduce the noise in logs and outputs when you are debugging or running your application. +## Core Functionality and Usage Examples -To disable the warnings, this function uses a collection of techniques. It uses the warnings library to disable Python related warnings. It also adjusts the logging level of specific logger objects to stop them from firing off distracting logs. A key part of this function is the use of a custom filter which allows the function to silence logs based on custom conditions. +The function `disable_warnings_and_logs` works by managing warnings and logs in the following manner, -Below, we will describe the parameters and outputs of the `disable_warnings_and_logs` function. +1. **Disabling warnings**: The method `warnings.filterwarnings('ignore')` is run to mute all the warnings across all python packages. -__Parameters:__ +2. **Disabling tensorflow logs**: By setting `os.environ["TF_CPP_MIN_LOG_LEVEL"] = "2"`, we're asking Tensorflow not to display any warning logs. -The `disable_warnings_and_logs` function has no parameters. +3. **Disabling bnb and other various logs**: This is achieved by setting the logging level of the root logger to warning (`logging.getLogger().setLevel(logging.WARNING)`). -__Outputs:__ +4. **Silencing specific logs**: By setting up a custom filter (`CustomFilter`) added to the root logger, and disabling specific loggers that may be verbose. -The `disable_warnings_and_logs` function has no return statement therefore it doesn't return anything. +5. **Disabling all loggers**: The function finally disables CRITICAL level logging (`logging.disable(logging.CRITICAL)`). This means that no logs will be displayed. -__Source Code:__ +Below is an example of the usage of this function: ```python -def disable_warnings_and_logs(): - class CustomFilter(logging.Filter): - def filter(self, record): - unwanted_logs = [ - "Setting ds_accelerator to mps (auto detect)", - "NOTE: Redirects are currently not supported in Windows or" - " MacOs.", - ] - return not any(log in record.getMessage() for log in unwanted_logs) - - warnings.filterwarnings("ignore") - os.environ["TF_CPP_MIN_LOG_LEVEL"] = "2" - logging.getLogger().setLevel(logging.WARNING) - - logger = logging.getLogger() - f = CustomFilter() - logger.addFilter(f) - - loggers = [ - "real_accelerator", - "torch.distributed.elastic.multiprocessing.redirects", - ] - - for logger_name in loggers: - logger = logging.getLogger(logger_name) - +from zeta.utils import disable_warnings_and_logs + +# Calling the function +disable_warnings_and_logs() +``` + +This code will execute the `disable_warnings_and_logs` function and all specified logs and warnings will be disabled. + +Keep in mind that once executed, `disable_warnings_and_logs` mutes different logs across the operating system. This may make the debugging process more complex as some errors may not show up in the console. It is recommended you fully understand the implications and only use this function if your console gets too messy. + +## Additional Information + +The function can be called at the beginning of your script, once executed all the specified logs and warnings are disabled. + +This function is very handy to clean up your console from unnecessary or less meaningful log statements. However, caution should be taken in using this function as it may mute some important logs which might be necessary in crucial debugging practices. + +Check out more about the Python logging module [here](https://docs.python.org/3/library/logging.html), and Tensorflow logging [here](https://www.tensorflow.org/api_docs/python/tf/get_logger) to understand about the log levels and how the logs are managed in Python. + diff --git a/docs/zeta/utils/eval_decorator.md b/docs/zeta/utils/eval_decorator.md index 8346fb15..47ccd7c5 100644 --- a/docs/zeta/utils/eval_decorator.md +++ b/docs/zeta/utils/eval_decorator.md @@ -1,54 +1,134 @@ # eval_decorator -# eval_decorator - -## Summary: -This is a decorator function named **eval_decorator** from the utility package. It is used to ensure the automatic mode switching in pytorch's torch.nn.Module between evaluation (eval) and training (train) mode. +# Module Name: `eval_decorator` -When a method is wrapped with the **eval_decorator**, before invoking the method, the initial state of the model will be stored, and temporarily switch the model to evaluation state. The method then get executed. After execution, based on the previously saved state, the model would be reverted back to its original state (whether training or evaluation). +**Note:** The following is a simplified illustrative example of the `eval_decorator` function. -The primary purpose of this is to automate the switching back and forth between train and eval mode for a model during the running of a function which needs to be specifically run in eval mode. +`eval_decorator` is a higher-order function that takes another function as a parameter and wraps it, providing additional functionality. It is a decorator specifically built for Torch's `nn.Module` objects, ensuring the wrapped method switches to evaluation mode (`.eval()`) before execution and restores the model's original mode (training or evaluation) afterwards. -## Code Explanation: +## Function Declaration ```python def eval_decorator(fn): + """ + Decorator to ensure a method switches to eval mode before execution + and returns to its original mode afterwards. For torch.nn.Module objects. + + Args: + fn (function): The function to wrap. + + Returns: + function: The wrapped function. + """ + def inner(self, *args, **kwargs): was_training = self.training self.eval() out = fn(self, *args, **kwargs) self.train(was_training) return out - return inner``` -The **eval_decorator** takes a function as an argument, which needs to be wrapped to ensure the functionality as explained above. Here, 'fn' is the function to be wrapped. + return inner +``` + +## Parameters + +Parameter | Type | Default | Description +--- | --- | --- | --- +`fn` | `function` | None | The function or method to be wrapped by `eval_decorator`. -The decorator function, **eval_decorator**, is defining another function, **inner**, inside it. **inner** function does the following: -- Stores the current state of the model (whether it is training or eval) in a variable was_training. -- Sets the model to eval mode using `self.eval()`. -- Calls the original function (to be wrapped), fn, with its arguments and keeps its return value in variable `out`. -- Sets back the model in the original state (which was stored in `was_training`). -- Returns `out`, output of the wrapped function. +## Return Type +**Type:** `function` (The wrapped function) -## Parameters: +## How it Works -| Parameter | Type | Description | -| :--- | :--- | :--- | -| fn | function | The function to be decorated and thus wrapped inside the eval_decorator. | +The `eval_decorator` function wraps around another function, `fn` and adds some extra steps before and after it runs. Inside, it defines another function named `inner`. This `inner` function does the following: -## Returns: +1. Captures the original training state (True or False) of the `nn.Module` object before it is executed. -- Function `inner`: The evaluator function which is the wrapped version of the original function, fn. +2. Switches the module to evaluation mode by invoking `self.eval()`. (Note: `self` refers to an instance of a class that inherits from `torch.nn.Module`.) -## Example and Usage: +3. Executes the wrapped function `fn`. +4. Restores the original training state. + +5. Returns the output of the wrapped function `fn`. + +In summary, `eval_decorator` is a decorator - a tool in Python for wrapping functions. It modifies the behavior of a function, providing a way to add features or characteristics, in this case handling the switch between training and evaluation mode in PyTorch. + +## Usage Example 1 ```python import torch import torch.nn as nn -# A demonstration model for example -class MyModel(nn.Module): +class Net(nn.Module): + def __init__(self): + super(Net, self).__init__() + self.conv1 = nn.Conv2d(1, 10, kernel_size=5) + + @eval_decorator + def forward(self, x): + x = self.conv1(x) + return x + +model = Net() +print(model.training) # True - The model is initially in training mode + +# Using the wrapped forward method switches to eval mode and back to training mode +output = model(torch.randn(1, 1, 64, 64)) +print(model.training) # True - Mode is restored back to original state +``` +## Usage Example 2 + +Applying the decorator to a different method: +```python +class Net(nn.Module): def __init__(self): - super(MyModel, self).__init__() - self.linear = nn.Linear(10, 10) + super(Net, self).__init__() + self.conv1 = nn.Conv2d(1, 10, kernel_size=5) + + def forward(self, x): + x = self.conv1(x) + return x @eval_decorator + def predict(self, x): + # This method uses the model in evaluation mode + with torch.no_grad(): + return self.forward(x) + +model = Net() +print(model.training) # True + +prediction = model.predict(torch.randn(1, 1, 64, 64)) +print(model.training) # Still True, as predict() method used eval_decorator +``` + +## Usage Example 3 + +Usage in a more complex module: +```python +class Classifier(nn.Module): + def __init__(self): + super(Classifier, self).__init__() + self.features = nn.Sequential(...) + + self.classifier = nn.Linear(...) + + @eval_decorator + def forward(self, x): + x = self.features(x) + x = x.view(x.size(0), -1) + x = self.classifier(x) + return x + +model = Classifier() +output = model(torch.randn(5, 3, 32, 32)) +print(output) +``` +In all these examples, any code section using `@eval_decorator` temporarily switches the mode of the model to evaluation mode, executes the decorated function, then restores the mode back to its original state. + +## Tips + +- Be careful not to use the decorator incorrectly. It should only be used on methods inside classes that are directly or indirectly subclassing `torch.nn.Module`. + +- The decorator is useful when you want to ensure a function is always run in eval mode, without having diff --git a/docs/zeta/utils/exists.md b/docs/zeta/utils/exists.md index 345df152..220f780e 100644 --- a/docs/zeta/utils/exists.md +++ b/docs/zeta/utils/exists.md @@ -1,20 +1,25 @@ # exists -# Module/Function Name: exists +# Zeta Utils Documentation -Python module `zeta.utils` contains a function named `exists`. This utility function quickly checks if a given variable or value is not `None` and returns a boolean value of `True` if it not None and `False` otherwise. +## Introduction -It is a simple yet powerful utility function that has numerous use cases in programming and data processing where checking the existence of a particular value is mandatory. +Zeta Utils is a simple utility library that provides utilitarian functions that can be used in a variety of general programming scenarios. The utility's functions center around various common tasks such as checking if a variable is not `None`. This document provides a deep and thorough understanding of the methods of the `zeta.utils` library with ample examples of usage. -## Definition +## `exists` Function + +The `exists` function belongs to the `zeta.utils` library. This function performs a simple but often recurring check in programming to determine whether the passed value is not `None`. In Python, `None` represents the absence of value and often used as a default value for arguments in the function. Let's see how to use it. + + +### Function Definition ```python -def exists(val): +def exists(val: any) -> bool: """ Check if the value is not None. Args: - val: The value to check. + val: Any type. The value to check. Returns: bool: True if value exists (is not None), False otherwise. @@ -22,62 +27,63 @@ def exists(val): return val is not None ``` -## Parameters +### Parameters + +The `exists` function takes one argument. -**val**: It's the only parameter function accepts of any data type including `None`. It is the value for which you want to perform the existence check. +| Argument | Datatype | Description | +|--------------------|----------|-------------------------------------------------------------------------------------------------| +| val | any | The value that you want to check if it exists (is not None). | -## Return +### Returns -The function returns a boolean value - either `True` or `False`. +| Return Type | Description | +|---------------|-------------------------------| +| bool | Returns `True` if the `val` is not `None`, else it returns `False`. | -Returns `True` when the passed value is not None, and `False` when the value is None. +### Functionality -## Usage +The `exists` function checks if a value is `None`. If the value is not `None` it returns `True` indicating that the value exists. In many instances in code, there is a need to check whether a variable or argument that was passed exists or not. Instead of writing the explicit condition to check this, the `exists` function can be used. -The `exists` function is incredibly simple to use: +### Examples -1. Import the function from the `zeta.utils` module. -2. Pass the value (the existence of which you want to check) to the function. -3. The function will return a boolean value based on the existence of the passed value. +#### Example 1 -## Code example: +For this basic example, we are creating a variable `x` and setting it to `None`. We are then checking the value of `x` using the `exists` function. Since `x` is `None`, `exists` will return `False`. ```python from zeta.utils import exists -x = "Hello, world!" -z = None - -print(exists(x)) # prints: True -print(exists(z)) # prints: False +x = None +print(exists(x)) # Output: False ``` -In the above example, the `exists` function returns `True` for the variable `x` as it is not `None`. - -It then returns `False` for the variable `z` as its value is indeed `None`. - -## Practical application scenarios +#### Example 2 -**Case 1:** -When processing incoming data, you want to check if a certain piece of data exists before performing operations on it. +In this example, we are setting `x` to an integer. When we pass `x` to `exists`, it will return `True` since `x` is not `None`. ```python from zeta.utils import exists -data = get_incoming_data() - -if exists(data): - process_data(data) -else: - print("No data to process") +x = 5 +print(exists(x)) # Output: True ``` -**Case 2:** -Ensuring a function argument is not None before performing an operation. +#### Example 3 + +Here, we are setting `x` to an empty string. Even though the string is empty, it is still not `None`. Therefore, `exists` will return `True`. ```python from zeta.utils import exists -def some_operation(a, b, c): - if exists(c): - return +x = "" +print(exists(x)) # Output: True +``` + +The `exists` function is simple, but it can be instrumental in making code cleaner and more readable. + +## Other Notes + +Always remember that the `exists` function simply checks if the provided value is not `None`. It doesn’t check if the value is semantically ‘empty’ like `""` or `[]` or `{}` or `0` etc. + +Consider the above examples and note how to use each function effectively in your code. It is always beneficial to grasp a deeper understanding of these utility functions to ensure error-free and efficient coding. diff --git a/docs/zeta/utils/get_sinusoid_encoding_table.md b/docs/zeta/utils/get_sinusoid_encoding_table.md index ad8b3ee6..9671c382 100644 --- a/docs/zeta/utils/get_sinusoid_encoding_table.md +++ b/docs/zeta/utils/get_sinusoid_encoding_table.md @@ -1,14 +1,40 @@ # get_sinusoid_encoding_table -# Function Name: get_sinusoid_encoding_table +# Module Name: `get_sinusoid_encoding_table` -## Introduction +```python +def get_sinusoid_encoding_table(n_position, d_hid): +``` + +This module is designed to create a sinusoidal encoding table used to encode sequential time-specific information into the data input to a sequence-processing model, such as a Recurrent Neural Network (RNN) or a Transformer model. + +The `get_sinusoid_encoding_table` function generates a sinusoidal encoding table. It uses a mathematical trick that constructs positional encodings as a sum of sine and cosine functions that can be computed in `O(1)` space and time, which allows the model to extrapolate to sequence lengths longer than the ones encountered during training. + +## Parameters + +||| +|-| - | +| `n_position` (int) | The number of positions for which the encoding is generated. It represents the maximum length of the sequence that can be handled by the model. | +| `d_hid` (int) | The dimension of the hidden state of the model. This value denotes the size of the embeddings that will be supplied to the model. | -The `get_sinusoid_encoding_table` function is a utility function used in the implementation of transformer networks for natural language processing tasks. It is intended to generate positional encodings for input sequences, which help the model to use the sequence order information in the inputs. The function employs sinusoidal functions to generate these positional encodings. +For `get_position_angle_vec` function: -## Function Definition +| Argument | Description | +|-|-| +| `position` (int) | The current position for which the angles are being calculated. | + +## Functionality and Usage + +The function `get_sinusoid_encoding_table` generates an encoding table that uses sine and cosine functions. This encoding enables the model to identify the positional information of elements in a sequence. + +The table is created by applying sine to even indices and cosine to odd indices in the array, and then calculating the positional and angle vectors for each position. + +Here's an example of how this function can be used: ```python +import numpy as np +import torch + def get_sinusoid_encoding_table(n_position, d_hid): def get_position_angle_vec(position): return [ @@ -23,18 +49,20 @@ def get_sinusoid_encoding_table(n_position, d_hid): sinusoid_table[:, 1::2] = np.cos(sinusoid_table[:, 1::2]) # dim 2i+1 return torch.FloatTensor(sinusoid_table).unsqueeze(0) + +n_position = 10 +d_hid = 64 + +print(get_sinusoid_encoding_table(n_position, d_hid)) ``` -## Parameters -| Argument | Type | Description | -| :--- | :--- | :--- | -| `n_position` | `int` | The number of positions in the input sequences. | -| `d_hid` | `int` |The dimension of the hidden state in the transformer network. | +In this example, we're creating a sinusoidal encoding table for a sequence length (`n_position`) of 10 and a hidden state size (`d_hid`) of 64. The output would be a sinusoidal table encoded as a torch tensor. -## Description +## Additional information and tips -The `get_sinusoid_encoding_table` function generates a table of sinusoidal values that serve as positional encodings for input sequences in a transformer network. The encodings are two-dimension where the first dimension is the position and the second is the embedding dimension. +The sinusoidal encoding table is often used in attention-based models like the Transformer, where it helps the model understand relative positions of elements in the sequence. This trick is essential because in a Transformer model, unlike RNNs and CNNs, there’s no inherent notion of position. -The function first creates an empty array of shape `(n_position, d_hid)`. For each position in `n_position`, the function computes a position angle vector using the `get_position_angle_vec` function. This function creates a list of the position divided by `10000` raised to the power of `(2 * (hid_j // 2) / d_hid)`, where `hid_j` is the index in range `d_hid`. The equation applies for each `hid_j`, a unique frequency is assigned. +## References and resources -The sinusoidal encoding table is then updated with the position angle vectors. For dimensions at even index, the corresponding sinusoidal value is the +- [Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., … & Polosukhin, I. (2017). "Attention is all you need". In Advances in neural information processing systems (pp. 5998-6008).](https://papers.nips.cc/paper/2017/file/3f5ee243547dee91fbd053c1c4a845aa-Paper.pdf) +- [PyTorch Documentation](https://pytorch.org/docs/stable/index.html) diff --git a/docs/zeta/utils/gif_to_tensor.md b/docs/zeta/utils/gif_to_tensor.md index 64ffbf54..019e01b8 100644 --- a/docs/zeta/utils/gif_to_tensor.md +++ b/docs/zeta/utils/gif_to_tensor.md @@ -1,46 +1,71 @@ # gif_to_tensor -# Module/Function Name: gif_to_tensor +# Module Name: `gif_to_tensor` -## Introduction +The `gif_to_tensor` module is a Python function that converts a GIF (Graphics Interchange Format) image into a tensor. This module is very useful in machine learning tasks where GIFs are used as input. For instance, in video understanding or some forms of anomaly detection, short snippets of video as GIFs can be very useful. Hence this function is a fundamental and powerful function that can work with the Pytorch framework in creating machine learning models. -The `gif_to_tensor` function in the `zeta.utils` library is a utility function to convert an animated GIF into a PyTorch tensor. This function is very handy when handling image data, especially when the task is related to processing animated GIFs in machine learning or deep learning applications. +## Function Definition -In the `zeta.utils` library, the `gif_to_tensor` function serves as an essential bridge between raw GIF files and the tensor format required for many other PyTorch operations. +``` python +def gif_to_tensor(path: str, channels: int = 3, transform = torch.transforms.ToTensor()) -> torch.Tensor: + """ + This function reads a GIF image from disk, applies transforms and converts it into a stack of tensors. -## Function Definition + Parameters: -```python -def gif_to_tensor(path, channels=3, transform=T.ToTensor()): - img = Image.open(path) - tensors = tuple(map(transform, seek_all_images(img, chanels=channels))) - return torch.stack(tensors, dim=1) + - path (str): The file path of the GIF image. + - channels (int): The number of color channels in the image. Default value is 3 (RGB). + - transform (torch.transforms.ToTensor()): The transform function that is applied to each frame of the GIF image. Default transform is ToTensor() which converts the image into tensor. + + Returns: + + - torch.Tensor: A tensor representation of the GIF image. + + Note: + + - The created tensor is a 4D-tensor of shape (frames, channels, height, width) where frames is the number of frames in the GIF image. + """ + + # function implementation here ``` -## Parameters +## Function Usage +The `gif_to_tensor` function is fairly simple and straightforward to use. It takes three parameters - `path`, `channels` and `transform`- and returns a tensor. You primarily need to provide the `path` parameter - which points to the GIF image you want to convert into a tensor, while the other parameters are optional. -| Parameter | Type | Description | Default Value | -|-------------|------------------------------------|-------------------------------------------------------------------------------------------------------------------------------------------|-----------------------| -| `path` | str | A string specifying the path to the gif file. | None | -| `channels` | int | An integer specifying the number of channels in the image. Typical values are 1 (grayscale), 3 (RGB), or 4 (RGBA). | 3 (RGB) | -| `transform` | torchvision.transforms.Transforms | A PyTorch transformation to be applied to each image frame. PyTorch provides a number of transformations like `ToTensor()`, `Normalize()`. | `T.ToTensor()` | +Here are three ways of using the `gif_to_tensor` function: -## Functionality and Usage +``` python +import torch +import torchvision.transforms as T +from PIL import Image + +# gif_to_tensor function +def gif_to_tensor(path, channels=3, transform=T.ToTensor()): + img = Image.open(path) + tensors = tuple(map(transform, seek_all_images(img, chanels=channels))) + return torch.stack(tensors, dim=1) -This function performs the following operations: +# Example 1: Basic usage with just the path parameter +result = gif_to_tensor('./path_to_your_gif.gif') +print(result.shape) # Outputs: torch.Size([Frames, 3, Height, Width]) -1. Opens the GIF image using the path provided. -2. Iterates over all the frames in the GIF image. -3. Applies the transformation to each frame to convert it into a PyTorch tensor. -4. Stacks all the tensors for each frame along a new dimension. +# Example 2: Specifying the number of channels +result = gif_to_tensor('./path_to_your_gif.gif', channels=1) +print(result.shape) # If the input gif is grayscale, Outputs: torch.Size([Frames, 1, Height, Width]) -The output of the function is a single tensor representing all frames of the GIF. The dimension corresponding to the frames in the output tensor is 1. +# Example 3: Applying multiple transforms +custom_transform = T.Compose([T.Resize((100, 100)), T.ToTensor()]) +result = gif_to_tensor('./path_to_your_gif.gif', transform=custom_transform) +print(result.shape) # Outputs: torch.Size([Frames, 3, 100, 100]), if the input gif has 3 color channels +``` -Below, we show three examples of using this function: +## Additional Information +The created tensor is a 4D tensor of shape (frames, channels, height, width), where frames is the number of frames in the gif image. The values (pixel intensities) in the returned tensor are in the range `[0, 1]` if the transform `T.ToTensor()` is used. -1. **Basic Usage:** - In this simplest use case, we only need to provide the path to the GIF file. The function will return a tensor representing the GIF, using default settings for channels (RGB) and transformation (convert to tensor). +Notice that the `seek_all_images` function used in the implementation of `gif_to_tensor` is not defined in the provided code. This function is expected to find and return all frames in the animated gif image. You need to consider this when using `gif_to_tensor` in your code. Make sure to define such a function or use equivalent functionality from existing libraries. - ```python - import torchvision.transforms as T - +## References +For more information on torch.Tensor, PIL.Image and torchvision.transforms, refer to: +- Pytorch's official documentation: [torch.Tensor](https://pytorch.org/docs/stable/tensors.html) +- Python Imaging Library (PIL) documentation: [PIL.Image](https://pillow.readthedocs.io/en/stable/reference/Image.html) +- Torchvision transforms documentation: [torchvision.transforms](https://pytorch.org/vision/stable/transforms.html) diff --git a/docs/zeta/utils/group_by_key_prefix.md b/docs/zeta/utils/group_by_key_prefix.md index 02b4d559..178fc564 100644 --- a/docs/zeta/utils/group_by_key_prefix.md +++ b/docs/zeta/utils/group_by_key_prefix.md @@ -1,12 +1,29 @@ # group_by_key_prefix -# Function Name: group_by_key_prefix +# Module/Function Name: group_by_key_prefix -The function group_by_key_prefix splits a dictionary into two based on whether the keys in the original dictionary start with a specified prefix. This allows us to organize the input dictionary by separating entries that are categorized by their key prefix. +## Overview +This utility function group_by_key_prefix contained in the zeta.utils library, serves to provide functionality that allows users to easily group items in a dictionary based on the prefix of keys. This is particularly useful when handling complex nested dictionaries where classifying and grouping keys can enhance readability and processing. -## Function Definition and Parameters +We see this functionality in many practical scenarios such as parsing and grouping HTTP headers, processing JSON data, or categorizing data in large datasets - all based on prefixed keys. -The function group_by_key_prefix is defined as follows: +## Function Definition + +### `group_by_key_prefix(prefix, d)` + +#### Parameters: + +| Parameter | Type | Description | Default | +|-----------|------|-------------|---------| +| prefix | str | This is the prefix that the function checks for in each key of the passed dictionary | - | +| d | dict | This is the dictionary that needs to be processed and grouped | - | + +The function takes two parameters: `prefix` which is a string and `d` which is a dictionary. + +The function checks each key of the passed dictionary `d` and groups them based on whether they start with the specified `prefix` or not. + +#### Returns: +The function returns a tuple of two dictionaries. One dictionary contains all items where keys start with the given prefix and the other dictionary contains all items where keys do not start with the given prefix. ```python def group_by_key_prefix(prefix, d): @@ -14,51 +31,79 @@ def group_by_key_prefix(prefix, d): Group dictionary items by keys that start with a specific prefix. Args: - prefix (str): The prefix to check for. - d (dict): The dictionary to group. + prefix (str): The prefix to check for. + d (dict): The dictionary to group. Returns: - tuple: Two dictionaries split based on the prefix condition. + tuple: Two dictionaries split based on the prefix condition. """ return group_dict_by_key(partial(string_begins_with, prefix), d) ``` -Here, the function takes two parameters. They are: +## Function Usage & Examples -1. prefix - - Type: str - Description: It is the prefix string that the function uses to check if the keys in the dictionary start with this piece of string. +Let's go through examples that illustrate the usage of this function: -2. d - - Type: dict - Description: This is the dictionary that the function is required to perform the operation on. The function traverses the keys of this dictionary and groups them into two dictionaries based on whether or not they start with the specified prefix. +### Example 1 - Basic Scenario: -## Usage Examples +In a scenario where we have a dictionary of various fruits and we wish to group them based on the first letter of the fruit's name. For example, we can choose "a" as our prefix. Here's how we can process the dictionary: -Now, let's run through some examples of how to use this function and what kind of output we can expect in different scenarios: +```python +import zeta.utils as zutils + +fruits = { + "apple": 5, + "avocado": 2, + "banana": 4, + "blackberry": 3, + "cherry": 7, + "apricot": 1 +} + +prefix = "a" +grouped_fruits = zutils.group_by_key_prefix(prefix, fruits) +print(grouped_fruits) +``` -### Example 1: Handling general case +### Example 2 - Empty Dictionary: -First, let's look at how the function handles a general case. +In the scenario where we pass an empty dictionary, we will receive two empty dictionaries in return as there are no keys to process: ```python -# First, we define a dictionary to be used for this example -example_dict = {"pear" : 1, "apple" : 2, "banana" : 3, "peach" : 4, "peanut" : 5} +import zeta.utils as zutils -# Now, let's use the function to split this dictionary based on the prefix "pea" -split_dict = group_by_key_prefix("pea", example_dict) +empty_dict = {} -# This will output two dictionaries: -# The first containing all those entries whose keys start with "pea", and the second containing the rest. +prefix = "a" +grouped_dict = zutils.group_by_key_prefix(prefix, empty_dict) +print(grouped_dict) # output: ({}, {}) ``` -### Example 2: Handling an empty input dictionary +### Example 3 - No Keys With Specified Prefix: -Next, let's examine how the function handles an empty input dictionary. +If there are no keys in the dictionary that start with the specified prefix, then one of the dictionaries returned in the tuple will be empty: ```python -# In this case, we use an empty dictionary as our input -empty_dict = {} +import zeta.utils as zutils + +fruits = { + "banana": 4, + "blackberry": 3, + "cherry": 7 +} + +prefix = "a" +grouped_fruits = zutils.group_by_key_prefix(prefix, fruits) +print(grouped_fruits) # output: ({}, {'banana': 4, 'blackberry': 3, 'cherry': 7}) +``` + +## Additional Tips & Best Practices: +1. Prefix search is case-sensitive. If keys contain capital letters, make sure to provide a capital letter as the prefix too if you're looking for an exact match. +2. This function does not search prefixes recursively. If dictionary values are themselves dictionaries, the function will not process keys for those nested dictionaries. +3. Be mindful of dictionary key types. This function will not work if keys are not string type. + +## References & Further Reading: +1. Python Dictionary Official Documentation: https://docs.python.org/3/tutorial/datastructures.html#dictionaries +2. Functional Programming in Python: https://docs.python.org/3/howto/functional.html -# Then we split this empty dictionary based on any prefix, say "test" -split_dict +This documentation provides an explanation on using the `group_by_key_prefix` utility function. For details on other functions provided by the `zeta.utils` library, refer to the respective documentation. diff --git a/docs/zeta/utils/group_dict_by_key.md b/docs/zeta/utils/group_dict_by_key.md index 1dd28f26..b377b410 100644 --- a/docs/zeta/utils/group_dict_by_key.md +++ b/docs/zeta/utils/group_dict_by_key.md @@ -1,47 +1,129 @@ # group_dict_by_key -# Module/Function Name: group_dict_by_key (Internally within `zeta.utils`) +# Module Name: Zeta.Utils -Function `group_dict_by_key` is a utility function which is designed to split specific dictionary based on the condition provided by the user. This function accepts two arguments: a condition (a function), and a dictionary. The key feature of this function is the implicit usage of the user-defined function to be used as a condition to split the dictionary on. This function allows users to take a very flexible approach in handling, processing, and manipulating dictionary objects in Python. +## Group dictionary keys `group_dict_by_key` based on a condition function -## Function Signature +The `group_dict_by_key` function in `Zeta.Utils` is a utility function that facilitates grouping keys of a dictionary based on a specified condition. The condition is defined by a custom function. + +The function returns two dictionaries where one dictionary contains the keys that meet the condition and the other dictionary contains keys that do not meet the condition. This can be useful in scenarios where you would like to separate out dictionary entries based on specific conditions. + +### Function Definition + +The following is the definition of the `group_dict_by_key` function: ```python -def group_dict_by_key(cond: function, d: dict) -> Tuple[dict, dict] +def group_dict_by_key(cond, d): + """ + Group dictionary keys based on a condition. + + Args: + cond (function): Condition to split dictionary. + d (dict): The dictionary to group. + + Returns: + tuple: Two dictionaries split based on the condition. + """ + return_val = [dict(), dict()] + for key in d.keys(): + match = bool(cond(key)) + ind = int(not match) + return_val[ind][key] = d[key] + return (*return_val,) ``` -This function takes in a `function` parameter which will be used to divide the dictionary into two parts, and the `dictionary` to be divided. The function can be named according to the condition of use, and its definition is entirely up to the user. The dictionary `d` is the dictionary to be divided. +### Arguments: + +The `group_dict_by_key` function accepts the following two arguments: -## Function Parameters +| Argument | Type | Description | +| --- | --- | --- | +| `cond` | function | A function that defines the condition based on which the dictionary keys will be split. This function should take a key as input and return a Boolean value indicating whether the key meets the condition or not. | +| `d` | dict | The dictionary that will be split into two dictionaries based on the condition provided by the `cond` function. | -| Parameter | Type | Description | Default Value | -| ------- | -------- | ------------------------------------------------------ | ---------------- | -| cond | function | User-defined function to be used to split the dictionary | NA | -| d | dict | Dictionary to be divided | NA | +### Returns: -## Returns +The `group_dict_by_key` function returns two dictionaries: -This function returns a `Tuple[dict, dict]`. Specifically, it outputs a tuple of dictionaries divided based on the condition provided. +1. The first dictionary contains keys that satisfy the condition specified by the `cond` function. -## How it Works +2. The second dictionary contains keys that do not satisfy the `cond` function. -The function `group_dict_by_key` starts by initializing two empty dictionaries `return_val`. It then iterates through every key in the input dictionary `d`. For each key, it evaluates the user-defined condition function `cond(key)`. If the condition is matched, the current key and value pair is added to the first new dictionary. If the condition is not matched, the current element is added to the second new dictionary. Therefore, the function iterates through all key-value pairs in the input dictionary and divide them into two dictionaries based on whether or not they meet the user-defined condition. +The returned dictionaries have the same values mapped to the same keys as the original dictionary. -## Examples and Usage +### Usage Example: -#### Import +#### Example 1: -In order to use this function, you must first understand how to import it. Here is an example of how you might do this: +Consider having a dictionary of student marks and the goal is to group the students into those who have scored 60 and above (pass) and below 60 (fail). The `cond` function will check if the marks are greater than or equal to 60. ```python -from zeta.utils import group_dict_by_key +students_marks = { + "John": 85, + "Peter": 60, + "Tracy": 72, + "Paul": 50, + "Angela": 67, + "Robert": 40 +} + +# define the condition function to check if marks >= 60 +cond = lambda marks : marks >= 60 + +pass_students, fail_students = group_dict_by_key(cond, students_marks) ``` -#### Use +The two dictionaries returned from `group_dict_by_key` would be: + +```python +pass_students = { + "John": 85, + "Peter": 60, + "Tracy": 72, + "Angela": 67, +} + +fail_students = { + "Paul": 50, + "Robert": 40 +} +``` -Here are three different examples of how you'd use `group_dict_by_key` function: +#### Example 2: -1. Grouping dictionary keys based on length: +If you have a dictionary of items and their prices, and you want to separate them into items that are below or equal to $20 and items that cost more than $20: ```python -cond = +items_prices = { + "apple": 2, + "orange": 3, + "mango": 1, + "blueberry": 5, + "grape": 10, + "guava": 25, + "dragon fruit": 50, +} + +# define the condition function to check if price > 20 +cond = lambda price : price > 20 + +pricey, affordable = group_dict_by_key(cond, items_prices) +``` + +The returned dictionaries would be: + +```python +pricey = { + "guava": 25, + "dragon fruit": 50, +} + +affordable = { + "apple": 2, + "orange": 3, + "mango": 1, + "blueberry": 5, + "grape": 10, +} +``` + diff --git a/docs/zeta/utils/gumbel_noise.md b/docs/zeta/utils/gumbel_noise.md index bb67c9d6..f5603626 100644 --- a/docs/zeta/utils/gumbel_noise.md +++ b/docs/zeta/utils/gumbel_noise.md @@ -1,46 +1,87 @@ # gumbel_noise -# Module Name: Gumbel Noise +# gumbel_noise Function Documentation -Function Name: gumbel_noise(t) +## Function Definition + +`gumbel_noise(t)` + +The `gumbel_noise` function generates Gumbel-distributed noise given a tensor object `t`. The Gumbel distribution, often used in modeling extremes, is used here to generate noise with similar characteristics. To add randomness or noise to your models, this function is crucial especially when working with GANs, Variational Autoencoders or other stochastic architectures where random sampling is a key component. + + +## Parameters: + +| Parameter | Type | Description | +|---------------|------------------------------------------------------|--------------------------------------------------------------| +| `t` | A tensor object | Any PyTorch's tensor onto which noise would be generated | + +## Returns: + +`noise`: A tensor object of the same shape as `t`, comprising of noise data sampled from Gumbel distribution. + +## Function Usage + +Before we jump onto the function usage, here's a brief about the Gumbel Distribution: The Gumbel Distribution, also known as Smallest Extreme Value (SEV) or Type I Extreme Value distribution, is a continuous probability distribution named after Emil Julius Gumbel. It is widely used in modeling extreme value problems in fields such as hydrology, structural engineering and climate data analysis. + +Now let's go through a few examples illustrating the usage of `gumbel_noise` function: + +### Import Necessary Libraries ```python -def gumbel_noise(t): - noise = torch.zeros_like(t).uniform_(0, 1) - return -log(-log(noise)) +import torch ``` -This function generates Gumbel noise, a type of statistical noise named after the Emil Julius Gumbel who was a German statistician, applied to a tensor 't' with similar attributes. It generates a tensor with the same size as 't', filled with random numbers uniformlly distributed between 0 (inclusive) and 1 (exclusive). Then, the Gumbel noise is computed which is a perturbation method to draw samples from discrete distributions. -The Gumbel distribution is used in sampling methods, for example in the Gumbel-Softmax trick, for producing one-hot encodings or to sample from a discrete distribution with an unspecified number of classes. +#### Example 1: Generation of Gumbel-Distributed Noise for a 1D Tensor Object -Parameters: -- t (torch.Tensor) : Input tensor. +```python +# Define a tensor +tensor = torch.tensor([1.0, 2.0, 3.0, 4.0, 5.0]) -Return: -- Tensor: Gumbel noise added tensor with the same type as t. The equals to negative logarithm of negative logarithm of uniform noise. +# Generate Gumbel noise +gumbel_noise_data = gumbel_noise(tensor) -## Example: +# Output +print(gumbel_noise_data) +``` + +In this example, gumbel_noise_data is a tensor of the same size as the input tensor, but filled with noise sampled from the Gumbel distribution. + +#### Example 2: Generation of Gumbel-Distributed Noise for a 2D Tensor Object ```python -import torch -from math import log +# Define a 2D tensor +tensor_2D = torch.tensor([[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]]) -def gumbel_noise(t): - noise = torch.zeros_like(t).uniform_(0, 1) - return -log(-log(noise)) +# Generate Gumbel noise +gumbel_noise_data2D = gumbel_noise(tensor_2D) -# Creating a tensor -x = torch.tensor([2.0, 1.0, 3.0, 4.0]) -print("Original Tensor: ",x) +# Output +print(gumbel_noise_data2D) +``` -# Applying gumbel noise -y = gumbel_noise(x) -print("Tensor after applying Gumbel noise function: ",y) +In this example, gumbel_noise_data2D is a 2D tensor of the same size as the input tensor, but filled with noise sampled from the Gumbel distribution. + +#### Example 3: Generation of Gumbel-Distributed Noise for a 3D Tensor Object + +```python +# Define a 3D tensor +tensor_3D = torch.rand((2,2,2)) + +# Generate Gumbel noise +gumbel_noise_data3D = gumbel_noise(tensor_3D) + +# Output +print(gumbel_noise_data3D) ``` -## Issues and Recommendations -- It should be noted that the function torch.zeros_like() can be replaced by the torch.empty_like() function if wanting to save time when generating the tensor. The former sets all values as zeros while the latter does not initialize the values, a step that isn't necessary since we are just overwriting these values with uniform noise. +In this example, gumbel_noise_data3D is a 3D tensor of the same size as the input tensor, but filled with noise sampled from the Gumbel distribution. + +This function, `gumbel_noise`, can be utilized in modelling various Machine Learning tasks - such as classification and generation tasks, and in building deep learning architectures, where learning from noise is beneficial, such as Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs) etc. + +## Notes and Additional Information + +When dealing with statistical modelling problems in Machine Learning, it's quite important and frequent to add statistical noise into the data. Because random noise makes the model more robust and generalizable. There are many types of noise that can be added into the data, Gumbel noise being one of them. + +The purpose of adding this Gumbel noise is to provide a stochastic element to the PyTorch tensor, resulting in a distribution of values which can be manipulated or studied. The Gumbel noise added onto `t` by `gumbel_noise` essentially provides a simple way of getting a version of `t` that has been noise-adjusted. This can be important for methods which need a stochastic element or for testing the robustness of different architectures to noise. -- Note that the function is computing the logarithm of noise. In the case where noise is very low and close to zero, the inner logarithm will give negative infinity. Subsequently, negative of negative infinity is positive infinity. Users should be aware of potential overflow issues in their computations. - -- If the function is used in machine learning models for training, it should be noted that the function is not different +It's worth noting that the Gumbel distribution has heavier tails than the normal distribution, so adding Gumbel noise to a variable will add extreme values (i.e., very large or very small numbers) more frequently than adding Gaussian noise. This means that using Gumbel noise can be a good way to test the stability and robustness of your model: if your model works well when you add Gumbel noise to the inputs, it's likely to also perform diff --git a/docs/zeta/utils/init_zero_.md b/docs/zeta/utils/init_zero_.md index 98cad120..f1a03622 100644 --- a/docs/zeta/utils/init_zero_.md +++ b/docs/zeta/utils/init_zero_.md @@ -1,64 +1,110 @@ # init_zero_ -# Module Name: zeta.utils +# **Zeta.utils** -## Function Name: init_zero_ +## **Overview** -The `init_zero_` function is used to initialize the weights and bias of a PyTorch layer to zero. Initialization of the weights and biases of a layer play a crucial role regarding the performance of a deep learning model. Here, we're initializing every parameter to zero, turning the model into a "zero model". This is useful for certain tasks where you need your model to start with a clean slate. +`zeta.utils` is a small set of utility functions designed specifically to work in Pytorch-based environments. The primary purpose of these utilities is to streamline common operations and data manipulations that are frequently used when working with Pytorch. -This function is designed to work with any layer type available in the `torch.nn.Module` of PyTorch framework. However, it should be noted that if we initialize parameters of all layers as zero, then all the neurons at each layer will learn the same features during training. This function should be used when you're sure that initializing parameters to zero fits your specific needs. +In this particular module, most of the functions are generally geared towards simplifying and optimizing weight and bias initialization of torch layers. In neural network architectures, appropriate initialization of weights and biases is crucial to ensuring models converge during training. -Below is the function definition and description of the parameters: - -| Function parameters | Description | -|---------------------|--------------------------------------------------------------------------------------------------------------------| -| layer |A `torch.nn.Module` object: The layer to initialize.| +## **Function Definition: `init_zero_`** +### **Function Signature** ```python -def init_zero_(layer): - """ - Initialize the weights and bias of a torch layer to zero. - - Args: - layer (torch.nn.Module): The layer to initialize. - """ - nn.init.constant_(layer.weight, 0.0) - if layer.bias is not None: - nn.init.constant_(layer.bias, 0.0) +def init_zero_(layer:torch.nn.Module): ``` +Initializes all the weights and biases of a specified torch layer to zero. + + ++ +### **Functionality and Usage** -## How to Use init_zero_ +`init_zero_` performs weight and bias initialization by filling the provided layer tensor with zeros. Zero initialization is typically used for debugging purposes and is generally not recommended for training models. -Below we provide three different examples showing the usage of `init_zero_` function. +However, in some cases, zero initialization can serve a useful purpose in assigning uniform initial importance to all input features. Additionally, using zero initialization can avoid potential issues with exploding or vanishing gradients, especially in larger and more complex models. -### Example 1: Initializing a Linear Layer with `init_zero_` +Function Parameters
++ +| Argument | Type | Default Value | Description | +| --- | --- | --- | --- | +| `layer` | torch.nn.Module | None | The layer whose weights and bias you want to initialize to zero. | + +
+++ +### **Additional Information** + +When working with this utility, it's important to remember that although zero initializing weights and biases can be useful for debugging, it is generally not effective for training deep learning models. This is because all neurons in the network start producing the same output and subsequent layers receive virtually identical signals; breaking the symmetry is crucial for the model to learn from various features in the dataset. + +Moreover, this function preserves the data type and device of the original tensor, so you do not have to worry about device or dtype mismatches. + +### **External Resources** -# define a 2d convolutional layer -conv_layer = nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=1) +For further exploration and understanding, you may refer to the following resources and references - +1. PyTorch Documentation: [torch.nn.init.constant_](https://pytorch.org/docs/stable/nn.init.html#torch.nn.init.constant_) +2. Blog post on Initialization Techniques: [Weight Initialization in Neural Networks: A Journey From the Basics to Kaiming](https://towardsdatascience.com/weight-initialization-in-neural-networks-a-journey-from-the-basics-to-kaiming-954fb9b47c79) -# initialize the layer with zeros -utils.init_zero_(conv_layer) +That concludes the documentation for the `init_zero_` function in `zeta.utils`. For usage and technical details on other functions in the module, refer to their respective documentation. -# print the weights and the bias of the layer +--- +## **Function Definition: `exists`** +[comment]: <> (This is a placeholder for the `exists` function from `zeta.utils`. It should be documented in the similar exhaustive manner) diff --git a/docs/zeta/utils/interpolate_pos_encoding_2d.md b/docs/zeta/utils/interpolate_pos_encoding_2d.md index 06caa0e4..7db1f5a7 100644 --- a/docs/zeta/utils/interpolate_pos_encoding_2d.md +++ b/docs/zeta/utils/interpolate_pos_encoding_2d.md @@ -1,56 +1,74 @@ # interpolate_pos_encoding_2d -# Module Name: interpolate_pos_encoding_2d - -## Introduction: - -This utility function named `interpolate_pos_encoding_2d` handles the -interpolation of position embeddings for sequences and is commonly used -in the Deep learning models dealing with sequential data like Recurrent Neural -Networks (RNNs) and variants, Transformers etc. - -Positional embeddings help these models to distinguish the order of presented -values, this becomes especially relevant when dealing with transformer models -as transformers lack recurrent or convolutional structure to handle this -information natively. - -If the target spatial size and the original spatial size are equal, the -original positional embeddings are returned directly. However, if the sizes differ, -this function uses the bicubic interpolation method provided by PyTorch's -`nn.functional.interpolate()` to adjust the size of the positional embeddings as per -the target spatial size. - -To ensure computational efficiency along with numerical precision, this function -also includes an option to convert the original data type of the positional -embeddings to float32 during the interpolation process (if originally in -bfloat16). After the interpolation process, the data is converted back to bfloat16. +# Zeta.utils Function: interpolate_pos_encoding_2d + +The function `interpolate_pos_encoding_2d` is part of the `zeta.utils` module, and its purpose is to resize a 2D positional encoding to a given target spatial size. The function does this by using bicubic interpolation, which is a method for resampling or interpolating data points on a two-dimensional regular grid. + +This function takes in the target spatial size and the positional encoding (pos_embed) as arguments and returns the resized positional encoding. + +## Arguments and Return Types + +| Arguments | Type | Description | +|------------------------|-------------------------------------------------------|------------------------------------------------------------------------------------------------------| +| target_spatial_size | int | The desired size for the resized positional encoding. | +| pos_embed | Tensor | The input positional encoding that needs resizing. | + | +| Return | Tensor | Returns the positional encoding resized to the given target spatial size. | + +## Function Definition +```python +def interpolate_pos_encoding_2d(target_spatial_size, pos_embed): + N = pos_embed.shape[1] + if N == target_spatial_size: + return pos_embed + dim = pos_embed.shape[-1] + pos_embed, updated = cast_if_src_dtype( + pos_embed, torch.bfloat16, torch.float32 + ) + pos_embed = nn.functional.interpolate( + pos_embed.reshape(1, int(math.sqrt(N)), int(math.sqrt(N)), dim).permute( + 0, 3, 1, 2 + ), + scale_factor=math.sqrt(target_spatial_size / N), + mode="bicubic", + ) + if updated: + pos_embed, _ = cast_if_src_dtype( + pos_embed, torch.float32, torch.bfloat16 + ) + pos_embed = pos_embed.permute(0, 2, 3, 1).view(1, -1, dim) + return pos_embed +``` +## Function Usage and Examples -## Function Definition: +Here is an example of how to use this function in a general scenario: -`interpolate_pos_encoding_2d(target_spatial_size, pos_embed)` +Example 1: +```python +import torch +import math +from torch import nn -``` -Performs interpolation on 2D positional embeddings as per the given target spatial size. +def cast_if_src_dtype(src, src_dtype, target_dtype): + if src.dtype == src_dtype: + return src.to(target_dtype), True + return src, False -Parameters: -- target_spatial_size (int): Target spatial size for the embeddings. -- pos_embed (Tensor): Initial 2D positional embeddings. +# Creating a random positional encoding +pos_embed = torch.randn(1, 16, 64) # 2-dimensional, size=(1,16,64) -Returns: -- pos_embed (Tensor): 2D positional embeddings after necessary interpolations and type conversions. +# Interpolating the positional encoding to a larger spatial size +new_pos_embed = interpolate_pos_encoding_2d(32, pos_embed) +print('Old size:', pos_embed.shape) +print('New size:', new_pos_embed.shape) ``` +In this example, an artificial positional encoding of size 1x16x64 is being interpolated to have 32 spatial size, resulting in a new size of 1x1024x64. -## Functionality and Usage: - -### Functionality: +## Common Usage Mistakes -Here is the step-wise functionality of the `interpolate_pos_encoding_2d` function: +One common mistake when using the `interpolate_pos_encoding_2d` function may be not checking the original spatial size of the positional encoding. If a positional encoding has the same spatial size as the target size that you want to resize it to, then the function will return the input positional encoding without resizing. -1. Fetches the initial spatial size of the positional embeddings. -2. If the initial and target spatial sizes are the same, it returns the original positional embeddings directly. -3. If the sizes differ, it proceeds with the interpolation. -4. Interpolation process: - 1. First, it checks if the initial positional embeddings are in `bfloat16` format. If so, converts them to `float32`. This is achieved by calling the function `cast_if_src_dtype`. - 2. Reshapes the positional embeddings and applies the bicubic interpolation by using `nn.functional.interpolate()` method to adjust the size. - 3. If the original data type was `bfloat16`, +## References and Further Reading +- [PyTorch nn.functional.interpolate](https://pytorch.org/docs/stable/generated/torch.nn.functional.interpolate.html) +- [Resampling or Interpolating](https://en.wikipedia.org/wiki/Resampling_(bitmap)) diff --git a/docs/zeta/utils/l2norm.md b/docs/zeta/utils/l2norm.md index 21650b96..57c0b6d1 100644 --- a/docs/zeta/utils/l2norm.md +++ b/docs/zeta/utils/l2norm.md @@ -1,8 +1,27 @@ # l2norm -# Module Name: zeta.utils +# Module Name: `l2norm` +--- + +Function: `l2norm(t, groups=1)` + +The `l2norm` is a function written in Python that uses the PyTorch library to normalize tensors. This particular function uses the `L2` or Euclidean norm. The function also handles grouped tensors and normalizes over each group separately. This function can be crucial in many scenarios where input tensors need to be normalized. + +## Parameters: + +| Parameter | Type | Default value | Description | +|-----------|------|---------------|-------------| +| t | Tensor | N/A | Input tensor to be normalized. | +| groups | int | 1 | Number of groups to split the tensor in. | + +## Returns: + +| Output | Type | Description | +|--------|------|-------------| +| Tensor | Tensor | The L2-normalized tensor. + +_Source Code:_ -## Function: l2norm ```python def l2norm(t, groups=1): t = rearrange(t, "... (g d) -> ... g d", g=groups) @@ -10,51 +29,56 @@ def l2norm(t, groups=1): return rearrange(t, "... g d -> ... (g d)") ``` -### Overview -The function `l2norm` as the name suggests, is used for L2 normalization of tensors. L2 normalization is the process of dividing a feature vector by its L2 norm, which results in a vector on the unit sphere. It helps deal with issues involving scale variance in data. - -The `l2norm` function takes in a tensor and an optional `groups` parameter, rearranges the elements of the tensor as per the `groups` parameter, performs the normalization and then again rearranges elements to their original order. +This function first rearranges the tensor `t` into the specified number of `groups`. After this rearrangement, it normalizes each group using the PyTorch function `F.normalize()` with `p=2`, which indicates the use of L2 or Euclidean norm and `dim=-1`, which normalizes over the last dimension. Finally, the function returns the tensor after rearranging it back to its original structure. -The function makes use of the `rearrange` function from the `einops` library and the `normalize` function from PyTorch's `torch.nn.functional` library. +## Usage Examples : -### Parameters -The `l2norm` function has the following parameters: +### Example 1: +```python +# Ignore import errors, they are part of the example code +from torch import randn +from einops import rearrange -| Argument | Type | Description | Default Value | -| --- | --- | ---| --- | -| t | torch.Tensor | The tensor that requires L2 normalization. | - | -| groups | int | The number of groups to divide the tensor into before applying normalization. | 1 | +t = randn(2, 2, 3) +result = l2norm(t, groups=2) +``` -### Usage -Here are three examples showcasing the usage of the `l2norm` function: +In this example, we generate a random tensor `t` with dimensions (2,2,3) using the `torch.randn()` function. Then we call the `l2norm` function with this tensor as the argument and normalize over 2 groups. -#### Example 1 +### Example 2: ```python -from zeta.utils import l2norm -import torch +# Ignore import errors, they are part of the example code +from torch import randn +from einops import rearrange + +t = randn(3, 3, 3) +result = l2norm(t, groups=1) +``` -# Creating a 3-dimensional tensor -tensor = torch.rand(4,2,2) +In this example, we generate a random tensor `t` with dimensions (3,3,3) using the `torch.randn()` function. Then we call the `l2norm` function with this tensor as the argument and normalize over a single group. -# Using l2norm without specifying groups -normalized_tensor = l2norm(tensor) +### Example 3: +```python +# Ignore import errors, they are part of the example code +from torch import randn +from einops import rearrange -# Print the output -print(normalized_tensor) +t = randn(4, 4, 2) +result = l2norm(t, groups=4) ``` -In this example, we create a random 3-dimensional tensor and use the `l2norm` function to normalize it without specifying the `groups` parameter. Thus, the tensor will not be divided into groups before normalization. +In this example, we generate a random tensor `t` with dimensions (4,4,2) using the `torch.randn()` function. Then we call the `l2norm` function with this tensor as the argument and normalize over 4 groups. -#### Example 2 -```python -from zeta.utils import l2norm -import torch +--- + +_Tips on usage_: + +While using the `l2norm` function, it is necessary to understand the dimensions of the input tensor and the number of groups that we wish to normalize over. More groups would mean more `dim` divisions, followed by individual normalization. This could potentially improve the accuracy of certain ML models where normalization is important. -# Creating a 3-dimensional tensor -tensor = torch.rand(4,2,2) +A suitable value for `groups` would depend entirely on the task at hand and would often need to be determined through experimentation. -# Using l2norm specifying groups as 2 -normalized_tensor = l2norm(tensor, groups=2) +Possible errors may arise if the number of groups is not a divisor of the number of dimensions in the tensor. In such a case, a more suitable value for `groups` should be selected. -# Print the output +--- +_For more detailed information, please refer to the Pytorch documentation linked [here](https://pytorch.org/docs/stable/tensors.html) and the Einops documentation linked [here](https://einops.rocks/)_. diff --git a/docs/zeta/utils/log.md b/docs/zeta/utils/log.md index 1f048f1e..195040f5 100644 --- a/docs/zeta/utils/log.md +++ b/docs/zeta/utils/log.md @@ -1,58 +1,72 @@ # log -# Module Name: zeta.utils.log - -## Table of Contents - -- [Introduction](#Introduction) -- [Arguments](#Arguments) -- [Methods](#Methods) -- [Examples](#Examples) -- [Tips](#Tips) -- [References](#References) +# zeta.utils.log ## Introduction -This document is a detailed and comprehensive guide on how to use the `log` module that exists within the `zeta.utils` library. -`log` is a utility function signature within the `zeta.utils` library, which specifically takes in a PyTorch Tensor and returns its natural logarithm (base `e`) after applying a clamp operation. Clamping refers to setting the value within an interval `min` and `max`. Here we only want to ensure that the tensor values are not lower than a small value `eps` which is often taken to prevent division by zero or log of zero errors. +The `log` function serves as a small utility helper to calculate the natural logarithm of a tensor using PyTorch's `torch.log` function, while safeguarding against division by zero error by setting a minimum clamp value. -## Arguments +The minimum clamp value serves as a protection from taking the log of 0 which would result in undefined mathematical operation (division by zero). The aim of this is to ensure computational stability, especially in context where the input tensor contains zero or near-zero values. -This function accepts two arguments: `t` and `eps`. +## Function Definition -| Argument | Type | Default | Description | -| ------- | ---- | ------- | ----------- | -| `t` | torch.Tensor | N/A | The input tensor on which the natural logarithm operation is performed. | -| `eps` | float | 1e-20 | A very small value to which tensor values are set if they are less than `eps`. This helps in avoiding computation errors when we evaluate log of these tensor values.| +This function, `zeta.utils.log(t, eps=1e-20)`, has the following parameters: -All arguments are compulsory, but you can omit `eps` during a function call; in this case, its default value (1e-20) would be used. +* `t` : A PyTorch tensor that the logarithm will be taken from. This tensor can have any shape. +* `eps` (default: `1e-20`): A small value which sets the minimum value for clamping. This essentially serves as a "safety net" preventing the input tensor from being zero or negative, which would result in an error when we take the log. -## Methods +## Return Value +The function `zeta.utils.log(t, eps=1e-20)` returns a tensor of the same shape, where each element represents the natural logarithm of the corresponding element from the input tensor `t` with a minimum clamp established by `eps`. -`log` is a standalone function and does not have any class or instance-specific methods. +## Functionality and Usage -To call it, use `zeta.utils.log(t, eps)` where `t` is the tensor and `eps` is the optional small value as explained above. +The implementation of the function is as follows: + +```python +def log(t, eps=1e-20): + return torch.log(t.clamp(min=eps)) +``` -## Examples +`t.clamp(min=eps)` restricts the values within tensor `t` to be greater or equal to the `eps` value. This is to avoid any fraudulent computations involving negative or zero values when the logarithm function is applied to these clamp restricted values by `torch.log`. -These examples demonstrate how to utilize the `log` function within the `zeta.utils` library. +This function is typically used in situations where it's necessary to calculate the natural log of tensor values in machine learning models, especially in those contexts where the input tensor might contain zero or near-zero values due to computations in the model or the nature of the input data. -- First, import the necessary libraries: +Here is a simple example usage of `zeta.utils.log`: ```python - import torch - from zeta.utils import log +import torch +import zeta.utils as zutils + +t = torch.tensor([0.0, 0.1, 1.0, 10.0]) +res = zutils.log(t) + +print(res) +``` +```console +tensor([-46.0517, -2.3026, 0.0000, 2.3026]) ``` -- Using `log` function with a simple tensor: +**Note**: As seen in the example above, instead of `inf` which is typically what we get by applying log to zero, our log utility function gives a large negative number (-46.0517), thanks to the `eps` clamping. + +## Additional Tips + +As mentioned earlier, the purpose of the `eps` parameter is to prevent possible mathematical errors when taking the log of zero or negative numbers. However, the default value of `eps` is set to `1e-20` which can be too small in some contexts, leading to extreme values when taking the log. + +Depending on the scale and the nature of your data, it may be useful to adjust `eps` to a larger value to avoid very large negative numbers but remember, setting `eps` too high might introduce a bias. As always, it’s a balance and the right value of `eps` depends on your specific situation. + +Here is another example of how adjusting `eps` can affect your results: ```python - # Define tensor - t = torch.tensor([0.0, 1.0, 2.0, 3.0]) - - # Apply log transformation - log_t = log(t) +import torch +import zeta.utils as zutils - print(log_t) +t = torch.tensor([0.0, 0.1, 1.0, 10.0]) +res = zutils.log(t, eps=1e-10) + +print(res) +``` +```console +tensor([-23.0259, -2.3026, 0.0000, 2.3026]) ``` -The expected output should + +In this example, by setting `eps` to `1e-10` we've effectively "softened" the result from applying log to zero from `-46.0517` to `-23.0259`. diff --git a/docs/zeta/utils/maybe.md b/docs/zeta/utils/maybe.md index 900526ab..d3e8f7b3 100644 --- a/docs/zeta/utils/maybe.md +++ b/docs/zeta/utils/maybe.md @@ -1,26 +1,47 @@ # maybe -# Module Name: maybe +# Module/Function Name: maybe -## Overview: +```python +def maybe(fn): + """ + Decorator that calls a function if the first argument exists. + + Args: + fn (function): The function to wrap. + + Returns: + function: The wrapped function. + """ + + @wraps(fn) + def inner(x, *args, **kwargs): + if not exists(x): + return x + return fn(x, *args, **kwargs) + + return inner +``` -The `maybe` function is a Python decorator, that wraps a function and calls it only if the first argument to the function exists. This can help in implementing conditional function calls based on the existence of the first input argument. It is intended to improve code organization and readability, and it can be particularly useful when dealing with functions that require the existence of an input argument for successful execution. +## Description: -## Module Interface: +The `maybe` function is a Python decorator that wraps a given function (`fn`) and alters its behavior in such a way that it only calls this function if the first argument provided (`x`) exists. In the context of this decorator, "exists" typically means that `x` is not `None` although this could be adjusted to accommodate any variations on what it means for `x` to "exist" depending on your specific use case. -The module provides a function wrapper `maybe` that accepts one input parameter, the function to be wrapped. The wrapped function `inner(x, *args, **kwargs)` has the ability to take any positional and keyword arguments. +This type of decorator can be tremendously useful in a number of contexts, including data preprocessing, data validation, error handling, and more. -Hereafter is a detailed table demonstrating `maybe` module interface. +## Parameters: -| Function Name | Argument | Description | Type | Default | -|---------------|----------|---------------------------------------------------------------------------------------------------|------|---------| -| maybe | fn | This argument refers to the function that needs to be wrapped. This function should be callable. | Any | None | +| Parameter | Type | Description | +|-----------|-------------|--------------------------------| +| fn | function | The function to be decorated | -## Example Usage: +## Returns: -In this section, we will provide several examples to demonstrate how you can use the `maybe` function. +| Return | Type | Description | +|-----------|-------------|--------------------------------| +| function | function | The decorated function | -### Example 1 - Basic Usage: +## Usage Example: ```python from functools import wraps @@ -40,27 +61,18 @@ def maybe(fn): def add_one(x): return x + 1 -print(add_one(4)) # Output: 5 -print(add_one(None)) # Output: None +print(add_one(None)) # Returns: None +print(add_one(2)) # Returns: 3 ``` -In this snippet, we define a decorator `maybe` which wraps the function `add_one`. When the input to `add_one` is None, no operation is done and None is returned. +In this example, we have created a `maybe` decorator using the given `maybe` function and applied it to the `add_one` function. When we call `add_one` with `None` as the argument, the `maybe` decorator checks if `None` exists (which it does not), and so it simply returns `None` without calling the `add_one` function. -### Example 2 - Varied Input: +However, when we call `add_one` with `2` as the argument, the `maybe` decorator checks if `2` exists (which it does), and so it proceeds to call the `add_one` function, resulting in `3`. -```python -@maybe -def add(x, y): - return x + y +## Additional Information: -print(add(4, 5)) # Output: 9 -print(add(None, 5)) # Output: None -``` - -In this example, we wrap a function `add` which takes two arguments. When the first argument is None, `maybe` prevents `add` from being executed and returns `None` instead. +The `maybe` decorator utilises the `@wraps` decorator from the `functools` module which updates the wrapper function to look like the wrapped function. This includes the function name, docstring, and module, amongst other attributes. -### Example 3 - Complex Functions: +The `if not exists(x)` part of the `inner` function acts as a short-circuit evaluation. This means `fn(x, *args, **kwargs)` is not executed if the `x` argument does not exist, thus preventing potential errors or exceptions from occurring. -```python -@maybe -def complex_func(x +Please ensure to define an `exists` function according to your requirement, as it works with the `maybe` decorator to determine whether or not the function `fn` should be invoked. diff --git a/docs/zeta/utils/module_device.md b/docs/zeta/utils/module_device.md index 0224ab90..64d655e7 100644 --- a/docs/zeta/utils/module_device.md +++ b/docs/zeta/utils/module_device.md @@ -2,12 +2,13 @@ # Module Name: module_device -This decorator provides an extended functionality to PyTorch's nn.Module. PyTorch's nn.Module does not have a specific property that explicitly points out which device it resides on. This decorator provides the `device` property to the class that can be used to return the device of a particular PyTorch's nn.Module class. +The `module_device` is a Python decorator function that efficiently manages a device on which a PyTorch neural network models, which is a subclass of `torch.nn.Module`, is loaded. This decorator helps in tracking the device on which different components (such as tensors) of the model are, especially in complex design models where different tensors can be on separate devices. This helps to avoid any device mismatch errors during computation. -## Function Definition +Moreover, it allows the developers to add their custom functions or operations that could be performed whenever the device changes. Also, it has an in-built compatibility check feature, which elegantly handles the case of trying to transfer to GPUs when CUDA is not available. -The decorator is defined as follows: +To dive deep, let's see the main components and details of this function. +## Class Defintion: ```python def module_device( device_property_name: str = "device", @@ -15,42 +16,69 @@ def module_device( compatibility_check: bool = False, ): ``` +This function has three parameters – `device_property_name`, `on_device_transfer`, and `compatibility_check`. -### Parameters +| Parameter | Type | Default | Description | +|------------------------|--------|-----------|---------------------------------------------------------------------------------------------------------------------------------------------| +| device_property_name | string | "device" | Name of the attribute which would track the device of the decorated class. | +| on_device_transfer | callable/disable | None | A callable function that will be invoked whenever the device changes. This function will be executed after the object is transferred to a new device. If None, no function will be executed. | +| compatibility_check | boolean | False | If True, checks the compatibility of the device change in case of CUDA not being available when trying to transfer to GPUs. | -| Parameter | Type | Default Value | Description | -|------------------------|---------|---------------|-------------| -| device_property_name | str | "device" | The name of the device property. | -| on_device_transfer | function| None | A function to be called whenever the device is transferred.| -| compatibility_check | bool | False | If set to True, raises an exception if "cuda" is in the device string while CUDA is not available. | +Here, `_dummy` is a registered buffer, a PyTorch state that is not a parametric tensor of the model but you want to save the model, so it persists across saving/loading roundtrips. -## Inner Functions and Properties +In case of multiple GPUs and your model spans them, this decorator will store all the devices. -### decorator +The `decorator` function wraps around a user-defined class. It keeps track of the device and throws an error when an incompatible device is used and updates the new device property in case of valid device change. It can also assist in performing user defined operations in case of device change using `on_device_transfer` function. -```python -def decorator(klass): -``` -The function takes a class as input and then checks if the input `klass` is a subclass of torch.nn.Module. +## Usage Examples: +Let's look at three ways to use this function. -### \_\_init\_\_ +### Example 1: +In the first example, we simply use this decorator to add a new device property (named "my_cuda_device" here) to our model, which always stores the current device of our model. ```python -def __init__(self, *args, **kwargs): +from torch.nn import Module +from torch import tensor + +@module_device(device_property_name="my_cuda_device") +class MyModel(Module): + def __init__(self, input_size, output_size): + super(MyModel, self).__init__() + self.fc1 = nn.Linear(input_size, output_size) + +MyModel_obj = MyModel(10, 10) +MyModel_obj.to('cuda') + +print(MyModel_obj.my_cuda_device) # Output: cuda:Usage Examples
++ +Before we proceed, let us first import the required modules and dependencies. ```python -import torch.nn as nn -import zeta.utils as utils +import torch +from torch import nn +from zeta.utils import init_zero_, exists +``` -# define a linear layer -linear_layer = nn.Linear(10, 5) +**Example 1: Initializing a Single Linear Layer** + +```python +# Create a single linear layer +layer = nn.Linear(10, 5) -# initialize the layer with zeros -utils.init_zero_(linear_layer) +# Initialize weights and bias to zero +init_zero_(layer) -# print the weights and the bias of the layer -print(linear_layer.weight) -print(linear_layer.bias) +print("Weights:", layer.weight) +print("Bias:", layer.bias) ``` -### Example 2: Initializing a Convolutional Layer with `init_zero_` +In this example, you can observe that after applying `init_zero_()`, all the weights and biases of the layer are initialized to zero. + +**Example 2: Initializing All Layers in a Neural Network Model** ```python -import torch.nn as nn -import zeta.utils as utils +# Create a simple neural network +model = nn.Sequential( + nn.Linear(10, 5), + nn.ReLU(), + nn.Linear(5, 1) +) + +# Loop through each layer in the model +for layer in model: + # Check if the layer has a weight, i.e., is a nn.Linear() layer + if exists(layer, 'weight'): + init_zero_(layer) + +# Check weights of first layer +print("Weights of First Layer:", model[0].weight) +print("Bias of First Layer:", model[0].bias) + +# Check weights of third layer +print("Weights of Third Layer:", model[2].weight) +print("Bias of Third Layer:", model[2].bias) +``` + +In this example, `init_zero_` is used to initialize all the weights and biases in a neural network model to zero. + +
+``` -It overrides the original `__init__` method of the class and registers a buffer named "_dummy", which is a non-persistent tensor containing a single zero. +### Example 2: -### \_\_to +In the second example, we will define a function that will be executed whenever the device changes. Here for simplicity, we will just print a simple message. ```python -def __to(self, device, *args, **kwargs): +def transfer_fn(self, device): + print(f"Transferred to {device}") + +@module_device(on_device_transfer=transfer_fn) +class SecondModel(Module): + pass + +SecondModel_obj = SecondModel() +SecondModel_obj.to('cuda') # Output: Transferred to cuda: ``` -This function is overloading the `to()` method of the torch.nn.Module class. It first checks if the `compatibility_check` flag is true and CUDA is not available, but the device is "cuda". If this is the case, a RuntimeError is raised. Otherwise, the `to()` method of torch.nn.Module is called with the specified parameters. +### Example 3: -### _device_property +In the third example, we will use both the features discussed above together: ```python -@property -def _device_property(self): +def transfer_fn(self, device): + print(f"Transferred to {device}") + +@module_device(device_property_name="my_device", on_device_transfer=transfer_fn) +class ThirdModel(Module): + pass + +ThirdModel_obj = ThirdModel() +ThirdModel_obj.to('cuda') # Output: Transferred to cuda: +print(ThirdModel_obj.my_device) # Output: cuda: ``` -The `_device_property` helps in fetching the device property of the object. It does not take any parameters and returns the device on which the model is residing. It does this by checking the device of all parameters and buffers of the model. if the model resides on more than one device, it returns all the diff --git a/docs/zeta/utils/once.md b/docs/zeta/utils/once.md index 07597e42..9f1b7ceb 100644 --- a/docs/zeta/utils/once.md +++ b/docs/zeta/utils/once.md @@ -1,53 +1,24 @@ # once -# Zeta Utils Library Documentation +# Function Name: once -## Contents +## Overview and Introduction -1. [Overview](#overview) -2. [Detailed Function Documentation](#Detailed-Function-Documentation) - - [once](#once) -3. [Usage Guides](#Usage-Guides) +In a variety of contexts, whether while initializing some variables, setting up logging, or ensuring some heavy computation isn't undertaken multiple times, there are scenarios where you might want to ensure a function is executed only once. The `once` function is a Python decorator that took up this challenge. By using it, we guarantee a wrapped function is called only for the first time it is invoked. -## Overview +The `once` function meets this requirement by retaining a flag `called` in its closure. This flag tracks whether or not a function has been called before. When the function is called, it checks the flag. If the flag is false (`False`), implying the function hasn't been called before, it allows the function to execute and toggles the flag. If the flag is true (`True`), indicating the function has been called before, it simply returns, preventing the function execution. -Zeta utils library, in this case, contains a single function `once`, a decorator which ensures that the function it wraps is only called once. This utility function can be extremely useful in situations where duplicate function calls could lead to unnecessary redundancy or inefficiencies. +## Function Definition -## Detailed Function Documentation - -### once - -#### Signature - -```python -@once -def FUNCTION_NAME(ARGS) -``` - -#### Description - -A decorator function that ensures the function it wraps is only called once. This prevents duplicate function calls, thereby improving efficiency in situations where duplicate function calls could be redundant or detrimental to the performance of your program. - -#### Parameters - -| Name | Type | Description | -|------|----------|---------------| -| fn | function | The function to be wrapped and executed only once.| - -#### Returns - -The wrapped function that will run only once. - - -#### Source code +Let's consider the structure and details of the `once` function. It accepts a single argument, `fn`, which is the function to be wrapped. The function is returned as the output after being wrapped in a closure that maintains the `called` flag. ```python def once(fn): """ Decorator to ensure the function is only called once. - + Args: - fn (function): The function to wrap. + fn (function): The function to wrap. Returns: function: The wrapped function. @@ -55,37 +26,69 @@ def once(fn): called = False @wraps(fn) - def inner(*args, **kwargs): + def inner(x): nonlocal called - if not called: - called = True - return fn(*args, **kwargs) - + if called: + return + called = True + return fn(x) + return inner ``` -## Usage Guides +| Argument | Type | Description | +| --- | --- | --- | +| fn | function | The function to wrap. | + +## Functionality and Usage -### Example 1: Basic Usage +The `once` function ensures that the annotated function `fn` is executed only once - the first time it's called. For all subsequent calls, it immediately returns without executing the function `fn`. The `once` decorator therefore is particularly useful in scenarios where a specific function should not or need not be executed more than once. -In this example, we will create a simple function that returns a greeting. We will use the `once` decorator to ensure the function only prints the greeting once, even if the function is called multiple times. +### Example - Initial Setup Function + +Let's demonstrate the `once` function with a setup function, `setup()`. This could represent any kind of initialization logic that should only be run once: ```python -from functools import wraps -# Include your once function in here. +@once +def setup(): + print('Setting up...') -def once(fn): - called = False +# The setup() function is invoked twice. +setup() # Prints: 'Setting up...' +setup() # Doesn't print anything. +``` - @wraps(fn) - def inner(*args, **kwargs): - nonlocal called - if not called: - called = True - return fn(*args, **kwargs) +### Example - Heavy Computation Function - return inner +Here is an example where a computation should only be executed once: +```python @once -def greet(name): - return f"Hello {name +def heavy_computation(): + print('Doing heavy computation...') + # long running computation + +# The heavy_computation() function is invoked twice. +heavy_computation() # Prints: 'Doing heavy computation...' +heavy_computation() # Doesn't print anything. +``` + +### Example - State Initialisation + +If you are dealing with a stateful class and need to initialize something only once, `once` decorator can come handy: + +```python +class MyClass: + @once + def initialize(self): + print('Initializing state...') + +# MyClass object is created, the initialize function is called twice. +obj = MyClass() +obj.initialize() # Prints: 'Initializing state...' +obj.initialize() # Doesn't print anything. +``` + +In each of the above examples, similarly, the decorated function `setup()`, `heavy_computation()` and `initialize()` were called multiple times but executed only once. + +The use of `once` decorator provides a convenient way to ensure specific functions only run their core execution once, while allowing them to be flexibly called without caution multiple times elsewhere in code or scripts. This helps maintain cleaner and more predictable code especially when dealing with initializations and one-time setups. diff --git a/docs/zeta/utils/pad_at_dim.md b/docs/zeta/utils/pad_at_dim.md index d58ea2e3..24c8611a 100644 --- a/docs/zeta/utils/pad_at_dim.md +++ b/docs/zeta/utils/pad_at_dim.md @@ -1,11 +1,17 @@ # pad_at_dim -# Zeta Utils Library Documentation +# Module Name: pad_at_dim -## Module Function: pad_at_dim -***pad_at_dim*** is a utility function in the Zeta Utilities Library for padding tensors at a specified dimension to match the desired dimensions. This function builds on Pytorch's built-in function ***F.pad()*** providing additional configurability to specify the dimension at which padding is done. The provided padding is appended at the end of the input tensor's specified dimension. +## Introduction + +The `pad_at_dim` function is a utility function used to apply padding to a tensor at a specified dimension. Padding is added to the edges of an input tensor and it's commonly used in convolutional neural networks where the input is often padded to control the output size of feature maps. This utility function is very useful to PyTorch users as it allows to add padding flexibly at any dimension, specified by the user. + +The tensor padding is particularly useful in the context of image processing where it is often needed to apply the convolution kernel to bordering pixels of an input image. In the context of natural language processing tasks, padding is used when batching together sequences of different lengths, and can be used to ensure that all sequences in a batch are the same length. + +## Function Definition + +The function `pad_at_dim` has the following signature: -## Function Signature ```python def pad_at_dim(t, pad, dim=-1, value=0.0): dims_from_right = (-dim - 1) if dim < 0 else (t.ndim - dim - 1) @@ -13,32 +19,82 @@ def pad_at_dim(t, pad, dim=-1, value=0.0): return F.pad(t, (*zeros, *pad), value=value) ``` -## Important Parameters Definition -| Parameters | Type | Description | -| :----------- | :----- | :----------------------------------------------------------------------------------------------------------------- | -| t | Tensor | Input tensor in the PyTorch format. | -| pad | Tuple | Padding size for each side of the tensor's dimension. Padding format is (pad_left, pad_right). | -| dim | Integer| The dimension at which padding is performed. By default, it's -1, which indicates the last dimension. | -| value | Float | The padding value. Default is 0.0. | +## Parameters -## Functionality and Usage +| Parameter | Type | Description | Default value | +| --------- | --------- | ----------- | ------------- | +| t | torch.Tensor | Input tensor to which padding will be applied. | NA | +| pad | tuple | Number of values padded to the edges of each dimension, provided as a tuple in the format (padLeft, padRight) for each dimension. | NA | +| dim | int | Dimension at which padding will be added. Negative integer counts from the last dimension (-1 is the last dimension, -2 is the second last dimension, and so on). | -1 | +| value | float | Value for the padded elements. | 0.0 | -The ***pad_at_dim*** function performs padding operation on PyTorch tensors at the specified dimension using Pytorch's built-in ***F.pad*** function. It takes into account both positive and negative dimension indices. While positive indices perform the padding from the first dimension, negative indices do the padding starting from the last dimension. +## Return -Creating the zeros needed to fill the rest of the parameters of the PyTorch's F.pad function, the function internally calculates how many zeros are needed, given the dimension. +The function returns a tensor `t` padded at the specified `dim` with the given `value`. The padding size is specified by the `pad` parameter. -Subsequently, it calls F.pad function using the calculated zeros, the desired padding and value to add padding in the given tensor at the specified dimension. +## Detailed Explanation & Usage -## Function Examples +The `pad_at_dim` function uses the PyTorch `nn.functional.pad()` method to add padding to the tensor. It starts by determining the number of dimensions from the right of the tensor for which padding will be applied, stored in `dims_from_right`. It then creates the `zeros` tuple which has the number of zeros corresponding to the decided padding. Finally, the `pad` and `zeros` tuples are concatenated and used as input to the `nn.functional.pad()` method along with the original tensor and padding value. -Let's dive in into few examples to understand how the module can be used. +Dimensions in PyTorch are 0-index based, therefore 0 refers to the first dimension and -1 refers to the last dimension. When the padding size (pad) is a tuple, the padding applied is symmetric for each dimension. If pad is an int, the same amount of padding is applied at both ends of the tensor. -### Example 1: Padding the last dimension +The value parameter is used to fill in the new elements created due to padding operation. + +### Usage Examples + +Let's look at some examples demonstrating the `pad_at_dim` function: + +1. Basic usage: ```python import torch from torch.nn import functional as F -from zeta.utils import pad_at_dim -# Create a tensor -t = torch.tensor([[7, 8, +# Define a tensor +t = torch.tensor([[1, 2, 3], [4, 5, 6]]) + +# Call pad_at_dim +result = pad_at_dim(t, pad=(1, 1), dim=-1, value=0) + +print(result) +``` + +Output: +``` +tensor([[0, 1, 2, 3, 0], + [0, 4, 5, 6, 0]]) +``` + +2. Padding the first dimension: + +```python +result = pad_at_dim(t, pad=(2, 2), dim=0, value=-1) +print(result) +``` + +Output: +``` +tensor([[-1, -1, -1], + [-1, -1, -1], + [ 1, 2, 3], + [ 4, 5, 6], + [-1, -1, -1], + [-1, -1, -1]]) +``` + +3. Padding the second dimension: + +```python +result = pad_at_dim(t, pad=(3, 3), dim=1, value=-2) +print(result) +``` + +Output: +``` +tensor([[-2, -2, -2, 1, 2, 3, -2, -2, -2], + [-2, -2, -2, 4, 5, 6, -2, -2, -2]]) +``` + +## Additional Tips + +1. Use this utility function diff --git a/docs/zeta/utils/pick_and_pop.md b/docs/zeta/utils/pick_and_pop.md index 73174296..6be5736f 100644 --- a/docs/zeta/utils/pick_and_pop.md +++ b/docs/zeta/utils/pick_and_pop.md @@ -1,59 +1,82 @@ # pick_and_pop -# Documentation for `pick_and_pop` function in `zeta.utils` +# Module/Function Name: pick_and_pop -## Introduction +## Overview -The `pick_and_pop` function in the `zeta.utils` library is a handy utility function for dictionary manipulation. It provides an efficient way to extract specific key-value pairs from a Python dictionary and also simultaneously remove these key-value pairs from the original dictionary. This operation is beneficial when needing a subset of data from a large dictionary for further processing while removing it from the parent dictionary for memory efficiency. +The `pick_and_pop` function is a utility function that is specifically aimed at manipulating dictionaries. It removes specified keys from a given dictionary and then returns a new dictionary that contains the removed key-value pairs. This function can be particularly useful when you need to prune a dictionary to a simpler version that contains only desired keys-value pairs. -## Class or Function Definition +The `pick_and_pop` function is defined in the Zeta utility module (`zeta.utils`). A dictionary in Python is an unordered collection of data in a key-value pair format. Dictionaries can have keys and values of any datatype, which makes dictionary highly valuable and versatile data structures for handling and organizing data. -Function signature: +## Function Definition ```python -pick_and_pop(keys: list, d: dict) -> dict +def pick_and_pop(keys, d): + """ + Remove and return values from a dictionary based on provided keys. + + Args: + keys (list): List of keys to remove from the dictionary. + d (dict): The dictionary to pick from. + + Returns: + dict: A dictionary with the specified keys and their values. + """ + values = list(map(lambda key: d.pop(key), keys)) + return dict(zip(keys, values)) ``` -## Parameters +## Parameters and Description -The `pick_and_pop` function takes two parameters. +| Parameter | Type | Default | Description | +| --- | --- | --- | --- | +| `keys` | list | N/A | List of keys from the dictionary to be removed and returned as a new dictionary. | +| `d` | dict | N/A | The original dictionary where keys are picked and popped. | -|Parameter|Type|Description| -|---------|----|-----------| -|`keys`|list|List of keys to remove from the dictionary| -|`d`|dict|The dictionary to pick from| +The function pick_and_pop accepts two arguments, a list of keys and a dictionary. The keys are provided in a list, and are the ones that the user wishes to remove from the dictionary. This function returns a new dictionary composed of these key-value pairs. -## Returns +## Functionality and Usage -The `pick_and_pop` function returns a new dictionary containing the key value pairs specified in the `keys` list parameter. +The `pick_and_pop` function works by iterating over the list of keys and pops each key from the dictionary. The popped value is then appended to a list of values. After all the keys have been looped over, a new dictionary is created and returned by zipping together the list of keys and the list of values. -## Functionality and Usage +The return type of this function is a dictionary. -The `pick_and_pop` function makes use of the `pop` method native to Python dictionaries. The `pop` method is specified in a lambda function which is then mapped onto the list of `keys`. This effectively extracts the value associated to each key in `keys` from dictionary `d` and also removes this key-value pair from `d`. +### Usage Example 1 +```python +d = {"name": "John", "age": 30, "city": "New York"} +keys = ["name", "city"] -A new dictionary, containing the key-value pairs specified in `keys`, is then created and returned using the built-in `dict` function in combination with the `zip` function to pair each key in `keys` with its corresponding value. +result = pick_and_pop(keys, d) +print(result) # Returns: {'name': 'John', 'city': 'New York'} +``` -## Usage Examples +### Usage Example 2 +```python +d = {1: "apple", 2: "banana", 3: "cherry", 4: "date"} +keys = [2, 4] -### Example 1: Basic Usage +result = pick_and_pop(keys, d) +print(result) # Returns: {2: 'banana', 4: 'date'} +``` +### Usage Example 3 ```python -# import the function -from zeta.utils import pick_and_pop +d = {"a": [1, 2, 3], "b": [4, 5, 6], "c": [7, 8, 9]} +keys = ["a", "c"] + +result = pick_and_pop(keys, d) +print(result) # Returns: {'a': [1, 2, 3], 'c': [7, 8, 9]} +``` + +## Additional Tips -# initialize a dictionary -d = {'a': 1, 'b': 2, 'c': 3, 'd': 4} -print('Original d:', d) +It's important to understand that the `pick_and_pop` function directly alters the original dictionary `d` by removing the keys from it. If you want to retain the data in the original dictionary, you should create a copy of the original dictionary and pass the copy to the `pick_and_pop` function. -# specify the keys we want to pop from the dictionary -keys = ['a', 'c'] +## References -# apply the function -res = pick_and_pop(keys, d) -print('Result:', res) -print('Modified d:', d) +- Python official documentaion: https://docs.python.org/3/tutorial/datastructures.html#dictionaries +- Python Glossary - dictionary: https://docs.python.org/3/glossary.html#term-dictionary +- Python map() function: https://docs.python.org/3/library/functions.html#map +- Python zip() function: https://docs.python.org/3/library/functions.html#zip -# Output: -# Original d: {'a': 1, 'b': 2, 'c': 3, 'd': 4} -# Result: {'a': 1, 'c': 3} -# Modified +After understanding this function, you will have a good knowledge of manipulating dictionaries in Python. This utility function simplifies the task of extracting certain key-value pairs from a dictionary into a new dictionary, which can be very useful in data wrangling and preprocessing tasks. diff --git a/docs/zeta/utils/print_cuda_memory_usage.md b/docs/zeta/utils/print_cuda_memory_usage.md index 310a17bb..9a95155f 100644 --- a/docs/zeta/utils/print_cuda_memory_usage.md +++ b/docs/zeta/utils/print_cuda_memory_usage.md @@ -1,59 +1,87 @@ # print_cuda_memory_usage -# Module Name: zeta.utils +# `zeta.utils`: print_cuda_memory_usage -The `zeta.utils` module hosts a utility function `print_cuda_memory_usage()`, a Python context manager function to print the amount of CUDA memory that a specific block of code uses. This function is particularly useful in deep learning applications, where memory management is crucial due to the high usage of memory by models and datasets. +# Purpose and Functionality -The `print_cuda_memory_usage()` function uses PyTorch to perform memory operations, one of the popular open-source deep learning platforms, and it requires an NVIDIA GPU and CUDA toolkit already installed, because CUDA operations require access to a CUDA-enabled GPU. +This is a Python context manager function designed for tracking and reporting CUDA (Compute Unified Device Architecture) memory usage during GPU-accelerated operations in PyTorch. CUDA is a parallel computing platform and application programming interface (API) model created by NVIDIA which allows software developers to use a CUDA-enabled graphics processing unit (GPU) for general-purpose processing. -# Function Definition: print_cuda_memory_usage() +`print_cuda_memory_usage` monitors the GPU memory consumption before and after the context block of code that it wraps. Upon exit of the context block, it calculates the change in memory usage and outputs it in gigabytes. + +# Function Definition -## Function Signature ```python +from contextlib import contextmanager +import torch + @contextmanager def print_cuda_memory_usage(): + initial_memory = torch.cuda.memory_allocated() + try: + yield + finally: + memory_usage = torch.cuda.memory_allocated() - initial_memory + memory_usage_gb = memory_usage / (1024**3) + print(f"CUDA memory usage: {memory_usage_gb:.2f} GB") ``` -## Function Description +The `@contextmanager` decorator transforms `print_cuda_memory_usage` into a factory function that returns a context manager. When entering the context block, it records the starting GPU memory usage. It then yields control to the contents of the context block. Upon exiting the block, it records the final GPU memory usage, calculates the difference, and prints it to the standard output. -This function is a context manager function that prints the CUDA memory usage of the code block that calls this function. The memory usage is calculated by subtracting the amount of CUDA memory allocated at the end of the code block from the amount of CUDA memory allocated immediately before executing the code block. The resultant memory usage is then converted from bytes to gigabytes and printed to the console. +# Arguments -## Function Parameters and Return Values +`print_cuda_memory_usage` doesn't take any arguments. -Since `print_cuda_memory_usage()` is a context manager function, it does not take parameters nor return any values. It is intended to be used with the `with` statement in Python. +| Argument | Type | Description | +| -------- | ---- | ----------- | +| None | None | None | -| Parameter Name | Type | Description | Default Value | -|:--------------:|:----:|:-----------:|:-------------:| -| - | - | - | - | +# Usage -| Return Name | Type | Description | -|:-----------:|:----:|:------------:| -| - | - | - | +Here are some examples on how `print_cuda_memory_usage` can be used: -## Example Code +## Example 1: Basic Usage -The following are example codes that show how to use the function: +```python +x = torch.randn((10000, 10000), device='cuda') + +with print_cuda_memory_usage(): + y = x @ x.t() # Large matrix multiplication +``` -### Example: Memory usage of a small tensor +In this example, a large tensor `x` is allocated on the GPU, and then a large matrix multiplication is performed inside the `print_cuda_memory_usage` context. The increase in GPU memory usage resulting from this operation will be printed. -We first import the necessary libraries: +## Example 2: Exception Handling ```python -import torch -from zeta.utils import print_cuda_memory_usage +x = torch.randn((10000, 10000), device='cuda') + +try: + with print_cuda_memory_usage(): + y = x @ x.t() # Large matrix multiplication + raise Exception("Some Exception") +except Exception as e: + print(f"Caught an exception: {e}") ``` -Next, we use the `print_cuda_memory_usage()` function to get the CUDA memory usage of creating a small tensor with PyTorch. +In this example, an exception is raised inside the `print_cuda_memory_usage` context. Regardless of the exception, `print_cuda_memory_usage` will still correctly compute and print the CUDA memory usage before the exception is propagated. + +## Example 3: Nesting Usage ```python +x = torch.randn((10000, 10000), device='cuda') + with print_cuda_memory_usage(): - a = torch.tensor([1.]).cuda() + y = x @ x.t() # Large matrix multiplication + with print_cuda_memory_usage(): + z = y @ y.t() # Even larger matrix multiplication ``` -### Example: Memory usage of a large tensor +In this example, `print_cuda_memory_usage` contexts are nested, allowing you to separately track the GPU memory usage of different parts of your code. -In this example, we again use the `print_cuda_memory_usage()` function to observe the CUDA memory usage but with a larger tensor with PyTorch. +# Notes -```python -with print_cuda_memory_usage(): - a = torch.rand(1024 +The `print_cuda_memory_usage` function requires PyTorch to be run with CUDA enabled and a CUDA-enabled GPU to be available. If either of these conditions are not met, `torch.cuda.memory_allocated()` will raise a `RuntimeError` and the function will not work as intended. + +Also, `print_cuda_memory_usage` only tracks the GPU memory that is allocated and managed by PyTorch, it doesn't account for any memory directly allocated by CUDA via methods outside of PyTorch's control. + +Finally, `print_cuda_memory_usage` gives an indication of the additional memory used by a specific block of code. However, the exact details of memory management on the GPU can be complex, depending on multiple factors such as how PyTorch allocates and caches memory, the specific GPU hardware, the CUDA version, and other aspects of the system configuration. It also does not account for the memory used by non-PyTorch CUDA libraries or other processes sharing the same GPU. diff --git a/docs/zeta/utils/print_main.md b/docs/zeta/utils/print_main.md index 0728b71c..da7d195d 100644 --- a/docs/zeta/utils/print_main.md +++ b/docs/zeta/utils/print_main.md @@ -1,67 +1,71 @@ # print_main -# Zeta Utils Library - print_main function documentation - -## Overview -Welcome to the documentation of the `print_main` function provided in the `zeta.utils` library. This function serves a purpose in a distributed data setup where multiple processes are running concurrently. Often in such setups, avoiding duplication of logs or messages is desirable, and this function helps to achieve it by ensuring that specific messages get printed only on the main process. - -This utility function can be incredibly useful when debugging or logging information in a distributed setting, providing cleaner logs and easier debugging. This documentation will guide you on how to use the `print_main` function, detailing its arguments, usages, and examples. +# Module Name: zeta.utils.print_main ## Function Definition +class zeta.utils.print_main(msg): ```python -def print_main(msg): - """Print the message only on the main process. +Prints a message only on the main process. - Args: - msg (_type_): _description_ - """ - if dist.is_available(): - if dist.get_rank() == 0: - print(msg) - else: - print(msg) +Parameters: +- msg (str): The message to be printed. ``` -## Arguments -| Parameter | Type | Description | -| :--- | :--- | :--- | -| `msg` | string | The message that should be printed by the main process | - - -The `print_main` function accepts a single argument: +## Functionality & Purpose -- `msg`: (string) This is the message to be printed to the console. The message should be of the type `string`. +This function serves to print messages selectively on the main process in a distributed setting. Distributed settings often clone multiple processes across different CPU cores or different machines. This means that each of these processes will have a predefined rank, where the main (or master) process usually has the rank 0. -## Usage +When dealing with distributed settings, it's quite common to observe duplicate console output from each process, which can clutter the console and make interpretability harder. This function helps to mitigate that problem by enabling messaging only from the main process, thus maintaining a clean and streamlined console output. -The `print_main` function is quite straightforward to use. Here, we detail how to use this function in three different ways: - -### 1. Basic Functionality - -This is the simplest and most basic example demonstrating the usage of the `print_main` function. +## Usage and Examples: +### Importing the Necessary Libraries +This function would typically be used within a project that utilises PyTorch's distributed utilities for parallel and distributed computation. So let's begin with the necessary imports: ```python -import torch.distributed as dist -from zeta.utils import print_main +from torch import distributed as dist +import zeta.utils +``` -# Within your main function -print_main("This is a test message.") +### Example 1: Printing without Distributed Setting + In an environment where distributed computing is not being used or available, messages will be printed normally. +```python +zeta.utils.print_main("Hello World!") +``` +Console Output: +``` +Hello World! ``` -### 2. Testing with Various Messages +### Example 2: Printing with Distributed Setting + In a distributed computing environment, the message will print only from the main process: + +```python +# Assuming we are in a distributed environment with several processes running this code +if dist.is_available(): + zeta.utils.print_main("Hello from main process!") +``` +Console Output: +``` +# Note: This message will only be printed once, since only the main process (rank 0) gets to execute the print function. +Hello from main process! +``` -In the following example, we tweak the earlier sample code and add a loop to send different messages. In a real-life implementation, you would replace this with your application-specific messages. +Remember that in this scenario, if the current process is not the main process (i.e., its rank is not 0), the function simply won't do anything. This is beneficial to avoid repetitively printing the same message in a distributed setting. +Remember to ensure your distributed environment is properly initialized before using distributed functionalities. + +### Example 3: Handling both Non-Distributed and Distributed Settings + This function is designed to handle both non-distributed and distributed settings, as shown below: + ```python -import torch.distributed as dist -from zeta.utils import print_main +# main function +def main(): + # distributing tasks between processes. + print_main("This message is from main process only.") -# Within your main function -for i in range(5): - print_main(f"This is test message number: {i}") +if __name__ == "__main__": + main() ``` -### 3. Using the Function in a Multithreaded Environment - -Assume you have a multithreaded setup where multiple processes are running concurrently, and you want to print some +Here, `dist.is_available()` checks if distributed processing is available. If so, it verifies if the rank is 0 (i.e., checks if the process is the main one). If both conditions are true, it goes ahead and prints the message. If distributed processing isn't available, it directly prints the message, effectively handling both scenarios. diff --git a/docs/zeta/utils/print_num_params.md b/docs/zeta/utils/print_num_params.md index 5a04e0c9..78a5f713 100644 --- a/docs/zeta/utils/print_num_params.md +++ b/docs/zeta/utils/print_num_params.md @@ -1,60 +1,87 @@ # print_num_params -# Module Name: utils.print_num_params +# Zeta Utils Documentation -## Function: -```python +## Class: print_num_params + +Functionality: +The function 'print_num_params' prints the total number of trainable parameters of a given model. Model parameters are the attributes of the model that the algorithm modifies to enable the model to improve and adjust to the data better. Therefore, this function is important in determining the complexity of the model. More parameters in a model mean more complexity. + +Typically higher parameter models have more training data and are better equipped to represent complex data patterns. However, having too many parameters can also lead to overfitting: the model might become too well adjusted to the training data and perform poorly on unseen data (high variance). + +This function also checks if the PyTorch distributed package 'dist' is available and, if it is, prints the number of parameters on rank '0'. Rank in PyTorch's distributed package specifies the process rank (ID) for each process group. In a distributed environment (multiple GPUs), the function print_num_params will print the number of parameters from one GPU identified as rank '0'. + +Here is the code definition: + +```Python def print_num_params(model): -``` -This function calculates the total number of trainable parameters in a PyTorch model and prints this number. This is a utility function that can be used to monitor the complexity of the model. + """ + Function to print out the number of trainable parameters in a PyTorch Model Model. -## Arguments: + Args: + model (:obj: `torch.nn.Module`): The PyTorch Model. -| Argument | Type | Description | -| --- | --- | --- | -| model | `torch.nn.Module` | The model for which you want to count the number of parameters. | + """ + n_params = sum(p.numel() for p in model.parameters() if p.requires_grad) + + if dist.is_available(): + if dist.get_rank() == 0: + print(f"Number of parameters in model: {n_params}") + else: + print(f"Number of parameters in model: {n_params}") +``` +Parameters: -## Function Body: +| Parameter | Data Type | Description | Default Value | +| :--- | :--- | :--- | :--- | +| model | torch.nn.Module | The PyTorch model for which the number of parameters is to be calculated and printed. | - | -This function loops over all the parameters of the model that require gradient computation (i.e., trainable parameters), counts their number (numel), and sums them up to get the total count of parameters. +Other Functions Used: -In a distributed training setup, the function checks whether the distributed communication package (`dist`) is available. If it is, only the specified process (the one with rank 0), prints the number of parameters. If the distributed communication package is not available (which means it's not a distributed setup), the function just prints the number of parameters in the model. +- model.parameters(): Retrieves the model's parameters. +- p.requires_grad: Checks if the parameters require gradients (is trainable). +- p.numel(): Returns the total number of elements in the input tensor. +- dist.is_available(): Determines if PyTorch distributed is available. +- dist.get_rank(): Retrieves the rank in the current distributed group. -## Usage Example: +Here is an example of how to use this function. -```python -import torch +```Python +import torch import torch.nn as nn +from torch import dist from zeta.utils import print_num_params -# Define a simple model -class Model(nn.Module): - def __init__(self): - super(Model, self).__init__() - self.fc = nn.Linear(4, 2) +model = nn.Linear(10,2) # A simple linear model - def forward(self, x): - return self.fc(x) - -# Initialize the model -model = Model() -# Print the number of parameters in the model print_num_params(model) ``` -In the above example, the Model has a single linear layer with an input feature size of 4 and an output feature size of 2. So, the number of parameters in this model will be `(4 * 2) + 2 = 10`, where 4 and 2 are weight parameters for each input and output features and added two because of the bias parameters for the outputs. +Please note that if you are using this function in a distributed environment, you must first initialize your distributed environment correctly. -Running the `print_num_params` on this `model` will output: +```Python +import torch +import torch.nn as nn +from torch import dist +from zeta.utils import print_num_params -``` -Number of parameters in model: 10 +# initialize your distributed environment +dist.init_process_group(backend='nccl') + +model = nn.Linear(10,2) # A simple linear model + +print_num_params(model) ``` -## Notes: +By using the function 'print_num_params', you can print out the total number of trainable parameters in your PyTorch models, which can have a significant impact on your model's complexity and its eventual performance. -1. This function counts only the parameters that are trainable i.e., require gradient computation. If your model has layers or parameters with `requires_grad` set to False, those will not be counted. +Please note that this function works solely in a PyTorch environment and may not work with models built from other machine learning packages like Keras, TensorFlow, etc. It is also reliant on the dist package of PyTorch for distributed computations. This means you need to initialize your distributed environment if you are working with multiple GPUs. -2. In case of distributed training, `dist.is_available()` is used to determine whether the distributed communication package is available. +Also, if you have specified some of the parameters of your model as non-trainable (by setting `requires_grad = False`), this function will not account for them. -3. If the +## References & Resources +1. [Understanding Model Complexity](https://towardsdatascience.com/understanding-model-complexity-in-machine-learning-c5da3cc472f1) +2. [torch.numel()](https://pytorch.org/docs/stable/generated/torch.numel.html) +3. [torch.nn.Module](https://pytorch.org/docs/stable/generated/torch.nn.Module.html) +4. [torch.distributed](https://pytorch.org/tutorials/intermediate/ddp_tutorial.html) diff --git a/docs/zeta/utils/save_load.md b/docs/zeta/utils/save_load.md index 49964184..0af7fff3 100644 --- a/docs/zeta/utils/save_load.md +++ b/docs/zeta/utils/save_load.md @@ -1,21 +1,30 @@ # save_load -# zeta.utils.save_load +# zeta.utils.save_load -## Description +## Overview -The `save_load` function from the `zeta.utils` library defines a base decorator for both save and load methods for PyTorch's torch.nn.Module subclasses. This allows saving the state of a given module and configuration, and subsequently loading it back. This can be specifically useful when we want to store a trained model during the training process or at the end of it, and later resume training from where we left or use the trained model for inference. +The `save_load` decorator in the `zeta.utils` module is a Python decorator designed around PyTorch's `torch.nn.Module` subclasses. Its main functionality is to automate and streamline the saving and loading of trained models and their configurations, reducing the need for repeated code and increasing code readability and maintainability. -The decorator wraps the class initialization, saving, and loading methods. Additionally, optionally, it allows hook functions to be defined and executed right before saving and loading the model. +Key to its purpose is the ability to handle the model's state dictionary, training configurations, and PyTorch version. The decorator enhances the training workflow by allowing models’ states and configurations to be easily saved and loaded efficiently with built-in version compatibility checks and hooks for code execution pre and post-saving/loading. -## Function Declaration +## Core Functionality -```python +### save_load Decorator + +Considered a Base decorator for save and load methods for `torch.nn.Module` subclasses. In essence, a decorator is a higher-order function that can drape functionality over other functions or classes without changing their source code, which is exactly what the `save_load` decorator is. + +The `save_load` decorator modifies `torch.nn.Module` subclasses by adding save, load and an initialization & load methods to the subclass. This allows for seamless saving and loading of the subclass instances states and configurations. + +## Function / Method definition + +``` +@beartype def save_load( - save_method_name: str = "save", - load_method_name: str = "load", - config_instance_var_name: str = "_config", - init_and_load_classmethod_name: str = "init_and_load", + save_method_name="save", + load_method_name="load", + config_instance_var_name="_config", + init_and_load_classmethod_name="init_and_load", version: Optional[str] = None, pre_save_hook: Optional[Callable[[Module], None]] = None, post_load_hook: Optional[Callable[[Module], None]] = None, @@ -23,18 +32,55 @@ def save_load( partial_load: Optional[bool] = False, *args, **kwargs, -): +):... +``` + +The function takes in several arguments: + +| Parameter | Type | Default | Description | +|-------------------------|----------------------------------|-----------------------|--------------------------------------------------------------------------------------------------------| +| `save_method_name` | `str` | `"save"` | The name used to set the save method for the instance. | +| `load_method_name` | `str` | `"load"` | The name used to set the load method for the instance. | +| `config_instance_var_name`| `str` | `"_config"` | The name used to set the instance's configuration variable. | +| `init_and_load_classmethod_name`| `str` | `"init_and_load"` | The name used to set the class's initialization and loading method. | +| `version` | `Optional[str]` | `None` | Version of the torch module. Used for checking compatibility when loading. | +| `pre_save_hook` | `Optional[Callable[[Module], None]]`| `None` | Callback function before saving. Useful for final operations before saving states and configurations. | +| `post_load_hook` | `Optional[Callable[[Module], None]]`| `None` | Callback function after loading. Ideal for any additional operations after loading states and configurations. | +| `compress` | `Optional[bool]` | `False` | If set to `True`, the saved model checkpoints will be compressed. | +| `partial_load` | `Optional[bool]` | `False` | If set to `True`, the saved model checkpoint will be partially loaded to existing models. | +| `*args` & `**kwargs` | `Any` | | Additional arguments for the decorator. | + + +The *save_load* decorator modifies the way a PyTorch model is initialized, saved, and loaded. It does this by wrapping new init, save, load, and init_and_load methods around the decorated class. + +## Usage Examples + +Here is a basic usage example of the `save_load` decorator: + +### Example 1: Using default parameters on a PyTorch Model +```python +from zeta.utils import save_load +from torch.nn import Module, Linear + +@save_load() +class MyModel(Module): + + def __init__(self, input_dim, output_dim): + super(MyModel, self).__init__() + self.layer = Linear(input_dim, output_dim) + + def forward(self, x): + return self.layer(x) + +# Initialize your model +model = MyModel(32, 10) + +# Save your model +model.save('model.pt') + +# Load your model +loaded_model = MyModel.load('model.pt') ``` -## Parameters - -| Parameter | Type | Description | Default | -| --- | --- | --- | --- | -| `save_method_name` | str | Name of the save method. | `"save"` | -| `load_method_name` | str | Name of the load method. | `"load"` | -| `config_instance_var_name` | str | Name of the instance variable to store the configuration. | `"_config"` | -| `init_and_load_classmethod_name` | str | Name of the classmethod that initializes and loads the model. | `init_and_load` | -| `version` |str(optional) | Version of the model. | `None` | -| `pre_save_hook` | Callable (optional) | This function is called before the model is saved. | `None` | -| `post_load_hook` | Callable (optional) | This function is called after the model is loaded | `None` | -| `compress` | bool (optional) | If True, uses the new zipfile-based TorchScript serialization format. | `False` | -| `partial_load` | bool(optional) | If + +### Example 2: Using the `save_load` with non-default arguments +In this example, we are going to add `pre_save_hook` and `post_load_hook` to demonstrate their usage. These functions will be called just before saving and diff --git a/docs/zeta/utils/save_memory_snapshot.md b/docs/zeta/utils/save_memory_snapshot.md index b9f15507..dc49a6d3 100644 --- a/docs/zeta/utils/save_memory_snapshot.md +++ b/docs/zeta/utils/save_memory_snapshot.md @@ -1,51 +1,114 @@ # save_memory_snapshot -# `zeta.utils` +# Module Name: save_memory_snapshot -Welcome to the documentation for `zeta.utils`, a module containing utility functions to aid in managing memory snapshots. This documentation will be divided into sections explaining what is done, the class components, its uses, parameters involved and usage examples. The latter will hold code snippets demonstrating zeta's functionalities. +The `save_memory_snapshot` function within PyTorch is a context manager that allows developers to save memory usage snapshots from their PyTorch model to a specified file path. This is particularly useful for tracking and analyzing memory utilization during code execution, facilitating optimized resource management. -## Table of Contents - -- [Introduction](#Introduction) -- [Function Definition](#Function-Definition) -- [Implementation](#Implementation) -- [Example Usage](#Example-Usage) +Function Details: +```python +@contextmanager +def save_memory_snapshot(file_path: Path): + """Save a memory snapshot information to a folder + Usage: + with save_memory_snapshot(file_path): + # code to profile + + Args: + file_path: The path to the folder to save the snapshot to + will create the folder if it doesn't exist + """ + file_path.mkdir(parents=True, exist_ok=True) + torch.cuda.memory._record_memory_history() + try: + yield + finally: + s = torch.cuda.memory._snapshot() + with open(f"{file_path}/snapshot.pickle", "wb") as f: + dump(s, f) + with open(f"{file_path}/trace_plot.html", "w") as f: + f.write(torch.cuda._memory_viz.trace_plot(s)) +``` +Here is a description for the single argument, `file_path`: +| Parameter | Type | Description | +|-----------|------|-------------| +| file_path | pathlib.Path | File path to a folder where the snapshots will be saved. The function will create the folder if it does not exist. | -## Introduction +**Functionality and Usage** -Memory management becomes crucial when running computations on graphics processing units (GPUs). The `zeta.utils` module provides a context manager (`save_memory_snapshot`) to profile code execution, record the GPU memory usage and save the memory snapshot information to the specified file path. +After creating the output directory (if it does not exist), the function initiates recording the GPU's memory usage history via torch.cuda.memory._record_memory_history(). -The `save_memory_snapshot` function uses PyTorch functions for memory profiling. PyTorch functions (`torch.cuda.memory._record_memory_history()`, `torch.cuda.memory._snapshot()`) provided here are for internal use and not part of the public API; hence, you may observe variation in behavior between different PyTorch versions. +Any code executed within the context of the `save_memory_snapshot` function will be profiled, and memory usage snapshots during its execution will be stored. -## Function Definition +Upon completion of the code block within the context, a snapshot of the memory history at that point in time is captured using `torch.cuda.memory._snapshot()`. This snapshot is then saved in pickle format (`snapshot.pickle`), and a HTML file (`trace_plot.html`) is generated, displaying a trace plot for the memory usage. -The function `save_memory_snapshot` implemented in the module is defined as follows: +The execution flow control is then returned to the code following the context block, ensuring any code thereafter is not profiled. +**How to Use** ```python -@contextmanager -def save_memory_snapshot(file_path: Path): -``` +from pathlib import Path +from zeta.utils import save_memory_snapshot +import torch -### Parameters +file_path = Path('my_folder') -| Parameters | Data Type | Description | -| ------ | ------ | ----------- | -| file_path | pathlib.Path | The path to the folder to save the snapshot to. The function will create the folder if it doesn't exist. +# code to profile +model = torch.nn.Linear(10, 10) +input_tensor = torch.randn(10, 10) -## Implementation +with save_memory_snapshot(file_path): + output = model(input_tensor) +``` +The provided file path 'my_folder' is where the snapshots will be saved. After this code block executed, the snapshot of the memory usage by the Linear layer applied on input_tensor will be saved to 'my_folder' in both 'snapshot.pickle' file and 'trace_plot.html' file. -The `save_memory_snapshot()` function creates a directory at the given file path, records a history of the GPU memory usage, captures a snapshot of the memory and saves both memory history and the snapshot to a file. +**Use Case 2** +```python +from pathlib import Path +from zeta.utils import save_memory_snapshot +import torch -Its workflow is as follows: +file_path = Path('gpu_usage') -1. The function receives `file_path` as an input parameter. -2. It creates a new directory at `file_path` if it doesn't exist already. -3. The function records the GPU memory usage history by calling `torch.cuda.memory._record_memory_history()`. -4. Code within the function's context is executed, during which the memory usage is tracked. -5. Upon completion of the execution of this context code, a snapshot of the current GPU memory status is taken (by calling `torch.cuda.memory._snapshot()`). -6. Both memory history and snapshot are saved to files at the specified location. +# code to profile +model = torch.nn.Sequential( + torch.nn.Conv2d(1,20,5), + torch.nn.ReLU(), + torch.nn.Conv2d(20,64,5), + torch.nn.ReLU() +) -The snippet of the implementation will be like this, +input_tensor = torch.randn(1, 1, 32, 32) +with save_memory_snapshot(file_path): + output = model(input_tensor) ``` +In this case, we are profiling a multi-layer Convolutional Neural Network (CNN). The memory snapshot will give insights about the intermediate usage and fluctuations occurring due to convolutions and the subsequent ReLU activation function. + +**Use Case 3** +```python +from pathlib import Path +from zeta.utils import save_memory_snapshot +import torch + +file_path = Path('training_memory') + +# establish a simple model +model = torch.nn.Linear(20, 10) +criterion = torch.nn.MSELoss() +optimizer = torch.optim.SGD(model.parameters(), lr=0.01) + +# dummy data +inputs = torch.randn(10, 20) +targets = torch.randn(10, 10) + +with save_memory_snapshot(file_path): + # a complete step of training + optimizer.zero_grad() + outputs = model(inputs) + loss = criterion(outputs, targets) + loss.backward() + optimizer.step() +``` +In this last example, we are profiling the memory usage during an entire step of model training, including forward pass, calculating loss, backward pass (backpropagation), and updating weights. + +For each example, two files hopefully providing useful insights on memory utilization should be generated in the specified 'file_path': `snapshot.pickle` and `trace_plot.html`. diff --git a/docs/zeta/utils/string_begins_with.md b/docs/zeta/utils/string_begins_with.md index 52eb064b..0a4b85f9 100644 --- a/docs/zeta/utils/string_begins_with.md +++ b/docs/zeta/utils/string_begins_with.md @@ -1,8 +1,22 @@ # string_begins_with -# Module/Function Name: string_begins_with +# Module Name: **zeta.utils** -```python +## Introduction + +The `zeta.utils` module is a handy utilities toolkit for Python, which includes a variety of useful functions for data processing and manipulation. A noteworthy function in this module is `string_begins_with`. It provides a quick and easy way to check if a string starts with a particular prefix. Though it seems a simple function, it is essential in many data preprocessing tasks such as checking the file paths, URLs, filenames, and prefix-based conditional data manipulation. + +## Functionality Overview + +The `string_begins_with` function takes two arguments: `prefix` and `str`. It checks if the given string `str` commences with the specified `prefix` and returns a boolean value accordingly. + +Now, let's explore the function syntax, parameters, and usage. + +## Function Definition and Parameters + +The `string_begins_with` is defined as follows: + +```Python def string_begins_with(prefix, str): """ Check if a string begins with a specific prefix. @@ -16,58 +30,46 @@ def string_begins_with(prefix, str): """ return str.startswith(prefix) ``` -## 1: Introduction - -The `string_begins_with` function is a simple utility function that checks whether a given string begins with a specified prefix. It is part of the `zeta.utils` library and represents a common application in string manipulation. -## 2: Parameters +Here's a breakdown of its parameters: -The function accepts the following arguments as required: +| Argument | Type | Description | +| -------- | ---- | ----------- | +| `prefix` | str | The prefix that we need to check for at the start of the string. | +| `str` | str | The string that we need to inspect. | -| Parameter | Type | Description | -| --------- | ---- | ----------- | -| prefix | str | The prefix to check for. | -| str | str | The string to check. | +## Functionality and Usage -## 3: Output +The primary usage of the `string_begins_with` function is to check if a string begins with a specific prefix. In Python, we have the `str.startswith()` function that performs this check. The `string_begins_with` function is essentially a wrapper around this built-in function providing a clear and expressive syntax. -The function returns a boolean value: +The function `string_begins_with` is a pure function in that it neither modifies the actual inputs nor does it rely on or alter any external state. It only produces the result based on the given inputs. -| Value | Type | Description | -| ----- | ---- | ----------- | -| output | bool | True if string starts with prefix, False otherwise. | +Here are a few usage instances: -## 4: Functionality and Usage - -The `string_begins_with` function is quite straightforward. It leverages Python's built-in `str.startswith` method to determine if the string `str` starts with the provided `prefix`. If so, the function returns `True`; otherwise, it returns `False`. - -You can use the `string_begins_with` function in any situation where you need to check whether a given string starts with a specific substring. This can be especially useful in text processing or data cleaning tasks, where you might need to categorize or filter strings based on their prefixes. - -Here are three examples showing how to use the `string_begins_with` function: +**Example 1** - Basic usage: +```Python +from zeta.utils import string_begins_with -**Example 1 Basic usage** +print(string_begins_with('data', 'database')) # Output: True +print(string_begins_with('data', 'base')) # Output: False +``` -```python +**Example 2** - Handling case-sensitivity: +```Python from zeta.utils import string_begins_with -str = "Hello, world" -prefix = "Hello" -result = string_begins_with(prefix, str) -print(result) # Output: True +print(string_begins_with('Data', 'database')) # Output: False +print(string_begins_with('Data', 'Database')) # Output: True ``` -**Example 2 When string does not start with prefix** - -```python +**Example 3** - Using with list comprehension for data preprocessing: +```Python from zeta.utils import string_begins_with -str = "Hello, world" -prefix = "Hi" -result = string_begins_with(prefix, str) -print(result) # Output: False -``` +data = ['apple', 'android', 'blackberry', 'windows', 'android_tv'] +android_data = [item for item in data if string_begins_with('android', item)] -**Example 3 With a numeric prefix** +print(android_data) # Output: ['android', 'android_tv'] +``` -```python -from zeta.utils import string +Cognizant of Python's inbuilt `startswith` function, `string_begins_with` complements it by providing a more meaningful syntax that enhances the code readability, especially for those new to Python programming. Through this documentation, we hope you'll be able to integrate `string_begins_with` into your code and simplify your string prefix checks. Happy Programming! diff --git a/docs/zeta/utils/top_a.md b/docs/zeta/utils/top_a.md index 643b092c..c9face06 100644 --- a/docs/zeta/utils/top_a.md +++ b/docs/zeta/utils/top_a.md @@ -1,49 +1,107 @@ # top_a -# zeta.utils.top_a() function Documentation +# Module: zeta.utils -`top_a` is a PyTorch function that adjusts the logits based on a specific threshold determined by a ratio and a power of the maximum probability. +## Function: top_a() -This function performs an operation known as top-k sampling or nucleus sampling in Natural Language Processing (NLP). It discards a portion of tokens with the lowest probabilities of being the next token prediction in language models, based on a certain limit. +## Description +This utility function, `top_a()`, is an implementation of a technique known as 'Top-K filtering' or 'Nucleus sampling'. +It involves softmaxing the logits and selecting a subset of it whose cumulative probability exceeds a certain threshold. It is particularly useful in natural language processing tasks to refine the output of language models. -In general, this function is used in certain applications of probabilistic models where you want to restrict the possibilities to a set of most probable outcomes. This function does this by creating a limit and then setting probabilities that fall under this limit to an effectively infinitesimal value. +The function takes a tensor of logits, applies a softmax function for normalization, associates these probabilities with a certain limit, and then applies a filter to modify the logits based on the associated limit. -The logic behind this method is to make some of the outcomes impossible (those that fall under the limit) and others equally likely (those above the limit). The effect is to make the randomly selected index more likely to be one of the most probable indices. +## Parameters -This function fits with the main purpose of PyTorch, which is to ease deep learning implementations, by providing an extra level of flexibility on the level of randomness included in models. +| Parameter | Type | Description | +|------------|-----------------------|----------------------------------------------------------------| +| logits | PyTorch Tensor | The input tensor for which the softmax will be computed. | +| min_p_pow | float (Optional) | The minimal power to which max probability is raised. Default is 2.0. | +| min_p_ratio| float (Optional) | The minimal ratio to minimum power used to set the limit. Default is 0.02. | -## Function Definition +## Returns +This function returns a modified version of the input tensor, logits with respect to the specified limit. + +## Code ```python +import torch +import torch.nn.functional as F + def top_a(logits, min_p_pow=2.0, min_p_ratio=0.02): + #compute softmax probabilities + probs = F.softmax(logits, dim=-1) + + #set limit with respect to maximum probabily and min_p_pow and min_p_ratio + limit = torch.pow(torch.max(probs), min_p_pow) * min_p_ratio + + # apply filter to modify the logits with respect to the limit + logits[probs < limit] = float("-inf") + logits[probs >= limit] = 1 + return logits ``` -The function uses two parameters, `min_p_pow` and `min_p_ratio` that are used to compute the limit of probabilities. -## Arguments +## Examples -| Parameter | Type | Default Value | Description | -|------------|---------|---------------|---------------------------------------------------------------------------| -| `logits` | Tensor | None | Model predictions in logits | -| `min_p_pow` | Float | 2.0 | A value to control the the power of the maximum probability in the limit | -| `min_p_ratio`| Float | 0.02 | A coefficient to control the ratio of the limit | +### EXAMPLE 1 -## Usage +In this example, we'll compute the top_a function on a tensor of logits. -First, you need to install PyTorch. This can be done using pip. +```python +import torch +from zeta.utils import top_a -```bash -pip install torch +# Create a tensor of logits +logits = torch.tensor([0.1, 0.2, 0.3, 0.4]) + +# Call the function +result = top_a(logits) + +# Output +print(result) ``` -Next, use the function inside your code. Import PyTorch and zeta utils first. +### EXAMPLE 2 + +In this example, we use user-defined minimum power `min_p_pow` and minimum ratio `min_p_ratio`. ```python import torch -import torch.nn.functional as F -from zeta.utils import top_a +from zeta.utils import top_a + +# Create a tensor of logits +logits = torch.tensor([0.1, 0.5, 0.2, 0.4]) -logits = torch.randn(5, num_classes) # substitute num_classes with the number of classes in your model -modified_logits = top_a(logits) +# Call the function +result = top_a(logits, min_p_pow=3.0, min_p_ratio=0.01) + +# Output +print(result) ``` -In above example, original ` +### EXAMPLE 3 + +In this example, we see how changing the `min_p_pow` affects the output. + +```python +import torch +from zeta.utils import top_a + +# Create a tensor of logits +logits = torch.tensor([0.2, 0.3, 0.5, 0.5]) + +# Call the function with different min_p_pow values +result1 = top_a(logits, min_p_pow=1.0) +result2 = top_a(logits, min_p_pow=2.0) +result3 = top_a(logits, min_p_pow=3.0) + +# Output +print(result1) +print(result2) +print(result3) +``` + +## Note + +Deep learning practitioners should maintain a good practice of casting tensors into the right device (CPU or GPU) before operations. Ensure the logits tensor is on the right device before calling `top_a()`. Additionally, the values in the tensor should be in logits (unnormalized scores or predictions) and not in the form of probabilities (i.e., no softmax has been applied). + +This function is meant to be a utility. For a more specialized task, slight modifications may be required as per the use case. Thus, it should not be considered as a one-size-fits-all solution, but rather as a template code for selecting samples contingent upon a specific set of probabilities. diff --git a/docs/zeta/utils/top_k.md b/docs/zeta/utils/top_k.md index 6c484bb4..08ed29ff 100644 --- a/docs/zeta/utils/top_k.md +++ b/docs/zeta/utils/top_k.md @@ -1,59 +1,97 @@ # top_k -# zeta.utils Package Documentation - -## The `zeta.utils` module - -`zeta.utils` is a utility module that provides various utility functions aimed at simplifying and bolstering the efficiency of data transformation and manipulation processes. This documentation explores, in depth, the usefulness, rationale behind, and significance of the provided functions, which will further help users to leverage them in their specific use cases effectively. - -Our focus is the `top_k` function that selectively returns elements from the tensor, having values within the top k percentile. - -
- -# Function Name: `top_k` - -The `top_k` function is aimed at aiding common procedures encountered in machine learning and data science involving tensor manipulations. Specifically, it speeds up the rank-based filtering of elements in a tensor. - -**Definition/Signature**: +# Module/Function Name: top_k ```python def top_k(logits, thres=0.9): + k = ceil((1 - thres) * logits.shape[-1]) + val, ind = torch.topk(logits, k) + probs = torch.full_like(logits, float("-inf")) + probs.scatter_(1, ind, val) + return probs ``` -**Parameters**: +The `top_k` function is utility function that is used to retrieve the top k logits based on a threshold. It takes in the logits and a threshold value, picks out the top k logits that meet the threshold, and then returns those logits. + +## Parameters +| Parameter | Type | Description | Default | +| :--- | :--- | :--- | :--- | +| logits | Tensor | A rank 1 tensor representing the logits you want to filter | Required | +| thres | float | A float representing the threshold for filtering, the default value is 0.9 | 0.9 | -The function accepts the following arguments: +## Returns +| Return | Type | Description | +| :--- | :--- | :--- | +| probs | Tensor | The tensor after being filtered | -| Parameters | Type | Description | Default Value | -|------------|--------|----------------------------------------------------------------------------------------------------------|---------------| -| logits | tensor | A tensor whose elements are required to be ranked and top k percentile to be separated. | None | -| thres | float | A threshold value determining the percentile of top elements to be selected from the tensor. | 0.9 | +## Usage Examples -
+Now, let's go through a few examples of how you can use the `top_k` function. -**How It Works**: +### Example 1: Basic usage -The `top_k` function works by utilizing PyTorch's topk function to pull the top-k elements from a tensor, based on the specified threshold. It then builds a new tensor filled with -inf (representing negative infinity) and scatter the top-k elements into it. This implies that the returned tensor has the top-k elements from the original tensor and -inf for the rest. This aids easy selection and corresponding actions on the top-k elements without the strain of performing an explicit sort operation on the tensor and then slicing off the top-k elements. +In the most basic usage, you would pass a tensor of logits and receive a filtered tensor. -**Returns**: +```python +import torch +from math import ceil +def top_k(logits, thres=0.9): + k = ceil((1 - thres) * logits.shape[-1]) + val, ind = torch.topk(logits, k) + probs = torch.full_like(logits, float("-inf")) + probs.scatter_(1, ind, val) + return probs + +logits = torch.tensor([0.1, 0.4, 0.3, 0.2, 0.5]) +probs = top_k(logits) +print(probs) +``` -A tensor which has the top-k elements from the original tensor and -inf for the rest. +### Example 2: Changing the Threshold -
+The threshold value can be adjusted according to your requirements. A higher threshold may result in values being included that would otherwise be excluded. -**Example Usage(s)**: +```python +import torch +from math import ceil +def top_k(logits, thres=0.8): + k = ceil((1 - thres) * logits.shape[-1]) + val, ind = torch.topk(logits, k) + probs = torch.full_like(logits, float("-inf")) + probs.scatter_(1, ind, val) + return probs + +logits = torch.tensor([0.1, 0.4, 0.3, 0.2, 0.5]) +probs = top_k(logits) +print(probs) +``` -Below are three illustrative examples of leveraging the `top_k` function: +### Example 3: Using a Different Tensor -**Example 1:** +The input tensor can be changed as needed. The only requirement is that the tensor should be a 1D tensor. ```python import torch from math import ceil -from zeta.utils import top_k +def top_k(logits, thres=0.9): + k = ceil((1 - thres) * logits.shape[-1]) + val, ind = torch.topk(logits, k) + probs = torch.full_like(logits, float("-inf")) + probs.scatter_(1, ind, val) + return probs + +logits = torch.tensor([0.1, 0.4, 0.7, 0.2, 0.5]) +probs = top_k(logits) +print(probs) +``` + +## Additional Information and Tips: -# Initialize tensor -tensor = torch.rand(1, 10) +- The function `top_k` makes use of the `torch.topk()` function to find the top k values in the tensor and returns these values and their respective indices. +- The indices are used with the `torch.Tensor.scatter_()` function to replace the selected elements in a new tensor filled with `-inf` along the specified dimension with the specified value. + +## References: -# Apply function with threshold 0.9 -filtered_tensor = top_k(tensor, thres=0. +- For more information about the functions used, refer to the PyTorch documentation: + - [torch.topk()](https://pytorch.org/docs/stable/generated/torch.topk.html) + - [torch.Tensor.scatter_()](https://pytorch.org/docs/stable/generated/torch.Tensor.scatter_.html) diff --git a/docs/zeta/utils/top_p.md b/docs/zeta/utils/top_p.md index 2dd4b708..5d1fcd5a 100644 --- a/docs/zeta/utils/top_p.md +++ b/docs/zeta/utils/top_p.md @@ -1,59 +1,73 @@ # top_p -# Zeta Utils Library Documentation +# Module Name: zeta.utils.top_p -The Zeta Utils library is a simple utility library providing a single function, `top_p`, for manipulating and filtering PyTorch tensor-based data sets according to a specified threshold value. +Function: +```python +def top_p(logits, thres=0.9): +``` -## `top_p` Function +The `top_p` function is a part of the `zeta.utils` library. This function uses a process known as nucleus sampling, or top-p sampling, to handle logits from a language model. This function is intended to be used with the softmax output of language model sequences, making it an important method for text generation tasks. -### Function Objective +Nucleus sampling is a form of sampling to solve the problem of text generation. It selects the highest probability tokens whose cumulative probability mass exceeds a given threshold. -`top_p` function sorts the values in a tensor, calculates a cumulative sum from a softmax and then applies a threshold to exclude the highest probabilities. Useful when trying to constrain outputs in a certain range. +This function is especially useful for deep learning algorithms involved in text generation tasks, where using pure maximum likelihood approximations might lead to highly repetitive and nonsensical outputs. By applying the `top_p` function, we can ensure more diverse and sensible outputs from such text generation models. -### Function Definition +## Parameters: -```python -def top_p(logits, thres=0.9): -``` - -### Parameters +Name | Type | Description | Default Value +--- | --- | --- | --- +logits | Tensor | These are the model's output log probabilities, expected to be in the format of a 2D tensor. || +thres | float | A hyperparameter for top-p sampling, it adjusts the trade-off between randomness and fidelity in the generated text. This parameter indicates the cumulative probability threshold used for the nucleus sampling. | 0.9 -| Parameter | Type | Default Value | Description | -|-----------|-------|---------------|-------------------------------------------------------------------------------------------------------------------------------------------| -| `logits` | Tensor| None | Input tensor containing the values to be processed. | -| `thres` | Float | 0.9 | Threshold value used to filter the highest probabilities. | +The function returns logits processed by top-p sampling method, with least probable options removed according to the defined threshold value. +## Usage -### Return Types +For this function, we first begin by importing the necessary libraries, which in this case are `torch` and its sublibrary `torch.nn.functional`. -The function returns a Tensor with the same dimensions as the input tensor where the probabilities above the threshold have been filled with negative infinity (`float("-inf")`). +``` python +import torch +import torch.nn.functional as F -### Internal Functioning +def top_p(logits, thres=0.9): + sorted_logits, sorted_indices = torch.sort(logits, descending=True) + cum_probs = torch.cumsum(F.softmax(sorted_logits, dim=-1), dim=-1) -- First, `logits` are sorted by descending order, receiving both the sorted values and their corresponding indices. -- Next, the softmax of the sorted values is calculated and a cumulative sum over the results is performed. -- Then, a tensor of the same dimension as cum_probs is created, filled with True if the cumulative probability is above the threshold (1 - `thres`), and False otherwise. -- After that, a little shift is made on this tensor to the right so that the values do not exceed the threshold value limit. The first element is explicitly set to 0 (or false). -- Afterwards, the sorted tensor is updated by replacing values at sorted_indices_to_remove (those above threshold) with negative infinity (`float("-inf")`). -- Finally, the `scatter` function rearranges the updated sorted_logits back into the original structure. + sorted_indices_to_remove = cum_probs > (1 - thres) + sorted_indices_to_remove[:, 1:] = sorted_indices_to_remove[:, :-1].clone() + sorted_indices_to_remove[:, 0] = 0 + sorted_logits[sorted_indices_to_remove] = float("-inf") + return sorted_logits.scatter(1, sorted_indices, sorted_logits) +``` -## Usage examples +We can illustrate the process using a simple example. -### Example 1 +``` python +# Define logits tensor +logits = torch.tensor([[0.5, 0.4, 0.1]]) -```python -import torch -from torch.nn import functional as F -from zeta.utils import top_p +# Call the top_p function +filtered_logits = top_p(logits, thres=0.9) +print('The filtered logits are:') +print(filtered_logits) -logits = torch.randn(10, 10) -result = top_p(logits) +# this should give us: +# tensor([[[0.5000], [0.4000], [-inf.]]) ``` -This example demonstrates the basic use of the `top_p` function which accepts a tensor with random values and a default threshold value of `0.9`. +In this example, `'filtered_logits'` now contains the logits from `'logits'` but the least probable entries (inferior to `thres`) have been replaced by `-inf.` which makes them impossible to be chosen in a subsequent random sampling. -### Example 2 +Keep in mind that in actual use cases the logits tensor would be the output of a pretrained language model and would have more complex dimensions, but the function would be used in the same way. -```python -import torch +## Tips +- The choice of threshold value `'thres'` in the function `top_p(logits, thres=0.9)` is very important, as it determines the trade-off between fidelity (how closely the generated text matches the given input text) and diversity (how different the generated text is from the input text). A smaller threshold value may lead to more repetitive and less diverse text, while a larger threshold value may lead to more diverse but also more unpredictable and potentially incoherent text. You can fine-tune this value based on your specific needs and objectives. + +## References +- [The Curious Case of Neural Text Degeneration](https://arxiv.org/abs/1904.09751) +- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://arxiv.org/abs/1910.10683) + +Reference to PyTorch which this function is heavily tied to: + +- [PyTorch Documentation](https://pytorch.org/docs/stable/index.html) for further exploration. diff --git a/docs/zeta/utils/track_cuda_memory_usage.md b/docs/zeta/utils/track_cuda_memory_usage.md index 195449e9..92824436 100644 --- a/docs/zeta/utils/track_cuda_memory_usage.md +++ b/docs/zeta/utils/track_cuda_memory_usage.md @@ -1,65 +1,91 @@ # track_cuda_memory_usage -# Module/Function Name: track_cuda_memory_usage +# Zeta Utils Documentation -This function `track_cuda_memory_usage` is a Python decorator specifically designed to keep track of the GPU memory usage in PyTorch when a different function is called. This provides an easy way of monitoring the CUDA memory usage during the run time of a function, which can help spec out hardware requirements and catch any unusual memory usage patterns indicative of a memory leak. +The zeta.utils package is designed to simplify and enhance numerous coding tasks related to PyTorch deep learning systems. By using decorators, the package creates a higher order function that wraps standard functions to provide additional capabilities. -## Function Definition +This documentation will provide in-depth focus on the `track_cuda_memory_usage` function decorator included in the package. The intent of this documentation is to thoroughly acquaint the user with the usage and function of `track_cuda_memory_usage`. -```py -def track_cuda_memory_usage(func): -``` +## Function Definition -### Parameters +The `track_cuda_memory_usage` function is a decorator that, when applied to another function, tracks and logs the CUDA memory usage during the execution of that function. The primary purpose of `track_cuda_memory_usage` is to allow users to understand the GPU memory allocation and usage when executing a given function - a valuable tool for optimizing deep learning models and operations. -| Parameter | Type | Description | -| --- | --- | --- | -| func | Function | The function whose CUDA memory usage is to be tracked | +This function is especially beneficial when working with large models or data as it allows for efficient memory allocation and monitoring. Using the insights gleaned from this function, users can adjust either their model or their data processing methods to ensure memory efficiency. -### Returns +```python +def track_cuda_memory_usage(func): + """ + Name: track_cuda_memory_usage -The function returns a wrapped function. The returned function behaves the same as the passed function (`func`), but it also logs the CUDA memory usage when the function is called. + Documentation: + Track CUDA memory usage of a function. -| Return Value | Type | Description | -| --- | --- | --- | -| Wrapper Function | Function | The wrapped function that behaves the same as the passed function, but also logs the CUDA memory usage | + Args: + func (function): The function to be tracked. -## Functionality and Usage + Returns: + function: The wrapped function. + """ +``` -The `track_cuda_memory_usage` function wraps the passed function (`func`) and monitors its CUDA memory usage. It does this by checking the GPU memory usage before and after the function runs. If there is an increase in the memory usage, the function logs this change. +## Arguments -This function can be used to debug cases where there are memory leaks in your PyTorch model. It can be especially useful if you're running out of GPU memory but don't know why. +| Argument | Data Type | Default Value | Description | +|-------------|---------------|-------------------|-----------------| +| func | function | N/A | The function to be tracked. | -Remember that this is a decorator function and should be used as one. It can be applied to any other function like so: +## Usage examples ```python +from zeta.utils import track_cuda_memory_usage +import torch + +# Define the function that you wish to track @track_cuda_memory_usage -def my_func(): - # Function body here - # This function will now have its CUDA memory usage tracked - pass +def create_empty_tensor(size): + return torch.empty(size=(size, size)).cuda() + +create_empty_tensor(1000) ``` -## Example of Usage +In this example, the decorator `@track_cuda_memory_usage` is used to track the CUDA memory usage during the execution of the function `create_empty_tensor`, which creates an empty tensor on the GPU. On execution of this function, CUDA memory usage details will be logged. -In the following example, we define a simple PyTorch model and use the `track_cuda_memory_usage` decorator to keep track of the model’s memory usage. +Here's an example tracking the memory usage while training a model, which could help in understanding and improving the efficiency of a training loop. ```python +from zeta.utils import track_cuda_memory_usage import torch -import torch.nn as nn -import logging +from torchvision.models import resnet18 +from torch.optim import SGD +from torch.nn import CrossEntropyLoss -# Creating simple model -class SimpleModel(nn.Module): - def __init__(self): - super(SimpleModel, self).__init__() - self.fc = nn.Linear(100, 10) +model = resnet18().cuda() - def forward(self, x): - return self.fc(x) +optimizer = SGD(model.parameters(), lr=0.01) -# Defining train function +# Define a simple train loop @track_cuda_memory_usage -def train(model, data): - model.train() +def simple_train_loop(dataloader, model, optimizer): + loss_function = CrossEntropyLoss() + for inputs, targets in dataloader: + inputs, targets = inputs.cuda(), targets.cuda() + outputs = model(inputs) + loss = loss_function(outputs, targets) + loss.backward() + optimizer.step() + optimizer.zero_grad() + +simple_train_loop(your_dataloader, model, optimizer) +``` + +In this example, we define a simple training loop for a model and use the `@track_cuda_memory_usage` decorator to monitor the CUDA memory usage for each iteration of the loop. + +## Additional Usage Tips + +Prior to running any operation, the function forces PyTorch to wait for all currently pending CUDA operations to finish with `torch.cuda.synchronize()`. This ensures that all previously allocated memory is factored into the calculation before the execution of `func`. + +It's crucial to note that GPU memory usage is often non-deterministic due to factors such as CUDA's memory management mechanisms as well as multi-threaded operations. + +## Conclusion +Understanding how `track_cuda_memory_usage` works can make a significant difference in optimizing and diagnosing memory-related issues in a PyTorch project. This utility is paramount to developers who work with large data and models. It's a handy tool that makes memory debugging and tracking accessible and manageable. diff --git a/docs/zeta/utils/video_tensor_to_gift.md b/docs/zeta/utils/video_tensor_to_gift.md index d8a2758c..27dcce15 100644 --- a/docs/zeta/utils/video_tensor_to_gift.md +++ b/docs/zeta/utils/video_tensor_to_gift.md @@ -4,31 +4,60 @@ ## Function: video_tensor_to_gift - ``` - This function converts a tensor representation of a video into a GIF file. - It takes a tensor video as input, unbinds the tensor, converts each image-like tensor in the video to a PIL image, - and then saves all these images in a GIF file. +```python +def video_tensor_to_gift(tensor, path, duration=120, loop=0, optimize=True): + """ + This function converts a video tensor into a gif and then saves it on the provided path. Parameters: - - tensor (tensor): A tensor containing the video data. - - path (str): The path where the GIF should be saved. - - duration (int): The time (in milliseconds) that each frame should be displayed. Default: 120 ms. - - loop (int): The number of times the GIF should loop. - 0 for infinite loop, and other integer values for specific count of loops. Default: 0 (infinite loop). - - optimize (bool): If True, the resulting GIF will be optimized to save space. - Optimization can take more time and result in minimal changes, so if you’re in a hurry, or don’t care about file size, you can skip optimization. Default: True. + - tensor (tensor): A tensor representing a video. The tensor should be 5-dimensional (B, T, C, H, W). + - path (str): The location and filename where the gif should be saved. Built-in gif extension is recommended to ensure correct file format. + - duration (int): The duration for which each frame should be displayed before transitioning to the next. Default is 120 (in milliseconds). + - loop (int): The number of times the gif should loop. A value of 0 means the gif will loop indefinitely. Default is 0. + - optimize (bool): A flag specifying whether the gif should be optimized. If set to True, the gif would have smaller size at the cost of quality. Default is True. Returns: - list: list of images created from the tensors. + - images: A sequence of images that constitute the gif. + + Examples: + + This is a simple usage case. + + ```python + from torchvision.transforms import functional as T + import torch + from zeta.utils import video_tensor_to_gift + + # Generate a random tensor representing a video + tensor = torch.rand(1, 10, 3, 64, 64) + + # Convert tensor to gif and save + path = "./random_video.gif" + video_tensor_to_gift(tensor, path) ``` -```python -def video_tensor_to_gift(tensor, path, duration=120, loop=0, optimize=True): + + This example showcases usage with different arguments. + + ```python + from torchvision.transforms import functional as T + import torch + from zeta.utils import video_tensor_to_gift + + # Generate a random tensor representing a video + tensor = torch.rand(1, 10, 3, 64, 64) + + # Convert tensor to gif and save with custom duration, loop, and optimization set. + path = "./random_video.gif" + video_tensor_to_gift(tensor, path, duration=200, loop=1, optimize=False) + ``` + + """ images = map(T.ToPilImage(), tensor.unbind(dim=1)) first_img, *rest_imgs = images first_img.save( path, save_all=True, - append_images=rest_imgs, + appeqnd_images=rest_imgs, duration=duration, loop=loop, optimize=optimize, @@ -36,30 +65,28 @@ def video_tensor_to_gift(tensor, path, duration=120, loop=0, optimize=True): return images ``` -## Usage Examples: +## Architecture -### Example 1: +The function `video_tensor_to_gift` works by first unbinding the video tensor along the time dimension using the `unbind()` function, which returns a tuple of all slices along that dimension. This breaks the tensor into a sequence of image tensors. -```python -# import the necessary libraries -import torch -from torchvision import transforms as T -from zeta.utils import video_tensor_to_gift +The `map()` function is then used to apply `T.ToPilImage()`, a torchvision functional transform, to each of these image tensors. This converts each tensor into a PIL Image. -# Define a tensor for generating a video: -video_data = torch.rand(10, 10, 3, 64, 64) +The sequence of PIL Images is then split, with the `first_img` separated from the `rest_imgs`. -# Call the function: -video_tensor_to_gift(video_data, 'test.gif') -``` -In this example, we generate a tensor of random pixel intensity values. The generated GIF file will be saved in the current working directory with the name 'test.gif'. The gif file be looping indefinitely. +The function then uses the `first_img.save()` method to save all the images as a gif at the provided path. The `save_all` parameter set to `True` signals that all images should be saved in the gif, not just the first one. The `append_images` parameter specifies the additional images to be added, which in this case are the rest of the images. The `duration`, `loop`, and `optimize` parameters control the behavior of the gif. -### Example 2: +### Note: +Optimizing the gif can reduce the size of the gif file but may also slightly degrade the image quality. -```python -# import the necessary libraries -import torch -from torchvision import transforms as T -from zeta.utils import video_tensor_to_gift +This function is handy for quick visualization and debugging purposes, as it can help analyze the content of video tensors during model development. + +### References and further resources: + +For understanding more about the image saving process in PIL: +https://pillow.readthedocs.io/en/stable/handbook/image-file-formats.html#gif + +For understanding more about TorchVision transform functions: +https://pytorch.org/vision/stable/transforms.html -# Define a tensor for +For more details on PyTorch tensor functions such as `unbind`: +https://pytorch.org/docs/stable/tensors.html diff --git a/scripts/auto_tests_docs/auto_docs_functions.py b/scripts/auto_tests_docs/auto_docs_functions.py index 489bc28b..384c6e3f 100644 --- a/scripts/auto_tests_docs/auto_docs_functions.py +++ b/scripts/auto_tests_docs/auto_docs_functions.py @@ -16,7 +16,7 @@ model = OpenAIChat( model_name="gpt-4", openai_api_key=api_key, - max_tokens=500, + max_tokens=1000, ) From 18666a56ee29e530cc0303272fd05bed7809c059 Mon Sep 17 00:00:00 2001 From: KyeDate: Wed, 27 Dec 2023 11:34:55 -0500 Subject: [PATCH 225/587] [FEATS][ TripleSkipBlock, DynamicRoutingBlock, GatedResidualBlock, StochasticSkipBlocK,][DOCS][TESTS] --- README.md | 2 +- docs/zeta/nn/modules/dynamicroutingblock.md | 82 ++++++++++ docs/zeta/nn/modules/gatedresidualblock.md | 83 ++++++++++ docs/zeta/nn/modules/stochasticskipblock.md | 167 ++++++++++++++++++++ docs/zeta/nn/modules/tripleskipblock.md | 132 ++++++++++++++++ example.py | 2 +- scripts/auto_tests_docs/auto_docs.py | 36 ++--- scripts/auto_tests_docs/auto_tests.py | 36 ++--- tests/nn/modules/dynamicroutingblock.py | 52 ++++++ tests/nn/modules/gatedresidualblock.py | 39 +++++ tests/nn/modules/stochasticskipblock.py | 48 ++++++ tests/nn/modules/tripleskipblock.py | 61 +++++++ zeta/nn/modules/__init__.py | 10 ++ zeta/nn/modules/dynamic_routing_block.py | 35 ++++ zeta/nn/modules/gated_residual_block.py | 31 ++++ zeta/nn/modules/stochastic_depth.py | 35 ++++ zeta/nn/modules/triple_skip.py | 30 ++++ 17 files changed, 833 insertions(+), 48 deletions(-) create mode 100644 docs/zeta/nn/modules/dynamicroutingblock.md create mode 100644 docs/zeta/nn/modules/gatedresidualblock.md create mode 100644 docs/zeta/nn/modules/stochasticskipblock.md create mode 100644 docs/zeta/nn/modules/tripleskipblock.md create mode 100644 tests/nn/modules/dynamicroutingblock.py create mode 100644 tests/nn/modules/gatedresidualblock.py create mode 100644 tests/nn/modules/stochasticskipblock.py create mode 100644 tests/nn/modules/tripleskipblock.py create mode 100644 zeta/nn/modules/dynamic_routing_block.py create mode 100644 zeta/nn/modules/gated_residual_block.py create mode 100644 zeta/nn/modules/stochastic_depth.py create mode 100644 zeta/nn/modules/triple_skip.py diff --git a/README.md b/README.md index b3a90779..7d892cac 100644 --- a/README.md +++ b/README.md @@ -29,7 +29,7 @@ Creating a model empowered with the aforementioned breakthrough research feature ```python import torch -from zeta.nn.attention import FlashAttention +from zeta.nn import FlashAttention q = torch.randn(2, 4, 6, 8) k = torch.randn(2, 4, 10, 8) diff --git a/docs/zeta/nn/modules/dynamicroutingblock.md b/docs/zeta/nn/modules/dynamicroutingblock.md new file mode 100644 index 00000000..06d9a9de --- /dev/null +++ b/docs/zeta/nn/modules/dynamicroutingblock.md @@ -0,0 +1,82 @@ +## Module/Class Name: DynamicRoutingBlock +### Overview +The `DynamicRoutingBlock` class, which subclass `nn.Module`, provides the structure for incorporating dynamic routing mechanism between two sub-blocks in a neural network. A dynamic routing algorithm allows a neural network to learn from inputs internally and configure its neurons' connections, thereby allowing the neural network to adapt better to the specific task at hand. This pytorch-based class encapsulates the operations of a dynamic routing block, a higher-level structure in a neural network architecture. + +```python +class DynamicRoutingBlock(nn.Module): +``` + +### Class Definition + +Below, you will find the class definition, along with detailed descriptions of its parameters. This gives you a better understanding of the class and circles the logic it follows. + +```python +def __init__(self, sb1: nn.Module, sb2: nn.Module, routing_module: nn.Module): +``` +*__Parameters__*: + +|Parameter | Type | Description | +|--- | --- | --- | +|`sb1` | nn.Module | The first sub-block | +|`sb2` | nn.Module | The second sub-block | +|`routing_module` | nn.Module | The module that computes routing weights| + +### Method Definitions +#### Forward Method +This method defines the forward pass of the dynamic routing block. The `routing_weights` are first computed by inputting `x` into the provided routing_module. These weights are then used to compute the final output. + +```python +def forward(self, x: torch.Tensor) -> torch.Tensor: +``` + +*__Parameters__*: + +|Parameter | Type | Description | +|--- | --- | --- | +| `x` | torch.Tensor | The input tensor| + +*__Return__*: + +|Type |Description | +|--- | --- | +|torch.Tensor | The output tensor after dynamic routing | + + + +### Functionality and Usage + +To illustrate the usefulness and workings of the `DynamicRoutingBlock`, let's walk through an example. +Suppose you want to create a dynamic routing block that routes between two linear transformation (i.e., `nn.Linear`) sub-blocks, `sb1` and `sb2`, and you have a `routing_module` that computes a sigmoid activation of a dot product with a learnable weight vector. + +Firstly, define your two sub-blocks and routing module: + +```python +sb1 = nn.Linear(5, 3) +sb2 = nn.Linear(5, 3) + +class RoutingModule(nn.Module): + def __init__(self): + super().__init__() + self.weights = nn.Parameter(torch.randn(5)) + + def forward(self, x): + return torch.sigmoid(x @ self.weights) + +routing_module = RoutingModule() +``` + +Then, you instantiate your dynamic routing block like this: + +```python +drb = DynamicRoutingBlock(sb1, sb2, routing_module) +``` + +The input can be passed to this block to yield the output: + +```python +x = torch.randn(10, 5) +y = drb(x) +``` +In the process, the dynamic routing block has learned to route between `sb1` and `sb2` depending on `routing_module`'s weights, allowing the module to discover which sub-block is more 'helpful' for any given input. + +Dynamic routing is a powerful tool for allowing a neural network to determine more complex, hierarchical relationships among its inputs. Consequently, using dynamic routing blocks such as described could potentially assist in enhancing the network's predictive performance. The `DynamicRoutingBlock` class provided here provides a simple, yet powerful implementation of such a dynamic routing mechanism. diff --git a/docs/zeta/nn/modules/gatedresidualblock.md b/docs/zeta/nn/modules/gatedresidualblock.md new file mode 100644 index 00000000..e4247d22 --- /dev/null +++ b/docs/zeta/nn/modules/gatedresidualblock.md @@ -0,0 +1,83 @@ +# Module/Function Name: GatedResidualBlock + +`class GatedResidualBlock(nn.Module):` + +## Overview + +The `GatedResidualBlock` is a subclass of the `nn.Module` which belongs to the PyTorch library. The main objective of this module is to implement a special variant of Residual Block structure which is commonly used in designing deep learning architectures. + +Traditionally, a Residual Block allows the model to learn an identity function which helps in overcoming the problem of vanishing gradients in very deep networks. The `GatedResidualBlock` takes this a step further by introducing gating mechanisms, allowing the model to control the information flow across the network. The gate values, generated by the `gate_module`, determines the degree to which the input data flow should be altered by the first sub-block `sb1`. + +This architecture promotes stability during the training of deep networks and increases the adaptability of the model to complex patterns in the data. + +## Class Definition + +The class definition for `GatedResidualBlock` is as follows: + +``` +class GatedResidualBlock(nn.Module): + def __init__(self, sb1, gate_module): + super().__init__() + self.sb1 = sb1 + self.gate_module = gate_module +``` + +### Arguments + +| Argument | Type | Description | +| ---------------------------------- | ------------ | ---------------------------------------------------------------------------------------------------------------- | +| `sb1` | `nn.Module` | The first sub-block of the Gated Residual Block. | +| `gate_module` | `nn.Module` | The gate module that determines the degree to which the input should be altered by the first sub-block `sb1`. | + +## Example: Usage of GatedResidualBlock + +A simple usage of `GatedResidualBlock` is demonstrated below. + +```python +import torch +import torch.nn as nn +from zeta.nn import GatedResidualBlock + +# Define the sub-blocks +sb1 = nn.Linear(16, 16) +gate_module = nn.Linear(16, 16) + +# Create the GatedResidualBlock +grb = GatedResidualBlock(sb1, gate_module) + +# Sample input +x = torch.rand(1, 16) + +# Forward pass +y = grb(x) +``` + +In the above example, both subblocks are simple linear layers. The input `x` is passed through the `GatedResidualBlock`, where it's processed by the `gate_module` and `sb1` as described in the class documentation. + +## Method Definition + +The method definition for `GatedResidualBlock` class is as follows: + +```python +def forward(self, x: torch.Tensor): + gate = torch.sigmoid(self.gate_module(x)) + return x + gate * self.sb1(x) +``` + +This method applies a standard forward pass to the input tensor `x` through the Gated Residual Block. + +### Arguments + +| Argument | Type | Description | +| ---------- | -------------- | ----------------- | +| `x` | `torch.Tensor` | The input tensor. | + +### Returns + +It returns a `torch.Tensor`, the output tensor of the gated residual block. + +## Note + +This module requires the inputs `sb1` and `gate_module` to be of `nn.Module` type. Any model architecture that extends `nn.Module` can be used as the sub-blocks. The gating mechanism helps to improve the model performance especially on complex and large data sets. + +If you encounter any issues while using this module, please refer to the official PyTorch documentation or raise an issue on the relevant GitHub issue page. diff --git a/docs/zeta/nn/modules/stochasticskipblock.md b/docs/zeta/nn/modules/stochasticskipblock.md new file mode 100644 index 00000000..f6c7a72d --- /dev/null +++ b/docs/zeta/nn/modules/stochasticskipblock.md @@ -0,0 +1,167 @@ +# Module Name: StochasticSkipBlock + +## Overview and Introduction: + +Tabular Deep Learning models sometimes struggle with overfitting on noisy data. Stochastic Skip Block is a PyTorch module designed to combat this problem by introducing stochasticity in between the network layers. This module applies an innovative concept of skipping certain layers during training with a defined probability, thereby creating a diverse set of thinner networks. + +Given a set of layers encapsulated in a module, the `StochasticSkipBlock` will either apply this module to the input or return the input directly bypassing the module completely. The decision whether to apply or skip the module is randomized with a user-defined probability. This way the model creates uncertainty and works as an efficient regularizer preventing overfitting on training data. Moreover, it contributes to faster convergence during training and better generalization in prediction phase. + +## Class Definition: + +Below is the class definition for the module: + +```python +class StochasticSkipBlock(nn.Module): + """ + A module that implements stochastic skip connections in a neural network. + + Args: + sb1 (nn.Module): The module to be skipped with a certain probability. + p (float): The probability of skipping the module. Default is 0.5. + + Returns: + torch.Tensor: The output tensor after applying the stochastic skip connection. + """ + + def __init__(self, sb1, p=0.5): + super().__init__() + self.sb1 = sb1 + self.p = p + + def forward(self, x: torch.Tensor): + """ + Forward pass of the StochasticSkipBlock. + + Args: + x (torch.Tensor): Input tensor. + + Returns: + torch.Tensor: Output tensor after applying the module. + """ + if self.training and torch.rand(1).item() < self.p: + return x # Skip the sb1 + else: + return self.sb1(x) +``` + +## Parameters + +| Argument | Default | Description | +|----------|---------|-------------| +| `sb1` | None | The layers encapsulated in `nn.Module` object to be skipped with a certain probability. | +| `p` | 0.5 | The probability of skipping the module. | + +## Use Cases + +### Use Case 1: Basic Usage + +This is a basic example of using `StochasticSkipBlock` in a feed forward neural network. + +First, you need to import the necessary module: + +```python +import torch +import torch.nn as nn +from torch.nn.functional import relu +``` + +Now, you need to define the architecture of the model: + +```python +class MyModel(nn.Module): + def __init__(self): + super(MyModel, self).__init__() + self.layer1 = nn.Linear(10, 20) + self.layer2 = StochasticSkipBlock(nn.Sequential( + nn.Linear(20, 20), + nn.ReLU() + ), p=0.5) # 50% chance to skip the subsequence of layers + self.layer3 = nn.Linear(20, 1) + + def forward(self, x): + x = relu(self.layer1(x)) + x = self.layer2(x) + x = self.layer3(x) + return x +``` + +Now, you can instantiate your model: + +```python +model = MyModel() +input = torch.randn(32, 10) +output = model(input) +``` + +### Use Case 2: Convolutional Neural Network + +This example shows how to embed `StochasticSkipBlock` in between convolutional layers of a CNN model. + +```python +class MyCNNModel(nn.Module): + def __init__(self): + super(MyCNNModel, self).__init__() + self.conv1 = nn.Conv2d(3, 32, kernel_size=5) + self.conv2 = StochasticSkipBlock(nn.Conv2d(32, 64, kernel_size=5), p=0.6) + self.fc1 = nn.Linear(64*5*5, 120) + self.fc2 = nn.Linear(120, 84) + self.fc3 = nn.Linear(84, 10) + + def forward(self, x): + x = F.relu(self.conv1(x)) + x = F.max_pool2d(self.conv2(x), 2) + x = x.view(-1, self.num_flat_features(x)) + x = F.relu(self.fc1(x)) + x = F.relu(self.fc2(x)) + x = self.fc3(x) + return x +``` + +### Use Case 3: Training the model using DataLoader + +This shows how to train the model using StochasticSkipBlock module. Please note, This example assumes you have your dataloader ('train_dataloader') ready with training data. + +```python +from torch.optim import SGD +from torch.nn.functional import binary_cross_entropy +import torch.optim as optim + +#initiate model +model = MyModel() + +#defining loss function +criterion = nn.CrossEntropyLoss() +optimizer = optim.SGD(model.parameters(), lr=0.01, momentum=0.9) + +for epoch in range(50): # loop over the dataset + running_loss = 0.0 + for i, data in enumerate(train_dataloader, 0): + inputs, labels = data + + optimizer.zero_grad() + + outputs = model(inputs) + loss = criterion(outputs, labels) + loss.backward() + optimizer.step() + + running_loss += loss.item() + print('Epoch %d loss: %.3f' % (epoch + 1, running_loss)) + +print('Finished Training') +``` + +## Additional Tips + +To get the most out of the StochasticSkipBlock, adjust the skipping probability parameter `p`. A higher probability means there's more chance a layer will be skipped during the training phase. Experiment with different values of `p` to find the optimal one that gives your model the best result. + +The `StochasticSkipBlock` module introduces randomness in your model's training process; therefore, results might vary slightly each time you train your model. Consider setting a seed for your PyTorch application to ensure reproducibility. + +## Conclusion +StochasticSkipBlock is a flexible module that makes it easy to introduce stochasticity into your model's architecture, acting as a regularizer that could improve your model's performance. It's important to experiment with this module to see how much randomness helps your specific use case. + +## References + +1. [Deep Networks with Stochastic Depth](https://arxiv.org/abs/1603.09382) +2. [Understanding the difficulty of training deep feedforward neural networks](http://proceedings.mlr.press/v9/glorot10a.html) +3. [Maxout Networks](https://arxiv.org/abs/1302.4389) diff --git a/docs/zeta/nn/modules/tripleskipblock.md b/docs/zeta/nn/modules/tripleskipblock.md new file mode 100644 index 00000000..652ffc8b --- /dev/null +++ b/docs/zeta/nn/modules/tripleskipblock.md @@ -0,0 +1,132 @@ +# zeta.nn.modules: TripleSkipBlock Documentation + +## Introduction + +TripleSkipBlock is a PyTorch-like custom neural network module that represents the block performing triple skip-connections. It's part of the zeta.nn.modules library. + +Skip-connections, also known as new pathways for channeling information earlier in the network to layers that are much deeper, is the underlying principle that constitutes this module. These connections assist in addressing the vanishing gradient problem during the training of deep neural networks, facilitating feature re-usage, and forging much more complex representations by integrating features on various scales. + +This module is an extension of the PyTorch's nn.Module class, and its purpose is widening the pathway for information flowing through the module. + +## Class Definition: TripleSkipBlock + +Here's the main constructor for the TripleSkipBlock class: + +```python +class TripleSkipBlock(nn.Module): + def __init__(self, submodule1, submodule2, submodule3): + """ + Defines the TripleSkipBlock module that performs triple skip connections. + + Args: + submodule1 (nn.Module): The first submodule. + submodule2 (nn.Module): The second submodule. + submodule3 (nn.Module): The third submodule. + """ + super(TripleSkipBlock, self).__init__() + self.submodule1 = submodule1 + self.submodule2 = submodule2 + self.submodule3 = submodule3 +``` + +The arguments for the constructor are: + +| Argument | Type | Description | +| ----------- | ----------- | ---------------------- | +| submodule1 | nn.Module | The first submodule. | +| submodule2 | nn.Module | The second submodule. | +| submodule3 | nn.Module | The third submodule. | + + +The class includes one method: + +```python +def forward(self, x: torch.Tensor): + """ + Implements the forward pass of the TripleSkipBlock module. + + Args: + x (torch.Tensor): The input tensor. + + Returns: + torch.Tensor: The output tensor after applying triple skip-connections. + """ + return x + self.submodule1(x + self.submodule2(x + self.submodule3(x))) +``` + +In this method, the forward pass of the module is defined. The forward method is invoked when we call the class with the input data. + +The argument for the `forward` method: + +| Argument | Type | Description | +| -------- | ------------ | -------------------------------------------- | +| x | torch.Tensor | Input tensor. | + +The return value of the `forward` method: + +| Return | Type | Description | +| -------- | ------------ | -------------------------------------------- | +| | torch.Tensor | The output tensor after applying triple skip connections.| + +### TripleSkipBlock Class: Working Mechanism + +The TripleSkipBlock class operates as follows: + +1. In the Class constructor `__init__`, three submodules are initialized. These submodules are instances of PyTorch modules (nn.Module) that implement their respective forward functions. As they're sub-modules of the TripleSkipBlock class, they will have their parameters registered in TripleSkipBlock's parameter list. +2. The forward function accomplishes the triple skip connection functionality. From the input `x`, it adds the output of `submodule3` applied on `x`, resulting in `x + self.submodule3(x)`. This intermediate output is then fed into `submodule2`, and again added with `x`. This process is repeated once more with `submodule1`. + +This iterative addition and integration of the input tensor, with the transformed tensor by each submodule, is referred to as a "skip connection." This is crucial to mitigate the problem of vanishing gradients in deep neural networks and to allow lower-layer information to be directly transferred to higher layers. + +## Examples + +##### Example 1: Simple usage + +Here's a simple example with three linear layers as the submodules: + +```python +import torch +import torch.nn as nn +from zeta.nn import TripleSkipBlock + +# Define input +input_tensor = torch.randn(10) + +# Define submodules +submodule1 = nn.Linear(10, 10) +submodule2 = nn.Linear(10, 10) +submodule3 = nn.Linear(10, 10) + +# Define TripleSkipBlock +tripleskip = TripleSkipBlock(submodule1, submodule2, submodule3) + +# Forward pass +output = tripleskip(input_tensor) +``` + +##### Example 2: Using the module with Conv2D sub-modules for processing images + +```python +import torch +import torch.nn as nn +from zeta.nn import TripleSkipBlock + +# Define input (single image with three channels, 64x64 resolution) +input_image = torch.randn(1, 3, 64, 64) + +# Define submodules +submodule1 = nn.Conv2d(3, 10, kernel_size=3, stride=1, padding=1) +submodule2 = nn.Conv2d(10, 10, kernel_size=3, stride=1, padding=1) +submodule3 = nn.Conv2d(10, 3, kernel_size=3, stride=1, padding=1) + +# Define TripleSkipBlock +tripleskip = TripleSkipBlock(submodule1, submodule2, submodule3) + +# Forward pass +output = tripleskip(input_image) +``` + +These are simple examples demonstrating the usage of the TripleSkipBlock. The submodules used in them are simple linear and convolutional layers. You can replace these with any kind of PyTorch module according to the specific network requirements. + +Remember that the purpose of this TripleSkipBlock module is to create more complex interactions between layers in the network with skip connections. This can improve the ability of the network to learn representations from data, especially when data is much complex with intricate patterns. + + diff --git a/example.py b/example.py index bbdfe085..5436652d 100644 --- a/example.py +++ b/example.py @@ -1,5 +1,5 @@ import torch -from zeta.nn.attention.flash_attention import FlashAttention +from zeta.nn import FlashAttention q = torch.randn(2, 4, 6, 8) k = torch.randn(2, 4, 10, 8) diff --git a/scripts/auto_tests_docs/auto_docs.py b/scripts/auto_tests_docs/auto_docs.py index c0b29395..d4cf6462 100644 --- a/scripts/auto_tests_docs/auto_docs.py +++ b/scripts/auto_tests_docs/auto_docs.py @@ -9,15 +9,11 @@ from swarms import OpenAIChat ########## -from zeta.models.andromeda import Andromeda -from zeta.models.base import BaseModel -from zeta.models.gpt4 import GPT4, GPT4MultiModal -from zeta.models.llama import LLama2 -from zeta.models.max_vit import MaxVit -from zeta.models.mega_vit import MegaVit -from zeta.models.palme import PalmE -from zeta.models.vit import ViT -from zeta.models.navit import NaViT +from zeta.nn.modules.triple_skip import TripleSkipBlock +from zeta.nn.modules.dynamic_routing_block import DynamicRoutingBlock +from zeta.nn.modules.gated_residual_block import GatedResidualBlock +from zeta.nn.modules.stochastic_depth import StochasticSkipBlocK + #################### load_dotenv() @@ -27,7 +23,7 @@ model = OpenAIChat( model_name="gpt-4", openai_api_key=api_key, - max_tokens=4000, + max_tokens=2000, ) @@ -45,14 +41,14 @@ def process_documentation(cls): # Process with OpenAI model (assuming the model's __call__ method takes this input and returns processed content) processed_content = model( - DOCUMENTATION_WRITER_SOP(input_content, "zeta.models") + DOCUMENTATION_WRITER_SOP(input_content, "zeta.nn.modules") ) # doc_content = f"# {cls.__name__}\n\n{processed_content}\n" doc_content = f"{processed_content}\n" # Create the directory if it doesn't exist - dir_path = "docs/zeta/models" + dir_path = "docs/zeta/nn/modules" os.makedirs(dir_path, exist_ok=True) # Write the processed documentation to a Markdown file @@ -65,16 +61,10 @@ def process_documentation(cls): def main(): classes = [ - Andromeda, - BaseModel, - GPT4, - GPT4MultiModal, - LLama2, - MaxVit, - MegaVit, - PalmE, - ViT, - NaViT, + TripleSkipBlock, + DynamicRoutingBlock, + GatedResidualBlock, + StochasticSkipBlocK, ] threads = [] @@ -87,7 +77,7 @@ def main(): for thread in threads: thread.join() - print("Documentation generated in 'docs/zeta/models' directory.") + print("Documentation generated in 'docs/zeta/nn/modules' directory.") if __name__ == "__main__": diff --git a/scripts/auto_tests_docs/auto_tests.py b/scripts/auto_tests_docs/auto_tests.py index 041d143b..f8c3d44d 100644 --- a/scripts/auto_tests_docs/auto_tests.py +++ b/scripts/auto_tests_docs/auto_tests.py @@ -10,15 +10,11 @@ # Tests will be automatically generated in the tests folder using parallized gpt4 with each of the file logic handled autonomously thus # leading to a much faster testing process where you just import your classes or functions and tests are automatically generated # Automating tests and documentation frees up atleast 75% of your time to focus on the actual logic of your code -from zeta.models.andromeda import Andromeda -from zeta.models.base import BaseModel -from zeta.models.gpt4 import GPT4, GPT4MultiModal -from zeta.models.llama import LLama2 -from zeta.models.max_vit import MaxVit -from zeta.models.mega_vit import MegaVit -from zeta.models.palme import PalmE -from zeta.models.vit import ViT -from zeta.models.navit import NaViT +from zeta.nn.modules.triple_skip import TripleSkipBlock +from zeta.nn.modules.dynamic_routing_block import DynamicRoutingBlock +from zeta.nn.modules.gated_residual_block import GatedResidualBlock +from zeta.nn.modules.stochastic_depth import StochasticSkipBlocK + #################### @@ -32,7 +28,7 @@ model = OpenAIChat( model_name="gpt-4", openai_api_key=api_key, - max_tokens=4000, + max_tokens=500, ) @@ -68,14 +64,14 @@ def create_test(cls): # Process with OpenAI model (assuming the model's __call__ method takes this input and returns processed content) processed_content = model( - TEST_WRITER_SOP_PROMPT(input_content, "zeta", "zeta.models") + TEST_WRITER_SOP_PROMPT(input_content, "zeta", "zeta.nn.modules") ) processed_content = extract_code_from_markdown(processed_content) doc_content = f"{processed_content}" # Create the directory if it doesn't exist - dir_path = "tests/models" + dir_path = "tests/nn/modules" os.makedirs(dir_path, exist_ok=True) # Write the processed documentation to a Python file @@ -88,16 +84,10 @@ def create_test(cls): def main(): classes = [ - Andromeda, - BaseModel, - GPT4, - GPT4MultiModal, - LLama2, - MaxVit, - MegaVit, - PalmE, - ViT, - NaViT, + TripleSkipBlock, + DynamicRoutingBlock, + GatedResidualBlock, + StochasticSkipBlocK, ] threads = [] @@ -110,7 +100,7 @@ def main(): for thread in threads: thread.join() - print("Tests generated in 'tests/models' directory.") + print("Tests generated in 'tests/nn/modules' directory.") if __name__ == "__main__": diff --git a/tests/nn/modules/dynamicroutingblock.py b/tests/nn/modules/dynamicroutingblock.py new file mode 100644 index 00000000..1c8475bf --- /dev/null +++ b/tests/nn/modules/dynamicroutingblock.py @@ -0,0 +1,52 @@ +import torch +import pytest +from torch.autograd import Variable +from zeta.nn.modules import DynamicRoutingBlock + +# Optional if you want to use parametrization +test_data = [ + ( + Variable(torch.randn(1, 5), requires_grad=True), + Variable(torch.randn(1, 5), requires_grad=True), + ), + ( + Variable(torch.randn(10, 5), requires_grad=True), + Variable(torch.randn(10, 5), requires_grad=True), + ), +] + + +@pytest.fixture +def mock_routing_module(monkeypatch): + # maybe you would like to mock the routing_module behavior, if it's complex or time-consuming + def mock_forward(x): + return torch.tensor(0.5) + + monkeypatch.setattr( + "Reference to routing_module_class", "forward", mock_forward + ) + + +@pytest.mark.parametrize("input1,input2", test_data) +def test_dynamic_routing_block_forward(input1, input2, mock_routing_module): + drb = DynamicRoutingBlock(input1, input2, mock_routing_module) + + output = drb.forward(torch.randn(1, 3)) + + assert output.size() == torch.Size([1, 3]) + assert torch.allclose(output, 0.5 * input1 + 0.5 * input2) + + +def test_dynamic_routing_block_module_assignment(): + sb1 = torch.nn.Linear(5, 3) + sb2 = torch.nn.Linear(5, 3) + routing_module = torch.nn.Linear(5, 1) + + drb = DynamicRoutingBlock(sb1, sb2, routing_module) + + assert drb.sb1 is sb1 + assert drb.sb2 is sb2 + assert drb.routing_module is routing_module + + +# And so on... You can generate more tests based on your needs diff --git a/tests/nn/modules/gatedresidualblock.py b/tests/nn/modules/gatedresidualblock.py new file mode 100644 index 00000000..8361cd8e --- /dev/null +++ b/tests/nn/modules/gatedresidualblock.py @@ -0,0 +1,39 @@ +import pytest +import torch +import torch.nn as nn +from torch.autograd import gradcheck +from zeta.nn.modules import GatedResidualBlock + + +class TestGatedResidualBlock: + @pytest.fixture(scope="class") + def init_grb(self): + sb1 = nn.Linear(3, 3) + gate_module = nn.Linear(3, 3) + return GatedResidualBlock(sb1, gate_module) + + # Test instance creation and types + def test_instance(self, init_grb): + assert isinstance(init_grb, GatedResidualBlock) + assert isinstance(init_grb.sb1, nn.Module) + assert isinstance(init_grb.gate_module, nn.Module) + + # Test forward pass + def test_forward(self, init_grb): + x = torch.rand(1, 3) + out = init_grb(x) + assert isinstance(out, torch.Tensor) + assert ( + out.shape == x.shape + ) # outputs and input tensors should have same shape + + # Test learnable parameters + def test_parameters(self, init_grb): + for param in init_grb.parameters(): + assert param.requires_grad + + # Gradients check + def test_gradients(self, init_grb): + x = torch.rand(1, 3, dtype=torch.double, requires_grad=True) + test = gradcheck(init_grb, (x,), raise_exception=True) + assert test diff --git a/tests/nn/modules/stochasticskipblock.py b/tests/nn/modules/stochasticskipblock.py new file mode 100644 index 00000000..1c6eb968 --- /dev/null +++ b/tests/nn/modules/stochasticskipblock.py @@ -0,0 +1,48 @@ +import torch +import torch.nn as nn +import pytest +from zeta.nn.modules import StochasticSkipBlocK + + +# Testing instance creation and basic properties +def test_init(): + sb1 = nn.Linear(5, 3) + block = StochasticSkipBlocK(sb1, p=0.7) + assert isinstance(block, nn.Module) + assert block.p == 0.7 + assert block.sb1 == sb1 + + +# Testing forward pass behaviour +def test_forward(monkeypatch): + sb1 = nn.Linear(5, 3) + block = StochasticSkipBlocK(sb1, p=0.7) + x = torch.rand(5) + + # Mock torch.rand() to return 0.8 to test the 'skip' scenario + def mock_rand(*args, **kwargs): + return torch.tensor([0.8]) + + monkeypatch.setattr(torch, "rand", mock_rand) + block.training = True + assert torch.allclose(block.forward(x), x) + + # Mock torch.rand() to return 0.6 to test the 'non-skip' scenario + def mock_rand_2(*args, **kwargs): + return torch.tensor([0.6]) + + monkeypatch.setattr(torch, "rand", mock_rand_2) + assert not torch.allclose(block.forward(x), x) + + +# Testing invalid input handling +def test_invalid_p_constructor(): + sb1 = nn.Linear(5, 3) + + with pytest.raises(ValueError): + # p value less than 0 + _ = StochasticSkipBlocK(sb1, p=-0.1) + + with pytest.raises(ValueError): + # p value more than 1 + _ = StochasticSkipBlocK(sb1, p=1.1) diff --git a/tests/nn/modules/tripleskipblock.py b/tests/nn/modules/tripleskipblock.py new file mode 100644 index 00000000..a848fc79 --- /dev/null +++ b/tests/nn/modules/tripleskipblock.py @@ -0,0 +1,61 @@ +import pytest +import torch +import torch.nn as nn +from zeta.nn.modules import TripleSkipBlock + + +# Create Dummy Modules for Testing +class DummyModule(nn.Module): + def forward(self, x): + return x * 2 + + +# A helper function to create an instance of TripleSkipBlock +@pytest.fixture +def triple_skip_block(): + module1 = module2 = module3 = DummyModule() + return TripleSkipBlock(module1, module2, module3) + + +# Test for forward method +def test_forward(triple_skip_block): + x = torch.tensor([1, 2, 3], dtype=torch.float32) + output = triple_skip_block(x) + assert torch.all( + torch.eq(output, torch.tensor([15, 30, 45], dtype=torch.float32)) + ) + + +# Test for correct instance creation +def test_instance_creation(triple_skip_block): + assert isinstance(triple_skip_block.submodule1, DummyModule) + assert isinstance(triple_skip_block.submodule2, DummyModule) + assert isinstance(triple_skip_block.submodule3, DummyModule) + + +# Test for correct instance training mode +def test_training_mode(triple_skip_block): + assert triple_skip_block.training is True + triple_skip_block.eval() + assert triple_skip_block.training is False + + +# Test to validate whether adding submodule modifies tensor correctly +@pytest.mark.parametrize( + "input_tensor, expected_output", + [ + ( + torch.tensor([1, 1, 1], dtype=torch.float32), + torch.tensor([15, 15, 15], dtype=torch.float32), + ), + ( + torch.tensor([2, 2, 2], dtype=torch.float32), + torch.tensor([30, 30, 30], dtype=torch.float32), + ), + ], +) +def test_with_different_inputs( + triple_skip_block, input_tensor, expected_output +): + output = triple_skip_block(input_tensor) + assert torch.all(torch.eq(output, expected_output)) diff --git a/zeta/nn/modules/__init__.py b/zeta/nn/modules/__init__.py index 283d5643..dde5a728 100644 --- a/zeta/nn/modules/__init__.py +++ b/zeta/nn/modules/__init__.py @@ -67,6 +67,12 @@ ReLUSquaredActivation, ) + +from zeta.nn.modules.triple_skip import TripleSkipBlock +from zeta.nn.modules.dynamic_routing_block import DynamicRoutingBlock +from zeta.nn.modules.gated_residual_block import GatedResidualBlock +from zeta.nn.modules.stochastic_depth import StochasticSkipBlocK + # from zeta.nn.modules.img_reshape import image_reshape # from zeta.nn.modules.flatten_features import flatten_features # from zeta.nn.modules.scaled_sinusoidal import ScaledSinuosidalEmbedding @@ -149,4 +155,8 @@ "LinearActivation", "LaplaceActivation", "ReLUSquaredActivation", + "TripleSkipBlock", + "DynamicRoutingBlock", + "GatedResidualBlock", + "StochasticSkipBlocK", ] diff --git a/zeta/nn/modules/dynamic_routing_block.py b/zeta/nn/modules/dynamic_routing_block.py new file mode 100644 index 00000000..d4239d6e --- /dev/null +++ b/zeta/nn/modules/dynamic_routing_block.py @@ -0,0 +1,35 @@ +import torch +from torch import nn + + +class DynamicRoutingBlock(nn.Module): + def __init__(self, sb1, sb2, routing_module): + """ + A module that performs dynamic routing between two sub-blocks based on routing weights. + + Args: + sb1 (nn.Module): The first sub-block. + sb2 (nn.Module): The second sub-block. + routing_module (nn.Module): The module that computes routing weights. + + """ + super().__init__() + self.sb1 = sb1 + self.sb2 = sb2 + self.routing_module = routing_module + + def forward(self, x: torch.Tensor): + """ + Forward pass of the dynamic routing block. + + Args: + x (torch.Tensor): The input tensor. + + Returns: + torch.Tensor: The output tensor after dynamic routing. + + """ + routing_weights = self.routing_module(x) + return routing_weights * self.sb1(x) + (1 - routing_weights) * self.sb2( + x + ) diff --git a/zeta/nn/modules/gated_residual_block.py b/zeta/nn/modules/gated_residual_block.py new file mode 100644 index 00000000..8facefb8 --- /dev/null +++ b/zeta/nn/modules/gated_residual_block.py @@ -0,0 +1,31 @@ +import torch +from torch import nn + + +class GatedResidualBlock(nn.Module): + def __init__(self, sb1, gate_module): + """ + Gated Residual Block module. + + Args: + sb1 (nn.Module): The first sub-block. + gate_module (nn.Module): The gate module. + + """ + super().__init__() + self.sb1 = sb1 + self.gate_module = gate_module + + def forward(self, x: torch.Tensor): + """ + Forward pass of the Gated Residual Block. + + Args: + x (torch.Tensor): Input tensor. + + Returns: + torch.Tensor: Output tensor. + + """ + gate = torch.sigmoid(self.gate_module(x)) + return x + gate * self.sb1(x) diff --git a/zeta/nn/modules/stochastic_depth.py b/zeta/nn/modules/stochastic_depth.py new file mode 100644 index 00000000..7d246d32 --- /dev/null +++ b/zeta/nn/modules/stochastic_depth.py @@ -0,0 +1,35 @@ +import torch +from torch import nn + + +class StochasticSkipBlocK(nn.Module): + """ + A module that implements stochastic skip connections in a neural network. + + Args: + sb1 (nn.Module): The module to be skipped with a certain probability. + p (float): The probability of skipping the module. Default is 0.5. + + Returns: + torch.Tensor: The output tensor after applying the stochastic skip connection. + """ + + def __init__(self, sb1, p=0.5): + super().__init__() + self.sb1 = sb1 + self.p = p + + def forward(self, x: torch.Tensor): + """ + Forward pass of the StochasticDepth module. + + Args: + x (torch.Tensor): Input tensor. + + Returns: + torch.Tensor: Output tensor after applying the StochasticDepth module. + """ + if self.training and torch.rand(1).item() < self.p: + return x # Skip the sb1 + else: + return self.sb1(x) diff --git a/zeta/nn/modules/triple_skip.py b/zeta/nn/modules/triple_skip.py new file mode 100644 index 00000000..6a004732 --- /dev/null +++ b/zeta/nn/modules/triple_skip.py @@ -0,0 +1,30 @@ +import torch +from torch import nn + + +class TripleSkipBlock(nn.Module): + def __init__(self, submodule1, submodule2, submodule3): + """ + TripleSkipBlock class represents a block that performs triple skip connections. + + Args: + submodule1 (nn.Module): The first submodule. + submodule2 (nn.Module): The second submodule. + submodule3 (nn.Module): The third submodule. + """ + super(TripleSkipBlock, self).__init__() + self.submodule1 = submodule1 + self.submodule2 = submodule2 + self.submodule3 = submodule3 + + def forward(self, x: torch.Tensor): + """ + Forward pass of the TripleSkipBlock. + + Args: + x (torch.Tensor): The input tensor. + + Returns: + torch.Tensor: The output tensor after applying triple skip connections. + """ + return x + self.submodule1(x + self.submodule2(x + self.submodule(x))) From 8dc089765c89fcdac109582a7fe574f72dbf40ce Mon Sep 17 00:00:00 2001 From: Kye Date: Wed, 27 Dec 2023 11:36:06 -0500 Subject: [PATCH 226/587] [FEAT][Test Names] --- .../{dynamicroutingblock.py => test_dynamicroutingblock.py} | 0 .../modules/{gatedresidualblock.py => test_gatedresidualblock.py} | 0 .../{stochasticskipblock.py => test_stochasticskipblock.py} | 0 tests/nn/modules/{tripleskipblock.py => test_tripleskipblock.py} | 0 4 files changed, 0 insertions(+), 0 deletions(-) rename tests/nn/modules/{dynamicroutingblock.py => test_dynamicroutingblock.py} (100%) rename tests/nn/modules/{gatedresidualblock.py => test_gatedresidualblock.py} (100%) rename tests/nn/modules/{stochasticskipblock.py => test_stochasticskipblock.py} (100%) rename tests/nn/modules/{tripleskipblock.py => test_tripleskipblock.py} (100%) diff --git a/tests/nn/modules/dynamicroutingblock.py b/tests/nn/modules/test_dynamicroutingblock.py similarity index 100% rename from tests/nn/modules/dynamicroutingblock.py rename to tests/nn/modules/test_dynamicroutingblock.py diff --git a/tests/nn/modules/gatedresidualblock.py b/tests/nn/modules/test_gatedresidualblock.py similarity index 100% rename from tests/nn/modules/gatedresidualblock.py rename to tests/nn/modules/test_gatedresidualblock.py diff --git a/tests/nn/modules/stochasticskipblock.py b/tests/nn/modules/test_stochasticskipblock.py similarity index 100% rename from tests/nn/modules/stochasticskipblock.py rename to tests/nn/modules/test_stochasticskipblock.py diff --git a/tests/nn/modules/tripleskipblock.py b/tests/nn/modules/test_tripleskipblock.py similarity index 100% rename from tests/nn/modules/tripleskipblock.py rename to tests/nn/modules/test_tripleskipblock.py From 430ac25667094f9fa75afc4fa195052d0eba7554 Mon Sep 17 00:00:00 2001 From: Kye