From 788f67e9ca1660d061777e8648410e9a3af291c3 Mon Sep 17 00:00:00 2001 From: Patrick von Platen Date: Fri, 24 May 2024 17:58:40 +0000 Subject: [PATCH] Add LoRA --- README.md | 3 + poetry.lock | 717 +++++++++++++++++++++++++++--- pyproject.toml | 4 +- src/mistral_inference/__init__.py | 2 +- src/mistral_inference/lora.py | 166 +++++++ src/mistral_inference/main.py | 12 +- src/mistral_inference/model.py | 29 +- 7 files changed, 850 insertions(+), 83 deletions(-) create mode 100644 src/mistral_inference/lora.py diff --git a/README.md b/README.md index 1a71d27..5c2134d 100644 --- a/README.md +++ b/README.md @@ -233,3 +233,6 @@ Instructions to run the image can be found in the [official documentation](https - Use Mistral models on [Mistral AI official API](https://console.mistral.ai/) (La Plateforme) - Use Mistral models via [cloud providers](https://docs.mistral.ai/deployment/cloud/overview/) +## References + +[1]: [LoRA](https://arxiv.org/abs/2106.09685): Low-Rank Adaptation of Large 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= "mistral_inference" -version = "v1.0.4" +version = "v1.1.0" description = "" authors = ["bam4d "] readme = "README.md" @@ -24,7 +24,7 @@ exclude = ["docs", "tools", "build"] [tool.poetry.dependencies] python = "^3.9.10" -xformers = ">=0.0.25" +xformers = ">=0.0.24" simple-parsing = ">=0.1.5" fire = ">=0.6.0" mistral_common = "^1.0.0" diff --git a/src/mistral_inference/__init__.py b/src/mistral_inference/__init__.py index 92192ee..6849410 100644 --- a/src/mistral_inference/__init__.py +++ b/src/mistral_inference/__init__.py @@ -1 +1 @@ -__version__ = "1.0.4" +__version__ = "1.1.0" diff --git a/src/mistral_inference/lora.py b/src/mistral_inference/lora.py new file mode 100644 index 0000000..a3846e2 --- /dev/null +++ b/src/mistral_inference/lora.py @@ -0,0 +1,166 @@ +import logging +from dataclasses import dataclass +from pathlib import Path +from typing import Dict, NamedTuple, Union + +import safetensors.torch +import torch +import torch.nn as nn +from simple_parsing.helpers import Serializable + + +@dataclass +class LoraArgs(Serializable): + rank: int + scaling: float + + def __post_init__(self): + assert self.rank > 0 + assert self.scaling > 0.0 + + +class LoRALinear(nn.Module): + """ + Implementation of: + - LoRA: https://arxiv.org/abs/2106.09685 + + Notes: + - Freezing is handled at network level, not layer level. + - Scaling factor controls relative importance of LoRA skip + connection versus original frozen weight. General guidance is + to keep it to 2.0 and sweep over learning rate when changing + the rank. + """ + + def __init__( + self, + in_features: int, + out_features: int, + rank: int, + scaling: float, + bias: bool = False, + ): + super().__init__() + + self.in_features = in_features + self.out_features = out_features + assert not bias + self.bias = bias + self.rank = rank + self.scaling = scaling + + self.lora_A = nn.Linear( + self.in_features, + self.rank, + bias=self.bias, + ) + self.lora_B = nn.Linear( + self.rank, + self.out_features, + bias=self.bias, + ) + + self.linear = nn.Linear(self.in_features, self.out_features, bias=self.bias) + + # make sure no LoRA weights are marked as "missing" in load_state_dict + def ignore_missing_keys(m: nn.Module, incompatible_keys: NamedTuple): + incompatible_keys.missing_keys[:] = [] # type: ignore + + self.register_load_state_dict_post_hook(ignore_missing_keys) + + def forward(self, x: torch.Tensor): + lora = self.lora_B(self.lora_A(x)) + return self.linear(x) + lora * self.scaling + + def _load_from_state_dict(self, state_dict, prefix, *args, **kwargs): + key_name = prefix + "weight" + + # full checkpoint + if key_name in state_dict: + w_ref = state_dict[key_name] + + # load frozen weights + state_dict = { + "linear.weight": w_ref, + "lora_A.weight": torch.zeros_like( + self.lora_A.weight, device=w_ref.device, dtype=w_ref.dtype + ), + "lora_B.weight": torch.zeros_like( + self.lora_B.weight, device=w_ref.device, dtype=w_ref.dtype + ), + } + self.load_state_dict(state_dict, assign=True, strict=True) + + +class LoRALoaderMixin: + def load_lora(self, lora_path: Union[Path, str], scaling: float = 2.0): + """Loads LoRA checkpoint""" + + lora_path = Path(lora_path) + assert lora_path.is_file(), f"{lora_path} does not exist or is not a file" + + state_dict = safetensors.torch.load_file(lora_path) + + self._load_lora_state_dict(state_dict, scaling=scaling) + + def _load_lora_state_dict( + self, lora_state_dict: Dict[str, torch.Tensor], scaling: float = 2.0 + ): + """Loads LoRA state_dict""" + + lora_dtypes = set([p.dtype for p in lora_state_dict.values()]) + assert ( + len(lora_dtypes) == 1 + ), f"LoRA weights have multipe different dtypes {lora_dtypes}. All weights need to have the same dtype" + lora_dtype = lora_dtypes.pop() + assert ( + lora_dtype == self.dtype + ), f"LoRA weights dtype differs from model's dtype {lora_dtype} != {self.dtype}" + assert all("lora" in key for key in lora_state_dict.keys()) + + # move tensors to device + lora_state_dict = {k: v.to(self.device) for k, v in lora_state_dict.items()} + + state_dict = self.state_dict() + + if self.args.lora is None: + logging.info("Loading and merging LoRA weights...") + + # replace every nn.Linear with a LoRALinear with 'meta' device except the output layer + named_modules = dict(self.named_modules()) + for name, module in named_modules.items(): + if isinstance(module, nn.Linear) and name != "output": + layer_id = name.split(".")[1] + if layer_id not in self.layers: + logging.debug( + "Skipping parameter %s at pipeline rank %d", + name, + self.pipeline_rank, + ) + else: + weight = ( + module.weight + + ( + lora_state_dict[name + ".lora_B.weight"] + @ lora_state_dict[name + ".lora_A.weight"] + ) + * scaling + ) + + state_dict[name + ".weight"] = weight + else: + logging.info("Loading LoRA weights...") + for k, v in lora_state_dict.items(): + state_dict.update(lora_state_dict) + + layer_id = k.split(".")[1] + if layer_id in self.layers: + state_dict[k] = v + else: + logging.debug( + "Skipping parameter %s at pipeline rank %d", + k, + self.pipeline_rank, + ) + + self.load_state_dict(state_dict, strict=True) diff --git a/src/mistral_inference/main.py b/src/mistral_inference/main.py index 5ee3c88..a5ef3a0 100644 --- a/src/mistral_inference/main.py +++ b/src/mistral_inference/main.py @@ -1,7 +1,7 @@ import logging import os from pathlib import Path -from typing import List +from typing import List, Optional import fire # type: ignore import torch @@ -46,6 +46,7 @@ def interactive( temperature: float = 0.7, num_pipeline_ranks: int = 1, instruct: bool = False, + lora_path: Optional[str] = None, ) -> None: if is_torchrun(): torch.distributed.init_process_group() @@ -64,6 +65,10 @@ def interactive( Path(model_path), max_batch_size=3, num_pipeline_ranks=num_pipeline_ranks ) + # load LoRA + if lora_path is not None: + transformer.load_lora(Path(lora_path)) + prompt: str = "" messages: List[UserMessage | AssistantMessage] = [] @@ -117,6 +122,7 @@ def demo( model_path: str, max_tokens: int = 35, temperature: float = 0, + lora_path: Optional[str] = None, ) -> None: if is_torchrun(): torch.distributed.init_process_group() @@ -131,6 +137,10 @@ def demo( transformer = Transformer.from_folder( Path(model_path), max_batch_size=3, num_pipeline_ranks=num_pipeline_ranks ) + # load LoRA + if lora_path is not None: + transformer.load_lora(Path(lora_path)) + mistral_tokenizer: MistralTokenizer = load_tokenizer(Path(model_path)) tokenizer: Tokenizer = mistral_tokenizer.instruct_tokenizer.tokenizer diff --git a/src/mistral_inference/model.py b/src/mistral_inference/model.py index 43d2e95..8295796 100644 --- a/src/mistral_inference/model.py +++ b/src/mistral_inference/model.py @@ -2,6 +2,7 @@ import logging import math from dataclasses import dataclass +from functools import partial from pathlib import Path from typing import Any, List, Mapping, Optional, Tuple, Union @@ -16,6 +17,7 @@ CacheInputMetadata, CacheView, ) +from mistral_inference.lora import LoraArgs, LoRALinear, LoRALoaderMixin from mistral_inference.moe import MoeArgs, MoeLayer from mistral_inference.rope import apply_rotary_emb, precompute_freqs_cis @@ -37,6 +39,8 @@ class ModelArgs(Serializable): rope_theta: Optional[float] = None # If this is set, we will use MoE layers instead of dense layers. moe: Optional[MoeArgs] = None + # If this is set, we will load LoRA linear layers instead of linear layers. + lora: Optional[LoraArgs] = None @dataclass @@ -61,6 +65,13 @@ def repeat_kv( return keys, values +def maybe_lora(args: ModelArgs) -> Union[nn.Linear, LoRALinear]: + if args.lora is None: + return nn.Linear + else: + return partial(LoRALinear, rank=args.lora.rank, scaling=args.lora.scaling) + + class Attention(nn.Module): def __init__(self, args: ModelArgs): super().__init__() @@ -74,10 +85,11 @@ def __init__(self, args: ModelArgs): self.scale = self.args.head_dim**-0.5 - self.wq = nn.Linear(args.dim, args.n_heads * args.head_dim, bias=False) - self.wk = nn.Linear(args.dim, args.n_kv_heads * args.head_dim, bias=False) - self.wv = nn.Linear(args.dim, args.n_kv_heads * args.head_dim, bias=False) - self.wo = nn.Linear(args.n_heads * args.head_dim, args.dim, bias=False) + MaybeLora = maybe_lora(args) + self.wq = MaybeLora(args.dim, args.n_heads * args.head_dim, bias=False) + self.wk = MaybeLora(args.dim, args.n_kv_heads * args.head_dim, bias=False) + self.wv = MaybeLora(args.dim, args.n_kv_heads * args.head_dim, bias=False) + self.wo = MaybeLora(args.n_heads * args.head_dim, args.dim, bias=False) def forward( self, @@ -127,9 +139,10 @@ class FeedForward(nn.Module): def __init__(self, args: ModelArgs): super().__init__() - self.w1 = nn.Linear(args.dim, args.hidden_dim, bias=False) - self.w2 = nn.Linear(args.hidden_dim, args.dim, bias=False) - self.w3 = nn.Linear(args.dim, args.hidden_dim, bias=False) + MaybeLora = maybe_lora(args) + self.w1 = MaybeLora(args.dim, args.hidden_dim, bias=False) + self.w2 = MaybeLora(args.hidden_dim, args.dim, bias=False) + self.w3 = MaybeLora(args.dim, args.hidden_dim, bias=False) def forward(self, x: torch.Tensor) -> torch.Tensor: return self.w2(nn.functional.silu(self.w1(x)) * self.w3(x)) # type: ignore @@ -179,7 +192,7 @@ def forward( return out -class Transformer(nn.Module): +class Transformer(nn.Module, LoRALoaderMixin): def __init__( self, args: ModelArgs,