add mixtral model patch

Signed-off-by: Anh Uong <[email protected]>

plugins/fused-ops-and-kernels/src/fms_acceleration_foak/kernels/liger/fused_linear_cross_entropy_loss.py

@@ -34,6 +34,14 @@
     _CONFIG_FOR_DOC,
     LLAMA_INPUTS_DOCSTRING,
 )
+from transformers.models.mixtral.modeling_mixtral import (
+    _CONFIG_FOR_DOC,
+    MIXTRAL_INPUTS_DOCSTRING,
+)
+from transformers.modeling_outputs import (
+    MoeCausalLMOutputWithPast,
+    MoeModelOutputWithPast,
+)
 from transformers.utils import (
     add_start_docstrings_to_model_forward,
     replace_return_docstrings,
@@ -289,7 +297,8 @@ def forward(self, lin_weight, _input, target, bias=None):
             self.reduction,
         )
 
-@add_start_docstrings_to_model_forward(LLAMA_INPUTS_DOCSTRING)
+# TODO: how to add diff docstrings for diff model types? what if the loss functions aren't the same across models?
+# @add_start_docstrings_to_model_forward(LLAMA_INPUTS_DOCSTRING)
 @replace_return_docstrings(
     output_type=CausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC
 )
@@ -328,9 +337,9 @@ def lce_forward(
     Example:
 
     ```python
-    >>> from transformers import AutoTokenizer, LlamaForCausalLM
+    >>> from transformers import AutoTokenizer, AutoModelForCausalLM
 
-    >>> model = LlamaForCausalLM.from_pretrained("meta-llama/Llama-2-7b-hf")
+    >>> model = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-2-7b-hf")
     >>> tokenizer = AutoTokenizer.from_pretrained("meta-llama/Llama-2-7b-hf")
 
     >>> prompt = "Hey, are you conscious? Can you talk to me?"
@@ -374,6 +383,7 @@ def lce_forward(
     loss = None
     logits = None
 
+    # patch change
     if self.training and (labels is not None):
         shift_hidden_states = hidden_states[..., :-1, :].contiguous()
         shift_labels = labels[..., 1:].contiguous()
@@ -425,4 +435,143 @@ def lce_forward(
         past_key_values=outputs.past_key_values,
         hidden_states=outputs.hidden_states,
         attentions=outputs.attentions,
+    )
+
+# TODO: is adding a separate copy of lce_forward() the right path or should the additional logic for Moe models be in the single lce_forward?
+@add_start_docstrings_to_model_forward(MIXTRAL_INPUTS_DOCSTRING)
+@replace_return_docstrings(output_type=MoeCausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC)
+# Ignore copy
+def lce_forward_mixtral(
+    self,
+    input_ids: torch.LongTensor = None,
+    attention_mask: Optional[torch.Tensor] = None,
+    position_ids: Optional[torch.LongTensor] = None,
+    past_key_values: Optional[List[torch.FloatTensor]] = None,
+    inputs_embeds: Optional[torch.FloatTensor] = None,
+    labels: Optional[torch.LongTensor] = None,
+    use_cache: Optional[bool] = None,
+    output_attentions: Optional[bool] = None,
+    output_hidden_states: Optional[bool] = None,
+    output_router_logits: Optional[bool] = None,
+    return_dict: Optional[bool] = None,
+    cache_position: Optional[torch.LongTensor] = None,
+    num_logits_to_keep: int = 0,
+) -> Union[Tuple, MoeCausalLMOutputWithPast]:
+    r"""
+    Args:
+        labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+            Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
+            config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
+            (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
+
+        num_logits_to_keep (`int`, *optional*):
+            Calculate logits for the last `num_logits_to_keep` tokens. If `0`, calculate logits for all
+            `input_ids` (special case). Only last token logits are needed for generation, and calculating them only for that
+            token can save memory, which becomes pretty significant for long sequences or large vocabulary size.
+
+    Returns:
+
+    Example:
+
+    ```python
+    >>> from transformers import AutoTokenizer, MixtralForCausalLM
+
+    >>> model = MixtralForCausalLM.from_pretrained("mistralai/Mixtral-8x7B-v0.1")
+    >>> tokenizer = AutoTokenizer.from_pretrained("mistralai/Mixtral-8x7B-v0.1")
+
+    >>> prompt = "Hey, are you conscious? Can you talk to me?"
+    >>> inputs = tokenizer(prompt, return_tensors="pt")
+
+    >>> # Generate
+    >>> generate_ids = model.generate(inputs.input_ids, max_length=30)
+    >>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
+    "Hey, are you conscious? Can you talk to me?\nI'm not conscious, but I can talk to you."
+    ```"""
+
+    output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+    output_router_logits = (
+        output_router_logits if output_router_logits is not None else self.config.output_router_logits
+    )
+
+    output_hidden_states = (
+        output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+    )
+    return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+    # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
+    outputs = self.model(
+        input_ids=input_ids,
+        attention_mask=attention_mask,
+        position_ids=position_ids,
+        past_key_values=past_key_values,
+        inputs_embeds=inputs_embeds,
+        use_cache=use_cache,
+        output_attentions=output_attentions,
+        output_hidden_states=output_hidden_states,
+        output_router_logits=output_router_logits,
+        return_dict=return_dict,
+        cache_position=cache_position,
+    )
+
+    hidden_states = outputs[0]
+
+    loss = None
+    logits = None
+
+    # patch change
+    if self.training and (labels is not None):
+        shift_hidden_states = hidden_states[..., :-1, :].contiguous()
+        shift_labels = labels[..., 1:].contiguous()
+
+        # flatten tokens
+        shift_hidden_states = shift_hidden_states.view(-1, self.config.hidden_size)
+        shift_labels = shift_labels.view(-1)
+
+        lce = LigerFusedLinearCrossEntropyLoss()
+        loss = lce(self.lm_head.weight, shift_hidden_states, shift_labels)
+    else:
+        # Only compute necessary logits, and do not upcast them to float if we are not computing the loss
+        logits = self.lm_head(hidden_states[:, -num_logits_to_keep:, :])
+
+        if labels is not None:
+            # Upcast to float if we need to compute the loss to avoid potential precision issues
+            logits = logits.float()
+            # Shift so that tokens < n predict n
+            shift_logits = logits[..., :-1, :].contiguous()
+            shift_labels = labels[..., 1:].contiguous()
+            # Flatten the tokens
+            loss_fct = CrossEntropyLoss()
+            shift_logits = shift_logits.view(-1, self.config.vocab_size)
+            shift_labels = shift_labels.view(-1)
+            # Enable model parallelism
+            shift_labels = shift_labels.to(shift_logits.device)
+            loss = loss_fct(shift_logits, shift_labels)
+
+    # TODO: unique differing part to mixtral model forward
+    aux_loss = None
+    if output_router_logits:
+        aux_loss = load_balancing_loss_func(
+            outputs.router_logits if return_dict else outputs[-1],
+            self.num_experts,
+            self.num_experts_per_tok,
+            attention_mask,
+        )
+        # TODO: should this loss manipulation be indented in?? or should it be added to even the liger loss?
+        if labels is not None:
+            loss += self.router_aux_loss_coef * aux_loss.to(loss.device)  # make sure to reside in the same device
+
+    if not return_dict:
+        output = (logits,) + outputs[1:]
+        if output_router_logits:
+            output = (aux_loss,) + output
+        return (loss,) + output if loss is not None else output
+
+    return MoeCausalLMOutputWithPast(
+        loss=loss,
+        aux_loss=aux_loss,
+        logits=logits,
+        past_key_values=outputs.past_key_values,
+        hidden_states=outputs.hidden_states,
+        attentions=outputs.attentions,
+        router_logits=outputs.router_logits,
     )

plugins/fused-ops-and-kernels/src/fms_acceleration_foak/models/mixtral.py

@@ -23,6 +23,7 @@
     combine_triggers,
 )
 from transformers.models.mixtral.modeling_mixtral import (
+    MixtralForCausalLM,
     MixtralAttention,
     MixtralRMSNorm,
 )
@@ -31,6 +32,7 @@
 from ..kernels.unsloth.cross_entropy_loss import FastCrossEntropyLoss
 from ..kernels.unsloth.rms_layernorm import fast_rms_layernorm
 from ..kernels.unsloth.rope_embedding import fast_rope_embedding
+from ..kernels.liger.fused_linear_cross_entropy_loss import lce_forward_mixtral
 from .utils import KEY_O, KEY_QKV, build_lora_fused_ops, trigger_fused_ops
 
 
@@ -93,6 +95,11 @@ def get_mp_rules(base_type):
                 "transformers.models.mixtral.modeling_mixtral",
             ),
         ),
+        ModelPatcherRule(
+            rule_id="mixtral-fused-lce",
+            trigger=ModelPatcherTrigger(check=MixtralForCausalLM),
+            forward=lce_forward_mixtral,
+        ),
         ModelPatcherRule(
             rule_id="mixtral-rope",
             import_and_maybe_reload=(