fix: Fix llava (#743)

## Summary
<!--- This is a required section; please describe the main purpose of
this proposed code change. --->
Resolve a part of #723 
<!---
## Details
This is an optional section; is there anything specific that reviewers
should be aware of?
--->

## Testing Done
<!--- This is a required section; please describe how this change was
tested. --->
Convergence Test:
bf16/test_mini_models:
```
python -m pytest test/convergence/bf16/test_mini_models.py -k llava
===================================================================================== test session starts =====================================================================================
platform linux -- Python 3.10.14, pytest-8.3.5, pluggy-1.5.0
rootdir: /home/jobuser/Liger-Kernel
configfile: pyproject.toml
plugins: xdist-3.7.0, rerunfailures-15.1, anyio-4.9.0, lipy-config-base-32.9.0, lipy-fabric-36.1.5, lipy-test-9.1.34, datadir-1.6.1
collecting ... 
------------------------------------------------------------------------------------- live log collection -------------------------------------------------------------------------------------
INFO     datasets:config.py:54 PyTorch version 2.7.0 available.
collected 16 items / 15 deselected / 1 selected                                                                                                                                               

test/convergence/bf16/test_mini_models.py::test_mini_model[mini_llava-32-0.0001-dtype1-0.001-0.01-0.1-0.01-0.01-0.01] PASSED                                                            [100%]

```

bf16/test_mini_models_multimodal:
```
python -m pytest test/convergence/bf16/test_mini_models_multimodal.py -k llava
===================================================================================== test session starts =====================================================================================
platform linux -- Python 3.10.14, pytest-8.3.5, pluggy-1.5.0
rootdir: /home/jobuser/Liger-Kernel
configfile: pyproject.toml
plugins: xdist-3.7.0, rerunfailures-15.1, anyio-4.9.0, lipy-config-base-32.9.0, lipy-fabric-36.1.5, lipy-test-9.1.34, datadir-1.6.1
collecting ... 
------------------------------------------------------------------------------------- live log collection -------------------------------------------------------------------------------------
INFO     datasets:config.py:54 PyTorch version 2.7.0 available.
collected 7 items / 6 deselected / 1 selected                                                                                                                                                 

test/convergence/bf16/test_mini_models_multimodal.py::test_mini_model_multimodal[mini_llava-32-0.0001-dtype1-0.001-0.01-0.1-0.01-0.01-0.01] PASSED
```
fp32/test_mini_models:
```
python -m pytest test/convergence/fp32/test_mini_models.py -k llava
===================================================================================== test session starts =====================================================================================
platform linux -- Python 3.10.14, pytest-8.3.5, pluggy-1.5.0
rootdir: /home/jobuser/Liger-Kernel
configfile: pyproject.toml
plugins: xdist-3.7.0, rerunfailures-15.1, anyio-4.9.0, lipy-config-base-32.9.0, lipy-fabric-36.1.5, lipy-test-9.1.34, datadir-1.6.1
collecting ... 
------------------------------------------------------------------------------------- live log collection -------------------------------------------------------------------------------------
INFO     datasets:config.py:54 PyTorch version 2.7.0 available.
collected 17 items / 16 deselected / 1 selected                                                                                                                                               

test/convergence/fp32/test_mini_models.py::test_mini_model[mini_llava-32-0.0001-dtype1-1e-08-1e-05-0.005-1e-05-0.005-1e-05] PASSED                                                      [100%]
```

fp32/test_mini_models_multimodal:
```
python -m pytest test/convergence/fp32/test_mini_models_multimodal.py -k llava
===================================================================================== test session starts =====================================================================================
platform linux -- Python 3.10.14, pytest-8.3.5, pluggy-1.5.0
rootdir: /home/jobuser/Liger-Kernel
configfile: pyproject.toml
plugins: xdist-3.7.0, rerunfailures-15.1, anyio-4.9.0, lipy-config-base-32.9.0, lipy-fabric-36.1.5, lipy-test-9.1.34, datadir-1.6.1
collecting ... 
------------------------------------------------------------------------------------- live log collection -------------------------------------------------------------------------------------
INFO     datasets:config.py:54 PyTorch version 2.7.0 available.
collected 7 items / 6 deselected / 1 selected                                                                                                                                                 

test/convergence/fp32/test_mini_models_multimodal.py::test_mini_model_multimodal[mini_llava-32-0.0001-dtype1-1e-08-1e-05-0.005-1e-05-0.005-1e-05] PASSED                                [100%]
```

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Complete the following tasks before sending your PR, and replace `[ ]`
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- Hardware Type: <BLANK>
- [ ] run `make test` to ensure correctness
- [ ] run `make checkstyle` to ensure code style
- [ ] run `make test-convergence` to ensure convergence

---------

Co-authored-by: Shao Tang <[email protected]>
Co-authored-by: Vaibhav Jindal <[email protected]>

Author: 3 people

src/liger_kernel/transformers/model/llava.py

@@ -7,10 +7,9 @@
 
 from torch.nn import CrossEntropyLoss
 from transformers.models.llava.modeling_llava import LlavaCausalLMOutputWithPast
-from transformers.utils import is_torchdynamo_compiling
-from transformers.utils.deprecation import deprecate_kwarg
 
 from liger_kernel.transformers.fused_linear_cross_entropy import LigerFusedLinearCrossEntropyLoss
+from liger_kernel.transformers.model.loss_utils import LigerForCausalLMLoss
 
 
 def lce_forward_deprecated(
@@ -28,6 +27,11 @@ def lce_forward_deprecated(
     output_attentions: Optional[bool] = None,
     output_hidden_states: Optional[bool] = None,
     return_dict: Optional[bool] = None,
+    cache_position: Optional[torch.LongTensor] = None,
+    logits_to_keep: Union[int, torch.Tensor] = 0,
+    image_sizes: torch.Tensor = None,
+    skip_logits: Optional[bool] = None,
+    **lm_kwargs,
 ) -> Union[Tuple, LlavaCausalLMOutputWithPast]:
     r"""
     Args:
@@ -36,10 +40,12 @@ def lce_forward_deprecated(
             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
+        logits_to_keep (`int` or `torch.Tensor`, *optional*):
+            If an `int`, compute logits for the last `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.
+            If a `torch.Tensor`, must be 1D corresponding to the indices to keep in the sequence length dimension.
+            This is useful when using packed tensor format (single dimension for batch and sequence length).
 
 
     Returns:
@@ -65,7 +71,6 @@ def lce_forward_deprecated(
     >>> processor.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
     "USER:  \nWhat's the content of the image? ASSISTANT: The image features a busy city street with a stop sign prominently displayed"
     ```"""
-
     output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
     output_hidden_states = (
         output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
@@ -89,73 +94,24 @@ def lce_forward_deprecated(
         )
 
     if inputs_embeds is None:
-        # 1. Extra the input embeddings
         inputs_embeds = self.get_input_embeddings()(input_ids)
 
-        # 2. Merge text and images
-        if pixel_values is not None and input_ids.shape[1] != 1:
-            image_outputs = self.vision_tower(pixel_values, output_hidden_states=True)
-            # this is not memory efficient at all (output_hidden_states=True) will save all the hidden stated.
-            selected_image_feature = image_outputs.hidden_states[vision_feature_layer]
-
-            if vision_feature_select_strategy == "default":
-                selected_image_feature = selected_image_feature[:, 1:]
-            elif vision_feature_select_strategy == "full":
-                selected_image_feature = selected_image_feature
-            else:
-                raise ValueError(f"Unexpected select feature strategy: {self.config.vision_feature_select_strategy}")
-
-            image_features = self.multi_modal_projector(selected_image_feature)
-            inputs_embeds = inputs_embeds.to(image_features.dtype)
-            inputs_embeds, attention_mask, labels, position_ids = self._merge_input_ids_with_image_features(
-                image_features, inputs_embeds, input_ids, attention_mask, labels
-            )
-
-        # In case input_ids.shape[1] == 1 & pixel_values==None & past_key_values != None, we are in the case of
-        # generation with cache
-        elif past_key_values is not None and pixel_values is not None and input_ids.shape[1] == 1:
-            # Retrieve the first layer to inspect the logits and mask out the hidden states
-            # that are set to 0
-            first_layer_past_key_value = past_key_values[0][0][:, :, :, 0]
-
-            # Sum all dimensions of head_dim (-2) to avoid random errors such as: https://github.com/huggingface/transformers/pull/28032#issuecomment-1863691941
-            batch_index, non_attended_tokens = torch.where(first_layer_past_key_value.float().sum(-2) == 0)
-
-            # Get the target length
-            target_length = input_ids.shape[1]
-            past_length = first_layer_past_key_value.shape[-1]
-
-            extended_attention_mask = torch.ones(
-                (attention_mask.shape[0], past_length),
-                dtype=attention_mask.dtype,
-                device=attention_mask.device,
-            )
-
-            # Filter out only the tokens that can be un-attended, this can happen
-            # if one uses Llava + Fused modules where the cache on the
-            # first iteration is already big enough, or if one passes custom cache
-            valid_indices = non_attended_tokens < extended_attention_mask.size(-1)
-            new_batch_index = batch_index[valid_indices]
-            new_non_attended_tokens = non_attended_tokens[valid_indices]
-
-            # Zero-out the places where we don't need to attend
-            extended_attention_mask[new_batch_index, new_non_attended_tokens] = 0
-
-            attention_mask = torch.cat((extended_attention_mask, attention_mask[:, -target_length:]), dim=1)
-            position_ids = torch.sum(attention_mask, dim=1).unsqueeze(-1) - 1
-
-    # TODO: @raushan retain only the new behavior after v4.47
-    elif image_features is not None:
-        n_image_tokens = (input_ids == self.config.image_token_index).sum().item()
-        n_image_features = image_features.shape[0] * image_features.shape[1]
+    if pixel_values is not None:
+        image_features = self.get_image_features(
+            pixel_values=pixel_values,
+            vision_feature_layer=vision_feature_layer,
+            vision_feature_select_strategy=vision_feature_select_strategy,
+            image_sizes=image_sizes,
+        )
 
-        if n_image_tokens != n_image_features:
+        special_image_mask = (input_ids == self.config.image_token_index).unsqueeze(-1)
+        special_image_mask = special_image_mask.expand_as(inputs_embeds).to(inputs_embeds.device)
+        if not is_torchdynamo_compiling() and inputs_embeds[special_image_mask].numel() != image_features.numel():
+            n_image_tokens = (input_ids == self.config.image_token_index).sum()
+            n_image_features = image_features.shape[0] * image_features.shape[1]
             raise ValueError(
                 f"Image features and image tokens do not match: tokens: {n_image_tokens}, features {n_image_features}"
             )
-        special_image_mask = (
-            (input_ids == self.config.image_token_index).unsqueeze(-1).expand_as(inputs_embeds).to(inputs_embeds.device)
-        )
         image_features = image_features.to(inputs_embeds.device, inputs_embeds.dtype)
         inputs_embeds = inputs_embeds.masked_scatter(special_image_mask, image_features)
 
@@ -168,13 +124,19 @@ def lce_forward_deprecated(
         output_attentions=output_attentions,
         output_hidden_states=output_hidden_states,
         return_dict=return_dict,
+        cache_position=cache_position,
+        logits_to_keep=logits_to_keep,
+        **lm_kwargs,
     )
     hidden_states = outputs[0]
 
     loss = None
     logits = None
 
-    if self.training and (labels is not None):
+    # Overwrite skip_logits, since llava never materializes logits
+    skip_logits = labels is not None
+
+    if skip_logits:
         # Shift so that tokens < n predict n
         if attention_mask is not None:
             # we use the input attention mask to shift the logits and labels, because it is 2D.
@@ -189,21 +151,34 @@ def lce_forward_deprecated(
             shift_labels = labels[..., 1:].contiguous()
 
         lce = LigerFusedLinearCrossEntropyLoss()
-        loss = lce(self.language_model.lm_head.weight, shift_hidden_states, shift_labels)
+        loss = lce(
+            self.language_model.lm_head.weight,
+            shift_hidden_states.view(-1, shift_hidden_states.size(-1)),
+            shift_labels.view(-1).to(shift_hidden_states.device),
+        )
     else:
         logits = self.language_model.lm_head(hidden_states)
         if labels is not None:
-            # Shift so that tokens < n predict n
+            # Upcast to float if we need to compute the loss to avoid potential precision issues
+            logits = logits.float()
+            shift_logits = logits[..., :-1, :]
+            shift_labels = labels[..., 1:]
             if attention_mask is not None:
-                shift_attention_mask = attention_mask[..., 1:]
-                shift_logits = logits[..., :-1, :][shift_attention_mask.to(logits.device) != 0].contiguous()
-                shift_labels = labels[..., 1:][shift_attention_mask.to(labels.device) != 0].contiguous()
+                # we use the input attention mask to shift the logits and labels, because it is 2D.
+                # we also crop attn mask in case it is longer, which happens in PrefixTuning with peft
+                shift_attention_mask = attention_mask[:, -shift_logits.shape[1] :].to(logits.device)
+                shift_logits = shift_logits[shift_attention_mask.to(logits.device) != 0].contiguous()
+                shift_labels = shift_labels[shift_attention_mask.to(shift_labels.device) != 0].contiguous()
             else:
-                shift_logits = logits[..., :-1, :].contiguous()
-                shift_labels = labels[..., 1:].contiguous()
+                shift_logits = shift_logits.contiguous()
+                shift_labels = shift_labels.contiguous()
             # Flatten the tokens
             loss_fct = CrossEntropyLoss()
-            loss = loss_fct(shift_logits.view(-1, shift_logits.size(-1)), shift_labels.view(-1).to(shift_logits.device))
+
+            flat_logits = shift_logits.view(-1, self.config.text_config.vocab_size)
+            flat_labels = shift_labels.view(-1).to(shift_logits.device)
+            loss = loss_fct(flat_logits, flat_labels)
+
     if not return_dict:
         # NOTE: This part has not been tested.
         output = outputs[1:]
@@ -215,10 +190,9 @@ def lce_forward_deprecated(
         past_key_values=outputs.past_key_values,
         hidden_states=outputs.hidden_states,
         attentions=outputs.attentions,
+        image_hidden_states=image_features if pixel_values is not None else None,
     )
 
-
-@deprecate_kwarg("num_logits_to_keep", version="4.50", new_name="logits_to_keep")
 def lce_forward(
     self,
     input_ids: torch.LongTensor = None,
@@ -292,103 +266,59 @@ def lce_forward(
         else self.config.vision_feature_select_strategy
     )
 
-    if (input_ids is None) ^ (inputs_embeds is not None):
-        raise ValueError("You must specify exactly one of input_ids or inputs_embeds")
-
-    if pixel_values is not None and inputs_embeds is not None:
-        raise ValueError(
-            "You cannot specify both pixel_values and inputs_embeds at the same time, and must specify either one"
-        )
-
-    if inputs_embeds is None:
-        inputs_embeds = self.get_input_embeddings()(input_ids)
-
-    if pixel_values is not None:
-        image_features = self.get_image_features(
-            pixel_values=pixel_values,
-            vision_feature_layer=vision_feature_layer,
-            vision_feature_select_strategy=vision_feature_select_strategy,
-            image_sizes=image_sizes,
-        )
-
-        special_image_mask = (input_ids == self.config.image_token_index).unsqueeze(-1)
-        special_image_mask = special_image_mask.expand_as(inputs_embeds).to(inputs_embeds.device)
-        if not is_torchdynamo_compiling() and inputs_embeds[special_image_mask].numel() != image_features.numel():
-            n_image_tokens = (input_ids == self.config.image_token_index).sum()
-            n_image_features = image_features.shape[0] * image_features.shape[1]
-            raise ValueError(
-                f"Image features and image tokens do not match: tokens: {n_image_tokens}, features {n_image_features}"
-            )
-        image_features = image_features.to(inputs_embeds.device, inputs_embeds.dtype)
-        inputs_embeds = inputs_embeds.masked_scatter(special_image_mask, image_features)
-
-    outputs = self.language_model.model(
+    outputs = self.model(
+        input_ids=input_ids,
+        pixel_values=pixel_values,
         attention_mask=attention_mask,
         position_ids=position_ids,
         past_key_values=past_key_values,
         inputs_embeds=inputs_embeds,
+        vision_feature_layer=vision_feature_layer,
+        vision_feature_select_strategy=vision_feature_select_strategy,
         use_cache=use_cache,
         output_attentions=output_attentions,
         output_hidden_states=output_hidden_states,
-        return_dict=return_dict,
+        return_dict=True,
         cache_position=cache_position,
-        logits_to_keep=logits_to_keep,
+        image_sizes=image_sizes,
         **lm_kwargs,
     )
     hidden_states = outputs[0]
+    # Only compute necessary logits, and do not upcast them to float if we are not computing the loss
+    slice_indices = slice(-logits_to_keep, None) if isinstance(logits_to_keep, int) else logits_to_keep
+    kept_hidden_states = hidden_states[:, slice_indices, :]
 
-    loss = None
+    shift_labels = lm_kwargs.pop("shift_labels", None)
     logits = None
+    loss = None
 
-    # Overwrite skip_logits, since llava never materializes logits
-    skip_logits = labels is not None
+    if skip_logits and labels is None and shift_labels is None:
+        raise ValueError("skip_logits is True, but labels and shift_labels are None")
 
-    if skip_logits:
-        # Shift so that tokens < n predict n
-        if attention_mask is not None:
-            # we use the input attention mask to shift the logits and labels, because it is 2D.
-            # we also crop attn mask in case it is longer, which happens in PrefixTuning with peft
-            shift_attention_mask = attention_mask[:, -(hidden_states.shape[1] - 1) :].to(hidden_states.device)
-            shift_hidden_states = hidden_states[..., :-1, :][
-                shift_attention_mask.to(hidden_states.device) != 0
-            ].contiguous()
-            shift_labels = labels[..., 1:][shift_attention_mask.to(labels.device) != 0].contiguous()
-        else:
-            shift_hidden_states = hidden_states[..., :-1, :].contiguous()
-            shift_labels = labels[..., 1:].contiguous()
+    if skip_logits is None:
+        # By default, if in training mode, don't materialize logits
+        skip_logits = self.training and (labels is not None or shift_labels is not None)
 
-        lce = LigerFusedLinearCrossEntropyLoss()
-        loss = lce(
-            self.language_model.lm_head.weight,
-            shift_hidden_states.view(-1, shift_hidden_states.size(-1)),
-            shift_labels.view(-1).to(shift_hidden_states.device),
+    if skip_logits:
+        loss = LigerForCausalLMLoss(
+            hidden_states=kept_hidden_states,
+            lm_head_weight=self.lm_head.weight,
+            labels=labels,
+            shift_labels=shift_labels,
+            hidden_size=self.config.text_config.hidden_size,
+            **lm_kwargs,
         )
+
     else:
-        logits = self.language_model.lm_head(hidden_states)
+        logits = self.lm_head(kept_hidden_states)
         if labels is not None:
-            # Upcast to float if we need to compute the loss to avoid potential precision issues
-            logits = logits.float()
-            shift_logits = logits[..., :-1, :]
-            shift_labels = labels[..., 1:]
-            if attention_mask is not None:
-                # we use the input attention mask to shift the logits and labels, because it is 2D.
-                # we also crop attn mask in case it is longer, which happens in PrefixTuning with peft
-                shift_attention_mask = attention_mask[:, -shift_logits.shape[1] :].to(logits.device)
-                shift_logits = shift_logits[shift_attention_mask.to(logits.device) != 0].contiguous()
-                shift_labels = shift_labels[shift_attention_mask.to(shift_labels.device) != 0].contiguous()
-            else:
-                shift_logits = shift_logits.contiguous()
-                shift_labels = shift_labels.contiguous()
-            # Flatten the tokens
-            loss_fct = CrossEntropyLoss()
+            loss = self.loss_function(
+                logits=logits, labels=labels, vocab_size=self.config.text_config.vocab_size, **lm_kwargs
+            )
 
-            flat_logits = shift_logits.view(-1, self.config.text_config.vocab_size)
-            flat_labels = shift_labels.view(-1).to(shift_logits.device)
-            loss = loss_fct(flat_logits, flat_labels)
 
     if not return_dict:
-        # NOTE: This part has not been tested.
-        output = outputs[1:]
+        output = (logits,) + outputs[1:]
         return (loss,) + output if loss is not None else output
 
     return LlavaCausalLMOutputWithPast(
@@ -397,5 +327,5 @@ def lce_forward(
         past_key_values=outputs.past_key_values,
         hidden_states=outputs.hidden_states,
         attentions=outputs.attentions,
-        image_hidden_states=image_features if pixel_values is not None else None,
+        image_hidden_states=outputs.image_hidden_states,
     )

src/liger_kernel/transformers/monkey_patch.py

@@ -314,13 +314,14 @@ def apply_liger_kernel_to_llava(
         logger.warning(TRANSFORMER_DEPRECATION_WARNING)
         modeling_llava.nn.CrossEntropyLoss = LigerCrossEntropyLoss
     if fused_linear_cross_entropy:
-        if transformer_version >= version.parse("4.49.0"):
+        if transformer_version >= version.parse("4.52.0"):
             modeling_llava.LlavaForConditionalGeneration.forward = llava_lce_forward
+        elif transformer_version >= version.parse("4.49.0") and transformer_version < version.parse("4.52.0"): 
+            modeling_llava.LlavaForConditionalGeneration.forward = llava_lce_forward_deprecated
         else:  # if version < 4.49.0
             logger.warning(
-                "Support for transformers versions < 4.49.0 will soon be discontinued due to issues with incorrect legacy processing. \n Please consider upgrading to avoid potential issues. See details: https://github.com/huggingface/transformers/pull/35526"
+                "The latest version of Liger does not support transformers < 4.49.0 for llava. Please downgrade your liger version or upgrade your transformer version."
             )
-            modeling_llava.LlavaForConditionalGeneration.forward = llava_lce_forward_deprecated
 
     if model is not None:
         text_model_name, vision_model_name = model.config.text_config.model_type, model.config.vision_config.model_type