Add SmolVLM2 support

src/liger_kernel/transformers/__init__.py

@@ -57,6 +57,7 @@
     from liger_kernel.transformers.monkey_patch import apply_liger_kernel_to_qwen3_moe  # noqa: F401
     from liger_kernel.transformers.monkey_patch import apply_liger_kernel_to_qwen3_next  # noqa: F401
     from liger_kernel.transformers.monkey_patch import apply_liger_kernel_to_smollm3  # noqa: F401
+    from liger_kernel.transformers.monkey_patch import apply_liger_kernel_to_smolvlm  # noqa: F401
 
 
 # Check if 'transformers' is installed
@@ -120,6 +121,7 @@ def __getattr__(name: str):
         "apply_liger_kernel_to_qwen3_moe",
         "apply_liger_kernel_to_qwen3_next",
         "apply_liger_kernel_to_smollm3",
+        "apply_liger_kernel_to_smolvlm",
     }
 
     if name in monkey_patch_symbols:
@@ -189,5 +191,6 @@ def __getattr__(name: str):
             "apply_liger_kernel_to_qwen3_moe",
             "apply_liger_kernel_to_qwen3_next",
             "apply_liger_kernel_to_smollm3",
+            "apply_liger_kernel_to_smolvlm",
         ]
     )

src/liger_kernel/transformers/model/smolvlm.py

@@ -0,0 +1,158 @@
+from typing import TYPE_CHECKING
+from typing import Optional
+from typing import Union
+from typing import Unpack
+
+import torch
+
+from transformers.models.smolvlm.modeling_smolvlm import SmolVLMCausalLMOutputWithPast
+from transformers.utils.generic import can_return_tuple
+
+from liger_kernel.transformers.model.loss_utils import LigerForCausalLMLoss
+
+if TYPE_CHECKING:
+    from transformers.cache_utils import Cache
+    from transformers.utils.generic import TransformersKwargs
+
+
+# Forward adapted to enable fused Linear + CE without materializing logits.
+# Mirrors the pattern used for other multimodal models (e.g., InternVL, LLaVA).
+@can_return_tuple
+def lce_forward(
+    self,
+    input_ids: Optional[torch.LongTensor] = None,
+    attention_mask: Optional[torch.Tensor] = None,
+    position_ids: Optional[torch.LongTensor] = None,
+    past_key_values: Optional["Cache"] = None,
+    inputs_embeds: Optional[torch.FloatTensor] = None,
+    pixel_values: Optional[torch.FloatTensor] = None,
+    pixel_attention_mask: Optional[torch.BoolTensor] = None,
+    image_hidden_states: 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,
+    cache_position: Optional[torch.LongTensor] = None,
+    return_dict: Optional[bool] = None,
+    logits_to_keep: Union[int, torch.Tensor] = 0,
+    skip_logits: Optional[bool] = None,  # Added argument for liger-kernel
+    **lm_kwargs: Unpack["TransformersKwargs"],  # renamed from kwargs
+) -> Union[tuple, SmolVLMCausalLMOutputWithPast]:
+    r"""
+    pixel_attention_mask (`torch.Tensor` of shape `(batch_size, image_size, image_size)`, *optional*):
+        Mask to avoid performing attention on padding pixel indices.
+    image_hidden_states (`torch.FloatTensor` of shape `(batch_size, num_channels, image_size, image_size)`):
+        The hidden states of the image encoder after modality projection.
+    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 `model.image_token_id`. Tokens with indices set to `model.image_token_id` are
+        ignored (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
+
+    Example:
+
+    ```python
+    >>> import requests
+    >>> import torch
+    >>> from PIL import Image
+    >>> from io import BytesIO
+
+    >>> from transformers import AutoProcessor, AutoModelForImageTextToText
+    >>> from transformers.image_utils import load_image
+
+    >>> # Note that passing the image urls (instead of the actual pil images) to the processor is also possible
+    >>> image1 = load_image("https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg")
+    >>> image2 = load_image("https://cdn.britannica.com/59/94459-050-DBA42467/Skyline-Chicago.jpg")
+    >>> image3 = load_image("https://cdn.britannica.com/68/170868-050-8DDE8263/Golden-Gate-Bridge-San-Francisco.jpg")
+
+    >>> processor = AutoProcessor.from_pretrained("HuggingFaceTB/SmolVLM2-2.2B-Instruct")
+    >>> model = AutoModelForImageTextToText.from_pretrained("HuggingFaceTB/SmolVLM2-2.2B-Instruct", dtype=torch.bfloat16, device_map="auto")
+
+    >>> # Create inputs
+    >>> messages = [
+    ...     {
+    ...         "role": "user",
+    ...         "content": [
+    ...             {"type": "video", "path": path/to/video},
+    ...             {"type": "text", "text": "What is happening in this video?"},
+    ...         ]
+    ...     }
+    ... ]
+
+    >>> inputs = processor.apply_chat_template([messages], add_generation_prompt=True)
+
+    >>> # Generate
+    >>> generated_ids = model.generate(**inputs, max_new_tokens=256)
+    >>> generated_texts = processor.batch_decode(generated_ids, skip_special_tokens=True)
+
+    >>> print(generated_texts)
+    ```"""
+    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
+    )
+    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,
+        pixel_values=pixel_values,
+        pixel_attention_mask=pixel_attention_mask,
+        image_hidden_states=image_hidden_states,
+        use_cache=use_cache,
+        output_attentions=output_attentions,
+        output_hidden_states=output_hidden_states,
+        cache_position=cache_position,
+        return_dict=True,
+        **lm_kwargs,
+    )
+
+    # Copied from llava.py
+    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, :]
+
+    shift_labels = lm_kwargs.pop("shift_labels", None)
+    logits = None
+    loss = 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 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)
+
+    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.lm_head(kept_hidden_states)
+        if labels is not None:
+            loss = self.loss_function(
+                logits=logits, labels=labels, vocab_size=self.config.text_config.vocab_size, **lm_kwargs
+            )
+
+    if not return_dict:
+        output = (logits,) + outputs[1:]
+        return (loss,) + output if loss is not None else output
+
+    return SmolVLMCausalLMOutputWithPast(
+        loss=loss,
+        logits=logits,
+        past_key_values=outputs.past_key_values,
+        hidden_states=outputs.hidden_states,
+        attentions=outputs.attentions,
+        image_hidden_states=outputs.image_hidden_states,
+    )

src/liger_kernel/transformers/monkey_patch.py

@@ -2055,7 +2055,6 @@ def apply_liger_kernel_to_internvl(
             `cross_entropy` and `fused_linear_cross_entropy` cannot both be True.
             If `fused_linear_cross_entropy` is True, the logits will not be materialized but more memory efficient.
         rms_norm (bool): Whether to apply Liger's RMSNorm. Default is True.
-        swiglu (bool): Whether to apply Liger's SwiGLU MLP. Default is True.
         model (PreTrainedModel): The model instance to apply Liger kernels to, if the model has already been
         loaded. Default is None.
     """
@@ -2068,8 +2067,11 @@ def apply_liger_kernel_to_internvl(
     from liger_kernel.transformers.model.internvl import lce_forward as internvl_lce_forward
 
     if cross_entropy:
-        logger.warning(TRANSFORMER_DEPRECATION_WARNING)
-        modeling_internvl.nn.CrossEntropyLoss = LigerCrossEntropyLoss
+        logger.info("Apply liger cross entropy")
+
+        from transformers.loss.loss_utils import nn
+
+        nn.functional.cross_entropy = liger_cross_entropy
     if fused_linear_cross_entropy:
         modeling_internvl.InternVLForConditionalGeneration.forward = internvl_lce_forward
     if rms_norm:
@@ -2112,6 +2114,86 @@ def apply_liger_kernel_to_internvl(
             logger.warning(f"{vision_model_name} is not supported by Liger kernel.")
 
 
+def apply_liger_kernel_to_smolvlm(
+    cross_entropy: bool = False,
+    fused_linear_cross_entropy: bool = True,
+    rms_norm: bool = True,
+    model: Optional[PreTrainedModel] = None,
+    **kwargs,
+) -> None:
+    """
+    Apply Liger kernels to replace original implementation in HuggingFace SmolVLM models.
+    Due to the characteristics of SmolVLM, the model must be passed to apply Liger-Kernel's patch to other models connected to SmolVLM.
+    However, if an LM not supported by Liger-Kernel is connected to SmolVLM, unexpected side effects may occur.
+    NOTE: SmolVLM is not available in transformers<4.50.0
+
+    Args:
+        rope (bool): Whether to apply Liger's rotary position embedding. Default is True.
+        cross_entropy (bool): Whether to apply Liger's cross entropy loss. Default is False.
+        fused_linear_cross_entropy (bool):
+            Whether to apply Liger's fused linear cross entropy loss. Default is True.
+            `cross_entropy` and `fused_linear_cross_entropy` cannot both be True.
+            If `fused_linear_cross_entropy` is True, the logits will not be materialized but more memory efficient.
+        rms_norm (bool): Whether to apply Liger's RMSNorm. Default is True.
+        model (PreTrainedModel): The model instance to apply Liger kernels to, if the model has already been
+        loaded. Default is None.
+    """
+    assert not (cross_entropy and fused_linear_cross_entropy), (
+        "cross_entropy and fused_linear_cross_entropy cannot both be True."
+    )
+
+    from transformers.models.smolvlm import modeling_smolvlm
+
+    from liger_kernel.transformers.model.smolvlm import lce_forward as smolvlm_lce_forward
+
+    if cross_entropy:
+        logger.info("Apply liger cross entropy")
+
+        from transformers.loss.loss_utils import nn
+
+        nn.functional.cross_entropy = liger_cross_entropy
+    if fused_linear_cross_entropy:
+        modeling_smolvlm.SmolVLMForConditionalGeneration.forward = smolvlm_lce_forward
+    if rms_norm:
+        modeling_smolvlm.SmolVLMRMSNorm = LigerRMSNorm
+
+    if model is not None:
+        text_model_name, vision_model_name = model.config.text_config.model_type, model.config.vision_config.model_type
+        text_liger_fn = MODEL_TYPE_TO_APPLY_LIGER_FN.get(text_model_name, None)
+        vision_liger_fn = MODEL_TYPE_TO_APPLY_LIGER_FN.get(vision_model_name, None)
+
+        kwargs = {"cross_entropy": False, "fused_linear_cross_entropy": False, **kwargs} | {"rms_norm": rms_norm}
+        if text_liger_fn:
+            accept_params = inspect.signature(text_liger_fn).parameters
+            remain_params = set(kwargs) - (set(accept_params) & set(kwargs))
+            text_kwargs = {k: v for k, v in kwargs.items() if k not in remain_params}
+
+            if remain_params:
+                logger.warning(
+                    f"These parameters are not supported by {text_model_name}. Enter the remaining {list(text_kwargs.keys())} except for {list(remain_params)}\n"
+                    f"Parameters accepted by {text_model_name}: {list(accept_params.keys())}"
+                )
+            text_kwargs["model"] = model.model.text_model
+            text_liger_fn(**text_kwargs)
+        elif text_model_name not in MODEL_TYPE_TO_APPLY_LIGER_FN:
+            logger.warning(f"{text_model_name} is not supported by Liger kernel.")
+
+        if vision_liger_fn:
+            accept_params = inspect.signature(vision_liger_fn).parameters
+            remain_params = set(kwargs) - (set(accept_params) & set(kwargs))
+            vision_kwargs = {k: v for k, v in kwargs.items() if k not in remain_params}
+
+            if remain_params:
+                logger.warning(
+                    f"These parameters are not supported by {vision_model_name}. Enter the remaining {list(vision_kwargs.keys())} except for {list(remain_params)}\n"
+                    f"Parameters accepted by {vision_model_name}: {list(accept_params.keys())}"
+                )
+            vision_kwargs["model"] = model.model.vision_model
+            vision_liger_fn(**vision_kwargs)
+        elif vision_model_name not in MODEL_TYPE_TO_APPLY_LIGER_FN:
+            logger.warning(f"{vision_model_name} is not supported by Liger kernel.")
+
+
 def apply_liger_kernel_to_falcon_h1(
     rope: bool = True,
     cross_entropy: bool = False,
@@ -2304,6 +2386,7 @@ def apply_liger_kernel_to_qwen3_next(
     "phi3": apply_liger_kernel_to_phi3,
     "paligemma": apply_liger_kernel_to_paligemma,
     "falcon_h1": apply_liger_kernel_to_falcon_h1,
+    "smolvlm": apply_liger_kernel_to_smolvlm,
 }