first commit

src/liger_kernel/transformers/model/olmo2.py

+from typing import List
+from typing import Optional
+from typing import Tuple
+from typing import Union
+
+import torch
+
+from liger_kernel.transformers.model.loss_utils import LigerForCausalLMLoss
+from liger_kernel.transformers.model.loss_utils import unpack_cross_entropy_result
+from liger_kernel.transformers.model.output_classes import LigerCausalLMOutputWithPast
+
+
+def lce_forward(
+    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,
+    return_dict: Optional[bool] = None,
+    cache_position: Optional[torch.LongTensor] = None,
+    logits_to_keep: Union[int, torch.Tensor] = 0,
+    skip_logits: Optional[bool] = None,
+    **kwargs,
+) -> Union[Tuple, LigerCausalLMOutputWithPast]:
+    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]`.
+
+        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:
+
+    Example:
+
+    ```python
+    >>> from transformers import AutoTokenizer, Olmo2ForCausalLM
+
+    >>> model = Olmo2ForCausalLM.from_pretrained("allenai/Olmo2-1B-hf")
+    >>> tokenizer = AutoTokenizer.from_pretrained("allenai/Olmo2-1B-hf")
+
+    >>> 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 sure if you’re conscious of this, but I’m'
+    ```
+    """
+    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,
+        use_cache=use_cache,
+        output_attentions=output_attentions,
+        output_hidden_states=output_hidden_states,
+        return_dict=return_dict,
+        cache_position=cache_position,
+        **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, :]
+
+    shift_labels = kwargs.pop("shift_labels", None)
+    logits = None
+    loss = None
+    token_accuracy = None
+    predicted_tokens = 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)
+
+    # Compute loss
+    if skip_logits:
+        result = LigerForCausalLMLoss(
+            hidden_states=kept_hidden_states,
+            lm_head_weight=self.lm_head.weight,
+            labels=labels,
+            shift_labels=shift_labels,
+            hidden_size=self.config.hidden_size,
+            **kwargs,
+        )
+        loss, _, token_accuracy, predicted_tokens = unpack_cross_entropy_result(result)
+
+    else:
+        logits = self.lm_head(kept_hidden_states)
+        if labels is not None or shift_labels is not None:
+            loss = self.loss_function(
+                logits=logits,
+                labels=labels,
+                shift_labels=shift_labels,
+                vocab_size=self.config.vocab_size,
+                **kwargs,
+            )
+
+    if not return_dict:
+        output = (logits,) + outputs[1:]
+        output = ((loss,) + output) if loss is not None else output
+        output = output + (token_accuracy,) if token_accuracy is not None else output
+        output = output + (predicted_tokens,) if predicted_tokens is not None else output
+        return output
+
+    # Return custom output class with token_accuracy field
+    return LigerCausalLMOutputWithPast(
+        loss=loss,
+        logits=logits,
+        past_key_values=outputs.past_key_values,
+        hidden_states=outputs.hidden_states,
+        attentions=outputs.attentions,
+        token_accuracy=token_accuracy,
+        predicted_tokens=predicted_tokens,
+    )

src/liger_kernel/transformers/model/olmo3.py

+from typing import List
+from typing import Optional
+from typing import Tuple
+from typing import Union
+
+import torch
+
+from transformers.modeling_outputs import BaseModelOutputWithPast
+
+from liger_kernel.transformers.model.loss_utils import LigerForCausalLMLoss
+from liger_kernel.transformers.model.loss_utils import unpack_cross_entropy_result
+from liger_kernel.transformers.model.output_classes import LigerCausalLMOutputWithPast
+
+
+def lce_forward(
+    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,
+    return_dict: Optional[bool] = None,
+    cache_position: Optional[torch.LongTensor] = None,
+    logits_to_keep: Union[int, torch.Tensor] = 0,
+    skip_logits: Optional[bool] = None,
+    **kwargs,
+) -> Union[Tuple, LigerCausalLMOutputWithPast]:
+    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]`.
+
+        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:
+
+    Example:
+
+    ```python
+    >>> from transformers import AutoTokenizer, Olmo3ForCausalLM
+
+    >>> model = Olmo3ForCausalLM.from_pretrained("allenai/Olmo-3-7B-Instruct")
+    >>> tokenizer = AutoTokenizer.from_pretrained("allenai/Olmo-3-7B-Instruct")
+
+    >>> 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 sure if you’re conscious of this, but I’m'
+    ```
+    """
+    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: BaseModelOutputWithPast = 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,
+        return_dict=return_dict,
+        cache_position=cache_position,
+        **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, :]
+
+    shift_labels = kwargs.pop("shift_labels", None)
+    logits = None
+    loss = None
+    token_accuracy = None
+    predicted_tokens = 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)
+
+    # Compute loss
+    if skip_logits:
+        result = LigerForCausalLMLoss(
+            hidden_states=kept_hidden_states,
+            lm_head_weight=self.lm_head.weight,
+            labels=labels,
+            shift_labels=shift_labels,
+            hidden_size=self.config.hidden_size,
+            **kwargs,
+        )
+        loss, _, token_accuracy, predicted_tokens = unpack_cross_entropy_result(result)
+
+    else:
+        logits = self.lm_head(kept_hidden_states)
+        if labels is not None or shift_labels is not None:
+            loss = self.loss_function(
+                logits=logits,
+                labels=labels,
+                shift_labels=shift_labels,
+                vocab_size=self.config.vocab_size,
+                **kwargs,
+            )
+
+    if not return_dict:
+        output = (logits,) + outputs[1:]
+        output = ((loss,) + output) if loss is not None else output
+        output = output + (token_accuracy,) if token_accuracy is not None else output
+        output = output + (predicted_tokens,) if predicted_tokens is not None else output
+        return output
+
+    # Return custom output class with token_accuracy field
+    return LigerCausalLMOutputWithPast(
+        loss=loss,
+        logits=logits,
+        past_key_values=outputs.past_key_values,
+        hidden_states=outputs.hidden_states,
+        attentions=outputs.attentions,
+        token_accuracy=token_accuracy,
+        predicted_tokens=predicted_tokens,
+    )

src/liger_kernel/transformers/model/output_classes.py

+"""
+Custom output classes for Liger-Kernel that extend transformers' ModelOutput classes
+with optional token accuracy field.
+"""
+
+from dataclasses import dataclass
+from typing import Optional
+
+import torch
+
+from transformers.modeling_outputs import CausalLMOutputWithPast
+from transformers.modeling_outputs import MoeCausalLMOutputWithPast
+
+# The following model-specific outputs are optional and depend on the installed
+# transformers version. Guard their imports so our module remains importable
+# even when those models are not available in the environment.
+try:
+    from transformers.models.gemma3.modeling_gemma3 import Gemma3CausalLMOutputWithPast as _Gemma3CausalLMOutputWithPast
+except Exception:
+    _Gemma3CausalLMOutputWithPast = None
+
+try:
+    from transformers.models.glm4v_moe.modeling_glm4v_moe import (
+        Glm4vMoeCausalLMOutputWithPast as _Glm4vMoeCausalLMOutputWithPast,
+    )
+except Exception:
+    _Glm4vMoeCausalLMOutputWithPast = None
+
+try:
+    from transformers.models.internvl.modeling_internvl import (
+        InternVLCausalLMOutputWithPast as _InternVLCausalLMOutputWithPast,
+    )
+except Exception:
+    _InternVLCausalLMOutputWithPast = None
+
+try:
+    from transformers.models.llava.modeling_llava import LlavaCausalLMOutputWithPast as _LlavaCausalLMOutputWithPast
+except Exception:
+    _LlavaCausalLMOutputWithPast = None
+
+try:
+    from transformers.models.paligemma.modeling_paligemma import (
+        PaliGemmaCausalLMOutputWithPast as _PaliGemmaCausalLMOutputWithPast,
+    )
+except Exception:
+    _PaliGemmaCausalLMOutputWithPast = None
+
+try:
+    from transformers.models.qwen2_5_vl.modeling_qwen2_5_vl import (
+        Qwen2_5_VLCausalLMOutputWithPast as _Qwen2_5_VLCausalLMOutputWithPast,
+    )
+except Exception:
+    _Qwen2_5_VLCausalLMOutputWithPast = None
+
+try:
+    from transformers.models.qwen2_vl.modeling_qwen2_vl import (
+        Qwen2VLCausalLMOutputWithPast as _Qwen2VLCausalLMOutputWithPast,
+    )
+except Exception:
+    _Qwen2VLCausalLMOutputWithPast = None
+
+try:
+    from transformers.models.qwen3_vl.modeling_qwen3_vl import (
+        Qwen3VLCausalLMOutputWithPast as _Qwen3VLCausalLMOutputWithPast,
+    )
+except Exception:
+    _Qwen3VLCausalLMOutputWithPast = None
+
+try:
+    from transformers.models.qwen3_vl_moe.modeling_qwen3_vl_moe import (
+        Qwen3VLMoeCausalLMOutputWithPast as _Qwen3VLMoeCausalLMOutputWithPast,
+    )
+except Exception:
+    _Qwen3VLMoeCausalLMOutputWithPast = None
+
+try:
+    from transformers.models.qwen3_5.modeling_qwen3_5 import (
+        Qwen3_5CausalLMOutputWithPast as _Qwen3_5CausalLMOutputWithPast,
+    )
+except Exception:
+    _Qwen3_5CausalLMOutputWithPast = None
+
+
+@dataclass
+class LigerCausalLMOutputWithPast(CausalLMOutputWithPast):
+    token_accuracy: Optional[torch.FloatTensor] = None
+    predicted_tokens: Optional[torch.LongTensor] = None
+
+
+@dataclass
+class LigerMoeCausalLMOutputWithPast(MoeCausalLMOutputWithPast):
+    token_accuracy: Optional[torch.FloatTensor] = None
+    predicted_tokens: Optional[torch.LongTensor] = None
+
+
+if _Gemma3CausalLMOutputWithPast is not None:
+
+    @dataclass
+    class LigerGemma3CausalLMOutputWithPast(_Gemma3CausalLMOutputWithPast):
+        token_accuracy: Optional[torch.FloatTensor] = None
+        predicted_tokens: Optional[torch.LongTensor] = None
+
+
+if _Glm4vMoeCausalLMOutputWithPast is not None:
+
+    @dataclass
+    class LigerGlm4vMoeCausalLMOutputWithPast(_Glm4vMoeCausalLMOutputWithPast):
+        token_accuracy: Optional[torch.FloatTensor] = None
+        predicted_tokens: Optional[torch.LongTensor] = None
+
+
+if _LlavaCausalLMOutputWithPast is not None:
+
+    @dataclass
+    class LigerLlavaCausalLMOutputWithPast(_LlavaCausalLMOutputWithPast):
+        token_accuracy: Optional[torch.FloatTensor] = None
+        predicted_tokens: Optional[torch.LongTensor] = None
+
+
+if _InternVLCausalLMOutputWithPast is not None:
+
+    @dataclass
+    class LigerInternVLCausalLMOutputWithPast(_InternVLCausalLMOutputWithPast):
+        token_accuracy: Optional[torch.FloatTensor] = None
+        predicted_tokens: Optional[torch.LongTensor] = None
+
+
+if _PaliGemmaCausalLMOutputWithPast is not None:
+
+    @dataclass
+    class LigerPaliGemmaCausalLMOutputWithPast(_PaliGemmaCausalLMOutputWithPast):
+        token_accuracy: Optional[torch.FloatTensor] = None
+        predicted_tokens: Optional[torch.LongTensor] = None
+
+
+if _Qwen2_5_VLCausalLMOutputWithPast is not None:
+
+    @dataclass
+    class LigerQwen2_5_VLCausalLMOutputWithPast(_Qwen2_5_VLCausalLMOutputWithPast):
+        token_accuracy: Optional[torch.FloatTensor] = None
+        predicted_tokens: Optional[torch.LongTensor] = None
+
+
+if _Qwen2VLCausalLMOutputWithPast is not None:
+
+    @dataclass
+    class LigerQwen2VLCausalLMOutputWithPast(_Qwen2VLCausalLMOutputWithPast):
+        token_accuracy: Optional[torch.FloatTensor] = None
+        predicted_tokens: Optional[torch.LongTensor] = None
+
+
+if _Qwen3VLCausalLMOutputWithPast is not None:
+
+    @dataclass
+    class LigerQwen3VLCausalLMOutputWithPast(_Qwen3VLCausalLMOutputWithPast):
+        token_accuracy: Optional[torch.FloatTensor] = None
+        predicted_tokens: Optional[torch.LongTensor] = None
+
+
+if _Qwen3VLMoeCausalLMOutputWithPast is not None:
+
+    @dataclass
+    class LigerQwen3VLMoeCausalLMOutputWithPast(_Qwen3VLMoeCausalLMOutputWithPast):
+        token_accuracy: Optional[torch.FloatTensor] = None
+        predicted_tokens: Optional[torch.LongTensor] = None
+
+
+if _Qwen3_5CausalLMOutputWithPast is not None:
+
+    @dataclass
+    class LigerQwen3_5CausalLMOutputWithPast(_Qwen3_5CausalLMOutputWithPast):
+        token_accuracy: Optional[torch.FloatTensor] = None
+        predicted_tokens: Optional[torch.LongTensor] = None

src/liger_kernel/transformers/model/paligemma.py

+from typing import List
+from typing import Optional
+from typing import Tuple
+from typing import Union
+
+import torch
+
+from torch.nn import CrossEntropyLoss
+from transformers.cache_utils import Cache
+from transformers.utils import is_torchdynamo_compiling
+from transformers.utils import logging
+
+from liger_kernel.transformers.model.loss_utils import LigerForCausalLMLoss
+from liger_kernel.transformers.model.loss_utils import unpack_cross_entropy_result
+from liger_kernel.transformers.model.output_classes import LigerPaliGemmaCausalLMOutputWithPast
+
+logger = logging.get_logger(__name__)
+
+
+def lce_forward(
+    self,
+    input_ids: torch.LongTensor = None,
+    pixel_values: torch.FloatTensor = None,
+    attention_mask: Optional[torch.Tensor] = None,
+    position_ids: Optional[torch.LongTensor] = None,
+    past_key_values: Optional[Union[List[torch.FloatTensor], Cache]] = None,
+    token_type_ids: Optional[torch.LongTensor] = None,
+    cache_position: Optional[torch.LongTensor] = 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,
+    return_dict: Optional[bool] = None,
+    logits_to_keep: Union[int, torch.Tensor] = 0,
+    skip_logits: Optional[bool] = None,
+    **lm_kwargs,
+) -> Union[Tuple, LigerPaliGemmaCausalLMOutputWithPast]:
+    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.text_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.text_config.vocab_size]`.
+
+        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:
+
+    Example:
+
+    ```python
+    >>> from PIL import Image
+    >>> import requests
+    >>> from transformers import AutoProcessor, PaliGemmaForConditionalGeneration
+
+    >>> model = PaliGemmaForConditionalGeneration.from_pretrained("google/PaliGemma-test-224px-hf")
+    >>> processor = AutoProcessor.from_pretrained("google/PaliGemma-test-224px-hf")
+
+    >>> prompt = "answer en Where is the cow standing?"
+    >>> url = "https://huggingface.co/gv-hf/PaliGemma-test-224px-hf/resolve/main/cow_beach_1.png"
+    >>> image = Image.open(requests.get(url, stream=True).raw)
+
+    >>> inputs = processor(images=image, text=prompt,  return_tensors="pt")
+
+    >>> # Generate
+    >>> generate_ids = model.generate(**inputs, max_length=30)
+    >>> processor.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
+    "answer en Where is the cow standing?\nbeach"
+    ```"""
+
+    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"
+        )
+
+    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
+
+    is_training = token_type_ids is not None and labels is not None
+
+    if inputs_embeds is None:
+        inputs_embeds = self.get_input_embeddings()(input_ids)
+
+    if cache_position is None:
+        past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
+        cache_position = torch.arange(
+            past_seen_tokens, past_seen_tokens + inputs_embeds.shape[1], device=inputs_embeds.device
+        )
+
+    if position_ids is None:
+        position_ids = cache_position.unsqueeze(0) + 1  # Paligemma positions are 1-indexed
+
+    # Merge text and images
+    if pixel_values is not None:
+        image_features = self.get_image_features(pixel_values)
+
+        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():
+            image_tokens_in_text = torch.sum(input_ids == self.config.image_token_index)
+            raise ValueError(
+                f"Number of images does not match number of special image tokens in the input text. "
+                f"Got {image_tokens_in_text} image tokens in the text but {image_features.shape[0] * image_features.shape[1]} "
+                "tokens from image embeddings."
+            )
+        image_features = image_features.to(inputs_embeds.device, inputs_embeds.dtype)
+        inputs_embeds = inputs_embeds.masked_scatter(special_image_mask, image_features)
+
+    # mask out pad-token-ids in labels for BC
+    if labels is not None and self.pad_token_id in labels:
+        logger.warning_once(
+            "`labels` contains `pad_token_id` which will be masked with `config.ignore_index`. "
+            "You have to mask out `pad_token_id` when preparing `labels`, this behavior will be removed in v.4.46.",
+        )
+        labels = torch.where(input_ids == self.pad_token_id, self.config.ignore_index, labels)
+
+    causal_mask = self._update_causal_mask(
+        attention_mask, token_type_ids, past_key_values, cache_position, inputs_embeds, is_training
+    )
+
+    outputs = self.language_model.model(
+        attention_mask=causal_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,
+        return_dict=return_dict,
+        cache_position=cache_position,
+        logits_to_keep=logits_to_keep,
+        **lm_kwargs,
+    )
+
+    shift_labels = lm_kwargs.pop("shift_labels", None)
+    hidden_states = outputs[0]
+
+    loss = None
+    logits = None
+    token_accuracy = None
+    predicted_tokens = None
+
+    if skip_logits and labels is None:
+        raise ValueError("skip_logits is True, but labels is None")
+
+    if skip_logits is None:
+        skip_logits = self.training and (labels is not None)
+
+    if skip_logits:
+        shift_hidden_states = hidden_states[..., :-1, :]
+        shift_labels = labels[..., 1:]
+
+        hidden_device = shift_hidden_states.device
+
+        if attention_mask is not None:
+            # we use the input attention mask to shift the hidden_states 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_hidden_states.shape[1] :].to(hidden_device)
+            shift_hidden_states = shift_hidden_states[shift_attention_mask.to(hidden_device) != 0].contiguous()
+            shift_labels = shift_labels[shift_attention_mask.to(shift_labels.device) != 0].contiguous()
+        else:
+            shift_hidden_states = shift_hidden_states.contiguous()
+            shift_labels = shift_labels.contiguous()
+
+        # Flatten hidden state
+        shift_hidden_states = shift_hidden_states.view(-1, self.config.text_config.hidden_size)
+        shift_labels = shift_labels.view(-1).to(hidden_device)
+
+        # Use LigerForCausalLMLoss with accuracy support and pass already shifted labels
+        result = LigerForCausalLMLoss(
+            hidden_states=shift_hidden_states,
+            lm_head_weight=self.language_model.lm_head.weight,
+            labels=None,
+            shift_labels=shift_labels,
+            hidden_size=self.config.text_config.hidden_size,
+            **lm_kwargs,
+        )
+        loss, _, token_accuracy, predicted_tokens = unpack_cross_entropy_result(result)
+    else:
+        logits = self.language_model.lm_head(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()
+
+            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)
+        elif shift_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, :]
+            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()
+
+            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:
+        output = (logits,) + outputs[1:]
+        output = (loss,) + output if loss is not None else output
+        output = output + (token_accuracy,) if token_accuracy is not None else output
+        output = output + (predicted_tokens,) if predicted_tokens is not None else output
+        return output
+
+    # Return PaliGemma output with token_accuracy field
+    return LigerPaliGemmaCausalLMOutputWithPast(
+        loss=loss,
+        logits=logits,
+        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,
+        token_accuracy=token_accuracy,
+        predicted_tokens=predicted_tokens,
+    )

src/liger_kernel/transformers/model/phi3.py

+from typing import List
+from typing import Optional
+from typing import Tuple
+from typing import Union
+
+import torch
+
+from transformers.modeling_outputs import BaseModelOutputWithPast
+
+from liger_kernel.transformers.model.loss_utils import LigerForCausalLMLoss
+from liger_kernel.transformers.model.loss_utils import unpack_cross_entropy_result
+from liger_kernel.transformers.model.output_classes import LigerCausalLMOutputWithPast
+
+
+def lce_forward(
+    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,
+    return_dict: Optional[bool] = None,
+    cache_position: Optional[torch.LongTensor] = None,
+    logits_to_keep: Union[int, torch.Tensor] = 0,
+    skip_logits: Optional[bool] = None,
+    **kwargs,
+) -> Union[Tuple, LigerCausalLMOutputWithPast]:
+    r"""
+    Example:
+
+    ```python
+    >>> from transformers import AutoTokenizer, Phi3ForCausalLM
+
+    >>> model = Phi3ForCausalLM.from_pretrained("meta-phi3/Phi3-2-7b-hf")
+    >>> tokenizer = AutoTokenizer.from_pretrained("meta-phi3/Phi3-2-7b-hf")
+
+    >>> 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_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
+
+    outputs: BaseModelOutputWithPast = 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,
+        cache_position=cache_position,
+        **kwargs,
+    )
+
+    hidden_states = outputs.last_hidden_state
+    # 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 = kwargs.pop("shift_labels", None)
+    logits = None
+    loss = None
+    token_accuracy = None
+    predicted_tokens = 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)
+
+    # Compute loss
+    if skip_logits:
+        result = LigerForCausalLMLoss(
+            hidden_states=kept_hidden_states,
+            lm_head_weight=self.lm_head.weight,
+            labels=labels,
+            shift_labels=shift_labels,
+            hidden_size=self.config.hidden_size,
+            **kwargs,
+        )
+        loss, _, token_accuracy, predicted_tokens = unpack_cross_entropy_result(result)
+    else:
+        logits = self.lm_head(kept_hidden_states)
+        if labels is not None or shift_labels is not None:
+            loss = self.loss_function(
+                logits=logits,
+                labels=labels,
+                shift_labels=shift_labels,
+                vocab_size=self.config.vocab_size,
+                **kwargs,
+            )
+
+    if not return_dict:
+        output_tuple = (logits,) + outputs[1:]
+        output = (loss,) + output_tuple if loss is not None else output_tuple
+        output = output + (token_accuracy,) if token_accuracy is not None else output
+        output = output + (predicted_tokens,) if predicted_tokens is not None else output
+        return output
+
+    # Return custom output class with token_accuracy field
+    return LigerCausalLMOutputWithPast(
+        loss=loss,
+        logits=logits,
+        past_key_values=outputs.past_key_values,
+        hidden_states=outputs.hidden_states,
+        attentions=outputs.attentions,
+        token_accuracy=token_accuracy,
+        predicted_tokens=predicted_tokens,
+    )

src/liger_kernel/transformers/model/pixtral.py

+# Pixtral vision encoder does not require a custom forward function.
+# The Liger kernel optimizations for Pixtral (RMSNorm, SwiGLU, RoPE) are applied
+# via class/function-level monkey patching in monkey_patch.py, which is sufficient
+# since the vision encoder has no cross-entropy loss to fuse.

src/liger_kernel/transformers/model/qwen2.py

+from typing import List
+from typing import Optional
+from typing import Tuple
+from typing import Union
+
+import torch
+
+from torch.nn import CrossEntropyLoss
+from transformers.modeling_outputs import CausalLMOutputWithPast
+
+from liger_kernel.transformers.fused_linear_cross_entropy import LigerFusedLinearCrossEntropyLoss
+from liger_kernel.transformers.model.loss_utils import LigerForCausalLMLoss
+from liger_kernel.transformers.model.loss_utils import unpack_cross_entropy_result
+from liger_kernel.transformers.model.output_classes import LigerCausalLMOutputWithPast
+
+
+def lce_forward_deprecated(
+    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,
+    return_dict: Optional[bool] = None,
+    cache_position: Optional[torch.LongTensor] = None,
+    skip_logits: Optional[bool] = None,
+) -> Union[Tuple, CausalLMOutputWithPast]:
+    r"""
+    Copy paste Qwen2's forward but replace torch cross entropy with liger fused linear cross entropy
+
+
+    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]`.
+
+    Returns:
+
+    Example:
+
+    ```python
+    >>> from transformers import AutoTokenizer, LlamaForCausalLM
+
+    >>> model = Qwen2ForCausalLM.from_pretrained("Qwen/Qwen2-1.5B")
+    >>> tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2-1.5B")
+
+    >>> 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_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,
+        return_dict=return_dict,
+        cache_position=cache_position,
+    )
+
+    hidden_states = outputs[0]
+
+    loss = None
+    logits = None
+
+    if skip_logits and labels is None:
+        raise ValueError("skip_logits is True, but labels is 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
+
+    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:
+        logits = self.lm_head(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 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)
+
+    if not return_dict:
+        output = (logits,) + outputs[1:]
+        return (loss,) + output if loss is not None else output
+
+    return CausalLMOutputWithPast(
+        loss=loss,
+        logits=logits,
+        past_key_values=outputs.past_key_values,
+        hidden_states=outputs.hidden_states,
+        attentions=outputs.attentions,
+    )
+
+
+def lce_forward(
+    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,
+    return_dict: Optional[bool] = None,
+    cache_position: Optional[torch.LongTensor] = None,
+    logits_to_keep: Union[int, torch.Tensor] = 0,
+    skip_logits: Optional[bool] = None,
+    **kwargs,
+) -> Union[Tuple, LigerCausalLMOutputWithPast]:
+    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]`.
+
+        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:
+
+    Example:
+
+    ```python
+    >>> from transformers import AutoTokenizer, Qwen2ForCausalLM
+
+    >>> model = Qwen2ForCausalLM.from_pretrained(PATH_TO_CONVERTED_WEIGHTS)
+    >>> tokenizer = AutoTokenizer.from_pretrained(PATH_TO_CONVERTED_TOKENIZER)
+
+    >>> 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_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,
+        return_dict=return_dict,
+        cache_position=cache_position,
+        **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, :]
+
+    shift_labels = kwargs.pop("shift_labels", None)
+    logits = None
+    loss = None
+    token_accuracy = None
+    predicted_tokens = 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)
+
+    # Compute loss
+    if skip_logits:
+        result = LigerForCausalLMLoss(
+            hidden_states=kept_hidden_states,
+            lm_head_weight=self.lm_head.weight,
+            labels=labels,
+            shift_labels=shift_labels,
+            hidden_size=self.config.hidden_size,
+            **kwargs,
+        )
+        loss, _, token_accuracy, predicted_tokens = unpack_cross_entropy_result(result)
+
+    else:
+        logits = self.lm_head(kept_hidden_states)
+        if labels is not None or shift_labels is not None:
+            loss = self.loss_function(
+                logits=logits,
+                labels=labels,
+                shift_labels=shift_labels,
+                vocab_size=self.config.vocab_size,
+                **kwargs,
+            )
+
+    if not return_dict:
+        output_tuple = (logits,) + outputs[1:]
+        output = (loss,) + output_tuple if loss is not None else output_tuple
+        output = output + (token_accuracy,) if token_accuracy is not None else output
+        output = output + (predicted_tokens,) if predicted_tokens is not None else output
+        return output
+
+    # Return custom output class with token accuracy field
+    return LigerCausalLMOutputWithPast(
+        loss=loss,
+        logits=logits,
+        past_key_values=outputs.past_key_values,
+        hidden_states=outputs.hidden_states,
+        attentions=outputs.attentions,
+        token_accuracy=token_accuracy,
+        predicted_tokens=predicted_tokens,
+    )

src/liger_kernel/transformers/model/qwen2_5_vl.py

+from typing import List
+from typing import Optional
+from typing import Tuple
+from typing import Union
+
+import torch
+
+from packaging import version
+from transformers import __version__ as transformers_version
+from transformers.utils import can_return_tuple
+
+from liger_kernel.transformers.model.loss_utils import LigerForCausalLMLoss
+from liger_kernel.transformers.model.loss_utils import unpack_cross_entropy_result
+from liger_kernel.transformers.model.output_classes import LigerQwen2_5_VLCausalLMOutputWithPast
+
+_TRANSFORMERS_V5_OR_LATER = version.parse(transformers_version) >= version.parse("5.0.0")
+
+
+def _get_hidden_size(config) -> int:
+    """Get hidden_size from Qwen2.5VLConfig in a version-aware manner."""
+    if _TRANSFORMERS_V5_OR_LATER:
+        return config.text_config.hidden_size
+    return config.hidden_size
+
+
+def _get_vocab_size(config) -> int:
+    """Get vocab_size from Qwen2.5VLConfig in a version-aware manner."""
+    if _TRANSFORMERS_V5_OR_LATER:
+        return config.text_config.vocab_size
+    return config.vocab_size
+
+
+@can_return_tuple
+def lce_forward(
+    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,
+    return_dict: Optional[bool] = None,
+    pixel_values: Optional[torch.Tensor] = None,
+    pixel_values_videos: Optional[torch.FloatTensor] = None,
+    image_grid_thw: Optional[torch.LongTensor] = None,
+    video_grid_thw: Optional[torch.LongTensor] = None,
+    rope_deltas: Optional[torch.LongTensor] = None,
+    mm_token_type_ids: Optional[torch.IntTensor] = None,
+    cache_position: Optional[torch.LongTensor] = None,
+    second_per_grid_ts: Optional[torch.Tensor] = None,
+    skip_logits: Optional[bool] = None,
+    **kwargs,
+) -> Union[Tuple, LigerQwen2_5_VLCausalLMOutputWithPast]:
+    r"""
+    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]`.
+    pixel_values_videos (`torch.FloatTensor` of shape `(seq_length, num_channels * temporal_size * image_size * image_size)):
+        The tensors corresponding to the input videos. Pixel values can be obtained using
+        [`AutoImageProcessor`]. See [`Qwen2_5_VLImageProcessor.__call__`] for details. [`Qwen2_5_VLProcessor`] uses
+        [`Qwen2_5_VLImageProcessor`] for processing videos.
+    image_grid_thw (`torch.LongTensor` of shape `(num_images, 3)`, *optional*):
+        The temporal, height and width of feature shape of each image in LLM.
+    video_grid_thw (`torch.LongTensor` of shape `(num_videos, 3)`, *optional*):
+        The temporal, height and width of feature shape of each video in LLM.
+    rope_deltas (`torch.LongTensor` of shape `(batch_size, )`, *optional*):
+        The rope index difference between sequence length and multimodal rope.
+    second_per_grid_ts (`torch.Tensor` of shape `(num_videos)`, *optional*):
+        The time interval (in seconds) for each grid along the temporal dimension in the 3D position IDs.
+
+    Example:
+
+    ```python
+    >>> from PIL import Image
+    >>> import requests
+    >>> from transformers import AutoProcessor, Qwen2_5_VLForConditionalGeneration
+
+    >>> model = Qwen2_5_VLForConditionalGeneration.from_pretrained("Qwen/Qwen2.5-VL-7B-Instruct")
+    >>> processor = AutoProcessor.from_pretrained("Qwen/Qwen2.5-VL-7B-Instruct")
+
+    >>> messages = [
+        {
+            "role": "user",
+            "content": [
+                {"type": "image"},
+                {"type": "text", "text": "What is shown in this image?"},
+            ],
+        },
+    ]
+    >>> url = "https://www.ilankelman.org/stopsigns/australia.jpg"
+    >>> image = Image.open(requests.get(url, stream=True).raw)
+
+    >>> text = processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+    >>> inputs = processor(text=[text], images=[image], vision_infos=[vision_infos])
+
+    >>> # 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]
+    "The image shows a street scene with a red stop sign in the foreground. In the background, there is a large red gate with Chinese characters ..."
+    ```"""
+
+    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
+
+    outputs = self.model(
+        input_ids=input_ids,
+        pixel_values=pixel_values,
+        pixel_values_videos=pixel_values_videos,
+        image_grid_thw=image_grid_thw,
+        video_grid_thw=video_grid_thw,
+        second_per_grid_ts=second_per_grid_ts,
+        position_ids=position_ids,
+        attention_mask=attention_mask,
+        past_key_values=past_key_values,
+        inputs_embeds=inputs_embeds,
+        use_cache=use_cache,
+        output_attentions=output_attentions,
+        output_hidden_states=output_hidden_states,
+        return_dict=return_dict,
+        mm_token_type_ids=mm_token_type_ids,
+        cache_position=cache_position,
+        **kwargs,
+    )
+
+    hidden_states = outputs[0]
+
+    shift_labels = kwargs.pop("shift_labels", None)
+    loss = None
+    logits = None
+    token_accuracy = None
+    predicted_tokens = 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:
+        skip_logits = self.training and (labels is not None or shift_labels is not None)
+
+    # Compute loss
+    if skip_logits:
+        result = LigerForCausalLMLoss(
+            hidden_states=hidden_states,
+            lm_head_weight=self.lm_head.weight,
+            labels=labels,
+            shift_labels=shift_labels,
+            hidden_size=_get_hidden_size(self.config),
+            **kwargs,
+        )
+        loss, _, token_accuracy, predicted_tokens = unpack_cross_entropy_result(result)
+    else:
+        logits = self.lm_head(hidden_states)
+
+        loss = None
+        if labels is not None or shift_labels is not None:
+            loss = self.loss_function(
+                logits=logits,
+                labels=labels,
+                shift_labels=shift_labels,
+                vocab_size=_get_vocab_size(self.config),
+            )
+
+    if not return_dict:
+        output_tuple = (logits,) + outputs[1:]
+        output = (loss,) + output_tuple if loss is not None else output_tuple
+        output = output + (token_accuracy,) if token_accuracy is not None else output
+        output = output + (predicted_tokens,) if predicted_tokens is not None else output
+        return output
+
+    # Return Qwen2.5-VL output with token accuracy
+    return LigerQwen2_5_VLCausalLMOutputWithPast(
+        loss=loss,
+        logits=logits,
+        past_key_values=outputs.past_key_values,
+        hidden_states=outputs.hidden_states,
+        attentions=outputs.attentions,
+        rope_deltas=outputs.rope_deltas,
+        token_accuracy=token_accuracy,
+        predicted_tokens=predicted_tokens,
+    )

src/liger_kernel/transformers/model/qwen2_vl.py

+from typing import List
+from typing import Optional
+from typing import Tuple
+from typing import Union
+
+import torch
+
+from packaging import version
+from transformers import __version__ as transformers_version
+from transformers.utils import can_return_tuple
+
+from liger_kernel.transformers.model.loss_utils import LigerForCausalLMLoss
+from liger_kernel.transformers.model.loss_utils import unpack_cross_entropy_result
+from liger_kernel.transformers.model.output_classes import LigerQwen2VLCausalLMOutputWithPast
+
+_TRANSFORMERS_V5_OR_LATER = version.parse(transformers_version) >= version.parse("5.0.0")
+
+
+def _get_hidden_size(config) -> int:
+    """Get hidden_size from Qwen2VLConfig in a version-aware manner."""
+    if _TRANSFORMERS_V5_OR_LATER:
+        return config.text_config.hidden_size
+    return config.hidden_size
+
+
+def _get_vocab_size(config) -> int:
+    """Get vocab_size from Qwen2VLConfig in a version-aware manner."""
+    if _TRANSFORMERS_V5_OR_LATER:
+        return config.text_config.vocab_size
+    return config.vocab_size
+
+
+@can_return_tuple
+def lce_forward(
+    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,
+    return_dict: Optional[bool] = None,
+    pixel_values: Optional[torch.Tensor] = None,
+    pixel_values_videos: Optional[torch.FloatTensor] = None,
+    image_grid_thw: Optional[torch.LongTensor] = None,
+    video_grid_thw: Optional[torch.LongTensor] = None,
+    rope_deltas: Optional[torch.LongTensor] = None,
+    mm_token_type_ids: Optional[torch.IntTensor] = None,
+    cache_position: Optional[torch.LongTensor] = None,
+    skip_logits: Optional[bool] = None,
+    **kwargs,
+) -> Union[Tuple, LigerQwen2VLCausalLMOutputWithPast]:
+    r"""
+    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]`.
+    pixel_values_videos (`torch.FloatTensor` of shape `(seq_length, num_channels * temporal_size * image_size * image_size)):
+        The tensors corresponding to the input videos. Pixel values can be obtained using
+        [`AutoImageProcessor`]. See [`Qwen2VLImageProcessor.__call__`] for details. [`Qwen2VLProcessor`] uses
+        [`Qwen2VLImageProcessor`] for processing videos.
+    image_grid_thw (`torch.LongTensor` of shape `(num_images, 3)`, *optional*):
+        The temporal, height and width of feature shape of each image in LLM.
+    video_grid_thw (`torch.LongTensor` of shape `(num_videos, 3)`, *optional*):
+        The temporal, height and width of feature shape of each video in LLM.
+    rope_deltas (`torch.LongTensor` of shape `(batch_size, )`, *optional*):
+        The rope index difference between sequence length and multimodal rope.
+
+    Example:
+
+    ```python
+    >>> from PIL import Image
+    >>> import requests
+    >>> from transformers import AutoProcessor, Qwen2VLForConditionalGeneration
+
+    >>> model = Qwen2VLForConditionalGeneration.from_pretrained("Qwen/Qwen2-VL-7B-Instruct")
+    >>> processor = AutoProcessor.from_pretrained("Qwen/Qwen2-VL-7B-Instruct")
+
+    >>> messages = [
+        {
+            "role": "user",
+            "content": [
+                {"type": "image"},
+                {"type": "text", "text": "What is shown in this image?"},
+            ],
+        },
+    ]
+    >>> url = "https://www.ilankelman.org/stopsigns/australia.jpg"
+    >>> image = Image.open(requests.get(url, stream=True).raw)
+
+    >>> text = processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+    >>> inputs = processor(text=[text], images=[image], vision_infos=[vision_infos])
+
+    >>> # 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]
+    "The image shows a street scene with a red stop sign in the foreground. In the background, there is a large red gate with Chinese characters ..."
+    ```"""
+
+    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
+
+    outputs = self.model(
+        input_ids=input_ids,
+        pixel_values=pixel_values,
+        pixel_values_videos=pixel_values_videos,
+        image_grid_thw=image_grid_thw,
+        video_grid_thw=video_grid_thw,
+        position_ids=position_ids,
+        attention_mask=attention_mask,
+        past_key_values=past_key_values,
+        inputs_embeds=inputs_embeds,
+        use_cache=use_cache,
+        output_attentions=output_attentions,
+        output_hidden_states=output_hidden_states,
+        return_dict=return_dict,
+        mm_token_type_ids=mm_token_type_ids,
+        cache_position=cache_position,
+        **kwargs,
+    )
+
+    hidden_states = outputs[0]
+
+    shift_labels = kwargs.pop("shift_labels", None)
+    loss = None
+    logits = None
+    token_accuracy = None
+    predicted_tokens = 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:
+        skip_logits = self.training and (labels is not None or shift_labels is not None)
+
+    # Compute loss
+    if skip_logits:
+        result = LigerForCausalLMLoss(
+            hidden_states=hidden_states,
+            lm_head_weight=self.lm_head.weight,
+            labels=labels,
+            shift_labels=shift_labels,
+            hidden_size=_get_hidden_size(self.config),
+            **kwargs,
+        )
+        loss, _, token_accuracy, predicted_tokens = unpack_cross_entropy_result(result)
+    else:
+        logits = self.lm_head(hidden_states)
+
+        loss = None
+        if labels is not None or shift_labels is not None:
+            loss = self.loss_function(
+                logits=logits,
+                labels=labels,
+                shift_labels=shift_labels,
+                vocab_size=_get_vocab_size(self.config),
+            )
+
+    if not return_dict:
+        output_tuple = (logits,) + outputs[1:]
+        output = (loss,) + output_tuple if loss is not None else output_tuple
+        output = output + (token_accuracy,) if token_accuracy is not None else output
+        output = output + (predicted_tokens,) if predicted_tokens is not None else output
+        return output
+
+    # Return Qwen2VL output with token accuracy
+    return LigerQwen2VLCausalLMOutputWithPast(
+        loss=loss,
+        logits=logits,
+        past_key_values=outputs.past_key_values,
+        hidden_states=outputs.hidden_states,
+        attentions=outputs.attentions,
+        rope_deltas=outputs.rope_deltas,
+        token_accuracy=token_accuracy,
+        predicted_tokens=predicted_tokens,
+    )

src/liger_kernel/transformers/model/qwen3.py

+from typing import List
+from typing import Optional
+from typing import Union
+
+import torch
+
+from liger_kernel.transformers.model.loss_utils import LigerForCausalLMLoss
+from liger_kernel.transformers.model.loss_utils import unpack_cross_entropy_result
+from liger_kernel.transformers.model.output_classes import LigerCausalLMOutputWithPast
+
+
+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[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,
+    cache_position: Optional[torch.LongTensor] = None,
+    logits_to_keep: Union[int, torch.Tensor] = 0,
+    skip_logits: Optional[bool] = None,
+    return_dict: Optional[bool] = None,
+    **kwargs,
+) -> LigerCausalLMOutputWithPast:
+    r"""
+        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]`.
+
+        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:
+
+    Example:
+
+    ```python
+    >>> from transformers import AutoTokenizer, Qwen3ForCausalLM
+
+    >>> model = Qwen3ForCausalLM.from_pretrained("Qwen/Qwen3-8B")
+    >>> tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen3-8B")
+
+    >>> 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_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,
+        cache_position=cache_position,
+        **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, :]
+
+    shift_labels = kwargs.pop("shift_labels", None)
+    # Remove output-control parameters that shouldn't be passed to loss functions
+    kwargs.pop("return_dict", None)
+    logits = None
+    loss = None
+    token_accuracy = None
+    predicted_tokens = 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)
+
+    # Compute loss
+    if skip_logits:
+        result = LigerForCausalLMLoss(
+            hidden_states=kept_hidden_states,
+            lm_head_weight=self.lm_head.weight,
+            labels=labels,
+            shift_labels=shift_labels,
+            hidden_size=self.config.hidden_size,
+            **kwargs,
+        )
+        loss, _, token_accuracy, predicted_tokens = unpack_cross_entropy_result(result)
+
+    else:
+        logits = self.lm_head(kept_hidden_states)
+        if labels is not None or shift_labels is not None:
+            loss = self.loss_function(
+                logits=logits,
+                labels=labels,
+                shift_labels=shift_labels,
+                vocab_size=self.config.vocab_size,
+                **kwargs,
+            )
+
+    if not return_dict:
+        output = (logits,) + outputs[1:]
+        output = ((loss,) + output) if loss is not None else output
+        output = output + (token_accuracy,) if token_accuracy is not None else output
+        output = output + (predicted_tokens,) if predicted_tokens is not None else output
+        return output
+
+    # Return custom output class with accuracy field
+    return LigerCausalLMOutputWithPast(
+        loss=loss,
+        logits=logits,
+        past_key_values=outputs.past_key_values,
+        hidden_states=outputs.hidden_states,
+        attentions=outputs.attentions,
+        token_accuracy=token_accuracy,
+        predicted_tokens=predicted_tokens,
+    )

src/liger_kernel/transformers/model/qwen3_5.py

+from typing import List
+from typing import Optional
+from typing import Union
+
+import torch
+
+from liger_kernel.transformers.model.loss_utils import LigerForCausalLMLoss
+from liger_kernel.transformers.model.loss_utils import unpack_cross_entropy_result
+from liger_kernel.transformers.model.output_classes import LigerCausalLMOutputWithPast
+from liger_kernel.transformers.model.output_classes import LigerQwen3_5CausalLMOutputWithPast
+
+
+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[List[torch.FloatTensor]] = None,
+    inputs_embeds: Optional[torch.FloatTensor] = None,
+    labels: Optional[torch.LongTensor] = None,
+    use_cache: Optional[bool] = None,
+    cache_position: Optional[torch.LongTensor] = None,
+    logits_to_keep: Union[int, torch.Tensor] = 0,
+    skip_logits: Optional[bool] = None,
+    **kwargs,
+) -> LigerCausalLMOutputWithPast:
+    r"""
+        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]`.
+
+        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:
+
+    Example:
+
+    ```python
+    >>> from transformers import AutoTokenizer, Qwen3_5ForCausalLM
+
+    >>> model = Qwen3_5ForCausalLM.from_pretrained("Qwen/Qwen3.5-9B")
+    >>> tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen3.5-9B")
+
+    >>> 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."
+    ```"""
+    return_dict = kwargs.pop("return_dict", None)
+    if return_dict is None:
+        return_dict = self.config.use_return_dict
+
+    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,
+        cache_position=cache_position,
+        **kwargs,
+    )
+
+    hidden_states = outputs.last_hidden_state
+    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 = kwargs.pop("shift_labels", None)
+    logits = None
+    loss = None
+    token_accuracy = None
+    predicted_tokens = None
+
+    if skip_logits is None:
+        skip_logits = self.training and (labels is not None or shift_labels is not None)
+
+    if skip_logits:
+        result = LigerForCausalLMLoss(
+            hidden_states=kept_hidden_states,
+            lm_head_weight=self.lm_head.weight,
+            labels=labels,
+            shift_labels=shift_labels,
+            hidden_size=self.config.hidden_size,
+            **kwargs,
+        )
+        loss, _, token_accuracy, predicted_tokens = unpack_cross_entropy_result(result)
+    else:
+        logits = self.lm_head(kept_hidden_states)
+        if labels is not None or shift_labels is not None:
+            loss = self.loss_function(
+                logits=logits,
+                labels=labels,
+                shift_labels=shift_labels,
+                vocab_size=self.config.vocab_size,
+                **kwargs,
+            )
+
+    if not return_dict:
+        output = (logits,) + outputs[1:]
+        output = ((loss,) + output) if loss is not None else output
+        output = output + (token_accuracy,) if token_accuracy is not None else output
+        output = output + (predicted_tokens,) if predicted_tokens is not None else output
+        return output
+
+    return LigerCausalLMOutputWithPast(
+        loss=loss,
+        logits=logits,
+        past_key_values=outputs.past_key_values,
+        hidden_states=outputs.hidden_states,
+        attentions=outputs.attentions,
+        token_accuracy=token_accuracy,
+        predicted_tokens=predicted_tokens,
+    )
+
+
+def lce_forward_for_multimodal(
+    self,
+    input_ids: Optional[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,
+    pixel_values: Optional[torch.Tensor] = None,
+    pixel_values_videos: Optional[torch.FloatTensor] = None,
+    image_grid_thw: Optional[torch.LongTensor] = None,
+    video_grid_thw: Optional[torch.LongTensor] = None,
+    mm_token_type_ids: Optional[torch.IntTensor] = None,
+    logits_to_keep: Union[int, torch.Tensor] = 0,
+    skip_logits: Optional[bool] = None,
+    **kwargs,
+) -> Union[tuple, LigerQwen3_5CausalLMOutputWithPast]:
+    r"""
+    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]`.
+    image_grid_thw (`torch.LongTensor` of shape `(num_images, 3)`, *optional*):
+        The temporal, height and width of feature shape of each image in LLM.
+    video_grid_thw (`torch.LongTensor` of shape `(num_videos, 3)`, *optional*):
+        The temporal, height and width of feature shape of each video in LLM.
+
+    Example:
+
+    ```python
+    >>> from transformers import AutoProcessor, Qwen3_5ForConditionalGeneration
+
+    >>> model = Qwen3_5ForConditionalGeneration.from_pretrained("Qwen/Qwen3-VL-8B-Instruct")
+    >>> processor = AutoProcessor.from_pretrained("Qwen/Qwen3-VL-8B-Instruct")
+
+    >>> messages = [
+        {
+            "role": "user",
+            "content": [
+                {
+                    "type": "image",
+                    "image": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/pipeline-cat-chonk.jpeg",
+                },
+                {"type": "text", "text": "Describe the image."},
+            ],
+        }
+    ]
+
+    >>> inputs = processor.apply_chat_template(
+        messages,
+        tokenize=True,
+        add_generation_prompt=True,
+        return_dict=True,
+        return_tensors="pt"
+    )
+
+    >>> # Generate
+    >>> generated_ids = model.generate(**inputs, max_new_tokens=1024)
+    >>> generated_ids_trimmed = [out_ids[len(in_ids) :] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)]
+    >>> output_text = processor.batch_decode(generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
+    >>> print(output_text)
+    ```
+    """
+    return_dict = kwargs.pop("return_dict", None)
+    if return_dict is None:
+        return_dict = self.config.use_return_dict
+
+    outputs = self.model(
+        input_ids=input_ids,
+        pixel_values=pixel_values,
+        pixel_values_videos=pixel_values_videos,
+        image_grid_thw=image_grid_thw,
+        video_grid_thw=video_grid_thw,
+        position_ids=position_ids,
+        attention_mask=attention_mask,
+        past_key_values=past_key_values,
+        inputs_embeds=inputs_embeds,
+        mm_token_type_ids=mm_token_type_ids,
+        **kwargs,
+    )
+
+    hidden_states = outputs[0]
+    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 = kwargs.pop("shift_labels", None)
+    logits = None
+    loss = None
+    token_accuracy = None
+    predicted_tokens = None
+
+    if skip_logits is None:
+        skip_logits = self.training and (labels is not None or shift_labels is not None)
+
+    if skip_logits:
+        result = 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,
+            **kwargs,
+        )
+        loss, _, token_accuracy, predicted_tokens = unpack_cross_entropy_result(result)
+    else:
+        logits = self.lm_head(kept_hidden_states)
+        if labels is not None or shift_labels is not None:
+            loss = self.loss_function(
+                logits=logits,
+                labels=labels,
+                shift_labels=shift_labels,
+                vocab_size=self.config.text_config.vocab_size,
+                **kwargs,
+            )
+
+    if not return_dict:
+        output = (logits,) + outputs[1:]
+        output = ((loss,) + output) if loss is not None else output
+        output = output + (token_accuracy,) if token_accuracy is not None else output
+        output = output + (predicted_tokens,) if predicted_tokens is not None else output
+        return output
+
+    return LigerQwen3_5CausalLMOutputWithPast(
+        loss=loss,
+        logits=logits,
+        past_key_values=outputs.past_key_values,
+        hidden_states=outputs.hidden_states,
+        attentions=outputs.attentions,
+        rope_deltas=outputs.rope_deltas,
+        token_accuracy=token_accuracy,
+        predicted_tokens=predicted_tokens,
+    )

src/liger_kernel/transformers/model/qwen3_5_moe.py

+from typing import TYPE_CHECKING
+from typing import List
+from typing import Optional
+from typing import Union
+
+import torch
+
+from transformers.modeling_outputs import MoeModelOutputWithPast
+
+if TYPE_CHECKING:
+    from transformers.models.qwen3_5_moe.modeling_qwen3_5_moe import load_balancing_loss_func
+
+from liger_kernel.transformers.model.loss_utils import LigerForCausalLMLoss
+from liger_kernel.transformers.model.loss_utils import unpack_cross_entropy_result
+from liger_kernel.transformers.model.output_classes import LigerMoeCausalLMOutputWithPast
+
+
+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[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,
+    mm_token_type_ids: Optional[torch.IntTensor] = None,
+    cache_position: Optional[torch.LongTensor] = None,
+    logits_to_keep: Union[int, torch.Tensor] = 0,
+    skip_logits: Optional[bool] = None,
+    return_dict: Optional[bool] = None,
+    **kwargs,
+) -> LigerMoeCausalLMOutputWithPast:
+    r"""
+        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]`.
+
+        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:
+
+    Example:
+
+    ```python
+    >>> from transformers import AutoModelForCausalLM, AutoTokenizer
+
+    >>> model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3.5-35B-A3B-Instruct")
+    >>> tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen3.5-35B-A3B-Instruct")
+
+    >>> prompt = "Give me a short introduction to large language model."
+    >>> 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: MoeModelOutputWithPast = 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,
+        mm_token_type_ids=mm_token_type_ids,
+        cache_position=cache_position,
+        **kwargs,
+    )
+
+    hidden_states = outputs.last_hidden_state
+    # 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 = kwargs.pop("shift_labels", None)
+    logits = None
+    loss = None
+    token_accuracy = None
+    predicted_tokens = None
+
+    if skip_logits is None:
+        skip_logits = self.training and (labels is not None or shift_labels is not None)
+
+    if skip_logits:
+        result = LigerForCausalLMLoss(
+            hidden_states=kept_hidden_states,
+            lm_head_weight=self.lm_head.weight,
+            labels=labels,
+            shift_labels=shift_labels,
+            hidden_size=self.config.hidden_size,
+            **kwargs,
+        )
+        loss, _, token_accuracy, predicted_tokens = unpack_cross_entropy_result(result)
+    else:  # if in inference model materialize logits
+        logits = self.lm_head(kept_hidden_states)
+        if labels is not None or shift_labels is not None:
+            loss = self.loss_function(
+                logits=logits,
+                labels=labels,
+                shift_labels=shift_labels,
+                vocab_size=self.vocab_size,
+                **kwargs,
+            )
+
+    aux_loss = None
+    if output_router_logits:
+        aux_loss = load_balancing_loss_func(
+            outputs.router_logits,
+            self.num_experts,
+            self.num_experts_per_tok,
+            attention_mask,
+        )
+        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:]
+        output = ((aux_loss,) + output) if aux_loss is not None else output
+        output = ((loss,) + output) if loss is not None else output
+        output = output + (token_accuracy,) if token_accuracy is not None else output
+        output = output + (predicted_tokens,) if predicted_tokens is not None else output
+        return output
+
+    return LigerMoeCausalLMOutputWithPast(
+        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,
+        token_accuracy=token_accuracy,
+        predicted_tokens=predicted_tokens,
+    )

src/liger_kernel/transformers/model/qwen3_moe.py

+from typing import List
+from typing import Optional
+from typing import Union
+
+import torch
+
+from transformers.modeling_outputs import MoeModelOutputWithPast
+from transformers.models.mixtral.modeling_mixtral import load_balancing_loss_func
+
+from liger_kernel.transformers.model.loss_utils import LigerForCausalLMLoss
+from liger_kernel.transformers.model.loss_utils import unpack_cross_entropy_result
+from liger_kernel.transformers.model.output_classes import LigerMoeCausalLMOutputWithPast
+
+
+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[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,
+    cache_position: Optional[torch.LongTensor] = None,
+    logits_to_keep: Union[int, torch.Tensor] = 0,
+    skip_logits: Optional[bool] = None,
+    return_dict: Optional[bool] = None,
+    **kwargs,
+) -> LigerMoeCausalLMOutputWithPast:
+    r"""
+        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]`.
+
+        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:
+
+    Example:
+
+    ```python
+    >>> from transformers import AutoTokenizer, Qwen3MoeForCausalLM
+
+    >>> model = Qwen3MoeForCausalLM.from_pretrained("Qwen/Qwen3-MoE-15B-A2B")
+    >>> tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen3-MoE-15B-A2B")
+
+    >>> 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: MoeModelOutputWithPast = 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,
+        cache_position=cache_position,
+        **kwargs,
+    )
+
+    hidden_states = outputs.last_hidden_state
+    # 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 = kwargs.pop("shift_labels", None)
+    logits = None
+    loss = None
+    token_accuracy = None
+    predicted_tokens = None
+
+    if skip_logits is None:
+        skip_logits = self.training and (labels is not None or shift_labels is not None)
+
+    # Compute loss
+    if skip_logits:
+        result = LigerForCausalLMLoss(
+            hidden_states=kept_hidden_states,
+            lm_head_weight=self.lm_head.weight,
+            labels=labels,
+            shift_labels=shift_labels,
+            hidden_size=self.config.hidden_size,
+            **kwargs,
+        )
+        loss, _, token_accuracy, predicted_tokens = unpack_cross_entropy_result(result)
+    else:  # if in inference model materialize logits
+        logits = self.lm_head(kept_hidden_states)
+        if labels is not None or shift_labels is not None:
+            loss = self.loss_function(
+                logits=logits,
+                labels=labels,
+                shift_labels=shift_labels,
+                vocab_size=self.vocab_size,
+                **kwargs,
+            )
+
+    aux_loss = None
+    if output_router_logits:
+        aux_loss = load_balancing_loss_func(
+            outputs.router_logits,
+            self.num_experts,
+            self.num_experts_per_tok,
+            attention_mask,
+        )
+        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:]
+        output = ((aux_loss,) + output) if aux_loss is not None else output
+        output = ((loss,) + output) if loss is not None else output
+        output = output + (token_accuracy,) if token_accuracy is not None else output
+        output = output + (predicted_tokens,) if predicted_tokens is not None else output
+        return output
+
+    # Return custom output class with accuracy field
+    return LigerMoeCausalLMOutputWithPast(
+        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,
+        token_accuracy=token_accuracy,
+        predicted_tokens=predicted_tokens,
+    )

src/liger_kernel/transformers/model/qwen3_next.py

+from typing import TYPE_CHECKING
+from typing import List
+from typing import Optional
+from typing import Union
+
+import torch
+
+from transformers.modeling_outputs import MoeModelOutputWithPast
+
+if TYPE_CHECKING:
+    from transformers.models.qwen3_next.modeling_qwen3_next import load_balancing_loss_func
+
+from liger_kernel.transformers.model.loss_utils import LigerForCausalLMLoss
+from liger_kernel.transformers.model.loss_utils import unpack_cross_entropy_result
+from liger_kernel.transformers.model.output_classes import LigerMoeCausalLMOutputWithPast
+
+
+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[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,
+    cache_position: Optional[torch.LongTensor] = None,
+    logits_to_keep: Union[int, torch.Tensor] = 0,
+    skip_logits: Optional[bool] = None,
+    return_dict: Optional[bool] = None,
+    **kwargs,
+) -> LigerMoeCausalLMOutputWithPast:
+    r"""
+        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]`.
+
+        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:
+
+    Example:
+
+    ```python
+    >>> from transformers import AutoModelForCausalLM, AutoTokenizer
+
+    >>> model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3-Next-80B-A3B-Instruct")
+    >>> tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen3-Next-80B-A3B-Instruct")
+
+    >>> prompt = "Give me a short introduction to large language model."
+    >>> 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: MoeModelOutputWithPast = 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,
+        cache_position=cache_position,
+        **kwargs,
+    )
+
+    hidden_states = outputs.last_hidden_state
+    # 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 = kwargs.pop("shift_labels", None)
+    logits = None
+    loss = None
+    token_accuracy = None
+    predicted_tokens = None
+
+    if skip_logits is None:
+        skip_logits = self.training and (labels is not None or shift_labels is not None)
+
+    if skip_logits:
+        result = LigerForCausalLMLoss(
+            hidden_states=kept_hidden_states,
+            lm_head_weight=self.lm_head.weight,
+            labels=labels,
+            shift_labels=shift_labels,
+            hidden_size=self.config.hidden_size,
+            **kwargs,
+        )
+        loss, _, token_accuracy, predicted_tokens = unpack_cross_entropy_result(result)
+    else:  # if in inference model materialize logits
+        logits = self.lm_head(kept_hidden_states)
+        if labels is not None or shift_labels is not None:
+            loss = self.loss_function(
+                logits=logits,
+                labels=labels,
+                shift_labels=shift_labels,
+                vocab_size=self.vocab_size,
+                **kwargs,
+            )
+
+    aux_loss = None
+    if output_router_logits:
+        aux_loss = load_balancing_loss_func(
+            outputs.router_logits,
+            self.num_experts,
+            self.num_experts_per_tok,
+            attention_mask,
+        )
+        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:]
+        output = ((aux_loss,) + output) if aux_loss is not None else output
+        output = ((loss,) + output) if loss is not None else output
+        output = output + (token_accuracy,) if token_accuracy is not None else output
+        output = output + (predicted_tokens,) if predicted_tokens is not None else output
+        return output
+
+    return LigerMoeCausalLMOutputWithPast(
+        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,
+        token_accuracy=token_accuracy,
+        predicted_tokens=predicted_tokens,
+    )

src/liger_kernel/transformers/model/qwen3_vl.py

+from typing import List
+from typing import Optional
+from typing import Tuple
+from typing import Union
+
+import torch
+
+from transformers.utils import can_return_tuple
+
+from liger_kernel.transformers.model.loss_utils import LigerForCausalLMLoss
+from liger_kernel.transformers.model.loss_utils import unpack_cross_entropy_result
+from liger_kernel.transformers.model.output_classes import LigerQwen3VLCausalLMOutputWithPast
+
+
+@can_return_tuple
+def lce_forward(
+    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,
+    return_dict: Optional[bool] = None,
+    pixel_values: Optional[torch.Tensor] = None,
+    pixel_values_videos: Optional[torch.FloatTensor] = None,
+    image_grid_thw: Optional[torch.LongTensor] = None,
+    video_grid_thw: Optional[torch.LongTensor] = None,
+    rope_deltas: Optional[torch.LongTensor] = None,
+    mm_token_type_ids: Optional[torch.IntTensor] = None,
+    cache_position: Optional[torch.LongTensor] = None,
+    second_per_grid_ts: Optional[torch.Tensor] = None,
+    skip_logits: Optional[bool] = None,
+    **kwargs,
+) -> Union[Tuple, LigerQwen3VLCausalLMOutputWithPast]:
+    """
+    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]`.
+    pixel_values_videos (`torch.FloatTensor` of shape `(seq_length, num_channels * temporal_size * image_size * image_size)):
+        The tensors corresponding to the input videos. Pixel values can be obtained using
+        [`AutoImageProcessor`]. See [`Qwen2_5_VLImageProcessor.__call__`] for details. [`Qwen2_5_VLProcessor`] uses
+        [`Qwen2_5_VLImageProcessor`] for processing videos.
+    image_grid_thw (`torch.LongTensor` of shape `(num_images, 3)`, *optional*):
+        The temporal, height and width of feature shape of each image in LLM.
+    video_grid_thw (`torch.LongTensor` of shape `(num_videos, 3)`, *optional*):
+        The temporal, height and width of feature shape of each video in LLM.
+    rope_deltas (`torch.LongTensor` of shape `(batch_size, )`, *optional*):
+        The rope index difference between sequence length and multimodal rope.
+    second_per_grid_ts (`torch.Tensor` of shape `(num_videos)`, *optional*):
+        The time interval (in seconds) for each grid along the temporal dimension in the 3D position IDs.
+    Example:
+    ```python
+    >>> from PIL import Image
+    >>> import requests
+    >>> from transformers import AutoProcessor, Qwen3VLForConditionalGeneration
+    >>> model = Qwen3VLForConditionalGeneration.from_pretrained("Qwen/Qwen3-VL")
+    >>> processor = AutoProcessor.from_pretrained("Qwen/Qwen3-VL")
+    >>> messages = [
+        {
+            "role": "user",
+            "content": [
+                {"type": "image"},
+                {"type": "text", "text": "What is shown in this image?"},
+            ],
+        },
+    ]
+    >>> url = "https://www.ilankelman.org/stopsigns/australia.jpg"
+    >>> image = Image.open(requests.get(url, stream=True).raw)
+    >>> text = processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+    >>> inputs = processor(text=[text], images=[image], vision_infos=[vision_infos])
+    >>> # 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]
+    "The image shows a street scene with a red stop sign in the foreground. In the background, there is a large red gate with Chinese characters ..."
+    ```"""
+
+    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
+
+    outputs = self.model(
+        input_ids=input_ids,
+        pixel_values=pixel_values,
+        pixel_values_videos=pixel_values_videos,
+        image_grid_thw=image_grid_thw,
+        video_grid_thw=video_grid_thw,
+        second_per_grid_ts=second_per_grid_ts,
+        position_ids=position_ids,
+        attention_mask=attention_mask,
+        past_key_values=past_key_values,
+        inputs_embeds=inputs_embeds,
+        use_cache=use_cache,
+        output_attentions=output_attentions,
+        output_hidden_states=output_hidden_states,
+        return_dict=return_dict,
+        mm_token_type_ids=mm_token_type_ids,
+        cache_position=cache_position,
+        **kwargs,
+    )
+
+    hidden_states = outputs[0]
+
+    shift_labels = kwargs.pop("shift_labels", None)
+    loss = None
+    logits = None
+    token_accuracy = None
+    predicted_tokens = 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:
+        skip_logits = self.training and (labels is not None or shift_labels is not None)
+
+    if skip_logits:
+        result = LigerForCausalLMLoss(
+            hidden_states=hidden_states,
+            lm_head_weight=self.lm_head.weight,
+            labels=labels,
+            shift_labels=shift_labels,
+            hidden_size=self.config.text_config.hidden_size,
+            **kwargs,
+        )
+        loss, _, token_accuracy, predicted_tokens = unpack_cross_entropy_result(result)
+    else:
+        logits = self.lm_head(hidden_states)
+
+        loss = None
+        if labels is not None:
+            loss = self.loss_function(logits=logits, labels=labels, vocab_size=self.config.text_config.vocab_size)
+
+    if not return_dict:
+        output = (logits,) + outputs[1:]
+        output = (loss,) + output if loss is not None else output
+        output = output + (token_accuracy,) if token_accuracy is not None else output
+        output = output + (predicted_tokens,) if predicted_tokens is not None else output
+        return output
+
+    return LigerQwen3VLCausalLMOutputWithPast(
+        loss=loss,
+        logits=logits,
+        past_key_values=outputs.past_key_values,
+        hidden_states=outputs.hidden_states,
+        attentions=outputs.attentions,
+        rope_deltas=outputs.rope_deltas,
+        token_accuracy=token_accuracy,
+        predicted_tokens=predicted_tokens,
+    )

src/liger_kernel/transformers/model/qwen3_vl_moe.py

+from typing import List
+from typing import Optional
+from typing import Tuple
+from typing import Union
+
+import torch
+
+from transformers.models.qwen3_vl_moe.modeling_qwen3_vl_moe import load_balancing_loss_func
+from transformers.utils import can_return_tuple
+
+from liger_kernel.transformers.model.loss_utils import LigerForCausalLMLoss
+from liger_kernel.transformers.model.loss_utils import unpack_cross_entropy_result
+from liger_kernel.transformers.model.output_classes import LigerQwen3VLMoeCausalLMOutputWithPast
+
+
+@can_return_tuple
+def lce_forward(
+    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,
+    return_dict: Optional[bool] = None,
+    pixel_values: Optional[torch.Tensor] = None,
+    pixel_values_videos: Optional[torch.FloatTensor] = None,
+    image_grid_thw: Optional[torch.LongTensor] = None,
+    video_grid_thw: Optional[torch.LongTensor] = None,
+    rope_deltas: Optional[torch.LongTensor] = None,
+    mm_token_type_ids: Optional[torch.IntTensor] = None,
+    cache_position: Optional[torch.LongTensor] = None,
+    second_per_grid_ts: Optional[torch.Tensor] = None,
+    skip_logits: Optional[bool] = None,
+    **kwargs,
+) -> Union[Tuple, LigerQwen3VLMoeCausalLMOutputWithPast]:
+    """
+    Qwen3-VL-MoE forward with fused linear cross entropy support mirroring Qwen3-VL behaviour.
+    """
+
+    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
+
+    outputs = self.model(
+        input_ids=input_ids,
+        pixel_values=pixel_values,
+        pixel_values_videos=pixel_values_videos,
+        image_grid_thw=image_grid_thw,
+        video_grid_thw=video_grid_thw,
+        second_per_grid_ts=second_per_grid_ts,
+        position_ids=position_ids,
+        attention_mask=attention_mask,
+        past_key_values=past_key_values,
+        inputs_embeds=inputs_embeds,
+        use_cache=use_cache,
+        output_attentions=output_attentions,
+        output_hidden_states=output_hidden_states,
+        return_dict=return_dict,
+        mm_token_type_ids=mm_token_type_ids,
+        cache_position=cache_position,
+        **kwargs,
+    )
+
+    hidden_states = outputs[0]
+
+    shift_labels = kwargs.pop("shift_labels", None)
+    loss = None
+    logits = None
+    token_accuracy = None
+    predicted_tokens = 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:
+        skip_logits = self.training and (labels is not None or shift_labels is not None)
+
+    if skip_logits:
+        result = LigerForCausalLMLoss(
+            hidden_states=hidden_states,
+            lm_head_weight=self.lm_head.weight,
+            labels=labels,
+            shift_labels=shift_labels,
+            hidden_size=self.config.text_config.hidden_size,
+            **kwargs,
+        )
+        loss, _, token_accuracy, predicted_tokens = unpack_cross_entropy_result(result)
+    else:
+        logits = self.lm_head(hidden_states)
+
+        if labels is not None:
+            loss = self.loss_function(logits=logits, labels=labels, vocab_size=self.config.text_config.vocab_size)
+
+    # Compute auxiliary load-balancing loss for MoE when requested
+    aux_loss = None
+    if kwargs.get("output_router_logits", False):
+        aux_loss = load_balancing_loss_func(
+            outputs.router_logits,
+            self.config.text_config.num_experts,
+            self.config.text_config.num_experts_per_tok,
+            attention_mask,
+        )
+        # If we computed training loss, add the scaled aux loss to it
+        if loss is not None and aux_loss is not None:
+            loss = loss + self.config.text_config.router_aux_loss_coef * aux_loss.to(loss.device)
+
+    if not return_dict:
+        output = (logits,) + outputs[1:]
+        output = (loss,) + output if loss is not None else output
+        output = output + (aux_loss,) if aux_loss is not None else output
+        output = output + (token_accuracy,) if token_accuracy is not None else output
+        output = output + (predicted_tokens,) if predicted_tokens is not None else output
+        return output
+
+    return LigerQwen3VLMoeCausalLMOutputWithPast(
+        loss=loss,
+        logits=logits,
+        past_key_values=outputs.past_key_values,
+        hidden_states=outputs.hidden_states,
+        attentions=outputs.attentions,
+        rope_deltas=outputs.rope_deltas,
+        aux_loss=aux_loss,
+        token_accuracy=token_accuracy,
+        predicted_tokens=predicted_tokens,
+    )

src/liger_kernel/transformers/model/smollm3.py

+from typing import TYPE_CHECKING
+from typing import List
+from typing import Optional
+from typing import Tuple
+from typing import Union
+
+import torch
+
+from torch.distributed.fsdp import FullyShardedDataParallel
+
+from liger_kernel.transformers.fsdp import _FSDPForwardRedirection
+from liger_kernel.transformers.model.loss_utils import LigerForCausalLMLoss
+from liger_kernel.transformers.model.loss_utils import unpack_cross_entropy_result
+from liger_kernel.transformers.model.output_classes import LigerCausalLMOutputWithPast
+from liger_kernel.utils import PEFT_AVAILABLE
+
+if TYPE_CHECKING:
+    from transformers.cache_utils import Cache
+
+if PEFT_AVAILABLE:
+    from peft.utils.other import ModulesToSaveWrapper
+
+
+def lce_forward(
+    self,
+    input_ids: torch.LongTensor = None,
+    attention_mask: Optional[torch.Tensor] = None,
+    position_ids: Optional[torch.LongTensor] = None,
+    past_key_values: Optional[Union["Cache", 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,
+    return_dict: Optional[bool] = None,
+    cache_position: Optional[torch.LongTensor] = None,
+    logits_to_keep: Union[int, torch.Tensor] = 0,
+    skip_logits: Optional[bool] = None,
+    **kwargs,
+) -> Union[Tuple, LigerCausalLMOutputWithPast]:
+    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]`.
+
+        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:
+
+    Example:
+
+    ```python
+    >>> from transformers import AutoTokenizer, Smollm3ForCausalLM
+
+    >>> model = Smollm3ForCausalLM.from_pretrained("HuggingFaceTB/SmolLM3-3B")
+    >>> tokenizer = AutoTokenizer.from_pretrained("HuggingFaceTB/SmolLM3-3B")
+
+    >>> 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_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,
+        return_dict=return_dict,
+        cache_position=cache_position,
+        **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, :]
+
+    shift_labels = kwargs.pop("shift_labels", None)
+    logits = None
+    loss = None
+    token_accuracy = None
+    predicted_tokens = None
+
+    # if in training mode, don't materialize logits
+    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)
+
+    # Compute loss
+    if skip_logits:
+        result = lce_maybe_trainable_lm_head(
+            self,
+            hidden_states=kept_hidden_states,
+            hidden_size=self.config.hidden_size,
+            labels=labels,
+            shift_labels=shift_labels,
+            **kwargs,
+        )
+        loss, _, token_accuracy, predicted_tokens = unpack_cross_entropy_result(result)
+
+    else:
+        logits = self.lm_head(kept_hidden_states)
+        if labels is not None or shift_labels is not None:
+            loss = self.loss_function(
+                logits=logits,
+                labels=labels,
+                shift_labels=shift_labels,
+                vocab_size=self.config.vocab_size,
+                **kwargs,
+            )
+
+    if not return_dict:
+        output_tuple = (logits,) + outputs[1:]
+        output = (loss,) + output_tuple if loss is not None else output_tuple
+        output = output + (token_accuracy,) if token_accuracy is not None else output
+        output = output + (predicted_tokens,) if predicted_tokens is not None else output
+        return output
+
+    # Return custom output class with token_accuracy field
+    return LigerCausalLMOutputWithPast(
+        loss=loss,
+        logits=logits,
+        past_key_values=outputs.past_key_values,
+        hidden_states=outputs.hidden_states,
+        attentions=outputs.attentions,
+        token_accuracy=token_accuracy,
+        predicted_tokens=predicted_tokens,
+    )
+
+
+def lce_maybe_trainable_lm_head(self, hidden_states, hidden_size, labels, shift_labels, **loss_kwargs):
+    lm_head = self.lm_head
+
+    # Unwrap the module if lm_head has been added as trainable module in PEFT LoRA configuration,
+    # i.e. listed in the modules_to_save field of LoraConfig, so the lm_head weights are read
+    # from the unwrapped module.
+    # See https://huggingface.co/docs/peft/package_reference/lora for reference.
+    if PEFT_AVAILABLE and isinstance(lm_head, ModulesToSaveWrapper):
+        lm_head = lm_head.modules_to_save.default
+
+    # If FSDP is used and lm_head is trainable, e.g., during full fine-tuning or with LoRA,
+    # reading the lm_head module weights and calling the kernel must be done within FSDP forward pass
+    # so the module entire parameters are summoned and kept in memory during the kernel execution.
+    if isinstance(lm_head, FullyShardedDataParallel):
+        return _FSDPForwardRedirection()(
+            lm_head,
+            _liger_for_causal_lm_loss,
+            lm_head.module,
+            hidden_states,
+            hidden_size,
+            labels,
+            shift_labels,
+            **loss_kwargs,
+        )
+
+    # FSDP is not used so we can read the lm_head weights and call the kernel directly
+    return _liger_for_causal_lm_loss(
+        lm_head=self.lm_head,
+        hidden_states=hidden_states,
+        hidden_size=hidden_size,
+        labels=labels,
+        shift_labels=shift_labels,
+        **loss_kwargs,
+    )
+
+
+def _liger_for_causal_lm_loss(lm_head, hidden_states, hidden_size, labels, shift_labels, **loss_kwargs):
+    return LigerForCausalLMLoss(
+        hidden_states=hidden_states,
+        lm_head_weight=lm_head.weight,
+        labels=labels,
+        hidden_size=hidden_size,
+        shift_labels=shift_labels,
+        **loss_kwargs,
+    )

src/liger_kernel/transformers/model/smolvlm.py

+from typing import TYPE_CHECKING
+from typing import Optional
+from typing import Union
+
+import torch
+
+from transformers.models.smolvlm.modeling_smolvlm import SmolVLMCausalLMOutputWithPast
+from transformers.processing_utils import Unpack
+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 or shift_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

+import inspect
+import logging
+
+from functools import partial
+from types import MethodType
+from typing import Callable
+from typing import Optional
+
+import transformers
+
+from packaging import version
+from transformers import PreTrainedModel
+
+from liger_kernel.transformers.cross_entropy import LigerCrossEntropyLoss
+from liger_kernel.transformers.functional import liger_cross_entropy
+from liger_kernel.transformers.geglu import LigerGEGLUMLP
+from liger_kernel.transformers.layer_norm import LigerLayerNorm
+from liger_kernel.transformers.model.falcon_h1 import lce_forward as falcon_h1_lce_forward
+from liger_kernel.transformers.model.gemma import lce_forward as gemma_lce_forward
+from liger_kernel.transformers.model.gemma2 import lce_forward as gemma2_lce_forward
+from liger_kernel.transformers.model.gpt_oss import lce_forward as gpt_oss_lce_forward
+from liger_kernel.transformers.model.llama import lce_forward as llama_lce_forward
+from liger_kernel.transformers.model.llava import lce_forward as llava_lce_forward
+from liger_kernel.transformers.model.mistral import lce_forward as mistral_lce_forward
+from liger_kernel.transformers.model.mixtral import lce_forward as mixtral_lce_forward
+from liger_kernel.transformers.model.phi3 import lce_forward as phi3_lce_forward
+from liger_kernel.transformers.model.qwen2 import lce_forward as qwen2_lce_forward
+from liger_kernel.transformers.model.smollm3 import lce_forward as smollm3_lce_forward
+from liger_kernel.transformers.qwen2vl_mrope import liger_multimodal_rotary_pos_emb
+from liger_kernel.transformers.rms_norm import LigerRMSNorm
+from liger_kernel.transformers.rope import liger_rotary_pos_emb
+from liger_kernel.transformers.rope import liger_rotary_pos_emb_vision
+from liger_kernel.transformers.swiglu import LigerBlockSparseTop2MLP
+from liger_kernel.transformers.swiglu import LigerExperts
+from liger_kernel.transformers.swiglu import LigerPhi3SwiGLUMLP
+from liger_kernel.transformers.swiglu import LigerSwiGLUMLP
+
+try:
+    import peft
+
+    PEFT_AVAILABLE = True
+except ImportError:
+    PEFT_AVAILABLE = False
+
+transformer_version = version.parse(transformers.__version__)
+
+logger = logging.getLogger(__name__)
+
+MIN_SUPPORTED_TRANSFORMERS_VERSION = version.parse("4.52.0")
+if transformer_version < MIN_SUPPORTED_TRANSFORMERS_VERSION:
+    raise ImportError(
+        f"liger-kernel requires transformers >= {MIN_SUPPORTED_TRANSFORMERS_VERSION}, got {transformers.__version__}. "
+        "Please install an older version of liger-kernel that is compatible with your transformers version."
+    )
+
+IS_TRANSFORMERS_V5_OR_LATER = version.parse(transformers.__version__) >= version.parse("5.0.0")
+
+
+def _bind_method_to_module(module, method_name: str, new_method: Callable):
+    # Binds a new method to a module instance so that self is passed as the first argument
+    module.__dict__[method_name] = new_method.__get__(module, module.__class__)
+
+
+def _patch_rms_norm_module(module, offset=0.0, eps=1e-6, casting_mode="llama", in_place=True, row_mode=None):
+    # Check if the module is a PEFT ModulesToSaveWrapper
+    # If it is, we need to patch the modules_to_save.default and original_modules
+    if PEFT_AVAILABLE and isinstance(module, peft.utils.other.ModulesToSaveWrapper):
+        module.modules_to_save.default.offset = offset
+        module.modules_to_save.default.casting_mode = casting_mode
+        module.modules_to_save.default.variance_epsilon = (
+            getattr(module, "variance_epsilon", None) or getattr(module, "eps", None) or eps
+        )
+        module.modules_to_save.default.in_place = in_place
+        module.modules_to_save.default.row_mode = row_mode
+        module.original_module.offset = offset
+        module.original_module.casting_mode = casting_mode
+        module.original_module.variance_epsilon = (
+            getattr(module, "variance_epsilon", None) or getattr(module, "eps", None) or eps
+        )
+        module.original_module.in_place = in_place
+        module.original_module.row_mode = row_mode
+        _bind_method_to_module(module.modules_to_save.default, "forward", LigerRMSNorm.forward)
+        _bind_method_to_module(module.modules_to_save.default, "extra_repr", LigerRMSNorm.extra_repr)
+        _bind_method_to_module(module.original_module, "forward", LigerRMSNorm.forward)
+        _bind_method_to_module(module.original_module, "extra_repr", LigerRMSNorm.extra_repr)
+        _bind_method_to_module(module.modules_to_save.default, "_get_name", lambda self: LigerRMSNorm.__name__)
+        _bind_method_to_module(module.original_module, "_get_name", lambda self: LigerRMSNorm.__name__)
+    else:
+        module.offset = offset
+        module.casting_mode = casting_mode
+        module.variance_epsilon = getattr(module, "variance_epsilon", None) or getattr(module, "eps", None) or eps
+        module.in_place = in_place
+        module.row_mode = row_mode
+        _bind_method_to_module(module, "forward", LigerRMSNorm.forward)
+        _bind_method_to_module(module, "extra_repr", LigerRMSNorm.extra_repr)
+        _bind_method_to_module(module, "_get_name", lambda self: LigerRMSNorm.__name__)
+
+
+def _patch_layer_norm_module(module, eps=1e-6):
+    # Check if the module is a PEFT ModulesToSaveWrapper
+    # If it is, we need to patch the modules_to_save.default and original_modules
+    if PEFT_AVAILABLE and isinstance(module, peft.utils.other.ModulesToSaveWrapper):
+        module.hidden_size = module.normalized_shape
+        _bind_method_to_module(module, "forward", LigerLayerNorm.forward)
+        _bind_method_to_module(module, "extra_repr", LigerLayerNorm.extra_repr)
+        module.modules_to_save.default.variance_epsilon = (
+            getattr(module, "variance_epsilon", None) or getattr(module, "eps", None) or eps
+        )
+        module.original_module.hidden_size = getattr(module, "hidden_size", None) or getattr(
+            module, "normalized_shape", None
+        )
+        module.original_module.variance_epsilon = (
+            getattr(module, "variance_epsilon", None) or getattr(module, "eps", None) or eps
+        )
+        module.original_module.hidden_size = getattr(module, "hidden_size", None) or getattr(
+            module, "normalized_shape", None
+        )
+        _bind_method_to_module(module.modules_to_save.default, "forward", LigerLayerNorm.forward)
+        _bind_method_to_module(module.modules_to_save.default, "extra_repr", LigerLayerNorm.extra_repr)
+        _bind_method_to_module(module.original_module, "forward", LigerLayerNorm.forward)
+        _bind_method_to_module(module.original_module, "extra_repr", LigerLayerNorm.extra_repr)
+        _bind_method_to_module(module.modules_to_save.default, "_get_name", lambda self: LigerLayerNorm.__name__)
+        _bind_method_to_module(module.original_module, "_get_name", lambda self: LigerLayerNorm.__name__)
+    else:
+        module.variance_epsilon = getattr(module, "variance_epsilon", None) or getattr(module, "eps", None) or eps
+        module.hidden_size = getattr(module, "hidden_size", None) or getattr(module, "normalized_shape", None)
+        _bind_method_to_module(module, "forward", LigerLayerNorm.forward)
+        _bind_method_to_module(module, "extra_repr", LigerLayerNorm.extra_repr)
+        _bind_method_to_module(module, "_get_name", lambda self: LigerLayerNorm.__name__)
+
+
+def _patch_swiglu_module(module, liger_module):
+    _bind_method_to_module(module, "forward", liger_module.forward)
+    _bind_method_to_module(module, "_get_name", lambda self: liger_module.__name__)
+
+
+def _patch_geglu_module(module):
+    _bind_method_to_module(module, "forward", LigerGEGLUMLP.forward)
+    _bind_method_to_module(module, "_get_name", lambda self: LigerGEGLUMLP.__name__)
+
+
+def apply_liger_kernel_to_granite(
+    rope: bool = True,
+    cross_entropy: bool = True,
+    fused_linear_cross_entropy: bool = False,
+    rms_norm: bool = True,
+    swiglu: bool = True,
+    model: PreTrainedModel = None,
+) -> None:
+    """
+    Apply Liger kernels to replace original implementation in HuggingFace Granite 3 models
+
+    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 True.
+        fused_linear_cross_entropy (bool):
+            Whether to apply Liger's fused linear cross entropy loss. Default is False.
+            `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.
+
+
+
+    Debugging notes:
+        If LigerSwiGLUMLP is OK for Llama, it should be fine for Granite, but it's not.
+    """
+
+    assert not (cross_entropy and fused_linear_cross_entropy), (
+        "cross_entropy and fused_linear_cross_entropy cannot both be True."
+    )
+
+    from transformers.models.granite import modeling_granite
+    from transformers.models.granite.modeling_granite import GraniteModel
+
+    if swiglu:
+        modeling_granite.GraniteMLP = LigerSwiGLUMLP
+
+    if rms_norm:
+        modeling_granite.GraniteRMSNorm = LigerRMSNorm
+
+    if rope:
+        modeling_granite.apply_rotary_pos_emb = liger_rotary_pos_emb
+
+    if cross_entropy:
+        from transformers.loss.loss_utils import nn
+
+        nn.functional.cross_entropy = liger_cross_entropy
+
+    if fused_linear_cross_entropy:
+        raise NotImplementedError("LigerFusedLinearCrossEntropy is not available for Granite models.")
+        # NOTE: Granite model `GraniteForCausalLM.forward` scales logits each
+        # call, so we can't sidestep logit materialization. A bit more work
+        # would be needed to add a scaling term to the `LigerFusedLinearCrossEntropyFunction`
+        # for the logit output.
+
+    if model is not None:
+        # The model instance already exists, so we need to additionally patch the
+        # instance variables that reference already-instantiated modules (e.g. GraniteRMSNorm or GraniteMLP)
+
+        # get the base model from the model instance
+        base_model: GraniteModel = getattr(model, model.base_model_prefix, model)
+
+        if rms_norm:
+            _patch_rms_norm_module(base_model.norm)
+
+        for decoder_layer in base_model.layers:
+            if swiglu:
+                _patch_swiglu_module(decoder_layer.mlp, LigerSwiGLUMLP)
+            if rms_norm:
+                _patch_rms_norm_module(decoder_layer.input_layernorm)
+                _patch_rms_norm_module(decoder_layer.post_attention_layernorm)
+
+
+def apply_liger_kernel_to_llama(
+    rope: bool = True,
+    cross_entropy: bool = False,
+    fused_linear_cross_entropy: bool = True,
+    rms_norm: bool = True,
+    swiglu: bool = True,
+    model: PreTrainedModel = None,
+) -> None:
+    """
+    Apply Liger kernels to replace original implementation in HuggingFace Llama models (2 and 3)
+
+    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.
+        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.
+    """
+
+    assert not (cross_entropy and fused_linear_cross_entropy), (
+        "cross_entropy and fused_linear_cross_entropy cannot both be True."
+    )
+
+    from transformers.models.llama import modeling_llama
+    from transformers.models.llama.modeling_llama import LlamaModel
+
+    if rope:
+        modeling_llama.apply_rotary_pos_emb = liger_rotary_pos_emb
+    if rms_norm:
+        modeling_llama.LlamaRMSNorm = LigerRMSNorm
+    if swiglu:
+        modeling_llama.LlamaMLP = LigerSwiGLUMLP
+
+    if cross_entropy:
+        from transformers.loss.loss_utils import nn
+
+        nn.functional.cross_entropy = liger_cross_entropy
+
+    if fused_linear_cross_entropy:
+        if model is not None:
+            model.forward = MethodType(llama_lce_forward, model)
+        else:
+            modeling_llama.LlamaForCausalLM.forward = llama_lce_forward
+
+    if model is not None:
+        # The model instance already exists, so we need to additionally patch the
+        # instance variables that reference already-instantiated modules (e.g. LlamaRMSNorm or LlamaMLP)
+
+        # get the base model from the model instance
+        base_model: LlamaModel = getattr(model, model.base_model_prefix, model)
+
+        if rms_norm:
+            _patch_rms_norm_module(base_model.norm)
+
+        for decoder_layer in base_model.layers:
+            if swiglu:
+                _patch_swiglu_module(decoder_layer.mlp, LigerSwiGLUMLP)
+            if rms_norm:
+                _patch_rms_norm_module(decoder_layer.input_layernorm)
+                _patch_rms_norm_module(decoder_layer.post_attention_layernorm)
+
+
+def apply_liger_kernel_to_smollm3(
+    rope: bool = True,
+    cross_entropy: bool = False,
+    fused_linear_cross_entropy: bool = True,
+    rms_norm: bool = True,
+    swiglu: bool = True,
+    model: PreTrainedModel = None,
+) -> None:
+    """
+    Apply Liger kernels to replace original implementation in HuggingFace SmolLM3 model
+
+    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.
+        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.
+    """
+
+    assert not (cross_entropy and fused_linear_cross_entropy), (
+        "cross_entropy and fused_linear_cross_entropy cannot both be True."
+    )
+
+    from transformers.models.smollm3 import modeling_smollm3
+    from transformers.models.smollm3.modeling_smollm3 import SmolLM3Model
+
+    if rope:
+        modeling_smollm3.apply_rotary_pos_emb = liger_rotary_pos_emb
+    if rms_norm:
+        modeling_smollm3.SmolLM3RMSNorm = LigerRMSNorm
+    if swiglu:
+        modeling_smollm3.SmolLM3MLP = LigerSwiGLUMLP
+
+    if cross_entropy:
+        from transformers.loss.loss_utils import nn
+
+        nn.functional.cross_entropy = liger_cross_entropy
+
+    if fused_linear_cross_entropy:
+        if model is not None:
+            model.forward = MethodType(smollm3_lce_forward, model)
+        else:
+            modeling_smollm3.SmolLM3ForCausalLM.forward = smollm3_lce_forward
+
+    if model is not None:
+        # The model instance already exists, so we need to additionally patch the
+        # instance variables that reference already-instantiated modules (e.g. SmolLM3RMSNorm or SmolLM3MLP)
+
+        # get the base model from the model instance
+        base_model: SmolLM3Model = getattr(model, model.base_model_prefix, model)
+
+        if rms_norm:
+            _patch_rms_norm_module(base_model.norm)
+
+        for decoder_layer in base_model.layers:
+            if swiglu:
+                _patch_swiglu_module(decoder_layer.mlp, LigerSwiGLUMLP)
+            if rms_norm:
+                _patch_rms_norm_module(decoder_layer.input_layernorm)
+                _patch_rms_norm_module(decoder_layer.post_attention_layernorm)
+
+
+def apply_liger_kernel_to_llava(
+    cross_entropy: bool = False,
+    fused_linear_cross_entropy: bool = True,
+    model: PreTrainedModel = None,
+    **kwargs,
+) -> None:
+    """
+    Apply Liger kernels to replace original implementation in HuggingFace Llava models.
+    Due to the characteristics of LlaVa, the model must be passed to apply Liger-Kernel's patch to other models connected to LLaVa.
+    However, if an LM not supported by Liger-Kernel is connected to LLaVa, unexpected side effects may occur.
+    NOTE: Llava is not available in transformers<4.36.0
+
+    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.
+        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.
+    """
+    assert not (cross_entropy and fused_linear_cross_entropy), (
+        "cross_entropy and fused_linear_cross_entropy cannot both be True."
+    )
+
+    from transformers.models.llava import modeling_llava
+
+    if cross_entropy:
+        from transformers.loss.loss_utils import nn
+
+        nn.functional.cross_entropy = liger_cross_entropy
+    if fused_linear_cross_entropy:
+        if model is not None:
+            model.forward = MethodType(llava_lce_forward, model)
+        else:
+            modeling_llava.LlavaForConditionalGeneration.forward = llava_lce_forward
+
+    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}
+        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.language_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_tower
+            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_llama4(
+    rope: bool = True,
+    cross_entropy: bool = False,
+    fused_linear_cross_entropy: bool = True,
+    rms_norm: bool = True,
+    swiglu: bool = True,
+    model: PreTrainedModel = None,
+    layer_norm: bool = True,
+) -> None:
+    """
+    Apply Liger kernels to replace original implementation in HuggingFace Llama4 models.
+
+    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.
+        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.
+    """
+    assert not (cross_entropy and fused_linear_cross_entropy), (
+        "cross_entropy and fused_linear_cross_entropy cannot both be True."
+    )
+
+    from transformers.models.llama4 import modeling_llama4
+    from transformers.models.llama4.modeling_llama4 import Llama4ForCausalLM
+    from transformers.models.llama4.modeling_llama4 import Llama4ForConditionalGeneration
+    from transformers.models.llama4.modeling_llama4 import Llama4TextModel
+    from transformers.models.llama4.modeling_llama4 import Llama4VisionModel
+
+    from liger_kernel.transformers.model.llama4 import lce_forward as llama4_lce_forward
+
+    if rope:
+        from liger_kernel.transformers.llama4_rope import apply_liger_llama4_rope_full
+
+        apply_liger_llama4_rope_full(modeling_llama4)
+    if rms_norm:
+        modeling_llama4.Llama4TextRMSNorm = LigerRMSNorm
+    if swiglu:
+        modeling_llama4.Llama4TextMLP = LigerSwiGLUMLP
+
+    if cross_entropy:
+        modeling_llama4.CrossEntropyLoss = LigerCrossEntropyLoss
+
+    if fused_linear_cross_entropy:
+        modeling_llama4.Llama4ForCausalLM.forward = llama4_lce_forward
+
+    if model is not None:
+        # The model instance already exists, so we need to additionally patch the
+        # instance variables that reference already-instantiated modules
+        if isinstance(model, Llama4ForConditionalGeneration):
+            language_model: Llama4ForCausalLM = model.language_model
+            vision_model: Llama4VisionModel = model.vision_model
+            text_model: Llama4TextModel = language_model.model
+        elif isinstance(model, Llama4ForCausalLM):
+            text_model = model.model
+            vision_model = None
+        elif isinstance(model, Llama4TextModel):
+            text_model = model
+            vision_model = None
+
+        else:
+            raise ValueError(f"Unsupported Llama4 model type: {type(model)}")
+
+        if text_model:
+            if rms_norm:
+                _patch_rms_norm_module(text_model.norm)
+            for decoder_layer in text_model.layers:
+                if swiglu:
+                    if decoder_layer.is_moe_layer:
+                        _patch_swiglu_module(decoder_layer.feed_forward.shared_expert, LigerSwiGLUMLP)
+                    else:
+                        _patch_swiglu_module(decoder_layer.feed_forward, LigerSwiGLUMLP)
+                if rms_norm:
+                    _patch_rms_norm_module(decoder_layer.input_layernorm)
+                    _patch_rms_norm_module(decoder_layer.post_attention_layernorm)
+
+        if vision_model:
+            _patch_layer_norm_module(vision_model.layernorm_pre)
+            _patch_layer_norm_module(vision_model.layernorm_post)
+
+            for layer in vision_model.model.layers:
+                if layer_norm:
+                    _patch_layer_norm_module(layer.input_layernorm)
+                    _patch_layer_norm_module(layer.post_attention_layernorm)
+
+
+def apply_liger_kernel_to_mllama(
+    rope: bool = True,
+    cross_entropy: bool = False,
+    fused_linear_cross_entropy: bool = True,
+    layer_norm: bool = True,
+    rms_norm: bool = True,
+    swiglu: bool = True,
+    model: PreTrainedModel = None,
+) -> None:
+    """
+    Apply Liger kernels to replace original implementation in HuggingFace MLlama models.
+    NOTE: MLlama is not available in transformers<4.45.0
+
+    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.
+        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.
+    """
+
+    assert not (cross_entropy and fused_linear_cross_entropy), (
+        "cross_entropy and fused_linear_cross_entropy cannot both be True."
+    )
+
+    from transformers.models.mllama import modeling_mllama
+    from transformers.models.mllama.modeling_mllama import MllamaForCausalLM
+    from transformers.models.mllama.modeling_mllama import MllamaForConditionalGeneration
+    from transformers.models.mllama.modeling_mllama import MllamaTextModel
+    from transformers.models.mllama.modeling_mllama import MllamaVisionModel
+
+    from liger_kernel.transformers.model.mllama import lce_forward as mllama_lce_forward
+
+    if rope:
+        modeling_mllama.apply_rotary_pos_emb = liger_rotary_pos_emb
+    if layer_norm and model is None:
+        modeling_mllama.nn.LayerNorm = LigerLayerNorm
+    if rms_norm:
+        modeling_mllama.MllamaTextRMSNorm = LigerRMSNorm
+    if swiglu:
+        modeling_mllama.MllamaTextMLP = LigerSwiGLUMLP
+    if cross_entropy:
+        from transformers.loss.loss_utils import nn
+
+        nn.functional.cross_entropy = liger_cross_entropy
+    if fused_linear_cross_entropy:
+        if model is not None:
+            model.forward = MethodType(mllama_lce_forward, model)
+        else:
+            modeling_mllama.MllamaForCausalLM.forward = mllama_lce_forward
+
+    if model is not None:
+        # The model instance already exists, so we need to additionally patch the
+        # instance variables that reference already-instantiated modules
+
+        if isinstance(model, MllamaForConditionalGeneration):
+            language_model: MllamaForCausalLM = model.model.language_model
+            vision_model: MllamaVisionModel = model.model.vision_model
+            if isinstance(language_model, MllamaForCausalLM):
+                text_model: MllamaTextModel = language_model.model
+            else:
+                text_model = language_model
+        elif isinstance(model, MllamaForCausalLM):
+            text_model = model.model
+            vision_model = None
+        elif isinstance(model, MllamaTextModel):
+            text_model = model
+            vision_model = None
+
+        else:
+            raise ValueError(f"Unsupported Mllama model type: {type(model)}")
+
+        if text_model:
+            if rms_norm:
+                _patch_rms_norm_module(text_model.norm)
+            for decoder_layer in text_model.layers:
+                if swiglu:
+                    _patch_swiglu_module(decoder_layer.mlp, LigerSwiGLUMLP)
+                if rms_norm:
+                    _patch_rms_norm_module(decoder_layer.input_layernorm)
+                    _patch_rms_norm_module(decoder_layer.post_attention_layernorm)
+
+        if vision_model:
+            _patch_layer_norm_module(vision_model.layernorm_pre)
+            _patch_layer_norm_module(vision_model.layernorm_post)
+
+            for layer in vision_model.transformer.layers:
+                if layer_norm:
+                    _patch_layer_norm_module(layer.input_layernorm)
+                    _patch_layer_norm_module(layer.post_attention_layernorm)
+
+            for layer in vision_model.global_transformer.layers:
+                if layer_norm:
+                    _patch_layer_norm_module(layer.input_layernorm)
+                    _patch_layer_norm_module(layer.post_attention_layernorm)
+
+
+def apply_liger_kernel_to_mistral(
+    rope: bool = True,
+    cross_entropy: bool = False,
+    fused_linear_cross_entropy: bool = True,
+    rms_norm: bool = True,
+    swiglu: bool = True,
+    model: PreTrainedModel = None,
+) -> None:
+    """
+    Apply Liger kernels to replace original implementation in HuggingFace Mistral models
+
+    Args:
+        rope (bool): Whether to apply Liger's rotary position embedding. Default is False.
+        cross_entropy (bool): Whether to apply Liger's cross entropy loss. Default is True.
+        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.
+        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.
+    """
+    assert not (cross_entropy and fused_linear_cross_entropy), (
+        "cross_entropy and fused_linear_cross_entropy cannot both be True."
+    )
+
+    from transformers.models.mistral import modeling_mistral
+    from transformers.models.mistral.modeling_mistral import MistralModel
+
+    if rope:
+        modeling_mistral.apply_rotary_pos_emb = liger_rotary_pos_emb
+    if rms_norm:
+        modeling_mistral.MistralRMSNorm = LigerRMSNorm
+    if cross_entropy:
+        modeling_mistral.CrossEntropyLoss = LigerCrossEntropyLoss
+    if fused_linear_cross_entropy:
+        if model is not None:
+            model.forward = MethodType(mistral_lce_forward, model)
+        else:
+            modeling_mistral.MistralForCausalLM.forward = mistral_lce_forward
+
+    if swiglu:
+        modeling_mistral.MistralMLP = LigerSwiGLUMLP
+
+    if model is not None:
+        # The model instance already exists, so we need to additionally patch the
+        # instance variables that reference already-instantiated modules
+
+        # get the base model from the model instance
+        base_model: MistralModel = getattr(model, model.base_model_prefix, model)
+
+        if rms_norm:
+            _patch_rms_norm_module(base_model.norm)
+
+        for decoder_layer in base_model.layers:
+            if swiglu:
+                _patch_swiglu_module(decoder_layer.mlp, LigerSwiGLUMLP)
+            if rms_norm:
+                _patch_rms_norm_module(decoder_layer.input_layernorm)
+                _patch_rms_norm_module(decoder_layer.post_attention_layernorm)
+
+
+def apply_liger_kernel_to_mixtral(
+    rope: bool = True,
+    cross_entropy: bool = False,
+    fused_linear_cross_entropy: bool = True,
+    rms_norm: bool = True,
+    swiglu: bool = True,
+    model: PreTrainedModel = None,
+) -> None:
+    """
+    Apply Liger kernels to replace original implementation in HuggingFace Mixtral models
+
+    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.
+        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.
+    """
+
+    assert not (cross_entropy and fused_linear_cross_entropy), (
+        "cross_entropy and fused_linear_cross_entropy cannot both be True."
+    )
+
+    from transformers.models.mixtral import modeling_mixtral
+    from transformers.models.mixtral.modeling_mixtral import MixtralModel
+
+    if rope:
+        modeling_mixtral.apply_rotary_pos_emb = liger_rotary_pos_emb
+    if rms_norm:
+        modeling_mixtral.MixtralRMSNorm = LigerRMSNorm
+    if cross_entropy:
+        from transformers.loss.loss_utils import nn
+
+        nn.functional.cross_entropy = liger_cross_entropy
+
+    if fused_linear_cross_entropy:
+        if model is not None:
+            model.forward = MethodType(mixtral_lce_forward, model)
+        else:
+            modeling_mixtral.MixtralForCausalLM.forward = mixtral_lce_forward
+    if swiglu:
+        if IS_TRANSFORMERS_V5_OR_LATER:
+            modeling_mixtral.MixtralExperts = LigerExperts
+        else:
+            modeling_mixtral.MixtralBlockSparseTop2MLP = LigerBlockSparseTop2MLP
+
+    if model is not None:
+        # The model instance already exists, so we need to additionally patch the
+        # instance variables that reference already-instantiated modules
+
+        # get the base model from the model instance
+        base_model: MixtralModel = getattr(model, model.base_model_prefix, model)
+
+        if rms_norm:
+            _patch_rms_norm_module(base_model.norm)
+
+        for decoder_layer in base_model.layers:
+            if swiglu:
+                if IS_TRANSFORMERS_V5_OR_LATER:
+                    _patch_swiglu_module(decoder_layer.mlp.experts, LigerExperts)
+                else:
+                    for expert in decoder_layer.block_sparse_moe.experts:
+                        _patch_swiglu_module(expert, LigerBlockSparseTop2MLP)
+            if rms_norm:
+                _patch_rms_norm_module(decoder_layer.input_layernorm)
+                _patch_rms_norm_module(decoder_layer.post_attention_layernorm)
+
+
+def apply_liger_kernel_to_pixtral(
+    rope: bool = True,
+    rms_norm: bool = True,
+    swiglu: bool = True,
+    model: PreTrainedModel = None,
+) -> None:
+    """
+    Apply Liger kernels to replace original implementation in HuggingFace Pixtral vision models.
+
+    Note: Pixtral's vision encoder does not have a cross-entropy loss, so there is no
+    `fused_linear_cross_entropy` or `cross_entropy` option. The language model side of
+    Pixtral uses Mistral, which can be patched separately via `apply_liger_kernel_to_mistral`.
+
+    Args:
+        rope (bool): Whether to apply Liger's rotary position embedding. Default is True.
+        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.
+    """
+    from transformers.models.pixtral import modeling_pixtral
+    from transformers.models.pixtral.modeling_pixtral import PixtralVisionModel
+
+    if rope:
+        modeling_pixtral.apply_rotary_pos_emb = liger_rotary_pos_emb
+    if rms_norm:
+        modeling_pixtral.PixtralRMSNorm = LigerRMSNorm
+    if swiglu:
+        modeling_pixtral.PixtralMLP = LigerSwiGLUMLP
+
+    if model is not None:
+        # The model instance already exists, so we need to additionally patch the
+        # instance variables that reference already-instantiated modules.
+        if isinstance(model, PixtralVisionModel):
+            transformer = model.transformer
+        else:
+            raise ValueError(f"Unsupported Pixtral model type: {type(model)}")
+
+        if rms_norm:
+            _patch_rms_norm_module(model.ln_pre, eps=1e-5)
+
+        for layer in transformer.layers:
+            if swiglu:
+                _patch_swiglu_module(layer.feed_forward, LigerSwiGLUMLP)
+            if rms_norm:
+                _patch_rms_norm_module(layer.attention_norm, eps=1e-5)
+                _patch_rms_norm_module(layer.ffn_norm, eps=1e-5)
+
+
+def apply_liger_kernel_to_gemma(
+    rope: bool = True,
+    cross_entropy: bool = False,
+    fused_linear_cross_entropy: bool = True,
+    rms_norm: bool = True,
+    geglu: bool = True,
+    model: PreTrainedModel = None,
+) -> None:
+    """
+    Apply Liger kernels to replace original implementation in HuggingFace Gemma
+    (Gemma 1 and 1.1 supported, for Gemma2 please use `apply_liger_kernel_to_gemma2` ) to make GPU go burrr.
+
+    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.
+        geglu (bool): Whether to apply Liger's GeGLU MLP. 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.gemma import modeling_gemma
+    from transformers.models.gemma.modeling_gemma import GemmaModel
+
+    from liger_kernel.transformers.rms_norm import LigerRMSNormForGemma
+
+    _patch_rms_norm_module_for_gemma = partial(_patch_rms_norm_module, casting_mode="gemma", offset=1.0)
+
+    if rope:
+        modeling_gemma.apply_rotary_pos_emb = liger_rotary_pos_emb
+    if rms_norm:
+        modeling_gemma.GemmaRMSNorm = LigerRMSNormForGemma
+    if cross_entropy:
+        from transformers.loss.loss_utils import nn
+
+        nn.functional.cross_entropy = liger_cross_entropy
+    if geglu:
+        modeling_gemma.GemmaMLP = LigerGEGLUMLP
+    if fused_linear_cross_entropy:
+        if model is not None:
+            model.forward = MethodType(gemma_lce_forward, model)
+        else:
+            modeling_gemma.GemmaForCausalLM.forward = gemma_lce_forward
+
+    if model is not None:
+        # The model instance already exists, so we need to additionally patch the
+        # instance variables that reference already-instantiated modules
+
+        # get the base model from the model instance
+        base_model: GemmaModel = getattr(model, model.base_model_prefix, model)
+
+        if rms_norm:
+            _patch_rms_norm_module_for_gemma(base_model.norm)
+
+        for decoder_layer in base_model.layers:
+            if geglu:
+                _patch_geglu_module(decoder_layer.mlp)
+            if rms_norm:
+                _patch_rms_norm_module_for_gemma(decoder_layer.input_layernorm)
+                _patch_rms_norm_module_for_gemma(decoder_layer.post_attention_layernorm)
+
+
+def apply_liger_kernel_to_gemma2(
+    rope: bool = True,
+    cross_entropy: bool = False,
+    fused_linear_cross_entropy: bool = True,
+    rms_norm: bool = True,
+    geglu: bool = True,
+    model: PreTrainedModel = None,
+) -> None:
+    """
+    Apply Liger kernels to replace original implementation in HuggingFace Gemma2
+    (for Gemma1 please use `apply_liger_kernel_to_gemma`) to make GPU go burrr.
+
+    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.
+        geglu (bool): Whether to apply Liger's GeGLU MLP. 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.gemma2 import modeling_gemma2
+    from transformers.models.gemma2.modeling_gemma2 import Gemma2Model
+
+    from liger_kernel.transformers.rms_norm import LigerRMSNormForGemma2
+
+    _patch_rms_norm_module_for_gemma2 = partial(
+        _patch_rms_norm_module, offset=1.0, casting_mode="gemma", in_place=False
+    )
+
+    if rope:
+        modeling_gemma2.apply_rotary_pos_emb = liger_rotary_pos_emb
+    if rms_norm:
+        # https://github.com/huggingface/transformers/blob/v4.44.2/src/transformers/models/gemma/modeling_gemma.py#L109
+        modeling_gemma2.Gemma2RMSNorm = LigerRMSNormForGemma2
+    if cross_entropy:
+        from transformers.loss.loss_utils import nn
+
+        nn.functional.cross_entropy = liger_cross_entropy
+    if fused_linear_cross_entropy:
+        if model is not None:
+            model.forward = MethodType(gemma2_lce_forward, model)
+        else:
+            modeling_gemma2.Gemma2ForCausalLM.forward = gemma2_lce_forward
+    if geglu:
+        modeling_gemma2.Gemma2MLP = LigerGEGLUMLP
+
+    if model is not None:
+        # The model instance already exists, so we need to additionally patch the
+        # instance variables that reference already-instantiated modules
+
+        # get the base model from the model instance
+        base_model: Gemma2Model = getattr(model, model.base_model_prefix, model)
+
+        if rms_norm:
+            _patch_rms_norm_module_for_gemma2(base_model.norm)
+
+        for decoder_layer in base_model.layers:
+            if geglu:
+                _patch_geglu_module(decoder_layer.mlp)
+            if rms_norm:
+                _patch_rms_norm_module_for_gemma2(decoder_layer.input_layernorm)
+                _patch_rms_norm_module_for_gemma2(decoder_layer.post_attention_layernorm)
+                _patch_rms_norm_module_for_gemma2(decoder_layer.pre_feedforward_layernorm)
+                _patch_rms_norm_module_for_gemma2(decoder_layer.post_feedforward_layernorm)
+
+
+def apply_liger_kernel_to_gemma3_text(
+    rope: bool = True,
+    cross_entropy: bool = False,
+    fused_linear_cross_entropy: bool = True,
+    rms_norm: bool = True,
+    geglu: bool = True,
+    model: PreTrainedModel = None,
+) -> None:
+    """
+    Apply Liger kernels to replace original implementation in HuggingFace Gemma3
+
+    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.
+        geglu (bool): Whether to apply Liger's GeGLU MLP. 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.gemma3 import modeling_gemma3
+    from transformers.models.gemma3.modeling_gemma3 import Gemma3DecoderLayer
+    from transformers.models.gemma3.modeling_gemma3 import Gemma3ForCausalLM
+    from transformers.models.gemma3.modeling_gemma3 import Gemma3TextModel
+
+    from liger_kernel.transformers.model.gemma3 import causal_forward
+    from liger_kernel.transformers.rms_norm import LigerRMSNormForGemma3
+
+    _patch_rms_norm_module_for_gemma3 = partial(
+        _patch_rms_norm_module, offset=1.0, casting_mode="gemma", in_place=False
+    )
+
+    if rope:
+        modeling_gemma3.apply_rotary_pos_emb = liger_rotary_pos_emb
+
+    if rms_norm:
+        modeling_gemma3.Gemma3RMSNorm = LigerRMSNormForGemma3
+
+    if geglu:
+        modeling_gemma3.Gemma3MLP = LigerGEGLUMLP
+
+    # Handle loss function
+    if cross_entropy:
+        from transformers.loss.loss_utils import nn
+
+        nn.functional.cross_entropy = liger_cross_entropy
+
+    if fused_linear_cross_entropy:
+        if model is not None:
+            model.forward = MethodType(causal_forward, model)
+        else:
+            modeling_gemma3.Gemma3ForCausalLM.forward = causal_forward
+
+    if model is not None:
+        # The model instance already exists, so we need to additionally patch the
+        # instance variables that reference already-instantiated modules
+
+        if isinstance(model, Gemma3ForCausalLM) or isinstance(model, Gemma3TextModel):
+            # get the base model from the model instance
+            base_model = model.model if isinstance(model, Gemma3ForCausalLM) else model
+
+            if rms_norm:
+                _patch_rms_norm_module_for_gemma3(base_model.norm)
+
+            for decoder_layer in base_model.layers:
+                decoder_layer: Gemma3DecoderLayer
+                if geglu:
+                    _bind_method_to_module(decoder_layer.mlp, "forward", LigerGEGLUMLP.forward)
+                if rms_norm:
+                    _patch_rms_norm_module_for_gemma3(decoder_layer.input_layernorm)
+                    _patch_rms_norm_module_for_gemma3(decoder_layer.post_attention_layernorm)
+                    _patch_rms_norm_module_for_gemma3(decoder_layer.pre_feedforward_layernorm)
+                    _patch_rms_norm_module_for_gemma3(decoder_layer.post_feedforward_layernorm)
+                    _patch_rms_norm_module_for_gemma3(decoder_layer.self_attn.q_norm)
+                    _patch_rms_norm_module_for_gemma3(decoder_layer.self_attn.k_norm)
+
+        else:
+            raise TypeError("The model must be Gemma3ForCausalLM.")
+
+
+def apply_liger_kernel_to_gemma3(
+    rope: bool = True,
+    cross_entropy: bool = False,
+    fused_linear_cross_entropy: bool = True,
+    layer_norm: bool = True,
+    rms_norm: bool = True,
+    geglu: bool = True,
+    model: PreTrainedModel = None,
+) -> None:
+    """
+    Apply Liger kernels to replace original implementation in HuggingFace Gemma3
+
+    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.
+        layer_norm (bool): Whether to apply Liger's LayerNorm. Default is True.
+        rms_norm (bool): Whether to apply Liger's RMSNorm. Default is True.
+        geglu (bool): Whether to apply Liger's GeGLU MLP. 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.gemma3 import modeling_gemma3
+    from transformers.models.gemma3.modeling_gemma3 import Gemma3ForConditionalGeneration
+    from transformers.models.siglip import modeling_siglip
+    from transformers.models.siglip.modeling_siglip import SiglipEncoderLayer
+    from transformers.models.siglip.modeling_siglip import SiglipVisionModel
+
+    from liger_kernel.transformers.model.gemma3 import multimodal_forward
+
+    _patch_rms_norm_module_for_gemma3 = partial(
+        _patch_rms_norm_module, offset=1.0, casting_mode="gemma", in_place=False
+    )
+
+    if layer_norm and model is None:
+        modeling_siglip.nn.LayerNorm = LigerLayerNorm
+
+    apply_liger_kernel_to_gemma3_text(
+        rope=rope, cross_entropy=False, fused_linear_cross_entropy=False, rms_norm=rms_norm, geglu=geglu
+    )
+
+    if cross_entropy:
+        modeling_gemma3.nn.CrossEntropyLoss = LigerCrossEntropyLoss
+
+    if fused_linear_cross_entropy:
+        if model is not None:
+            model.forward = MethodType(multimodal_forward, model)
+        else:
+            modeling_gemma3.Gemma3ForConditionalGeneration.forward = multimodal_forward
+
+    if model is not None:
+        # The model instance already exists, so we need to additionally patch the
+        # instance variables that reference already-instantiated modules
+
+        if isinstance(model, Gemma3ForConditionalGeneration):
+            if isinstance(model.model.vision_tower, SiglipVisionModel):
+                vision_tower = model.model.vision_tower
+
+                _patch_layer_norm_module(vision_tower.vision_model.post_layernorm)
+
+                for layer in vision_tower.vision_model.encoder.layers:
+                    layer: SiglipEncoderLayer
+                    if layer_norm:
+                        _patch_layer_norm_module(layer.layer_norm1)
+                        _patch_layer_norm_module(layer.layer_norm2)
+            else:
+                raise TypeError("The vision tower must be SiglipVisionModel")
+
+            if rms_norm:
+                _patch_rms_norm_module_for_gemma3(model.model.multi_modal_projector.mm_soft_emb_norm)
+
+            apply_liger_kernel_to_gemma3_text(
+                rope=rope,
+                cross_entropy=False,
+                fused_linear_cross_entropy=False,
+                rms_norm=rms_norm,
+                geglu=geglu,
+                model=model.model.language_model,
+            )
+
+        else:
+            raise TypeError("The model must be Gemma3ForConditionalGeneration.")
+
+
+def apply_liger_kernel_to_paligemma(
+    rope: bool = True,
+    cross_entropy: bool = False,
+    fused_linear_cross_entropy: bool = True,
+    layer_norm: bool = True,
+    rms_norm: bool = True,
+    geglu: bool = True,
+    model: PreTrainedModel = None,
+) -> None:
+    """
+    Apply Liger kernels to replace original implementation in HuggingFace PaliGemma
+
+    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.
+        layer_norm (bool): Whether to apply Liger's LayerNorm. Default is True.
+        rms_norm (bool): Whether to apply Liger's RMSNorm. Default is True.
+        geglu (bool): Whether to apply Liger's GeGLU MLP. 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."
+    )
+
+    # PaliGemma submodules are ['vision_tower', 'multi_modal_projector', 'language_model']
+
+    from transformers.models.gemma.modeling_gemma import GemmaForCausalLM
+    from transformers.models.gemma.modeling_gemma import GemmaModel
+    from transformers.models.gemma2.modeling_gemma2 import Gemma2ForCausalLM
+    from transformers.models.gemma2.modeling_gemma2 import Gemma2Model
+    from transformers.models.paligemma import modeling_paligemma
+    from transformers.models.paligemma.modeling_paligemma import PaliGemmaForConditionalGeneration
+    from transformers.models.siglip import modeling_siglip
+    from transformers.models.siglip.modeling_siglip import SiglipEncoderLayer
+    from transformers.models.siglip.modeling_siglip import SiglipVisionModel
+
+    from liger_kernel.transformers.model.paligemma import lce_forward
+
+    # The vision_tower is a SiglipVisionModel
+    if layer_norm and model is None:
+        modeling_siglip.nn.LayerNorm = LigerLayerNorm
+
+    # SiglipMLP is standard FFN so LigerGEGLUMLP is not compatible
+    # The multi_modal_projector is Linear, nothing to do
+
+    # The language_model is GemmaForCausalLM or Gemma2ForCausalLM
+    apply_liger_kernel_to_gemma(
+        rope=rope, cross_entropy=False, fused_linear_cross_entropy=False, rms_norm=rms_norm, geglu=geglu
+    )
+    apply_liger_kernel_to_gemma2(
+        rope=rope, cross_entropy=False, fused_linear_cross_entropy=False, rms_norm=rms_norm, geglu=geglu
+    )
+    # Handle loss function
+    if cross_entropy:
+        modeling_paligemma.nn.CrossEntropyLoss = LigerCrossEntropyLoss
+    if fused_linear_cross_entropy:
+        if model is not None:
+            model.forward = MethodType(lce_forward, model)
+        else:
+            modeling_paligemma.PaliGemmaForConditionalGeneration.forward = lce_forward
+
+    if model is not None:
+        # The model instance already exists, so we need to additionally patch the
+        # instance variables that reference already-instantiated modules
+
+        if not isinstance(model, PaliGemmaForConditionalGeneration):
+            raise TypeError("model have to be of type PaliGemmaForConditionalGeneration")
+
+        vision_tower: SiglipVisionModel = model.model.vision_tower
+
+        _patch_layer_norm_module(vision_tower.vision_model.post_layernorm)
+
+        for layer in vision_tower.vision_model.encoder.layers:
+            layer: SiglipEncoderLayer
+            if layer_norm:
+                _patch_layer_norm_module(layer.layer_norm1)
+                _patch_layer_norm_module(layer.layer_norm2)
+
+        language_model = model.model.language_model
+
+        if isinstance(language_model, (GemmaForCausalLM, GemmaModel)):
+            apply_liger_kernel_to_gemma(
+                rope=rope,
+                cross_entropy=False,
+                fused_linear_cross_entropy=False,
+                rms_norm=rms_norm,
+                geglu=geglu,
+                model=language_model,
+            )
+
+        elif isinstance(language_model, (Gemma2ForCausalLM, Gemma2Model)):
+            apply_liger_kernel_to_gemma2(
+                rope=rope,
+                cross_entropy=False,
+                fused_linear_cross_entropy=False,
+                rms_norm=rms_norm,
+                geglu=geglu,
+                model=language_model,
+            )
+        else:
+            raise TypeError(
+                "The language_model of a PaliGemma model must be either GemmaForCausalLM or Gemma2ForCausalLM."
+            )
+
+
+def apply_liger_kernel_to_qwen2(
+    rope: bool = True,
+    cross_entropy: bool = False,
+    fused_linear_cross_entropy: bool = True,
+    rms_norm: bool = True,
+    swiglu: bool = True,
+    model: PreTrainedModel = None,
+) -> None:
+    """
+    Apply Liger kernels to replace original implementation in HuggingFace Qwen2 models
+
+    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.
+        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.
+    """
+    assert not (cross_entropy and fused_linear_cross_entropy), (
+        "cross_entropy and fused_linear_cross_entropy cannot both be True."
+    )
+
+    from transformers.models.qwen2 import modeling_qwen2
+    from transformers.models.qwen2.modeling_qwen2 import Qwen2Model
+
+    if rope:
+        modeling_qwen2.apply_rotary_pos_emb = liger_rotary_pos_emb
+    if rms_norm:
+        modeling_qwen2.Qwen2RMSNorm = LigerRMSNorm
+
+    if cross_entropy:
+        from transformers.loss.loss_utils import nn
+
+        nn.functional.cross_entropy = liger_cross_entropy
+
+    if fused_linear_cross_entropy:
+        if model is not None:
+            model.forward = MethodType(qwen2_lce_forward, model)
+        else:
+            modeling_qwen2.Qwen2ForCausalLM.forward = qwen2_lce_forward
+
+    if swiglu:
+        modeling_qwen2.Qwen2MLP = LigerSwiGLUMLP
+
+    if model is not None:
+        # The model instance already exists, so we need to additionally patch the
+        # instance variables that reference already-instantiated modules
+
+        # get the base model from the model instance
+        base_model: Qwen2Model = getattr(model, model.base_model_prefix, model)
+
+        if rms_norm:
+            _patch_rms_norm_module(base_model.norm)
+
+        for decoder_layer in base_model.layers:
+            if swiglu:
+                _patch_swiglu_module(decoder_layer.mlp, LigerSwiGLUMLP)
+            if rms_norm:
+                _patch_rms_norm_module(decoder_layer.input_layernorm)
+                _patch_rms_norm_module(decoder_layer.post_attention_layernorm)
+
+
+def apply_liger_kernel_to_qwen3(
+    rope: bool = True,
+    cross_entropy: bool = False,
+    fused_linear_cross_entropy: bool = True,
+    rms_norm: bool = True,
+    swiglu: bool = True,
+    model: PreTrainedModel = None,
+) -> None:
+    """
+    Apply Liger kernels to replace original implementation in HuggingFace Qwen3 models.
+    """
+    assert not (cross_entropy and fused_linear_cross_entropy), (
+        "cross_entropy and fused_linear_cross_entropy cannot both be True."
+    )
+
+    from transformers.models.qwen3 import modeling_qwen3
+    from transformers.models.qwen3.modeling_qwen3 import Qwen3Model
+
+    from liger_kernel.transformers.model.qwen3 import lce_forward as qwen3_lce_forward
+
+    if rope:
+        modeling_qwen3.apply_rotary_pos_emb = liger_rotary_pos_emb
+
+    if rms_norm:
+        modeling_qwen3.Qwen3RMSNorm = LigerRMSNorm
+
+    if cross_entropy:
+        from transformers.loss.loss_utils import nn
+
+        nn.functional.cross_entropy = liger_cross_entropy
+
+    if fused_linear_cross_entropy:
+        if model is not None:
+            model.forward = MethodType(qwen3_lce_forward, model)
+        else:
+            modeling_qwen3.Qwen3ForCausalLM.forward = qwen3_lce_forward
+
+    if swiglu:
+        modeling_qwen3.Qwen3MLP = LigerSwiGLUMLP
+
+    if model is not None:
+        # The model instance already exists, so we need to additionally patch the
+        # instance variables that reference already-instantiated modules
+
+        # get the base model from the model instance
+        base_model: Qwen3Model = getattr(model, model.base_model_prefix, model)
+
+        if rms_norm:
+            _patch_rms_norm_module(base_model.norm)
+        for decoder_layer in base_model.layers:
+            if swiglu:
+                _patch_swiglu_module(decoder_layer.mlp, LigerSwiGLUMLP)
+            if rms_norm:
+                _patch_rms_norm_module(decoder_layer.input_layernorm)
+                _patch_rms_norm_module(decoder_layer.post_attention_layernorm)
+
+
+def apply_liger_kernel_to_qwen3_moe(
+    rope: bool = True,
+    cross_entropy: bool = False,
+    fused_linear_cross_entropy: bool = True,
+    rms_norm: bool = True,
+    swiglu: bool = True,
+    model: PreTrainedModel = None,
+) -> None:
+    """
+    Apply Liger kernels to replace original implementation in HuggingFace Qwen3 models.
+    """
+    assert not (cross_entropy and fused_linear_cross_entropy), (
+        "cross_entropy and fused_linear_cross_entropy cannot both be True."
+    )
+
+    from transformers.models.qwen3_moe import modeling_qwen3_moe
+    from transformers.models.qwen3_moe.modeling_qwen3_moe import Qwen3MoeModel
+
+    from liger_kernel.transformers.model.qwen3_moe import lce_forward as qwen3_lce_forward
+    from liger_kernel.transformers.swiglu import LigerQwen3MoeSwiGLUMLP
+
+    if rope:
+        modeling_qwen3_moe.apply_rotary_pos_emb = liger_rotary_pos_emb
+
+    if rms_norm:
+        modeling_qwen3_moe.Qwen3MoeRMSNorm = LigerRMSNorm
+
+    if cross_entropy:
+        from transformers.loss.loss_utils import nn
+
+        nn.functional.cross_entropy = liger_cross_entropy
+
+    if fused_linear_cross_entropy:
+        if model is not None:
+            model.forward = MethodType(qwen3_lce_forward, model)
+        else:
+            modeling_qwen3_moe.Qwen3MoeForCausalLM.forward = qwen3_lce_forward
+
+    if swiglu:
+        if IS_TRANSFORMERS_V5_OR_LATER:
+            modeling_qwen3_moe.Qwen3MoeExperts = LigerExperts
+        else:
+            modeling_qwen3_moe.Qwen3MoeMLP = LigerQwen3MoeSwiGLUMLP
+
+    if model is not None:
+        # The model instance already exists, so we need to additionally patch the
+        # instance variables that reference already-instantiated modules
+
+        # get the base model from the model instance
+        base_model: Qwen3MoeModel = getattr(model, model.base_model_prefix, model)
+
+        if rms_norm:
+            _patch_rms_norm_module(base_model.norm)
+        for decoder_layer in base_model.layers:
+            if swiglu:
+                if IS_TRANSFORMERS_V5_OR_LATER:
+                    _patch_swiglu_module(decoder_layer.mlp.experts, LigerExperts)
+                else:
+                    for mlp_expert in decoder_layer.mlp.experts:
+                        _patch_swiglu_module(mlp_expert, LigerQwen3MoeSwiGLUMLP)
+            if rms_norm:
+                _patch_rms_norm_module(decoder_layer.input_layernorm)
+                _patch_rms_norm_module(decoder_layer.post_attention_layernorm)
+
+
+def apply_liger_kernel_to_gpt_oss(
+    rope: bool = True,
+    cross_entropy: bool = False,
+    fused_linear_cross_entropy: bool = True,
+    rms_norm: bool = True,
+    swiglu: bool = False,  # Set to False by default since GPT-OSS has custom expert implementation
+    model: PreTrainedModel = None,
+) -> None:
+    """
+    Apply Liger kernels to replace original implementation in HuggingFace GPT-OSS models.
+    NOTE: GPT-OSS is supported in transformers >= 4.55.0
+    NOTE: SwiGLU patching is disabled by default for GPT-OSS as it uses a custom expert
+          implementation with clamping and MXFP4 quantization.
+
+    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.
+        swiglu (bool): Whether to apply Liger's SwiGLU MLP. Default is False.
+            Note: GPT-OSS uses a custom expert implementation, so SwiGLU patching is disabled by default.
+        model (PreTrainedModel): The model instance to apply Liger kernels to, if the model has already been
+            loaded. Default is None.
+    """
+    if version.parse(transformers.__version__) < version.parse("4.55.0"):
+        logger.warning("GPT-OSS support requires transformers >= 4.55.0")
+        return
+
+    assert not (cross_entropy and fused_linear_cross_entropy), (
+        "cross_entropy and fused_linear_cross_entropy cannot both be True."
+    )
+
+    from transformers.models.gpt_oss import modeling_gpt_oss
+    from transformers.models.gpt_oss.modeling_gpt_oss import GptOssModel
+
+    if rope:
+        modeling_gpt_oss.apply_rotary_pos_emb = liger_rotary_pos_emb
+
+    if rms_norm:
+        modeling_gpt_oss.GptOssRMSNorm = LigerRMSNorm
+
+    if cross_entropy:
+        from transformers.loss.loss_utils import nn
+
+        nn.functional.cross_entropy = liger_cross_entropy
+
+    if fused_linear_cross_entropy:
+        if model is not None:
+            model.forward = MethodType(gpt_oss_lce_forward, model)
+        else:
+            modeling_gpt_oss.GptOssForCausalLM.forward = gpt_oss_lce_forward
+
+    # Note: SwiGLU patching is not implemented for GPT-OSS due to custom expert implementation
+    # with clamping (swiglu_limit=7.0) and MXFP4 quantization
+
+    if model is not None:
+        # The model instance already exists, so we need to additionally patch the
+        # instance variables that reference already-instantiated modules
+
+        # get the base model from the model instance
+        base_model: GptOssModel = getattr(model, model.base_model_prefix, model)
+
+        if rms_norm:
+            _patch_rms_norm_module(base_model.norm)
+        for decoder_layer in base_model.layers:
+            if rms_norm:
+                _patch_rms_norm_module(decoder_layer.input_layernorm)
+                _patch_rms_norm_module(decoder_layer.post_attention_layernorm)
+
+
+def apply_liger_kernel_to_qwen2_vl(
+    rope: bool = True,
+    cross_entropy: bool = False,
+    fused_linear_cross_entropy: bool = True,
+    rms_norm: bool = True,
+    layer_norm: bool = True,
+    swiglu: bool = True,
+    model: PreTrainedModel = None,
+) -> None:
+    """
+    Apply Liger kernels to replace original implementation in HuggingFace Qwen2-VL models.
+    NOTE: Qwen2-VL is not supported in transformers<4.52.4
+
+    Args:
+        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.
+        layer_norm (bool): Whether to apply Liger's LayerNorm. 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.
+    """
+    if transformer_version < version.parse("4.52.4"):
+        logger.warning("Qwen2-VL support is only compatible with transformers >= 4.52.4")
+        return
+
+    assert not (cross_entropy and fused_linear_cross_entropy), (
+        "cross_entropy and fused_linear_cross_entropy cannot both be True."
+    )
+
+    from transformers.models.qwen2_vl import modeling_qwen2_vl
+    from transformers.models.qwen2_vl.modeling_qwen2_vl import Qwen2VisionTransformerPretrainedModel
+    from transformers.models.qwen2_vl.modeling_qwen2_vl import Qwen2VLForConditionalGeneration
+    from transformers.models.qwen2_vl.modeling_qwen2_vl import Qwen2VLModel
+    from transformers.models.qwen2_vl.modeling_qwen2_vl import Qwen2VLTextModel
+
+    from liger_kernel.transformers.model.qwen2_vl import lce_forward as qwen2_vl_lce_forward
+
+    if rope:
+        modeling_qwen2_vl.apply_multimodal_rotary_pos_emb = liger_multimodal_rotary_pos_emb
+    if rms_norm:
+        # https://github.com/huggingface/transformers/blob/main/src/transformers/models/qwen2_vl/modeling_qwen2_vl.py#L439
+        modeling_qwen2_vl.Qwen2RMSNorm = LigerRMSNorm
+    if layer_norm and model is None:
+        modeling_qwen2_vl.LayerNorm = LigerLayerNorm
+    if cross_entropy:
+        modeling_qwen2_vl.CrossEntropyLoss = LigerCrossEntropyLoss
+    if fused_linear_cross_entropy:
+        if model is not None:
+            model.forward = MethodType(qwen2_vl_lce_forward, model)
+        else:
+            modeling_qwen2_vl.Qwen2VLForConditionalGeneration.forward = qwen2_vl_lce_forward
+    if swiglu:
+        modeling_qwen2_vl.Qwen2MLP = LigerSwiGLUMLP
+
+    if model is not None:
+        # The model instance already exists, so we need to additionally patch the
+        # instance variables that reference already-instantiated modules
+        if isinstance(model, Qwen2VLForConditionalGeneration):
+            text_model: Qwen2VLTextModel = model.model.language_model
+            vision_model: Qwen2VisionTransformerPretrainedModel = model.model.visual
+        elif isinstance(model, Qwen2VLModel):
+            text_model: Qwen2VLTextModel = model.language_model
+            vision_model: Qwen2VisionTransformerPretrainedModel = model.visual
+        elif isinstance(model, Qwen2VLTextModel):
+            text_model: Qwen2VLTextModel = model
+            vision_model = None
+        else:
+            # Note: Currently there's no support for patching vision model only. Feel free to raise an issue if needed.
+            raise TypeError(
+                f"Unsupported Qwen2VL model type. `model` must be `Qwen2VLForConditionalGeneration`, `Qwen2VLModel` or `Qwen2VLTextModel`. Got: {type(model)}"
+            )
+
+        # Patch Qwen2VisionTransformerPretrainedModel
+        if vision_model is not None:
+            for vision_block in vision_model.blocks:
+                if layer_norm:
+                    _patch_layer_norm_module(vision_block.norm1)
+                    _patch_layer_norm_module(vision_block.norm2)
+
+        # Patch Qwen2VisionTextModel
+        if text_model is not None:
+            if rms_norm:
+                _patch_rms_norm_module(text_model.norm)
+            for decoder_layer in text_model.layers:
+                if swiglu:
+                    _patch_swiglu_module(decoder_layer.mlp, LigerSwiGLUMLP)
+                if rms_norm:
+                    _patch_rms_norm_module(decoder_layer.input_layernorm)
+                    _patch_rms_norm_module(decoder_layer.post_attention_layernorm)
+
+
+def apply_liger_kernel_to_qwen2_5_vl(
+    rope: bool = True,
+    cross_entropy: bool = False,
+    fused_linear_cross_entropy: bool = True,
+    rms_norm: bool = True,
+    swiglu: bool = True,
+    model: PreTrainedModel = None,
+) -> None:
+    """
+    Apply Liger kernels to replace original implementation in HuggingFace Qwen2.5-VL models.
+    NOTE: Qwen2.5-VL is not available in transformers<4.48.2
+
+    Args:
+        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.
+        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.
+    """
+    if transformer_version < version.parse("4.52.4"):
+        logger.warning("Qwen2.5-VL support is only compatible with transformers >= 4.52.4")
+        return
+
+    assert not (cross_entropy and fused_linear_cross_entropy), (
+        "cross_entropy and fused_linear_cross_entropy cannot both be True."
+    )
+
+    from transformers.models.qwen2_5_vl import modeling_qwen2_5_vl
+    from transformers.models.qwen2_5_vl.modeling_qwen2_5_vl import Qwen2_5_VisionTransformerPretrainedModel
+    from transformers.models.qwen2_5_vl.modeling_qwen2_5_vl import Qwen2_5_VLForConditionalGeneration
+    from transformers.models.qwen2_5_vl.modeling_qwen2_5_vl import Qwen2_5_VLModel
+    from transformers.models.qwen2_5_vl.modeling_qwen2_5_vl import Qwen2_5_VLTextModel
+
+    from liger_kernel.transformers.model.qwen2_5_vl import lce_forward as qwen2_5_vl_lce_forward
+
+    if rope:
+        modeling_qwen2_5_vl.apply_multimodal_rotary_pos_emb = liger_multimodal_rotary_pos_emb
+    if rms_norm:
+        modeling_qwen2_5_vl.Qwen2RMSNorm = LigerRMSNorm
+    if cross_entropy:
+        modeling_qwen2_5_vl.CrossEntropyLoss = LigerCrossEntropyLoss
+    if fused_linear_cross_entropy:
+        if model is not None:
+            model.forward = MethodType(qwen2_5_vl_lce_forward, model)
+        else:
+            modeling_qwen2_5_vl.Qwen2_5_VLForConditionalGeneration.forward = qwen2_5_vl_lce_forward
+    if swiglu:
+        modeling_qwen2_5_vl.Qwen2MLP = LigerSwiGLUMLP
+
+    if model is not None:
+        # The model instance already exists, so we need to additionally patch the
+        # instance variables that reference already-instantiated modules
+        if isinstance(model, Qwen2_5_VLForConditionalGeneration):
+            text_model: Qwen2_5_VLTextModel = model.model.language_model
+            vision_model: Qwen2_5_VisionTransformerPretrainedModel = model.model.visual
+        elif isinstance(model, Qwen2_5_VLModel):
+            text_model: Qwen2_5_VLTextModel = model.language_model
+            vision_model: Qwen2_5_VisionTransformerPretrainedModel = model.visual
+        elif isinstance(model, Qwen2_5_VLTextModel):
+            text_model: Qwen2_5_VLTextModel = model
+            vision_model = None
+        else:
+            # Note: Currently there's no support for patching vision model only. Feel free to raise an issue if needed.
+            raise TypeError(
+                f"Unsupported Qwen2VL model type. `model` must be `Qwen2VLForConditionalGeneration`, `Qwen2VLModel` or `Qwen2VLTextModel`. Got: {type(model)}"
+            )
+
+        if vision_model is not None:
+            # Patch Qwen2_5_VisionTransformerPretrainedModel
+            for vision_block in vision_model.blocks:
+                if rms_norm:
+                    _patch_rms_norm_module(vision_block.norm1)
+                    _patch_rms_norm_module(vision_block.norm2)
+
+        if text_model is not None:
+            if rms_norm:
+                _patch_rms_norm_module(text_model.norm)
+            for decoder_layer in text_model.layers:
+                if swiglu:
+                    _patch_swiglu_module(decoder_layer.mlp, LigerSwiGLUMLP)
+                if rms_norm:
+                    _patch_rms_norm_module(decoder_layer.input_layernorm)
+                    _patch_rms_norm_module(decoder_layer.post_attention_layernorm)
+
+
+def apply_liger_kernel_to_qwen3_vl(
+    rope: bool = True,
+    cross_entropy: bool = False,
+    fused_linear_cross_entropy: bool = True,
+    rms_norm: bool = True,
+    swiglu: bool = False,
+    model: PreTrainedModel = None,
+) -> None:
+    """
+    Apply Liger kernels to replace original implementation in HuggingFace Qwen3-VL models.
+
+    Args:
+        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.
+        swiglu (bool): Whether to apply Liger's SwiGLU MLP. Default is False.
+        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.qwen3_vl import modeling_qwen3_vl
+    from transformers.models.qwen3_vl.modeling_qwen3_vl import Qwen3VLForConditionalGeneration
+    from transformers.models.qwen3_vl.modeling_qwen3_vl import Qwen3VLModel
+    from transformers.models.qwen3_vl.modeling_qwen3_vl import Qwen3VLTextModel
+
+    from liger_kernel.transformers.model.qwen3_vl import lce_forward as qwen3_vl_lce_forward
+
+    if rope:
+        modeling_qwen3_vl.apply_rotary_pos_emb = liger_rotary_pos_emb
+        modeling_qwen3_vl.apply_rotary_pos_emb_vision = liger_rotary_pos_emb_vision
+
+    if rms_norm:
+        modeling_qwen3_vl.Qwen3VLTextRMSNorm = LigerRMSNorm
+
+    if cross_entropy:
+        from transformers.loss.loss_utils import nn
+
+        nn.functional.cross_entropy = liger_cross_entropy
+
+    if fused_linear_cross_entropy:
+        if model is not None:
+            model.forward = MethodType(qwen3_vl_lce_forward, model)
+        else:
+            modeling_qwen3_vl.Qwen3VLForConditionalGeneration.forward = qwen3_vl_lce_forward
+
+    if model is not None and rms_norm:
+        if isinstance(model, Qwen3VLForConditionalGeneration):
+            text_model: Qwen3VLTextModel = model.model.language_model
+        elif isinstance(model, Qwen3VLModel):
+            text_model: Qwen3VLTextModel = model.language_model
+        elif isinstance(model, Qwen3VLTextModel):
+            text_model = model
+        else:
+            raise TypeError(
+                f"Unsupported Qwen3VL model type. `model` must be `Qwen3VLForConditionalGeneration`, `Qwen3VLModel` or `Qwen3VLTextModel`. Got: {type(model)}"
+            )
+
+        _patch_qwen3_vl_rms_norm = partial(_patch_rms_norm_module, offset=0.0, casting_mode="llama")
+
+        if text_model is not None:
+            _patch_qwen3_vl_rms_norm(text_model.norm)
+            for decoder_layer in text_model.layers:
+                _patch_qwen3_vl_rms_norm(decoder_layer.input_layernorm)
+                _patch_qwen3_vl_rms_norm(decoder_layer.post_attention_layernorm)
+                self_attn = getattr(decoder_layer, "self_attn", None)
+                if self_attn is not None:
+                    if hasattr(self_attn, "q_norm") and self_attn.q_norm is not None:
+                        _patch_qwen3_vl_rms_norm(self_attn.q_norm)
+                    if hasattr(self_attn, "k_norm") and self_attn.k_norm is not None:
+                        _patch_qwen3_vl_rms_norm(self_attn.k_norm)
+
+
+def apply_liger_kernel_to_qwen3_vl_moe(
+    rope: bool = True,
+    cross_entropy: bool = False,
+    fused_linear_cross_entropy: bool = True,
+    rms_norm: bool = True,
+    swiglu: bool = False,
+    model: PreTrainedModel = None,
+) -> None:
+    """
+    Apply Liger kernels to replace original implementation in HuggingFace Qwen3-VL MoE models.
+
+    Args:
+        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 False.
+        rms_norm (bool): Whether to apply Liger's RMSNorm. Default is True.
+        swiglu (bool): Whether to apply Liger's SwiGLU MLP. Default is False.
+        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.qwen3_vl_moe import modeling_qwen3_vl_moe
+    from transformers.models.qwen3_vl_moe.modeling_qwen3_vl_moe import Qwen3VLMoeForConditionalGeneration
+    from transformers.models.qwen3_vl_moe.modeling_qwen3_vl_moe import Qwen3VLMoeModel
+    from transformers.models.qwen3_vl_moe.modeling_qwen3_vl_moe import Qwen3VLMoeTextModel
+
+    from liger_kernel.transformers.model.qwen3_vl_moe import lce_forward as qwen3_vl_moe_lce_forward
+
+    if rope:
+        modeling_qwen3_vl_moe.apply_rotary_pos_emb = liger_rotary_pos_emb
+        modeling_qwen3_vl_moe.apply_rotary_pos_emb_vision = liger_rotary_pos_emb_vision
+
+    if rms_norm:
+        modeling_qwen3_vl_moe.Qwen3VLMoeTextRMSNorm = LigerRMSNorm
+
+    if cross_entropy:
+        from transformers.loss.loss_utils import nn
+
+        nn.functional.cross_entropy = liger_cross_entropy
+
+    if fused_linear_cross_entropy:
+        if model is not None:
+            model.forward = MethodType(qwen3_vl_moe_lce_forward, model)
+        else:
+            modeling_qwen3_vl_moe.Qwen3VLMoeForConditionalGeneration.forward = qwen3_vl_moe_lce_forward
+
+    if model is not None and rms_norm:
+        if isinstance(model, Qwen3VLMoeForConditionalGeneration):
+            text_model: Qwen3VLMoeTextModel = model.model.language_model
+        elif isinstance(model, Qwen3VLMoeModel):
+            text_model: Qwen3VLMoeTextModel = model.language_model
+        elif isinstance(model, Qwen3VLMoeTextModel):
+            text_model = model
+        else:
+            raise TypeError(
+                f"Unsupported Qwen3VLMoe model type. `model` must be `Qwen3VLMoeForConditionalGeneration`, `Qwen3VLMoeModel` or `Qwen3VLMoeTextModel`. Got: {type(model)}"
+            )
+
+        _patch_qwen3_vl_moe_rms_norm = partial(_patch_rms_norm_module, offset=0.0, casting_mode="llama")
+
+        if text_model is not None:
+            _patch_qwen3_vl_moe_rms_norm(text_model.norm)
+            for decoder_layer in text_model.layers:
+                _patch_qwen3_vl_moe_rms_norm(decoder_layer.input_layernorm)
+                _patch_qwen3_vl_moe_rms_norm(decoder_layer.post_attention_layernorm)
+                self_attn = getattr(decoder_layer, "self_attn", None)
+                if self_attn is not None:
+                    if hasattr(self_attn, "q_norm") and self_attn.q_norm is not None:
+                        _patch_qwen3_vl_moe_rms_norm(self_attn.q_norm)
+                    if hasattr(self_attn, "k_norm") and self_attn.k_norm is not None:
+                        _patch_qwen3_vl_moe_rms_norm(self_attn.k_norm)
+
+
+def apply_liger_kernel_to_phi3(
+    rope: bool = True,
+    cross_entropy: bool = False,
+    fused_linear_cross_entropy: bool = True,
+    rms_norm: bool = True,
+    swiglu: bool = True,
+    model: PreTrainedModel = None,
+) -> None:
+    """
+    Apply Liger kernels to replace original implementation in HuggingFace Phi3 models.
+
+    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.
+        swiglu (bool): Whether to apply Liger's SwiGLU Phi3MLP. 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.phi3 import modeling_phi3
+    from transformers.models.phi3.modeling_phi3 import Phi3Model
+
+    if rope:
+        modeling_phi3.apply_rotary_pos_emb = liger_rotary_pos_emb  # Same as Gemma
+    if rms_norm:
+        modeling_phi3.Phi3RMSNorm = LigerRMSNorm  # Same as Llama
+    if swiglu:
+        modeling_phi3.Phi3MLP = LigerPhi3SwiGLUMLP
+    if cross_entropy:
+        from transformers.loss.loss_utils import nn
+
+        nn.functional.cross_entropy = liger_cross_entropy
+    if fused_linear_cross_entropy:
+        if model is not None:
+            model.forward = MethodType(phi3_lce_forward, model)
+        else:
+            modeling_phi3.Phi3ForCausalLM.forward = phi3_lce_forward
+
+    if model is not None:
+        # The model instance already exists, so we need to additionally patch the
+        # instance variables that reference already-instantiated modules
+
+        # get the base model from the model instance
+        base_model: Phi3Model = getattr(model, model.base_model_prefix, model)
+
+        if rms_norm:
+            _patch_rms_norm_module(base_model.norm)
+
+        for decoder_layer in base_model.layers:
+            if swiglu:
+                _patch_swiglu_module(decoder_layer.mlp, LigerPhi3SwiGLUMLP)
+            if rms_norm:
+                _patch_rms_norm_module(decoder_layer.input_layernorm)
+                _patch_rms_norm_module(decoder_layer.post_attention_layernorm)
+
+
+def apply_liger_kernel_to_olmo2(
+    rope: bool = True,
+    cross_entropy: bool = False,
+    fused_linear_cross_entropy: bool = True,
+    rms_norm: bool = True,
+    swiglu: bool = True,
+    model: PreTrainedModel = None,
+) -> None:
+    """
+    Apply Liger kernels to replace original implementation in HuggingFace OLMO2 models.
+
+    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.
+        swiglu (bool): Whether to apply Liger's SwiGLU Olmo2MLP. 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.olmo2 import modeling_olmo2
+    from transformers.models.olmo2.modeling_olmo2 import Olmo2Model
+
+    from liger_kernel.transformers.model.olmo2 import lce_forward as olmo2_lce_forward
+    from liger_kernel.transformers.rms_norm import LigerRMSNormForOlmo2
+
+    if rope:
+        modeling_olmo2.apply_rotary_pos_emb = liger_rotary_pos_emb
+    if rms_norm:
+        modeling_olmo2.Olmo2RMSNorm = LigerRMSNormForOlmo2
+    if swiglu:
+        modeling_olmo2.Olmo2MLP = LigerSwiGLUMLP
+    if cross_entropy:
+        from transformers.loss.loss_utils import nn
+
+        nn.functional.cross_entropy = liger_cross_entropy
+    if fused_linear_cross_entropy:
+        if model is not None:
+            model.forward = MethodType(olmo2_lce_forward, model)
+        else:
+            modeling_olmo2.Olmo2ForCausalLM.forward = olmo2_lce_forward
+
+    if model is not None:
+        # The model instance already exists, so we need to additionally patch the
+        # instance variables that reference already-instantiated modules
+
+        # get the base model from the model instance
+        base_model: Olmo2Model = getattr(model, model.base_model_prefix, model)
+
+        if rms_norm:
+            _patch_rms_norm_module(base_model.norm)
+
+        for decoder_layer in base_model.layers:
+            if swiglu:
+                _patch_swiglu_module(decoder_layer.mlp, LigerSwiGLUMLP)
+            if rms_norm:
+                _patch_rms_norm_module(decoder_layer.post_attention_layernorm, in_place=False)
+                _patch_rms_norm_module(decoder_layer.post_feedforward_layernorm, in_place=False)
+
+
+def apply_liger_kernel_to_olmo3(
+    rope: bool = True,
+    cross_entropy: bool = False,
+    fused_linear_cross_entropy: bool = True,
+    rms_norm: bool = True,
+    swiglu: bool = True,
+    model: PreTrainedModel = None,
+) -> None:
+    """
+    Apply Liger kernels to replace original implementation in HuggingFace Olmo3 models.
+
+    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.
+        swiglu (bool): Whether to apply Liger's SwiGLU to Olmo3MLP. 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.olmo3 import modeling_olmo3
+    from transformers.models.olmo3.modeling_olmo3 import Olmo3Model
+
+    from liger_kernel.transformers.model.olmo3 import lce_forward as olmo3_lce_forward
+    from liger_kernel.transformers.rms_norm import LigerRMSNormForOlmo2
+
+    # Olmo3 arch is very similar to Olmo2, so we can reuse all these components in the same way.
+    if rope:
+        modeling_olmo3.apply_rotary_pos_emb = liger_rotary_pos_emb
+    if rms_norm:
+        modeling_olmo3.Olmo3RMSNorm = LigerRMSNormForOlmo2  # same as olmo2
+    if swiglu:
+        modeling_olmo3.Olmo3MLP = LigerSwiGLUMLP
+    if cross_entropy:
+        from transformers.loss.loss_utils import nn
+
+        nn.functional.cross_entropy = liger_cross_entropy
+    if fused_linear_cross_entropy:
+        if model is not None:
+            model.forward = MethodType(olmo3_lce_forward, model)
+        else:
+            modeling_olmo3.Olmo3ForCausalLM.forward = olmo3_lce_forward
+
+    if model is not None:
+        # The model instance already exists, so we need to additionally patch the
+        # instance variables that reference already-instantiated modules
+
+        # get the base model from the model instance
+        base_model: Olmo3Model = getattr(model, model.base_model_prefix, model)
+
+        if rms_norm:
+            _patch_rms_norm_module(base_model.norm)
+
+        for decoder_layer in base_model.layers:
+            if swiglu:
+                _patch_swiglu_module(decoder_layer.mlp, LigerSwiGLUMLP)
+            if rms_norm:
+                _patch_rms_norm_module(decoder_layer.post_attention_layernorm, in_place=False)
+                _patch_rms_norm_module(decoder_layer.post_feedforward_layernorm, in_place=False)
+
+
+def apply_liger_kernel_to_glm4(
+    rope: bool = False,
+    cross_entropy: bool = False,
+    fused_linear_cross_entropy: bool = True,
+    rms_norm: bool = True,
+    swiglu: bool = True,
+    model: PreTrainedModel = None,
+) -> None:
+    """
+    Apply Liger kernels to replace original implementation in HuggingFace GLM-4 models.
+
+    Args:
+        rope (bool): Whether to apply Liger's rotary position embedding. Default is False.
+        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.
+        swiglu (bool): Whether to apply Liger's SwiGLU Glm4MLP. 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.glm4 import modeling_glm4
+    from transformers.models.glm4.modeling_glm4 import Glm4Model
+
+    from liger_kernel.transformers.model.glm4 import lce_forward as glm4_lce_forward
+    from liger_kernel.transformers.rms_norm import LigerRMSNormForGlm4
+
+    if rope:
+        raise NotImplementedError("liger_rotary_pos_emb is not available for Glm4 models.")
+    if rms_norm:
+        modeling_glm4.Glm4RMSNorm = LigerRMSNormForGlm4
+    if swiglu:
+        modeling_glm4.Glm4MLP = LigerPhi3SwiGLUMLP
+    if cross_entropy:
+        from transformers.loss.loss_utils import nn
+
+        nn.functional.cross_entropy = liger_cross_entropy
+    if fused_linear_cross_entropy:
+        if model is not None:
+            model.forward = MethodType(glm4_lce_forward, model)
+        else:
+            modeling_glm4.Glm4ForCausalLM.forward = glm4_lce_forward
+
+    if model is not None:
+        # The model instance already exists, so we need to additionally patch the
+        # instance variables that reference already-instantiated modules
+
+        # get the base model from the model instance
+        base_model: Glm4Model = getattr(model, model.base_model_prefix, model)
+
+        if rms_norm:
+            _patch_rms_norm_module(base_model.norm, in_place=False)
+
+        for decoder_layer in base_model.layers:
+            if swiglu:
+                _patch_swiglu_module(decoder_layer.mlp, LigerPhi3SwiGLUMLP)
+            if rms_norm:
+                _patch_rms_norm_module(decoder_layer.input_layernorm, in_place=False)
+                _patch_rms_norm_module(decoder_layer.post_attention_layernorm, in_place=False)
+                _patch_rms_norm_module(decoder_layer.post_self_attn_layernorm, in_place=False)
+                _patch_rms_norm_module(decoder_layer.post_mlp_layernorm, in_place=False)
+
+
+def apply_liger_kernel_to_glm4v(
+    rope: bool = False,
+    cross_entropy: bool = False,
+    fused_linear_cross_entropy: bool = True,
+    rms_norm: bool = True,
+    swiglu: bool = True,
+    model: PreTrainedModel = None,
+) -> None:
+    """
+    Apply Liger kernels to replace original implementation in HuggingFace GLM-4v models.
+
+    Args:
+        rope (bool): Whether to apply Liger's rotary position embedding. Default is False.
+        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.
+        swiglu (bool): Whether to apply Liger's SwiGLU Glm4MLP. 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.glm4v import modeling_glm4v
+    from transformers.models.glm4v.modeling_glm4v import Glm4vForConditionalGeneration
+    from transformers.models.glm4v.modeling_glm4v import Glm4vModel
+    from transformers.models.glm4v.modeling_glm4v import Glm4vTextModel
+    from transformers.models.glm4v.modeling_glm4v import Glm4vVisionModel
+
+    from liger_kernel.transformers.model.glm4v import lce_forward as glm4v_lce_forward
+    from liger_kernel.transformers.rms_norm import LigerRMSNormForGlm4
+
+    if rope:
+        raise NotImplementedError("liger_rotary_pos_emb is not available for Glm4 models.")
+    if rms_norm:
+        modeling_glm4v.Glm4vRMSNorm = LigerRMSNormForGlm4
+    if cross_entropy:
+        from transformers.loss.loss_utils import nn
+
+        nn.functional.cross_entropy = liger_cross_entropy
+    if fused_linear_cross_entropy:
+        if model is not None:
+            model.forward = MethodType(glm4v_lce_forward, model)
+        else:
+            modeling_glm4v.Glm4vForConditionalGeneration.forward = glm4v_lce_forward
+
+    if model is not None:
+        # The model instance already exists, so we need to additionally patch the
+        # instance variables that reference already-instantiated modules
+        if isinstance(model, Glm4vForConditionalGeneration):
+            text_model: Glm4vTextModel = model.model.language_model
+            vision_model: Glm4vVisionModel = model.model.visual
+        elif isinstance(model, Glm4vModel):
+            text_model: Glm4vTextModel = model.language_model
+            vision_model: Glm4vVisionModel = model.visual
+        elif isinstance(model, Glm4vTextModel):
+            text_model: Glm4vTextModel = model
+            vision_model = None
+        else:
+            # Note: Currently there's no support for patching vision model only. Feel free to raise an issue if needed.
+            raise TypeError(
+                f"Unsupported glm4.1v model type. `model` must be `Glm4VLForConditionalGeneration`, `Glm4vVisionModel` or `Glm4vTextModel`. Got: {type(model)}"
+            )
+
+        if vision_model is not None:
+            for vision_block in vision_model.blocks:
+                if rms_norm:
+                    _patch_rms_norm_module(vision_block.norm1)
+                    _patch_rms_norm_module(vision_block.norm2)
+                if swiglu:
+                    _patch_swiglu_module(vision_block.mlp, LigerSwiGLUMLP)
+
+        if text_model is not None:
+            if rms_norm:
+                _patch_rms_norm_module(text_model.norm)
+            for decoder_layer in text_model.layers:
+                if swiglu:
+                    _patch_swiglu_module(decoder_layer.mlp, LigerPhi3SwiGLUMLP)
+                if rms_norm:
+                    _patch_rms_norm_module(decoder_layer.input_layernorm)
+                    _patch_rms_norm_module(decoder_layer.post_attention_layernorm)
+                    _patch_rms_norm_module(decoder_layer.post_self_attn_layernorm)
+                    _patch_rms_norm_module(decoder_layer.post_mlp_layernorm)
+
+
+def apply_liger_kernel_to_glm4v_moe(
+    rope: bool = False,
+    cross_entropy: bool = False,
+    fused_linear_cross_entropy: bool = True,
+    rms_norm: bool = True,
+    swiglu: bool = True,
+    model: PreTrainedModel = None,
+) -> None:
+    """
+    Apply Liger kernels to replace original implementation in HuggingFace GLM4v_moe models.
+
+    Args:
+        rope (bool): Whether to apply Liger's rotary position embedding. Default is False.
+        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.
+        swiglu (bool): Whether to apply Liger's SwiGLUMLP. 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.glm4v_moe import modeling_glm4v_moe
+    from transformers.models.glm4v_moe.modeling_glm4v_moe import Glm4vMoeForConditionalGeneration
+    from transformers.models.glm4v_moe.modeling_glm4v_moe import Glm4vMoeModel
+    from transformers.models.glm4v_moe.modeling_glm4v_moe import Glm4vMoeTextModel
+    from transformers.models.glm4v_moe.modeling_glm4v_moe import Glm4vMoeVisionModel
+
+    from liger_kernel.transformers.model.glm4v_moe import lce_forward as glm4v_moe_lce_forward
+    from liger_kernel.transformers.rms_norm import LigerRMSNormForGlm4
+
+    if rope:
+        raise NotImplementedError("liger_rotary_pos_emb is not available for Glm4 models.")
+    if rms_norm:
+        modeling_glm4v_moe.Glm4vMoeRMSNorm = LigerRMSNormForGlm4
+        modeling_glm4v_moe.Glm4vMoeTextRMSNorm = LigerRMSNormForGlm4
+    if cross_entropy:
+        from transformers.loss.loss_utils import nn
+
+        nn.functional.cross_entropy = liger_cross_entropy
+    if fused_linear_cross_entropy:
+        if model is not None:
+            model.forward = MethodType(glm4v_moe_lce_forward, model)
+        else:
+            modeling_glm4v_moe.Glm4vMoeForConditionalGeneration.forward = glm4v_moe_lce_forward
+
+    if model is not None:
+        # The model instance already exists, so we need to additionally patch the
+        # instance variables that reference already-instantiated modules
+        if isinstance(model, Glm4vMoeForConditionalGeneration):
+            text_model: Glm4vMoeTextModel = model.model.language_model
+            vision_model: Glm4vMoeVisionModel = model.model.visual
+            Glm4vMoeTextMoE = modeling_glm4v_moe.Glm4vMoeTextMoE
+        elif isinstance(model, Glm4vMoeModel):
+            text_model: Glm4vMoeTextModel = model.language_model
+            vision_model: Glm4vMoeVisionModel = model.visual
+            Glm4vMoeTextMoE = modeling_glm4v_moe.Glm4vMoeTextMoE
+        elif isinstance(model, Glm4vMoeTextModel):
+            text_model: Glm4vMoeTextModel = model
+            vision_model = None
+        else:
+            # Note: Currently there's no support for patching vision model only. Feel free to raise an issue if needed.
+            raise TypeError(
+                f"Unsupported glm4v_moe model type. `model` must be `Glm4vMoeForConditionalGeneration`, `Glm4vMoeVisionModel` or `Glm4vMoeTextModel`. Got: {type(model)}"
+            )
+
+        if vision_model is not None:
+            _patch_rms_norm_module(vision_model.post_conv_layernorm)
+            _patch_rms_norm_module(vision_model.post_layernorm)
+            for vision_block in vision_model.blocks:
+                if rms_norm:
+                    _patch_rms_norm_module(vision_block.norm1)
+                    _patch_rms_norm_module(vision_block.norm2)
+                if swiglu:
+                    _patch_swiglu_module(vision_block.mlp, LigerSwiGLUMLP)
+
+        if text_model is not None:
+            if rms_norm:
+                _patch_rms_norm_module(text_model.norm)
+            for decoder_layer in text_model.layers:
+                if swiglu:
+                    decoder_layer.mlp = _patch_swiglu_module(decoder_layer.mlp, LigerSwiGLUMLP)
+                if rms_norm:
+                    _patch_rms_norm_module(decoder_layer.input_layernorm)
+                    _patch_rms_norm_module(decoder_layer.post_attention_layernorm)
+        if isinstance(Glm4vMoeTextMoE, type) and isinstance(decoder_layer.mlp, Glm4vMoeTextMoE):
+            experts = getattr(decoder_layer.mlp, "experts", None)
+            if experts is not None:
+                for expert in experts:
+                    _patch_swiglu_module(expert, LigerSwiGLUMLP)
+            if decoder_layer.mlp.shared_experts is not None:
+                _patch_swiglu_module(decoder_layer.mlp.shared_experts, LigerSwiGLUMLP)
+            for decoder_layer in text_model.layers:
+                if rms_norm:
+                    _patch_rms_norm_module(decoder_layer.input_layernorm)
+                    _patch_rms_norm_module(decoder_layer.post_attention_layernorm)
+
+
+def apply_liger_kernel_to_internvl(
+    cross_entropy: bool = False,
+    fused_linear_cross_entropy: bool = True,
+    rms_norm: bool = True,
+    layer_norm: bool = True,
+    model: Optional[PreTrainedModel] = None,
+    **kwargs,
+) -> None:
+    """
+    Apply Liger kernels to replace original implementation in HuggingFace InternVL models.
+    Due to the characteristics of InternVL, the model must be passed to apply Liger-Kernel's patch to other models connected to InternVL.
+    However, if an LM not supported by Liger-Kernel is connected to InternVL, unexpected side effects may occur.
+    NOTE: InternVL is not available in transformers<4.52.1
+
+    Args:
+        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.
+        layer_norm (bool): Whether to apply Liger's LayerNorm. 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."
+    )
+    import torch.nn as torch_nn
+
+    from transformers.models.internvl import modeling_internvl
+    from transformers.models.internvl.modeling_internvl import InternVLForConditionalGeneration
+    from transformers.models.internvl.modeling_internvl import InternVLModel
+    from transformers.models.internvl.modeling_internvl import InternVLVisionLayer
+    from transformers.models.internvl.modeling_internvl import InternVLVisionModel
+    from transformers.models.internvl.modeling_internvl import InternVLVisionRMSNorm
+
+    from liger_kernel.transformers.layer_norm import LigerLayerNorm
+    from liger_kernel.transformers.model.internvl import lce_forward as internvl_lce_forward
+    from liger_kernel.transformers.rms_norm import LigerRMSNorm
+
+    if layer_norm and model is None:
+        modeling_internvl.nn.LayerNorm = LigerLayerNorm
+
+    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_internvl.InternVLForConditionalGeneration.forward = internvl_lce_forward
+    if rms_norm:
+        modeling_internvl.InternVLVisionRMSNorm = LigerRMSNorm
+
+    if model is not None:
+        # The model instance already exists, so we need to additionally patch the
+        # instance variables that reference already-instantiated modules
+        if isinstance(model, InternVLForConditionalGeneration):
+            text_model = model.model.language_model
+            vision_model: InternVLVisionModel = model.model.vision_tower
+        elif isinstance(model, InternVLModel):
+            text_model = model.language_model
+            vision_model: InternVLVisionModel = model.vision_tower
+        else:
+            raise TypeError(
+                f"Unsupported internvl model type. `model` must be `InternVLForConditionalGeneration`, `InternVLModel`. Got: {type(model)}"
+            )
+
+        text_model_name = model.config.text_config.model_type
+        text_liger_fn = MODEL_TYPE_TO_APPLY_LIGER_FN.get(text_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"] = 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.")
+
+        # Patch vision model RMSNorm layers
+        if rms_norm:
+            for encoder_layer in vision_model.encoder.layer:
+                encoder_layer: InternVLVisionLayer
+                if isinstance(encoder_layer.attention.q_norm, InternVLVisionRMSNorm):
+                    _patch_rms_norm_module(encoder_layer.attention.q_norm)
+                if isinstance(encoder_layer.attention.k_norm, InternVLVisionRMSNorm):
+                    _patch_rms_norm_module(encoder_layer.attention.k_norm)
+
+        # Patch vision model LayerNorm layers
+        if layer_norm:
+            # Patch layernorm
+            if isinstance(vision_model.layernorm, torch_nn.LayerNorm):
+                _patch_layer_norm_module(vision_model.layernorm)
+
+            # Patch encoder layers
+            for encoder_layer in vision_model.encoder.layer:
+                encoder_layer: InternVLVisionLayer
+                if isinstance(encoder_layer.layernorm_before, torch_nn.LayerNorm):
+                    _patch_layer_norm_module(encoder_layer.layernorm_before)
+                if isinstance(encoder_layer.layernorm_after, torch_nn.LayerNorm):
+                    _patch_layer_norm_module(encoder_layer.layernorm_after)
+
+
+def apply_liger_kernel_to_smolvlm(
+    cross_entropy: bool = False,
+    fused_linear_cross_entropy: bool = True,
+    rms_norm: bool = True,
+    layer_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:
+        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.
+        layer_norm (bool): Whether to apply Liger's LayerNorm. 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 transformers.models.smolvlm.modeling_smolvlm import SmolVLMEncoderLayer
+    from transformers.models.smolvlm.modeling_smolvlm import SmolVLMForConditionalGeneration
+    from transformers.models.smolvlm.modeling_smolvlm import SmolVLMModel
+    from transformers.models.smolvlm.modeling_smolvlm import SmolVLMVisionTransformer
+
+    from liger_kernel.transformers.model.smolvlm import lce_forward as smolvlm_lce_forward
+
+    # Patch LayerNorm for vision model if model is not provided (pre-initialization)
+    if layer_norm and model is None:
+        modeling_smolvlm.nn.LayerNorm = LigerLayerNorm
+
+    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:
+        if model is not None:
+            model.forward = MethodType(smolvlm_lce_forward, model)
+        else:
+            modeling_smolvlm.SmolVLMForConditionalGeneration.forward = smolvlm_lce_forward
+    if rms_norm:
+        modeling_smolvlm.SmolVLMRMSNorm = LigerRMSNorm
+
+    if model is not None:
+        # The model instance already exists, so we need to additionally patch the
+        # instance variables that reference already-instantiated modules
+        if isinstance(model, SmolVLMForConditionalGeneration):
+            text_model = model.model.text_model
+            vision_model: SmolVLMVisionTransformer = model.model.vision_model
+        elif isinstance(model, SmolVLMModel):
+            text_model = model.text_model
+            vision_model: SmolVLMVisionTransformer = model.vision_model
+        else:
+            raise TypeError(
+                f"Unsupported smolvlm model type. `model` must be `SmolVLMForConditionalGeneration`, `SmolVLMModel`. Got: {type(model)}"
+            )
+
+        text_model_name = model.config.text_config.model_type
+        text_liger_fn = MODEL_TYPE_TO_APPLY_LIGER_FN.get(text_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"] = 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.")
+
+        # Patch vision model LayerNorm layers
+        if layer_norm:
+            # Patch post_layernorm
+            _patch_layer_norm_module(vision_model.post_layernorm)
+
+            # Patch encoder layers
+            for encoder_layer in vision_model.encoder.layers:
+                encoder_layer: SmolVLMEncoderLayer
+                _patch_layer_norm_module(encoder_layer.layer_norm1)
+                _patch_layer_norm_module(encoder_layer.layer_norm2)
+
+
+def apply_liger_kernel_to_falcon_h1(
+    rope: bool = True,
+    cross_entropy: bool = False,
+    fused_linear_cross_entropy: bool = True,
+    rms_norm: bool = True,
+    swiglu: bool = False,
+    model: PreTrainedModel = None,
+) -> None:
+    """
+    Apply Liger kernels to replace original implementation in HuggingFace Falcon-H1 models
+    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 True.
+        fused_linear_cross_entropy (bool):
+            Whether to apply Liger's fused linear cross entropy loss. Default is False.
+            `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 False.
+        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.
+    """
+
+    assert not (cross_entropy and fused_linear_cross_entropy), (
+        "cross_entropy and fused_linear_cross_entropy cannot both be True."
+    )
+
+    from transformers.models.falcon_h1 import modeling_falcon_h1
+    from transformers.models.falcon_h1.modeling_falcon_h1 import FalconH1Model
+
+    if rope:
+        logger.info("Apply liger rotary pos emb.")
+        modeling_falcon_h1.apply_rotary_pos_emb = liger_rotary_pos_emb
+    if rms_norm:
+        logger.info("Apply liger RMSNorm")
+        modeling_falcon_h1.FalconH1RMSNorm = LigerRMSNorm
+    if swiglu:
+        logger.warning("LigerSwiGLUMLP is not available for Falcon-H1 models. There will be no effect.")
+
+    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:
+        if model is not None:
+            model.forward = MethodType(falcon_h1_lce_forward, model)
+        else:
+            modeling_falcon_h1.FalconH1ForCausalLM.forward = falcon_h1_lce_forward
+
+    if model is not None:
+        # The model instance already exists, so we need to additionally patch the
+        # instance variables that reference already-instantiated modules (e.g. LlamaRMSNorm or LlamaMLP)
+
+        # get the base model from the model instance
+        base_model: FalconH1Model = getattr(model, model.base_model_prefix, model)
+
+        if rms_norm:
+            _patch_rms_norm_module(base_model.final_layernorm)
+
+        for decoder_layer in base_model.layers:
+            if swiglu:
+                _patch_swiglu_module(decoder_layer.mlp, LigerSwiGLUMLP)
+            if rms_norm:
+                _patch_rms_norm_module(decoder_layer.input_layernorm)
+                _patch_rms_norm_module(decoder_layer.pre_ff_layernorm)
+
+
+def apply_liger_kernel_to_qwen3_next(
+    rope: bool = False,
+    cross_entropy: bool = False,
+    fused_linear_cross_entropy: bool = True,
+    rms_norm: bool = True,
+    swiglu: bool = True,
+    model: PreTrainedModel = None,
+) -> None:
+    """
+    Apply Liger kernels to replace original implementation in HuggingFace GLM4v_moe models.
+
+    Args:
+        rope (bool): Whether to apply Liger's rotary position embedding. Default is False.
+        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.
+        swiglu (bool): Whether to apply Liger's SwiGLUMLP. 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.qwen3_next import modeling_qwen3_next
+    from transformers.models.qwen3_next.modeling_qwen3_next import Qwen3NextForCausalLM
+    from transformers.models.qwen3_next.modeling_qwen3_next import Qwen3NextMLP
+    from transformers.models.qwen3_next.modeling_qwen3_next import Qwen3NextModel
+    from transformers.models.qwen3_next.modeling_qwen3_next import Qwen3NextSparseMoeBlock
+
+    from liger_kernel.transformers.model.qwen3_next import lce_forward as qwen3_next_lce_forward
+    from liger_kernel.transformers.rms_norm import LigerRMSNormForQwen3Next
+    from liger_kernel.transformers.swiglu import LigerQwen3MoeSwiGLUMLP
+
+    if rope:
+        # It might enocunter nan issue
+        # modeling_qwen3_next.apply_rotary_pos_emb = liger_rotary_pos_emb
+        raise NotImplementedError("liger_rotary_pos_emb is not available for Qwen3Next models.")
+    if rms_norm:
+        modeling_qwen3_next.Qwen3NextRMSNorm = LigerRMSNormForQwen3Next
+    if cross_entropy:
+        from transformers.loss.loss_utils import nn
+
+        nn.functional.cross_entropy = liger_cross_entropy
+    if fused_linear_cross_entropy:
+        if model is not None:
+            if isinstance(model, Qwen3NextForCausalLM):
+                model.forward = MethodType(qwen3_next_lce_forward, model)
+            else:
+                raise TypeError(
+                    f" fused_linear_cross_entropy is only applicable on Qwen3NextForCausalLM. Got: {type(model)}"
+                )
+        else:
+            modeling_qwen3_next.Qwen3NextForCausalLM.forward = qwen3_next_lce_forward
+    if swiglu:
+        if IS_TRANSFORMERS_V5_OR_LATER:
+            modeling_qwen3_next.Qwen3NextExperts = LigerExperts
+        else:
+            # Qwen3MoeMLP and Qwen3NextMLP are identical, hence we reuse LigerQwen3MoeSwiGLUMLP
+            modeling_qwen3_next.Qwen3NextMLP = LigerQwen3MoeSwiGLUMLP
+
+    if model is not None:
+        # The model instance already exists, so we need to additionally patch the
+        # instance variables that reference already-instantiated modules
+        if isinstance(model, (Qwen3NextForCausalLM, Qwen3NextModel)):
+            base_model: Qwen3NextForCausalLM = getattr(model, model.base_model_prefix, model)
+        else:
+            raise TypeError(
+                f"Unsupported qwen3_next model type. `model` must be `Qwen3NextForCausalLM`, `Qwen3NextModel`. Got: {type(model)}"
+            )
+
+        _patch_rms_norm_module_for_qwen3_next = partial(
+            _patch_rms_norm_module, offset=1.0, casting_mode="gemma", in_place=False
+        )
+
+        if rms_norm:
+            _patch_rms_norm_module_for_qwen3_next(base_model.norm)
+
+        for decoder_layer in base_model.layers:
+            if rms_norm:
+                _patch_rms_norm_module_for_qwen3_next(decoder_layer.input_layernorm)
+                _patch_rms_norm_module_for_qwen3_next(decoder_layer.post_attention_layernorm)
+
+            # Qwen3MoeMLP and Qwen3NextMLP are identical, hence we reuse LigerQwen3MoeSwiGLUMLP
+            if swiglu:
+                if isinstance(decoder_layer.mlp, Qwen3NextMLP):
+                    _patch_swiglu_module(decoder_layer.mlp, LigerQwen3MoeSwiGLUMLP)
+                if isinstance(decoder_layer.mlp, Qwen3NextSparseMoeBlock):
+                    _patch_swiglu_module(decoder_layer.mlp.shared_expert, LigerQwen3MoeSwiGLUMLP)
+                    experts = getattr(decoder_layer.mlp, "experts", None)
+                    if experts is not None:
+                        if IS_TRANSFORMERS_V5_OR_LATER:
+                            _patch_swiglu_module(experts, LigerExperts)
+                        else:
+                            for expert in experts:
+                                _patch_swiglu_module(expert, LigerQwen3MoeSwiGLUMLP)
+
+
+def apply_liger_kernel_to_qwen3_5(
+    rope: bool = False,
+    cross_entropy: bool = False,
+    fused_linear_cross_entropy: bool = True,
+    rms_norm: bool = True,
+    swiglu: bool = True,
+    model: PreTrainedModel = None,
+) -> None:
+    """
+    Apply Liger kernels to replace original implementation in HuggingFace Qwen3.5 dense models.
+
+    Args:
+        rope (bool): Whether to apply Liger's rotary position embedding. Default is False.
+            Not yet supported for Qwen3.5 due to hybrid attention (Gated DeltaNet + Gated Attention).
+        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.
+        swiglu (bool): Whether to apply Liger's SwiGLUMLP. 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.qwen3_5 import modeling_qwen3_5
+    from transformers.models.qwen3_5.modeling_qwen3_5 import Qwen3_5ForCausalLM
+    from transformers.models.qwen3_5.modeling_qwen3_5 import Qwen3_5TextModel
+
+    try:
+        from transformers.models.qwen3_5.modeling_qwen3_5 import Qwen3_5ForConditionalGeneration
+    except ImportError:
+        Qwen3_5ForConditionalGeneration = None
+
+    from liger_kernel.transformers.model.qwen3_5 import lce_forward as qwen3_5_lce_forward
+    from liger_kernel.transformers.model.qwen3_5 import lce_forward_for_multimodal as qwen3_5_lce_forward_for_multimodal
+    from liger_kernel.transformers.monkey_patch import _patch_rms_norm_module
+    from liger_kernel.transformers.monkey_patch import _patch_swiglu_module
+    from liger_kernel.transformers.rms_norm import LigerRMSNormForQwen3Next
+    from liger_kernel.transformers.swiglu import LigerQwen3MoeSwiGLUMLP
+
+    if rope:
+        raise NotImplementedError("liger_rotary_pos_emb is not available for Qwen3_5 models.")
+
+    if rms_norm:
+        modeling_qwen3_5.Qwen3_5RMSNorm = LigerRMSNormForQwen3Next
+
+    if cross_entropy:
+        from transformers.loss.loss_utils import nn
+
+        from liger_kernel.transformers.cross_entropy import liger_cross_entropy
+
+        nn.functional.cross_entropy = liger_cross_entropy
+
+    if fused_linear_cross_entropy:
+        if model is not None:
+            if isinstance(model, Qwen3_5ForCausalLM):
+                model.forward = MethodType(qwen3_5_lce_forward, model)
+            elif isinstance(model, Qwen3_5ForConditionalGeneration):
+                model.forward = MethodType(qwen3_5_lce_forward_for_multimodal, model)
+            else:
+                raise TypeError(
+                    f"fused_linear_cross_entropy is only applicable on Qwen3_5ForCausalLM or Qwen3_5ForConditionalGeneration. Got: {type(model)}"
+                )
+        else:
+            modeling_qwen3_5.Qwen3_5ForCausalLM.forward = qwen3_5_lce_forward
+            if Qwen3_5ForConditionalGeneration is not None:
+                modeling_qwen3_5.Qwen3_5ForConditionalGeneration.forward = qwen3_5_lce_forward_for_multimodal
+
+    if swiglu:
+        modeling_qwen3_5.Qwen3_5MLP = LigerQwen3MoeSwiGLUMLP
+
+    if model is not None:
+        if isinstance(model, (Qwen3_5ForCausalLM, Qwen3_5TextModel)):
+            text_model: Qwen3_5TextModel = getattr(model, model.base_model_prefix, model)
+        elif Qwen3_5ForConditionalGeneration is not None and isinstance(model, Qwen3_5ForConditionalGeneration):
+            text_model = model.model.language_model
+        else:
+            raise TypeError(f"Unsupported qwen3_5 model type. Got: {type(model)}")
+
+        _patch_rms_norm_module_for_qwen3_5 = partial(
+            _patch_rms_norm_module, offset=1.0, casting_mode="gemma", in_place=False
+        )
+
+        if rms_norm:
+            _patch_rms_norm_module_for_qwen3_5(text_model.norm)
+
+        for decoder_layer in text_model.layers:
+            if rms_norm:
+                _patch_rms_norm_module_for_qwen3_5(decoder_layer.input_layernorm)
+                _patch_rms_norm_module_for_qwen3_5(decoder_layer.post_attention_layernorm)
+
+            if swiglu:
+                _patch_swiglu_module(decoder_layer.mlp, LigerQwen3MoeSwiGLUMLP)
+
+
+def apply_liger_kernel_to_qwen3_5_moe(
+    rope: bool = False,
+    cross_entropy: bool = False,
+    fused_linear_cross_entropy: bool = True,
+    rms_norm: bool = True,
+    swiglu: bool = True,
+    model: PreTrainedModel = None,
+) -> None:
+    """
+    Apply Liger kernels to replace original implementation in HuggingFace Qwen3.5 MoE models.
+
+    Args:
+        rope (bool): Whether to apply Liger's rotary position embedding. Default is False.
+        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.
+        swiglu (bool): Whether to apply Liger's SwiGLUMLP. 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.qwen3_5_moe import modeling_qwen3_5_moe
+    from transformers.models.qwen3_5_moe.modeling_qwen3_5_moe import Qwen3_5MoeForCausalLM
+    from transformers.models.qwen3_5_moe.modeling_qwen3_5_moe import Qwen3_5MoeTextModel
+
+    from liger_kernel.transformers.model.qwen3_5_moe import lce_forward as qwen3_5_moe_lce_forward
+    from liger_kernel.transformers.rms_norm import LigerRMSNormForQwen3Next
+    from liger_kernel.transformers.swiglu import LigerQwen3MoeSwiGLUMLP
+
+    if rope:
+        raise NotImplementedError("liger_rotary_pos_emb is not available for Qwen3_5Moe models.")
+    if rms_norm:
+        modeling_qwen3_5_moe.Qwen3_5MoeRMSNorm = LigerRMSNormForQwen3Next
+    if cross_entropy:
+        from transformers.loss.loss_utils import nn
+
+        nn.functional.cross_entropy = liger_cross_entropy
+    if fused_linear_cross_entropy:
+        if model is not None:
+            if isinstance(model, Qwen3_5MoeForCausalLM):
+                model.forward = MethodType(qwen3_5_moe_lce_forward, model)
+            else:
+                raise TypeError(
+                    f" fused_linear_cross_entropy is only applicable on Qwen3_5MoeForCausalLM. Got: {type(model)}"
+                )
+        else:
+            modeling_qwen3_5_moe.Qwen3_5MoeForCausalLM.forward = qwen3_5_moe_lce_forward
+    if swiglu:
+        modeling_qwen3_5_moe.Qwen3_5MoeExperts = LigerExperts
+
+    if model is not None:
+        # The model instance already exists, so we need to additionally patch the
+        # instance variables that reference already-instantiated modules
+        if isinstance(model, (Qwen3_5MoeForCausalLM, Qwen3_5MoeTextModel)):
+            base_model: Qwen3_5MoeTextModel = getattr(model, model.base_model_prefix, model)
+        else:
+            raise TypeError(
+                f"Unsupported qwen3_5_moe model type. `model` must be `Qwen3_5MoeForCausalLM`, `Qwen3_5MoeTextModel`. Got: {type(model)}"
+            )
+
+        _patch_rms_norm_module_for_qwen3_5_moe = partial(
+            _patch_rms_norm_module, offset=1.0, casting_mode="gemma", in_place=False
+        )
+
+        if rms_norm:
+            _patch_rms_norm_module_for_qwen3_5_moe(base_model.norm)
+
+        for decoder_layer in base_model.layers:
+            if rms_norm:
+                _patch_rms_norm_module_for_qwen3_5_moe(decoder_layer.input_layernorm)
+                _patch_rms_norm_module_for_qwen3_5_moe(decoder_layer.post_attention_layernorm)
+
+            if swiglu:
+                _patch_swiglu_module(decoder_layer.mlp.shared_expert, LigerQwen3MoeSwiGLUMLP)
+                experts = getattr(decoder_layer.mlp, "experts", None)
+                if experts is not None:
+                    _patch_swiglu_module(experts, LigerExperts)
+
+
+def apply_liger_kernel_to_hunyuan_v1_dense(
+    rope: bool = True,
+    cross_entropy: bool = False,
+    fused_linear_cross_entropy: bool = True,
+    rms_norm: bool = True,
+    swiglu: bool = True,
+    model: PreTrainedModel = None,
+) -> None:
+    """
+    Apply Liger kernels to replace original implementation in HuggingFace Hunyuan v1 dense models.
+    """
+    assert not (cross_entropy and fused_linear_cross_entropy), (
+        "cross_entropy and fused_linear_cross_entropy cannot both be True."
+    )
+
+    from transformers.models.hunyuan_v1_dense import modeling_hunyuan_v1_dense
+    from transformers.models.hunyuan_v1_dense.modeling_hunyuan_v1_dense import HunYuanDenseV1Model
+
+    from liger_kernel.transformers.model.hunyuan_v1 import lce_forward as hunyuan_v1_lce_forward
+    from liger_kernel.transformers.swiglu import LigerHunyuanV1SwiGLUMLP
+
+    if rope:
+        modeling_hunyuan_v1_dense.apply_rotary_pos_emb = liger_rotary_pos_emb
+
+    if rms_norm:
+        modeling_hunyuan_v1_dense.HunYuanDenseV1RMSNorm = LigerRMSNorm
+
+    if cross_entropy:
+        from transformers.loss.loss_utils import nn
+
+        nn.functional.cross_entropy = liger_cross_entropy
+
+    if fused_linear_cross_entropy:
+        if model is not None:
+            model.forward = MethodType(hunyuan_v1_lce_forward, model)
+        else:
+            modeling_hunyuan_v1_dense.HunYuanDenseV1ForCausalLM.forward = hunyuan_v1_lce_forward
+
+    if swiglu:
+        modeling_hunyuan_v1_dense.HunYuanDenseV1MLP = LigerHunyuanV1SwiGLUMLP
+
+    if model is not None:
+        # The model instance already exists, so we need to additionally patch the
+        # instance variables that reference already-instantiated modules
+
+        # get the base model from the model instance
+        base_model: HunYuanDenseV1Model = getattr(model, model.base_model_prefix, model)
+
+        if rms_norm:
+            _patch_rms_norm_module(base_model.norm)
+        for decoder_layer in base_model.layers:
+            if swiglu:
+                _patch_swiglu_module(decoder_layer.mlp, LigerHunyuanV1SwiGLUMLP)
+            if rms_norm:
+                _patch_rms_norm_module(decoder_layer.input_layernorm)
+                _patch_rms_norm_module(decoder_layer.post_attention_layernorm)
+
+
+def apply_liger_kernel_to_hunyuan_v1_moe(
+    rope: bool = True,
+    cross_entropy: bool = False,
+    fused_linear_cross_entropy: bool = True,
+    rms_norm: bool = True,
+    swiglu: bool = True,
+    model: PreTrainedModel = None,
+) -> None:
+    """
+    Apply Liger kernels to replace original implementation in HuggingFace Qwen3 models.
+    """
+    assert not (cross_entropy and fused_linear_cross_entropy), (
+        "cross_entropy and fused_linear_cross_entropy cannot both be True."
+    )
+
+    from transformers.models.hunyuan_v1_moe import modeling_hunyuan_v1_moe
+    from transformers.models.hunyuan_v1_moe.modeling_hunyuan_v1_moe import HunYuanMoEV1Model
+
+    from liger_kernel.transformers.model.hunyuan_v1 import lce_forward as hunyuan_v1_moe_lce_forward
+    from liger_kernel.transformers.swiglu import LigerHunyuanV1SwiGLUMLP
+
+    if rope:
+        modeling_hunyuan_v1_moe.apply_rotary_pos_emb = liger_rotary_pos_emb
+
+    if rms_norm:
+        modeling_hunyuan_v1_moe.HunYuanMoEV1RMSNorm = LigerRMSNorm
+
+    if cross_entropy:
+        from transformers.loss.loss_utils import nn
+
+        nn.functional.cross_entropy = liger_cross_entropy
+
+    if fused_linear_cross_entropy:
+        if model is not None:
+            model.forward = MethodType(hunyuan_v1_moe_lce_forward, model)
+        else:
+            modeling_hunyuan_v1_moe.HunYuanMoEV1ForCausalLM.forward = hunyuan_v1_moe_lce_forward
+
+    if swiglu:
+        if IS_TRANSFORMERS_V5_OR_LATER:
+            modeling_hunyuan_v1_moe.HunYuanMoEV1Experts = LigerExperts
+        else:
+            modeling_hunyuan_v1_moe.HunYuanMoEV1MLP = LigerHunyuanV1SwiGLUMLP
+
+    if model is not None:
+        # The model instance already exists, so we need to additionally patch the
+        # instance variables that reference already-instantiated modules
+
+        # get the base model from the model instance
+        base_model: HunYuanMoEV1Model = getattr(model, model.base_model_prefix, model)
+
+        if rms_norm:
+            _patch_rms_norm_module(base_model.norm)
+        for decoder_layer in base_model.layers:
+            if swiglu:
+                if IS_TRANSFORMERS_V5_OR_LATER:
+                    _patch_swiglu_module(decoder_layer.mlp.experts, LigerExperts)
+                else:
+                    for mlp_expert in decoder_layer.mlp.experts:
+                        _patch_swiglu_module(mlp_expert, LigerHunyuanV1SwiGLUMLP)
+            if rms_norm:
+                _patch_rms_norm_module(decoder_layer.input_layernorm)
+                _patch_rms_norm_module(decoder_layer.post_attention_layernorm)
+
+
+def apply_liger_kernel_to_exaone4(
+    rope: bool = True,
+    cross_entropy: bool = False,
+    fused_linear_cross_entropy: bool = True,
+    rms_norm: bool = True,
+    swiglu: bool = True,
+    model: PreTrainedModel = None,
+) -> None:
+    """
+    Apply Liger kernels to replace original implementation in HuggingFace EXAONE4 models.
+
+    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.
+        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.
+    """
+    assert not (cross_entropy and fused_linear_cross_entropy), (
+        "cross_entropy and fused_linear_cross_entropy cannot both be True."
+    )
+
+    from transformers.models.exaone4 import modeling_exaone4
+    from transformers.models.exaone4.modeling_exaone4 import Exaone4Model
+
+    from liger_kernel.transformers.model.exaone4 import lce_forward as exaone4_lce_forward
+
+    if rope:
+        modeling_exaone4.apply_rotary_pos_emb = liger_rotary_pos_emb
+
+    if rms_norm:
+        # EXAONE4 requires in_place=False to avoid gradient issues
+        class Exaone4LigerRMSNorm(LigerRMSNorm):
+            def __init__(self, hidden_size, eps=1e-6, **kwargs):
+                super().__init__(hidden_size, eps, **kwargs)
+                self.in_place = False
+
+        modeling_exaone4.Exaone4RMSNorm = Exaone4LigerRMSNorm
+
+    if cross_entropy:
+        from transformers.loss.loss_utils import nn
+
+        nn.functional.cross_entropy = liger_cross_entropy
+
+    if fused_linear_cross_entropy:
+        if model is not None:
+            model.forward = MethodType(exaone4_lce_forward, model)
+        else:
+            modeling_exaone4.Exaone4ForCausalLM.forward = exaone4_lce_forward
+
+    if swiglu:
+        modeling_exaone4.Exaone4MLP = LigerSwiGLUMLP
+
+    if model is not None:
+        # The model instance already exists, so we need to additionally patch the
+        # instance variables that reference already-instantiated modules
+
+        # get the base model from the model instance
+        base_model: Exaone4Model = getattr(model, model.base_model_prefix, model)
+
+        if rms_norm:
+            _patch_rms_norm_module(base_model.norm, in_place=False)
+        for decoder_layer in base_model.layers:
+            if swiglu:
+                _bind_method_to_module(decoder_layer.mlp, "forward", LigerSwiGLUMLP.forward)
+            if rms_norm:
+                _patch_rms_norm_module(decoder_layer.post_attention_layernorm, in_place=False)
+                _patch_rms_norm_module(decoder_layer.post_feedforward_layernorm, in_place=False)
+                _patch_rms_norm_module(decoder_layer.self_attn.q_norm, in_place=False)
+                _patch_rms_norm_module(decoder_layer.self_attn.k_norm, in_place=False)
+
+
+# Model type corresponds to the keys defined in transformers/models/auto/modeling_auto.py
+MODEL_TYPE_TO_APPLY_LIGER_FN = {
+    "gemma": apply_liger_kernel_to_gemma,
+    "gemma2": apply_liger_kernel_to_gemma2,
+    "gemma3_text": apply_liger_kernel_to_gemma3_text,
+    "gemma3": apply_liger_kernel_to_gemma3,
+    "glm4": apply_liger_kernel_to_glm4,
+    "glm4v": apply_liger_kernel_to_glm4v,
+    "glm4v_moe": apply_liger_kernel_to_glm4v_moe,
+    "gpt_oss": apply_liger_kernel_to_gpt_oss,
+    "internvl": apply_liger_kernel_to_internvl,
+    "llama": apply_liger_kernel_to_llama,
+    "llama4_text": apply_liger_kernel_to_llama4,
+    "llama4": apply_liger_kernel_to_llama4,
+    "llava": apply_liger_kernel_to_llava,
+    "granite": apply_liger_kernel_to_granite,
+    "mllama": apply_liger_kernel_to_mllama,
+    "mllama_text_model": apply_liger_kernel_to_mllama,
+    "mistral": apply_liger_kernel_to_mistral,
+    "mixtral": apply_liger_kernel_to_mixtral,
+    "olmo2": apply_liger_kernel_to_olmo2,
+    "pixtral": apply_liger_kernel_to_pixtral,
+    "olmo3": apply_liger_kernel_to_olmo3,
+    "qwen2": apply_liger_kernel_to_qwen2,
+    "qwen3": apply_liger_kernel_to_qwen3,
+    "qwen3_moe": apply_liger_kernel_to_qwen3_moe,
+    "qwen2_vl": apply_liger_kernel_to_qwen2_vl,
+    "qwen2_vl_text": apply_liger_kernel_to_qwen2_vl,
+    "qwen2_5_vl": apply_liger_kernel_to_qwen2_5_vl,
+    "qwen2_5_vl_text": apply_liger_kernel_to_qwen2_5_vl,
+    "qwen3_next": apply_liger_kernel_to_qwen3_next,
+    "qwen3_5": apply_liger_kernel_to_qwen3_5,
+    "qwen3_5_text": apply_liger_kernel_to_qwen3_5,
+    "qwen3_5_moe": apply_liger_kernel_to_qwen3_5_moe,
+    "qwen3_5_moe_text": apply_liger_kernel_to_qwen3_5_moe,
+    "qwen3_vl": apply_liger_kernel_to_qwen3_vl,
+    "qwen3_vl_text": apply_liger_kernel_to_qwen3_vl,
+    "qwen3_vl_moe": apply_liger_kernel_to_qwen3_vl_moe,
+    "qwen3_vl_moe_text": apply_liger_kernel_to_qwen3_vl_moe,
+    "smollm3": apply_liger_kernel_to_smollm3,
+    "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,
+    "hunyuan_v1_dense": apply_liger_kernel_to_hunyuan_v1_dense,
+    "hunyuan_v1_moe": apply_liger_kernel_to_hunyuan_v1_moe,
+    "exaone4": apply_liger_kernel_to_exaone4,
+}
+
+
+def _apply_liger_kernel(model_type: str, **kwargs) -> None:
+    """
+    Applies Liger kernels based on the specified model type. The custom
+    kernels for the specified model type will be applied with the provided
+    keyword arguments, otherwise the default configuration will be used.
+
+    ** Note: Calling _apply_liger_kernel() after model initialization
+    will not be able to fully patch models. This must be called before model initialization.
+    If the model has already been instantiated
+
+    Args:
+        - model_type: the model types as defined in transformers/models/auto/modeling_auto.py
+          and specified in the model's config.json
+        - kwargs: keyword arguments that are passed to the corresponding apply_liger_kernel_to_* function.
+    """
+    if not model_type:
+        logger.info("Model type was not provided. No Liger kernels will be applied.")
+        return
+
+    if model_type not in MODEL_TYPE_TO_APPLY_LIGER_FN.keys():
+        logger.info(f"There are currently no Liger kernels supported for model type: {model_type}.")
+        return
+
+    apply_fn = MODEL_TYPE_TO_APPLY_LIGER_FN[model_type]
+    apply_fn_signature = inspect.signature(apply_fn)
+
+    # Filter out the keyword arguments that are not supported by the apply function
+    applicable_kwargs = {key: value for key, value in kwargs.items() if key in apply_fn_signature.parameters}
+
+    logger.info(f"Applying Liger kernels for model type: {model_type} with kwargs: {applicable_kwargs}")
+
+    # Assume this is invoked pre-model initialization, so we only need to patch transformers code
+    apply_fn(**applicable_kwargs)
+
+
+def _apply_liger_kernel_to_instance(model: PreTrainedModel, **kwargs) -> None:
+    """
+    Applies Liger kernels to the provided model instance.
+
+    Args:
+        - model: the model instance to apply Liger kernels to
+        - kwargs: keyword arguments that are passed to the corresponding apply_liger_kernel_to_* function.
+    """
+    model_type = getattr(model, "config", None) and getattr(model.config, "model_type", None)
+
+    if not model_type:
+        logger.info("Model type could not be determined from model config. No Liger kernels will be applied.")
+        return
+
+    if model_type not in MODEL_TYPE_TO_APPLY_LIGER_FN.keys():
+        logger.info(f"There are currently no Liger kernels supported for model type: {model_type}.")
+        return
+
+    apply_fn = MODEL_TYPE_TO_APPLY_LIGER_FN[model_type]
+    apply_fn_signature = inspect.signature(apply_fn)
+
+    # Filter out the keyword arguments that are not supported by the apply function
+    applicable_kwargs = {key: value for key, value in kwargs.items() if key in apply_fn_signature.parameters}
+    logger.info(
+        f"Applying Liger kernels to model instance with model type: {model_type} with kwargs: {applicable_kwargs}"
+    )
+
+    apply_fn(model=model, **applicable_kwargs)

src/liger_kernel/transformers/multi_token_attention.py

+import math
+
+import torch
+import torch.nn as nn
+
+from torch.nn.modules.utils import _pair
+
+from liger_kernel.ops import LigerMultiTokenAttentionFunction
+
+
+class LigerMultiTokenAttention(nn.Module):
+    r"""
+    Multi-Token Attention:
+        out = mask_{0}(conv2d(softmax(mask_{-\inf}(scores))))
+
+    Reference: https://arxiv.org/pdf/2504.00927
+    """
+
+    def __init__(
+        self,
+        in_channels: int,
+        out_channels: int,
+        kernel_size: int,
+        stride: int = 1,
+        padding: int = 0,
+        dilation: int = 1,
+        groups: int = 1,
+        bias: bool = True,
+        sparse: bool = False,
+    ):
+        super().__init__()
+        self.in_channels = in_channels
+        self.out_channels = out_channels
+        self.kernel_size = _pair(kernel_size)
+        self.stride = _pair(stride)
+        self.padding = _pair(padding)
+        self.dilation = _pair(dilation)
+        self.groups = groups
+        self.sparse = sparse
+
+        self.weight = nn.Parameter(torch.empty(out_channels, in_channels // groups, *self.kernel_size))
+        if bias:
+            self.bias = nn.Parameter(torch.empty(out_channels))
+        else:
+            self.register_parameter("bias", None)
+
+        self.reset_parameters()
+
+    def reset_parameters(self):
+        nn.init.kaiming_uniform_(self.weight, a=math.sqrt(5))
+        if self.bias is not None:
+            nn.init.zeros_(self.bias)
+
+    def forward(self, scores: torch.Tensor) -> torch.Tensor:
+        return LigerMultiTokenAttentionFunction.apply(
+            scores,
+            self.weight,
+            self.bias,
+            self.stride,
+            self.padding,
+            self.dilation,
+            self.groups,
+            self.sparse,
+        )