Add support for Qwen3.5 MoE

src/liger_kernel/transformers/__init__.py

@@ -63,6 +63,7 @@
     from liger_kernel.transformers.monkey_patch import apply_liger_kernel_to_qwen2_5_vl  # noqa: F401
     from liger_kernel.transformers.monkey_patch import apply_liger_kernel_to_qwen2_vl  # noqa: F401
     from liger_kernel.transformers.monkey_patch import apply_liger_kernel_to_qwen3  # noqa: F401
+    from liger_kernel.transformers.monkey_patch import apply_liger_kernel_to_qwen3_5_moe  # noqa: F401
     from liger_kernel.transformers.monkey_patch import apply_liger_kernel_to_qwen3_moe  # noqa: F401
     from liger_kernel.transformers.monkey_patch import apply_liger_kernel_to_qwen3_next  # noqa: F401
     from liger_kernel.transformers.monkey_patch import apply_liger_kernel_to_qwen3_vl  # noqa: F401
@@ -133,6 +134,7 @@ def __getattr__(name: str):
         "apply_liger_kernel_to_qwen2_vl",
         "apply_liger_kernel_to_qwen3",
         "apply_liger_kernel_to_qwen3_moe",
+        "apply_liger_kernel_to_qwen3_5_moe",
         "apply_liger_kernel_to_qwen3_next",
         "apply_liger_kernel_to_qwen3_vl",
         "apply_liger_kernel_to_qwen3_vl_moe",
@@ -213,6 +215,7 @@ def __getattr__(name: str):
             "apply_liger_kernel_to_qwen2_vl",
             "apply_liger_kernel_to_qwen3",
             "apply_liger_kernel_to_qwen3_moe",
+            "apply_liger_kernel_to_qwen3_5_moe",
             "apply_liger_kernel_to_qwen3_next",
             "apply_liger_kernel_to_qwen3_vl",
             "apply_liger_kernel_to_qwen3_vl_moe",

src/liger_kernel/transformers/model/qwen3_5_moe.py

@@ -0,0 +1,155 @@
+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,
+    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,
+        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_next.py

@@ -115,7 +115,13 @@ def lce_forward(
     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, labels, self.vocab_size, **kwargs)
+            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:

src/liger_kernel/transformers/monkey_patch.py

@@ -2652,13 +2652,17 @@ def apply_liger_kernel_to_qwen3_next(
                 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(base_model.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(decoder_layer.input_layernorm)
-                _patch_rms_norm_module(decoder_layer.post_attention_layernorm)
+                _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:
@@ -2675,6 +2679,91 @@ def apply_liger_kernel_to_qwen3_next(
                                 _patch_swiglu_module(expert, 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,
@@ -2906,6 +2995,8 @@ def __init__(self, hidden_size, eps=1e-6, **kwargs):
     "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_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,