[feat]: Add support for gpt-oss

README.md

@@ -264,6 +264,7 @@ loss.backward()
 | OLMo2   | `liger_kernel.transformers.apply_liger_kernel_to_olmo2`     | RoPE, RMSNorm, SwiGLU, CrossEntropyLoss, FusedLinearCrossEntropy |
 | Olmo3   | `liger_kernel.transformers.apply_liger_kernel_to_olmo3`     | RoPE, RMSNorm, SwiGLU, CrossEntropyLoss, FusedLinearCrossEntropy |
 | GLM-4   | `liger_kernel.transformers.apply_liger_kernel_to_glm4`     | RoPE, RMSNorm, SwiGLU, CrossEntropyLoss, FusedLinearCrossEntropy |
+| GPT-OSS   | `liger_kernel.transformers.apply_liger_kernel_to_gpt_oss`     | RoPE, RMSNorm, CrossEntropyLoss, FusedLinearCrossEntropy |
 | InternVL3   | `liger_kernel.transformers.apply_liger_kernel_to_internvl`     | RoPE, RMSNorm, SwiGLU, CrossEntropyLoss, FusedLinearCrossEntropy |
 | HunyuanV1   | `liger_kernel.transformers.apply_liger_kernel_to_hunyuan_v1_dense`    |  RoPE, RMSNorm, SwiGLU, CrossEntropyLoss, FusedLinearCrossEntropy       |
 | HunyuanV1 MoE | `liger_kernel.transformers.apply_liger_kernel_to_hunyuan_v1_moe` | RoPE, RMSNorm, SwiGLU, CrossEntropyLoss, FusedLinearCrossEntropy       |

src/liger_kernel/transformers/__init__.py

@@ -41,6 +41,7 @@
     from liger_kernel.transformers.monkey_patch import apply_liger_kernel_to_glm4  # noqa: F401
     from liger_kernel.transformers.monkey_patch import apply_liger_kernel_to_glm4v  # noqa: F401
     from liger_kernel.transformers.monkey_patch import apply_liger_kernel_to_glm4v_moe  # noqa: F401
+    from liger_kernel.transformers.monkey_patch import apply_liger_kernel_to_gpt_oss  # noqa: F401
     from liger_kernel.transformers.monkey_patch import apply_liger_kernel_to_granite  # noqa: F401
     from liger_kernel.transformers.monkey_patch import apply_liger_kernel_to_hunyuan_v1_dense  # noqa: F401
     from liger_kernel.transformers.monkey_patch import apply_liger_kernel_to_hunyuan_v1_moe  # noqa: F401
@@ -110,6 +111,7 @@ def __getattr__(name: str):
         "apply_liger_kernel_to_glm4",
         "apply_liger_kernel_to_glm4v",
         "apply_liger_kernel_to_glm4v_moe",
+        "apply_liger_kernel_to_gpt_oss",
         "apply_liger_kernel_to_granite",
         "apply_liger_kernel_to_internvl",
         "apply_liger_kernel_to_llama",
@@ -187,6 +189,7 @@ def __getattr__(name: str):
             "apply_liger_kernel_to_glm4",
             "apply_liger_kernel_to_glm4v",
             "apply_liger_kernel_to_glm4v_moe",
+            "apply_liger_kernel_to_gpt_oss",
             "apply_liger_kernel_to_granite",
             "apply_liger_kernel_to_internvl",
             "apply_liger_kernel_to_llama",

src/liger_kernel/transformers/model/gpt_oss.py

@@ -0,0 +1,138 @@
+from typing import List
+from typing import Optional
+from typing import Union
+
+import torch
+
+from transformers.modeling_outputs import MoeCausalLMOutputWithPast
+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
+
+
+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,
+    **kwargs,
+) -> MoeCausalLMOutputWithPast:
+    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, GptOssForCausalLM
+
+    >>> model = GptOssForCausalLM.from_pretrained("openai/gpt-oss-20b")
+    >>> tokenizer = AutoTokenizer.from_pretrained("openai/gpt-oss-20b")
+
+    >>> 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
+    )
+
+    # 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
+
+    if skip_logits is None:
+        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.hidden_size,
+            **kwargs,
+        )
+    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
+
+    return MoeCausalLMOutputWithPast(
+        loss=loss,
+        aux_loss=aux_loss,
+        logits=logits,
+        past_key_values=outputs.past_key_values,
+        hidden_states=outputs.hidden_states,
+        attentions=outputs.attentions,
+        router_logits=outputs.router_logits,
+    )

src/liger_kernel/transformers/monkey_patch.py

@@ -20,6 +20,7 @@
 from liger_kernel.transformers.model.gemma import lce_forward_deprecated as gemma_lce_forward_deprecated
 from liger_kernel.transformers.model.gemma2 import lce_forward as gemma2_lce_forward
 from liger_kernel.transformers.model.gemma2 import lce_forward_deprecated as gemma2_lce_forward_deprected
+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.llama import lce_forward_deprecated as llama_lce_forward_deprecated
 from liger_kernel.transformers.model.llava import lce_forward as llava_lce_forward
@@ -1459,6 +1460,79 @@ def apply_liger_kernel_to_qwen3_moe(
                 _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,
@@ -2752,6 +2826,7 @@ def apply_liger_kernel_to_hunyuan_v1_moe(
     "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,

test/convergence/bf16/test_mini_models.py

@@ -29,6 +29,7 @@
 from liger_kernel.transformers import apply_liger_kernel_to_glm4
 from liger_kernel.transformers import apply_liger_kernel_to_glm4v
 from liger_kernel.transformers import apply_liger_kernel_to_glm4v_moe
+from liger_kernel.transformers import apply_liger_kernel_to_gpt_oss
 from liger_kernel.transformers import apply_liger_kernel_to_granite
 from liger_kernel.transformers import apply_liger_kernel_to_hunyuan_v1_dense
 from liger_kernel.transformers import apply_liger_kernel_to_hunyuan_v1_moe
@@ -64,6 +65,7 @@
 from test.utils import revert_liger_kernel_to_glm4
 from test.utils import revert_liger_kernel_to_glm4v
 from test.utils import revert_liger_kernel_to_glm4v_moe
+from test.utils import revert_liger_kernel_to_gpt_oss
 from test.utils import revert_liger_kernel_to_granite
 from test.utils import revert_liger_kernel_to_hunyuan_v1
 from test.utils import revert_liger_kernel_to_hunyuan_v1_moe
@@ -267,6 +269,15 @@
 except ImportError:
     FALCONH1_AVAILABLE = False
 
+try:
+    # GPT-OSS is only available in transformers>=4.55.0
+    from transformers.models.gpt_oss.configuration_gpt_oss import GptOssConfig
+    from transformers.models.gpt_oss.modeling_gpt_oss import GptOssForCausalLM
+
+    GPT_OSS_AVAILABLE = True
+except ImportError:
+    GPT_OSS_AVAILABLE = False
+
 try:
     # Qwen3Next is only available in transformers>=4.57.0
     from transformers.models.qwen3_next.configuration_qwen3_next import Qwen3NextConfig
@@ -613,6 +624,43 @@
         ),
     )
 
+if GPT_OSS_AVAILABLE:
+    MINI_MODEL_SETUPS["mini_gpt_oss"] = MiniModelConfig(
+        liger_kernel_patch_func=apply_liger_kernel_to_gpt_oss,
+        liger_kernel_patch_revert_func=revert_liger_kernel_to_gpt_oss,
+        model_class=GptOssForCausalLM,
+        mini_model_config=GptOssConfig(
+            vocab_size=32000,  # 201088
+            hidden_size=896,
+            intermediate_size=896,  # Same as hidden_size for GPT-OSS
+            num_hidden_layers=4,
+            num_attention_heads=8,
+            num_key_value_heads=2,
+            head_dim=64,
+            hidden_act="silu",
+            max_position_embeddings=8192,
+            initializer_range=0.02,
+            rms_norm_eps=1e-5,
+            use_cache=True,
+            tie_word_embeddings=False,
+            rope_parameters={
+                "rope_type": "yarn",
+                "factor": 8.0,
+                "beta_fast": 32.0,
+                "beta_slow": 1.0,
+                "truncate": False,
+                "original_max_position_embeddings": 4096,
+            },
+            attention_dropout=0.0,
+            num_local_experts=8,  # Reduced from 32 for mini model
+            num_experts_per_tok=2,  # Reduced from 4 for mini model
+            router_aux_loss_coef=0.9,
+            output_router_logits=False,
+            sliding_window=128,
+            layer_types=["sliding_attention" if bool((i + 1) % 2) else "full_attention" for i in range(4)],
+        ),
+    )
+
 if GEMMA3_AVAILABLE:
     MINI_MODEL_SETUPS["mini_gemma3_text"] = MiniModelConfig(
         liger_kernel_patch_func=apply_liger_kernel_to_gemma3_text,
@@ -1664,6 +1712,25 @@ def run_mini_model(
                 ),
             ],
         ),
+        pytest.param(
+            "mini_gpt_oss",
+            32,
+            1e-5,
+            torch.bfloat16,
+            5e-2,
+            5e-2,
+            1e-1,
+            1e-1,
+            1e-2,
+            1e-2,
+            marks=[
+                pytest.mark.skipif(not supports_bfloat16(), reason="bfloat16 not supported on this GPU"),
+                pytest.mark.skipif(
+                    not GPT_OSS_AVAILABLE,
+                    reason="GPT-OSS not available in this version of transformers",
+                ),
+            ],
+        ),
         pytest.param(
             "mini_qwen2_vl",
             32,