FusedMoE support for the Transformers backend (#22650)

Signed-off-by: Harry Mellor <19981378+hmellor@users.noreply.github.com>
Signed-off-by: yewentao256 <zhyanwentao@126.com>

docs/models/supported_models.md

@@ -17,12 +17,12 @@ These models are what we list in [supported-text-models][supported-text-models]
 
 ### Transformers
 
-vLLM also supports model implementations that are available in Transformers. You should expect the performance of a Transformers model implementation used in vLLM to be within <1% of the performance of a dedicated vLLM model implementation. We call this feature the "Transformers backend".
+vLLM also supports model implementations that are available in Transformers. You should expect the performance of a Transformers model implementation used in vLLM to be within <5% of the performance of a dedicated vLLM model implementation. We call this feature the "Transformers backend".
 
 Currently, the Transformers backend works for the following:
 
 - Modalities: embedding models, language models and vision-language models*
-- Architectures: encoder-only, decoder-only
+- Architectures: encoder-only, decoder-only, mixture-of-experts
 - Attention types: full attention and/or sliding attention
 
 _*Vision-language models currently accept only image inputs. Support for video inputs will be added in a future release._
@@ -31,6 +31,7 @@ If the Transformers model implementation follows all the steps in [writing a cus
 
 - All the features listed in the [compatibility matrix](../features/README.md#feature-x-feature)
 - Any combination of the following vLLM parallelisation schemes:
+    - Data parallel
     - Pipeline parallel
     - Tensor parallel
 

tests/models/registry.py

@@ -661,6 +661,10 @@ _TRANSFORMERS_BACKEND_MODELS = {
     "TransformersForSequenceClassification": _HfExamplesInfo("papluca/xlm-roberta-base-language-detection", min_transformers_version="4.57.0.dev0"),  # noqa: E501
     "TransformersForCausalLM": _HfExamplesInfo("hmellor/Ilama-3.2-1B", trust_remote_code=True),  # noqa: E501
     "TransformersForMultimodalLM": _HfExamplesInfo("BAAI/Emu3-Chat-hf"),
+    "TransformersMoEForCausalLM": _HfExamplesInfo("allenai/OLMoE-1B-7B-0924", min_transformers_version="4.57.0.dev0"),  # noqa: E501
+    "TransformersMoEForMultimodalLM": _HfExamplesInfo("Qwen/Qwen3-VL-30B-A3B-Instruct", min_transformers_version="4.57.0.dev0"),  # noqa: E501
+    "TransformersMoEEmbeddingModel": _HfExamplesInfo("Qwen/Qwen3-30B-A3B", min_transformers_version="4.57.0.dev0"),  # noqa: E501
+    "TransformersMoEForSequenceClassification": _HfExamplesInfo("Qwen/Qwen3-30B-A3B", min_transformers_version="4.57.0.dev0"),  # noqa: E501
 }
 
 _EXAMPLE_MODELS = {

tests/models/test_transformers.py

@@ -66,6 +66,7 @@ def check_implementation(
     [
         ("meta-llama/Llama-3.2-1B-Instruct", "transformers"),
         ("hmellor/Ilama-3.2-1B", "auto"),  # CUSTOM CODE
+        ("allenai/OLMoE-1B-7B-0924", "transformers"),  # MoE
     ])  # trust_remote_code=True by default
 def test_models(
     hf_runner: type[HfRunner],
@@ -74,6 +75,14 @@ def test_models(
     model: str,
     model_impl: str,
 ) -> None:
+    import transformers
+    from packaging.version import Version
+    installed = Version(transformers.__version__)
+    required = Version("4.57.0.dev0")
+    if model == "allenai/OLMoE-1B-7B-0924" and installed < required:
+        pytest.skip("MoE models with the Transformers backend require "
+                    f"transformers>={required}, but got {installed}")
+
     check_implementation(hf_runner,
                          vllm_runner,
                          example_prompts,

tests/models/utils.py

@@ -430,17 +430,26 @@ def dummy_hf_overrides(
 
     update_dict = {
         "num_layers": num_layers,
-        "num_experts": num_experts,
-        "num_experts_per_tok": 2,
-        "num_local_experts": num_experts,
-        # Otherwise there will not be any expert layers
-        "first_k_dense_replace": 0,
-        # To avoid OOM on DeepSeek-V3
-        "n_routed_experts": num_experts,
         # For Gemma-3n
         "num_kv_shared_layers": 1,
     }
 
+    class DummyConfig:
+        hf_text_config = text_config
+
+    # Only set MoE related config when the model has MoE layers.
+    # Otherwise all models detected as MoE by _get_transformers_backend_cls.
+    if ModelConfig.get_num_experts(DummyConfig) > 0:
+        update_dict.update({
+            "num_experts": num_experts,
+            "num_experts_per_tok": 2,
+            "num_local_experts": num_experts,
+            # Otherwise there will not be any expert layers
+            "first_k_dense_replace": 0,
+            # To avoid OOM on DeepSeek-V3
+            "n_routed_experts": num_experts,
+        })
+
     # Update num_hidden_layers for non-Longcat architectures
     if model_arch != "LongcatFlashForCausalLM" \
             and model_arch != "LongCatFlashMTPModel":

vllm/config/model.py

@@ -20,7 +20,7 @@ from vllm.config.multimodal import (MMCacheType, MMEncoderTPMode,
                                     MultiModalConfig)
 from vllm.config.pooler import PoolerConfig
 from vllm.config.scheduler import RunnerType
-from vllm.config.utils import assert_hashable, config
+from vllm.config.utils import assert_hashable, config, getattr_iter
 from vllm.logger import init_logger
 from vllm.platforms import current_platform
 from vllm.transformers_utils.config import (
@@ -667,6 +667,8 @@ class ModelConfig:
     def _get_transformers_backend_cls(self) -> str:
         """Determine which Transformers backend class will be used if
         `model_impl` is set to `transformers` or `auto`."""
+        prefix = "Transformers"
+        prefix += "MoE" if self.get_num_experts() > 1 else ""
         # Check if the architecture we're wrapping has defaults
         runner = None
         convert = None
@@ -685,15 +687,15 @@ class ModelConfig:
         # Resolve Transformers backend pooling classes
         if runner == "pooling":
             if convert == "embed":
-                return "TransformersEmbeddingModel"
+                return prefix + "EmbeddingModel"
             if convert == "classify":
-                return "TransformersForSequenceClassification"
+                return prefix + "ForSequenceClassification"
         # Resolve Transformers backend generate classes
         if self.hf_config != self.hf_text_config:
             # If 'hf_text_config' is the same as 'hf_config'. If not, it is
             # probably a composite config, i.e. multimodal
-            return "TransformersForMultimodalLM"
-        return "TransformersForCausalLM"
+            return prefix + "ForMultimodalLM"
+        return prefix + "ForCausalLM"
 
     def using_transformers_backend(self) -> bool:
         """Check if the model is using the Transformers backend class."""
@@ -1025,17 +1027,7 @@ class ModelConfig:
             self.enforce_eager = True
 
     def _verify_with_expert_parallelism(self) -> None:
-        num_expert_names = [
-            "moe_num_experts",  # Dbrx
-            "num_experts",  # Jamba
-            "n_routed_experts",  # DeepSeek
-            "num_local_experts",  # Mixtral
-        ]
-        num_experts = 0
-        for name in num_expert_names:
-            num_experts = getattr(self.hf_text_config, name, 0)
-            if num_experts > 0:
-                break
+        num_experts = self.get_num_experts()
         if num_experts < 1:
             raise ValueError(
                 "Number of experts in the model must be greater than 0 "
@@ -1220,6 +1212,21 @@ class ModelConfig:
         num_heads = getattr(self.hf_text_config, "num_attention_heads", 0)
         return num_heads // parallel_config.tensor_parallel_size
 
+    def get_num_experts(self) -> int:
+        """Returns the number of experts in the model."""
+        num_expert_names = [
+            "num_experts",  # Jamba
+            "moe_num_experts",  # Dbrx
+            "n_routed_experts",  # DeepSeek
+            "num_local_experts",  # Mixtral
+        ]
+        num_experts = getattr_iter(self.hf_text_config, num_expert_names, 0)
+        if isinstance(num_experts, list):
+            # Ernie VL's remote code uses list[int]...
+            # The values are always the same so we just take the first one.
+            return num_experts[0]
+        return num_experts
+
     def get_layers_start_end_indices(
             self, parallel_config: ParallelConfig) -> tuple[int, int]:
         from vllm.distributed.utils import get_pp_indices

vllm/model_executor/layers/fused_moe/layer.py

@@ -960,6 +960,7 @@ class FusedMoE(CustomOp):
         is_sequence_parallel=False,
         zero_expert_num: Optional[int] = 0,
         zero_expert_type: Optional[str] = None,
+        expert_mapping: Optional[list[tuple[str, str, int, str]]] = None,
     ):
         super().__init__()
         if params_dtype is None:
@@ -996,6 +997,9 @@ class FusedMoE(CustomOp):
         self.zero_expert_num = zero_expert_num
         self.zero_expert_type = zero_expert_type
 
+        # Expert mapping used in self.load_weights
+        self.expert_mapping = expert_mapping
+
         # Round up hidden size if needed.
         hidden_size = maybe_roundup_hidden_size(hidden_size, moe_in_dtype,
                                                 quant_config,
@@ -1617,6 +1621,33 @@ class FusedMoE(CustomOp):
 
         return False if return_success else None
 
+    def load_weights(
+            self, weights: Iterable[tuple[str,
+                                          torch.Tensor]]) -> Iterable[str]:
+        if (expert_mapping := self.expert_mapping) is None:
+            raise ValueError("`self.expert_mapping` must be provided to "
+                             "load weights using `self.load_weights`.")
+        for expert_name, loaded_weight in weights:
+            qual_name = f"{self.layer_name}.{expert_name}"
+            for param_name, weight_name, expert_id, shard_id in expert_mapping:
+                if weight_name not in qual_name:
+                    continue
+                weight_name = qual_name.replace(weight_name, param_name)
+                param_name = weight_name.removeprefix(f"{self.layer_name}.")
+                param = getattr(self, param_name)
+                success = self.weight_loader(
+                    param=param,
+                    loaded_weight=loaded_weight,
+                    weight_name=weight_name,
+                    shard_id=shard_id,
+                    expert_id=expert_id,
+                    return_success=True,
+                )
+                if success:
+                    logger.debug("Loaded %s for expert %d into %s", param_name,
+                                 expert_id, self.layer_name)
+                    yield param_name
+
     def get_expert_weights(self) -> Iterable[torch.Tensor]:
         weights = list(self.named_parameters())
         assert all(weight.is_contiguous() for _, weight in weights)

vllm/model_executor/models/registry.py

@@ -307,10 +307,14 @@ _TRANSFORMERS_SUPPORTED_MODELS = {
 }
 
 _TRANSFORMERS_BACKEND_MODELS = {
-    "TransformersEmbeddingModel": ("transformers_pooling", "TransformersEmbeddingModel"),  # noqa: E501
-    "TransformersForSequenceClassification": ("transformers_pooling", "TransformersForSequenceClassification"),  # noqa: E501
     "TransformersForCausalLM": ("transformers", "TransformersForCausalLM"),
     "TransformersForMultimodalLM": ("transformers", "TransformersForMultimodalLM"),  # noqa: E501
+    "TransformersMoEForCausalLM": ("transformers_moe", "TransformersMoEForCausalLM"),  # noqa: E501
+    "TransformersMoEForMultimodalLM": ("transformers_moe", "TransformersMoEForMultimodalLM"),  # noqa: E501
+    "TransformersEmbeddingModel": ("transformers_pooling", "TransformersEmbeddingModel"),  # noqa: E501
+    "TransformersForSequenceClassification": ("transformers_pooling", "TransformersForSequenceClassification"),  # noqa: E501
+    "TransformersMoEForSequenceClassification": ("transformers_pooling", "TransformersMoEForSequenceClassification"),  # noqa: E501
+    "TransformersMoEEmbeddingModel": ("transformers_pooling", "TransformersMoEEmbeddingModel"),  # noqa: E501
 }
 # yapf: enable
 

vllm/model_executor/models/transformers.py

@@ -22,6 +22,8 @@ from typing import Literal, Optional, Union
 
 import regex as re
 import torch
+import transformers
+from packaging.version import Version
 from torch import nn
 from transformers import (AutoModel, BatchFeature, PretrainedConfig,
                           PreTrainedModel)
@@ -35,6 +37,7 @@ from vllm.config.utils import getattr_iter
 from vllm.distributed import get_pp_group, get_tensor_model_parallel_world_size
 from vllm.distributed.utils import get_pp_indices
 from vllm.logger import init_logger
+from vllm.model_executor.layers.layernorm import RMSNorm
 from vllm.model_executor.layers.linear import (ColumnParallelLinear,
                                                ReplicatedLinear,
                                                RowParallelLinear)
@@ -121,10 +124,14 @@ def can_enable_torch_compile(vllm_config: VllmConfig) -> bool:
     return enable
 
 
+Style = Literal["colwise", "colwise_rep", "rowwise", "rowwise_rep",
+                "replicate"]
+
+
 def replace_linear_class(
     linear: nn.Linear,
-    style: Literal["colwise", "rowwise"],
-    quant_config: QuantizationConfig,
+    style: Style = "replicate",
+    quant_config: Optional[QuantizationConfig] = None,
     *,
     prefix: str = "",
 ) -> Union[ColumnParallelLinear, RowParallelLinear, ReplicatedLinear]:
@@ -132,11 +139,11 @@ def replace_linear_class(
     Replace nn.Linear with one of vLLM's tensor parallel linear classes.
 
     Args:
-        linear (nn.Linear): `nn.Linear` to be replaced.
-        style (str): Tensor parallel style of the new linear, e.g. "colwise".
-        quant_config (QuantConfig): Quantization config for the new linear.
+        linear: `nn.Linear` to be replaced.
+        style: Tensor parallel style of the new linear, e.g. "colwise".
+        quant_config: Quantization config for the new linear.
     Returns:
-        Union[ColumnParallelLinear, RowParallelLinear]: The new linear.
+        The new linear.
     """
 
     if not isinstance(style, str):
@@ -166,6 +173,31 @@ def replace_linear_class(
     )
 
 
+def replace_rms_norm_class(rms_norm: nn.Module, hidden_size: int) -> RMSNorm:
+    """Replace a Transformers RMSNorm with vLLM's RMSNorm.
+
+    This method assumes:
+    - Weight is stored as `weight`.
+    - Epsilon is stored as `eps` or `variance_epsilon`.
+    - `with_scale` indicates whether the layer has a weight (Gemma3n only).
+    - `var_hidden_size` is only ever used for Intern vision encoder in vLLM
+    and Transformers doesn't appear to have the same concept.
+    """
+    kwargs = {
+        "hidden_size": hidden_size,
+        "eps": getattr_iter(rms_norm, ("eps", "variance_epsilon"), 1e-6),
+        "has_weight": getattr(rms_norm, "with_scale", True)
+    }
+    if (weight := getattr(rms_norm, "weight", None)) is not None:
+        # If weight is a Parameter, get its data tensor
+        weight = getattr(weight, "data", weight)
+        kwargs["dtype"] = weight.dtype
+    else:
+        # No weight, fall back to weightless RMSNorm
+        kwargs["has_weight"] = False
+    return RMSNorm(**kwargs)
+
+
 # Copied from `accelerate`
 @contextmanager
 def init_on_device_without_buffers(device: torch.device):
@@ -463,9 +495,15 @@ class TransformersBase(nn.Module, SupportsQuant, SupportsLoRA, SupportsPP):
         self.ignore_unexpected_suffixes: list[str] = []
         """Ignore unexpected weights whose qualname ends with these suffixes."""
 
-        # Skip loading extra bias for GPTQ models.
-        if self.quant_config and "gptq" in self.quant_config.get_name():
-            self.ignore_unexpected_suffixes.append(".bias")
+        if self.quant_config:
+            quant_method_name = self.quant_config.get_name()
+            # Check for unsupported quantization methods.
+            if quant_method_name == "mxfp4":
+                raise NotImplementedError("Transformers backend does not "
+                                          "support MXFP4 quantization yet.")
+            # Skip loading extra bias for GPTQ models.
+            if "gptq" in quant_method_name:
+                self.ignore_unexpected_suffixes.append(".bias")
 
         # Set correct attn and init on "meta" to delay allocating GPU tensors
         # TODO: @raushan, use the public `model.set_attn_implementation()`
@@ -478,8 +516,12 @@ class TransformersBase(nn.Module, SupportsQuant, SupportsLoRA, SupportsPP):
                 trust_remote_code=self.model_config.trust_remote_code,
             )
 
+        # Remove layers not on this pipeline parallel rank
         self.pipeline_parallel()
-        self.tensor_parallel()
+        # Substitute remaining layers with vLLM's layers as needed
+        self.recursive_replace()
+        # Create attention instances for KV cache allocation
+        self.attention_instances = self.create_attention_instances()
 
         # Input embeddings
         if not isinstance(self.model.get_input_embeddings(), PPMissingLayer):
@@ -494,12 +536,10 @@ class TransformersBase(nn.Module, SupportsQuant, SupportsLoRA, SupportsPP):
                     quant_config=self.quant_config,
                 ))
 
-        # Attention layers
-        self.attention_instances = self.create_attention_instances()
-
         # Initialize any parameters that have not had their modules replaced
         self.init_parameters(self.model)
 
+        # Pipeline parallel intermediate tensors
         self.make_empty_intermediate_tensors = (
             make_empty_intermediate_tensors_factory(
                 ["hidden_states"], self.text_config.hidden_size))
@@ -558,56 +598,53 @@ class TransformersBase(nn.Module, SupportsQuant, SupportsLoRA, SupportsPP):
             if not self.pp_group.is_last_rank:
                 setattr(self.model, name, PPMissingLayer())
 
-    def tensor_parallel(self):
-        """
-        Apply the model's tensor parallelization plan.
-        Currently only supports linear layers.
-        """
-        # Look for tp plans in all of the PreTrainedModels found in self.model
-        is_pretrained_model = lambda m: isinstance(m, PreTrainedModel)
-        supports_tp_plan = lambda m: m.config.base_model_tp_plan is not None
-        pretrained_models = filter(is_pretrained_model, self.model.modules())
-        models_with_tp_plan = filter(supports_tp_plan, pretrained_models)
+    def recursive_replace(self):
+        """Recursively replace modules in the model as needed.
 
-        if not any(models_with_tp_plan) and self.tp_size > 1:
+        Currently, this replaces:
+
+        - `nn.Linear` with vLLM's tensor parallel linear classes
+        - `*RMSNorm` with vLLM's `RMSNorm`
+        """
+        tp_plan = self.model.tp_plan
+
+        if not tp_plan and self.tp_size > 1:
             tip = get_feature_request_tip(self.model_config.model,
                                           self.model_config.trust_remote_code)
             raise ValueError(
                 f"{type(self.model)} does not support tensor parallel. {tip}")
 
-        def _tensor_parallel(module: nn.Module, prefix: str, tp_plan=None):
-            tp_plan = tp_plan or {}
+        # Prefix the patterns because we always start from `self.model`
+        tp_plan = {maybe_prefix("model", k): v for k, v in tp_plan.items()}
 
-            # If the current module is a PreTrainedModel, set the tp_plan for
-            # all of its children
-            if isinstance(module, PreTrainedModel):
-                tp_plan = module.config.base_model_tp_plan or {}
-                tp_plan = {
-                    maybe_prefix(prefix, k): v
-                    for k, v in tp_plan.items()
-                }
-
-            # Some weight loaders expect linear layers to inherit from vLLM's
-            # LinearBase class, so we set a default style which causes any
-            # unspecified linear layers to be replaced with ReplicatedLinear
+        def _recursive_replace(module: nn.Module, prefix: str):
             for child_name, child_module in module.named_children():
+                new_module = child_module
                 qual_name = maybe_prefix(prefix, child_name)
                 if isinstance(child_module, nn.Linear):
                     generator = (p for p in tp_plan if re.match(p, qual_name))
                     pattern = next(generator, None)
+                    # Some weight loaders expect all linear layers to inherit
+                    # LinearBase, so we set a default style which causes any
+                    # unspecified layers to be replaced with ReplicatedLinear
                     style = tp_plan.get(pattern, "replicate")
                     new_module = replace_linear_class(child_module,
                                                       style,
                                                       self.quant_config,
                                                       prefix=qual_name)
+                # TODO(hmellor): Enable RMSNorm replacement once we have a way
+                # to choose RMSNorm vs GemmaRMSNorm
+                # elif child_module.__class__.__name__.endswith("RMSNorm"):
+                #     new_module = replace_rms_norm_class(
+                #         child_module, self.config.hidden_size)
+                else:
+                    _recursive_replace(child_module, prefix=qual_name)
+
+                if new_module is not child_module:
                     setattr(module, child_name, new_module)
                     log_replacement(qual_name, child_module, new_module)
-                else:
-                    _tensor_parallel(child_module,
-                                     prefix=qual_name,
-                                     tp_plan=tp_plan)
 
-        _tensor_parallel(self.model, prefix="model")
+        _recursive_replace(self.model, prefix="model")
 
     def create_attention_instances(
         self,
@@ -657,15 +694,21 @@ class TransformersBase(nn.Module, SupportsQuant, SupportsLoRA, SupportsPP):
             self.model: PreTrainedModel = AutoModel.from_config(...)
         ```
         """
-        for name, param in module.named_parameters(recurse=False):
-            if param.device == torch.device("meta"):
-                new_param = nn.Parameter(
-                    torch.empty_like(param.data,
-                                     dtype=dtype or self.model_config.dtype,
-                                     device=self.device_config.device))
-                setattr(module, name, new_param)
-        for child in module.children():
-            self.init_parameters(child, dtype)
+
+        def _init_parameters(module: nn.Module, dtype: Optional[torch.dtype]):
+            for name, param in module.named_parameters(recurse=False):
+                if param.device == torch.device("meta"):
+                    new_param = nn.Parameter(
+                        torch.empty_like(
+                            param.data,
+                            dtype=dtype or self.model_config.dtype,
+                            device=self.device_config.device,
+                        ))
+                    setattr(module, name, new_param)
+            for child in module.children():
+                _init_parameters(child, dtype)
+
+        _init_parameters(module, dtype)
 
     def forward(
         self,
@@ -702,8 +745,10 @@ class TransformersBase(nn.Module, SupportsQuant, SupportsLoRA, SupportsPP):
 
         return hidden_states
 
-    def load_weights(self, weights: Iterable[tuple[str,
-                                                   torch.Tensor]]) -> set[str]:
+    def load_weights(
+        self,
+        weights: Iterable[tuple[str, torch.Tensor]],
+    ) -> set[str]:
         loader = AutoWeightsLoader(
             self,
             skip_prefixes=self.skip_prefixes,
@@ -713,6 +758,14 @@ class TransformersBase(nn.Module, SupportsQuant, SupportsLoRA, SupportsPP):
         )
         return loader.load_weights(weights, mapper=self.hf_to_vllm_mapper)
 
+    def check_version(self, min_version: str, feature: str):
+        installed = Version(transformers.__version__)
+        required = Version(min_version)
+        if installed < required:
+            raise ImportError(
+                f"Transformers backend requires transformers>={required} "
+                f"for {feature}, but got {installed}")
+
 
 @support_torch_compile(enable_if=can_enable_torch_compile)
 class TransformersForCausalLM(TransformersBase):

vllm/model_executor/models/transformers_moe.py

@@ -0,0 +1,268 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+# Copyright 2024 The vLLM team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Wrapper around `transformers` MoE models."""
+from typing import Any
+
+import torch
+import torch.nn as nn
+
+from vllm.compilation.decorators import support_torch_compile
+from vllm.config.utils import getattr_iter
+from vllm.forward_context import ForwardContext, get_forward_context
+from vllm.model_executor.custom_op import CustomOp
+from vllm.model_executor.layers.fused_moe import FusedMoE
+from vllm.platforms import current_platform
+from vllm.utils import direct_register_custom_op
+
+from .transformers import (TransformersBase, TransformersForCausalLM,
+                           TransformersForMultimodalLM,
+                           can_enable_torch_compile, log_replacement)
+from .utils import maybe_prefix
+
+
+@CustomOp.register("transformers_fused_moe")
+class TransformersFusedMoE(FusedMoE):
+    """Custom FusedMoE for the Transformers backend."""
+
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        self._top_k_index: torch.Tensor = None
+
+        def custom_routing_function(hidden_states, gating_output, topk,
+                                    renormalize):
+            """Return `top_k_weights` from `gating_output` and the
+            `top_k_index` we stored in the layer earlier."""
+            return gating_output, self._top_k_index
+
+        self.custom_routing_function = custom_routing_function
+
+    def forward(self, hidden_states: torch.Tensor, top_k_index: torch.Tensor,
+                top_k_weights: torch.Tensor, **kwargs: Any) -> torch.Tensor:
+        """In Transformers `experts.forward` will have this signature.
+
+        We discard any extra kwargs because we cannot use them here."""
+        return torch.ops.vllm.transformers_moe_forward(hidden_states,
+                                                       top_k_index,
+                                                       top_k_weights,
+                                                       self.layer_name)
+
+
+def transformers_moe_forward(hidden_states: torch.Tensor,
+                             top_k_index: torch.Tensor,
+                             top_k_weights: torch.Tensor,
+                             layer_name: str) -> torch.Tensor:
+    """Store the `top_k_index` in the layer and call the actual forward."""
+    forward_context: ForwardContext = get_forward_context()
+    self = forward_context.no_compile_layers[layer_name]
+    self._top_k_index = top_k_index
+    # Clone hidden_states because it will be mutated in-place in FusedMoE
+    return self.forward_impl(hidden_states.clone(), top_k_weights)
+
+
+def transformers_moe_forward_fake(hidden_states: torch.Tensor,
+                                  top_k_index: torch.Tensor,
+                                  top_k_weights: torch.Tensor,
+                                  layer_name: str) -> torch.Tensor:
+    return torch.empty_like(hidden_states)
+
+
+direct_register_custom_op(
+    op_name="transformers_moe_forward",
+    op_func=transformers_moe_forward,
+    mutates_args=["hidden_states"],
+    fake_impl=transformers_moe_forward_fake,
+    dispatch_key=current_platform.dispatch_key,
+    tags=(torch.Tag.needs_fixed_stride_order, ),
+)
+
+
+class TransformersMoEBase(TransformersBase):
+
+    def __init__(self, *, vllm_config, prefix=""):
+        self.check_version("4.57.0.dev0", "MoE models support")
+        super().__init__(vllm_config=vllm_config, prefix=prefix)
+
+        if self.parallel_config.enable_expert_parallel:
+            raise NotImplementedError(
+                "Transformers backend does not support expert parallel yet.")
+        if self.parallel_config.enable_eplb:
+            raise NotImplementedError(
+                "Transformers backend does not support expert parallel load "
+                "balancing yet.")
+
+    def get_expert_mapping(self) -> list[tuple[str, str, int, str]]:
+        """
+        Params for weights, fp8 weight scales, fp8 activation scales
+        (param_name, weight_name, expert_id, shard_id)
+        """
+        ckpt_names = [
+            # (ckpt_gate_proj_name, ckpt_down_proj_name, ckpt_up_proj_name)
+            ("gate_proj", "down_proj", "up_proj"),  # Most common MoE style
+            ("w1", "w2", "w3"),  # Granite, Mixtral, Phi MoE style
+            ("linear", "linear_1", "linear_v"),  # Grok1 style
+        ]
+        expert_mapping = []
+        for gate_proj, down_proj, up_proj in ckpt_names:
+            expert_mapping.extend(
+                FusedMoE.make_expert_params_mapping(
+                    ckpt_gate_proj_name=gate_proj,
+                    ckpt_down_proj_name=down_proj,
+                    ckpt_up_proj_name=up_proj,
+                    num_experts=self.model_config.get_num_experts(),
+                    num_redundant_experts=0,  # TODO: enable EPLB
+                ))
+        return expert_mapping
+
+    def recursive_replace(self):
+        """Initialize the MoE layers."""
+        text_config = self.text_config
+
+        # Positional arguments
+        num_experts = self.model_config.get_num_experts()
+        top_k = getattr_iter(text_config, ["num_experts_per_tok", "top_k"],
+                             None)
+        assert top_k is not None
+        hidden_size = text_config.hidden_size
+        intermediate_size = getattr_iter(
+            text_config, ["moe_intermediate_size", "intermediate_size"], None)
+        assert intermediate_size is not None
+
+        # If there are shared experts, the results are
+        # reduced after mlp.forward() not inside FusedMoE
+        num_experts_shared = getattr_iter(text_config, [
+            "num_experts_shared", "n_shared_experts", "moe_num_shared_experts"
+        ], 0)
+        reduce_results = num_experts_shared == 0
+
+        def add_all_reduce(mlp: nn.Module):
+            """Adds an all-reduce to the output of `mlp.forward()`."""
+
+            class MLPWithAllReduce(mlp.__class__):
+
+                def forward(self, *args, **kwargs):
+                    output = super().forward(*args, **kwargs)
+                    return self.experts.maybe_all_reduce_tensor_model_parallel(
+                        output)
+
+            mlp.__class__ = MLPWithAllReduce
+
+        # Unused kwargs since we use custom_routing_function:
+        # - `scoring_func` and `e_score_correction_bias` only used for grouped
+        #    topk routing inside vLLM and are non-trivial to infer
+        #    and hard code `use_grouped_topk=False`
+        # - `renormalize` passed anyway because it's easy to infer
+        # - `num_expert_group` and `topk_group` used for inferring expert
+        #    placement strategy in FusedMoE
+        # - `apply_router_weight_on_input` is already applied in Transformers
+        renormalize = getattr(text_config, "norm_topk_prob", top_k > 1)
+        num_expert_group = getattr(text_config, "n_group", None)
+        topk_group = getattr(text_config, "topk_group", None)
+
+        # MoE activation function
+        activation = "silu"
+        wrapped_arch = self.config.architectures[0].lower()
+        if "gptoss" in wrapped_arch:
+            activation = "swigluoai"
+        elif "grok1" in wrapped_arch:
+            activation = "gelu"
+
+        # Expert mapping for `AutoWeightsLoader`
+        expert_mapping = self.get_expert_mapping()
+
+        # Configs
+        parallel_config = self.parallel_config
+        eplb_config = parallel_config.eplb_config
+
+        # Expert parallel load balancing kwargs
+        enable_eplb = parallel_config.enable_eplb
+        num_redundant_experts = eplb_config.num_redundant_experts
+
+        # Recursively fuse MoE layers
+        def _recursive_replace(module: nn.Module, prefix: str):
+            for child_name, child_module in module.named_children():
+                qual_name = maybe_prefix(prefix, child_name)
+                if (child_name == "experts"
+                        and isinstance(child_module, nn.ModuleList)):
+                    # Alias for readability
+                    mlp = module
+                    experts = child_module
+                    # Do the experts have biases
+                    has_bias = False
+                    for experts_param_name, _ in experts.named_parameters():
+                        if "bias" in experts_param_name:
+                            has_bias = True
+                            break
+                    # Double check there are no shared experts
+                    nonlocal reduce_results
+                    if reduce_results:
+                        for mlp_param_name, _ in mlp.named_parameters():
+                            if "shared_expert" in mlp_param_name:
+                                reduce_results = False
+                                break
+                    # Replace experts module with FusedMoE
+                    fused_experts = TransformersFusedMoE(
+                        num_experts=num_experts,
+                        top_k=top_k,
+                        hidden_size=hidden_size,
+                        intermediate_size=intermediate_size,
+                        reduce_results=reduce_results,
+                        renormalize=renormalize,
+                        # Hard coded because topk happens in Transformers
+                        use_grouped_topk=False,
+                        num_expert_group=num_expert_group,
+                        topk_group=topk_group,
+                        quant_config=self.quant_config,
+                        prefix=qual_name,
+                        activation=activation,
+                        enable_eplb=enable_eplb,
+                        num_redundant_experts=num_redundant_experts,
+                        has_bias=has_bias,
+                        expert_mapping=expert_mapping,
+                    )
+                    mlp.experts = fused_experts
+                    log_replacement(qual_name, experts, fused_experts)
+                    # If results are not all-reduced in FusedMoE, ensure they
+                    # are all-reduced at the end of mlp.forward() if tensor
+                    # parallel or expert parallel is enabled
+                    if not reduce_results and (fused_experts.tp_size > 1
+                                               or fused_experts.ep_size > 1):
+                        add_all_reduce(mlp)
+                else:
+                    _recursive_replace(child_module, prefix=qual_name)
+
+        _recursive_replace(self.model, prefix="model")
+        # Continue with the replacement of layers in TransformersBase
+        super().recursive_replace()
+
+
+@support_torch_compile(enable_if=can_enable_torch_compile)
+class TransformersMoEForCausalLM(TransformersMoEBase, TransformersForCausalLM):
+    pass
+
+
+@support_torch_compile(
+    # set `positions` to last dim to support Qwen-mrope
+    dynamic_arg_dims={
+        "input_ids": 0,
+        "positions": -1,
+        "intermediate_tensors": 0,
+        "inputs_embeds": 0,
+    },
+    enable_if=can_enable_torch_compile)
+class TransformersMoEForMultimodalLM(TransformersMoEForCausalLM,
+                                     TransformersForMultimodalLM):
+    pass

vllm/model_executor/models/transformers_pooling.py

@@ -20,7 +20,7 @@ from typing import Optional, Union
 import torch
 from transformers import AutoModelForSequenceClassification
 
-from vllm.attention import AttentionType
+from vllm.attention import Attention, AttentionType
 from vllm.compilation.decorators import support_torch_compile
 from vllm.config import VllmConfig
 from vllm.model_executor.layers.pooler import (ClassifierPooler, CLSPool,
@@ -29,6 +29,7 @@ from vllm.sequence import IntermediateTensors
 
 from .interfaces_base import VllmModelForPooling
 from .transformers import TransformersBase, can_enable_torch_compile
+from .transformers_moe import TransformersMoEBase
 from .utils import WeightsMapper
 
 
@@ -79,7 +80,9 @@ class TransformersPoolingBase(TransformersBase, VllmModelForPooling):
         self.padding_idx = self.text_config.pad_token_id
 
     def create_attention_instances(
-            self, attn_type: AttentionType = AttentionType.DECODER):
+        self,
+        attn_type: AttentionType = AttentionType.DECODER
+    ) -> dict[int, Attention]:
         # TODO(hmellor): Better way to detect encoder models
         # In encoder models, the attention layers will have `is_causal=False`
         is_encoder = lambda m: not getattr(m, "is_causal", True)
@@ -90,14 +93,7 @@ class TransformersPoolingBase(TransformersBase, VllmModelForPooling):
 
         # Check minimum transformers version for encoder models support
         if attn_type == AttentionType.ENCODER_ONLY:
-            import transformers
-            from packaging.version import Version
-            installed = Version(transformers.__version__)
-            required = Version("4.57.0.dev0")
-            if installed < required:
-                raise ValueError(
-                    "Encoder models with the Transformers backend require "
-                    f"transformers>={required}, but got {installed}")
+            self.check_version("4.57.0.dev0", "encoder models support")
 
         return super().create_attention_instances(attn_type)
 
@@ -198,3 +194,15 @@ class TransformersForSequenceClassification(TransformersPoolingBase):
                     vllm_config.model_config),
             ),
         })
+
+
+@support_torch_compile(enable_if=can_enable_torch_compile)
+class TransformersMoEEmbeddingModel(TransformersMoEBase,
+                                    TransformersEmbeddingModel):
+    pass
+
+
+@support_torch_compile(enable_if=can_enable_torch_compile)
+class TransformersMoEForSequenceClassification(
+        TransformersMoEBase, TransformersForSequenceClassification):
+    pass