DP/EP Support for gpt-oss with deepep-ht comm kernel on SM100

vllm/distributed/device_communicators/base_device_communicator.py

@@ -255,7 +255,7 @@ def prepare_communication_buffer_for_model(self,
             if module.__class__.__name__ == "FusedMoE"
         ]
         for module in moe_modules:
-            module.quant_method.init_prepare_finalize()
+            module.quant_method.init_prepare_finalize(module)
 
     def dispatch(
             self, hidden_states: torch.Tensor,

vllm/model_executor/layers/fused_moe/config.py

@@ -450,6 +450,12 @@ def make(
             if quant_dtype is None and isinstance(quant_config, Fp8Config):
                 quant_dtype = torch.float8_e4m3fn
 
+            from vllm.model_executor.layers.quantization.mxfp4 import (
+                Mxfp4Config)
+            if (quant_dtype is None and isinstance(quant_config, Mxfp4Config)
+                    and envs.VLLM_USE_FLASHINFER_MOE_MXFP4_MXFP8):
+                quant_dtype = "mxfp8"
+
             from vllm.model_executor.layers.quantization.modelopt import (
                 ModelOptNvFp4Config)
             if quant_dtype is None and isinstance(quant_config,

vllm/model_executor/layers/fused_moe/layer.py

@@ -200,7 +200,7 @@ def maybe_make_prepare_finalize(
 
     # Note: init_prepare_finalize should only be called by
     # prepare_communication_buffer_for_model.
-    def init_prepare_finalize(self):
+    def init_prepare_finalize(self, layer: torch.nn.Module):
         assert self.moe is not None
         prepare_finalize = self.maybe_make_prepare_finalize(self.moe)
 
@@ -211,7 +211,7 @@ def init_prepare_finalize(self):
             assert self.fused_experts is None, \
                 f"Attempt to override experts for {id(self)}!"
             self.topk_indices_dtype = prepare_finalize.topk_indices_dtype()
-            experts = self.select_gemm_impl(prepare_finalize, self.moe)
+            experts = self.select_gemm_impl(prepare_finalize, self.moe, layer)
             self.fused_experts = FusedMoEModularKernel(
                 prepare_finalize,
                 experts,
@@ -221,6 +221,7 @@ def select_gemm_impl(
         self,
         prepare_finalize: FusedMoEPrepareAndFinalize,
         moe: FusedMoEConfig,
+        layer: torch.nn.Module,
     ) -> FusedMoEPermuteExpertsUnpermute:
         # based on the all2all implementation, select the appropriate
         # gemm implementation
@@ -273,6 +274,7 @@ def select_gemm_impl(
         prepare_finalize: FusedMoEPrepareAndFinalize,
         # TODO(bnell): Remove. Every layer should have an moe config object.
         moe: FusedMoEConfig,
+        layer: torch.nn.Module,
     ) -> FusedMoEPermuteExpertsUnpermute:
         if (prepare_finalize.activation_format ==
                 FusedMoEActivationFormat.BatchedExperts):

vllm/model_executor/layers/fused_moe/trtllm_moe.py

@@ -0,0 +1,197 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+from typing import Optional
+
+import torch
+
+import vllm.model_executor.layers.fused_moe.modular_kernel as mk
+from vllm.model_executor.layers.fused_moe.config import FusedMoEConfig
+from vllm.model_executor.layers.fused_moe.topk_weight_and_reduce import (
+    TopKWeightAndReduceNoOP)
+from vllm.utils import next_power_of_2
+
+
+class TrtLlmGenExperts(mk.FusedMoEPermuteExpertsUnpermute):
+
+    def __init__(
+        self,
+        moe: FusedMoEConfig,
+        gemm1_alpha,
+        gemm1_beta,
+        gemm1_clamp_limit,
+        w13_bias,
+        w2_bias,
+        max_capture_size,
+    ):
+        super().__init__(moe.quant_config)
+        self.moe = moe
+        self.gemm1_alpha = gemm1_alpha
+        self.gemm1_beta = gemm1_beta
+        self.gemm1_clamp_limit = gemm1_clamp_limit
+        self.w13_bias = w13_bias
+        self.w2_bias = w2_bias
+        self.max_capture_size = max_capture_size
+
+    @property
+    def activation_formats(
+        self
+    ) -> tuple[mk.FusedMoEActivationFormat, mk.FusedMoEActivationFormat]:
+        return (mk.FusedMoEActivationFormat.Standard,
+                mk.FusedMoEActivationFormat.Standard)
+
+    def supports_chunking(self) -> bool:
+        return True
+
+    def supports_expert_map(self) -> bool:
+        return True
+
+    def finalize_weight_and_reduce_impl(self) -> mk.TopKWeightAndReduce:
+        return TopKWeightAndReduceNoOP()
+
+    def workspace_shapes(
+        self,
+        a: torch.Tensor,
+        aq: torch.Tensor,
+        M: int,
+        N: int,
+        K: int,
+        topk: int,
+        global_num_experts: int,
+        local_num_experts: int,
+        expert_tokens_meta: Optional[mk.ExpertTokensMetadata],
+    ) -> tuple[tuple[int, ...], tuple[int, ...], tuple[int, ...], torch.dtype]:
+        # The workspaces for this implementation are managed by flashinfer.
+        # TODO(varun) : workspace1 is could be used as the output tensor. This
+        # is error-prone. Allow the `workspace_shapes` to return None workspaces
+        workspace1 = (M, K)
+        workspace2 = (0, 0)
+        output = (M, K)
+        return (workspace1, workspace2, output, a.dtype)
+
+    def _get_tile_tokens_dim(self, x: torch.Tensor, top_k: int,
+                             local_num_experts: int):
+        # Number of tokens in the input tensor.
+        num_tokens = x.shape[0]
+        # Factor to account for the imbalance of the experts.
+        # factor equals to the
+        # max_real_num_tokens_per_expert / perfect_num_tokens_per_expert
+        # 1.0 means perfect expert distribution.
+        # > 1.0 means some experts have more tokens than the perfect
+        # distribution.
+        # < 1.0 does not make sense.
+        imbalance_factor = 1.3
+        # Calculate the number of tokens per expert assuming perfect
+        # distribution.
+        num_tokens_per_expert = (num_tokens * top_k) // local_num_experts
+        # Apply the imbalance factor.
+        num_tokens_per_expert = int(num_tokens_per_expert * imbalance_factor)
+        # And pad the number to the next power of 2.
+        tile_tokens_dim = next_power_of_2(num_tokens_per_expert)
+        # Cap to 8-64 tokens per CTA tile as it's the range supported by the
+        #  kernel.
+        tile_tokens_dim = min(max(tile_tokens_dim, 8), 64)
+
+        return tile_tokens_dim
+
+    def apply(
+        self,
+        output: torch.Tensor,
+        hidden_states: torch.Tensor,
+        w1: torch.Tensor,
+        w2: torch.Tensor,
+        topk_weights: torch.Tensor,
+        topk_ids: torch.Tensor,
+        activation: str,
+        global_num_experts: int,
+        expert_map: Optional[torch.Tensor],
+        w1_scale: Optional[torch.Tensor],
+        w2_scale: Optional[torch.Tensor],
+        w1_zp: Optional[torch.Tensor],
+        w2_zp: Optional[torch.Tensor],
+        a1q_scale: Optional[torch.Tensor],
+        a2_scale: Optional[torch.Tensor],
+        workspace13: torch.Tensor,
+        workspace2: torch.Tensor,
+        expert_tokens_meta: Optional[mk.ExpertTokensMetadata],
+        apply_router_weight_on_input: bool,
+    ):
+        topk = topk_ids.size(-1)
+        local_num_experts = w1.size(0)
+        intermediate_size = w2.size(1)
+        local_expert_offset = self.moe.ep_rank * local_num_experts
+
+        x_quant = hidden_states
+        x_scale = a1q_scale
+        if x_scale is not None:
+            x_scale = x_scale.view(torch.float8_e4m3fn).reshape(
+                *x_quant.shape[:-1], -1)
+
+        packed_tensor = (topk_ids.to(torch.int32) << 16) | topk_weights.to(
+            torch.bfloat16).view(torch.int16)
+
+        assert w1_scale is not None
+        assert w2_scale is not None
+        kwargs = {
+            "topk_ids":
+            packed_tensor,
+            "routing_bias":
+            None,
+            "hidden_states":
+            x_quant,
+            "hidden_states_scale":
+            x_scale,
+            "gemm1_weights":
+            w1,
+            "gemm1_weights_scale":
+            w1_scale,
+            "gemm1_bias":
+            self.w13_bias,
+            "gemm1_alpha":
+            self.gemm1_alpha,
+            "gemm1_beta":
+            self.gemm1_beta,
+            "gemm1_clamp_limit":
+            self.gemm1_clamp_limit,
+            "gemm2_weights":
+            w2,
+            "gemm2_weights_scale":
+            w2_scale,
+            "gemm2_bias":
+            self.w2_bias,
+            "output1_scale_scalar":
+            None,
+            "output1_scale_gate_scalar":
+            None,
+            "output2_scale_scalar":
+            None,
+            "num_experts":
+            global_num_experts,
+            "top_k":
+            topk,
+            "n_group":
+            None,
+            "topk_group":
+            None,
+            "intermediate_size":
+            intermediate_size,
+            "local_expert_offset":
+            local_expert_offset,
+            "local_num_experts":
+            local_num_experts,
+            "routed_scaling_factor":
+            None,
+            "tile_tokens_dim":
+            self._get_tile_tokens_dim(x_quant, topk, local_num_experts),
+            "routing_method_type":
+            1,
+            "do_finalize":
+            True,
+            "output":
+            output,
+            "tune_max_num_tokens":
+            self.max_capture_size,
+        }
+
+        from flashinfer import trtllm_fp4_block_scale_routed_moe
+        trtllm_fp4_block_scale_routed_moe(**kwargs)
+        return output

vllm/model_executor/layers/fused_moe/utils.py

@@ -12,6 +12,8 @@
     per_token_group_quant_int8, per_token_quant_int8)
 from vllm.model_executor.layers.quantization.utils.mxfp4_utils import (
     quant_dequant_mxfp4)
+from vllm.model_executor.layers.quantization.utils.mxfp8_utils import (
+    mxfp8_quantize)
 from vllm.platforms import current_platform
 from vllm.triton_utils import tl, triton
 from vllm.utils import cdiv
@@ -177,6 +179,18 @@ def _mxfp4_quantize(
     return A, None
 
 
+def _mxfp8_quantize(
+    A: torch.Tensor,
+    A_scale: Optional[torch.Tensor],
+    per_act_token_quant: bool,
+    block_shape: Optional[list[int]] = None,
+) -> tuple[torch.Tensor, torch.Tensor]:
+    assert A_scale is None
+    assert not per_act_token_quant
+    assert block_shape is None
+    return mxfp8_quantize(A)
+
+
 def moe_kernel_quantize_input(
     A: torch.Tensor,
     A_scale: Optional[torch.Tensor],
@@ -195,6 +209,8 @@ def moe_kernel_quantize_input(
                              is_sf_swizzled_layout=is_fp4_scale_swizzled)
     elif quant_dtype == "mxfp4":
         return _mxfp4_quantize(A, A_scale, per_act_token_quant, block_shape)
+    elif quant_dtype == "mxfp8":
+        return _mxfp8_quantize(A, A_scale, per_act_token_quant, block_shape)
     else:
         return A, A_scale
 

vllm/model_executor/layers/quantization/compressed_tensors/compressed_tensors_moe.py

@@ -3,7 +3,7 @@
 
 import enum
 from enum import Enum
-from typing import Callable, Optional
+from typing import Any, Callable, Optional
 
 import torch
 from compressed_tensors import CompressionFormat
@@ -292,6 +292,7 @@ def select_gemm_impl(
         self,
         prepare_finalize: mk.FusedMoEPrepareAndFinalize,
         moe: FusedMoEConfig,
+        layer: Any,
     ) -> mk.FusedMoEPermuteExpertsUnpermute:
         """Return the appropriate GEMM experts implementation."""
         experts = select_nvfp4_gemm_impl(
@@ -688,11 +689,9 @@ def process_weights_after_loading(self, layer: torch.nn.Module) -> None:
                 device=device,
                 dtype=torch.int64)
 
-    def select_gemm_impl(
-        self,
-        prepare_finalize: FusedMoEPrepareAndFinalize,
-        moe: FusedMoEConfig,
-    ) -> FusedMoEPermuteExpertsUnpermute:
+    def select_gemm_impl(self, prepare_finalize: FusedMoEPrepareAndFinalize,
+                         moe: FusedMoEConfig,
+                         layer: Any) -> FusedMoEPermuteExpertsUnpermute:
         # cutlass path
         if self.use_cutlass:
             from vllm.model_executor.layers.fused_moe import (

vllm/model_executor/layers/quantization/fp8.py

@@ -897,6 +897,7 @@ def select_gemm_impl(
         self,
         prepare_finalize: FusedMoEPrepareAndFinalize,
         moe: FusedMoEConfig,
+        layer: Any,
     ) -> FusedMoEPermuteExpertsUnpermute:
         from vllm.model_executor.layers.fused_moe import (
             BatchedTritonOrDeepGemmExperts, TritonOrDeepGemmExperts)

vllm/model_executor/layers/quantization/modelopt.py

@@ -311,6 +311,7 @@ def select_gemm_impl(
         self,
         prepare_finalize: mk.FusedMoEPrepareAndFinalize,
         moe: FusedMoEConfig,
+        layer: Any,
     ) -> mk.FusedMoEPermuteExpertsUnpermute:
         experts = select_cutlass_fp8_gemm_impl(
             moe,
@@ -1034,6 +1035,7 @@ def select_gemm_impl(
         self,
         prepare_finalize: mk.FusedMoEPrepareAndFinalize,
         moe: FusedMoEConfig,
+        layer: Any,
     ) -> mk.FusedMoEPermuteExpertsUnpermute:
         experts = select_nvfp4_gemm_impl(
             moe,