[DSV4] Add BF16 and MXFP8 A2A support for flashinfer a2a one sided

docs/design/moe_kernel_features.md

@@ -36,7 +36,7 @@ th {
 | deepep_high_throughput | standard | fp8 | G(128),A,T<sup>2</sup> | Y | Y | [`DeepEPHTPrepareAndFinalize`][vllm.model_executor.layers.fused_moe.prepare_finalize.deepep_ht.DeepEPHTPrepareAndFinalize] |
 | deepep_low_latency | batched | fp8 | G(128),A,T<sup>3</sup> | Y | Y | [`DeepEPLLPrepareAndFinalize`][vllm.model_executor.layers.fused_moe.prepare_finalize.deepep_ll.DeepEPLLPrepareAndFinalize] |
 | flashinfer_nvlink_two_sided | standard | nvfp4,fp8 | G,A,T | N | N | [`FlashInferNVLinkTwoSidedPrepareAndFinalize`][vllm.model_executor.layers.fused_moe.prepare_finalize.flashinfer_nvlink_two_sided.FlashInferNVLinkTwoSidedPrepareAndFinalize] |
-| flashinfer_nvlink_one_sided | standard | nvfp4 | G,A,T | N | N | [`FlashInferNVLinkOneSidedPrepareAndFinalize`][vllm.model_executor.layers.fused_moe.prepare_finalize.flashinfer_nvlink_one_sided.FlashInferNVLinkOneSidedPrepareAndFinalize] |
+| flashinfer_nvlink_one_sided | standard | nvfp4,bf16,mxfp8 | G,A,T | N | N | [`FlashInferNVLinkOneSidedPrepareAndFinalize`][vllm.model_executor.layers.fused_moe.prepare_finalize.flashinfer_nvlink_one_sided.FlashInferNVLinkOneSidedPrepareAndFinalize] |
 
 !!! info "Table key"
     1. All types: mxfp4, nvfp4, int4, int8, fp8

vllm/distributed/device_communicators/all2all.py

@@ -577,6 +577,8 @@ def initialize(
         top_k: int,
         num_experts: int,
         hidden_size: int,
+        dispatch_dtype_bytes_per_elem: int = 0,
+        dispatch_scale_bytes_per_token: int = 0,
     ):
         """Initialize the MoeAlltoAll workspace."""
         if self.initialized:
@@ -607,9 +609,13 @@ def initialize(
         ep_config = MnnvlConfig(
             comm_backend=CustomCommunicator(self.cpu_group),
         )
+        if dispatch_dtype_bytes_per_elem == 0:
+            hidden_bytes = hidden_size // 2
+        else:
+            hidden_bytes = hidden_size * dispatch_dtype_bytes_per_elem
         total_dispatch_payload_size_per_token = (
-            hidden_size // 2  # nvfp4 hidden states
-            + hidden_size // 16  # fp8 scaling factors
+            hidden_bytes
+            + dispatch_scale_bytes_per_token
             + top_k * 4  # int32 topks ids
             + top_k * 4  # float32 topk weights
         )

vllm/model_executor/layers/fused_moe/all2all_utils.py

@@ -228,23 +228,37 @@ def maybe_make_prepare_finalize(
 
     elif moe.use_fi_nvl_one_sided_kernels:
         assert quant_config is not None
-        if quant_config.quant_dtype != "nvfp4":
-            raise ValueError(
-                "The 'flashinfer_nvlink_one_sided' all2all backend only "
-                "supports nvfp4 activation quantization, but got "
-                f"quant_dtype={quant_config.quant_dtype!r}. Use a different "
-                "all2all backend (e.g. 'flashinfer_nvlink_two_sided' or "
-                "'allgather_reducescatter') for non-nvfp4 models."
-            )
         max_num_tokens = (
             get_current_vllm_config().scheduler_config.max_num_batched_tokens
         )
+        if quant_config.quant_dtype is None:
+            dispatch_dtype_bytes_per_elem = 2
+            dispatch_scale_bytes_per_token = 0
+        elif quant_config.quant_dtype == "nvfp4":
+            dispatch_dtype_bytes_per_elem = 0
+            dispatch_scale_bytes_per_token = moe.hidden_dim // 16
+        elif quant_config.quant_dtype == "mxfp8":
+            dispatch_dtype_bytes_per_elem = 1
+            align = quant_config.mx_alignment
+            if align > 0:
+                padded_k = ((moe.hidden_dim + align - 1) // align) * align
+            else:
+                padded_k = moe.hidden_dim
+            dispatch_scale_bytes_per_token = padded_k // 32
+        else:
+            raise NotImplementedError(
+                "flashinfer_nvlink_one_sided dispatch supports nvfp4, mxfp8, "
+                "and bf16 (quant_dtype=None) today; got "
+                f"quant_dtype={quant_config.quant_dtype!r}"
+            )
         prepare_finalize = FlashInferNVLinkOneSidedPrepareAndFinalize(
             max_num_tokens=max_num_tokens,
             top_k=moe.experts_per_token,
             num_experts=moe.num_experts,
             hidden_size=moe.hidden_dim,
             num_dispatchers=all2all_manager.world_size,
+            dispatch_dtype_bytes_per_elem=dispatch_dtype_bytes_per_elem,
+            dispatch_scale_bytes_per_token=dispatch_scale_bytes_per_token,
         )
 
     elif moe.use_ag_rs_all2all_kernels and allow_new_interface:

vllm/model_executor/layers/fused_moe/config.py

@@ -254,6 +254,8 @@ class FusedMoEQuantConfig:
     gemm1_beta: float | None = None
     gemm1_clamp_limit: float | None = None
 
+    mx_alignment: int = 0
+
     def __post_init__(self):
         assert not self.per_act_token_quant or self.block_shape is None, (
             "illegal quantization"
@@ -712,6 +714,7 @@ def mxfp4_mxfp8_moe_quant_config(
     gemm1_alpha: float | None = None,
     gemm1_beta: float | None = None,
     gemm1_clamp_limit: float | None = None,
+    mx_alignment: int = 0,
 ) -> FusedMoEQuantConfig:
     """
     Construct a quant config for mxfp4 activations and mxfp4 weights.
@@ -724,6 +727,7 @@ def mxfp4_mxfp8_moe_quant_config(
         gemm1_alpha=gemm1_alpha,
         gemm1_beta=gemm1_beta,
         gemm1_clamp_limit=gemm1_clamp_limit,
+        mx_alignment=mx_alignment,
     )
 
 

vllm/model_executor/layers/fused_moe/experts/trtllm_mxfp4_moe.py

@@ -44,6 +44,9 @@ def __init__(
             moe_config.intermediate_size_per_partition
         )
         self.hidden_dim = moe_config.hidden_dim
+        self.hidden_dim_unpadded = (
+            moe_config.hidden_dim_unpadded or moe_config.hidden_dim
+        )
         self.local_num_experts = moe_config.num_local_experts
         self.ep_rank = moe_config.moe_parallel_config.ep_rank
 
@@ -82,9 +85,6 @@ def __init__(
             get_current_vllm_config().compilation_config.max_cudagraph_capture_size
         )
 
-        # P1-5 fix: use public quant_dtype property instead of private _a1
-        self.use_mxfp8_input = quant_config.quant_dtype == "mxfp8"
-
     @staticmethod
     def _supports_current_device() -> bool:
         p = current_platform
@@ -121,8 +121,7 @@ def supports_expert_map(self) -> bool:
 
     @property
     def expects_unquantized_inputs(self) -> bool:
-        # Expert handles MXFP8 quantization internally if needed
-        return True
+        return False
 
 
 class TrtLlmMxfp4ExpertsMonolithic(
@@ -181,24 +180,19 @@ def apply(
     ) -> torch.Tensor:
         from flashinfer import trtllm_fp4_block_scale_moe
 
-        # Handle input quantization
-        if self.use_mxfp8_input:
-            from flashinfer import mxfp8_quantize
-
-            x_quant, x_scale = mxfp8_quantize(
-                hidden_states,
-                is_sf_swizzled_layout=False,
-                alignment=256,
-            )
-            x_scale = x_scale.view(torch.float8_e4m3fn).reshape(
-                *hidden_states.shape[:-1], -1
-            )
+        if a1q_scale is not None:
+            x_quant = hidden_states
+            x_scale = a1q_scale.view(torch.float8_e4m3fn)
         else:
             assert hidden_states.dtype == torch.bfloat16
             x_quant = hidden_states
             x_scale = None
-
-        output = torch.empty_like(hidden_states)
+        output = torch.empty(
+            *hidden_states.shape[:-1],
+            self.hidden_dim_unpadded,
+            dtype=torch.bfloat16,
+            device=hidden_states.device,
+        )
 
         from vllm.utils.flashinfer import _is_fi_autotuning, autotune
 
@@ -244,10 +238,6 @@ class TrtLlmMxfp4ExpertsModular(TrtLlmMxfp4ExpertsBase, mk.FusedMoEExpertsModula
     Moved from trtllm_moe.py.
     """
 
-    @property
-    def expects_unquantized_inputs(self) -> bool:
-        return True
-
     @staticmethod
     def _supports_parallel_config(
         moe_parallel_config: FusedMoEParallelConfig,
@@ -284,7 +274,7 @@ def workspace_shapes(
         # The workspaces for this implementation are managed by flashinfer.
         workspace1 = (0,)
         workspace2 = (0,)
-        output = (M, K)
+        output = (M, self.hidden_dim_unpadded)
         return (workspace1, workspace2, output)
 
     def apply(
@@ -310,18 +300,9 @@ def apply(
         intermediate_size = self.intermediate_size_per_partition
         local_expert_offset = self.moe_config.ep_rank * local_num_experts
 
-        # Handle input quantization
-        if self.use_mxfp8_input:
-            from flashinfer import mxfp8_quantize
-
-            x_quant, x_scale = mxfp8_quantize(
-                hidden_states,
-                is_sf_swizzled_layout=False,
-                alignment=256,
-            )
-            x_scale = x_scale.view(torch.float8_e4m3fn).reshape(
-                *hidden_states.shape[:-1], -1
-            )
+        if a1q_scale is not None:
+            x_quant = hidden_states
+            x_scale = a1q_scale.view(torch.float8_e4m3fn)
         else:
             assert hidden_states.dtype == torch.bfloat16
             x_quant = hidden_states

vllm/model_executor/layers/fused_moe/oracle/mxfp4.py

@@ -1195,10 +1195,18 @@ def make_mxfp4_moe_quant_config(
             gemm1_beta=gemm1_beta,
             gemm1_clamp_limit=swiglu_limit,
         )
-    elif mxfp4_backend in (
-        Mxfp4MoeBackend.FLASHINFER_TRTLLM_MXFP4_MXFP8,
-        Mxfp4MoeBackend.FLASHINFER_CUTLASS_MXFP4_MXFP8,
-    ):
+    elif mxfp4_backend == Mxfp4MoeBackend.FLASHINFER_TRTLLM_MXFP4_MXFP8:
+        return mxfp4_mxfp8_moe_quant_config(
+            w1_bias=w1_bias,
+            w2_bias=w2_bias,
+            w1_scale=w1_scale,
+            w2_scale=w2_scale,
+            gemm1_alpha=gemm1_alpha,
+            gemm1_beta=gemm1_beta,
+            gemm1_clamp_limit=swiglu_limit,
+            mx_alignment=256,
+        )
+    elif mxfp4_backend == Mxfp4MoeBackend.FLASHINFER_CUTLASS_MXFP4_MXFP8:
         return mxfp4_mxfp8_moe_quant_config(
             w1_bias=w1_bias,
             w2_bias=w2_bias,
@@ -1250,7 +1258,6 @@ def make_mxfp4_moe_kernel(
     """Create a FusedMoEKernel for the given MXFP4 backend."""
     is_monolithic = issubclass(experts_cls, mk.FusedMoEExpertsMonolithic)
 
-    # Create Prepare/Finalize.
     prepare_finalize = maybe_make_prepare_finalize(
         moe=moe_config,
         quant_config=moe_quant_config,

vllm/model_executor/layers/fused_moe/prepare_finalize/flashinfer_nvlink_one_sided.py

@@ -31,13 +31,16 @@ def __init__(
         num_experts: int,
         hidden_size: int,
         num_dispatchers: int = 1,
+        dispatch_dtype_bytes_per_elem: int = 0,
+        dispatch_scale_bytes_per_token: int = 0,
     ):
         super().__init__()
         self.max_num_tokens = max_num_tokens
         self.top_k = top_k
         self.num_experts = num_experts
         self.hidden_size = hidden_size
         self.num_dispatchers_ = num_dispatchers
+        self.scale_elems_per_token = dispatch_scale_bytes_per_token
 
         device_communicator = get_ep_group().device_communicator
         assert device_communicator is not None
@@ -49,6 +52,8 @@ def __init__(
             top_k=self.top_k,
             num_experts=self.num_experts,
             hidden_size=self.hidden_size,
+            dispatch_dtype_bytes_per_elem=dispatch_dtype_bytes_per_elem,
+            dispatch_scale_bytes_per_token=dispatch_scale_bytes_per_token,
         )
 
     @property
@@ -92,19 +97,24 @@ def prepare(
             else a1.shape[0]
         )
 
-        a1q, a1q_scale = moe_kernel_quantize_input(
-            a1,
-            quant_config.a1_gscale,
-            quant_config.quant_dtype,
-            quant_config.per_act_token_quant,
-            quant_config.block_shape,
-            is_fp4_scale_swizzled=False,  # delay swizzle to after comm
-        )
+        if defer_input_quant:
+            a1q, a1q_scale = a1, None
+        else:
+            a1q, a1q_scale = moe_kernel_quantize_input(
+                a1,
+                quant_config.a1_gscale,
+                quant_config.quant_dtype,
+                quant_config.per_act_token_quant,
+                quant_config.block_shape,
+                is_fp4_scale_swizzled=False,  # delay swizzle to after comm
+                mx_alignment=quant_config.mx_alignment,
+            )
 
         payloads = []
         payloads.append(a1q)
         if a1q_scale is not None:
             payloads.append(a1q_scale)
+        topk_ids_payload_index = len(payloads)
         payloads.append(topk_ids)
         payloads.append(topk_weights)
 
@@ -113,6 +123,8 @@ def prepare(
             token_selected_experts=topk_ids,
             input_payloads=payloads,
             runtime_max_tokens_per_rank=self.runtime_max_tokens_per_rank,
+            invalid_token_expert_id=-1,  # Follow TRTLLM Pattern
+            expert_id_payload_index=topk_ids_payload_index,
         )
         if a1q_scale is not None:
             a1q_recv, a1q_scale_recv, topk_ids_recv, topk_weights_recv = recv_payloads
@@ -124,7 +136,8 @@ def prepare(
                 a1q_scale_recv = a1q_scale_recv.view(-1, a1q_scale_recv.shape[-1])
                 a1q_scale_recv = a1q_scale_recv.view(torch.uint8)
                 a1q_scale_recv = nvfp4_block_scale_interleave(a1q_scale_recv)
-            a1q_scale_recv = a1q_scale_recv.view(-1, self.hidden_size // 16)
+            assert self.scale_elems_per_token > 0
+            a1q_scale_recv = a1q_scale_recv.view(-1, self.scale_elems_per_token)
         else:
             a1q_recv, topk_ids_recv, topk_weights_recv = recv_payloads
             a1q_scale_recv = None

vllm/model_executor/layers/fused_moe/prepare_finalize/flashinfer_nvlink_two_sided.py

@@ -174,6 +174,7 @@ def flashinfer_alltoall_dispatch(
             # the hidden states, breaking the A2A kernel. So, we
             # delay the swizzling until after the A2A.
             is_fp4_scale_swizzled=False,
+            mx_alignment=quant_config.mx_alignment,
         )
 
         x = MnnvlMoe.mnnvl_moe_alltoallv(

vllm/model_executor/layers/fused_moe/prepare_finalize/naive_dp_ep.py

@@ -40,6 +40,7 @@ def _quantize_and_setup_dispatch(
             per_act_token_quant=quant_config.per_act_token_quant,
             block_shape=quant_config.block_shape,
             is_fp4_scale_swizzled=False,
+            mx_alignment=quant_config.mx_alignment,
         )
 
     # Skip gathering scales if we have static quantization

vllm/model_executor/layers/fused_moe/prepare_finalize/no_dp_ep.py

@@ -31,6 +31,7 @@ def _quantize_input(
         per_act_token_quant=quant_config.per_act_token_quant,
         block_shape=quant_config.block_shape,
         is_fp4_scale_swizzled=quant_config.is_nvfp4_scale_swizzled,
+        mx_alignment=quant_config.mx_alignment,
     )
 
     return a1q, a1q_scale

vllm/model_executor/layers/fused_moe/utils.py

@@ -208,11 +208,12 @@ def _mxfp8_e4m3_quantize(
     per_act_token_quant: bool,
     block_shape: list[int] | None = None,
     is_sf_swizzled_layout: bool = False,
+    mx_alignment: int = 0,
 ) -> tuple[torch.Tensor, torch.Tensor]:
     assert A_scale is None
     assert not per_act_token_quant
     assert block_shape is None or block_shape == [1, 32]
-    return mxfp8_e4m3_quantize(A, is_sf_swizzled_layout)
+    return mxfp8_e4m3_quantize(A, is_sf_swizzled_layout, mx_alignment)
 
 
 def _mxfp6_e3m2_quantize(
@@ -258,6 +259,7 @@ def moe_kernel_quantize_input(
     is_fp4_scale_swizzled: bool = True,
     ocp_mx_scheme: str | None = None,
     quantization_emulation: bool = False,
+    mx_alignment: int = 0,
 ) -> tuple[torch.Tensor, torch.Tensor | None]:
     # Handle OCP MX scheme that requires QDQ (quantize-dequantize) for emulation
     if ocp_mx_scheme is not None:
@@ -319,7 +321,8 @@ def moe_kernel_quantize_input(
             A_scale,
             per_act_token_quant,
             block_shape,
-            is_sf_swizzled_layout=is_fp4_scale_swizzled,
+            is_sf_swizzled_layout=False,
+            mx_alignment=mx_alignment,
         )
     elif quant_dtype == "mxfp6_e3m2":
         if not quantization_emulation: