support fused_moe_allgather_ep

tests/multicard/test_fused_moe_allgather_ep.py

@@ -0,0 +1,82 @@
+#
+# Copyright (c) 2025 Huawei Technologies Co., Ltd. All Rights Reserved.
+# Copyright 2023 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.
+#
+"""
+Execute the inference of fused_moe_allgather_ep and fused_moe_alltoall_ep.
+
+Run 'pytest tests/multicard/test_fused_moe_allgather_ep.py'.
+"""
+
+import os
+from unittest.mock import patch
+
+from modelscope import snapshot_download  # type: ignore
+from vllm import SamplingParams
+
+from tests.conftest import VllmRunner
+
+
+@patch.dict(
+    os.environ, {
+        "VLLM_USE_V1": "1",
+        "VLLM_WORKER_MULTIPROC_METHOD": "spawn",
+        "TASK_QUEUE_ENABLE": "1",
+        "VLLM_ENABLE_FUSED_EXPERTS_ALLGATHER_EP": "1"
+    })
+def test_generate_with_allgather():
+    example_prompts = ["Hello, my name is"]
+    sampling_params = SamplingParams(max_tokens=100, temperature=0.0)
+
+    with VllmRunner(snapshot_download("vllm-ascend/DeepSeek-V3-Pruning"),
+                    tensor_parallel_size=16,
+                    enforce_eager=True,
+                    max_model_len=1024,
+                    dtype="auto",
+                    enable_expert_parallel=True,
+                    additional_config={
+                        "ascend_scheduler_config": {
+                            "enabled": True,
+                            "chunked_prefill_enabled": False,
+                        },
+                        "expert_tensor_parallel_size": 1
+                    }) as vllm_model:
+        vllm_model.generate(example_prompts, sampling_params)
+
+
+@patch.dict(
+    os.environ, {
+        "VLLM_USE_V1": "1",
+        "VLLM_WORKER_MULTIPROC_METHOD": "spawn",
+        "TASK_QUEUE_ENABLE": "1"
+    })
+def test_generate_with_alltoall():
+    example_prompts = ["Hello, my name is"]
+    sampling_params = SamplingParams(max_tokens=100, temperature=0.0)
+
+    with VllmRunner(snapshot_download("vllm-ascend/DeepSeek-V3-Pruning"),
+                    tensor_parallel_size=16,
+                    enforce_eager=True,
+                    max_model_len=1024,
+                    dtype="auto",
+                    enable_expert_parallel=True,
+                    additional_config={
+                        "ascend_scheduler_config": {
+                            "enabled": True,
+                            "chunked_prefill_enabled": False,
+                        },
+                        "expert_tensor_parallel_size": 1
+                    }) as vllm_model:
+        vllm_model.generate(example_prompts, sampling_params)

vllm_ascend/envs.py

@@ -99,6 +99,11 @@
     # Whether to enable the trace recompiles from pytorch.
     "VLLM_ASCEND_TRACE_RECOMPILES":
     lambda: bool(int(os.getenv("VLLM_ASCEND_TRACE_RECOMPILES", '0'))),
+    # Whether to enable fused_experts_allgather_ep. MoeInitRoutingV3 and
+    # GroupedMatmulFinalizeRouting operators are combined to implement EP.
+    "VLLM_ENABLE_FUSED_EXPERTS_ALLGATHER_EP":
+    lambda: bool(int(os.getenv("VLLM_ENABLE_FUSED_EXPERTS_ALLGATHER_EP", '0'))
+                 ),
     "VLLM_ASCEND_ENABLE_DBO":
     lambda: bool(int(os.getenv("VLLM_ASCEND_ENABLE_DBO", '0'))),
     # Whether to enable the model execute time observe profile. Disable it when

vllm_ascend/ops/fused_moe.py

@@ -897,8 +897,9 @@ def apply(
         **kwargs,
     ) -> torch.Tensor:
 
+        is_deepseek_v3_r1 = global_num_experts == 256
         # NOTE: now npu_moe_gating_top_k can only support `group_count=256` pattern
-        if global_num_experts == 256:
+        if is_deepseek_v3_r1:
             topk_weights, topk_ids, _ = torch_npu.npu_moe_gating_top_k(
                 router_logits,
                 k=top_k,  # topk当前写8
@@ -934,7 +935,7 @@ def apply(
             topk_ids = torch.randint_like(topk_ids, 0, global_num_experts)
 
         fused_moe_state = get_fused_moe_state(self.ep_group.world_size,
-                                              is_prefill)
+                                              is_prefill, is_deepseek_v3_r1)
         if fused_moe_state == FusedMoEState.MC2:
             return fused_experts_with_mc2(
                 hidden_states=x,
@@ -1128,15 +1129,17 @@ def forward(self,
             real_top_k = self.top_k
 
         num_tokens, hidden_size = hidden_states.shape
+        is_deepseek_v3_r1 = self.global_num_experts == 256
 
         fused_moe_state = get_fused_moe_state(self.moe_parallel_config.ep_size,
-                                              is_prefill)
+                                              is_prefill, is_deepseek_v3_r1)
         if shared_experts:
             if not self.enable_multistream_moe or fused_moe_state != FusedMoEState.MC2:
                 shared_hidden_states = shared_experts(hidden_states)
 
         tp_size = get_tensor_model_parallel_world_size()
-        if tp_size > 1 and fused_moe_state != FusedMoEState.AllGather:
+        if (tp_size > 1 and fused_moe_state != FusedMoEState.AllGather
+                and fused_moe_state != FusedMoEState.AllGatherEP):
             if num_tokens < tp_size:
                 hidden_states = nn.functional.pad(
                     hidden_states, (0, 0, 0, tp_size - num_tokens))
@@ -1194,7 +1197,8 @@ def forward(self,
             if isinstance(e_hidden_states, tuple):
                 e_hidden_states, shared_hidden_states = e_hidden_states
 
-        if tp_size > 1 and fused_moe_state != FusedMoEState.AllGather:
+        if (tp_size > 1 and fused_moe_state != FusedMoEState.AllGather
+                and fused_moe_state != FusedMoEState.AllGatherEP):
             dist.all_gather(list(chunk_hidden_states), e_hidden_states,
                             self.tp_group)
             final_hidden_states = torch.cat(chunk_hidden_states, dim=0)
@@ -1212,7 +1216,8 @@ def forward(self,
         else:
             final_hidden_states = e_hidden_states
 
-        if tp_size > 1 and fused_moe_state == FusedMoEState.AllGather:
+        if tp_size > 1 and (fused_moe_state == FusedMoEState.AllGather
+                            or fused_moe_state == FusedMoEState.AllGatherEP):
             final_hidden_states = tensor_model_parallel_all_reduce(
                 final_hidden_states)
 

vllm_ascend/quantization/w8a8_dynamic.py

@@ -22,6 +22,7 @@
 import torch_npu
 from vllm.distributed import GroupCoordinator
 
+import vllm_ascend.envs as envs
 from vllm_ascend.ascend_config import get_ascend_config
 from vllm_ascend.distributed.parallel_state import get_ep_group
 from vllm_ascend.ops.fused_moe import select_experts
@@ -346,6 +347,95 @@ def fused_experts_with_all2all(
     return final_hidden_states
 
 
+def fused_experts_with_allgather(hidden_states: torch.Tensor,
+                                 w1: torch.Tensor,
+                                 w1_scale: torch.Tensor,
+                                 w2: torch.Tensor,
+                                 w2_scale: torch.Tensor,
+                                 topk_weights: torch.Tensor,
+                                 topk_ids: torch.Tensor,
+                                 top_k: int,
+                                 expert_map: torch.Tensor = None):
+    original_shape = hidden_states.shape
+    if len(original_shape) == 3:
+        hidden_states = hidden_states.view(-1, hidden_states.shape[-1])
+    num_tokens = hidden_states.shape[0]
+    batch_size, hidden_size = hidden_states.shape
+    topk_weights = topk_weights.to(hidden_states.dtype)
+
+    ep_group = get_ep_group().device_group
+    ep_rank = torch.distributed.get_rank(group=ep_group)
+    ep_size = torch.distributed.get_world_size(ep_group)
+
+    global_num_experts = len(expert_map)
+    local_num_experts = global_num_experts // ep_size
+
+    hidden_states, pertoken_scale = torch_npu.npu_dynamic_quant(hidden_states)
+
+    hidden_states, expanded_x_idx, expert_tokens, pertoken_scale = torch_npu.npu_moe_init_routing_v2(
+        hidden_states,
+        topk_ids,
+        scale=pertoken_scale,
+        offset=None,
+        active_num=num_tokens * top_k,
+        expert_num=global_num_experts,
+        expert_tokens_num_type=1,
+        expert_tokens_num_flag=True,
+        active_expert_range=[
+            ep_rank * local_num_experts, (ep_rank + 1) * local_num_experts
+        ],
+        quant_mode=-1,
+        row_idx_type=1)
+    group_list_type = 1
+
+    sorted_topk_weight = torch.index_select(topk_weights.view(-1), 0,
+                                            expanded_x_idx)
+    row_index = expanded_x_idx // topk_ids.shape[-1]
+    row_index = row_index.to(torch.int64)
+    share_input = torch.zeros((batch_size, hidden_size),
+                              dtype=torch.bfloat16,
+                              device="npu")
+
+    hidden_states = torch_npu.npu_grouped_matmul(
+        x=[hidden_states],
+        weight=[w1],
+        split_item=3,
+        group_list_type=group_list_type,
+        group_type=0,
+        group_list=expert_tokens,
+        output_dtype=torch.int32)[0]
+
+    # act_fn: swiglu
+    hidden_states, pertoken_scale = torch_npu.npu_dequant_swiglu_quant(
+        x=hidden_states,
+        weight_scale=w1_scale.to(torch.float32),
+        activation_scale=pertoken_scale,
+        bias=None,
+        quant_scale=None,
+        quant_offset=None,
+        group_index=expert_tokens,
+        activate_left=True,
+        quant_mode=1,
+    )
+
+    final_hidden_states = torch_npu.npu_grouped_matmul_finalize_routing(
+        hidden_states,
+        w2,
+        scale=w2_scale.to(torch.float32),
+        bias=None,
+        pertoken_scale=pertoken_scale.view(-1),
+        group_list=expert_tokens,
+        shared_input=share_input,
+        logit=sorted_topk_weight.to(torch.float32),
+        row_index=row_index,
+        output_bs=batch_size).to(torch.bfloat16)
+
+    if len(original_shape) == 3:
+        final_hidden_states = final_hidden_states.view(original_shape)
+
+    return final_hidden_states
+
+
 def fused_experts(hidden_states: torch.Tensor,
                   w1: torch.Tensor,
                   w1_scale: torch.Tensor,
@@ -623,8 +713,10 @@ def apply(
         assert router_logits.shape[
             1] == global_num_experts, "Number of global experts mismatch"
 
+        is_deepseek_v3_r1 = global_num_experts == 256
+
         # NOTE: now npu_moe_gating_top_k can only support `group_count=256` pattern
-        if global_num_experts == 256:
+        if is_deepseek_v3_r1:
             topk_weights, topk_ids, _ = torch_npu.npu_moe_gating_top_k(
                 router_logits,
                 k=top_k,  # topk当前写8
@@ -661,8 +753,19 @@ def apply(
         topk_weights = topk_weights.to(x.dtype)
 
         fused_moe_state = get_fused_moe_state(self.ep_group.world_size,
-                                              is_prefill)
-        if fused_moe_state == FusedMoEState.MC2:
+                                              is_prefill, is_deepseek_v3_r1)
+        if fused_moe_state == FusedMoEState.AllGatherEP:
+            return fused_experts_with_allgather(
+                hidden_states=x,
+                w1=layer.w13_weight,
+                w1_scale=layer.w13_weight_scale,
+                w2=layer.w2_weight,
+                w2_scale=layer.w2_weight_scale,
+                topk_weights=topk_weights,
+                topk_ids=topk_ids,
+                top_k=top_k,
+                expert_map=expert_map)
+        elif fused_moe_state == FusedMoEState.MC2:
             return fused_experts_with_mc2(
                 hidden_states=x,
                 w1=layer.w13_weight,
@@ -713,6 +816,8 @@ def process_weights_after_loading(self, layer):
                 1, 2).contiguous()
             layer.w2_weight.data = layer.w2_weight.data.transpose(
                 1, 2).contiguous()
+        if envs.VLLM_ENABLE_FUSED_EXPERTS_ALLGATHER_EP:
+            torch_npu.npu_format_cast_(layer.w2_weight, 29)
         layer.w13_weight_scale.data = layer.w13_weight_scale.data.view(
             layer.w13_weight_scale.data.shape[0], -1)
         layer.w13_weight_offset.data = layer.w13_weight_offset.data.view(

vllm_ascend/utils.py

@@ -283,11 +283,18 @@ class FusedMoEState(Enum):
     AllGather = 0
     All2All = 1
     MC2 = 2
+    AllGatherEP = 3
 
 
 # TODO(zzzzwwjj): add soc_version to choose branch
-def get_fused_moe_state(ep_size: int, with_prefill: bool):
-    if ep_size == 1:
+def get_fused_moe_state(ep_size: int, with_prefill: bool,
+                        is_deepseek_v3_r1: bool):
+    # the fusion operator torch_npu.npu_grouped_matmul_finalize_routing called by allgather ep
+    # only supports deepseek v3/r1
+    if (envs.VLLM_ENABLE_FUSED_EXPERTS_ALLGATHER_EP and ep_size > 1
+            and is_deepseek_v3_r1):
+        return FusedMoEState.AllGatherEP
+    elif ep_size == 1:
         return FusedMoEState.AllGather
     # NOTE: mc2 need ep_size >= 16 & all2all can't use in torchair graph.
     elif ep_size < 16 or with_prefill: