[0.9.1][Feature]Moe alltoallv communication optimization for unquantized RL training sence & alltoallv support dpo

tests/multicard/test_qwen3_moe.py

@@ -0,0 +1,75 @@
+# 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.
+# This file is a part of the vllm-ascend project.
+#
+"""
+Compare the outputs of vLLM with and without aclgraph.
+Run `pytest tests/multicard/test_data_parallel.py`.
+"""
+
+import os
+import subprocess
+import sys
+from unittest.mock import patch
+
+import pytest
+
+MODELS = ["vllm-ascend/Qwen3-30B-A3B-Puring"]
+
+
+@pytest.mark.parametrize("model", MODELS)
+@pytest.mark.parametrize("max_tokens", [32])
+@patch.dict(
+    os.environ, {
+        "ASCEND_RT_VISIBLE_DEVICES": "0,1,2,3",
+        "VLLM_ASCEND_ENABLE_MOE_ALL2ALL_SEQ": "1",
+        "VLLM_ASCEND_ENABLE_DBO": "1"
+    })
+def test_qwen3_moe_inference(model, max_tokens):
+    script = "examples/offline_data_parallel.py"
+
+    env = os.environ.copy()
+
+    cmd = [
+        sys.executable,
+        script,
+        "--model",
+        model,
+        "--dp-size",
+        "2",
+        "--tp-size",
+        "2",
+        "--node-size",
+        "1",
+        "--node-rank",
+        "0",
+        "--trust-remote-code",
+        "--enforce-eager",
+    ]
+
+    print(f"Running subprocess: {' '.join(cmd)}")
+    proc = subprocess.run(cmd,
+                          env=env,
+                          stdout=subprocess.PIPE,
+                          stderr=subprocess.STDOUT,
+                          timeout=600)
+    output = proc.stdout.decode()
+
+    print(output)
+
+    assert "DP rank 0 needs to process" in output
+    assert "DP rank 1 needs to process" in output
+    assert "Generated text:" in output
+    assert proc.returncode == 0

tests/ut/test_distributed_tensor_parallel.py

@@ -0,0 +1,127 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+# Copyright (c) 2025 Huawei Technologies Co., Ltd. All Rights Reserved.
+import importlib
+from unittest.mock import MagicMock, patch
+
+import pytest
+import torch
+
+from vllm_ascend.distributed.tensor_parallel import (
+    _gather_along_first_dim, _gather_along_last_dim,
+    _reduce_scatter_along_first_dim, _reduce_scatter_along_last_dim,
+    all_to_all_hp2sp, all_to_all_sp2hp)
+
+
+@pytest.fixture
+def test_tensor():
+    return torch.randn(8, 16)
+
+
+@pytest.fixture
+def test_tensor_last_dim():
+    return torch.randn(8, 16, 32)
+
+
+@pytest.fixture
+def mock_group():
+    return MagicMock()
+
+
+@pytest.fixture(autouse=True)
+def mock_dist():
+    with patch("torch.distributed") as mock:
+        mock.get_world_size.return_value = 4
+        mock.get_rank.return_value = 0
+        yield mock
+
+
+class TestDistributedCommunication:
+
+    @pytest.mark.parametrize("world_size", [1, 4])
+    def test_gather_along_first_dim(self, test_tensor, mock_group, mock_dist,
+                                    world_size):
+        """test _gather_along_first_dim"""
+        mock_dist.get_world_size.return_value = world_size
+
+        result = _gather_along_first_dim(test_tensor, mock_group)
+
+        if world_size == 1:
+            assert torch.equal(result, test_tensor)
+        else:
+            assert result.shape == (32, 16)  # 8*4=32
+
+    def test_gather_along_first_dim_unequal_split(self, test_tensor,
+                                                  mock_group):
+        """test unequal split"""
+        output_split_sizes = [5, 10, 15, 2]
+        result = _gather_along_first_dim(test_tensor, mock_group,
+                                         output_split_sizes)
+        assert result.shape == (32, 16)  # 5+10+15+2=32
+
+    @pytest.mark.parametrize("world_size", [1, 4])
+    def test_gather_along_last_dim(self, test_tensor_last_dim, mock_group,
+                                   mock_dist, world_size):
+        """test _gather_along_last_dim"""
+        mock_dist.get_world_size.return_value = world_size
+
+        result = _gather_along_last_dim(test_tensor_last_dim, mock_group)
+
+        if world_size == 1:
+            assert torch.equal(result, test_tensor_last_dim)
+        else:
+            assert result.shape == (8, 16, 32 * world_size)  # 8*4=32
+
+    @pytest.mark.parametrize("input_shape,expected_shape", [
+        ((32, 16), (8, 16)),
+        ((40, 10), (10, 10)),
+    ])
+    def test_reduce_scatter_along_first_dim(self, mock_group, input_shape,
+                                            expected_shape):
+        input_tensor = torch.randn(*input_shape)
+        result = _reduce_scatter_along_first_dim(input_tensor, mock_group)
+        assert result.shape == expected_shape
+
+    def test_reduce_scatter_along_last_dim(self, mock_group):
+        input_tensor = torch.randn(8, 16, 32)
+        result = _reduce_scatter_along_last_dim(input_tensor, mock_group)
+        assert result.shape == (8, 16, 8)  # 32/4=8
+
+    @pytest.mark.parametrize("func,input_shape,expected_shape", [
+        ("all_gather_last_dim_from_tensor_parallel_region", (8, 16, 32),
+         (8, 16, 128)),
+        ("reduce_scatter_to_sequence_parallel_region", (32, 16), (8, 16)),
+        ("reduce_scatter_last_dim_to_tensor_parallel_region", (8, 16, 32),
+         (8, 16, 8)),
+        ("gather_from_sequence_parallel_region", (8, 16), (32, 16)),
+    ])
+    def test_wrapper_functions(self, mock_group, func, input_shape,
+                               expected_shape):
+        """test wrapper funcs"""
+        mod = importlib.import_module(
+            'vllm_ascend.distributed.tensor_parallel')
+        globals = mod.__dict__
+        test_func = globals[func]
+        input_tensor = torch.randn(*input_shape)
+        result = test_func(input_tensor, mock_group)
+        assert result.shape == expected_shape
+
+    @pytest.mark.parametrize(
+        "input_shape,output_shape",
+        [
+            ((8, 16), (32, 4)),  # [num_tokens/TP, H] -> [num_tokens, H/TP]
+        ])
+    def test_all_to_all_sp2hp(self, mock_group, input_shape, output_shape):
+        input_tensor = torch.randn(*input_shape)
+        result = all_to_all_sp2hp(input_tensor, mock_group)
+        assert result.shape == output_shape
+
+    @pytest.mark.parametrize(
+        "input_shape,output_shape",
+        [
+            ((32, 4), (8, 16)),  # [num_tokens, H/TP] -> [num_tokens/TP, H]
+        ])
+    def test_all_to_all_hp2sp(self, mock_group, input_shape, output_shape):
+        input_tensor = torch.randn(*input_shape)
+        result = all_to_all_hp2sp(input_tensor, mock_group)
+        assert result.shape == output_shape

tests/ut/test_token_dispatcher.py

@@ -0,0 +1,62 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+# Copyright (c) 2025 Huawei Technologies Co., Ltd. All Rights Reserved.
+
+import pytest
+import torch
+from pytest_mock import MockerFixture
+
+from vllm_ascend.ops.moe_dispatcher.token_dispatcher import (
+    MoEAlltoAllSeqOverLapDispatcher, MoeDispatcherConfig)
+from vllm_ascend.utils import adapt_patch  # noqa E402
+
+import vllm_ascend.patch.worker.patch_common.patch_utils # type: ignore[import]  # isort: skip  # noqa
+
+
+
+adapt_patch(True)
+
+
+class TestMoEAlltoAllSeqOverLapDispatcher:
+
+    @pytest.fixture
+    def config(self):
+        config = MoeDispatcherConfig()
+        config.set_num_local_experts(2)
+        config.set_num_moe_experts(4)
+        config.set_moe_pad_expert_input_to_capacity(False)
+        config.set_moe_expert_capacity_factor(None)
+        config.set_moe_router_topk(2)
+        config.set_moe_grouped_gemm(False)
+        config.set_group_topk(0)
+        config.set_num_groups(1)
+        config.set_is_fused(False)
+        return config.build()
+
+    def mock_ep_group(self, mocker):
+        mock_group = mocker.MagicMock()
+        mock_group.rank_in_group = 0
+        mock_group.world_size = 2
+        mock_group.device_group = "mock_group"
+        return mock_group
+
+    @pytest.fixture
+    def dispatcher(self, config, mocker: MockerFixture):
+        mocker.patch(
+            "vllm_ascend.ops.moe_dispatcher.token_dispatcher.get_ep_group",
+            return_value=self.mock_ep_group(mocker))
+        return MoEAlltoAllSeqOverLapDispatcher(config)
+
+    def test_initialization(self, dispatcher, config):
+        assert dispatcher.num_local_experts == config.num_local_experts
+        assert dispatcher.num_experts == config.num_moe_experts
+        assert dispatcher.local_expert_indices == [0, 1]
+        assert dispatcher.ep_rank == 0
+        assert dispatcher.ep_size == 2
+        assert dispatcher.overlap_stream is not None
+
+    def test_routing(self, dispatcher):
+        probs = torch.randn(4, 4)  # 4 tokens, 4 experts
+        scores, routing_map = dispatcher.routing(probs)
+        assert scores.shape == (4, 4)  # topk=2
+        assert routing_map.shape == (4, 4)

vllm_ascend/ascend_forward_context.py

@@ -11,19 +11,26 @@
 
 import vllm_ascend.envs as envs
 
+import vllm_ascend.envs as envs_ascend
+
 
 class FusedMoEState(Enum):
     AllGather = 0
     All2All = 1
     MC2 = 2
     MC2_PREFILL = 3
+    All2AllSeq = 4
 
 
 # TODO(zzzzwwjj): add soc_version to choose branch
 def get_fused_moe_state(ep_size: int, with_prefill: bool):
     enable_chunk_mc2 = envs.VLLM_ASCEND_ENABLE_CHUNK_MC2
     if ep_size == 1:
         return FusedMoEState.AllGather
+    elif envs_ascend.VLLM_ASCEND_ENABLE_MOE_ALL2ALL_SEQ:
+        # MC2 Dispatch/Combine performs better than alltoall_seq in decoding stage.
+        return FusedMoEState.All2AllSeq if (
+            ep_size < 16 or with_prefill) else FusedMoEState.MC2
     elif ep_size >= 16 and with_prefill and enable_chunk_mc2:
         return FusedMoEState.MC2_PREFILL
     # NOTE: mc2 need ep_size >= 16 & all2all can't use in torchair graph.

vllm_ascend/attention/attention_v1.py

@@ -32,6 +32,7 @@
 
 from vllm_ascend.attention.utils import \
     AscendCommonAttentionMetadata as CommonAttentionMetadata
+from vllm_ascend.multistream.base import MSAttentionMetadataSplitConfig
 from vllm_ascend.ops.attention import vanilla_chunked_prefill
 from vllm_ascend.utils import get_graph_params
 
@@ -140,6 +141,18 @@ class AscendMetadata:
 
     enable_dbo_across_dp: bool = False
 
+    def split_metadata_for_multistream(
+        self,
+        ms_split_config: MSAttentionMetadataSplitConfig,
+    ) -> list["AscendMetadata"]:
+        """Split metadata for multi-stream with AscendMetadata"""
+        from vllm_ascend.multistream.ms_split import model_input_split_v1_attn
+        return model_input_split_v1_attn(
+            ms_split_config=ms_split_config,
+            attn_metadata=self,
+            _metadata_cls=AscendMetadata,
+        )
+
 
 class AscendAttentionMetadataBuilder: