[Misc][Main2Main] Upgrade vLLM to 0427

.github/workflows/pr_test_full.yaml

@@ -80,7 +80,7 @@ jobs:
     name: e2e-full
     strategy:
       matrix:
-        vllm_version: [d886c26d4d4fef7d079696beb4ece1cfb4b008a8]
+        vllm_version: [4d51588e2381018348f1022dfa3a7698899805b7]
     needs: [changes]
     if: ${{ needs.changes.outputs.e2e_tracker == 'true' || needs.changes.outputs.e2e_tracker == true }}
     uses: ./.github/workflows/_e2e_test.yaml

.github/workflows/pr_test_light.yaml

@@ -41,7 +41,7 @@ jobs:
   lint:
     uses: ./.github/workflows/_pre_commit.yml
     with:
-      vllm: d886c26d4d4fef7d079696beb4ece1cfb4b008a8
+      vllm: 4d51588e2381018348f1022dfa3a7698899805b7
   changes:
     runs-on: linux-aarch64-a2b3-0
     container:
@@ -154,7 +154,7 @@ jobs:
     if: ${{ needs.lint.result == 'success' && needs.changes.outputs.has_tests == 'true' }}
     strategy:
       matrix:
-        vllm_version: [d886c26d4d4fef7d079696beb4ece1cfb4b008a8]
+        vllm_version: [4d51588e2381018348f1022dfa3a7698899805b7]
     uses: ./.github/workflows/_optional_smart_e2e.yaml
     with:
       vllm: ${{ matrix.vllm_version }}
@@ -164,7 +164,7 @@ jobs:
     name: e2e-light
     strategy:
       matrix:
-        vllm_version: [d886c26d4d4fef7d079696beb4ece1cfb4b008a8]
+        vllm_version: [4d51588e2381018348f1022dfa3a7698899805b7]
     # Note (yikun): If CI resource are limited we can split job into two chain jobs
     needs: [lint, changes]
     # only trigger e2e test after lint passed and the change is e2e related with pull request.

.gitignore

@@ -210,6 +210,7 @@ kernel_meta/
 # generated by CANN
 fusion_result.json
 csrc/output/
+csrc/third_party/
 
 # claude code skills
 .claude/skills/*

docs/source/conf.py

@@ -81,7 +81,7 @@
     # CANN image tag
     "cann_image_tag": "8.5.1-910b-ubuntu22.04-py3.11",
     # vLLM commit hash for main branch
-    "main_vllm_commit": "d886c26d4d4fef7d079696beb4ece1cfb4b008a8",
+    "main_vllm_commit": "4d51588e2381018348f1022dfa3a7698899805b7",
     # vLLM tag for main branch
     "main_vllm_tag": "v0.19.1",
     # Python version for main branch

tests/ut/_310p/fused_moe/test_shared_fused_moe_310.py

@@ -20,7 +20,6 @@
 
 from vllm_ascend._310p.fused_moe.fused_moe import (
     AscendFusedMoE310,
-    AscendSharedFusedMoE310,
 )
 
 
@@ -48,8 +47,8 @@ def forward(self, hidden_states: torch.Tensor):
         return out
 
 
-def _build_layer(shared_experts: torch.nn.Module | None) -> AscendSharedFusedMoE310:
-    layer = AscendSharedFusedMoE310.__new__(AscendSharedFusedMoE310)
+def _build_layer(shared_experts: torch.nn.Module | None) -> AscendFusedMoE310:
+    layer = AscendFusedMoE310.__new__(AscendFusedMoE310)
     # The test bypasses full layer init with __new__, so we must initialize
     # nn.Module internals before assigning child modules.
     torch.nn.Module.__init__(layer)
@@ -80,7 +79,7 @@ def test_forward_impl_with_shared_experts_returns_tuple_310():
     routed_out = torch.randn(3, 8)
 
     with patch.object(AscendFusedMoE310, "forward_impl", return_value=routed_out):
-        shared_out, routed = layer.forward_impl(hidden_states, router_logits)
+        shared_out, routed = layer.shared_forward_impl(hidden_states, router_logits)
 
     expected_shared = 0.5 * (hidden_states * 2.0 + 1.0)
     torch.testing.assert_close(shared_out, expected_shared)
@@ -100,7 +99,7 @@ def test_forward_impl_without_shared_experts_returns_routed_only_310():
     routed_out = torch.randn(3, 8)
 
     with patch.object(AscendFusedMoE310, "forward_impl", return_value=routed_out):
-        output = layer.forward_impl(hidden_states, router_logits)
+        output = layer.shared_forward_impl(hidden_states, router_logits)
 
     torch.testing.assert_close(output, routed_out)
 

tests/ut/ops/test_fused_moe.py

@@ -236,6 +236,12 @@ def moe_method(mock_dist_env):
     return AscendUnquantizedFusedMoEMethod(moe)
 
 
+def test_ascend_unquantized_skips_upstream_modular_kernel_init():
+    method = AscendUnquantizedFusedMoEMethod.maybe_make_prepare_finalize
+
+    assert method(object()) is None
+
+
 class Device(TypedDict):
     device_id: int
     device_expert: list[int]

tests/ut/spec_decode/test_eagle_proposer.py

@@ -17,6 +17,17 @@
 from vllm_ascend.attention.attention_v1 import AscendAttentionState
 from vllm_ascend.spec_decode.draft_proposer import AscendDraftModelProposer
 from vllm_ascend.spec_decode.eagle_proposer import AscendEagleProposer
+from vllm_ascend.utils import vllm_version_is
+
+# vLLM #40732 moved `SpecDecodeBaseProposer` (and its `CpuGpuBuffer` import)
+# out of `vllm.v1.spec_decode.eagle` into `vllm.v1.spec_decode.llm_base_proposer`.
+# Pick the right patch path depending on the installed vllm version so the
+# tests can mock the buffer factory.
+_CPU_GPU_BUFFER_TARGET = (
+    "vllm.v1.spec_decode.eagle.CpuGpuBuffer"
+    if vllm_version_is("0.19.1")
+    else "vllm.v1.spec_decode.llm_base_proposer.CpuGpuBuffer"
+)
 
 
 class TestEagleProposerInitialization(TestBase):
@@ -51,13 +62,15 @@ def setUp(self):
         self.vllm_config.parallel_config.enable_expert_parallel = False
         self.vllm_config.speculative_config.draft_tensor_parallel_size = 1
         self.vllm_config.speculative_config.num_speculative_tokens = 2
+        self.vllm_config.speculative_config.parallel_drafting = False
         self.vllm_config.speculative_config.speculative_token_tree = str([(i + 1) * (0,) for i in range(2)])
+        self.vllm_config.speculative_config.draft_model_config.hf_config = MagicMock(spec=[])
         self.vllm_config.speculative_config.draft_model_config.uses_xdrope_dim = 0
         self.vllm_config.speculative_config.draft_model_config.uses_mrope = False
         self.vllm_config.speculative_config.disable_padded_drafter_batch = False
         self.vllm_config.additional_config = None
 
-        self.mock_cpugpubuffer = patch("vllm.v1.spec_decode.eagle.CpuGpuBuffer")
+        self.mock_cpugpubuffer = patch(_CPU_GPU_BUFFER_TARGET)
         self.mock_cpugpubuffer.start()
         self.mock_supports_multimodal_inputs = patch(
             "vllm.multimodal.registry.MultiModalRegistry.supports_multimodal_inputs", return_value=False
@@ -76,6 +89,7 @@ def tearDown(self):
     def test_initialization_eagle_graph(self):
         self.vllm_config.speculative_config.method = "eagle"
         self.vllm_config.speculative_config.draft_model_config.get_hidden_size.return_value = 4096
+        self.vllm_config.speculative_config.draft_model_config.get_inputs_embeds_size.return_value = 4096
         self.vllm_config.speculative_config.draft_model_config.uses_mrope = False
         self.vllm_config.compilation_config.mode = CompilationMode.VLLM_COMPILE
         self.vllm_config.model_config.enforce_eager = False
@@ -99,6 +113,7 @@ def test_initialization_eagle_graph(self):
     def test_initialization_eagle3_enforce_eager(self):
         self.vllm_config.speculative_config.method = "eagle3"
         self.vllm_config.speculative_config.draft_model_config.get_hidden_size.return_value = 2048
+        self.vllm_config.speculative_config.draft_model_config.get_inputs_embeds_size.return_value = 2048
         self.vllm_config.compilation_config.mode = CompilationMode.NONE
         self.vllm_config.compilation_config.pass_config = MagicMock()
         self.vllm_config.compilation_config.pass_config.enable_sp = False
@@ -116,6 +131,7 @@ def test_initialization_eagle3_enforce_eager(self):
     def test_initialization_eagle3_full_graph_async(self):
         self.vllm_config.speculative_config.method = "eagle3"
         self.vllm_config.speculative_config.draft_model_config.get_hidden_size.return_value = 2048
+        self.vllm_config.speculative_config.draft_model_config.get_inputs_embeds_size.return_value = 2048
         self.vllm_config.compilation_config.mode = CompilationMode.VLLM_COMPILE
         self.vllm_config.model_config.enforce_eager = False
         self.vllm_config.speculative_config.enforce_eager = False
@@ -133,6 +149,7 @@ def test_initialization_eagle3_full_graph_async(self):
     def test_initialization_mtp_full_graph_async(self):
         self.vllm_config.speculative_config.method = "mtp"
         self.vllm_config.speculative_config.draft_model_config.get_hidden_size.return_value = 2048
+        self.vllm_config.speculative_config.draft_model_config.get_inputs_embeds_size.return_value = 2048
         self.vllm_config.compilation_config.mode = CompilationMode.VLLM_COMPILE
         self.vllm_config.model_config.enforce_eager = False
         self.vllm_config.speculative_config.enforce_eager = False
@@ -196,7 +213,7 @@ def setUp(self):
         self.vllm_config.additional_config = None
         init_ascend_config(self.vllm_config)
 
-        self.mock_cpugpubuffer = patch("vllm.v1.spec_decode.eagle.CpuGpuBuffer")
+        self.mock_cpugpubuffer = patch(_CPU_GPU_BUFFER_TARGET)
         self.mock_cpugpubuffer.start()
         self.mock_supports_multimodal_inputs = patch(
             "vllm.multimodal.registry.MultiModalRegistry.supports_multimodal_inputs", return_value=False
@@ -332,7 +349,7 @@ def setUp(self):
         self.vllm_config.additional_config = None
         init_ascend_config(self.vllm_config)
 
-        self.mock_cpugpubuffer = patch("vllm.v1.spec_decode.eagle.CpuGpuBuffer")
+        self.mock_cpugpubuffer = patch(_CPU_GPU_BUFFER_TARGET)
         self.mock_cpugpubuffer.start()
         self.mock_supports_multimodal_inputs = patch(
             "vllm.multimodal.registry.MultiModalRegistry.supports_multimodal_inputs", return_value=False
@@ -483,7 +500,7 @@ def setUp(self):
         self.vllm_config.additional_config = None
         init_ascend_config(self.vllm_config)
 
-        self.mock_cpugpubuffer = patch("vllm.v1.spec_decode.eagle.CpuGpuBuffer")
+        self.mock_cpugpubuffer = patch(_CPU_GPU_BUFFER_TARGET)
         self.mock_cpugpubuffer.start()
         self.mock_supports_multimodal_inputs = patch(
             "vllm.multimodal.registry.MultiModalRegistry.supports_multimodal_inputs", return_value=False
@@ -558,7 +575,7 @@ def setUp_and_tearDown(self):
         self.vllm_config.additional_config = None
         init_ascend_config(self.vllm_config)
 
-        self.mock_cpugpubuffer = patch("vllm.v1.spec_decode.eagle.CpuGpuBuffer")
+        self.mock_cpugpubuffer = patch(_CPU_GPU_BUFFER_TARGET)
         self.mock_cpugpubuffer.start()
         self.mock_supports_multimodal_inputs = patch(
             "vllm.multimodal.registry.MultiModalRegistry.supports_multimodal_inputs", return_value=False
@@ -1263,7 +1280,7 @@ def setUp(self):
         self.vllm_config.additional_config = None
         init_ascend_config(self.vllm_config)
 
-        self.mock_cpugpubuffer = patch("vllm.v1.spec_decode.eagle.CpuGpuBuffer", MockCpuGpuBuffer)
+        self.mock_cpugpubuffer = patch(_CPU_GPU_BUFFER_TARGET, MockCpuGpuBuffer)
         self.mock_cpugpubuffer.start()
         self.mock_supports_multimodal_inputs = patch(
             "vllm.multimodal.registry.MultiModalRegistry.supports_multimodal_inputs", return_value=False
@@ -1747,6 +1764,7 @@ def setUp(self):
         self.vllm_config.speculative_config.use_local_argmax_reduction = False
         self.vllm_config.speculative_config.draft_tensor_parallel_size = 1
         self.vllm_config.speculative_config.speculative_token_tree = str([(i + 1) * (0,) for i in range(3)])
+        self.vllm_config.speculative_config.draft_model_config.hf_config = MagicMock(spec=[])
         self.vllm_config.speculative_config.draft_model_config.get_hidden_size.return_value = 4
         self.vllm_config.speculative_config.draft_model_config.get_inputs_embeds_size.return_value = 4
         self.vllm_config.speculative_config.draft_model_config.uses_mrope = False
@@ -1755,7 +1773,7 @@ def setUp(self):
         self.vllm_config.additional_config = None
         init_ascend_config(self.vllm_config)
 
-        self.mock_cpugpubuffer = patch("vllm.v1.spec_decode.eagle.CpuGpuBuffer", MockCpuGpuBuffer)
+        self.mock_cpugpubuffer = patch(_CPU_GPU_BUFFER_TARGET, MockCpuGpuBuffer)
         self.mock_cpugpubuffer.start()
         self.mock_supports_multimodal_inputs = patch(
             "vllm.multimodal.registry.MultiModalRegistry.supports_multimodal_inputs", return_value=False
@@ -1876,7 +1894,14 @@ def check_mock(self):
 
         import vllm.v1.spec_decode.eagle
 
-        assert hasattr(vllm.v1.spec_decode.eagle, "CpuGpuBuffer")
+        # `CpuGpuBuffer` was re-exported from `eagle` until vLLM #40732 moved
+        # `SpecDecodeBaseProposer` (and the import) into `llm_base_proposer`.
+        if vllm_version_is("0.19.1"):
+            assert hasattr(vllm.v1.spec_decode.eagle, "CpuGpuBuffer")
+        else:
+            import vllm.v1.spec_decode.llm_base_proposer
+
+            assert hasattr(vllm.v1.spec_decode.llm_base_proposer, "CpuGpuBuffer")
         RunnerCls = vllm.v1.spec_decode.eagle.SpecDecodeBaseProposer
         for attr in ("_get_positions", "_set_positions"):
             assert hasattr(RunnerCls, attr), f"SpecDecodeBaseProposer.{attr} not found"

vllm_ascend/_310p/fused_moe/fused_moe.py

@@ -20,7 +20,6 @@
 from vllm.distributed import get_dp_group, get_ep_group, get_tp_group
 from vllm.model_executor.layers.fused_moe.config import FusedMoEConfig
 from vllm.model_executor.layers.fused_moe.layer import FusedMoE, UnquantizedFusedMoEMethod
-from vllm.model_executor.layers.fused_moe.shared_fused_moe import SharedFusedMoE
 
 from vllm_ascend.ascend_forward_context import _EXTRA_CTX, MoECommType
 from vllm_ascend.ops.fused_moe.experts_selector import zero_experts_compute
@@ -40,6 +39,11 @@ def __init__(self, moe: FusedMoEConfig = None):
     def is_monolithic(self) -> bool:
         return False
 
+    def maybe_make_prepare_finalize(self, routing_tables=None):
+        # Ascend 310P uses its own MoE communication and forward_impl path.
+        # Do not let upstream modular-kernel initialization replace it.
+        return None
+
     def process_weights_after_loading(self, layer):
         super().process_weights_after_loading(layer)
 
@@ -119,6 +123,8 @@ class AscendFusedMoE310(FusedMoE):
     def __init__(self, *args, **kwargs):
         super().__init__(*args, **kwargs)
 
+        self._routed_input_transform = kwargs.get("routed_input_transform")
+        self._shared_experts = kwargs.get("shared_experts")
         self.global_num_experts = kwargs["num_experts"]
 
         if self.quant_config is None:
@@ -127,6 +133,10 @@ def __init__(self, *args, **kwargs):
             self.quant_method = self.quant_config.get_quant_method(self, self.layer_name)
 
         assert self.quant_method is not None
+        # Keep base_quant_method aligned with the Ascend-replaced quant_method
+        # so FusedMoE.maybe_init_modular_kernel doesn't dispatch into the
+        # upstream UnquantizedFusedMoEMethod.maybe_make_prepare_finalize.
+        self.base_quant_method = self.quant_method
 
         self.moe_config.tp_group = get_tp_group()
         self.moe_config.dp_group = get_dp_group()
@@ -175,6 +185,11 @@ def __init__(self, *args, **kwargs):
             self.vllm_config.parallel_config.enable_dbo,
         )
 
+    @property
+    def is_internal_router(self) -> bool:
+        # 310P Ascend path expects router logits from the model forward path.
+        return False
+
     def init_experts_map(self, moe_config):
         """
         Initialize expert mapping for MoE (Mixture of Experts) model.
@@ -260,68 +275,12 @@ def forward_impl(  # type: ignore[override]
 
         return routed_out
 
-
-class AscendSharedFusedMoE310(SharedFusedMoE, AscendFusedMoE310):
-    def __init__(
-        self,
-        shared_experts: torch.nn.Module,
-        gate: torch.nn.Module | None = None,
-        use_overlapped: bool = True,
-        routed_input_transform: torch.nn.Module | None = None,
-        **kwargs,
-    ):
-        AscendFusedMoE310.__init__(self, **kwargs)
-        self._routed_input_transform = routed_input_transform
-        self._shared_experts = shared_experts
-        self.use_overlapped = use_overlapped
-        self.shared_expert_stream = None
-        self._gate = gate
-        # Recreate runner after shared_experts/gate are set so custom op dispatch
-        # goes through moe_forward_shared.
-        # NOTE: must use self._shared_experts here, not self.shared_experts —
-        # FusedMoE.shared_experts is a property that reads self.runner.shared_experts,
-        # which at this point is still the stale runner built with shared_experts=None.
-        from vllm_ascend.ops.fused_moe.fused_moe import AscendMoERunner
-
-        self.runner = AscendMoERunner(
-            self.layer_name,
-            self.moe_config,
-            self.router,
-            self._routed_input_transform,
-            self._gate,
-            self._shared_experts,
-            self.quant_method,
-            self.reduce_results,
-            self.vllm_config.parallel_config.enable_dbo,
-        )
-
-    @property
-    def is_internal_router(self) -> bool:
-        # 310P Ascend path expects router logits from the model forward path.
-        return False
-
-    def forward(
-        self,
-        hidden_states: torch.Tensor,
-        router_logits: torch.Tensor,
-    ) -> tuple[torch.Tensor, torch.Tensor]:
-        result = AscendFusedMoE310.forward(
-            self,
-            hidden_states=hidden_states,
-            router_logits=router_logits,
-        )
-        # When shared experts are absent, the parent returns only fused_out;
-        # otherwise it returns a (shared_out, fused_out) tuple.
-        if self._shared_experts is None:
-            return None, result
-        return result
-
     def _forward_shared_experts(self, hidden_states: torch.Tensor):
         if self._shared_experts is None:
             return None
         return self._shared_experts(hidden_states)
 
-    def forward_impl(  # type: ignore[override]
+    def shared_forward_impl(  # type: ignore[override]
         self, hidden_states: torch.Tensor, router_logits: torch.Tensor
     ):
         routed_out = AscendFusedMoE310.forward_impl(