[Feat] 310p support MoE W8A8 quantizaition (#6641)

### What this PR does / why we need it?
This PR introduces support for W8A8 dynamic quantization for
Mixture-of-Experts (MoE) models on Ascend 310P devices. This is achieved
by:
- Implementing a new quantization scheme
`AscendW8A8DynamicFusedMoEMethod310`.
- Adding a unified MLP implementation (`unified_apply_mlp`) for 310P
that handles both quantized and unquantized paths.
- Refactoring the MoE and quantization configuration logic to correctly
route to the new 310P-specific implementations.
- Adding new e2e and unit tests to verify the functionality of MoE W8A8
quantization.

### Does this PR introduce _any_ user-facing change?
No

### How was this patch tested?
- Added a new e2e test `test_qwen3_moe_tp2_w8a8` to test MoE W8A8
quantization in a multi-card setup.
- Added several new unit tests for the 310P-specific MoE components,
including `experts_selector`, `fused_moe`, `moe_comm_method`, `moe_mlp`,
and the new `w8a8_dynamic` quantization method.

- vLLM version: v0.15.0
- vLLM main:
d7e17aaacd

---------

Signed-off-by: pu-zhe <zpuaa@outlook.com>

tests/ut/_310p/fused_moe/test_experts_selector_310.py

@@ -0,0 +1,42 @@
+#
+# Copyright (c) 2026 Huawei Technologies Co., Ltd. All Rights Reserved.
+#
+# 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.
+
+import pytest
+import torch
+
+from vllm_ascend._310p.fused_moe.experts_selector import select_experts
+
+
+class TestExpertsSelector310:
+    @pytest.mark.parametrize("global_num_experts", [256, 128])
+    def test_select_experts(self, global_num_experts):
+        x = torch.randn(8, 2)
+        router_logits = torch.randn(8, 2)
+        topk_weights, topk_ids = select_experts(
+            hidden_states=x,
+            router_logits=router_logits,
+            top_k=2,
+            use_grouped_topk=False,
+            renormalize=True,
+            topk_group=None,
+            num_expert_group=None,
+            custom_routing_function=None,
+            scoring_func="softmax",
+            e_score_correction_bias=None,
+            global_num_experts=global_num_experts,
+        )
+
+        assert topk_weights.shape == (8, 2)
+        assert topk_ids.shape == (8, 2)

tests/ut/_310p/fused_moe/test_moe_mlp_310.py

@@ -0,0 +1,132 @@
+#
+# Copyright (c) 2026 Huawei Technologies Co., Ltd. All Rights Reserved.
+#
+# 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.
+
+from unittest.mock import call, patch
+
+import torch
+
+from tests.ut.base import TestBase
+from vllm_ascend._310p.fused_moe.moe_mlp import unified_apply_mlp
+
+
+class TestUnifiedApplyMLP310(TestBase):
+    @patch("torch_npu.npu_grouped_matmul")
+    @patch("torch_npu.npu_swiglu")
+    def test_unified_apply_mlp_without_quantization_310(self, mock_npu_swiglu, mock_npu_grouped_matmul):
+        mock_gmm1_out = torch.randn(10, 40, dtype=torch.float16)
+        mock_gmm2_out = torch.randn(10, 20, dtype=torch.float16)
+        mock_npu_grouped_matmul.side_effect = [[mock_gmm1_out], [mock_gmm2_out]]
+
+        mock_npu_swiglu_output = torch.randn(10, 40, dtype=torch.float16)
+        mock_npu_swiglu.return_value = mock_npu_swiglu_output
+
+        hidden_states = torch.randn(10, 20, dtype=torch.float16)
+        w1 = torch.randn(5, 20, 40, dtype=torch.float16)
+        w2 = torch.randn(5, 40, 20, dtype=torch.float16)
+        group_list = torch.tensor([2, 4, 6, 8, 10], dtype=torch.int64)
+
+        result = unified_apply_mlp(
+            hidden_states=hidden_states,
+            w1=w1,
+            w1_scale=None,
+            w2=w2,
+            w2_scale=None,
+            group_list=group_list,
+            group_list_type=1,
+            with_quant=False,
+        )
+
+        self.assertEqual(mock_npu_grouped_matmul.call_count, 2)
+        mock_npu_grouped_matmul.assert_has_calls(
+            [
+                call(
+                    x=[hidden_states], weight=[w1], split_item=2, group_list_type=1, group_type=0, group_list=group_list
+                ),
+                call(
+                    x=[mock_npu_swiglu_output],
+                    weight=[w2],
+                    split_item=2,
+                    group_list_type=1,
+                    group_type=0,
+                    group_list=group_list,
+                ),
+            ],
+            any_order=True,
+        )
+        mock_npu_swiglu.assert_called_once()
+        mock_npu_swiglu.assert_called_with(mock_gmm1_out)
+
+        self.assertEqual(result.shape, hidden_states.shape)
+        self.assertEqual(result.dtype, torch.float16)
+
+    @patch("torch.cumsum")
+    @patch("torch_npu.npu_quant_grouped_matmul_dequant")
+    @patch("torch_npu.npu_swiglu")
+    def test_unified_apply_mlp_with_quantization_310(
+        self, mock_npu_swiglu, mock_npu_quant_grouped_matmul_dequant, mock_cumsum
+    ):
+        mock_cumsum_out = torch.arange(0, 10, dtype=torch.int64)
+        mock_cumsum.return_value = mock_cumsum_out
+        mock_gmm1_out = torch.randn(10, 40, dtype=torch.float16)
+        mock_gmm2_out = torch.randn(10, 20, dtype=torch.float16)
+        mock_npu_quant_grouped_matmul_dequant.side_effect = [mock_gmm1_out, mock_gmm2_out]
+
+        mock_npu_swiglu_output = torch.randn(10, 40, dtype=torch.float16)
+        mock_npu_swiglu.return_value = mock_npu_swiglu_output
+
+        hidden_states = torch.randn(10, 20, dtype=torch.float16)
+        w1 = torch.randn(5, 20, 40, dtype=torch.float16)
+        w1_scale = torch.rand(5, 40, dtype=torch.float32)
+        w2 = torch.randn(5, 40, 20, dtype=torch.float16)
+        w2_scale = torch.rand(5, 40, dtype=torch.float32)
+        group_list = torch.tensor([2, 4, 6, 8, 10], dtype=torch.int64)
+
+        result = unified_apply_mlp(
+            hidden_states=hidden_states,
+            w1=w1,
+            w1_scale=w1_scale,
+            w2=w2,
+            w2_scale=w2_scale,
+            group_list=group_list,
+            group_list_type=1,
+            with_quant=True,
+        )
+
+        mock_cumsum.assert_called_once()
+        self.assertEqual(mock_npu_quant_grouped_matmul_dequant.call_count, 2)
+        mock_npu_quant_grouped_matmul_dequant.assert_has_calls(
+            [
+                call(
+                    x=hidden_states,
+                    quantized_weight=w1,
+                    weight_scale=w1_scale,
+                    group_list=mock_cumsum_out,
+                    quant_mode="pertoken",
+                ),
+                call(
+                    x=mock_npu_swiglu_output,
+                    quantized_weight=w2,
+                    weight_scale=w2_scale,
+                    group_list=mock_cumsum_out,
+                    quant_mode="pertoken",
+                ),
+            ],
+            any_order=True,
+        )
+        mock_npu_swiglu.assert_called_once()
+        mock_npu_swiglu.assert_called_with(mock_gmm1_out)
+
+        self.assertEqual(result.shape, hidden_states.shape)
+        self.assertEqual(result.dtype, torch.float16)