[Feat] 310p support MoE W8A8 quantizaition

tests/e2e/310p/multicard/test_moe_model_multicard.py

@@ -44,3 +44,17 @@ def test_qwen3_moe_ep4_fp16():
             enable_expert_parallel=True
     ) as vllm_model:
         vllm_model.generate_greedy(example_prompts, max_tokens)
+
+def test_qwen3_moe_tp2_w8a8():
+    example_prompts = [
+        "Hello, my name is",
+    ]
+    max_tokens = 5
+    with VllmRunner(
+            "vllm-ascend/Qwen3-30B-A3B-W8A8",
+            tensor_parallel_size=2,
+            enforce_eager=True,
+            dtype="float16",
+            quantization="ascend"
+    ) as vllm_model:
+        vllm_model.generate_greedy(example_prompts, max_tokens)

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)

tests/ut/_310p/quantization/test_modelslim_config_310.py

@@ -1,10 +1,26 @@
+#
+# 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 MagicMock, patch
 
 from vllm.model_executor.layers.fused_moe import FusedMoE
-from vllm.model_executor.layers.fused_moe.config import FusedMoEConfig
+from vllm.model_executor.layers.fused_moe.config import FusedMoEConfig, FusedMoEParallelConfig
 from vllm.model_executor.layers.linear import LinearBase
 
 from tests.ut.base import TestBase
+from vllm_ascend._310p.fused_moe.fused_moe import AscendUnquantizedFusedMoEMethod310
 from vllm_ascend._310p.quantization.modelslim_config import AscendModelSlimConfig310
 from vllm_ascend.ops.linear import AscendUnquantizedLinearMethod
 
@@ -31,7 +47,7 @@ def test_get_quant_method_for_linear_310(self):
         # Test skipped layer
         with (
             patch("vllm_ascend._310p.quantization.modelslim_config.get_current_vllm_config", return_value=mock_config),
-            patch.object(self.ascend_config, "is_layer_skipped_ascend", return_value=True)
+            patch.object(self.ascend_config, "is_layer_skipped_ascend", return_value=True),
         ):
             method = self.ascend_config.get_quant_method(linear_layer, ".attn")
             self.assertIsInstance(method, AscendUnquantizedLinearMethod)
@@ -54,14 +70,35 @@ def test_get_quant_method_for_fused_moe_310(self):
         fused_moe_layer = MagicMock(spec=FusedMoE)
         fused_moe_layer.moe = MagicMock(spec=FusedMoEConfig)
         fused_moe_layer.moe_config = MagicMock(spec=FusedMoEConfig)
+        fused_moe_layer.moe_config.moe_parallel_config = MagicMock(spec=FusedMoEParallelConfig)
+        fused_moe_layer.moe_config.moe_parallel_config.use_ep = True
+        fused_moe_layer.moe_config.moe_parallel_config.dp_size = 1
         mock_config = MagicMock()
         mock_config.model_config.hf_config.model_type = None
+        mock_config.compilation_config.custom_ops = ["all"]
+        mock_scheme = MagicMock()
+        # Test skipped layer
+        with (
+            patch("vllm.config.vllm.get_current_vllm_config", return_value=mock_config),
+            patch("vllm_ascend._310p.quantization.modelslim_config.get_current_vllm_config", return_value=mock_config),
+            patch("vllm_ascend.quantization.modelslim_config.get_current_vllm_config", return_value=mock_config),
+            patch.object(self.ascend_config, "is_layer_skipped_ascend", return_value=True),
+        ):
+            method = self.ascend_config.get_quant_method(fused_moe_layer, ".moe")
+            self.assertIsInstance(method, AscendUnquantizedFusedMoEMethod310)
+
+        # Test quantized layer
         mock_scheme = MagicMock()
         with (
             patch.object(self.ascend_config, "is_layer_skipped_ascend", return_value=False),
+            patch("vllm.config.vllm.get_current_vllm_config", return_value=mock_config),
             patch("vllm_ascend._310p.quantization.modelslim_config.get_current_vllm_config", return_value=mock_config),
+            patch("vllm_ascend.quantization.modelslim_config.get_current_vllm_config", return_value=mock_config),
             patch("vllm_ascend._310p.quantization.modelslim_config.create_scheme_for_layer", return_value=mock_scheme),
-            patch("vllm_ascend._310p.quantization.modelslim_config.AscendLinearMethod", return_value=MagicMock()),
-            self.assertRaises(NotImplementedError),
+            patch(
+                "vllm_ascend._310p.quantization.modelslim_config.AscendFusedMoEMethod", return_value=MagicMock()
+            ) as fused_moe_method,
         ):
-            self.ascend_config.get_quant_method(fused_moe_layer, "moe_layer")
+            method = self.ascend_config.get_quant_method(fused_moe_layer, ".moe")
+            self.assertIs(method, fused_moe_method.return_value)
+            fused_moe_method.assert_called_once_with(mock_scheme, fused_moe_layer.moe_config)

tests/ut/_310p/quantization/test_w8a8_dynamic_310.py

@@ -0,0 +1,66 @@
+#
+# 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 Mock, patch
+
+import torch
+
+from tests.ut.base import TestBase
+from vllm_ascend._310p.quantization.methods.w8a8_dynamic import AscendW8A8DynamicFusedMoEMethod310
+
+
+class TestAscendW8A8FusedMoEMethod310(TestBase):
+    num_experts = 8
+    hidden_size = 128
+    intermediate_size = 128
+
+    @patch("vllm_ascend._310p.quantization.methods.w8a8_dynamic.get_ep_group")
+    def setUp(self, mock_get_ep_group):
+        with patch(
+            "vllm_ascend._310p.quantization.methods.w8a8_dynamic.get_current_vllm_config"
+        ) as mock_get_current_vllm_config:
+            mock_vllm_config = Mock()
+            mock_vllm_config.quant_config = Mock(quant_description={"group_size": 0})
+            mock_vllm_config.scheduler_config = Mock(
+                max_num_batched_tokens=2048, max_model_len=2048, enable_chunked_prefill=False
+            )
+            mock_get_current_vllm_config.return_value = mock_vllm_config
+            mock_ep_group = Mock()
+            mock_get_ep_group.return_value = mock_ep_group
+            mock_ascend_config = Mock()
+
+            mock_ascend_config.enable_chunked_prefill = False
+
+            self.quant_method = AscendW8A8DynamicFusedMoEMethod310()
+
+    def test_get_weight_310(self):
+        param_dict = self.quant_method.get_weight(
+            self.num_experts, self.intermediate_size, self.hidden_size, torch.float16
+        )
+        self.assertEqual(param_dict["w13_weight"].dtype, torch.int8)
+        self.assertEqual(
+            param_dict["w13_weight"].shape, (self.num_experts, 2 * self.intermediate_size, self.hidden_size)
+        )
+        self.assertEqual(param_dict["w2_weight"].dtype, torch.int8)
+        self.assertEqual(param_dict["w2_weight"].shape, (self.num_experts, self.hidden_size, self.intermediate_size))
+
+    def test_get_dynamic_quant_param_310(self):
+        param_dict = self.quant_method.get_dynamic_quant_param(
+            self.num_experts, self.intermediate_size, self.hidden_size, torch.float16
+        )
+        self.assertEqual(param_dict["w13_weight_scale"].dtype, torch.float32)
+        self.assertEqual(param_dict["w13_weight_scale"].shape, (self.num_experts, 2 * self.intermediate_size, 1))
+        self.assertEqual(param_dict["w2_weight_scale"].dtype, torch.float32)
+        self.assertEqual(param_dict["w2_weight_scale"].shape, (self.num_experts, self.hidden_size, 1))

tests/ut/_310p/quantization/test_w8a8_310.py → tests/ut/_310p/quantization/test_w8a8_static_310.py

@@ -1,3 +1,18 @@
+#
+# 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 MagicMock, patch
 
 import torch
@@ -16,19 +31,19 @@ def test_get_weight_310(self):
         self.assertEqual(weight["weight"].shape, (20, 10))
 
     def test_get_pertensor_param_310(self):
-        params = self.method.get_pertensor_param(torch.bfloat16)
-        self.assertEqual(params["input_scale"].dtype, torch.bfloat16)
+        params = self.method.get_pertensor_param(torch.float16)
+        self.assertEqual(params["input_scale"].dtype, torch.float16)
         self.assertEqual(params["input_offset"].dtype, torch.int8)
         self.assertEqual(params["input_scale"].shape, (1,))
         self.assertEqual(params["input_offset"].shape, (1,))
 
     def test_get_perchannel_param_310(self):
-        params = self.method.get_perchannel_param(10, torch.bfloat16)
+        params = self.method.get_perchannel_param(10, torch.float16)
 
         self.assertEqual(params["quant_bias"].dtype, torch.int32)
-        self.assertEqual(params["deq_scale"].dtype, torch.float32)
-        self.assertEqual(params["weight_scale"].dtype, torch.bfloat16)
-        self.assertEqual(params["weight_offset"].dtype, torch.bfloat16)
+        self.assertEqual(params["deq_scale"].dtype, torch.int64)
+        self.assertEqual(params["weight_scale"].dtype, torch.float16)
+        self.assertEqual(params["weight_offset"].dtype, torch.float16)
         self.assertEqual(params["quant_bias"].shape, (10,))
         self.assertEqual(params["deq_scale"].shape, (10,))
         self.assertEqual(params["weight_scale"].shape, (10, 1))

vllm_ascend/_310p/fused_moe/experts_selector.py

@@ -19,7 +19,6 @@
 import torch
 
 from vllm_ascend.ops.fused_moe.experts_selector import _native_select_experts
-from vllm_ascend.utils import get_weight_prefetch_method
 
 
 def select_experts(
@@ -55,9 +54,6 @@ def select_experts(
         topk_weights: router weights of shape (num_tokens, top_k).
         topk_ids: selected expert IDs of shape (num_tokens, top_k).
     """
-    # prefetch w1_w3_proj.weight preprocess
-    weight_prefetch_method = get_weight_prefetch_method()
-    weight_prefetch_method.maybe_prefetch_moe_weight_preprocess(hidden_states, "gate_up")
     topk_weights, topk_ids = _native_select_experts(
         hidden_states=hidden_states,
         router_logits=router_logits,