[Main] [Refactor] Enable MoECommMethod in Eager Mode

tests/ut/models/test_deepseek_v2.py

@@ -23,8 +23,7 @@
 
 from vllm_ascend.models.deepseek_v2 import (
     CustomDeepseekV2MergedReplicatedLinear, CustomDeepseekV2MLAAttention,
-    CustomDeepseekV2MLP, CustomDeepseekV2MoE,
-    CustomDeepseekV2RowParallelLinear,
+    CustomDeepseekV2MLP, CustomDeepseekV2RowParallelLinear,
     CustomDeepseekV2RowParallelLinearReplaceAllreduce,
     CustomDeepseekV2SiluAndMul, LogitsProcessor, ParallelLMHead)
 
@@ -213,22 +212,6 @@ def test_custom_deepseek_v2_mlp(mock_distributed, base_config):
                             quant_config=None)
 
 
-def test_custom_deepseek_v2_moe(mock_distributed, base_config,
-                                mock_forward_context):
-    base_config.n_shared_experts = 1
-    moe = CustomDeepseekV2MoE(config=base_config,
-                              quant_config=None,
-                              prefix="mlp")
-    assert moe.top_k == 2
-
-    x = torch.randn(2, 4, 128)
-    attn_metadata = Mock(num_prefills=1)
-    with patch("vllm_ascend.ops.fused_moe.AscendFusedMoE.__call__",
-               return_value=(torch.randn(2, 4, 128), torch.randn(2, 4, 128))):
-        output = moe(x, attn_metadata)
-        assert output.shape == (2, 4, 128)
-
-
 @patch("torch_npu.npu_rms_norm")
 def test_custom_deepseek_v2_mla_attention(mock_rms_norm, mock_distributed,
                                           base_config):

tests/ut/ops/test_fused_moe_prepare_and_finalize.py

@@ -6,7 +6,8 @@
 
 from vllm_ascend.ops.moe.fused_moe_prepare_and_finalize import (
     FusedMoEPrepareAndFinalizeWithAll2All,
-    FusedMoEPrepareAndFinalizeWithAllGather, FusedMoEPrepareAndFinalizeWithMC2)
+    FusedMoEPrepareAndFinalizeWithAllGather, FusedMoEPrepareAndFinalizeWithMC2,
+    FusedMoEPrepareAndFinalizeWithNaiveMulticast)
 
 
 class TestFusedMoEPrepareAndFinalize(unittest.TestCase):
@@ -216,3 +217,68 @@ def mock_reduce_scatter_func(tensor, dim):
         mock_tp_all_reduce.return_value = result
         result_with_tp = layer.finalize(h_out, reduce_results=True)
         self.assertEqual(result_with_tp.shape[0], 3)
+
+    @patch("vllm_ascend.ops.moe.fused_moe_prepare_and_finalize.get_dp_group")
+    @patch(
+        "vllm_ascend.ops.moe.fused_moe_prepare_and_finalize.tensor_model_parallel_all_reduce"
+    )
+    @patch(
+        "vllm_ascend.ops.moe.fused_moe_prepare_and_finalize.get_forward_context"
+    )
+    def test_naive_multicast_prepare_finalize(self, mock_get_forward_context,
+                                              mock_tp_all_reduce,
+                                              mock_get_dp_group):
+        # Mock forward context with DP metadata
+        mock_context = MagicMock()
+        mock_context.dp_metadata.cu_tokens_across_dp_cpu = torch.tensor(
+            [2, 5, 7])
+        mock_get_forward_context.return_value = mock_context
+
+        # Setup DP group mock
+        mock_dp_group = MagicMock()
+        mock_dp_group.broadcast = MagicMock()
+        mock_dp_group.all_reduce = MagicMock()
+        mock_get_dp_group.return_value = mock_dp_group
+
+        # Mock all_reduce to just return input (simulate sum)
+        def mock_all_reduce(tensor):
+            return tensor * 2
+
+        mock_dp_group.all_reduce.side_effect = mock_all_reduce
+
+        # Setup config
+        self.moe_config.dp_size = 3
+        self.moe_config.dp_rank = 1
+        self.moe_config.tp_size = 1
+        self.moe_config.ep_size = 1
+
+        layer = FusedMoEPrepareAndFinalizeWithNaiveMulticast(self.moe_config)
+
+        # Local inputs
+        hidden_states = torch.randn(3, 8)
+        router_logits = torch.randn(3, 2)
+
+        # Mock gate for router logits recomputation
+        mock_gate = MagicMock()
+        mock_gate.return_value = (torch.randn(7, 2), None)
+
+        # Run prepare
+        h_out, r_out, _ = layer.prepare(hidden_states,
+                                        router_logits,
+                                        rm_router_logits=False,
+                                        gate=mock_gate)
+
+        # Should be global tensor: [7, 8] and [7, 2]
+        self.assertEqual(h_out.shape, (7, 8))
+        self.assertEqual(r_out.shape, (7, 2))
+
+        # Run finalize
+        result = layer.finalize(h_out, reduce_results=False)
+
+        # Should slice back to local: [3, 8]
+        self.assertEqual(result.shape, (3, 8))
+
+        # Test with reduce_results=True and TP/EP > 1
+        mock_tp_all_reduce.return_value = result
+        result_with_tp = layer.finalize(h_out, reduce_results=True)
+        self.assertEqual(result_with_tp.shape, (3, 8))

tests/ut/ops/test_fused_ops.py

@@ -22,10 +22,7 @@
 from pytest_mock import MockerFixture
 from vllm.model_executor.layers.fused_moe import FusedMoEMethodBase
 
-import vllm_ascend.ops.moe.token_dispatcher as token_dispatcher_module
 from tests.ut.base import TestBase
-from vllm_ascend.ascend_forward_context import (FusedMoEState,
-                                                _get_fused_moe_state)
 from vllm_ascend.ops.fused_moe import (AscendFusedMoE,
                                        AscendUnquantizedFusedMoEMethod)
 from vllm_ascend.ops.moe.experts_selector import select_experts
@@ -60,68 +57,24 @@ def mock_npu_format_cast(weight_data, format):
 
 @pytest.fixture
 def mock_dist_env(mocker: MockerFixture):
-    mock_setup_token_dispatchers = MagicMock()
-    mock_token_dispatcher_with_allgather = MagicMock()
-    mock_token_dispatcher_with_all2allv = MagicMock()
-    mock_token_dispatcher_with_mc2 = MagicMock()
-
-    mock_dispatch_result_allgather = {
-        "hidden_states": torch.randn(16, 2),
-        "group_list": torch.tensor([8, 16], dtype=torch.int64),
-        "group_list_type": 0,
-    }
-    mock_combine_result_allgather = torch.randn(16, 2)
-
-    mock_token_dispatcher_with_allgather.token_dispatch.return_value = mock_dispatch_result_allgather
-    mock_token_dispatcher_with_allgather.token_combine.return_value = mock_combine_result_allgather
-
-    mock_dispatch_result_all2allv = {
-        "hidden_states": torch.randn(16, 2),
-        "group_list": torch.tensor([4, 8, 12, 16], dtype=torch.int64),
-        "group_list_type": 1,
-        "dynamic_scale": None,
-    }
-    mock_combine_result_all2allv = torch.randn(16, 2)
-    mock_token_dispatcher_with_all2allv.token_dispatch.return_value = mock_dispatch_result_all2allv
-    mock_token_dispatcher_with_all2allv.token_combine.return_value = mock_combine_result_all2allv
-
-    mock_dispatch_result_mc2 = {
-        "hidden_states": torch.randn(16, 2),
-        "group_list": torch.tensor([5, 10, 15, 16], dtype=torch.int64),
-        "group_list_type": 1,
-        "dynamic_scale": None,
-        "assist_info_for_combine": torch.randn(16, 2),
-        "ep_recv_counts": torch.tensor([4, 4, 4, 4], dtype=torch.int32),
-    }
-    mock_combine_result_mc2 = torch.randn(16, 2)
-    mock_token_dispatcher_with_mc2.token_dispatch.return_value = mock_dispatch_result_mc2
-    mock_token_dispatcher_with_mc2.token_combine.return_value = mock_combine_result_mc2
+    mock_moe_comm_method = MagicMock()
 
-    captured_dispatchers = {}
+    def mock_prepare(hidden_states, router_logits, **kwargs):
+        return hidden_states, router_logits
 
-    def capture_register(dispatcher_instance):
-        key = dispatcher_instance.__class__.__name__
-        captured_dispatchers[key] = dispatcher_instance
-        if key == 'TokenDispatcherWithAllGather':
-            captured_dispatchers[key] = mock_token_dispatcher_with_allgather
-        elif key == 'TokenDispatcherWithAll2AllV':
-            captured_dispatchers[key] = mock_token_dispatcher_with_all2allv
-        elif key == 'TokenDispatcherWithMC2':
-            captured_dispatchers[key] = mock_token_dispatcher_with_mc2
+    mock_moe_comm_method.prepare.side_effect = mock_prepare
 
-    mock_register_token_dispatcher_patcher = patch(
-        'vllm_ascend.ops.moe.token_dispatcher._register_token_dispatcher',
-        side_effect=capture_register)
+    mock_fused_experts_result = torch.randn(16, 2)
+    mock_moe_comm_method.fused_experts.return_value = mock_fused_experts_result
 
-    mock_get_token_dispatcher_patcher = patch(
-        'vllm_ascend.ops.moe.token_dispatcher.get_token_dispatcher',
-        side_effect=lambda name: captured_dispatchers.get(name))
+    def mock_finalize(hidden_states, **kwargs):
+        return hidden_states
 
-    default_mock_token_dispatcher = mock_token_dispatcher_with_allgather
+    mock_moe_comm_method.finalize.side_effect = mock_finalize
 
     mock_forward_context_obj = MagicMock(
-        fused_moe_state=FusedMoEState.AllGather,
-        token_dispatcher=default_mock_token_dispatcher,
+        moe_comm_method=mock_moe_comm_method,
+        moe_comm_method_name="mc2commimpl",
         max_tokens_across_dp=10,
         dp_metadata=MagicMock(cu_tokens_across_dp_cpu=[5, 10]),
         mc2_mask=torch.zeros(16, dtype=torch.bool),
@@ -131,14 +84,12 @@ def capture_register(dispatcher_instance):
     with patch('torch.distributed.get_rank', return_value=0), \
         patch('torch.distributed.get_world_size', return_value=4), \
         patch('vllm_ascend.ops.fused_moe.get_ep_group', return_value=mock_ep_and_mc2_group(mocker)), \
+        patch('vllm_ascend.ops.moe.token_dispatcher.get_ep_group', return_value=mock_ep_and_mc2_group(mocker)), \
         patch('vllm_ascend.ops.fused_moe.get_mc2_group', return_value=mock_ep_and_mc2_group(mocker)), \
         patch('vllm_ascend.ops.fused_moe.get_tp_group', return_value=mock_dp_and_tp_group(mocker)), \
         patch('vllm.distributed.parallel_state.get_tp_group', return_value=mock_dp_and_tp_group(mocker)), \
         patch('vllm_ascend.ops.fused_moe.get_dp_group', return_value=mock_dp_and_tp_group(mocker)), \
         patch('vllm.model_executor.layers.fused_moe.layer.get_dp_group', return_value=mock_dp_and_tp_group(mocker)), \
-        patch('torch.distributed.all_gather'), \
-        patch('torch.distributed.all_to_all_single'), \
-        patch('vllm_ascend.ops.fused_moe.tensor_model_parallel_all_reduce'), \
         patch('vllm.model_executor.layers.fused_moe.config.get_dp_group',
             return_value=mock_dp_and_tp_group(mocker)), \
         patch('vllm_ascend.ops.fused_moe.get_ascend_config',
@@ -150,29 +101,29 @@ def capture_register(dispatcher_instance):
             return_value=(3, torch.tensor([0, 1, 2, -1, -1, -1, -1, -1]))), \
         patch('vllm_ascend.ops.fused_moe.get_forward_context',
             return_value=mock_forward_context_obj), \
+        patch('vllm_ascend.ops.moe.fused_moe_prepare_and_finalize.get_forward_context',
+            return_value=mock_forward_context_obj), \
         patch('vllm_ascend.ops.fused_moe.get_current_vllm_config',
                 return_value=MagicMock(
                     parallel_config=MagicMock(tensor_parallel_size=2),
                     scheduler_config=MagicMock(max_num_seqs=4),
                     model_config=MagicMock(max_model_len=2048)
                 )), \
         patch("vllm_ascend.utils.get_ascend_soc_version", return_value=AscendSocVersion.A3), \
-        patch.object(token_dispatcher_module, 'setup_token_dispatchers', mock_setup_token_dispatchers), \
         patch('vllm_ascend.ops.moe.moe_mlp.get_forward_context',
-                return_value=mock_forward_context_obj):
+                return_value=mock_forward_context_obj), \
+        patch('vllm_ascend.ops.moe.moe_comm_method.MC2CommImpl._get_token_dispatcher',
+              return_value=None), \
+        patch('vllm_ascend.ops.moe.moe_comm_method.AlltoAllCommImpl._get_token_dispatcher',
+              return_value=None), \
+        patch('vllm_ascend.ops.moe.moe_comm_method.AllGatherCommImpl._get_token_dispatcher',
+              return_value=None):
 
         yield {
             'mock_forward_context_obj': mock_forward_context_obj,
-            'mock_token_dispatcher_with_allgather':
-            mock_token_dispatcher_with_allgather,
-            'mock_token_dispatcher_with_all2allv':
-            mock_token_dispatcher_with_all2allv,
-            'mock_token_dispatcher_with_mc2': mock_token_dispatcher_with_mc2,
+            'mock_moe_comm_method': mock_moe_comm_method,
         }
 
-    mock_register_token_dispatcher_patcher.stop()
-    mock_get_token_dispatcher_patcher.stop()
-
 
 @pytest.fixture
 def mock_moe_env(mocker: MockerFixture):
@@ -338,9 +289,7 @@ def test_forward(self, mock_dist_env, default_moe_config, others_param):
             moe.moe_parallel_config.ep_size = 1
 
         moe.quant_method = MockQuantMethod(shared_experts, num_tokens)
-        forward_context = MagicMock(mc2_mask=torch.zeros(num_tokens,
-                                                         dtype=torch.bool),
-                                    padded_num_tokens=num_tokens)
+        forward_context = mock_dist_env['mock_forward_context_obj']
         with patch("vllm_ascend.ops.fused_moe.get_forward_context",
                    return_value=forward_context):
             output = moe.forward(inputs,
@@ -394,25 +343,10 @@ def test_process_weights_after_loading(self, moe_method, mock_dist_env):
                              [[256, 4], [128, 1], [128, 1], [128, 4]])
     def test_apply_without_expert_map(self, moe_method, mock_dist_env,
                                       mock_moe_env, others_param):
-
         global_num_experts, ep_size = others_param
         is_prefill = False
-        is_deepseek_v3_r1 = global_num_experts == 256
-
-        if ep_size == 1:
-            selected_token_dispatcher = mock_dist_env[
-                'mock_token_dispatcher_with_allgather']
-        elif ep_size < 16:
-            selected_token_dispatcher = mock_dist_env[
-                'mock_token_dispatcher_with_all2allv']
-        else:
-            selected_token_dispatcher = mock_dist_env[
-                'mock_token_dispatcher_with_mc2']
 
-        forward_context = MagicMock(fused_moe_state=_get_fused_moe_state(
-            ep_size, is_prefill, is_deepseek_v3_r1),
-                                    with_quant=False,
-                                    token_dispatcher=selected_token_dispatcher)
+        forward_context = mock_dist_env['mock_forward_context_obj']
 
         with patch("vllm_ascend.ops.fused_moe.get_forward_context",
                    return_value=forward_context):
@@ -438,35 +372,22 @@ def test_apply_without_expert_map(self, moe_method, mock_dist_env,
                                       global_num_experts=global_num_experts,
                                       is_prefill=is_prefill)
 
-            expected_shape = (16, 2)
+            mock_moe_comm_method = mock_dist_env['mock_moe_comm_method']
+            mock_moe_comm_method.fused_experts.assert_called_once()
 
+            expected_shape = (16, 2)
             assert result.shape == expected_shape
 
     @pytest.mark.parametrize("others_param", [16, 1, 4])
     def test_apply_with_expert_map(self, moe_method, mock_dist_env,
                                    mock_moe_env, others_param):
-
         ep_size = others_param
         is_prefill = False
 
-        if ep_size == 1:
-            selected_token_dispatcher = mock_dist_env[
-                'mock_token_dispatcher_with_allgather']
-        elif ep_size < 16:
-            selected_token_dispatcher = mock_dist_env[
-                'mock_token_dispatcher_with_all2allv']
-        else:
-            selected_token_dispatcher = mock_dist_env[
-                'mock_token_dispatcher_with_mc2']
-
-        forward_context = MagicMock(fused_moe_state=_get_fused_moe_state(
-            ep_size, is_prefill, True),
-                                    with_quant=False,
-                                    token_dispatcher=selected_token_dispatcher)
+        forward_context = mock_dist_env['mock_forward_context_obj']
 
         with patch("vllm_ascend.ops.fused_moe.get_forward_context", return_value=forward_context), \
              patch("vllm_ascend.utils.get_ascend_soc_version", return_value=AscendSocVersion.A3):
-
             expert_map = torch.tensor([0, 1, 2, -1, -1, -1, -1, -1])
             moe_method.ep_size = ep_size
             x = torch.randn(8, 2, 2)
@@ -493,8 +414,10 @@ def test_apply_with_expert_map(self, moe_method, mock_dist_env,
                                       expert_map=expert_map,
                                       is_prefill=is_prefill)
 
-            expected_shape = (16, 2)
+            mock_moe_comm_method = mock_dist_env['mock_moe_comm_method']
+            mock_moe_comm_method.fused_experts.assert_called_once()
 
+            expected_shape = (16, 2)
             assert result.shape == expected_shape
 
 
@@ -574,7 +497,7 @@ def test_unified_apply_mlp_with_quantization_mc2(self, mock_npu_dequant,
                                                      mock_get_forward_context):
 
         mock_forward_context = MagicMock()
-        mock_forward_context.fused_moe_state = FusedMoEState.MC2
+        mock_forward_context.moe_comm_method_name = "mc2commimpl"
         mock_get_forward_context.return_value = mock_forward_context
 
         mock_is_310p.return_value = False
@@ -618,8 +541,6 @@ def test_unified_apply_mlp_with_quantization_mc2(self, mock_npu_dequant,
                                    with_quant=True)
 
         mock_get_forward_context.assert_called()
-        self.assertEqual(mock_forward_context.fused_moe_state,
-                         FusedMoEState.MC2)
 
         mock_npu_dynamic_quant.assert_called()