add ut of fused_moe.py

tests/ut/ops/test_fused_ops.py

@@ -0,0 +1,356 @@
+#
+# 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.
+#
+from typing import List, TypedDict
+from unittest.mock import MagicMock, patch
+
+import pytest
+import torch
+import torch.nn as nn
+from pytest_mock import MockerFixture
+
+from vllm_ascend.ops.fused_moe import (AscendFusedMoE,
+                                       AscendUnquantizedFusedMoEMethod)
+from vllm_ascend.utils import adapt_patch  # noqa E402
+
+adapt_patch(True)
+
+
+def mock_ep_group(mocker):
+    mock_group = mocker.MagicMock()
+    mock_group.rank_in_group = 0
+    mock_group.rank = 0
+    mock_group.world_size = 4
+    mock_group.device_group = "mock_group_ep"
+    mock_group.all_to_all = MagicMock(return_value=torch.randn(8, 8))
+    return mock_group
+
+
+def mock_dp_and_tp_group(mocker):
+    mock_group = mocker.MagicMock()
+    mock_group.rank_in_group = 0
+    mock_group.world_size = 2
+    mock_group.device_group = "mock_group"
+    mock_group.all_gather = MagicMock(return_value=torch.randn(10, 32))
+    return mock_group
+
+
+@pytest.fixture
+def mock_dist_env(mocker: MockerFixture):
+    # init dist env patch
+
+    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_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('torch.distributed.all_gather', return_value=MagicMock(return_value=torch.randn(10,32))), \
+         patch('torch.distributed.all_to_all_single', return_value=torch.randn(8, 32)), \
+         patch('vllm_ascend.ops.fused_moe.tensor_model_parallel_all_reduce',
+               return_value=torch.randn(5, 32)), \
+         patch('vllm_ascend.ops.fused_moe.data_parallel_reduce_scatter',
+               return_value=torch.randn(5, 32)), \
+         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',
+               return_value=MagicMock(
+                   torchair_graph_config=MagicMock(enabled=False, enable_multistream_moe=False),
+                   expert_map_path=None
+               )), \
+         patch('vllm_ascend.ops.fused_moe.determine_expert_map',
+               return_value=(3, torch.tensor([0, 1, 2, -1, -1, -1, -1, -1]))), \
+         patch('vllm_ascend.ops.fused_moe.get_forward_context',
+               return_value=MagicMock(
+                   attn_metadata=MagicMock(max_num_tokens_across_dp=10),
+                   dp_metadata=MagicMock(cu_tokens_across_dp_cpu=[5, 10])
+               )), \
+        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)
+               )):
+        yield
+
+
+@pytest.fixture
+def mock_moe_env(mocker: MockerFixture):
+    # init moe env patch
+
+    with patch('torch_npu.npu_moe_gating_top_k', return_value=(
+            torch.randn(8, 2),
+            torch.randint(0, 8, (8, 2)),
+            None
+        )), \
+        patch('torch_npu.npu_moe_init_routing', return_value=(
+                torch.randn(8, 2),
+                torch.randint(0, 8, (8, 2)),
+                torch.tensor([0, 1, 2, 4, 6, 2, 7, 1])
+        )), \
+        patch("torch_npu.npu_moe_compute_expert_tokens", return_value=(
+                torch.randn(8, 2)
+        )), \
+        patch("torch_npu.npu_moe_distribute_dispatch", return_value=(
+                torch.randn(16, 2)
+        )), \
+        patch("torch_npu.npu_moe_distribute_combine", return_value=(
+                torch.randn(16, 2)
+        )), \
+        patch("torch_npu.npu_grouped_matmul", return_value=(
+                (torch.randn(8, 2), torch.randn(8, 2))
+        )), \
+        patch("torch_npu.npu_swiglu", return_value=(
+                torch.randn(16, 2)
+        )), \
+        patch("torch_npu.npu_moe_gating_top_k_softmax", return_value=(
+                torch.randn(8, 2),
+                torch.randint(0, 8, (8, 2)),
+                torch.tensor([0, 1, 2, 4, 6, 2, 7, 1])
+        )), \
+        patch("torch_npu.npu_moe_finalize_routing", return_value=(
+                torch.randn(16, 2)
+        )):
+        yield
+
+
+@pytest.fixture
+def default_moe_config():
+    """default moe config"""
+    return {
+        'num_experts': 8,
+        'top_k': 2,
+        'hidden_size': 512,
+        'intermediate_size': 1024
+    }
+
+
+@pytest.fixture
+def moe_method(mock_dist_env):
+    moe = MagicMock()
+    moe.moe_parallel_config.return_value = MagicMock(ep_size=4)
+    return AscendUnquantizedFusedMoEMethod(moe)
+
+
+class Device(TypedDict):
+    device_id: int
+    device_expert: List[int]
+
+
+class Layer(TypedDict):
+    layer_id: int
+    device_count: int
+    device_list: List[Device]
+
+
+class MockData(TypedDict):
+    moe_layer_count: int
+    layer_list: List[Layer]
+
+
+class MockQuantMethod(nn.Module):
+
+    def __init__(self, shared_experts, num_tokens):
+        super().__init__()
+        if shared_experts:
+            self.apply = MagicMock(return_value=(torch.randn(num_tokens, 32),
+                                                 torch.randn(num_tokens, 10)))
+        else:
+            self.apply = MagicMock(return_value=(torch.randn(num_tokens, 32)))
+
+
+class TestAscendFusedMoe:
+
+    def test_init_no_quant(self, mock_dist_env, default_moe_config):
+        layer = AscendFusedMoE(**default_moe_config)
+
+        layer.w13_weight = nn.Parameter(
+            torch.randn(default_moe_config['num_experts'],
+                        default_moe_config['intermediate_size'] * 2,
+                        default_moe_config['hidden_size']))
+        layer.w2_weight = nn.Parameter(
+            torch.randn(default_moe_config['num_experts'],
+                        default_moe_config['hidden_size'],
+                        default_moe_config['intermediate_size']))
+
+        assert layer.num_experts == default_moe_config['num_experts']
+        assert layer.top_k == default_moe_config['top_k']
+        assert hasattr(layer, 'w13_weight')
+        assert hasattr(layer, 'w2_weight')
+        assert layer.moe_instance_id == 0
+
+        # check group_topk
+        with pytest.raises(AssertionError):
+            error_config = default_moe_config.copy()
+            error_config['use_grouped_topk'] = True
+            layer = AscendFusedMoE(**error_config)
+
+        # check scoring_func
+        with pytest.raises(ValueError):
+            error_config = default_moe_config.copy()
+            error_config['scoring_func'] = "random"
+            layer = AscendFusedMoE(**error_config)
+
+    def test_init_with_quant(self, mock_dist_env, default_moe_config):
+        mock_quant_config = MagicMock()
+        mock_quant_method = MagicMock()
+        mock_quant_config.get_quant_method.return_value = mock_quant_method
+
+        moe = AscendFusedMoE(**default_moe_config,
+                             quant_config=mock_quant_config)
+
+        assert moe.quant_method is not None
+        assert moe.quant_method == mock_quant_method
+
+    @pytest.mark.parametrize(
+        "others_param",
+        [[None,
+          MagicMock(return_value=torch.randn(5, 32)), False, 5, None],
+         [2, None, False, 5, None], [None, None, True, 5, None],
+         [None, None, False, 1, None], [None, None, True, 5, 1],
+         [None, None, False, 5, 1]])
+    def test_forward(self, mock_dist_env, default_moe_config, others_param):
+        """
+        1 test has shared_experts
+        2 test has top_k
+        3 test is_prefill is true
+        4 test single num_tokens(decode)
+        5 test ep_size is 1 and is_prefill is true
+        6 test ep_size is 1 and is_prefill is False
+        """
+        top_k, shared_experts, is_prefill, num_tokens, ep_size = others_param
+        inputs = torch.randn(num_tokens, 32)
+        router_logits = torch.randn(num_tokens, 8)
+        moe = AscendFusedMoE(**default_moe_config)
+
+        if ep_size == 1:
+            moe.moe_parallel_config.ep_size = 1
+
+        moe.quant_method = MockQuantMethod(shared_experts, num_tokens)
+        output = moe.forward(inputs,
+                             router_logits,
+                             is_prefill=is_prefill,
+                             top_k=top_k,
+                             shared_experts=shared_experts)
+
+        moe.quant_method.apply.assert_called_once()
+
+        if shared_experts:
+            assert output[0].shape == (num_tokens, 32)
+            assert output[1].shape == (num_tokens, 10)
+        else:
+            assert output.shape == (num_tokens, 32)
+
+    def test_forward_ms_fused_moe_comp(self, mock_dist_env,
+                                       default_moe_config):
+        inputs = torch.randn(5, 32)
+        router_logits = torch.randn(5, 8)
+        moe = AscendFusedMoE(**default_moe_config)
+
+        moe.quant_method = MockQuantMethod(None, 5)
+        output = moe._forward_ms_fused_moe_comp(inputs,
+                                                router_logits,
+                                                is_prefill=False,
+                                                real_top_k=1)
+
+        moe.quant_method.apply.assert_called_once()
+
+        assert output.shape == (5, 32)
+
+
+class TestAscendUnquantizedFusedMoEMethod:
+
+    def test_process_weights_after_loading(self, moe_method, mock_dist_env):
+        layer = MagicMock()
+        layer.w13_weight.data = torch.randn(16, 32)
+        layer.w2_weight.data = torch.randn(16, 32)
+
+        moe_method.process_weights_after_loading(layer)
+
+        assert isinstance(layer.w13_weight, torch.nn.Parameter)
+        assert isinstance(layer.w2_weight, torch.nn.Parameter)
+        assert not layer.w13_weight.requires_grad
+        assert not layer.w2_weight.requires_grad
+
+    @pytest.mark.parametrize(
+        "others_param",
+        [[256, 4, False], [128, 1, False], [128, 1, True], [128, 4, False]])
+    def test_apply_without_expert_map(self, moe_method, mock_dist_env,
+                                      mock_moe_env, others_param):
+        """
+        1 test is_deepseek_v3_r1=true and use fused_expters_with_all2all
+        2 test use_select_experts and fused_experts
+        3 test use select_gating_topk_softmax_experts and fused_experts
+        4 test use select_experts and fused_experts_with_all2all_buffer
+        """
+        global_num_experts, ep_size, select_softmax = others_param
+        with patch(
+                "vllm_ascend.ops.fused_moe.SELECT_GATING_TOPK_SOTFMAX_EXPERTS",
+                select_softmax):
+            moe_method.ep_size = ep_size
+            x = torch.randn(8, 2, 2)
+            router_logits = torch.randn(8, 8)
+            layer = MagicMock()
+            layer.w13_weight = torch.randn(8, 16, 1)
+            layer.w2_weight = torch.randn(16, 8, 1)
+            result = moe_method.apply(layer=layer,
+                                      x=x,
+                                      router_logits=router_logits,
+                                      top_k=2,
+                                      renormalize=True,
+                                      global_num_experts=global_num_experts,
+                                      is_prefill=False)
+
+            if ep_size == 1:
+                assert result.shape == (16, 2)
+            else:
+                assert result.shape == x.shape
+
+    @pytest.mark.parametrize("others_param",
+                             [[16, False], [1, True], [1, False], [4, False]])
+    def test_apply_with_expert_map(self, moe_method, mock_dist_env,
+                                   mock_moe_env, others_param):
+        """
+        1 test use_select_experts and use fused_expters_with_mc2
+        2 test use_select_experts and fused_experts_with_all2all_buffer
+        3 test use_select_experts and fused_experts_with_all2all
+        4 test use_select_experts and fused_experts
+        """
+        ep_size, alltoall_buffer = others_param
+        with patch("vllm_ascend.ops.fused_moe.MOE_ALL2ALL_BUFFER",
+                   alltoall_buffer):
+            expert_map = torch.tensor([0, 1, 2, -1, -1, -1, -1, -1])
+            moe_method.ep_size = ep_size
+            x = torch.randn(8, 2, 2)
+            if ep_size == 1:
+                x = x.view(-1, 2)
+            router_logits = torch.randn(8, 8)
+            if alltoall_buffer:
+                moe_method.max_model_len = 1
+            layer = MagicMock()
+            layer.w13_weight = torch.randn(8, 16, 1)
+            layer.w2_weight = torch.randn(16, 8, 1)
+            result = moe_method.apply(layer=layer,
+                                      x=x,
+                                      router_logits=router_logits,
+                                      top_k=2,
+                                      renormalize=True,
+                                      global_num_experts=128,
+                                      expert_map=expert_map,
+                                      is_prefill=False)
+
+            if ep_size == 16 or ep_size == 1:
+                assert result.shape == (16, 2)
+            else:
+                assert result.shape == x.shape