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[Refactor][MoE] remove redundant code after refactoring fused_moe (#2612)

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### What this PR does / why we need it?
There are a lot of redundant codes related to moe here, and the
structure is not very clear.
We did the following things：

we have placed the relatively independent code related to apply_mlp into
a separate file;
removed the environment variables of alltoall_buffer and alltoall_seq.
Remove the code related to alltoall_buffer and alltoall_seq, and retain
the sole TokenDispatcher inheritance class.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
e2e&ut

- vLLM version: v0.10.1.1
- vLLM main:
4071c76cf3

---------

Signed-off-by: Pr0Wh1teGivee <calvin_zhu0210@outlook.com>
Signed-off-by: weijinqian_v1 <weijinqian@huawei.com>
Co-authored-by: weijinqian0 <12153182+weijinqian0@users.noreply.github.com>
This commit is contained in:
weichen
2025-08-30 22:28:50 +08:00
committed by GitHub
parent 20ae71291d
commit 3a5fc5ee01
13 changed files with 417 additions and 1237 deletions
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69
tests/ut/ops/test_common_fused_moe.py Normal file
View File

@@ -0,0 +1,69 @@
#
# 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 unittest.mock import patch
import torch
from tests.ut.base import TestBase
from vllm_ascend.ops.common_fused_moe import fused_experts_moge
class TestFusedExpertsMoGE(TestBase):
def test_fused_experts_moge(self):
with patch('torch_npu.npu_grouped_matmul') as mock_grouped_matmul, \
patch('torch_npu.npu_swiglu') as mock_swiglu, \
patch('vllm_ascend.utils.is_310p') as mock_is_310p:
mock_is_310p.return_value = False
mock_grouped_matmul.side_effect = lambda x, weight, **kwargs: [
torch.randn(x[0].shape[0], weight[0].shape[1])
]
mock_swiglu.side_effect = lambda x: x
hidden_states = torch.randn(4, 128)
w1 = torch.randn(4, 256, 128)
w2 = torch.randn(4, 128, 128)
topk_weights = torch.rand(4, 1)
topk_ids = torch.tensor([[0], [1], [2], [3]], dtype=torch.long)
top_k = 1
global_num_experts = 4
moe_parallel_config = type(
'MockConfig', (), {
'ep_size': 1,
'tp_size': 1,
'dp_size': 1,
'tp_rank': 0,
'dp_rank': 0,
'ep_rank': 0,
'use_ep': True
})()
output = fused_experts_moge(
hidden_states=hidden_states,
w1=w1,
w2=w2,
moe_parallel_config=moe_parallel_config,
topk_weights=topk_weights,
topk_ids=topk_ids,
top_k=top_k,
global_num_experts=global_num_experts,
apply_router_weight_on_input=True,
)
self.assertEqual(output.shape, (4, 128))

88
tests/ut/ops/test_fused_ops.py
View File

@@ -27,9 +27,9 @@ 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,
unified_apply_mlp)
AscendUnquantizedFusedMoEMethod)
from vllm_ascend.ops.layers.experts_selector import select_experts
from vllm_ascend.ops.layers.moe_mlp import unified_apply_mlp
from vllm_ascend.utils import AscendSocVersion, adapt_patch
adapt_patch(True)
@@ -129,36 +129,38 @@ def mock_dist_env(mocker: MockerFixture):
with_quant=False)
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.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_ascend.ops.fused_moe.data_parallel_reduce_scatter'), \
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=mock_forward_context_obj), \
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.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_ascend.ops.fused_moe.data_parallel_reduce_scatter'), \
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=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)
)), \
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.object(token_dispatcher_module, 'setup_token_dispatchers', mock_setup_token_dispatchers), \
patch('vllm_ascend.ops.layers.moe_mlp.get_forward_context',
return_value=mock_forward_context_obj):
yield {
'mock_forward_context_obj': mock_forward_context_obj,
@@ -441,12 +443,11 @@ class TestAscendUnquantizedFusedMoEMethod:
assert result.shape == expected_shape
@pytest.mark.parametrize("others_param",
[[16, False], [1, True], [1, False], [4, False]])
@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, alltoall_buffer = others_param
ep_size = others_param
is_prefill = False
if ep_size == 1:
@@ -464,9 +465,7 @@ class TestAscendUnquantizedFusedMoEMethod:
with_quant=False,
token_dispatcher=selected_token_dispatcher)
with patch("vllm_ascend.ops.fused_moe.MOE_ALL2ALL_BUFFER",
alltoall_buffer), \
patch("vllm_ascend.ops.fused_moe.get_forward_context", return_value=forward_context), \
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])
@@ -475,8 +474,6 @@ class TestAscendUnquantizedFusedMoEMethod:
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()
local_num_experts = 2
@@ -529,9 +526,8 @@ class TestExpertsSelector:
class TestUnifiedApplyMLP(TestBase):
@patch('vllm_ascend.ops.fused_moe.get_forward_context')
@patch('vllm_ascend.ops.fused_moe.get_mc2_group')
@patch('vllm_ascend.ops.fused_moe.is_310p')
@patch('vllm_ascend.ops.layers.moe_mlp.get_forward_context')
@patch('vllm_ascend.ops.layers.moe_mlp.is_310p')
@patch('torch_npu.npu_grouped_matmul')
@patch('torch_npu.npu_dynamic_quant')
@patch('torch_npu.npu_dequant_swiglu_quant')
@@ -539,16 +535,12 @@ class TestUnifiedApplyMLP(TestBase):
mock_npu_dynamic_quant,
mock_npu_grouped_matmul,
mock_is_310p,
mock_get_mc2_group,
mock_get_forward_context):
mock_forward_context = MagicMock()
mock_forward_context.fused_moe_state = FusedMoEState.MC2
mock_get_forward_context.return_value = mock_forward_context
mock_mc2_group = MagicMock()
mock_get_mc2_group.return_value = mock_mc2_group
mock_is_310p.return_value = False
mock_npu_dynamic_quant.return_value = (torch.randint(-128,
@@ -601,7 +593,7 @@ class TestUnifiedApplyMLP(TestBase):
self.assertEqual(result.dtype, torch.bfloat16)
@patch('vllm_ascend.ops.fused_moe.is_310p')
@patch('vllm_ascend.ops.layers.moe_mlp.is_310p')
@patch('torch_npu.npu_grouped_matmul')
@patch('torch_npu.npu_swiglu')
@patch('torch_npu.npu_dynamic_quant')
@@ -643,7 +635,7 @@ class TestUnifiedApplyMLP(TestBase):
self.assertEqual(result.shape, hidden_states.shape)
self.assertEqual(result.dtype, torch.float16)
@patch('vllm_ascend.ops.fused_moe.get_forward_context')
@patch('vllm_ascend.ops.layers.moe_mlp.get_forward_context')
@patch('torch_npu.npu_grouped_matmul')
@patch('torch_npu.npu_swiglu')
@patch('torch_npu.npu_dynamic_quant')
@@ -703,7 +695,7 @@ class TestUnifiedApplyMLP(TestBase):
self.assertEqual(result.shape, hidden_states.shape)
self.assertEqual(result.dtype, torch.bfloat16)
@patch('vllm_ascend.ops.fused_moe.is_310p')
@patch('vllm_ascend.ops.layers.moe_mlp.is_310p')
@patch('torch_npu.npu_grouped_matmul')
@patch('torch_npu.npu_swiglu')
@patch('torch_npu.npu_dynamic_quant')

52
tests/ut/ops/test_token_dispatcher.py
View File

@@ -17,57 +17,13 @@
from unittest.mock import MagicMock, PropertyMock, patch
import pytest
import torch
from pytest_mock import MockerFixture
from tests.ut.base import PytestBase, TestBase
from tests.ut.base import TestBase
from vllm_ascend.ops.moe_dispatcher.token_dispatcher import (
AscendSocVersion, MoEAlltoAllSeqOverLapDispatcher, MoEDispatcherConfig,
TokenDispatcherWithAll2AllV, TokenDispatcherWithAllGather,
TokenDispatcherWithMC2, _Dispatchers, _register_token_dispatcher,
get_token_dispatcher, setup_token_dispatchers)
class TestMoEAlltoAllSeqOverLapDispatcher(PytestBase):
@pytest.fixture
def config(self):
config = MoEDispatcherConfig()
config.set_num_local_experts(2)
config.set_num_moe_experts(4)
config.set_moe_pad_expert_input_to_capacity(False)
config.set_moe_expert_capacity_factor(None)
config.set_moe_router_topk(2)
config.set_moe_grouped_gemm(False)
config.set_group_topk(0)
config.set_num_groups(1)
config.set_is_fused(False)
return config.build()
def mock_ep_group(self, mocker):
mock_group = mocker.MagicMock()
mock_group.rank_in_group = 0
mock_group.world_size = 2
mock_group.device_group = "mock_group"
return mock_group
@pytest.fixture
def dispatcher(self, config, mocker: MockerFixture):
mocker.patch(
"vllm_ascend.ops.moe_dispatcher.token_dispatcher.get_ep_group",
return_value=self.mock_ep_group(mocker))
mocker.patch("torch.npu.current_device", return_value="cpu")
mocker.patch("torch.npu.Stream", return_value=mocker.MagicMock)
return MoEAlltoAllSeqOverLapDispatcher(config)
def test_initialization(self, dispatcher, config):
assert dispatcher.num_local_experts == config.num_local_experts
assert dispatcher.num_experts == config.num_moe_experts
assert dispatcher.local_expert_indices == [0, 1]
assert dispatcher.ep_rank == 0
assert dispatcher.ep_size == 2
assert dispatcher.overlap_stream is not None
AscendSocVersion, TokenDispatcherWithAll2AllV,
TokenDispatcherWithAllGather, TokenDispatcherWithMC2, _Dispatchers,
_register_token_dispatcher, get_token_dispatcher, setup_token_dispatchers)
class TestTokenDispatcherWithMC2(TestBase):