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[Main] [Refactor] Enable MoECommMethod in Eager Mode (#2791)

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### What this PR does / why we need it?
1. Replace prepare/finalize operation in fused_moe.py by
moe_comm_method.prepare()/finalize()
2. Replace unified_fused_experts by moe_comm_method.fused_experts() in
fused_moe.py/w8a8_dynamic.py/w4a8_dynamic.py
3. Add calling _select_moe_comm_method in spec-decode proposers.
4. Currently, w4a8_dynamic does not support gatherep, use all2allv
instead.
5. Remove redundant code.
### Does this PR introduce _any_ user-facing change?
AllgatherEP switch is disabled in aclgraph/eager mode, just follow the
rules in modelrunner_v1._select_moe_comm_method()
### How was this patch tested?
e2e & ut


- vLLM version: v0.10.2
- vLLM main:
7f6f2c1182

Signed-off-by: Pr0Wh1teGivee <calvin_zhu0210@outlook.com>
Co-authored-by: weijinqian0 <12153182+weijinqian0@users.noreply.github.com>
This commit is contained in:
weichen
2025-09-16 11:06:00 +08:00
committed by GitHub
parent 0aba644633
commit 18ca7861f6
18 changed files with 523 additions and 596 deletions
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143
tests/ut/ops/test_fused_ops.py
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@@ -22,10 +22,7 @@ import torch_npu
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_moe_comm_method = 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)
def mock_prepare(hidden_states, router_logits, **kwargs):
return hidden_states, router_logits
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_moe_comm_method.prepare.side_effect = mock_prepare
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_fused_experts_result = torch.randn(16, 2)
mock_moe_comm_method.fused_experts.return_value = mock_fused_experts_result
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
def mock_finalize(hidden_states, **kwargs):
return hidden_states
captured_dispatchers = {}
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_register_token_dispatcher_patcher = patch(
'vllm_ascend.ops.moe.token_dispatcher._register_token_dispatcher',
side_effect=capture_register)
mock_get_token_dispatcher_patcher = patch(
'vllm_ascend.ops.moe.token_dispatcher.get_token_dispatcher',
side_effect=lambda name: captured_dispatchers.get(name))
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 mock_dist_env(mocker: MockerFixture):
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',
@@ -150,6 +101,8 @@ def mock_dist_env(mocker: MockerFixture):
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),
@@ -157,22 +110,20 @@ def mock_dist_env(mocker: MockerFixture):
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 @@ class TestAscendFusedMoe:
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 @@ class TestAscendUnquantizedFusedMoEMethod:
[[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 @@ class TestAscendUnquantizedFusedMoEMethod:
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 @@ class TestAscendUnquantizedFusedMoEMethod:
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 @@ class TestUnifiedApplyMLP(TestBase):
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 @@ class TestUnifiedApplyMLP(TestBase):
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()