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1327f9be1cea85b2750ac4145981ddd732064bb9
xc-llm-ascend/tests/ut/ops/test_fused_ops.py
Mengqing Cao 1327f9be1c Fix some ci issue and refactor modelrunner (#2445) 
### What this PR does / why we need it?
Fix some ci issue and refactor modelrunner

### Does this PR introduce _any_ user-facing change?
N/A

### How was this patch tested?
CI passed with existing test.

- vLLM version: v0.10.0
- vLLM main:
4d9c61993a

---------

Signed-off-by: wangli <wangli858794774@gmail.com>
Signed-off-by: MengqingCao <cmq0113@163.com>
Signed-off-by: weiguihua2 <weiguihua2@huawei.com>
Co-authored-by: wangli <wangli858794774@gmail.com>
Co-authored-by: weiguihua2 <weiguihua2@huawei.com>
2025-08-20 09:01:04 +08:00

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#
# 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
import torch_npu
from pytest_mock import MockerFixture
from vllm.model_executor.layers.fused_moe import FusedMoEMethodBase
from vllm_ascend.ascend_forward_context import _get_fused_moe_state
from vllm_ascend.ops.fused_moe import (AscendFusedMoE,
AscendUnquantizedFusedMoEMethod)
from vllm_ascend.ops.layers.experts_selector import select_experts
from vllm_ascend.utils import AscendSocVersion, adapt_patch # noqa E402
adapt_patch(True)
def mock_ep_and_mc2_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_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', 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(
max_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(16, 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)
)):
if hasattr(torch_npu, 'npu_moe_distribute_dispatch_v2'):
with patch("torch_npu.npu_moe_distribute_dispatch_v2", return_value=(
torch.randn(16, 2))), \
patch("torch_npu.npu_moe_distribute_combine_v2", return_value=(
torch.randn(16, 2))):
yield
else:
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 MockFusedMoEMethod(FusedMoEMethodBase):
# TODO(bnell): also pass quant_config?
moe = MagicMock()
def __init__(self):
super().__init__(self.moe)
def create_weights(self, layer: torch.nn.Module, num_experts: int,
hidden_size: int, intermediate_size_per_partition: int,
params_dtype: torch.dtype, **extra_weight_attrs):
pass
def apply(self, hidden_states: torch.Tensor,
expert_weights: torch.Tensor) -> torch.Tensor:
pass
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')
# 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 = MockFusedMoEMethod()
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)
forward_context = MagicMock(mc2_mask=torch.zeros(num_tokens,
dtype=torch.bool),
padded_num_tokens=num_tokens)
with patch("vllm_ascend.ops.fused_moe.get_forward_context",
return_value=forward_context):
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], [128, 1], [128, 1], [128, 4]])
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_experts_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 = others_param
is_prefill = False
is_deepseek_v3_r1 = global_num_experts == 256
forward_context = MagicMock(fused_moe_state=_get_fused_moe_state(
ep_size, is_prefill, is_deepseek_v3_r1))
with patch("vllm_ascend.ops.fused_moe.get_forward_context",
return_value=forward_context):
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=is_prefill)
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
is_prefill = False
forward_context = MagicMock(
fused_moe_state=_get_fused_moe_state(ep_size, is_prefill, True))
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), \
patch("vllm_ascend.ops.fused_moe.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)
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=is_prefill)
if ep_size == 16 or ep_size == 1:
assert result.shape == (16, 2)
else:
assert result.shape == x.shape
class TestExpertsSelector:
@pytest.mark.parametrize("global_num_experts", [[256], [128]])
def test_select_experts(self, mock_dist_env, mock_moe_env,
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)
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