0
tests/ut/_310p/fused_moe/__init__.py
Normal file
0
tests/ut/_310p/fused_moe/__init__.py
Normal file
85
tests/ut/_310p/fused_moe/test_experts_selector_310.py
Normal file
85
tests/ut/_310p/fused_moe/test_experts_selector_310.py
Normal file
@@ -0,0 +1,85 @@
|
||||
#
|
||||
# Copyright (c) 2026 Huawei Technologies Co., Ltd. All Rights Reserved.
|
||||
#
|
||||
# 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.
|
||||
|
||||
from unittest.mock import patch
|
||||
|
||||
import pytest
|
||||
import torch
|
||||
|
||||
from vllm_ascend._310p.fused_moe.experts_selector import select_experts
|
||||
|
||||
|
||||
class TestExpertsSelector310:
|
||||
@pytest.mark.parametrize("global_num_experts", [256, 128])
|
||||
def test_select_experts(self, global_num_experts):
|
||||
hidden_states = torch.randn(8, 16)
|
||||
router_logits = torch.randn(8, 8)
|
||||
|
||||
with patch("torch_npu.npu_moe_gating_top_k_softmax") as mock_npu:
|
||||
mock_npu.return_value = (
|
||||
torch.randn(8, 2),
|
||||
torch.randint(0, 8, (8, 2), dtype=torch.int32),
|
||||
None,
|
||||
)
|
||||
|
||||
topk_weights, topk_ids = select_experts(
|
||||
hidden_states=hidden_states,
|
||||
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,
|
||||
)
|
||||
|
||||
mock_npu.assert_called_once()
|
||||
|
||||
assert topk_weights.shape == (8, 2)
|
||||
assert topk_ids.shape == (8, 2)
|
||||
|
||||
def test_select_experts_chunks_large_token_batch(self):
|
||||
num_tokens = 2050
|
||||
hidden_states = torch.randn(num_tokens, 16)
|
||||
router_logits = torch.randn(num_tokens, 8)
|
||||
|
||||
def mock_gating(logits, k):
|
||||
return (
|
||||
torch.ones(logits.shape[0], k),
|
||||
torch.zeros(logits.shape[0], k, dtype=torch.int32),
|
||||
None,
|
||||
)
|
||||
|
||||
with patch(
|
||||
"torch_npu.npu_moe_gating_top_k_softmax",
|
||||
side_effect=mock_gating,
|
||||
) as mock_npu:
|
||||
topk_weights, topk_ids = select_experts(
|
||||
hidden_states=hidden_states,
|
||||
router_logits=router_logits,
|
||||
top_k=2,
|
||||
use_grouped_topk=False,
|
||||
renormalize=True,
|
||||
custom_routing_function=None,
|
||||
scoring_func="softmax",
|
||||
)
|
||||
|
||||
assert [call.args[0].shape[0] for call in mock_npu.call_args_list] == [1024, 1024, 2]
|
||||
assert topk_weights.shape == (num_tokens, 2)
|
||||
assert topk_ids.shape == (num_tokens, 2)
|
||||
assert torch.all(topk_weights == 0.5)
|
||||
179
tests/ut/_310p/fused_moe/test_moe_mlp_310.py
Normal file
179
tests/ut/_310p/fused_moe/test_moe_mlp_310.py
Normal file
@@ -0,0 +1,179 @@
|
||||
#
|
||||
# Copyright (c) 2026 Huawei Technologies Co., Ltd. All Rights Reserved.
|
||||
#
|
||||
# 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.
|
||||
|
||||
from unittest.mock import MagicMock, call, patch
|
||||
|
||||
import torch
|
||||
|
||||
from tests.ut.base import TestBase
|
||||
from vllm_ascend._310p.fused_moe.moe_comm_method import AllGatherCommImpl310
|
||||
from vllm_ascend._310p.fused_moe.moe_mlp import unified_apply_mlp
|
||||
from vllm_ascend.ops.fused_moe.moe_runtime_args import (
|
||||
MoEMlpComputeInput,
|
||||
MoEQuantParams,
|
||||
MoEWeights,
|
||||
)
|
||||
from vllm_ascend.quantization.quant_type import QuantType
|
||||
|
||||
|
||||
def build_mlp_compute_input_fixture(
|
||||
*,
|
||||
hidden_states: torch.Tensor,
|
||||
w1: torch.Tensor,
|
||||
w2: torch.Tensor,
|
||||
group_list: torch.Tensor,
|
||||
with_quant: bool,
|
||||
w1_scale: torch.Tensor | None = None,
|
||||
w2_scale: torch.Tensor | None = None,
|
||||
group_list_type: int = 1,
|
||||
) -> MoEMlpComputeInput:
|
||||
return MoEMlpComputeInput(
|
||||
hidden_states=hidden_states,
|
||||
group_list=group_list,
|
||||
group_list_type=group_list_type,
|
||||
dynamic_scale=None,
|
||||
topk_scales=None,
|
||||
weights=MoEWeights(w1=w1, w2=w2, w1_scale=w1_scale, w2_scale=w2_scale),
|
||||
quant=MoEQuantParams(quant_type=QuantType.W8A8 if with_quant else QuantType.NONE),
|
||||
fusion=False,
|
||||
activation="silu",
|
||||
need_trans=False,
|
||||
dynamic_eplb=False,
|
||||
)
|
||||
|
||||
|
||||
class TestUnifiedApplyMLP310(TestBase):
|
||||
@patch("vllm_ascend._310p.fused_moe.moe_comm_method.unified_apply_mlp")
|
||||
def test_all_gather_apply_mlp_returns_common_tuple_contract(self, mock_unified_apply_mlp):
|
||||
mlp_compute_input = MagicMock(spec=MoEMlpComputeInput)
|
||||
mlp_output = torch.randn(10, 20, dtype=torch.float16)
|
||||
mock_unified_apply_mlp.return_value = mlp_output
|
||||
|
||||
comm_impl = AllGatherCommImpl310.__new__(AllGatherCommImpl310)
|
||||
|
||||
output, before_gmm2_evt = comm_impl._apply_mlp(mlp_compute_input)
|
||||
|
||||
self.assertIs(output, mlp_output)
|
||||
self.assertIsNone(before_gmm2_evt)
|
||||
mock_unified_apply_mlp.assert_called_once_with(mlp_compute_input=mlp_compute_input)
|
||||
|
||||
@patch("torch_npu.npu_grouped_matmul", create=True)
|
||||
@patch("torch_npu.npu_swiglu")
|
||||
def test_unified_apply_mlp_without_quantization_310(self, mock_npu_swiglu, mock_npu_grouped_matmul):
|
||||
mock_gmm1_out = torch.randn(10, 40, dtype=torch.float16)
|
||||
mock_gmm2_out = torch.randn(10, 20, dtype=torch.float16)
|
||||
mock_npu_grouped_matmul.side_effect = [[mock_gmm1_out], [mock_gmm2_out]]
|
||||
|
||||
mock_npu_swiglu_output = torch.randn(10, 40, dtype=torch.float16)
|
||||
mock_npu_swiglu.return_value = mock_npu_swiglu_output
|
||||
|
||||
hidden_states = torch.randn(10, 20, dtype=torch.float16)
|
||||
w1 = torch.randn(5, 20, 40, dtype=torch.float16)
|
||||
w2 = torch.randn(5, 40, 20, dtype=torch.float16)
|
||||
group_list = torch.tensor([2, 4, 6, 8, 10], dtype=torch.int64)
|
||||
|
||||
result = unified_apply_mlp(
|
||||
mlp_compute_input=build_mlp_compute_input_fixture(
|
||||
hidden_states=hidden_states,
|
||||
w1=w1,
|
||||
w2=w2,
|
||||
group_list=group_list,
|
||||
with_quant=False,
|
||||
)
|
||||
)
|
||||
|
||||
self.assertEqual(mock_npu_grouped_matmul.call_count, 2)
|
||||
mock_npu_grouped_matmul.assert_has_calls(
|
||||
[
|
||||
call(
|
||||
x=[hidden_states], weight=[w1], split_item=2, group_list_type=1, group_type=0, group_list=group_list
|
||||
),
|
||||
call(
|
||||
x=[mock_npu_swiglu_output],
|
||||
weight=[w2],
|
||||
split_item=2,
|
||||
group_list_type=1,
|
||||
group_type=0,
|
||||
group_list=group_list,
|
||||
),
|
||||
],
|
||||
any_order=True,
|
||||
)
|
||||
mock_npu_swiglu.assert_called_once()
|
||||
mock_npu_swiglu.assert_called_with(mock_gmm1_out)
|
||||
|
||||
self.assertEqual(result.shape, hidden_states.shape)
|
||||
self.assertEqual(result.dtype, torch.float16)
|
||||
|
||||
@patch("torch.cumsum")
|
||||
@patch("torch_npu.npu_quant_grouped_matmul_dequant", create=True)
|
||||
@patch("torch_npu.npu_swiglu")
|
||||
def test_unified_apply_mlp_with_quantization_310(
|
||||
self, mock_npu_swiglu, mock_npu_quant_grouped_matmul_dequant, mock_cumsum
|
||||
):
|
||||
mock_cumsum_out = torch.arange(0, 10, dtype=torch.int64)
|
||||
mock_cumsum.return_value = mock_cumsum_out
|
||||
mock_gmm1_out = torch.randn(10, 40, dtype=torch.float16)
|
||||
mock_gmm2_out = torch.randn(10, 20, dtype=torch.float16)
|
||||
mock_npu_quant_grouped_matmul_dequant.side_effect = [mock_gmm1_out, mock_gmm2_out]
|
||||
|
||||
mock_npu_swiglu_output = torch.randn(10, 40, dtype=torch.float16)
|
||||
mock_npu_swiglu.return_value = mock_npu_swiglu_output
|
||||
|
||||
hidden_states = torch.randn(10, 20, dtype=torch.float16)
|
||||
w1 = torch.randn(5, 20, 40, dtype=torch.float16)
|
||||
w1_scale = torch.rand(5, 40, dtype=torch.float32)
|
||||
w2 = torch.randn(5, 40, 20, dtype=torch.float16)
|
||||
w2_scale = torch.rand(5, 40, dtype=torch.float32)
|
||||
group_list = torch.tensor([2, 4, 6, 8, 10], dtype=torch.int64)
|
||||
|
||||
result = unified_apply_mlp(
|
||||
mlp_compute_input=build_mlp_compute_input_fixture(
|
||||
hidden_states=hidden_states,
|
||||
w1=w1,
|
||||
w2=w2,
|
||||
group_list=group_list,
|
||||
with_quant=True,
|
||||
w1_scale=w1_scale,
|
||||
w2_scale=w2_scale,
|
||||
)
|
||||
)
|
||||
|
||||
mock_cumsum.assert_called_once()
|
||||
self.assertEqual(mock_npu_quant_grouped_matmul_dequant.call_count, 2)
|
||||
mock_npu_quant_grouped_matmul_dequant.assert_has_calls(
|
||||
[
|
||||
call(
|
||||
x=hidden_states,
|
||||
quantized_weight=w1,
|
||||
weight_scale=w1_scale,
|
||||
group_list=mock_cumsum_out,
|
||||
quant_mode="pertoken",
|
||||
),
|
||||
call(
|
||||
x=mock_npu_swiglu_output,
|
||||
quantized_weight=w2,
|
||||
weight_scale=w2_scale,
|
||||
group_list=mock_cumsum_out,
|
||||
quant_mode="pertoken",
|
||||
),
|
||||
],
|
||||
any_order=True,
|
||||
)
|
||||
mock_npu_swiglu.assert_called_once()
|
||||
mock_npu_swiglu.assert_called_with(mock_gmm1_out)
|
||||
|
||||
self.assertEqual(result.shape, hidden_states.shape)
|
||||
self.assertEqual(result.dtype, torch.float16)
|
||||
109
tests/ut/_310p/fused_moe/test_shared_fused_moe_310.py
Normal file
109
tests/ut/_310p/fused_moe/test_shared_fused_moe_310.py
Normal file
@@ -0,0 +1,109 @@
|
||||
#
|
||||
# Copyright (c) 2026 Huawei Technologies Co., Ltd. All Rights Reserved.
|
||||
#
|
||||
# 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.
|
||||
|
||||
from unittest.mock import patch
|
||||
|
||||
import torch
|
||||
import torch.nn.functional as F
|
||||
|
||||
from vllm_ascend._310p.fused_moe.fused_moe import (
|
||||
AscendFusedMoE310,
|
||||
)
|
||||
|
||||
|
||||
class _DummyGate(torch.nn.Module):
|
||||
def forward(self, hidden_states: torch.Tensor):
|
||||
# Keep gate output deterministic: sigmoid(0)=0.5.
|
||||
return torch.zeros(
|
||||
hidden_states.shape[0],
|
||||
1,
|
||||
dtype=hidden_states.dtype,
|
||||
device=hidden_states.device,
|
||||
), None
|
||||
|
||||
|
||||
class _DummySharedExperts(torch.nn.Module):
|
||||
def __init__(self, with_gate: bool):
|
||||
super().__init__()
|
||||
self.expert_gate = _DummyGate() if with_gate else None
|
||||
|
||||
def forward(self, hidden_states: torch.Tensor):
|
||||
out = hidden_states * 2.0 + 1.0
|
||||
if self.expert_gate is not None:
|
||||
gate_out, _ = self.expert_gate(hidden_states)
|
||||
out = F.sigmoid(gate_out) * out
|
||||
return out
|
||||
|
||||
|
||||
def _build_layer(shared_experts: torch.nn.Module | None) -> AscendFusedMoE310:
|
||||
layer = AscendFusedMoE310.__new__(AscendFusedMoE310)
|
||||
# The test bypasses full layer init with __new__, so we must initialize
|
||||
# nn.Module internals before assigning child modules.
|
||||
torch.nn.Module.__init__(layer)
|
||||
layer._shared_experts = shared_experts
|
||||
return layer
|
||||
|
||||
|
||||
def test_forward_shared_experts_without_gate_310():
|
||||
layer = _build_layer(_DummySharedExperts(with_gate=False))
|
||||
hidden_states = torch.randn(4, 8)
|
||||
output = layer._forward_shared_experts(hidden_states)
|
||||
expected = hidden_states * 2.0 + 1.0
|
||||
torch.testing.assert_close(output, expected)
|
||||
|
||||
|
||||
def test_forward_shared_experts_with_gate_310():
|
||||
layer = _build_layer(_DummySharedExperts(with_gate=True))
|
||||
hidden_states = torch.randn(4, 8)
|
||||
output = layer._forward_shared_experts(hidden_states)
|
||||
expected = 0.5 * (hidden_states * 2.0 + 1.0)
|
||||
torch.testing.assert_close(output, expected)
|
||||
|
||||
|
||||
def test_forward_impl_with_shared_experts_returns_tuple_310():
|
||||
layer = _build_layer(_DummySharedExperts(with_gate=True))
|
||||
hidden_states = torch.randn(3, 8)
|
||||
router_logits = torch.randn(3, 8)
|
||||
routed_out = torch.randn(3, 8)
|
||||
|
||||
with patch.object(AscendFusedMoE310, "forward_impl", return_value=routed_out):
|
||||
shared_out, routed = layer.shared_forward_impl(hidden_states, router_logits)
|
||||
|
||||
expected_shared = 0.5 * (hidden_states * 2.0 + 1.0)
|
||||
torch.testing.assert_close(shared_out, expected_shared)
|
||||
torch.testing.assert_close(routed, routed_out)
|
||||
|
||||
|
||||
def test_forward_impl_without_shared_experts_integration_310():
|
||||
layer = _build_layer(None)
|
||||
hidden_states = torch.randn(3, 8)
|
||||
assert layer._forward_shared_experts(hidden_states) is None
|
||||
|
||||
|
||||
def test_forward_impl_without_shared_experts_returns_routed_only_310():
|
||||
layer = _build_layer(None)
|
||||
hidden_states = torch.randn(3, 8)
|
||||
router_logits = torch.randn(3, 8)
|
||||
routed_out = torch.randn(3, 8)
|
||||
|
||||
with patch.object(AscendFusedMoE310, "forward_impl", return_value=routed_out):
|
||||
output = layer.shared_forward_impl(hidden_states, router_logits)
|
||||
|
||||
torch.testing.assert_close(output, routed_out)
|
||||
|
||||
|
||||
def test_is_internal_router_is_false_310():
|
||||
layer = _build_layer(_DummySharedExperts(with_gate=True))
|
||||
assert layer.is_internal_router is False
|
||||
Reference in New Issue
Block a user