70 lines
2.4 KiB
Python
70 lines
2.4 KiB
Python
#
|
|
# 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))
|