3a5fc5ee01
### 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>
70 lines
2.4 KiB
Python
70 lines
2.4 KiB
Python
#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# This file is a part of the vllm-ascend project.
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#
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from unittest.mock import patch
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import torch
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from tests.ut.base import TestBase
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from vllm_ascend.ops.common_fused_moe import fused_experts_moge
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class TestFusedExpertsMoGE(TestBase):
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def test_fused_experts_moge(self):
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with patch('torch_npu.npu_grouped_matmul') as mock_grouped_matmul, \
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patch('torch_npu.npu_swiglu') as mock_swiglu, \
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patch('vllm_ascend.utils.is_310p') as mock_is_310p:
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mock_is_310p.return_value = False
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mock_grouped_matmul.side_effect = lambda x, weight, **kwargs: [
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torch.randn(x[0].shape[0], weight[0].shape[1])
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]
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mock_swiglu.side_effect = lambda x: x
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hidden_states = torch.randn(4, 128)
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w1 = torch.randn(4, 256, 128)
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w2 = torch.randn(4, 128, 128)
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topk_weights = torch.rand(4, 1)
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topk_ids = torch.tensor([[0], [1], [2], [3]], dtype=torch.long)
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top_k = 1
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global_num_experts = 4
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moe_parallel_config = type(
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'MockConfig', (), {
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'ep_size': 1,
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'tp_size': 1,
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'dp_size': 1,
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'tp_rank': 0,
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'dp_rank': 0,
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'ep_rank': 0,
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'use_ep': True
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})()
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output = fused_experts_moge(
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hidden_states=hidden_states,
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w1=w1,
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w2=w2,
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moe_parallel_config=moe_parallel_config,
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topk_weights=topk_weights,
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topk_ids=topk_ids,
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top_k=top_k,
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global_num_experts=global_num_experts,
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apply_router_weight_on_input=True,
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)
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self.assertEqual(output.shape, (4, 128))
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