37a0715eda
### What this PR does / why we need it?
Fix issues mentioned in
https://github.com/vllm-project/vllm-ascend/pull/2791 and some minor
refactoring.
1. Use Enum instead of string.
2. Avoid setting a new property to forward_context in
AscendFusedMoE.forward().
3. Enabling TokenDispatcherWithMoge.
4. Remove redundant code.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
Qwen3-30B-A3B/Qwen3-30B-A3B-W8A8/DeepSeek-V3-W4A8-Pruing/deepseek-mtp/pangu-pro-moe-pruing:
1. Enable/Disable EP
2. Aclgraph & eager
- vLLM version: v0.10.2
- vLLM main:
9607d5eb44
Signed-off-by: Pr0Wh1teGivee <calvin_zhu0210@outlook.com>
Co-authored-by: weijinqian0 <12153182+weijinqian0@users.noreply.github.com>
57 lines
2.2 KiB
Python
57 lines
2.2 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 AscendFusedMoE
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class TestLoadWeight(TestBase):
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def test_load_w13_transpose(self):
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with patch.object(AscendFusedMoE, "__init__",
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lambda self, *args, **kwargs: None):
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moe = AscendFusedMoE(num_experts=4, top_k=2, hidden_size=8)
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expert_data = torch.randn(128, 8)
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loaded_weight = torch.randn(128, 4)
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moe._load_w13(expert_data, 1, "w1", loaded_weight, 0)
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expert_data = torch.randn(8, 128)
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loaded_weight = torch.randn(128, 4)
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moe._load_w13(expert_data, 1, "w1", loaded_weight, 0)
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expert_data = torch.randn(128, 8)
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loaded_weight = torch.randn(128, 4)
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moe._load_w13(expert_data, 1, "w3", loaded_weight, 0)
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expert_data = torch.randn(8, 128)
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loaded_weight = torch.randn(128, 4)
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moe._load_w13(expert_data, 1, "w3", loaded_weight, 0)
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def test_load_w2_transpose(self):
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with patch.object(AscendFusedMoE, "__init__",
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lambda self, *args, **kwargs: None):
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moe = AscendFusedMoE(num_experts=4, top_k=2, hidden_size=8)
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expert_data = torch.randn(128, 4)
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loaded_weight = torch.randn(128, 8)
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moe._load_w2(expert_data, 1, loaded_weight, 0)
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expert_data = torch.randn(4, 128)
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loaded_weight = torch.randn(128, 8)
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moe._load_w2(expert_data, 1, loaded_weight, 0)
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