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https://github.com/vllm-project/vllm-ascend.git
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9935d45728
### What this PR does / why we need it? Add model basic accuracy test(Qwen2.5-0.5B-Instruct) Signed-off-by: hfadzxy <starmoon_zhang@163.com>
104 lines
3.7 KiB
Python
104 lines
3.7 KiB
Python
# Copyright (c) 2025 Huawei Technologies Co., Ltd. All Rights Reserved.
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# Copyright 2023 The vLLM team.
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#
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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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# SPDX-License-Identifier: Apache-2.0
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# This file is a part of the vllm-ascend project.
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# Adapted from vllm/tests/kernels/test_moe.py
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"""Tests for the MOE layers.
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Run `pytest tests/ops/test_fused_moe.py`.
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"""
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import pytest
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import torch
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from vllm.config import VllmConfig, set_current_vllm_config
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from vllm.model_executor.layers.activation import SiluAndMul
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from vllm_ascend.ops.fused_moe import fused_experts
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NUM_EXPERTS = [8, 64]
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EP_SIZE = [1, 4]
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TOP_KS = [2, 6]
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DEVICE = ["npu"]
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def torch_moe(a, w1, w2, topk_weights, topk_ids, topk, expert_map):
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B, D = a.shape
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a = a.view(B, -1, D).repeat(1, topk, 1).reshape(-1, D)
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out = torch.zeros(B * topk, w2.shape[1], dtype=a.dtype, device=a.device)
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topk_weights = topk_weights.view(-1)
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topk_ids = topk_ids.view(-1)
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if expert_map is not None:
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topk_ids = expert_map[topk_ids]
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for i in range(w1.shape[0]):
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mask = topk_ids == i
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if mask.sum():
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out[mask] = SiluAndMul()(
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a[mask] @ w1[i].transpose(0, 1)) @ w2[i].transpose(0, 1)
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return (out.view(B, -1, w2.shape[1]) *
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topk_weights.view(B, -1, 1).to(out.dtype)).sum(dim=1)
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@pytest.mark.parametrize("m", [1, 33, 64, 222, 1024 * 128])
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@pytest.mark.parametrize("n", [128, 1024, 2048])
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@pytest.mark.parametrize("k", [128, 511, 1024])
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@pytest.mark.parametrize("e", NUM_EXPERTS)
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@pytest.mark.parametrize("topk", TOP_KS)
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@pytest.mark.parametrize("ep_size", EP_SIZE)
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@pytest.mark.parametrize("dtype", [torch.float16, torch.bfloat16])
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@pytest.mark.parametrize("device", DEVICE)
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def test_fused_experts(
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m: int,
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n: int,
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k: int,
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e: int,
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topk: int,
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ep_size: int,
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dtype: torch.dtype,
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device: str,
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):
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vllm_config = VllmConfig()
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with set_current_vllm_config(vllm_config):
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a = torch.randn((m, k), device=device, dtype=dtype) / 10
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w1 = torch.randn((e, 2 * n, k), device=device, dtype=dtype) / 10
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w2 = torch.randn((e, k, n), device=device, dtype=dtype) / 10
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score = torch.randn((m, e), device=device, dtype=dtype)
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if ep_size > 1:
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local_e = e // ep_size
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e_ids = torch.randint(0,
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e, (local_e, ),
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device=device,
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dtype=torch.int32)
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e_map = torch.full((e, ), -1, device=device, dtype=torch.int32)
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e_map[e_ids] = torch.arange(local_e,
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device=device,
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dtype=torch.int32)
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w1 = w1[e_ids]
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w2 = w2[e_ids]
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else:
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e_map = None
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score = torch.softmax(score, dim=-1, dtype=dtype)
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topk_weights, topk_ids = torch.topk(score, topk)
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topk_ids = topk_ids.to(torch.int32)
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output = fused_experts(a, w1, w2, topk_weights, topk_ids, topk, e_map)
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torch_output = torch_moe(a, w1, w2, topk_weights, topk_ids, topk,
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e_map)
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# TODO: The native params are: atol=2e-2, rtol=0, maybe related to the nan problem
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torch.testing.assert_close(output, torch_output, atol=4e-2, rtol=1)
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torch.npu.empty_cache()
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