6ee7f5cf71
### What this PR does / why we need it? Backport: https://github.com/vllm-project/vllm-ascend/pull/252 This support speculative decoding in Ascend, including speculating with a draft model、by matching n-grams in the prompt、using MLP speculators and using EAGLE based draft models. Backport: https://github.com/vllm-project/vllm-ascend/pull/423 spec decode MultiStepWorker support TP1DraftModelRunner fully, support run the draft_model_runner with multi-step prepare on the NPU directly and support draft_model_runner use MLA. 1. before this pr, `MultiStepWorker` would not step into the branch using NPU prepare, but only into the branch using CPU prepare (`line 52` of `vllm_ascend/patch/patch_multi_step_worker.py`). Although this has `no effect` on the `correct operation` of speculative decoding and the performance of the two branches is basically the same as of the current version, I support entering this branch in this PR. In general, there are two main changes in `patch_multi_step_worker.py`: first, the `is_cuda_like()` check is removed and the `TP1DraftModelRunner` rewritten in vllm_ascend is used; second, the `supports_gpu_multi_step()` function is made to return true on NPU devices when outer Multi_step_worker could work correct. 3. before this pr, `TP1DraftModelRunner` only supports Attention on NPU, but not MLA. The relevant adaptation is in `vllm_ascend/worker/draft_model_runner.py`. Although I don’t know why the `input_positions` of `model_input.attn_metadata` in vllm-ascend needs to be added in `execute_model`, it is done in `model_runner.py`, so I also made corresponding changes. Otherwise, when atten_backend is MLA, it will prompt that input_positions cannot be found. 4. I commented out two lines in `draft_model_runner.py` in `line118` to support the scenario of K>1. ``` # lora_mapping=model_input.lora_mapping, # lora_requests=model_input.lora_requests, ``` I added comments. In the future, when vllm-ascend supports lora feature, the changes here can be restored. TODO： - [ ] revert the patch when the related issues are addressed in vllm ### How was this patch tested? CI passed with new added test. - e2e test for medusa proposer: tests/singlecard/spec_decode/e2e/test_medusa_correctness.py - e2e test for mlp proposer: tests/singlecard/spec_decode/e2e/test_mlp_correctness.py - e2e test for n-gram proposer: tests/singlecard/spec_decode/e2e/test_ngram_correctness.py Tests for patched files: - tests/singlecard/spec_decode/test_dynamic_spec_decode.py - tests/singlecard/spec_decode/test_multi_step_worker.py - tests/singlecard/spec_decode/test_ngram_worker.py - tests/singlecard/spec_decode/test_spec_decode_worker.py --------- Signed-off-by: MengqingCao <cmq0113@163.com> Co-authored-by: mengwei805 <mengwei25@huawei.com>
29 lines
1005 B
Python
29 lines
1005 B
Python
#
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# Copyright (c) 2025 Huawei Technologies Co., Ltd. All Rights Reserved.
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# This file is a part of the vllm-ascend project.
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# Adapted from vllm-project/vllm/tests/spec_decode/conftest.py
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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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#
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import pytest
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@pytest.fixture(scope="function", autouse=True)
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def use_v0_only(monkeypatch):
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"""
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Since this module is V0 only, set VLLM_USE_V1=0 for
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all tests in the module.
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"""
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monkeypatch.setenv('VLLM_USE_V1', '0')
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