[SpecDecode] Add spec decode support (#500)
### 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>
2025-04-17 20:16:32 +08:00
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#
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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/utils.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 functools
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import os
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import signal
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2025-06-06 20:21:13 +08:00
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import subprocess
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import sys
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import time
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from typing import Callable, Optional
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[SpecDecode] Add spec decode support (#500)
### 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>
2025-04-17 20:16:32 +08:00
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2025-06-06 20:21:13 +08:00
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import openai
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import requests
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[SpecDecode] Add spec decode support (#500)
### 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>
2025-04-17 20:16:32 +08:00
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from typing_extensions import ParamSpec
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2025-06-06 20:21:13 +08:00
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from vllm.engine.arg_utils import AsyncEngineArgs
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from vllm.entrypoints.openai.cli_args import make_arg_parser
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from vllm.model_executor.model_loader import get_model_loader
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from vllm.utils import FlexibleArgumentParser, get_open_port
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[SpecDecode] Add spec decode support (#500)
### 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>
2025-04-17 20:16:32 +08:00
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_P = ParamSpec("_P")
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2025-06-06 20:21:13 +08:00
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class RemoteOpenAIServer:
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DUMMY_API_KEY = "token-abc123" # vLLM's OpenAI server does not need API key
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def __init__(self,
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model: str,
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vllm_serve_args: list[str],
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*,
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env_dict: Optional[dict[str, str]] = None,
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seed: Optional[int] = 0,
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auto_port: bool = True,
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max_wait_seconds: Optional[float] = None) -> None:
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if auto_port:
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if "-p" in vllm_serve_args or "--port" in vllm_serve_args:
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raise ValueError("You have manually specified the port "
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"when `auto_port=True`.")
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# Don't mutate the input args
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vllm_serve_args = vllm_serve_args + [
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"--port", str(get_open_port())
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]
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if seed is not None:
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if "--seed" in vllm_serve_args:
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raise ValueError("You have manually specified the seed "
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f"when `seed={seed}`.")
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vllm_serve_args = vllm_serve_args + ["--seed", str(seed)]
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parser = FlexibleArgumentParser(
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description="vLLM's remote OpenAI server.")
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parser = make_arg_parser(parser)
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args = parser.parse_args(["--model", model, *vllm_serve_args])
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self.host = str(args.host or 'localhost')
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self.port = int(args.port)
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self.show_hidden_metrics = \
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args.show_hidden_metrics_for_version is not None
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# download the model before starting the server to avoid timeout
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is_local = os.path.isdir(model)
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if not is_local:
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engine_args = AsyncEngineArgs.from_cli_args(args)
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model_config = engine_args.create_model_config()
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load_config = engine_args.create_load_config()
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model_loader = get_model_loader(load_config)
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model_loader.download_model(model_config)
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env = os.environ.copy()
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# the current process might initialize cuda,
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# to be safe, we should use spawn method
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env['VLLM_WORKER_MULTIPROC_METHOD'] = 'spawn'
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if env_dict is not None:
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env.update(env_dict)
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self.proc = subprocess.Popen(
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["vllm", "serve", model, *vllm_serve_args],
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env=env,
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stdout=sys.stdout,
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stderr=sys.stderr,
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)
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max_wait_seconds = max_wait_seconds or 240
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self._wait_for_server(url=self.url_for("health"),
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timeout=max_wait_seconds)
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def __enter__(self):
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return self
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def __exit__(self, exc_type, exc_value, traceback):
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self.proc.terminate()
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try:
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self.proc.wait(8)
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except subprocess.TimeoutExpired:
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# force kill if needed
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self.proc.kill()
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def _wait_for_server(self, *, url: str, timeout: float):
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# run health check
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start = time.time()
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while True:
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try:
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if requests.get(url).status_code == 200:
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break
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except Exception:
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# this exception can only be raised by requests.get,
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# which means the server is not ready yet.
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# the stack trace is not useful, so we suppress it
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# by using `raise from None`.
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result = self.proc.poll()
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if result is not None and result != 0:
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raise RuntimeError("Server exited unexpectedly.") from None
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time.sleep(0.5)
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if time.time() - start > timeout:
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raise RuntimeError(
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"Server failed to start in time.") from None
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@property
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def url_root(self) -> str:
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return f"http://{self.host}:{self.port}"
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def url_for(self, *parts: str) -> str:
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return self.url_root + "/" + "/".join(parts)
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def get_client(self, **kwargs):
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if "timeout" not in kwargs:
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kwargs["timeout"] = 600
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return openai.OpenAI(
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base_url=self.url_for("v1"),
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api_key=self.DUMMY_API_KEY,
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max_retries=0,
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**kwargs,
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)
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def get_async_client(self, **kwargs):
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if "timeout" not in kwargs:
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kwargs["timeout"] = 600
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return openai.AsyncOpenAI(base_url=self.url_for("v1"),
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api_key=self.DUMMY_API_KEY,
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max_retries=0,
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**kwargs)
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[SpecDecode] Add spec decode support (#500)
### 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>
2025-04-17 20:16:32 +08:00
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def fork_new_process_for_each_test(
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f: Callable[_P, None]) -> Callable[_P, None]:
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"""Decorator to fork a new process for each test function.
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See https://github.com/vllm-project/vllm/issues/7053 for more details.
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"""
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@functools.wraps(f)
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def wrapper(*args: _P.args, **kwargs: _P.kwargs) -> None:
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# Make the process the leader of its own process group
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# to avoid sending SIGTERM to the parent process
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os.setpgrp()
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from _pytest.outcomes import Skipped
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pid = os.fork()
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print(f"Fork a new process to run a test {pid}")
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if pid == 0:
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try:
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f(*args, **kwargs)
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except Skipped as e:
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# convert Skipped to exit code 0
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print(str(e))
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os._exit(0)
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except Exception:
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import traceback
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traceback.print_exc()
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os._exit(1)
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else:
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os._exit(0)
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else:
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pgid = os.getpgid(pid)
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_pid, _exitcode = os.waitpid(pid, 0)
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# ignore SIGTERM signal itself
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old_signal_handler = signal.signal(signal.SIGTERM, signal.SIG_IGN)
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# kill all child processes
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os.killpg(pgid, signal.SIGTERM)
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# restore the signal handler
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signal.signal(signal.SIGTERM, old_signal_handler)
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assert _exitcode == 0, (f"function {f} failed when called with"
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f" args {args} and kwargs {kwargs}")
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return wrapper
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