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[Misc][V0 Deprecation] Remove Draft Model Runner Used for V0 Spec Decode (#1810)

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### What this PR does / why we need it?
Remove draft model runner used for V0 spec decode.

This PR is a part of
https://github.com/vllm-project/vllm-ascend/issues/1620.

- vLLM version: v0.9.2
- vLLM main:
34cda778a0

---------

Signed-off-by: shen-shanshan <467638484@qq.com>
This commit is contained in:
Shanshan Shen
2025-07-16 10:51:23 +08:00
committed by GitHub
parent f96100fad5
commit f9e2e9bb31
4 changed files with 0 additions and 493 deletions
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15
vllm_ascend/patch/__init__.py
View File

@@ -73,21 +73,6 @@
# Future Plan:
# Keep this patch in vllm-ascend.
#
# ** File: worker/patch_common/patch_spec_decode_worker.py **
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# 1. `vllm.spec_decode.spec_decode_worker.SpecDecodeWorker.create_worker`
# Why:
# We need to use the patched `TP1DraftModelRunner` in `SpecDecodeWorker.create_worker`.
# The mainly reason to overwrite `TP1DraftModelRunner`is the hard code of
# `FlashAttentionMetadata`
# How：
# ditto
# Related PR (if no, explain why):
# - https://github.com/vllm-project/vllm/pull/15195
# - https://github.com/vllm-project/vllm-ascend/pull/395
# Future Plan:
# Revert it when the related pr is merged in vllm and vllm-ascend.
#
# ** File: worker/patch_common/patch_distributed.py **
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# 1. `vllm.distributed.parallel_state.GroupCoordinator`

1
vllm_ascend/patch/worker/patch_common/__init__.py
View File

@@ -21,4 +21,3 @@ import vllm_ascend.patch.worker.patch_common.patch_utils # noqa isort:skip
import vllm_ascend.patch.worker.patch_common.patch_distributed # noqa
import vllm_ascend.patch.worker.patch_common.patch_minicpm # noqa
import vllm_ascend.patch.worker.patch_common.patch_sampler # noqa
import vllm_ascend.patch.worker.patch_common.patch_spec_decode_worker # noqa

157
vllm_ascend/patch/worker/patch_common/patch_spec_decode_worker.py
View File

@@ -1,157 +0,0 @@
#
# Copyright (c) 2025 Huawei Technologies Co., Ltd. All Rights Reserved.
# This file is a part of the vllm-ascend project.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
from typing import Any, Dict, Optional
from vllm.config import ParallelConfig
from vllm.logger import logger
from vllm.model_executor.layers.rejection_sampler import RejectionSampler
from vllm.model_executor.layers.spec_decode_base_sampler import \
SpecDecodeBaseSampler
from vllm.model_executor.layers.typical_acceptance_sampler import \
TypicalAcceptanceSampler
from vllm.spec_decode.medusa_worker import MedusaWorker
from vllm.spec_decode.mlp_speculator_worker import MLPSpeculatorWorker
from vllm.spec_decode.multi_step_worker import MultiStepWorker
from vllm.spec_decode.ngram_worker import NGramWorker
from vllm.spec_decode.smaller_tp_proposer_worker import SmallerTpProposerWorker
from vllm.spec_decode.spec_decode_worker import SpecDecodeWorker
from vllm.worker.worker_base import WorkerBase
from vllm_ascend.worker.draft_model_runner import TP1DraftModelRunner
def create_worker(
cls,
scorer_worker: WorkerBase,
draft_worker_kwargs: Dict[str, Any],
disable_mqa_scorer: bool,
disable_by_batch_size: Optional[int],
draft_token_acceptance_method: str,
typical_acceptance_sampler_posterior_threshold: float,
typical_acceptance_sampler_posterior_alpha: float,
disable_logprobs: bool,
disable_log_stats: bool,
num_speculative_tokens: int,
) -> "SpecDecodeWorker":
allow_zero_draft_token_step = True
enable_lm_head_weight_load = False
num_spec_prefill_steps = 1
ngram_prompt_lookup_max = (
draft_worker_kwargs.pop("ngram_prompt_lookup_max"))
ngram_prompt_lookup_min = (
draft_worker_kwargs.pop("ngram_prompt_lookup_min"))
draft_model_config = draft_worker_kwargs["vllm_config"].model_config
draft_parallel_config: ParallelConfig = draft_worker_kwargs[
'vllm_config'].parallel_config
if ngram_prompt_lookup_max > 0:
draft_worker_kwargs[
"device_type"] = scorer_worker.device_config.device.type
proposer_worker = NGramWorker(**draft_worker_kwargs)
proposer_worker.set_ngram_window_size(ngram_prompt_lookup_min,
ngram_prompt_lookup_max)
else:
# TODO(Yizhou): A quick fix, must be refactored ASAP
# ngram need not this fix.
draft_worker_kwargs[
"vllm_config"].parallel_config.expert_parallel_size = 1
draft_worker_kwargs[
"vllm_config"].parallel_config.expert_tensor_parallel_size = 1
draft_tp = draft_parallel_config.tensor_parallel_size
target_tp = scorer_worker.parallel_config.tensor_parallel_size
if draft_model_config.hf_config.model_type == "mlp_speculator":
proposer_worker = MLPSpeculatorWorker(**draft_worker_kwargs)
elif draft_model_config.hf_config.model_type == "medusa":
proposer_worker = MedusaWorker(**draft_worker_kwargs)
else:
# Note: The current version of the MTP module doer not support
# the use of TP1DraftModelRunner
if draft_tp == 1 and draft_model_config.hf_config.model_type !=\
"deepseek_mtp":
draft_worker_kwargs["model_runner_cls"] = TP1DraftModelRunner
else:
if draft_model_config.hf_config.model_type == "eagle":
raise NotImplementedError(
f"{draft_model_config.hf_config.model_type} "
"does not support TP > 1 yet")
allow_zero_draft_token_step = False
# Load lm_head weight for eagle in init_device
if draft_model_config.hf_config.model_type == "eagle":
enable_lm_head_weight_load = True
proposer_worker = MultiStepWorker(**draft_worker_kwargs)
if draft_model_config.hf_config.model_type == "deepseek_mtp":
num_spec_prefill_steps = draft_model_config.hf_config.n_predict
proposer_worker = SmallerTpProposerWorker.maybe_wrap_worker(
proposer_worker, draft_tp, target_tp)
logger.info("Configuring SpecDecodeWorker with proposer=%s",
type(proposer_worker))
spec_decode_sampler: SpecDecodeBaseSampler = None
if draft_token_acceptance_method == "rejection_sampler":
spec_decode_sampler = RejectionSampler()
elif draft_token_acceptance_method == "typical_acceptance_sampler":
spec_decode_sampler = TypicalAcceptanceSampler(
posterior_threshold=\
typical_acceptance_sampler_posterior_threshold,
posterior_alpha=typical_acceptance_sampler_posterior_alpha,
)
logger.info(
"[Speculative Decoding] Configuring"
" SpecDecodeWorker with sampler=%s", type(spec_decode_sampler))
if not disable_mqa_scorer:
if scorer_worker.model_runner.attn_backend.get_name() != "FLASH_ATTN":
disable_mqa_scorer = True
logger.info("[Speculative Decoding] Disabling MQA scorer as the "
"MQA is only available with flash attn backend.")
if draft_model_config and \
draft_model_config.max_model_len < \
scorer_worker.model_config.max_model_len:
disable_mqa_scorer = True
logger.info("[Speculative Decoding] Disabling MQA scorer as the "
"draft model max_model_len is smaller than the target "
"model max_model_len.")
if not scorer_worker.model_runner.model_config.enforce_eager:
disable_mqa_scorer = True
logger.info("[Speculative Decoding] Disabling MQA scorer as the "
"target model is not running in eager mode.")
return SpecDecodeWorker(
proposer_worker,
scorer_worker,
disable_mqa_scorer=disable_mqa_scorer,
disable_logprobs=disable_logprobs,
disable_log_stats=disable_log_stats,
disable_by_batch_size=disable_by_batch_size,
spec_decode_sampler=spec_decode_sampler,
allow_zero_draft_token_step=allow_zero_draft_token_step,
enable_lm_head_weight_load=enable_lm_head_weight_load,
num_spec_prefill_steps=num_spec_prefill_steps)
SpecDecodeWorker.create_worker = classmethod(create_worker)