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[Misc][V0 Deprecation] Remove multi-step worker (#1809)

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### What this PR does / why we need it?
Remove multi-step worker

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

- vLLM version: v0.9.2
- vLLM main:
235bfd5dfe

---------

Signed-off-by: shen-shanshan <467638484@qq.com>
This commit is contained in:
Shanshan Shen
2025-07-15 19:48:47 +08:00
committed by GitHub
parent bf2549856f
commit a929699e98
4 changed files with 0 additions and 303 deletions
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1
vllm_ascend/patch/worker/patch_common/__init__.py
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@@ -20,6 +20,5 @@
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_multi_step_worker # noqa
import vllm_ascend.patch.worker.patch_common.patch_sampler # noqa
import vllm_ascend.patch.worker.patch_common.patch_spec_decode_worker # noqa

91
vllm_ascend/patch/worker/patch_common/patch_multi_step_worker.py
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@@ -1,91 +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 List, Set, Tuple
import torch
from vllm.model_executor.layers.sampler import SamplerOutput
from vllm.sequence import ExecuteModelRequest
from vllm.spec_decode.multi_step_worker import MultiStepWorker
from vllm_ascend.worker.draft_model_runner import TP1DraftModelRunner
def sampler_output(
self,
execute_model_req: ExecuteModelRequest,
sample_len: int,
seq_ids_with_bonus_token_in_last_step: Set[int],
) -> Tuple[List[SamplerOutput], bool]:
"""Run the model forward pass sample_len times. Returns the list of
sampler output, one per model forward pass, along with indicator of
whether torch tensor in sampler output need to be transposed in latter
sampler_output_to_torch logic.
For multi step worker, this indicator shall be True.
"""
self._raise_if_unsupported(execute_model_req)
# Expand the batch for sequences with a bonus token.
# Perform a forward pass on the expanded batch and filter the
# response to retain only the original sequences' responses.
expanded_request, indices_of_seq_with_bonus_tokens =\
self._expand_execute_model_request(
execute_model_req, seq_ids_with_bonus_token_in_last_step)
# Run model sample_len times.
model_outputs: List[SamplerOutput] = []
# TODO: supports_gpu_multi_step is False in ASCEND
if isinstance(self.model_runner, TP1DraftModelRunner) and \
self.model_runner.supports_gpu_multi_step(expanded_request):
# Here we run the draft_model_runner with multi-step prepare
# on the GPU directly
expanded_request.num_steps = sample_len
self.model_runner.set_indices_of_seq_with_bonus_tokens(
indices_of_seq_with_bonus_tokens)
model_outputs = self.execute_model(execute_model_req=expanded_request)
else:
# Here we run multi-step directly, with every step prepared
# on the CPU.
# TODO Remove this branch once DraftModelRunner supports TP>1
# and other restrictions that are part of DraftModelRunner's
# supports_gpu_multi_step(..)
if expanded_request.previous_hidden_states is not None:
self.worker.model_runner.return_hidden_states = True
for _ in range(sample_len):
model_output: List[SamplerOutput] = self.worker.execute_model(
execute_model_req=expanded_request)
assert (len(model_output) == 1
), "composing multistep workers not supported"
model_output = model_output[0]
self._maybe_update_previous_hidden_states(model_output,
expanded_request)
self._append_new_tokens(model_output,
expanded_request.seq_group_metadata_list,
indices_of_seq_with_bonus_tokens)
model_outputs.append(model_output)
# move indices to device to avoid stream sync
indices_of_seq_with_bonus_tokens = torch.tensor(
indices_of_seq_with_bonus_tokens, device=self.device)
filtered_model_outputs = self._filter_model_output(
model_outputs, indices_of_seq_with_bonus_tokens)
return filtered_model_outputs, True
MultiStepWorker.sampler_output = torch.inference_mode()(sampler_output)