df1ee8070d
### What this PR does / why we need it?
Implement a unified parallelized speculative decoding in VLLM
Ascend，which can simultaneously support parallel speculative inference
schemes such as Pard, P-Eagle, etc. refer to
https://github.com/vllm-project/vllm-ascend/pull/6565 and
https://github.com/vllm-project/vllm-ascend/pull/4078
### How was this patch tested?
run with parallel drafting script:
export target=/model/Llama-3.1-8B-Instruct
export draft=/model/PARD-Llama-3.2-1B
export CUDA_VISIBLE_DEVICES=6
export ASCEND_RT_VISIBLE_DEVICES=6
vllm serve $target \
--tensor-parallel-size 1 \
--max-model-len 4096 \
--no-enable-prefix-caching \
--port 8811 \
--speculative-config '{"model": "/model/PARD-Llama-3.2-1B", "method":
"draft_model", "num_speculative_tokens": 8, "parallel_drafting": true}'
base script:
export target=/model/Llama-3.1-8B-Instruct
export draft=/model/PARD-Llama-3.2-1B
export CUDA_VISIBLE_DEVICES=6
export ASCEND_RT_VISIBLE_DEVICES=6
vllm serve $target \
--tensor-parallel-size 1 \
--max-model-len 4096 \
--no-enable-prefix-caching \
--port 8811
benchmark script:
MAX_CONCURRENCY=1
NUM_PROMPTS=80
vllm bench serve --port 8811 \
--temperature 0 \
--model /model/Llama-3.1-8B-Instruct \
--backend openai-chat \
--endpoint /v1/chat/completions \
--dataset-name hf \
--dataset-path philschmid/mt-bench \
--num-prompts ${NUM_PROMPTS} \
--max-concurrency ${MAX_CONCURRENCY} \
--seed 1234
test results :
base(without spec decode): TTFT 79.46ms TPOT 26.99ms
output_tokens_throughput 36.75 tok/s
this pr(with parallel drafting): TTFT 72.24ms TPOT 13.45ms
output_tokens_throughput 72.98 tok/s
per-position acceptance(from position 0 to 7):
79.48%、56.93%、40%、27.90%、19.79%、14.25%、10.57%、7.61%.
----------------------------------------------------------------------
run on qwen3 model script ：
export target=/model/Qwen3-1.7B
export draft=/model/PARD-Qwen3-0.6B
export CUDA_VISIBLE_DEVICES=1
export ASCEND_RT_VISIBLE_DEVICES=1
vllm serve $target \
--tensor-parallel-size 1 \
--max-model-len 4096 \
--no-enable-prefix-caching \
--port 8811 \
--speculative-config '{"model": "/model/PARD-Qwen3-0.6B", "method":
"draft_model", "num_speculative_tokens": 8, "parallel_drafting": true}'
cc @NickJudyHvv
- vLLM version: v0.15.0
- vLLM main:
9562912cea
---------
Signed-off-by: 01267596 <xiongkai123@cmbchina.com>
Signed-off-by: kx <1670186653@qq.com>
Signed-off-by: HF-001 <1670186653@qq.com>
Co-authored-by: 01267596 <xiongkai123@cmbchina.com>
66 lines
2.6 KiB
Python
66 lines
2.6 KiB
Python
# Copyright (c) 2025 Huawei Technologies Co., Ltd. All Rights Reserved.
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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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# This file is a part of the vllm-ascend project.
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#
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# Adapted from https://github.com/vllm-project/vllm/blob/main/vllm/v1/spec_decode/utils.py
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from vllm.triton_utils import tl, triton
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@triton.jit
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def prepare_inputs_padded_kernel(
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cu_num_draft_tokens_ptr, # [num_reqs]
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valid_sampled_tokens_count_ptr, # [num_reqs]
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query_start_loc_gpu_ptr, # [num_reqs + 1]
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token_indices_to_sample_ptr, # [num_reqs] (output)
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num_rejected_tokens_gpu_ptr,
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num_reqs, # tl.int32
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BLOCK_SIZE: tl.constexpr,
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):
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pid = tl.program_id(axis=0)
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num_programs = tl.num_programs(axis=0)
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# Grid-Stride Loop:
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block_start_step = num_programs * BLOCK_SIZE
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for block_start in tl.range(pid * BLOCK_SIZE, num_reqs, block_start_step):
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offsets = block_start + tl.arange(0, BLOCK_SIZE)
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mask = offsets < num_reqs
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# Calculate num_draft_tokens from cu_num_draft_tokens, which is an inclusive
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# cumulative sum (first entry is the first value, not zero).
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cu_draft_curr = tl.load(cu_num_draft_tokens_ptr + offsets, mask=mask)
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prev_indices = offsets - 1
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has_prev = offsets > 0
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cu_draft_prev = tl.load(
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cu_num_draft_tokens_ptr + prev_indices,
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mask=mask & has_prev,
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other=0,
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)
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num_draft_tokens = tl.where(has_prev, cu_draft_curr - cu_draft_prev, cu_draft_curr)
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valid_count = tl.load(valid_sampled_tokens_count_ptr + offsets, mask=mask)
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num_rejected = num_draft_tokens + 1 - valid_count
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num_rejected = tl.where(num_draft_tokens > 0, num_rejected, 0)
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# query_start_loc[req_idx + 1] is the start position of the next request,
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# which is one past the last token of this request.
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q_last_tok_idx = tl.load(query_start_loc_gpu_ptr + offsets + 1, mask=mask) - 1
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index_to_sample = q_last_tok_idx - num_rejected
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tl.store(token_indices_to_sample_ptr + offsets, index_to_sample, mask=mask)
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tl.store(num_rejected_tokens_gpu_ptr + offsets, num_rejected, mask=mask)
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