xinyun/vllm
1
0
Fork 0
mirror of https://git.datalinker.icu/vllm-project/vllm.git synced 2026-03-18 21:47:34 +08:00
Code Issues Packages Projects Releases Wiki Activity

Merge a2b34802361df50da4ef3ac250707b71b2ce282a into 254f6b986720c92ddf97fbb1a6a6465da8e87e29

Browse Source
This commit is contained in:
ゆり 2025-12-25 00:07:15 +00:00 committed by GitHub
commit 1d3be5cbed
No known key found for this signature in database
GPG Key ID: B5690EEEBB952194
1 changed files with 41 additions and 30 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

71
vllm/v1/spec_decode/ngram_proposer.py
View File

@ -6,6 +6,7 @@ import numpy as np
from numba import get_num_threads, jit, njit, prange, set_num_threads
from vllm.config import VllmConfig
from vllm.distributed import get_tp_group
class NgramProposer:
@ -33,21 +34,17 @@ class NgramProposer:
# Threshold of total number of tokens in the batch to enable
# multi-threading in numba batch propose.
self.num_tokens_threshold = 8192
tp_size = vllm_config.parallel_config.tensor_parallel_size
cpu_count = os.cpu_count()
# Max number of threads for numba parallel processing.
# Since draft tokens are computed only on rank 0 and broadcast to other
# ranks (for TP consistency), rank 0 can use all available threads.
if cpu_count:
# Divide by 2 to use physical cores
# and not logical cores (hyper-threading).
# Cap the number of threads to 8 to avoid using too many threads
# since other components like frontend (incl tokenization)
# and Structured Outputs also use multiple threads.
# TODO(ekagra-ranjan): bump up the cap from 1 to 8
# when TP parallelization for ngram is implemented.
self.num_numba_thread_available = min(1, (cpu_count // 2))
# Divide by tp_size to ensure each tensor parallel rank
# has some threads since all ranks will run this.
self.num_numba_thread_available //= tp_size
self.num_numba_thread_available = min(8, cpu_count // 2)
else:
self.num_numba_thread_available = 1
@ -137,33 +134,47 @@ class NgramProposer:
token_ids_cpu: np.ndarray,
spec_decode_unsupported_reqs: set,
) -> list[list[int]]:
# find which requests need ngram proposals
valid_ngram_requests = []
for i, sampled_ids in enumerate(sampled_token_ids):
num_sampled_ids = len(sampled_ids)
if not num_sampled_ids:
# Skip speculative decoding.
continue
# Only compute draft tokens on TP rank 0 and broadcast to other ranks.
# This ensures all TP ranks have identical draft tokens, which is
# required because numba parallel execution can produce different
# results across ranks due to non-determinism.
tp_group = get_tp_group()
if tp_group.is_first_rank:
# find which requests need ngram proposals
valid_ngram_requests = []
for i, sampled_ids in enumerate(sampled_token_ids):
num_sampled_ids = len(sampled_ids)
if not num_sampled_ids:
# Skip speculative decoding.
continue
# Skip requests that require sampling parameters that are not
# supported with speculative decoding.
req_id = req_ids[i]
if req_id in spec_decode_unsupported_reqs:
continue
# Skip requests that require sampling parameters that are not
# supported with speculative decoding.
req_id = req_ids[i]
if req_id in spec_decode_unsupported_reqs:
continue
num_tokens = num_tokens_no_spec[i]
if num_tokens >= self.max_model_len:
# Skip requests that have already reached the max model length.
continue
num_tokens = num_tokens_no_spec[i]
if num_tokens >= self.max_model_len:
# Skip requests that have already reached the max model length.
continue
valid_ngram_requests.append(i)
valid_ngram_requests.append(i)
draft_token_ids = self.batch_propose(
len(sampled_token_ids),
valid_ngram_requests,
num_tokens_no_spec,
token_ids_cpu,
)
draft_token_ids = self.batch_propose(
len(sampled_token_ids),
valid_ngram_requests,
num_tokens_no_spec,
token_ids_cpu,
)
else:
draft_token_ids = None
# Broadcast draft tokens from rank 0 to all other ranks.
# Rank 0 always computes valid draft_token_ids, so broadcast
# will never return None.
draft_token_ids = tp_group.broadcast_object(draft_token_ids, src=0)
assert draft_token_ids is not None
return draft_token_ids