xinyun/vllm
1
0
Fork 0
mirror of https://git.datalinker.icu/vllm-project/vllm.git synced 2025-12-10 05:15:42 +08:00
Code Issues Packages Projects Releases Wiki Activity

Revert "[Core] Performance: Use list[np.ndarray] instead of list[list… (#28773)

Browse Source
This commit is contained in:
Nick Hill 2025-11-14 20:24:00 -08:00 committed by GitHub
parent edfe498189
commit ac86bff8cb
No known key found for this signature in database
GPG Key ID: B5690EEEBB952194
12 changed files with 76 additions and 102 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

3
tests/v1/core/test_async_scheduler.py
View File

@ -2,7 +2,6 @@
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
from collections import deque
import numpy as np
import pytest
from vllm.v1.core.sched.output import SchedulerOutput
@ -22,7 +21,7 @@ def _make_model_runner_output(
return ModelRunnerOutput(
req_ids=req_ids,
req_id_to_index={req_id: i for i, req_id in enumerate(req_ids)},
sampled_token_ids=[np.array([i]) for i in range(len(req_ids))],
sampled_token_ids=[[i] for i in range(len(req_ids))],
logprobs=None,
prompt_logprobs_dict={},
pooler_output=[],

76
tests/v1/core/test_scheduler.py
View File

@ -3,7 +3,6 @@
import dataclasses
from unittest.mock import Mock
import numpy as np
import pytest
import torch
@ -170,7 +169,7 @@ def test_schedule_partial_requests():
req_id_to_index=req_to_index,
# Only the first request has a sampled token id because
# the rest requests are still being prefilled.
sampled_token_ids=[np.array([0]), np.array([]), np.array([])],
sampled_token_ids=[[0], [], []],
logprobs=None,
prompt_logprobs_dict={},
pooler_output=[],
@ -217,7 +216,7 @@ def test_no_mm_input_chunking():
model_runner_output = ModelRunnerOutput(
req_ids=[request.request_id for request in requests],
req_id_to_index=req_to_index,
sampled_token_ids=[np.array([]) for _ in range(len(requests))],
sampled_token_ids=[[] for _ in range(len(requests))],
logprobs=None,
prompt_logprobs_dict={},
pooler_output=[],
@ -277,7 +276,7 @@ def test_schedule_concurrent_partial_requests(enable_prefix_caching: bool):
model_runner_output = ModelRunnerOutput(
req_ids=[request.request_id for request in requests],
req_id_to_index=req_to_index,
sampled_token_ids=[np.array([]) for _ in range(len(requests))],
sampled_token_ids=[[] for _ in range(len(requests))],
logprobs=None,
prompt_logprobs_dict={},
pooler_output=[],
@ -301,8 +300,7 @@ def test_schedule_concurrent_partial_requests(enable_prefix_caching: bool):
model_runner_output = ModelRunnerOutput(
req_ids=[request.request_id for request in requests],
req_id_to_index=req_to_index,
sampled_token_ids=[np.array([0]), np.array([0])]
+ [np.array([]) for _ in range(len(requests) - 2)],
sampled_token_ids=[[0], [0]] + [[] for _ in range(len(requests) - 2)],
logprobs=None,
prompt_logprobs_dict={},
pooler_output=[],
@ -349,8 +347,8 @@ def test_stop_via_update_from_output():
req_ids=[req.request_id for req in requests],
req_id_to_index={req.request_id: i for i, req in enumerate(requests)},
sampled_token_ids=[
np.array([EOS_TOKEN_ID]),
np.array([10, 11]),
[EOS_TOKEN_ID],
[10, 11],
], # First request hits EOS, second continues
logprobs=None,
prompt_logprobs_dict={},
@ -394,10 +392,7 @@ def test_stop_via_update_from_output():
model_output = ModelRunnerOutput(
req_ids=[req.request_id for req in requests],
req_id_to_index={req.request_id: i for i, req in enumerate(requests)},
sampled_token_ids=[
np.array([10, 42, 12]),
np.array([13, 14]),
], # First request hits stop token
sampled_token_ids=[[10, 42, 12], [13, 14]], # First request hits stop token
logprobs=None,
prompt_logprobs_dict={},
pooler_output=[],
@ -441,10 +436,7 @@ def test_stop_via_update_from_output():
model_output = ModelRunnerOutput(
req_ids=[req.request_id for req in requests],
req_id_to_index={req.request_id: i for i, req in enumerate(requests)},
sampled_token_ids=[
np.array([10, 11, 12]),
np.array([13]),
], # First request exceeds max_tokens
sampled_token_ids=[[10, 11, 12], [13]], # First request exceeds max_tokens
logprobs=None,
prompt_logprobs_dict={},
pooler_output=[],
@ -483,7 +475,7 @@ def test_stop_via_update_from_output():
model_output = ModelRunnerOutput(
req_ids=[requests[0].request_id],
req_id_to_index={requests[0].request_id: 0},
sampled_token_ids=[np.array([EOS_TOKEN_ID, 10, 11])],
sampled_token_ids=[[EOS_TOKEN_ID, 10, 11]],
logprobs=None,
prompt_logprobs_dict={},
pooler_output=[],
@ -624,7 +616,7 @@ def test_schedule_concurrent_batches(
model_runner_output = ModelRunnerOutput(
req_ids=[requests[0].request_id],
req_id_to_index={requests[0].request_id: 0},
sampled_token_ids=[np.array([0])],
sampled_token_ids=[[0]],
logprobs=None,
prompt_logprobs_dict={},
pooler_output=[],
@ -641,7 +633,7 @@ def test_schedule_concurrent_batches(
model_runner_output = ModelRunnerOutput(
req_ids=[requests[1].request_id],
req_id_to_index={requests[1].request_id: 0},
sampled_token_ids=[np.array([0])],
sampled_token_ids=[[0]],
logprobs=None,
prompt_logprobs_dict={},
pooler_output=[],
@ -678,7 +670,7 @@ def test_preempt_during_execution():
model_runner_output0 = ModelRunnerOutput(
req_ids=[requests[0].request_id],
req_id_to_index={requests[0].request_id: 0},
sampled_token_ids=[np.array([0])],
sampled_token_ids=[[0]],
logprobs=None,
prompt_logprobs_dict={},
pooler_output=[],
@ -695,7 +687,7 @@ def test_preempt_during_execution():
model_runner_output1 = ModelRunnerOutput(
req_ids=[requests[1].request_id],
req_id_to_index={requests[1].request_id: 0},
sampled_token_ids=[np.array([42])],
sampled_token_ids=[[42]],
logprobs=None,
prompt_logprobs_dict={},
pooler_output=[],
@ -712,18 +704,14 @@ def test_preempt_during_execution():
@pytest.mark.parametrize(
"spec_tokens,output_tokens,expected",
[
([[1, 2, 3]], [np.array([1, 2, 3, 4])], (1, 3, 3, [1, 1, 1])), # perfect match
([[1, 2, 3]], [np.array([1, 5])], (1, 3, 1, [1, 0, 0])), # early mismatch
(
[[1, 2], [3]],
[np.array([1, 2, 5]), np.array([3, 4])],
(2, 3, 3, [2, 1]),
), # multiple sequences
([[1]], [np.array([1, 2])], (1, 1, 1, [1])), # single token sequence
([[]], [np.array([5])], (0, 0, 0, [0])), # empty sequence
([[1, 2, 3]], [[1, 2, 3, 4]], (1, 3, 3, [1, 1, 1])), # perfect match
([[1, 2, 3]], [[1, 5]], (1, 3, 1, [1, 0, 0])), # early mismatch
([[1, 2], [3]], [[1, 2, 5], [3, 4]], (2, 3, 3, [2, 1])), # multiple sequences
([[1]], [[1, 2]], (1, 1, 1, [1])), # single token sequence
([[]], [[5]], (0, 0, 0, [0])), # empty sequence
(
[[1, 2, 3], [4, 5, 6]],
[np.array([1, 2, 7]), np.array([4, 8])],
[[1, 2, 7], [4, 8]],
(2, 6, 3, [2, 1, 0]),
), # multiple mismatches
],
@ -757,7 +745,7 @@ def test_schedule_spec_decoding_stats(spec_tokens, output_tokens, expected):
model_runner_output = ModelRunnerOutput(
req_ids=req_ids,
req_id_to_index=req_to_index,
sampled_token_ids=[np.array([0]) for _ in range(len(requests))],
sampled_token_ids=[[0] for _ in range(len(requests))],
logprobs=None,
prompt_logprobs_dict={},
pooler_output=[],
@ -984,7 +972,7 @@ def test_kv_connector_basic(is_async: bool):
MODEL_RUNNER_OUTPUT = ModelRunnerOutput(
req_ids=req_ids,
req_id_to_index=req_to_index,
sampled_token_ids=[np.array([1000])] * len(req_ids),
sampled_token_ids=[[1000]] * len(req_ids),
logprobs=None,
prompt_logprobs_dict={},
pooler_output=[],
@ -1037,7 +1025,7 @@ def test_kv_connector_basic(is_async: bool):
MODEL_RUNNER_OUTPUT = ModelRunnerOutput(
req_ids=req_ids,
req_id_to_index=req_to_index,
sampled_token_ids=[np.array([1000])] * len(req_ids),
sampled_token_ids=[[1000]] * len(req_ids),
logprobs=None,
prompt_logprobs_dict={},
pooler_output=[],
@ -1100,7 +1088,7 @@ def test_external_prefix_cache_metrics():
MODEL_RUNNER_OUTPUT = ModelRunnerOutput(
req_ids=[r.request_id for r in requests],
req_id_to_index={r.request_id: i for i, r in enumerate(requests)},
sampled_token_ids=[np.array([1000])] * NUM_REQUESTS,
sampled_token_ids=[[1000]] * NUM_REQUESTS,
logprobs=None,
prompt_logprobs_dict={},
pooler_output=[],
@ -1166,7 +1154,7 @@ def test_kv_connector_unable_to_allocate(use_ec_connector, ec_role):
MODEL_RUNNER_OUTPUT = ModelRunnerOutput(
req_ids=req_ids,
req_id_to_index=req_to_index,
sampled_token_ids=[np.array([1000])] * len(req_ids),
sampled_token_ids=[[1000]] * len(req_ids),
logprobs=None,
prompt_logprobs_dict={},
pooler_output=[],
@ -1251,7 +1239,7 @@ def test_kv_connector_handles_preemption(use_ec_connector, ec_role):
MODEL_RUNNER_OUTPUT = ModelRunnerOutput(
req_ids=req_ids,
req_id_to_index=req_to_index,
sampled_token_ids=[np.array([1000])] * len(req_ids),
sampled_token_ids=[[1000]] * len(req_ids),
logprobs=None,
prompt_logprobs_dict={},
pooler_output=[],
@ -1344,7 +1332,7 @@ def make_output(scheduler: Scheduler):
return ModelRunnerOutput(
req_ids=[req.request_id for req in scheduler.running],
req_id_to_index={req.request_id: i for i, req in enumerate(scheduler.running)},
sampled_token_ids=[np.array([1000])] * len(scheduler.running),
sampled_token_ids=[[1000]] * len(scheduler.running),
logprobs=None,
prompt_logprobs_dict={},
pooler_output=[],
@ -1761,7 +1749,7 @@ def test_priority_scheduling_preemption():
req_id_to_index={
req.request_id: i for i, req in enumerate(low_priority_requests)
},
sampled_token_ids=[np.array([100]) for _ in low_priority_requests],
sampled_token_ids=[[100] for _ in low_priority_requests],
logprobs=None,
prompt_logprobs_dict={},
pooler_output=[],
@ -1830,7 +1818,7 @@ def test_priority_scheduling_no_preemption_when_space_available():
req_id_to_index={
req.request_id: i for i, req in enumerate(low_priority_requests)
},
sampled_token_ids=[np.array([100]) for _ in low_priority_requests],
sampled_token_ids=[[100] for _ in low_priority_requests],
logprobs=None,
prompt_logprobs_dict={},
pooler_output=[],
@ -2076,7 +2064,7 @@ def test_priority_scheduling_heap_property():
model_output = ModelRunnerOutput(
req_ids=[req.req_id],
req_id_to_index={req.req_id: 0},
sampled_token_ids=[np.array([100])],
sampled_token_ids=[[100]],
logprobs=None,
prompt_logprobs_dict={},
pooler_output=[],
@ -2162,7 +2150,7 @@ def test_priority_scheduling_preemption_and_resumption_when_out_of_kv(
model_output = ModelRunnerOutput(
req_ids=[request_low.request_id],
req_id_to_index={request_low.request_id: 0},
sampled_token_ids=[np.array([100])],
sampled_token_ids=[[100]],
# spec_token_ids=None,
logprobs=None,
prompt_logprobs_dict={},
@ -2193,7 +2181,7 @@ def test_priority_scheduling_preemption_and_resumption_when_out_of_kv(
model_output = ModelRunnerOutput(
req_ids=[req.request_id for req in requests],
req_id_to_index={req.request_id: i for i, req in enumerate(requests)},
sampled_token_ids=[np.array([100]) for _ in requests],
sampled_token_ids=[[100] for _ in requests],
# spec_token_ids=None,
logprobs=None,
prompt_logprobs_dict={},
@ -2219,7 +2207,7 @@ def test_priority_scheduling_preemption_and_resumption_when_out_of_kv(
model_output = ModelRunnerOutput(
req_ids=[req.request_id for req in requests],
req_id_to_index={req.request_id: i for i, req in enumerate(requests)},
sampled_token_ids=[np.array([]), np.array([100])],
sampled_token_ids=[[], [100]],
# spec_token_ids=None,
logprobs=None,
prompt_logprobs_dict={},

3
tests/v1/kv_connector/unit/utils.py
View File

@ -7,7 +7,6 @@ from dataclasses import dataclass
from itertools import chain, count
from typing import Any
import numpy as np
import torch
from vllm import SamplingParams
@ -229,7 +228,7 @@ def create_model_runner_output(
# Make sampled tokens.
sampled_token = EOS_TOKEN_ID if use_eos else token_id
sampled_token_ids = [np.array([sampled_token]) for _ in req_ids]
sampled_token_ids = [[sampled_token] for _ in req_ids]
kv_connector_output = (
None

5
tests/v1/spec_decode/test_eagle.py
View File

@ -3,7 +3,6 @@
from unittest import mock
import numpy as np
import pytest
import torch
@ -113,9 +112,7 @@ def test_prepare_next_token_ids():
sampled_token_ids_tensor = torch.tensor(
sampled_token_ids, dtype=torch.int32, device=device
)
sampled_token_ids_cpu = [
np.array([i for i in seq if i != -1]) for seq in sampled_token_ids
]
sampled_token_ids_cpu = [[i for i in seq if i != -1] for seq in sampled_token_ids]
expected_next_token_ids_cpu = [1, 4, 30, 40]
expected_next_token_ids_tensor = torch.tensor(

18
tests/v1/spec_decode/test_ngram.py
View File

@ -77,7 +77,7 @@ def test_ngram_proposer():
# No match.
token_ids_cpu = np.array([[1, 2, 3, 4, 5]])
result = get_ngram_proposer(min_n=2, max_n=2, k=2).propose(
sampled_token_ids=[np.array([0])],
sampled_token_ids=[[0]],
req_ids=["0"],
num_tokens_no_spec=np.array([len(c) for c in token_ids_cpu]),
token_ids_cpu=token_ids_cpu,
@ -88,7 +88,7 @@ def test_ngram_proposer():
# No match for 4-gram.
token_ids_cpu = np.array([[1, 2, 3, 4, 1, 2, 3]])
result = get_ngram_proposer(min_n=4, max_n=4, k=2).propose(
sampled_token_ids=[np.array([0])],
sampled_token_ids=[[0]],
req_ids=["0"],
num_tokens_no_spec=np.array([len(c) for c in token_ids_cpu]),
token_ids_cpu=token_ids_cpu,
@ -99,7 +99,7 @@ def test_ngram_proposer():
# No match for 4-gram but match for 3-gram.
token_ids_cpu = np.array([[1, 2, 3, 4, 1, 2, 3]])
result = get_ngram_proposer(min_n=3, max_n=4, k=2).propose(
sampled_token_ids=[np.array([0])],
sampled_token_ids=[[0]],
req_ids=["0"],
num_tokens_no_spec=np.array([len(c) for c in token_ids_cpu]),
token_ids_cpu=token_ids_cpu,
@ -111,7 +111,7 @@ def test_ngram_proposer():
# In this case, the proposer should return the 4-gram match.
token_ids_cpu = np.array([[2, 3, 4, 5, 1, 2, 3, 4, 1, 2, 3, 4]])
result = get_ngram_proposer(min_n=3, max_n=4, k=2).propose(
sampled_token_ids=[np.array([0])],
sampled_token_ids=[[0]],
req_ids=["0"],
num_tokens_no_spec=np.array([len(c) for c in token_ids_cpu]),
token_ids_cpu=token_ids_cpu,
@ -122,7 +122,7 @@ def test_ngram_proposer():
# Match for 2-gram and 3-gram, but not 4-gram.
token_ids_cpu = np.array([[3, 4, 5, 2, 3, 4, 1, 2, 3, 4]])
result = get_ngram_proposer(min_n=2, max_n=4, k=2).propose(
sampled_token_ids=[np.array([0])],
sampled_token_ids=[[0]],
req_ids=["0"],
num_tokens_no_spec=np.array([len(c) for c in token_ids_cpu]),
token_ids_cpu=token_ids_cpu,
@ -133,7 +133,7 @@ def test_ngram_proposer():
# Multiple 3-gram matched, but always pick the first one.
token_ids_cpu = np.array([[1, 2, 3, 100, 1, 2, 3, 200, 1, 2, 3, 300, 1, 2, 3]])
result = get_ngram_proposer(min_n=3, max_n=3, k=2).propose(
sampled_token_ids=[np.array([0])],
sampled_token_ids=[[0]],
req_ids=["0"],
num_tokens_no_spec=np.array([len(c) for c in token_ids_cpu]),
token_ids_cpu=token_ids_cpu,
@ -144,7 +144,7 @@ def test_ngram_proposer():
# check empty input
token_ids_cpu = np.array([[]])
result = get_ngram_proposer(min_n=2, max_n=2, k=2).propose(
sampled_token_ids=[np.array([0])],
sampled_token_ids=[[0]],
req_ids=["0"],
num_tokens_no_spec=np.array([len(c) for c in token_ids_cpu]),
token_ids_cpu=token_ids_cpu,
@ -157,7 +157,7 @@ def test_ngram_proposer():
# second request has 3 tokens and no match. Padded with -1 for max len 5
token_ids_cpu = np.array([[1, 2, 3, 1, 2], [4, 5, 6, -1, -1]])
result = get_ngram_proposer(min_n=2, max_n=2, k=2).propose(
sampled_token_ids=[np.array([0]), np.array([1])],
sampled_token_ids=[[0], [1]],
req_ids=["0", "1"],
num_tokens_no_spec=np.array([5, 3]),
token_ids_cpu=token_ids_cpu,
@ -181,7 +181,7 @@ def test_ngram_proposer():
input_2[:3] = [4, 5, 6]
token_ids_cpu = np.array([input_1, input_2])
result = ngram_proposer.propose(
sampled_token_ids=[np.array([0]), np.array([1])],
sampled_token_ids=[[0], [1]],
req_ids=["0", "1"],
num_tokens_no_spec=np.array([len(input_1), 3]),
token_ids_cpu=token_ids_cpu,

4
vllm/v1/core/sched/scheduler.py
View File

@ -1010,8 +1010,8 @@ class Scheduler(SchedulerInterface):
continue
req_index = model_runner_output.req_id_to_index[req_id]
generated_token_ids: list[int] = (
sampled_token_ids[req_index].tolist() if sampled_token_ids else []
generated_token_ids = (
sampled_token_ids[req_index] if sampled_token_ids else []
)
scheduled_spec_token_ids = (

2
vllm/v1/outputs.py
View File

@ -158,7 +158,7 @@ class ModelRunnerOutput:
# num_generated_tokens is the number of tokens
# generated in the current step. It can be different for
# each request due to speculative/jump decoding.
sampled_token_ids: list[np.ndarray]
sampled_token_ids: list[list[int]]
# [num_reqs, max_num_logprobs + 1]
# [num_reqs, max_num_logprobs + 1]

8
vllm/v1/sample/rejection_sampler.py
View File

@ -3,7 +3,6 @@
from dataclasses import replace
import numpy as np
import torch
import torch.nn as nn
@ -205,7 +204,7 @@ class RejectionSampler(nn.Module):
def parse_output(
output_token_ids: torch.Tensor,
vocab_size: int,
) -> list[np.ndarray]:
) -> list[list[int]]:
"""Parse the output of the rejection sampler.
Args:
output_token_ids: The sampled token IDs in shape
@ -221,7 +220,10 @@ class RejectionSampler(nn.Module):
valid_mask = (output_token_ids_np != PLACEHOLDER_TOKEN_ID) & (
output_token_ids_np < vocab_size
)
return [row[valid_mask[i]] for i, row in enumerate(output_token_ids_np)]
outputs = [
row[valid_mask[i]].tolist() for i, row in enumerate(output_token_ids_np)
]
return outputs
def apply_logits_processors(
self,

7
vllm/v1/spec_decode/eagle.py
View File

@ -484,7 +484,7 @@ class EagleProposer:
def prepare_next_token_ids_cpu(
self,
sampled_token_ids: list[np.ndarray],
sampled_token_ids: list[list[int]],
requests: dict[str, CachedRequestState],
gpu_input_batch: InputBatch,
num_scheduled_tokens: dict[str, int],
@ -499,7 +499,7 @@ class EagleProposer:
req_ids = gpu_input_batch.req_ids
next_token_ids: list[int] = []
for i, token_ids in enumerate(sampled_token_ids):
if token_ids.shape[0] > 0:
if token_ids:
# Common case.
next_token_id = token_ids[-1]
else:
@ -510,9 +510,10 @@ class EagleProposer:
seq_len = req_state.num_computed_tokens + num_scheduled_tokens[req_id]
next_token_id = req_state.get_token_id(seq_len)
next_token_ids.append(next_token_id)
return torch.tensor(
next_token_ids = torch.tensor(
next_token_ids, dtype=torch.int32, device=self.input_ids.device
)
return next_token_ids
def prepare_next_token_ids_padded(
self,

6
vllm/v1/spec_decode/ngram_proposer.py
View File

@ -54,7 +54,7 @@ class NgramProposer:
# Trigger Numba JIT compilation for N-gram proposer.
# This usually takes less than 1 second.
self.propose(
[np.array([])] * 1024,
[[]] * 1024,
[""] * 1024,
np.zeros(1024, dtype=np.int32),
np.zeros((1024, self.max_model_len), dtype=np.int32),
@ -131,7 +131,7 @@ class NgramProposer:
def propose(
self,
sampled_token_ids: list[np.ndarray],
sampled_token_ids: list[list[int]],
req_ids: list[str],
num_tokens_no_spec: np.ndarray,
token_ids_cpu: np.ndarray,
@ -140,7 +140,7 @@ class NgramProposer:
# find which requests need ngram proposals
valid_ngram_requests = []
for i, sampled_ids in enumerate(sampled_token_ids):
num_sampled_ids = sampled_ids.shape[0]
num_sampled_ids = len(sampled_ids)
if not num_sampled_ids:
# Skip speculative decoding.
continue

10
vllm/v1/spec_decode/suffix_decoding.py
View File

@ -1,7 +1,5 @@
# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
import numpy as np
from vllm.config import VllmConfig
from vllm.v1.worker.gpu_input_batch import InputBatch
@ -34,16 +32,16 @@ class SuffixDecodingProposer:
def propose(
self,
input_batch: InputBatch,
sampled_token_ids: list[np.ndarray],
sampled_token_ids: list[list[int]],
) -> list[list[int]]:
"""
Propose speculative tokens for each request in the input batch. Suffix Decoding
will speculate a dynamic number of tokens for each request every decoding step,
so each entry in the returned list may have different lengths.
"""
draft_token_ids: list[np.ndarray] = []
draft_token_ids: list[list[int]] = []
for i, sampled_ids in enumerate(sampled_token_ids):
if sampled_ids.shape[0] == 0:
if not sampled_ids:
# Skip speculative decoding for partial prefills.
draft_token_ids.append([])
continue
@ -72,7 +70,7 @@ class SuffixDecodingProposer:
self.suffix_cache.start_request(req_id, prompt_token_ids)
# Append the newly sampled ids to the suffix cache for this request.
self.suffix_cache.add_active_response(req_id, sampled_ids.tolist())
self.suffix_cache.add_active_response(req_id, sampled_ids)
# Suffix decoding only uses the most recent tokens up to max_tree_depth, so
# we extract the pattern from the end of the input.

36
vllm/v1/worker/gpu_model_runner.py
View File

@ -216,11 +216,9 @@ class AsyncGPUModelRunnerOutput(AsyncModelRunnerOutput):
del self._logprobs_tensors
del self._sampled_token_ids
valid_sampled_token_ids: list[np.ndarray] = [
row for row in self.sampled_token_ids_cpu.numpy()
]
valid_sampled_token_ids = self.sampled_token_ids_cpu.tolist()
for i in self._invalid_req_indices:
valid_sampled_token_ids[i] = np.array([])
valid_sampled_token_ids[i].clear()
output = self._model_runner_output
output.sampled_token_ids = valid_sampled_token_ids
@ -2341,7 +2339,7 @@ class GPUModelRunner(
) -> tuple[
dict[str, int],
LogprobsLists | None,
list[np.ndarray],
list[list[int]],
dict[str, LogprobsTensors | None],
list[str],
dict[str, int],
@ -2367,7 +2365,6 @@ class GPUModelRunner(
num_sampled_tokens = sampler_output.sampled_token_ids.shape[0]
sampled_token_ids = sampler_output.sampled_token_ids
invalid_req_indices = []
valid_sampled_token_ids: list[np.ndarray]
if not self.use_async_scheduling:
# Get the valid generated tokens.
max_gen_len = sampled_token_ids.shape[-1]
@ -2382,7 +2379,7 @@ class GPUModelRunner(
)
# Mask out the sampled tokens that should not be sampled.
for i in discard_sampled_tokens_req_indices:
valid_sampled_token_ids[int(i)] = np.array([])
valid_sampled_token_ids[int(i)].clear()
else:
valid_sampled_token_ids = []
invalid_req_indices = discard_sampled_tokens_req_indices.tolist()
@ -2410,24 +2407,19 @@ class GPUModelRunner(
[0] if spec_decode_metadata and logprobs_tensors else None
)
for req_idx in range(num_sampled_tokens):
sampled_ids: np.ndarray | None
if self.use_async_scheduling:
sampled_ids = (
np.array([-1]) if req_idx not in invalid_req_indices_set else None
)
sampled_ids = [-1] if req_idx not in invalid_req_indices_set else None
else:
sampled_ids = valid_sampled_token_ids[req_idx]
num_sampled_ids: int = (
sampled_ids.shape[0] if sampled_ids is not None else 0
)
num_sampled_ids: int = len(sampled_ids) if sampled_ids else 0
if cu_num_accepted_tokens is not None:
cu_num_accepted_tokens.append(
cu_num_accepted_tokens[-1] + num_sampled_ids
)
if sampled_ids is None or num_sampled_ids == 0:
if not sampled_ids:
continue
start_idx = self.input_batch.num_tokens_no_spec[req_idx]
@ -2769,9 +2761,7 @@ class GPUModelRunner(
with record_function_or_nullcontext("gpu_model_runner: sample"):
sampler_output = self._sample(logits, spec_decode_metadata)
def propose_draft_token_ids(
sampled_token_ids: torch.Tensor | list[np.ndarray],
) -> None:
def propose_draft_token_ids(sampled_token_ids):
assert spec_decode_common_attn_metadata is not None
with record_function_or_nullcontext("gpu_model_runner: draft"):
self._draft_token_ids = self.propose_draft_token_ids(
@ -2893,14 +2883,14 @@ class GPUModelRunner(
def propose_draft_token_ids(
self,
scheduler_output: "SchedulerOutput",
sampled_token_ids: torch.Tensor | list[np.ndarray],
sampled_token_ids: torch.Tensor | list[list[int]],
sampling_metadata: SamplingMetadata,
hidden_states: torch.Tensor,
sample_hidden_states: torch.Tensor,
aux_hidden_states: list[torch.Tensor] | None,
spec_decode_metadata: SpecDecodeMetadata | None,
common_attn_metadata: CommonAttentionMetadata,
) -> torch.Tensor | list[list[int]]:
) -> list[list[int]] | torch.Tensor:
num_scheduled_tokens = scheduler_output.total_num_scheduled_tokens
if self.speculative_config.method == "ngram":
assert isinstance(sampled_token_ids, list)
@ -2932,7 +2922,7 @@ class GPUModelRunner(
for num_draft, tokens in zip(
spec_decode_metadata.num_draft_tokens, sampled_token_ids
):
indices.append(offset + tokens.shape[0] - 1)
indices.append(offset + len(tokens) - 1)
offset += num_draft + 1
indices = torch.tensor(indices, device=self.device)
hidden_states = sample_hidden_states[indices]
@ -4872,7 +4862,7 @@ class GPUModelRunner(
return kv_cache_spec
def _to_list(self, sampled_token_ids: torch.Tensor) -> list[np.ndarray]:
def _to_list(self, sampled_token_ids: torch.Tensor) -> list[list[int]]:
# This is a short term mitigation for issue mentioned in
# https://github.com/vllm-project/vllm/issues/22754.
# `tolist` would trigger a cuda wise stream sync, which
@ -4885,4 +4875,4 @@ class GPUModelRunner(
pinned.copy_(sampled_token_ids, non_blocking=True)
self.transfer_event.record()
self.transfer_event.synchronize()
return [row for row in pinned.numpy()]
return pinned.tolist()