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Nov 15, 2025
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Merged

[Redo] #26368 #28771

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172b255
[Core] Performance: Use list[np.ndarray] instead of list[list[int]] f…
Jialin Nov 15, 2025
d40ec3b
[CI/Build[ Fix V1 Test others test
DarkLight1337 Nov 15, 2025
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3 changes: 2 additions & 1 deletion tests/v1/core/test_async_scheduler.py
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Original file line number Diff line number Diff line change
Expand Up @@ -2,6 +2,7 @@
# 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
Expand All @@ -21,7 +22,7 @@ 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=[[i] for i in range(len(req_ids))],
sampled_token_ids=[np.array([i]) for i in range(len(req_ids))],
logprobs=None,
prompt_logprobs_dict={},
pooler_output=[],
Expand Down
6 changes: 4 additions & 2 deletions tests/v1/core/test_priority_scheduler_random.py
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Original file line number Diff line number Diff line change
Expand Up @@ -3,6 +3,7 @@
import random
import uuid

import numpy as np
import pytest

from vllm.config import VllmConfig
Expand Down Expand Up @@ -99,8 +100,7 @@ def _mock_execute_model(
random.randint(*num_output_tokens_range) for _ in range(len(request_ids))
]
sampled_token_ids = [
[random.randint(0, 100) for _ in range(num_tokens)]
for num_tokens in num_output_tokens
np.random.randint(0, 100, size=num_tokens) for num_tokens in num_output_tokens
]

return ModelRunnerOutput(
Expand Down Expand Up @@ -196,6 +196,8 @@ def test_priority_scheduling_blast(
num_blocks: int,
):
random.seed(42)
np.random.seed(42)

seen_request_prompt_length = dict[str, int]()
seen_request_ids = set[str]()
seen_mm_hashes = set[str]()
Expand Down
88 changes: 50 additions & 38 deletions tests/v1/core/test_scheduler.py
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Original file line number Diff line number Diff line change
Expand Up @@ -3,6 +3,7 @@
import dataclasses
from unittest.mock import Mock

import numpy as np
import pytest
import torch

Expand Down Expand Up @@ -169,7 +170,7 @@ 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=[[0], [], []],
sampled_token_ids=[np.array([0]), np.array([]), np.array([])],
logprobs=None,
prompt_logprobs_dict={},
pooler_output=[],
Expand Down Expand Up @@ -216,7 +217,7 @@ 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=[[] for _ in range(len(requests))],
sampled_token_ids=[np.array([]) for _ in range(len(requests))],
logprobs=None,
prompt_logprobs_dict={},
pooler_output=[],
Expand Down Expand Up @@ -276,7 +277,7 @@ 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=[[] for _ in range(len(requests))],
sampled_token_ids=[np.array([]) for _ in range(len(requests))],
logprobs=None,
prompt_logprobs_dict={},
pooler_output=[],
Expand All @@ -300,7 +301,8 @@ 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=[[0], [0]] + [[] for _ in range(len(requests) - 2)],
sampled_token_ids=[np.array([0]), np.array([0])]
+ [np.array([]) for _ in range(len(requests) - 2)],
logprobs=None,
prompt_logprobs_dict={},
pooler_output=[],
Expand Down Expand Up @@ -347,8 +349,8 @@ 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=[
[EOS_TOKEN_ID],
[10, 11],
np.array([EOS_TOKEN_ID]),
np.array([10, 11]),
], # First request hits EOS, second continues
logprobs=None,
prompt_logprobs_dict={},
Expand Down Expand Up @@ -392,7 +394,10 @@ 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=[[10, 42, 12], [13, 14]], # First request hits stop token
sampled_token_ids=[
np.array([10, 42, 12]),
np.array([13, 14]),
], # First request hits stop token
logprobs=None,
prompt_logprobs_dict={},
pooler_output=[],
Expand Down Expand Up @@ -436,7 +441,10 @@ 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=[[10, 11, 12], [13]], # First request exceeds max_tokens
sampled_token_ids=[
np.array([10, 11, 12]),
np.array([13]),
], # First request exceeds max_tokens
logprobs=None,
prompt_logprobs_dict={},
pooler_output=[],
Expand Down Expand Up @@ -475,7 +483,7 @@ 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=[[EOS_TOKEN_ID, 10, 11]],
sampled_token_ids=[np.array([EOS_TOKEN_ID, 10, 11])],
logprobs=None,
prompt_logprobs_dict={},
pooler_output=[],
Expand Down Expand Up @@ -616,7 +624,7 @@ 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=[[0]],
sampled_token_ids=[np.array([0])],
logprobs=None,
prompt_logprobs_dict={},
pooler_output=[],
Expand All @@ -633,7 +641,7 @@ 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=[[0]],
sampled_token_ids=[np.array([0])],
logprobs=None,
prompt_logprobs_dict={},
pooler_output=[],
Expand Down Expand Up @@ -670,7 +678,7 @@ 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=[[0]],
sampled_token_ids=[np.array([0])],
logprobs=None,
prompt_logprobs_dict={},
pooler_output=[],
Expand All @@ -687,7 +695,7 @@ 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=[[42]],
sampled_token_ids=[np.array([42])],
logprobs=None,
prompt_logprobs_dict={},
pooler_output=[],
Expand All @@ -704,14 +712,18 @@ def test_preempt_during_execution():
@pytest.mark.parametrize(
"spec_tokens,output_tokens,expected",
[
([[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]], [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], [4, 5, 6]],
[[1, 2, 7], [4, 8]],
[np.array([1, 2, 7]), np.array([4, 8])],
(2, 6, 3, [2, 1, 0]),
), # multiple mismatches
],
Expand Down Expand Up @@ -745,7 +757,7 @@ 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=[[0] for _ in range(len(requests))],
sampled_token_ids=[np.array([0]) for _ in range(len(requests))],
logprobs=None,
prompt_logprobs_dict={},
pooler_output=[],
Expand Down Expand Up @@ -972,7 +984,7 @@ 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=[[1000]] * len(req_ids),
sampled_token_ids=[np.array([1000])] * len(req_ids),
logprobs=None,
prompt_logprobs_dict={},
pooler_output=[],
Expand Down Expand Up @@ -1025,7 +1037,7 @@ 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=[[1000]] * len(req_ids),
sampled_token_ids=[np.array([1000])] * len(req_ids),
logprobs=None,
prompt_logprobs_dict={},
pooler_output=[],
Expand Down Expand Up @@ -1088,7 +1100,7 @@ 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=[[1000]] * NUM_REQUESTS,
sampled_token_ids=[np.array([1000])] * NUM_REQUESTS,
logprobs=None,
prompt_logprobs_dict={},
pooler_output=[],
Expand Down Expand Up @@ -1154,7 +1166,7 @@ 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=[[1000]] * len(req_ids),
sampled_token_ids=[np.array([1000])] * len(req_ids),
logprobs=None,
prompt_logprobs_dict={},
pooler_output=[],
Expand Down Expand Up @@ -1239,7 +1251,7 @@ 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=[[1000]] * len(req_ids),
sampled_token_ids=[np.array([1000])] * len(req_ids),
logprobs=None,
prompt_logprobs_dict={},
pooler_output=[],
Expand Down Expand Up @@ -1332,7 +1344,7 @@ 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=[[1000]] * len(scheduler.running),
sampled_token_ids=[np.array([1000])] * len(scheduler.running),
logprobs=None,
prompt_logprobs_dict={},
pooler_output=[],
Expand Down Expand Up @@ -1749,7 +1761,7 @@ def test_priority_scheduling_preemption():
req_id_to_index={
req.request_id: i for i, req in enumerate(low_priority_requests)
},
sampled_token_ids=[[100] for _ in low_priority_requests],
sampled_token_ids=[np.array([100]) for _ in low_priority_requests],
logprobs=None,
prompt_logprobs_dict={},
pooler_output=[],
Expand Down Expand Up @@ -1818,7 +1830,7 @@ 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=[[100] for _ in low_priority_requests],
sampled_token_ids=[np.array([100]) for _ in low_priority_requests],
logprobs=None,
prompt_logprobs_dict={},
pooler_output=[],
Expand Down Expand Up @@ -2064,7 +2076,7 @@ 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=[[100]],
sampled_token_ids=[np.array([100])],
logprobs=None,
prompt_logprobs_dict={},
pooler_output=[],
Expand Down Expand Up @@ -2150,7 +2162,7 @@ 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=[[100]],
sampled_token_ids=[np.array([100])],
# spec_token_ids=None,
logprobs=None,
prompt_logprobs_dict={},
Expand Down Expand Up @@ -2181,7 +2193,7 @@ 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=[[100] for _ in requests],
sampled_token_ids=[np.array([100]) for _ in requests],
# spec_token_ids=None,
logprobs=None,
prompt_logprobs_dict={},
Expand All @@ -2207,7 +2219,7 @@ 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=[[], [100]],
sampled_token_ids=[np.array([]), np.array([100])],
# spec_token_ids=None,
logprobs=None,
prompt_logprobs_dict={},
Expand Down Expand Up @@ -2624,7 +2636,7 @@ def test_ec_connector_with_partial_cache_hit_multi_round(use_kv_connector):
model_output = ModelRunnerOutput(
req_ids=[request1.request_id],
req_id_to_index={request1.request_id: 0},
sampled_token_ids=[[100]],
sampled_token_ids=[np.array([100])],
# spec_token_ids=None,
logprobs=None,
prompt_logprobs_dict={},
Expand Down Expand Up @@ -2830,7 +2842,7 @@ def test_ec_connector_unable_to_allocate(use_kv_connector):
MODEL_RUNNER_OUTPUT = ModelRunnerOutput(
req_ids=req_ids,
req_id_to_index=req_to_index,
sampled_token_ids=[[1000]] * len(req_ids),
sampled_token_ids=[np.array([1000])] * len(req_ids),
logprobs=None,
prompt_logprobs_dict={},
pooler_output=[],
Expand Down Expand Up @@ -2943,7 +2955,7 @@ def test_priority_scheduling_ec_connector_preemption_and_resumption(
model_output = ModelRunnerOutput(
req_ids=[request_low.request_id],
req_id_to_index={request_low.request_id: 0},
sampled_token_ids=[[100]],
sampled_token_ids=[np.array([100])],
# spec_token_ids=None,
logprobs=None,
prompt_logprobs_dict={},
Expand Down Expand Up @@ -2994,7 +3006,7 @@ def test_priority_scheduling_ec_connector_preemption_and_resumption(
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=[[100] for _ in requests],
sampled_token_ids=[np.array([100]) for _ in requests],
# spec_token_ids=None,
logprobs=None,
prompt_logprobs_dict={},
Expand Down Expand Up @@ -3029,7 +3041,7 @@ def test_priority_scheduling_ec_connector_preemption_and_resumption(
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=[[100], [100, 200]],
sampled_token_ids=[np.array([100]), np.array([100, 200])],
# spec_token_ids=None,
logprobs=None,
prompt_logprobs_dict={},
Expand Down Expand Up @@ -3215,7 +3227,7 @@ def test_ec_connector_allocate_encoder_tokens_with_external_load(use_kv_connecto
model_output = ModelRunnerOutput(
req_ids=[request1.request_id, request2.request_id],
req_id_to_index={request1.request_id: 0, request2.request_id: 1},
sampled_token_ids=[[100], [121]],
sampled_token_ids=[np.array([100]), np.array([121])],
# spec_token_ids=None,
logprobs=None,
prompt_logprobs_dict={},
Expand Down
7 changes: 4 additions & 3 deletions tests/v1/kv_connector/unit/test_nixl_connector.py
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Original file line number Diff line number Diff line change
Expand Up @@ -11,6 +11,7 @@
from collections import defaultdict
from unittest.mock import patch

import numpy as np
import pytest
import ray
import torch
Expand Down Expand Up @@ -823,7 +824,7 @@ def test_kv_connector_stats_aggregation():
output = ModelRunnerOutput(
req_ids=[f"req_{i}"],
req_id_to_index={f"req_{i}": 0},
sampled_token_ids=[[123]], # dummy token
sampled_token_ids=[np.array([123])], # dummy token
logprobs=None,
prompt_logprobs_dict={},
pooler_output=[None],
Expand Down Expand Up @@ -904,7 +905,7 @@ def make_multi_stats(nixl_count: int, foo_count: int) -> MultiKVConnectorStats:
output = ModelRunnerOutput(
req_ids=[f"req_{i}"],
req_id_to_index={f"req_{i}": 0},
sampled_token_ids=[[123]],
sampled_token_ids=[np.array([123])],
logprobs=None,
prompt_logprobs_dict={},
pooler_output=[None],
Expand Down Expand Up @@ -962,7 +963,7 @@ def test_scheduler_kv_connector_stats_aggregation():
model_output = ModelRunnerOutput(
req_ids=["req_0"],
req_id_to_index={"req_0": 0},
sampled_token_ids=[[123]],
sampled_token_ids=[np.array([123])],
logprobs=None,
prompt_logprobs_dict={},
pooler_output=[None],
Expand Down
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