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Revert "[Redo] #26368 (#28771)" (#29121)

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Signed-off-by: Jialin Ouyang <Jialin.Ouyang@gmail.com>
This commit is contained in:
Jialin Ouyang
2025-11-20 21:27:45 -08:00
committed by GitHub
parent 11857a00b0
commit 30b9c67743
16 changed files with 99 additions and 127 deletions
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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=[],

6
tests/v1/core/test_priority_scheduler_random.py
View File

@@ -3,7 +3,6 @@
import random
import uuid
import numpy as np
import pytest
from vllm.config import VllmConfig
@@ -100,7 +99,8 @@ def _mock_execute_model(
random.randint(*num_output_tokens_range) for _ in range(len(request_ids))
]
sampled_token_ids = [
np.random.randint(0, 100, size=num_tokens) for num_tokens in num_output_tokens
[random.randint(0, 100) for _ in range(num_tokens)]
for num_tokens in num_output_tokens
]
return ModelRunnerOutput(
@@ -196,8 +196,6 @@ 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]()

88
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={},
@@ -2636,7 +2624,7 @@ 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=[np.array([100])],
sampled_token_ids=[[100]],
# spec_token_ids=None,
logprobs=None,
prompt_logprobs_dict={},
@@ -2842,7 +2830,7 @@ 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=[np.array([1000])] * len(req_ids),
sampled_token_ids=[[1000]] * len(req_ids),
logprobs=None,
prompt_logprobs_dict={},
pooler_output=[],
@@ -2955,7 +2943,7 @@ 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=[np.array([100])],
sampled_token_ids=[[100]],
# spec_token_ids=None,
logprobs=None,
prompt_logprobs_dict={},
@@ -3006,7 +2994,7 @@ 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=[np.array([100]) for _ in requests],
sampled_token_ids=[[100] for _ in requests],
# spec_token_ids=None,
logprobs=None,
prompt_logprobs_dict={},
@@ -3041,7 +3029,7 @@ 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=[np.array([100]), np.array([100, 200])],
sampled_token_ids=[[100], [100, 200]],
# spec_token_ids=None,
logprobs=None,
prompt_logprobs_dict={},
@@ -3227,7 +3215,7 @@ 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=[np.array([100]), np.array([121])],
sampled_token_ids=[[100], [121]],
# spec_token_ids=None,
logprobs=None,
prompt_logprobs_dict={},

7
tests/v1/kv_connector/unit/test_nixl_connector.py
View File

@@ -11,7 +11,6 @@ import uuid
from collections import defaultdict
from unittest.mock import patch
import numpy as np
import pytest
import ray
import torch
@@ -827,7 +826,7 @@ def test_kv_connector_stats_aggregation():
output = ModelRunnerOutput(
req_ids=[f"req_{i}"],
req_id_to_index={f"req_{i}": 0},
sampled_token_ids=[np.array([123])], # dummy token
sampled_token_ids=[[123]], # dummy token
logprobs=None,
prompt_logprobs_dict={},
pooler_output=[None],
@@ -908,7 +907,7 @@ def test_multi_kv_connector_stats_aggregation():
output = ModelRunnerOutput(
req_ids=[f"req_{i}"],
req_id_to_index={f"req_{i}": 0},
sampled_token_ids=[np.array([123])],
sampled_token_ids=[[123]],
logprobs=None,
prompt_logprobs_dict={},
pooler_output=[None],
@@ -966,7 +965,7 @@ def test_scheduler_kv_connector_stats_aggregation():
model_output = ModelRunnerOutput(
req_ids=["req_0"],
req_id_to_index={"req_0": 0},
sampled_token_ids=[np.array([123])],
sampled_token_ids=[[123]],
logprobs=None,
prompt_logprobs_dict={},
pooler_output=[None],

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,