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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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88
tests/v1/core/test_scheduler.py
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@@ -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={},