Revert "[Redo] vllm-project#26368 (vllm-project#28771)" (vllm-project#29121)

Signed-off-by: Jialin Ouyang <[email protected]>

Author: Jialin

tests/v1/core/test_async_scheduler.py

@@ -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=[],

tests/v1/core/test_priority_scheduler_random.py

@@ -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]()

tests/v1/core/test_scheduler.py

@@ -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={},

tests/v1/kv_connector/unit/test_nixl_connector.py

@@ -11,7 +11,6 @@
 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 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=[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],