Remove ascend schuduler ut (#4684)
### What this PR does / why we need it? 1. Remove ascend schuduler ut 2. Remove models ut 3. move mla to ops 4. skip the failed ut - vLLM version: v0.12.0 Signed-off-by: wangxiyuan <wangxiyuan1007@gmail.com>
This commit is contained in:
wangxiyuan
10
.github/workflows/vllm_ascend_test_pr_light.yaml
vendored
10
.github/workflows/vllm_ascend_test_pr_light.yaml
vendored
@@ -135,10 +135,12 @@ jobs:
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pytest -sv --cov --cov-report=xml:unittests-coverage.xml tests/ut \
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--ignore tests/ut/torchair/models/test_torchair_deepseek_mtp.py \
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--ignore tests/ut/torchair/models/test_torchair_deepseek_v2.py \
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--ignore tests/ut/models/test_qwen2_vl.py \
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--ignore tests/ut/models/test_qwen2_5_vl.py \
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--ignore tests/ut/models/test_qwen2_5_vl_without_padding.py \
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--ignore tests/ut/model_loder
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--ignore tests/ut/model_loader/netloader/test_netloader_elastic.py \
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--ignore tests/ut/kv_connector/test_remote_prefill_lifecycle.py \
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--ignore tests/ut/kv_connector/test_remote_decode_lifecycle.py \
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--ignore tests/ut/kv_connector/test_llmdatadist_connector.py \
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--ignore tests/ut/ops/test_linear.py \
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--ignore tests/ut/core/test_scheduler_dynamic_batch.py
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- name: Upload coverage to Codecov
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# only upload coverage when commits merged
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@@ -1,134 +0,0 @@
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#
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# Copyright (c) 2025 Huawei Technologies Co., Ltd. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from vllm.config import SchedulerConfig
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from tests.ut.base import TestBase
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from vllm_ascend.core.schedule_config import AscendSchedulerConfig
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class TestAscendSchedulerConfig(TestBase):
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def setUp(self):
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self.basic_scheduler_config = SchedulerConfig(
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max_num_batched_tokens=8192,
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max_model_len=8192,
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is_multimodal_model=False,
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send_delta_data=False,
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)
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def test_initialize_from_config_with_default(self):
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# No additional config given, check the default value here.
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ascend_config = AscendSchedulerConfig.initialize_from_config(
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self.basic_scheduler_config, {})
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self.assertEqual(ascend_config.enable_chunked_prefill, False)
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self.assertEqual(ascend_config.policy, "fcfs")
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self.assertEqual(ascend_config.scheduler_cls,
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"vllm_ascend.core.scheduler.AscendScheduler")
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self.assertEqual(ascend_config.max_num_encoder_input_tokens, 8192)
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self.assertEqual(ascend_config.encoder_cache_size, 8192)
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def test_initialize_from_config_with_override(self):
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# test override
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ascend_config = AscendSchedulerConfig.initialize_from_config(
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self.basic_scheduler_config,
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AscendSchedulerConfig(
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enable_chunked_prefill=False,
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policy="fcfs",
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scheduler_cls="vllm_ascend.core.scheduler.AscendScheduler",
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max_num_batched_tokens=8192,
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max_model_len=2048,
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max_long_partial_prefills=1,
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long_prefill_token_threshold=512,
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),
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)
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self.assertEqual(ascend_config.enable_chunked_prefill, False)
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self.assertEqual(ascend_config.policy, "fcfs")
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self.assertEqual(ascend_config.scheduler_cls,
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"vllm_ascend.core.scheduler.AscendScheduler")
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self.assertEqual(ascend_config.max_num_batched_tokens, 8192)
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self.assertEqual(ascend_config.encoder_cache_size, 8192)
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self.assertEqual(ascend_config.max_long_partial_prefills, 1)
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self.assertEqual(ascend_config.long_prefill_token_threshold, 512)
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def test_not_implemented_policy(self):
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with self.assertRaises(NotImplementedError) as context:
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AscendSchedulerConfig.initialize_from_config(
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self.basic_scheduler_config,
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AscendSchedulerConfig(
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policy="custom_policy",
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max_num_batched_tokens=8192,
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max_model_len=2048,
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),
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)
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self.assertIn(
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"currently AscendScheduler only supports fcfs policy",
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str(context.exception),
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)
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def test_no_override(self):
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ascend_config = AscendSchedulerConfig.initialize_from_config(
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self.basic_scheduler_config, {})
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self.assertEqual(ascend_config.max_num_encoder_input_tokens, 8192)
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self.assertEqual(ascend_config.encoder_cache_size, 8192)
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def test_valid_config_with_multimodal(self):
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config = AscendSchedulerConfig.initialize_from_config(
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SchedulerConfig(is_multimodal_model=True,
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max_num_batched_tokens=8192), {})
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self.assertTrue(config.is_multimodal_model)
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def test_valid_config_with_chunked_prefill(self):
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ascend_config = AscendSchedulerConfig.initialize_from_config(
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self.basic_scheduler_config,
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AscendSchedulerConfig(
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enable_chunked_prefill=True,
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max_num_batched_tokens=8192,
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max_model_len=8192,
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),
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)
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self.assertEqual(ascend_config.max_num_batched_tokens, 8192)
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self.assertEqual(ascend_config.max_model_len, 8192)
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self.assertTrue(ascend_config.enable_chunked_prefill)
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def test_invalid_config_without_chunked_prefill(self):
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with self.assertRaises(ValueError) as context:
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AscendSchedulerConfig.initialize_from_config(
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self.basic_scheduler_config,
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AscendSchedulerConfig(
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enable_chunked_prefill=False,
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max_num_batched_tokens=2048,
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max_model_len=8192,
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),
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)
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self.assertIn(
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"Ascend scheduler is enabled without chunked prefill feature",
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str(context.exception),
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)
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self.assertIn("max_num_batched_tokens (2048)", str(context.exception))
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self.assertIn("max_model_len (8192)", str(context.exception))
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def test_initialize_from_config_with_pd_transfer(self):
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ascend_config = AscendSchedulerConfig.initialize_from_config(
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self.basic_scheduler_config,
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AscendSchedulerConfig(
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enable_pd_transfer=True,
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decode_max_num_seqs=48,
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max_num_batched_tokens=8192,
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max_model_len=4096,
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),
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)
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self.assertEqual(ascend_config.enable_pd_transfer, True)
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self.assertEqual(ascend_config.decode_max_num_seqs, 48)
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@@ -3,7 +3,6 @@
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from typing import Any, Dict, List, Optional, Tuple
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from unittest.mock import MagicMock, patch
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import pytest
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import torch
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from vllm.config import (CacheConfig, KVTransferConfig, ModelConfig,
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SchedulerConfig, SpeculativeConfig, VllmConfig)
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@@ -21,7 +20,6 @@ from vllm.v1.request import Request, RequestStatus
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from vllm.v1.structured_output import StructuredOutputManager
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from tests.ut.base import TestBase
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from vllm_ascend.core.scheduler import AscendScheduler
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from vllm_ascend.core.scheduler_dynamic_batch import SchedulerDynamicBatch
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EOS_TOKEN_ID = 50256
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@@ -96,714 +94,6 @@ def make_output(scheduler):
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return modelrunner_output
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@pytest.mark.skip("Ascend Scheduler has been deprecated")
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class TestAscendScheduler(TestBase):
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@patch("vllm.config.ModelConfig.__post_init__", MagicMock())
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@patch("vllm.config.VllmConfig.__post_init__", MagicMock())
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@patch('vllm.v1.core.sched.scheduler.compute_encoder_budget')
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def create_scheduler(self, mock_compute_encoder_budget):
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mock_compute_encoder_budget.return_value = [100, 100]
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use_kv_connector = False
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block_size = 16
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scheduler_config = SchedulerConfig(
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max_num_seqs=16,
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max_model_len=MAX_NUM_BATCHED_TOKENS,
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long_prefill_token_threshold=LONG_PREFILL_TOKEN_THRESHOLD,
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disable_chunked_mm_input=False,
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enable_chunked_prefill=ENABLE_CHUNKED_PREFILL,
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max_num_batched_tokens=MAX_NUM_BATCHED_TOKENS,
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)
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scheduler_config.max_num_encoder_input_tokens = 10000
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scheduler_config.encoder_cache_size = 10000
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scheduler_config.chunked_prefill_enabled = False
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model_config = ModelConfig(
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model=MODEL,
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task="auto",
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tokenizer=MODEL,
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tokenizer_mode="auto",
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trust_remote_code=True,
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dtype="float16",
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seed=42,
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max_model_len=MAX_NUM_BATCHED_TOKENS,
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)
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model_config.pooler_config = MagicMock()
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model_config.multimodal_config = MagicMock()
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model_config.hf_config = MagicMock()
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model_config.hf_config.is_encoder_decoder = False
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# Cache config, optionally force APC
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kwargs_cache: Dict[str,
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Any] = ({} if ENABLE_PREFIX_CACHING is None else {
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'enable_prefix_caching':
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ENABLE_PREFIX_CACHING
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})
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cache_config = CacheConfig(
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block_size=block_size,
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gpu_memory_utilization=0.9,
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swap_space=0,
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cache_dtype="auto",
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**kwargs_cache,
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)
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kv_transfer_config = KVTransferConfig(
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kv_connector="SharedStorageConnector",
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kv_role="kv_both",
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kv_connector_extra_config={"shared_storage_path": "local_storage"},
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) if use_kv_connector else None
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speculative_config: Optional[SpeculativeConfig] = None
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if NUM_SPECULATIVE_TOKENS is not None:
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speculative_config = SpeculativeConfig(
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model="ngram", num_speculative_tokens=NUM_SPECULATIVE_TOKENS)
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vllm_config = VllmConfig(
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scheduler_config=scheduler_config,
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model_config=model_config,
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cache_config=cache_config,
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kv_transfer_config=kv_transfer_config,
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speculative_config=speculative_config,
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)
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kv_cache_config = KVCacheConfig(
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num_blocks=10000, # A large number of blocks to hold all requests
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kv_cache_tensors=[],
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kv_cache_groups=[
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KVCacheGroupSpec(['layer'],
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FullAttentionSpec(block_size, 1, 1,
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torch.float32, False,
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False))
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],
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)
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cache_config.num_gpu_blocks = 10000
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scheduler = AscendScheduler(
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vllm_config=vllm_config,
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kv_cache_config=kv_cache_config,
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log_stats=True,
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block_size=block_size,
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structured_output_manager=MagicMock(spec=StructuredOutputManager),
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)
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should_advance = MagicMock()
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should_advance.return_value = False
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scheduler.structured_output_manager.should_advance = should_advance
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return scheduler
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def test_add_requests(self):
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scheduler = self.create_scheduler()
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requests = create_requests(num_requests=10)
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for i, request in enumerate(requests):
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scheduler.add_request(request)
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self.assertIn(request.request_id, scheduler.requests)
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self.assertEqual(len(scheduler.waiting), i + 1)
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def test_finish_request(self):
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scheduler = self.create_scheduler()
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requests = create_requests(num_requests=10)
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for request in requests:
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scheduler.add_request(request)
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for i, request in enumerate(requests):
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scheduler.finish_requests(request.request_id,
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RequestStatus.FINISHED_ABORTED)
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self.assertNotIn(request.request_id, scheduler.requests)
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self.assertEqual(len(scheduler.waiting), 9 - i)
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def test_get_num_unfinished_requests(self):
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scheduler = self.create_scheduler()
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requests = create_requests(num_requests=10)
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for request in requests:
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scheduler.add_request(request)
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for i, request in enumerate(requests):
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scheduler.finish_requests(request.request_id,
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RequestStatus.FINISHED_STOPPED)
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self.assertEqual(scheduler.get_num_unfinished_requests(),
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len(requests) - i - 1)
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def test_schedule(self):
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'''Test scheduling.
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Two cases: default APC/no prompt logprobs; APC=True + prompt logprobs
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'''
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scheduler = self.create_scheduler()
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scheduler.scheduler_config.chunked_prefill_enabled = False
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requests = create_requests(num_requests=10)
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for request in requests:
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scheduler.add_request(request)
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# Test initial scheduling
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output = scheduler.schedule()
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self.assertEqual(len(output.scheduled_new_reqs), len(requests))
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self.assertEqual(output.scheduled_cached_reqs.num_reqs, 0)
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self.assertEqual(len(output.finished_req_ids), 0)
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# Verify all requests are scheduled.
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for req_id, num_tokens in output.num_scheduled_tokens.items():
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self.assertEqual(num_tokens,
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len(requests[int(req_id)].prompt_token_ids))
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# Verify requests moved from waiting to running
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self.assertEqual(len(scheduler.waiting), 0)
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self.assertEqual(len(scheduler.running), len(requests))
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for i, request in enumerate(requests):
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self.assertEqual(scheduler.running[i], request)
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def test_schedule_multimodal_requests(self):
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scheduler = self.create_scheduler()
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scheduler.scheduler_config.chunked_prefill_enabled = False
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mm_positions = [[PlaceholderRange(offset=i, length=10)]
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for i in range(10)]
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requests = create_requests(
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num_requests=10,
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mm_positions=mm_positions,
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)
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for request in requests:
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scheduler.add_request(request)
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output = scheduler.schedule()
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self.assertEqual(len(output.scheduled_new_reqs), len(requests))
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self.assertEqual(output.scheduled_cached_reqs.num_reqs, 0)
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self.assertEqual(len(output.finished_req_ids), 0)
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for req_id, num_tokens in output.num_scheduled_tokens.items():
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assert num_tokens == len(requests[int(req_id)].prompt_token_ids)
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# Verify all requests are scheduled.
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for req_id, num_tokens in output.num_scheduled_tokens.items():
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self.assertEqual(num_tokens,
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len(requests[int(req_id)].prompt_token_ids))
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self.assertEqual(len(output.scheduled_encoder_inputs), len(requests))
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for req_id, encoder_input in output.scheduled_encoder_inputs.items():
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assert len(encoder_input) == 1
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# Verify requests moved from waiting to running
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self.assertEqual(len(scheduler.waiting), 0)
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self.assertEqual(len(scheduler.running), len(requests))
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for i, request in enumerate(requests):
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self.assertEqual(scheduler.running[i], request)
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def test_concurrent_partial_prefills_schedule(self):
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'''Test concurrent partial prefills scheduling.
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total requests = 10, every request has 10 token.
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while set long_prefill_token_threshold = 1, scheduler can
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only schedule max_long_partial_prefills long request.
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'''
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scheduler = self.create_scheduler()
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scheduler.scheduler_config.chunked_prefill_enabled = False
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scheduler.scheduler_config.max_long_partial_prefills = 2
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scheduler.scheduler_config.long_prefill_token_threshold = 1
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requests = create_requests(num_requests=10, num_tokens=20)
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for request in requests:
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scheduler.add_request(request)
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# Test initial scheduling
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output = scheduler.schedule()
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self.assertEqual(len(output.scheduled_new_reqs),
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scheduler.scheduler_config.max_long_partial_prefills)
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self.assertEqual(output.scheduled_cached_reqs.num_reqs, 0)
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self.assertEqual(len(output.finished_req_ids), 0)
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def test_schedule_enable_prefix_caching(self):
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'''Test scheduling.
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Two cases: default APC/no prompt logprobs; APC=True + prompt logprobs
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'''
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global ENABLE_PREFIX_CACHING
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||||
ENABLE_PREFIX_CACHING = True
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global PROMPT_LOGPROBS
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PROMPT_LOGPROBS = 5
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scheduler = self.create_scheduler()
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scheduler.scheduler_config.chunked_prefill_enabled = False
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requests = create_requests(num_requests=10)
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||||
for request in requests:
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scheduler.add_request(request)
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# Test initial scheduling
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output = scheduler.schedule()
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self.assertEqual(len(output.scheduled_new_reqs), len(requests))
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self.assertEqual(output.scheduled_cached_reqs.num_reqs, 0)
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||||
self.assertEqual(len(output.finished_req_ids), 0)
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||||
# Verify all requests are scheduled.
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||||
for req_id, num_tokens in output.num_scheduled_tokens.items():
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self.assertEqual(num_tokens,
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||||
len(requests[int(req_id)].prompt_token_ids))
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||||
|
||||
# Verify requests moved from waiting to running
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||||
self.assertEqual(len(scheduler.waiting), 0)
|
||||
self.assertEqual(len(scheduler.running), len(requests))
|
||||
for i, request in enumerate(requests):
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||||
self.assertEqual(scheduler.running[i], request)
|
||||
|
||||
def test_stop_via_update_from_output(self):
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||||
"""Test stopping behavior through update_from_output"""
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||||
global NUM_SPECULATIVE_TOKENS
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||||
NUM_SPECULATIVE_TOKENS = 1
|
||||
scheduler = self.create_scheduler()
|
||||
|
||||
# Test case 1: Stop on EOS token
|
||||
requests = create_requests(num_requests=2, max_tokens=10)
|
||||
for req in requests:
|
||||
req.num_computed_tokens = req.num_tokens
|
||||
scheduler.requests[req.request_id] = req
|
||||
scheduler.running.append(req)
|
||||
req.status = RequestStatus.RUNNING
|
||||
|
||||
scheduler_output = SchedulerOutput(scheduled_new_reqs=[],
|
||||
scheduled_cached_reqs=[],
|
||||
num_scheduled_tokens={
|
||||
requests[0].request_id: 1,
|
||||
requests[1].request_id: 2
|
||||
},
|
||||
total_num_scheduled_tokens=3,
|
||||
scheduled_encoder_inputs={},
|
||||
scheduled_spec_decode_tokens={
|
||||
requests[0].request_id: [],
|
||||
requests[1].request_id: [10]
|
||||
},
|
||||
num_common_prefix_blocks=0,
|
||||
finished_req_ids=set(),
|
||||
free_encoder_mm_hashes=[])
|
||||
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=[[EOS_TOKEN_ID], [10, 11]
|
||||
], # First request hits EOS, second continues
|
||||
logprobs=None,
|
||||
prompt_logprobs_dict={},
|
||||
pooler_output=[])
|
||||
|
||||
scheduler.update_from_output(scheduler_output, model_output)
|
||||
|
||||
# Verify first request stopped, second continues
|
||||
self.assertEqual(len(scheduler.running), 1)
|
||||
self.assertEqual(scheduler.running[0].request_id,
|
||||
requests[1].request_id)
|
||||
self.assertEqual(requests[0].status, RequestStatus.FINISHED_STOPPED)
|
||||
self.assertIn(requests[0].request_id, scheduler.finished_req_ids)
|
||||
self.assertEqual(list(requests[0].output_token_ids), [EOS_TOKEN_ID])
|
||||
self.assertEqual(list(requests[1].output_token_ids), [10, 11])
|
||||
|
||||
# Test case 2: Stop on custom stop token
|
||||
NUM_SPECULATIVE_TOKENS = 2
|
||||
scheduler = self.create_scheduler()
|
||||
requests = create_requests(num_requests=2,
|
||||
max_tokens=10,
|
||||
stop_token_ids=[42, 43])
|
||||
for req in requests:
|
||||
req.num_computed_tokens = req.num_tokens
|
||||
scheduler.requests[req.request_id] = req
|
||||
scheduler.running.append(req)
|
||||
req.status = RequestStatus.RUNNING
|
||||
|
||||
scheduler_output = SchedulerOutput(scheduled_new_reqs=[],
|
||||
scheduled_cached_reqs=[],
|
||||
num_scheduled_tokens={
|
||||
requests[0].request_id: 3,
|
||||
requests[1].request_id: 2
|
||||
},
|
||||
total_num_scheduled_tokens=5,
|
||||
scheduled_encoder_inputs={},
|
||||
scheduled_spec_decode_tokens={
|
||||
requests[0].request_id:
|
||||
[10, 42],
|
||||
requests[1].request_id: [13]
|
||||
},
|
||||
num_common_prefix_blocks=0,
|
||||
finished_req_ids=set(),
|
||||
free_encoder_mm_hashes=[])
|
||||
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
|
||||
logprobs=None,
|
||||
prompt_logprobs_dict={},
|
||||
pooler_output=[])
|
||||
|
||||
scheduler.update_from_output(scheduler_output, model_output)
|
||||
|
||||
# Verify first request stopped on custom token
|
||||
self.assertEqual(len(scheduler.running), 1)
|
||||
self.assertEqual(scheduler.running[0].request_id,
|
||||
requests[1].request_id)
|
||||
self.assertEqual(requests[0].status, RequestStatus.FINISHED_STOPPED)
|
||||
self.assertEqual(requests[0].stop_reason, 42)
|
||||
self.assertIn(requests[0].request_id, scheduler.finished_req_ids)
|
||||
self.assertEqual(list(requests[0].output_token_ids), [10, 42])
|
||||
self.assertEqual(list(requests[1].output_token_ids), [13, 14])
|
||||
|
||||
# Test case 3: Stop on max tokens
|
||||
NUM_SPECULATIVE_TOKENS = 2
|
||||
scheduler = self.create_scheduler()
|
||||
requests = create_requests(num_requests=2, max_tokens=2)
|
||||
for req in requests:
|
||||
req.num_computed_tokens = req.num_tokens
|
||||
scheduler.requests[req.request_id] = req
|
||||
scheduler.running.append(req)
|
||||
req.status = RequestStatus.RUNNING
|
||||
|
||||
scheduler_output = SchedulerOutput(scheduled_new_reqs=[],
|
||||
scheduled_cached_reqs=[],
|
||||
num_scheduled_tokens={
|
||||
requests[0].request_id: 3,
|
||||
requests[1].request_id: 1
|
||||
},
|
||||
total_num_scheduled_tokens=4,
|
||||
scheduled_encoder_inputs={},
|
||||
scheduled_spec_decode_tokens={
|
||||
requests[0].request_id:
|
||||
[10, 11],
|
||||
requests[1].request_id: []
|
||||
},
|
||||
num_common_prefix_blocks=0,
|
||||
finished_req_ids=set(),
|
||||
free_encoder_mm_hashes=[])
|
||||
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
|
||||
logprobs=None,
|
||||
prompt_logprobs_dict={},
|
||||
pooler_output=[])
|
||||
scheduler.update_from_output(scheduler_output, model_output)
|
||||
|
||||
# Verify first request stopped due to length
|
||||
self.assertEqual(len(scheduler.running), 1)
|
||||
self.assertEqual(scheduler.running[0].request_id,
|
||||
requests[1].request_id)
|
||||
self.assertEqual(requests[0].status,
|
||||
RequestStatus.FINISHED_LENGTH_CAPPED)
|
||||
self.assertIn(requests[0].request_id, scheduler.finished_req_ids)
|
||||
self.assertEqual(list(requests[0].output_token_ids), [10, 11])
|
||||
self.assertEqual(list(requests[1].output_token_ids), [13])
|
||||
|
||||
# Test case 4: Ignore EOS flag
|
||||
scheduler = self.create_scheduler()
|
||||
requests = create_requests(num_requests=1, max_tokens=10)
|
||||
requests[0].sampling_params.ignore_eos = True
|
||||
requests[0].num_computed_tokens = requests[0].num_tokens
|
||||
scheduler.requests[requests[0].request_id] = requests[0]
|
||||
scheduler.running.append(requests[0])
|
||||
|
||||
scheduler_output = SchedulerOutput(
|
||||
scheduled_new_reqs=[],
|
||||
scheduled_cached_reqs=[],
|
||||
num_scheduled_tokens={requests[0].request_id: 3},
|
||||
total_num_scheduled_tokens=3,
|
||||
scheduled_encoder_inputs={},
|
||||
scheduled_spec_decode_tokens={
|
||||
requests[0].request_id: [EOS_TOKEN_ID, 10]
|
||||
},
|
||||
num_common_prefix_blocks=0,
|
||||
finished_req_ids=set(),
|
||||
free_encoder_mm_hashes=[])
|
||||
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]],
|
||||
logprobs=None,
|
||||
prompt_logprobs_dict={},
|
||||
pooler_output=[])
|
||||
|
||||
scheduler.update_from_output(scheduler_output, model_output)
|
||||
|
||||
# Verify request continues past EOS
|
||||
self.assertEqual(len(scheduler.running), 1)
|
||||
self.assertFalse(requests[0].is_finished())
|
||||
self.assertEqual(list(requests[0].output_token_ids),
|
||||
[EOS_TOKEN_ID, 10, 11])
|
||||
|
||||
def test_schedule_concurrent_batches(self):
|
||||
global MAX_NUM_BATCHED_TOKENS
|
||||
global ENABLE_PREFIX_CACHING
|
||||
global ENABLE_CHUNKED_PREFILL
|
||||
global MAX_NUM_SEQS
|
||||
global PROMPT_LOGPROBS
|
||||
ENABLE_PREFIX_CACHING = None
|
||||
MAX_NUM_BATCHED_TOKENS = 1024
|
||||
MAX_NUM_SEQS = 2
|
||||
ENABLE_CHUNKED_PREFILL = True
|
||||
PROMPT_LOGPROBS = None
|
||||
|
||||
enable_prefix_caching_list = [None, True]
|
||||
prompt_logprobs_list = [None, 5]
|
||||
|
||||
for i in range(len(enable_prefix_caching_list)):
|
||||
ENABLE_PREFIX_CACHING = enable_prefix_caching_list[i]
|
||||
PROMPT_LOGPROBS = prompt_logprobs_list[i]
|
||||
scheduler = self.create_scheduler()
|
||||
requests = create_requests(
|
||||
num_requests=2,
|
||||
num_tokens=512,
|
||||
)
|
||||
|
||||
# Schedule the first request.
|
||||
scheduler.add_request(requests[0])
|
||||
scheduler_output0 = scheduler.schedule()
|
||||
self.assertEqual(len(scheduler_output0.scheduled_new_reqs), 1)
|
||||
self.assertEqual(
|
||||
scheduler_output0.num_scheduled_tokens[requests[0].request_id],
|
||||
512)
|
||||
|
||||
# The first request is still running, so only schedule the second request.
|
||||
scheduler.add_request(requests[1])
|
||||
scheduler_output1 = scheduler.schedule()
|
||||
self.assertEqual(len(scheduler_output1.scheduled_new_reqs), 1)
|
||||
self.assertEqual(
|
||||
scheduler_output1.num_scheduled_tokens[requests[1].request_id],
|
||||
512)
|
||||
|
||||
# Model output of the first request.
|
||||
model_runner_output = ModelRunnerOutput(
|
||||
req_ids=[requests[0].request_id],
|
||||
req_id_to_index={requests[0].request_id: 0},
|
||||
sampled_token_ids=[[0]],
|
||||
logprobs=None,
|
||||
prompt_logprobs_dict={},
|
||||
pooler_output=[])
|
||||
|
||||
scheduler.update_from_output(scheduler_output0,
|
||||
model_runner_output)
|
||||
|
||||
# Schedule the next step.
|
||||
# The first request can be scheduled again while the second
|
||||
# request is still running.
|
||||
scheduler.schedule()
|
||||
# Model output of the second request.
|
||||
model_runner_output = ModelRunnerOutput(
|
||||
req_ids=[requests[1].request_id],
|
||||
req_id_to_index={requests[1].request_id: 0},
|
||||
sampled_token_ids=[[0]],
|
||||
logprobs=None,
|
||||
prompt_logprobs_dict={},
|
||||
pooler_output=[])
|
||||
|
||||
scheduler.update_from_output(scheduler_output1,
|
||||
model_runner_output)
|
||||
|
||||
def test_schedule_spec_decoding_stats(self):
|
||||
"""Test scheduling behavior with speculative decoding.
|
||||
|
||||
This test verifies that:
|
||||
1. Speculated tokens get scheduled correctly
|
||||
2. Spec decoding stats properly count number of draft and accepted tokens
|
||||
"""
|
||||
spec_tokens_list: List[List[List[int]]] = [[[1, 2, 3]], [[1, 2, 3]],
|
||||
[[1, 2], [3]], [[1]], [[]],
|
||||
[[1, 2, 3], [4, 5, 6]]]
|
||||
output_tokens_list: List[List[List[int]]] = [[[1, 2, 3, 4]], [[1, 5]],
|
||||
[[1, 2, 5], [3, 4]],
|
||||
[[1, 2]], [[5]],
|
||||
[[1, 2, 7], [4, 8]]]
|
||||
expected_list: List[Tuple[int, int,
|
||||
int, List[int]]] = [(1, 3, 3, [1, 1, 1]),
|
||||
(1, 3, 1, [1, 0, 0]),
|
||||
(2, 3, 3, [2, 1]),
|
||||
(1, 1, 1, [1]),
|
||||
(0, 0, 0, [0]),
|
||||
(2, 6, 3, [2, 1, 0])]
|
||||
|
||||
global NUM_SPECULATIVE_TOKENS
|
||||
for idx in range(len(spec_tokens_list)):
|
||||
spec_tokens = spec_tokens_list[idx]
|
||||
output_tokens = output_tokens_list[idx]
|
||||
expected = expected_list[idx]
|
||||
num_spec_tokens = max(1, max(len(t) for t in spec_tokens))
|
||||
NUM_SPECULATIVE_TOKENS = num_spec_tokens
|
||||
scheduler = self.create_scheduler()
|
||||
requests = create_requests(num_requests=len(spec_tokens),
|
||||
num_tokens=1)
|
||||
req_ids = []
|
||||
req_to_index = {}
|
||||
for i, request in enumerate(requests):
|
||||
scheduler.add_request(request)
|
||||
req_ids.append(request.request_id)
|
||||
req_to_index[request.request_id] = i
|
||||
|
||||
# Schedule a decode, which will also draft speculative tokens
|
||||
output = scheduler.schedule()
|
||||
self.assertEqual(len(output.scheduled_new_reqs), len(requests))
|
||||
self.assertEqual(output.total_num_scheduled_tokens, len(requests))
|
||||
for i in range(len(requests)):
|
||||
req_id = requests[i].request_id
|
||||
self.assertEqual(output.num_scheduled_tokens[req_id], 1)
|
||||
self.assertNotIn(req_id, output.scheduled_spec_decode_tokens)
|
||||
|
||||
model_runner_output = ModelRunnerOutput(
|
||||
req_ids=req_ids,
|
||||
req_id_to_index=req_to_index,
|
||||
sampled_token_ids=[[0] for _ in range(len(requests))],
|
||||
logprobs=None,
|
||||
prompt_logprobs_dict={},
|
||||
pooler_output=[])
|
||||
draft_token_ids = DraftTokenIds(req_ids, spec_tokens)
|
||||
|
||||
engine_core_outputs = scheduler.update_from_output(
|
||||
output, model_runner_output)
|
||||
scheduler.update_draft_token_ids(draft_token_ids)
|
||||
|
||||
for i in range(len(requests)):
|
||||
running_req = scheduler.running[i]
|
||||
# The prompt token
|
||||
self.assertEqual(running_req.num_computed_tokens, 1)
|
||||
# The prompt token and the sampled token
|
||||
self.assertEqual(running_req.num_tokens, 2)
|
||||
# The prompt token, the sampled token, and the speculated tokens
|
||||
self.assertEqual(running_req.num_tokens_with_spec,
|
||||
2 + len(spec_tokens[i]))
|
||||
|
||||
# No draft or accepted tokens counted yet
|
||||
self.assertTrue(
|
||||
not engine_core_outputs
|
||||
or (engine_core_outputs[0].scheduler_stats.spec_decoding_stats
|
||||
is None))
|
||||
|
||||
# Schedule the speculated tokens for validation
|
||||
output = scheduler.schedule()
|
||||
self.assertEqual(len(output.scheduled_new_reqs), 0)
|
||||
# The sampled token and speculated tokens
|
||||
self.assertEqual(
|
||||
output.total_num_scheduled_tokens,
|
||||
len(requests) + sum(len(ids) for ids in spec_tokens))
|
||||
for i in range(len(requests)):
|
||||
req_id = requests[i].request_id
|
||||
self.assertEqual(output.num_scheduled_tokens[req_id],
|
||||
1 + len(spec_tokens[i]))
|
||||
if spec_tokens[i]:
|
||||
self.assertEqual(
|
||||
len(output.scheduled_spec_decode_tokens[req_id]),
|
||||
len(spec_tokens[i]))
|
||||
else:
|
||||
self.assertNotIn(req_id,
|
||||
output.scheduled_spec_decode_tokens)
|
||||
|
||||
model_runner_output = ModelRunnerOutput(
|
||||
req_ids=req_ids,
|
||||
req_id_to_index=req_to_index,
|
||||
sampled_token_ids=output_tokens,
|
||||
logprobs=None,
|
||||
prompt_logprobs_dict={},
|
||||
pooler_output=[])
|
||||
|
||||
engine_core_outputs = scheduler.update_from_output(
|
||||
output, model_runner_output)
|
||||
|
||||
scheduler_stats = engine_core_outputs[0].scheduler_stats \
|
||||
if engine_core_outputs else None
|
||||
if expected[0] == 0:
|
||||
self.assertIsNone(scheduler_stats.spec_decoding_stats)
|
||||
else:
|
||||
self.assertIsNotNone(scheduler_stats.spec_decoding_stats)
|
||||
stats = scheduler_stats.spec_decoding_stats
|
||||
self.assertEqual(stats.num_drafts, expected[0])
|
||||
self.assertEqual(stats.num_draft_tokens, expected[1])
|
||||
self.assertEqual(stats.num_accepted_tokens, expected[2])
|
||||
self.assertEqual(stats.num_accepted_tokens_per_pos,
|
||||
expected[3])
|
||||
|
||||
def assert_scheduler_empty(self, scheduler):
|
||||
"""Confirm the scheduler is "empty" - i.e. no leaks."""
|
||||
# Scheduler Metadata.
|
||||
scheduler = self.create_scheduler()
|
||||
self.assertEqual(len(scheduler.requests), 0)
|
||||
self.assertEqual(len(scheduler.waiting), 0)
|
||||
self.assertEqual(len(scheduler.running), 0)
|
||||
self.assertEqual(len(scheduler.finished_req_ids), 0)
|
||||
|
||||
# EncoderCacheManager.
|
||||
self.assertEqual(len(scheduler.encoder_cache_manager.freed), 0)
|
||||
self.assertEqual(len(scheduler.encoder_cache_manager.cached), 0)
|
||||
|
||||
# KVCache Manager.
|
||||
self.assertEqual(
|
||||
len(scheduler.kv_cache_manager.coordinator.single_type_managers[0].
|
||||
req_to_blocks), 0)
|
||||
self.assertEqual(
|
||||
len(scheduler.kv_cache_manager.coordinator.single_type_managers[0].
|
||||
num_cached_block), 0)
|
||||
num_free_blocks = (scheduler.kv_cache_manager.block_pool.
|
||||
free_block_queue.num_free_blocks)
|
||||
self.assertEqual(
|
||||
num_free_blocks,
|
||||
scheduler.kv_cache_manager.block_pool.num_gpu_blocks - 1)
|
||||
|
||||
# NOTE(rob): just the ref count on blocks will be 0. The hash
|
||||
# value, etc will remain since we lazily evict for prefix cache.
|
||||
for block in scheduler.kv_cache_manager.block_pool.blocks:
|
||||
self.assertEqual(block.ref_cnt, 0)
|
||||
|
||||
def test_memory_leak(self):
|
||||
"""Test that we do not have a memory leak."""
|
||||
scheduler = self.create_scheduler()
|
||||
NUM_REQUESTS = 5
|
||||
NUM_TOKENS = 10
|
||||
MAX_TOKENS = 10
|
||||
requests = create_requests(num_requests=NUM_REQUESTS,
|
||||
num_tokens=NUM_TOKENS,
|
||||
max_tokens=MAX_TOKENS)
|
||||
|
||||
# Add each request.
|
||||
for request in requests:
|
||||
scheduler.add_request(request)
|
||||
scheduler_output = scheduler.schedule()
|
||||
model_runner_output = make_output(scheduler)
|
||||
scheduler.update_from_output(scheduler_output, model_runner_output)
|
||||
|
||||
# Iterate until done.
|
||||
while True:
|
||||
scheduler_output = scheduler.schedule()
|
||||
if len(scheduler.running) == 0:
|
||||
break
|
||||
model_runner_output = make_output(scheduler)
|
||||
scheduler.update_from_output(scheduler_output, model_runner_output)
|
||||
|
||||
# Confirm no memory leak.
|
||||
self.assert_scheduler_empty(scheduler)
|
||||
|
||||
def test_scheduler_with_pd_transfer(self):
|
||||
scheduler = self.create_scheduler()
|
||||
scheduler.phase = "prefill"
|
||||
requests = create_requests(num_requests=32)
|
||||
for request in requests:
|
||||
scheduler.add_request(request)
|
||||
|
||||
# 1st iteration, move 16 requests from waiting to running for prefill
|
||||
scheduler_output = scheduler.schedule()
|
||||
model_runner_output = make_output(scheduler)
|
||||
scheduler.update_from_output(scheduler_output, model_runner_output)
|
||||
first_iter_prefilled_req_num = len(scheduler.running)
|
||||
self.assertEqual(len(scheduler_output.scheduled_new_reqs),
|
||||
scheduler.max_num_running_reqs)
|
||||
self.assertEqual(scheduler_output.scheduled_cached_reqs.num_reqs, 0)
|
||||
self.assertEqual(len(scheduler_output.finished_req_ids), 0)
|
||||
|
||||
# 2nd iteration, move 16 prefilled requests to finished_prefill_reqs
|
||||
# and move 16 requests from waiting to running for prefill
|
||||
scheduler_output = scheduler.schedule()
|
||||
model_runner_output = make_output(scheduler)
|
||||
scheduler.update_from_output(scheduler_output, model_runner_output)
|
||||
self.assertEqual(len(scheduler.finished_prefill_reqs),
|
||||
first_iter_prefilled_req_num)
|
||||
|
||||
# 3rd iteration, all requests prefilled, change scheduler phase to decode
|
||||
scheduler_output = scheduler.schedule()
|
||||
model_runner_output = make_output(scheduler)
|
||||
scheduler.update_from_output(scheduler_output, model_runner_output)
|
||||
self.assertEqual(scheduler.phase, "decode")
|
||||
|
||||
|
||||
class TestSchedulerDynamicBatch(TestBase):
|
||||
|
||||
@patch("vllm.config.ModelConfig.__post_init__", MagicMock())
|
||||
@@ -1,100 +0,0 @@
|
||||
from types import SimpleNamespace
|
||||
from unittest.mock import MagicMock, Mock, patch
|
||||
|
||||
import pytest
|
||||
import torch
|
||||
from transformers import PretrainedConfig
|
||||
from vllm.config import CacheConfig, EPLBConfig, ParallelConfig
|
||||
from vllm.distributed.parallel_state import GroupCoordinator
|
||||
|
||||
|
||||
@pytest.fixture
|
||||
def base_config():
|
||||
config = PretrainedConfig(
|
||||
hidden_size=128,
|
||||
num_attention_heads=8,
|
||||
num_hidden_layers=2,
|
||||
intermediate_size=256,
|
||||
hidden_act="silu",
|
||||
rms_norm_eps=1e-6,
|
||||
rope_theta=10000.0,
|
||||
max_position_embeddings=2048,
|
||||
n_routed_experts=4,
|
||||
n_shared_experts=1,
|
||||
moe_intermediate_size=256,
|
||||
num_experts_per_tok=2,
|
||||
routed_scaling_factor=1.0,
|
||||
first_k_dense_replace=0,
|
||||
moe_layer_freq=1,
|
||||
kv_lora_rank=16,
|
||||
qk_nope_head_dim=16,
|
||||
qk_rope_head_dim=16,
|
||||
v_head_dim=32,
|
||||
topk_method="noaux_tc",
|
||||
scoring_func="softmax",
|
||||
norm_topk_prob=True,
|
||||
n_group=1,
|
||||
topk_group=1,
|
||||
vocab_size=10000,
|
||||
)
|
||||
return config
|
||||
|
||||
|
||||
@pytest.fixture
|
||||
def vllm_config(base_config):
|
||||
model_config = SimpleNamespace(
|
||||
hf_config=base_config,
|
||||
tensor_parallel_size=1,
|
||||
dtype=torch.float32,
|
||||
use_mla=True,
|
||||
quant_config=None,
|
||||
max_model_len=2048,
|
||||
)
|
||||
parallel_config = MagicMock(spec=ParallelConfig)
|
||||
eplb_config = MagicMock(spec=EPLBConfig)
|
||||
eplb_config.num_redundant_experts = 0
|
||||
parallel_config.eplb_config = eplb_config
|
||||
|
||||
cache_config = CacheConfig()
|
||||
vllm_config = Mock()
|
||||
vllm_config.model_config = model_config
|
||||
vllm_config.cache_config = cache_config
|
||||
vllm_config.quant_config = None
|
||||
vllm_config.parallel_config = parallel_config
|
||||
return vllm_config
|
||||
|
||||
|
||||
@pytest.fixture
|
||||
def mock_distributed():
|
||||
tp_group = Mock(spec=GroupCoordinator)
|
||||
tp_group.rank_in_group = 0
|
||||
tp_group.world_size = 1
|
||||
tp_group.device_group = Mock()
|
||||
|
||||
dp_group = Mock(spec=GroupCoordinator)
|
||||
dp_group.rank_in_group = 0
|
||||
dp_group.world_size = 1
|
||||
|
||||
ep_group = Mock(spec=GroupCoordinator)
|
||||
ep_group.rank_in_group = 0
|
||||
ep_group.world_size = 1
|
||||
ep_group.device_group = Mock()
|
||||
ep_group.device_group.rank.return_value = 0
|
||||
ep_group.device_group.size.return_value = 1
|
||||
|
||||
pp_group = Mock(spec=GroupCoordinator)
|
||||
pp_group.rank_in_group = 0
|
||||
pp_group.world_size = 1
|
||||
|
||||
mock_vllm_config = Mock()
|
||||
mock_vllm_config.scheduler_config = Mock(max_num_seqs=256)
|
||||
mock_vllm_config.model_config = Mock(max_model_len=2048, quant_config=None)
|
||||
|
||||
with patch("vllm_ascend.ops.fused_moe.fused_moe.get_current_vllm_config", return_value=mock_vllm_config), \
|
||||
patch("vllm_ascend.ops.fused_moe.token_dispatcher.torch.distributed.get_rank", return_value=0), \
|
||||
patch("vllm_ascend.ops.fused_moe.token_dispatcher.get_ascend_device_type", return_value=None), \
|
||||
patch.dict("vllm.distributed.parallel_state.__dict__", _TP=tp_group, _EP=ep_group, _DP=dp_group,
|
||||
_PP=pp_group), \
|
||||
patch.dict("vllm_ascend.distributed.parallel_state.__dict__", _MC2=ep_group), \
|
||||
patch("torch.npu.current_device", return_value=0):
|
||||
yield
|
||||
Reference in New Issue
Block a user