e2a0c19cea
1. remove some useless test func and file 2. fix format.sh problem 3. enable full test for singlecard and multicard 4. move long term test to long_term folder. For this kind of test, it only runs by labeled and daily test. Include: spec decode、accuracy test ## After refactor: There are 4 test modules - `singlecard`: contains the test running on one NPU. It'll be run for each PR and daily test. - `multicard`: contains the test running on multi NPUs. It'll be run for each PR and daily test. - `long_term`: contains the test that cost much time(Now include `spec decode` and `accuracy` test). It'll be run for the PR with `long-term-test` labeled and daily test. - `e2e`: contains the test for doc and pd feature. It'll be run for the PR with `pd-test` labeled and daily test. ## Todo: 1. some test are skipped, they should be fixed and reenabled in the future. 2. pyhccl test for multicard doesn't work at all. It should be enabled as well. 3. ensure long-term-test pass by daily test. ### Know issue Now, `ready` labels is required to start pd test or long term test. And when `long-term-test` or `pd-test` is labeled after another one, the old labeled test will be re-run again. So the labeled test should be ran in the following step: 1. decide which test need run, then label it. `long-term-test` or `pd-test` or both. 2. add `ready-for-test` label, then the test will be ran. Signed-off-by: wangxiyuan <wangxiyuan1007@gmail.com>
106 lines
4.5 KiB
Python
106 lines
4.5 KiB
Python
#
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# Copyright (c) 2025 Huawei Technologies Co., Ltd. All Rights Reserved.
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# This file is a part of the vllm-ascend project.
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# Adapted from vllm-project/vllm/tests/spec_decode/test_dynamic_spec_decode.py
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# Copyright 2023 The vLLM team.
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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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#
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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.sequence import ExecuteModelRequest
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from vllm.spec_decode.metrics import AsyncMetricsCollector
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from vllm.spec_decode.multi_step_worker import MultiStepWorker
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from vllm.spec_decode.spec_decode_worker import SpecDecodeWorker
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from vllm.spec_decode.top1_proposer import Top1Proposer
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from tests.long_term.spec_decode.test_utils import mock_spec_decode_sampler
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from tests.long_term.spec_decode.utils import create_batch, mock_worker
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@pytest.mark.parametrize('queue_size', [4])
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@pytest.mark.parametrize('batch_size', [1])
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@pytest.mark.parametrize('k', [1])
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@pytest.mark.parametrize("acceptance_sampler_method",
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["rejection_sampler", "typical_acceptance_sampler"])
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@torch.inference_mode()
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def test_disable_spec_tokens(queue_size: int, batch_size: int, k: int,
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acceptance_sampler_method: str):
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"""Verify that speculative tokens are disabled when the batch size
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exceeds the threshold.
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"""
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disable_by_batch_size = 3
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draft_worker = mock_worker(cls=MultiStepWorker)
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target_worker = mock_worker()
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metrics_collector = MagicMock(spec=AsyncMetricsCollector)
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worker = SpecDecodeWorker(proposer_worker=draft_worker,
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scorer_worker=target_worker,
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spec_decode_sampler=mock_spec_decode_sampler(
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acceptance_sampler_method),
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disable_logprobs=False,
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metrics_collector=metrics_collector,
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disable_by_batch_size=disable_by_batch_size)
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exception_secret = 'artificial stop'
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draft_worker.get_spec_proposals.side_effect = ValueError(exception_secret)
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seq_group_metadata_list, _, _ = create_batch(batch_size, k)
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execute_model_req = ExecuteModelRequest(
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seq_group_metadata_list=seq_group_metadata_list,
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num_lookahead_slots=k,
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running_queue_size=queue_size)
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if queue_size > disable_by_batch_size:
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with patch.object(worker,
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'_run_no_spec',
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side_effect=ValueError(exception_secret)), \
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pytest.raises(ValueError, match=exception_secret):
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worker.execute_model(execute_model_req=execute_model_req)
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# When the batch size is larger than the threshold,
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# we expect no speculative tokens (0).
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expected_num_spec_tokens = None if queue_size < disable_by_batch_size else 0
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assert seq_group_metadata_list[
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0].num_speculative_tokens == expected_num_spec_tokens
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draft_worker.sampler_output.side_effect = ValueError(exception_secret)
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proposer = Top1Proposer(
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worker=draft_worker,
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device='cpu', # not used
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vocab_size=100, # not used
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# Must be long enough to avoid being skipped due to length.
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max_proposal_len=1024,
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)
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if queue_size < disable_by_batch_size:
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# Should raise exception when executing the mocked draft model.
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with pytest.raises(ValueError, match=exception_secret):
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proposer.get_spec_proposals(
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execute_model_req=ExecuteModelRequest(
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seq_group_metadata_list=seq_group_metadata_list,
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num_lookahead_slots=k),
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seq_ids_with_bonus_token_in_last_step=set())
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else:
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# Should not execute the draft model because spec decode is disabled
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# for all requests. Accordingly, the proposal length should be 0.
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proposals = proposer.get_spec_proposals(
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execute_model_req=ExecuteModelRequest(
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seq_group_metadata_list=seq_group_metadata_list,
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num_lookahead_slots=k),
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seq_ids_with_bonus_token_in_last_step=set())
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assert proposals.proposal_lens.tolist() == [0] * batch_size
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