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e489ad7a21
- **Add SPDX license headers to python source files**
- **Check for SPDX headers using pre-commit**
commit 9d7ef44c3cfb72ca4c32e1c677d99259d10d4745
Author: Russell Bryant <rbryant@redhat.com>
Date: Fri Jan 31 14:18:24 2025 -0500
Add SPDX license headers to python source files
This commit adds SPDX license headers to python source files as
recommended to
the project by the Linux Foundation. These headers provide a concise way
that is
both human and machine readable for communicating license information
for each
source file. It helps avoid any ambiguity about the license of the code
and can
also be easily used by tools to help manage license compliance.
The Linux Foundation runs license scans against the codebase to help
ensure
we are in compliance with the licenses of the code we use, including
dependencies. Having these headers in place helps that tool do its job.
More information can be found on the SPDX site:
- https://spdx.dev/learn/handling-license-info/
Signed-off-by: Russell Bryant <rbryant@redhat.com>
commit 5a1cf1cb3b80759131c73f6a9dddebccac039dea
Author: Russell Bryant <rbryant@redhat.com>
Date: Fri Jan 31 14:36:32 2025 -0500
Check for SPDX headers using pre-commit
Signed-off-by: Russell Bryant <rbryant@redhat.com>
---------
Signed-off-by: Russell Bryant <rbryant@redhat.com>
90 lines
3.8 KiB
Python
90 lines
3.8 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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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 .test_utils import mock_spec_decode_sampler
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from .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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