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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>
274 lines
9.2 KiB
Python
274 lines
9.2 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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import random
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from unittest.mock import MagicMock
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import pytest
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from transformers import PreTrainedTokenizer
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from vllm.core.scheduler import Scheduler
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from vllm.engine.output_processor.multi_step import MultiStepOutputProcessor
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from vllm.engine.output_processor.stop_checker import StopChecker
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from vllm.sampling_params import SamplingParams
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from vllm.sequence import (CompletionSequenceGroupOutput, Logprob,
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SequenceOutput, SequenceStatus)
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from vllm.transformers_utils.detokenizer import Detokenizer
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from vllm.utils import Counter
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from ...core.utils import create_seq_group
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@pytest.mark.parametrize("seq_output_len", [128])
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@pytest.mark.parametrize("num_new_tokens", [1, 12])
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@pytest.mark.skip_global_cleanup
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def test_appends_token_ids(num_new_tokens: int, seq_output_len: int):
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"""Verify multi-step decoding appends token ids correctly.
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We append token ids and verify all the token ids were appended correctly.
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Note that ignore_eos=True.
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"""
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detokenizer = MagicMock(spec=Detokenizer)
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scheduler = MagicMock(spec=Scheduler)
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stop_checker = MagicMock(spec=StopChecker)
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seq_counter = Counter()
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output_processor = MultiStepOutputProcessor(
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detokenizer=detokenizer,
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scheduler=[scheduler],
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seq_counter=seq_counter,
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get_tokenizer_for_seq=lambda _: mock_tokenizer(),
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stop_checker=stop_checker,
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)
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seq_group = create_seq_group(
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seq_prompt_len=1024,
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seq_output_lens=[seq_output_len],
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sampling_params=SamplingParams(max_tokens=seq_output_len +
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num_new_tokens,
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ignore_eos=True),
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)
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seq = seq_group.get_seqs()[0]
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seq.status = SequenceStatus.RUNNING
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new_token_ids = list(range(num_new_tokens))
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outputs = [
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CompletionSequenceGroupOutput(
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samples=[
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SequenceOutput(
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parent_seq_id=seq.seq_id,
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output_token=output_token,
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logprobs={output_token: Logprob(0.0)},
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)
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],
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prompt_logprobs=None,
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) for output_token in new_token_ids
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]
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assert seq.get_token_ids()[-len(new_token_ids):] != new_token_ids
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output_processor.process_outputs(seq_group, outputs)
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assert seq.get_token_ids()[-len(new_token_ids):] == new_token_ids
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@pytest.mark.parametrize("seq_prompt_len", [1024])
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@pytest.mark.parametrize("seq_output_len", [128])
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@pytest.mark.parametrize("num_new_tokens", [5, 6, 7, 8])
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@pytest.mark.parametrize("max_tokens", [128 + 3])
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@pytest.mark.skip_global_cleanup
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def test_respects_max_tokens(num_new_tokens: int, seq_prompt_len: int,
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seq_output_len: int, max_tokens: int):
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"""Verify tokens after max_tokens are dropped and not appended to the
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sequence.
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"""
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detokenizer = MagicMock(spec=Detokenizer)
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scheduler = MagicMock(spec=Scheduler)
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stop_checker = MagicMock(spec=StopChecker)
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seq_counter = Counter()
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output_processor = MultiStepOutputProcessor(
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detokenizer=detokenizer,
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scheduler=[scheduler],
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seq_counter=seq_counter,
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get_tokenizer_for_seq=lambda _: mock_tokenizer(),
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stop_checker=stop_checker,
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)
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seq_group = create_seq_group(
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seq_prompt_len=seq_prompt_len,
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seq_output_lens=[seq_output_len],
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sampling_params=SamplingParams(max_tokens=max_tokens, ),
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)
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seq = seq_group.get_seqs()[0]
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seq.status = SequenceStatus.RUNNING
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new_token_ids = list(range(num_new_tokens))
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outputs = [
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CompletionSequenceGroupOutput(
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samples=[
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SequenceOutput(
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parent_seq_id=seq.seq_id,
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output_token=output_token,
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logprobs={output_token: Logprob(0.0)},
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)
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],
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prompt_logprobs=None,
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) for output_token in new_token_ids
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]
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assert seq.get_len() == seq_prompt_len + seq_output_len
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output_processor.process_outputs(seq_group, outputs)
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# Expect the processed sequence to not go over max tokens in len.
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assert seq.get_len() == seq_prompt_len + max_tokens
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# Expect the correct tokens were appended.
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expected_appended_tokens = new_token_ids[:max_tokens - seq_output_len]
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assert seq.get_token_ids(
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)[-len(expected_appended_tokens):] == expected_appended_tokens
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@pytest.mark.parametrize("seq_prompt_len", [1024])
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@pytest.mark.parametrize("seq_output_len", [128])
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@pytest.mark.parametrize("num_new_tokens", [12])
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@pytest.mark.parametrize("seed", list(range(6)))
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@pytest.mark.skip_global_cleanup
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def test_respects_eos_token_id(num_new_tokens: int, seq_prompt_len: int,
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seq_output_len: int, seed: int):
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"""Verify the eos token id is included in the sequence, but subsequent
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tokens are dropped (not appended to sequence).
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"""
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random.seed(seed)
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detokenizer = MagicMock(spec=Detokenizer)
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scheduler = MagicMock(spec=Scheduler)
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stop_checker = MagicMock(spec=StopChecker)
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seq_counter = Counter()
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eos_token_id = 100
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output_processor = MultiStepOutputProcessor(
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detokenizer=detokenizer,
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scheduler=[scheduler],
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seq_counter=seq_counter,
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get_tokenizer_for_seq=lambda _: mock_tokenizer(eos_token_id),
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stop_checker=stop_checker,
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)
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seq_group = create_seq_group(
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seq_prompt_len=seq_prompt_len,
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seq_output_lens=[seq_output_len],
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sampling_params=SamplingParams(
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# Ensure enough space.
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max_tokens=seq_output_len + num_new_tokens, ),
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)
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seq = seq_group.get_seqs()[0]
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seq.status = SequenceStatus.RUNNING
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new_token_ids = list(range(num_new_tokens))
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assert eos_token_id not in new_token_ids
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eos_index = random.randint(0, len(new_token_ids) - 1)
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new_token_ids[eos_index] = eos_token_id
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outputs = [
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CompletionSequenceGroupOutput(
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samples=[
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SequenceOutput(
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parent_seq_id=seq.seq_id,
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output_token=output_token,
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logprobs={output_token: Logprob(0.0)},
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)
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],
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prompt_logprobs=None,
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) for output_token in new_token_ids
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]
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assert seq.get_len() == seq_prompt_len + seq_output_len
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output_processor.process_outputs(seq_group, outputs)
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# Expect the processed sequence to not go beyond provided eos.
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assert seq.get_len() == seq_prompt_len + seq_output_len + (eos_index + 1)
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# Expect the correct tokens were appended.
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expected_appended_tokens = new_token_ids[:eos_index + 1]
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assert seq.get_token_ids(
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)[-len(expected_appended_tokens):] == expected_appended_tokens
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@pytest.mark.parametrize("seq_prompt_len", [1024])
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@pytest.mark.parametrize("seq_output_len", [128])
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@pytest.mark.parametrize("num_new_tokens", [12])
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@pytest.mark.parametrize("seed", list(range(6)))
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@pytest.mark.skip_global_cleanup
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def test_ignores_eos_token_id(num_new_tokens: int, seq_prompt_len: int,
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seq_output_len: int, seed: int):
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"""When sampling parameters dictate that we should ignore the eos token id,
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ensure all token ids are appended even if the eos token id is emitted.
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"""
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random.seed(seed)
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detokenizer = MagicMock(spec=Detokenizer)
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scheduler = MagicMock(spec=Scheduler)
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stop_checker = MagicMock(spec=StopChecker)
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seq_counter = Counter()
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eos_token_id = 100
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output_processor = MultiStepOutputProcessor(
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detokenizer=detokenizer,
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scheduler=[scheduler],
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seq_counter=seq_counter,
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get_tokenizer_for_seq=lambda _: mock_tokenizer(eos_token_id),
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stop_checker=stop_checker,
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)
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seq_group = create_seq_group(
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seq_prompt_len=seq_prompt_len,
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seq_output_lens=[seq_output_len],
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sampling_params=SamplingParams(
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# Ensure enough space.
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max_tokens=seq_output_len + num_new_tokens,
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ignore_eos=True,
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),
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)
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seq = seq_group.get_seqs()[0]
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seq.status = SequenceStatus.RUNNING
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new_token_ids = list(range(num_new_tokens))
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assert eos_token_id not in new_token_ids
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eos_index = random.randint(0, len(new_token_ids) - 1)
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new_token_ids[eos_index] = eos_token_id
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outputs = [
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CompletionSequenceGroupOutput(
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samples=[
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SequenceOutput(
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parent_seq_id=seq.seq_id,
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output_token=output_token,
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logprobs={output_token: Logprob(0.0)},
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)
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],
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prompt_logprobs=None,
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) for output_token in new_token_ids
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]
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assert seq.get_len() == seq_prompt_len + seq_output_len
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output_processor.process_outputs(seq_group, outputs)
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# Expect the processed sequence to go beyond eos.
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assert seq.get_len() == seq_prompt_len + seq_output_len + num_new_tokens
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# Expect the correct tokens were appended.
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expected_appended_tokens = new_token_ids[:seq_output_len + num_new_tokens -
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seq_output_len]
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assert seq.get_token_ids(
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)[-len(expected_appended_tokens):] == expected_appended_tokens
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def mock_tokenizer(eos_token_id=1000):
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tokenizer = MagicMock(spec=PreTrainedTokenizer)
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tokenizer.eos_token_id = eos_token_id
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return tokenizer
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