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>
99 lines
3.5 KiB
Python
99 lines
3.5 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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import random
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from typing import Tuple
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from unittest.mock import patch
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import pytest
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import torch
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from vllm.model_executor.layers.logits_processor import LogitsProcessor
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from vllm.model_executor.sampling_metadata import SamplingMetadata
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from vllm.model_executor.utils import set_random_seed
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from vllm.sequence import SamplingParams, SequenceData, SequenceGroupMetadata
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from vllm.utils import is_pin_memory_available
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class MockLogitsProcessor(LogitsProcessor):
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def __init__(self, vocab_size: int, scale: float,
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fake_logits: torch.Tensor):
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super().__init__(vocab_size=vocab_size, scale=scale)
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self.fake_logits = fake_logits.clone()
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def forward(self, *args, **kwargs):
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with patch(
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"vllm.model_executor.layers.logits_processor._prune_hidden_states",
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lambda x, y: x
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), patch(
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"vllm.model_executor.layers.logits_processor.LogitsProcessor._get_logits",
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lambda *args, **kwargs: self.fake_logits):
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return super().forward(*args, **kwargs)
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def _prepare_test(
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batch_size: int
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) -> Tuple[torch.Tensor, torch.Tensor, MockLogitsProcessor]:
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vocab_size = 32000
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input_tensor = torch.rand((batch_size, 1024), dtype=torch.float16)
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fake_logits = torch.full((batch_size, vocab_size),
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1e-2,
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dtype=input_tensor.dtype)
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logits_processor = MockLogitsProcessor(32000, 0.5, fake_logits)
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return input_tensor, fake_logits, logits_processor
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RANDOM_SEEDS = list(range(128))
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CUDA_DEVICES = [
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f"cuda:{i}" for i in range(1 if torch.cuda.device_count() == 1 else 2)
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]
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@pytest.mark.parametrize("seed", RANDOM_SEEDS)
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@pytest.mark.parametrize("device", CUDA_DEVICES)
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def test_logits_processors(seed: int, device: str):
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set_random_seed(seed)
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torch.set_default_device(device)
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batch_size = random.randint(1, 256)
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input_tensor, fake_logits, logits_processor = _prepare_test(batch_size)
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# This sample logits processor gives infinite score to the i-th token,
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# where i is the length of the input sequence.
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# We therefore expect the output token sequence to be [0, 1, 2, ...]
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def pick_ith(token_ids, logits):
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logits[len(token_ids)] = float("inf")
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return logits
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seq_group_metadata_list = []
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seq_lens = []
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for i in range(batch_size):
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seq_group_metadata_list.append(
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SequenceGroupMetadata(
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request_id=f"test_{i}",
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is_prompt=True,
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seq_data={0: SequenceData.from_seqs([1, 2, 3])},
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sampling_params=SamplingParams(temperature=0,
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logits_processors=[pick_ith]),
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block_tables={0: [1]},
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))
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seq_lens.append(seq_group_metadata_list[-1].seq_data[0].get_len())
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sampling_metadata = SamplingMetadata.prepare(
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seq_group_metadata_list,
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seq_lens,
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query_lens=seq_lens,
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device=device,
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pin_memory=is_pin_memory_available())
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logits_processor_output = logits_processor(
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lm_head=None,
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hidden_states=input_tensor,
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sampling_metadata=sampling_metadata)
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assert torch.isinf(logits_processor_output[:, 0]).all()
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fake_logits *= logits_processor.scale
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torch.testing.assert_close(logits_processor_output[:, 1],
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fake_logits[:, 1],
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rtol=1e-4,
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atol=0.0)
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