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>
89 lines
3.1 KiB
Python
89 lines
3.1 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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from functools import lru_cache, partial
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from typing import Dict, FrozenSet, Iterable, List, Optional, Union
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import torch
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from vllm.sampling_params import LogitsProcessor
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from vllm.transformers_utils.tokenizer import AnyTokenizer
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class AllowedTokenIdsLogitsProcessor:
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"""Logits processor for constraining generated tokens to a
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specific set of token ids."""
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def __init__(self, allowed_ids: Iterable[int]):
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self.allowed_ids: Optional[List[int]] = list(allowed_ids)
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self.mask: Optional[torch.Tensor] = None
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def __call__(self, token_ids: List[int],
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logits: torch.Tensor) -> torch.Tensor:
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if self.mask is None:
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self.mask = torch.ones((logits.shape[-1], ),
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dtype=torch.bool,
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device=logits.device)
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self.mask[self.allowed_ids] = False
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self.allowed_ids = None
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logits.masked_fill_(self.mask, float("-inf"))
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return logits
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@lru_cache(maxsize=32)
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def _get_allowed_token_ids_logits_processor(
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allowed_token_ids: FrozenSet[int],
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vocab_size: int,
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) -> LogitsProcessor:
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if not allowed_token_ids:
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raise ValueError("Empty allowed_token_ids provided")
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if not all(0 <= tid < vocab_size for tid in allowed_token_ids):
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raise ValueError("allowed_token_ids contains "
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"out-of-vocab token id")
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return AllowedTokenIdsLogitsProcessor(allowed_token_ids)
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def logit_bias_logits_processor(
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logit_bias: Dict[int, float],
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token_ids: List[int],
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logits: torch.Tensor,
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) -> torch.Tensor:
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for token_id, bias in logit_bias.items():
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logits[token_id] += bias
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return logits
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def get_logits_processors(
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logit_bias: Optional[Union[Dict[int, float], Dict[str, float]]],
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allowed_token_ids: Optional[List[int]],
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tokenizer: AnyTokenizer,
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) -> List[LogitsProcessor]:
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logits_processors: List[LogitsProcessor] = []
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if logit_bias:
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try:
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# Convert token_id to integer
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# Clamp the bias between -100 and 100 per OpenAI API spec
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clamped_logit_bias: Dict[int, float] = {
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int(token_id): min(100.0, max(-100.0, bias))
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for token_id, bias in logit_bias.items()
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}
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except ValueError as exc:
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raise ValueError(
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"Found token_id in logit_bias that is not "
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"an integer or string representing an integer") from exc
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# Check if token_id is within the vocab size
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for token_id, bias in clamped_logit_bias.items():
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if token_id < 0 or token_id >= len(tokenizer):
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raise ValueError(f"token_id {token_id} in logit_bias contains "
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"out-of-vocab token id")
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logits_processors.append(
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partial(logit_bias_logits_processor, clamped_logit_bias))
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if allowed_token_ids is not None:
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logits_processors.append(
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_get_allowed_token_ids_logits_processor(
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frozenset(allowed_token_ids), len(tokenizer)))
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return logits_processors
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