Sync from v0.13

vllm/logits_process.py

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+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+from collections.abc import Callable, Sequence
+from typing import TypeAlias
+
+import torch
+
+from vllm.tokenizers import TokenizerLike
+
+LogitsProcessor: TypeAlias = (
+    Callable[[list[int], torch.Tensor], torch.Tensor]
+    | Callable[[list[int], list[int], torch.Tensor], torch.Tensor]
+)
+"""LogitsProcessor is a function that takes a list
+of previously generated tokens, the logits tensor
+for the next token and, optionally, prompt tokens as a
+first argument, and returns a modified tensor of logits
+to sample from."""
+
+
+def get_bad_words_logits_processors(
+    bad_words: list[str], tokenizer: TokenizerLike
+) -> list[LogitsProcessor]:
+    bad_words_ids: list[list[int]] = list()
+
+    for bad_word in bad_words:
+        # To prohibit words both at the beginning
+        # and in the middle of text
+        # (related to add_prefix_space tokenizer parameter)
+        for add_prefix_space in [False, True]:
+            prefix = " " if add_prefix_space else ""
+            prompt = prefix + bad_word.lstrip()
+
+            prompt_token_ids = tokenizer.encode(text=prompt, add_special_tokens=False)
+
+            # If no space at the beginning
+            # or if prefix space produces a new word token
+            if (not add_prefix_space) or (
+                add_prefix_space
+                and prompt_token_ids[0] != bad_words_ids[-1][0]
+                and len(prompt_token_ids) == len(bad_words_ids[-1])
+            ):
+                bad_words_ids.append(prompt_token_ids)
+
+    return [NoBadWordsLogitsProcessor(bad_words_ids=bad_words_ids)]
+
+
+class NoBadWordsLogitsProcessor:
+    _SMALLEST_LOGIT = float("-inf")
+    _NEUTRAL_LOGIT = 0.0
+
+    def __init__(self, bad_words_ids: list[list[int]]):
+        self.bad_words_ids = bad_words_ids
+        self.word_bias: torch.FloatTensor = None
+
+    def __call__(
+        self,
+        past_tokens_ids: Sequence[int],
+        logits: torch.FloatTensor,
+    ) -> torch.Tensor:
+        if self.word_bias is None:
+            self._init_word_bias(logits=logits)
+
+        last_token_bias = torch.zeros_like(logits)
+
+        for bad_word_ids in self.bad_words_ids:
+            if len(bad_word_ids) == 1:  # 1-token words already processed
+                continue
+
+            if len(bad_word_ids) > len(past_tokens_ids) + 1:
+                continue
+
+            prefix_length = len(bad_word_ids) - 1
+            last_token_id = bad_word_ids[-1]
+            actual_prefix = past_tokens_ids[-prefix_length:]
+            expected_prefix = bad_word_ids[:prefix_length]
+
+            assert len(actual_prefix) == len(expected_prefix)
+
+            is_match = tuple(actual_prefix) == tuple(expected_prefix)
+            last_token_bias[last_token_id] += (
+                self._SMALLEST_LOGIT if is_match else self._NEUTRAL_LOGIT
+            )
+
+        logits = logits + self.word_bias + last_token_bias
+
+        return logits
+
+    def _init_word_bias(self, logits: torch.FloatTensor) -> None:
+        # Code based on NoBadWordsLogitsProcessor and SequenceBiasLogitsProcessor  # noqa: E501
+        # from https://github.com/huggingface/transformers/blob/main/src/transformers/generation/logits_process.py
+
+        vocab_size = logits.shape[-1]
+
+        self._check_token_ids_bounds(vocab_size=vocab_size)
+
+        self.word_bias = torch.zeros(
+            (vocab_size,), dtype=torch.float, device=logits.device
+        )
+
+        for bad_word_ids in self.bad_words_ids:
+            if len(bad_word_ids) == 1:
+                bad_word_id = bad_word_ids[-1]
+                self.word_bias[bad_word_id] = self._SMALLEST_LOGIT
+
+    def _check_token_ids_bounds(self, vocab_size: int) -> None:
+        invalid_token_ids = []
+
+        for bad_word_ids in self.bad_words_ids:
+            for token_id in bad_word_ids:
+                if token_id < 0 or token_id >= vocab_size:
+                    invalid_token_ids.append(token_id)
+
+        if len(invalid_token_ids) > 0:
+            raise ValueError(
+                f"The model vocabulary size is {vocab_size},"
+                f" but the following tokens"
+                f" were specified as bad: {invalid_token_ids}."
+                f" All token id values should be integers satisfying:"
+                f" 0 <= token_id < {vocab_size}."
+            )