Sync from v0.13

vllm/tokenizers/detokenizer_utils.py

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+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+
+from vllm.tokenizers import TokenizerLike
+
+
+def _replace_none_with_empty(tokens: list[str | None]):
+    for i, token in enumerate(tokens):
+        if token is None:
+            tokens[i] = ""
+
+
+def _convert_tokens_to_string_with_added_encoders(
+    tokenizer: TokenizerLike,
+    output_tokens: list[str],
+    skip_special_tokens: bool,
+    spaces_between_special_tokens: bool,
+) -> str:
+    # Adapted from
+    # https://github.com/huggingface/transformers/blob/v4.28.0/src/transformers/tokenization_utils.py#L921
+    # NOTE(woosuk): The following code is slow because it runs a for loop over
+    # the output_tokens. In Python, running a for loop over a list can be slow
+    # even when the loop body is very simple.
+    # Performance improvements: avoid repeated attribute and function lookups;
+    # localize frequently used objects;
+
+    sub_texts: list[str] = []
+    current_sub_text: list[str] = []
+    convert_tokens_to_string = tokenizer.convert_tokens_to_string
+    added_vocab_set = set(tokenizer.get_added_vocab())
+    all_special_tokens = (
+        set(tokenizer.all_special_tokens) if skip_special_tokens else ()
+    )
+
+    for token in output_tokens:
+        # Use precomputed set for skip-special check
+        if token in all_special_tokens:
+            continue
+        if token in added_vocab_set:
+            if current_sub_text:
+                sub_texts.append(convert_tokens_to_string(current_sub_text))
+                current_sub_text.clear()
+            sub_texts.append(token)
+        else:
+            current_sub_text.append(token)
+    if current_sub_text:
+        sub_texts.append(convert_tokens_to_string(current_sub_text))
+    if spaces_between_special_tokens:
+        return " ".join(sub_texts)
+    return "".join(sub_texts)
+
+
+# 5 is an arbitrary value that should work for all
+# tokenizers (bigger = more conservative).
+INITIAL_INCREMENTAL_DETOKENIZATION_OFFSET = 5
+
+
+def convert_prompt_ids_to_tokens(
+    tokenizer: TokenizerLike,
+    prompt_ids: list[int],
+    skip_special_tokens: bool = False,
+) -> tuple[list[str], int, int]:
+    """Converts the prompt ids to tokens and returns the tokens and offsets
+    for incremental detokenization.
+
+    Note that not all tokens are converted to strings. Only the tokens that
+    are necessary for incremental detokenization are converted to strings.
+    """
+    # We do not need to convert the whole prompt to tokens.
+    # Offset a little more in case we have special tokens.
+    new_tokens = tokenizer.convert_ids_to_tokens(
+        prompt_ids[-INITIAL_INCREMENTAL_DETOKENIZATION_OFFSET - 2 :],
+        skip_special_tokens=skip_special_tokens,
+    )
+    read_offset = len(new_tokens)
+    prefix_offset = max(read_offset - INITIAL_INCREMENTAL_DETOKENIZATION_OFFSET, 0)
+    # This is required to guard against out-of-vocab prompt token ids
+    _replace_none_with_empty(new_tokens)  # type: ignore[arg-type]
+    return new_tokens, prefix_offset, read_offset
+
+
+def convert_ids_list_to_tokens(
+    tokenizer: TokenizerLike,
+    token_ids: list[int],
+) -> list[str]:
+    """Detokenize the input ids individually.
+
+    Args:
+      tokenizer: tokenizer used by model under test
+      token_ids: convert these tokens (Python list form)
+
+    Returns:
+      Python list of token string representations
+
+    """
+    token_str_lst = []
+    for token_id in token_ids:
+        # use default skip_special_tokens.
+        token_str = tokenizer.decode([token_id])
+        if token_str is None:
+            token_str = ""
+        token_str_lst.append(token_str)
+    return token_str_lst
+
+
+# Based on
+# https://github.com/huggingface/text-generation-inference/blob/v0.9.4/server/text_generation_server/models/model.py#L62C9-L62C15
+# under Apache 2.0 license
+def detokenize_incrementally(
+    tokenizer: TokenizerLike,
+    all_input_ids: list[int],
+    prev_tokens: list[str] | None,
+    prefix_offset: int,
+    read_offset: int,
+    skip_special_tokens: bool = False,
+    spaces_between_special_tokens: bool = True,
+) -> tuple[list[str], str, int, int]:
+    """Detokenizes the input ids incrementally and returns the new tokens
+    and the new text.
+
+    If `prev_tokens` is None, this function will convert the input ids to
+    tokens and return the tokens and the new text. Otherwise, it will return the
+    new tokens and the new text.
+
+    This function will also return the new prefix offset and the new read
+    offset to be used in the next iteration.
+
+    The offsets are necessary to defeat cleanup algorithms in the decode which
+    decide to add a space or not depending on the surrounding ids.
+
+    Args:
+        tokenizer: The tokenizer to use.
+        all_input_ids: The input ids. The last id is the new token id.
+        prev_tokens: The previous tokens. If None, this function will convert
+            the input ids to tokens and return the tokens and the new text.
+        prefix_offset: The prefix offset.
+        read_offset: The read offset.
+        skip_special_tokens: Whether to skip special tokens.
+        spaces_between_special_tokens: Whether to add spaces between special
+            tokens.
+    """
+    new_token_id = all_input_ids[-1]
+    # This is the first iteration for this sequence
+    is_first_iter = prev_tokens is None
+    if is_first_iter:
+        (prev_tokens, prefix_offset, read_offset) = convert_prompt_ids_to_tokens(
+            tokenizer, all_input_ids[:-1], skip_special_tokens=skip_special_tokens
+        )
+    assert prev_tokens is not None
+
+    # If the new token id is out of bounds, return an empty string.
+    if 0 <= new_token_id < len(tokenizer):
+        # Put new_token_id in a list so skip_special_tokens is respected
+        new_tokens = tokenizer.convert_ids_to_tokens(
+            [new_token_id], skip_special_tokens=skip_special_tokens
+        )
+        if isinstance(new_tokens, str):
+            new_tokens = [new_tokens]
+    else:
+        new_tokens = [""]
+    output_tokens = prev_tokens + new_tokens
+
+    # If this is the first iteration, return all tokens.
+    if is_first_iter:
+        new_tokens = output_tokens
+
+    # The prefix text is necessary only to defeat cleanup algorithms in
+    # the decode which decide to add a space or not depending on the
+    # surrounding ids.
+    if tokenizer.is_fast or not tokenizer.get_added_vocab():
+        prefix_text = tokenizer.convert_tokens_to_string(
+            output_tokens[prefix_offset:read_offset]
+        )
+        new_text = tokenizer.convert_tokens_to_string(output_tokens[prefix_offset:])
+    else:
+        prefix_text = _convert_tokens_to_string_with_added_encoders(
+            tokenizer,
+            output_tokens[prefix_offset:read_offset],
+            skip_special_tokens=skip_special_tokens,
+            spaces_between_special_tokens=spaces_between_special_tokens,
+        )
+        new_text = _convert_tokens_to_string_with_added_encoders(
+            tokenizer,
+            output_tokens[prefix_offset:],
+            skip_special_tokens=skip_special_tokens,
+            spaces_between_special_tokens=spaces_between_special_tokens,
+        )
+
+    if len(new_text) <= len(prefix_text) or new_text.endswith("<EFBFBD>"):
+        # utf-8 char at the end means it's a potential unfinished byte sequence
+        # from byte fallback tokenization.
+        # If it's in the middle, it's probably a real invalid id generated
+        # by the model
+        return new_tokens, "", prefix_offset, read_offset
+
+    new_text = new_text[len(prefix_text) :]
+    return new_tokens, new_text, read_offset, len(output_tokens)