# SPDX-License-Identifier: Apache-2.0 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project import itertools from collections.abc import Iterable from dataclasses import dataclass from vllm.logger import init_logger from vllm.logprobs import ( PromptLogprobs, SampleLogprobs, append_logprobs_for_next_position, create_prompt_logprobs, create_sample_logprobs, ) from vllm.tokenizers.detokenizer_utils import ( TokenizerLike, convert_ids_list_to_tokens, ) from vllm.v1.engine import EngineCoreOutput, EngineCoreRequest from vllm.v1.outputs import LogprobsLists, LogprobsTensors logger = init_logger(__name__) NONES = itertools.repeat(None) @dataclass class LogprobsProcessor: # Tokenizer for this request, # None if detokenization is disabled. tokenizer: TokenizerLike | None # Logprobs for this request logprobs: SampleLogprobs | None prompt_logprobs: PromptLogprobs | None cumulative_logprob: float | None num_logprobs: int | None num_prompt_logprobs: int | None @classmethod def from_new_request( cls, tokenizer: TokenizerLike | None, request: EngineCoreRequest, ) -> "LogprobsProcessor": sampling_params = request.sampling_params assert sampling_params is not None num_logprobs = sampling_params.logprobs num_prompt_logprobs = sampling_params.prompt_logprobs return cls( tokenizer=tokenizer, cumulative_logprob=(None if num_logprobs is None else 0.0), logprobs=( None if num_logprobs is None else create_sample_logprobs(sampling_params.flat_logprobs) ), prompt_logprobs=( None if num_prompt_logprobs is None else create_prompt_logprobs(sampling_params.flat_logprobs) ), num_prompt_logprobs=num_prompt_logprobs, num_logprobs=num_logprobs, ) def _update_sample_logprobs(self, logprobs_lists: LogprobsLists) -> None: """Update with sample logprobs from EngineCore. Outer lists are only of len > 1 if EngineCore made >1 tokens in prior step (e.g. in spec decoding). Args: logprobs_lists: the lists of logprob tokens, logprobs, and ranks. """ assert self.num_logprobs is not None assert self.logprobs is not None assert self.cumulative_logprob is not None token_ids_lst, logprobs_lst, ranks_lst, _ = logprobs_lists for rank_np, logprobs_np, token_ids_np in zip( ranks_lst, logprobs_lst, token_ids_lst ): rank = rank_np.tolist() logprobs = logprobs_np.tolist() token_ids = token_ids_np.tolist() # Detokenize (non-incrementally). decoded_tokens: list[str] | Iterable[None] if self.tokenizer is None: decoded_tokens = NONES else: decoded_tokens_list = convert_ids_list_to_tokens( self.tokenizer, token_ids ) decoded_tokens = self._verify_tokens( decoded_tokens_list=decoded_tokens_list, tokens=token_ids ) # Sampler puts the sampled logprob in first. sampled_token_logprob = logprobs[0] self.cumulative_logprob += sampled_token_logprob # Update with the Logprob container for this pos. append_logprobs_for_next_position( self.logprobs, token_ids, logprobs, decoded_tokens, rank, self.num_logprobs, ) def _update_prompt_logprobs( self, prompt_logprobs_tensors: LogprobsTensors, ) -> None: """Update with prompt logprobs from EngineCore. Args: prompt_logprobs_tensors: tuple containing the prompt logprobs tensors. """ # Prompt logprobs are enabled. assert self.num_prompt_logprobs is not None assert self.prompt_logprobs is not None token_ids, logprobs, ranks = prompt_logprobs_tensors # Recover shapes. num_prompt_tokens, num_logprobs = logprobs.shape # Detokenize non-incrementally. # Output is flat: [num_tok, num_lps] -> [num_tok * num_lps] all_decoded_tokens: list[str] | None = ( None if self.tokenizer is None else convert_ids_list_to_tokens( self.tokenizer, token_ids.flatten().tolist() ) ) # Pythonize the torch tensors. prompt_token_ranks = ranks.tolist() prompt_logprobs = logprobs.tolist() token_ids_list = token_ids.tolist() # Make Logprob for each position. for pos in range(num_prompt_tokens): # Handle flattening and UTF-8 correction per position offset = pos * num_logprobs offset_end = offset + num_logprobs decoded_tokens_for_pos: list[str] | Iterable[None] if all_decoded_tokens is None: decoded_tokens_for_pos = NONES else: # Extract decoded tokens for this position decoded_tokens_slice = all_decoded_tokens[offset:offset_end] # Apply UTF-8 correction within this position's token boundaries decoded_tokens_for_pos = self._verify_tokens( decoded_tokens_list=decoded_tokens_slice, tokens=token_ids_list[pos] ) # Update with the Logprob container for this pos. append_logprobs_for_next_position( self.prompt_logprobs, token_ids_list[pos], prompt_logprobs[pos], decoded_tokens_for_pos, prompt_token_ranks[pos], self.num_prompt_logprobs, ) def pop_prompt_logprobs(self) -> PromptLogprobs | None: """Pop and return all request prompt logprobs The logprobs processor aggregates prompt chunk logprobs over one or more prefill chunks. This method returns all prompt logprobs at once and then forgets them. Ensures correct RequestOutputKind.DELTA semantics wherein all prompt logprobs are returned at once at the end of prefill. Returns: None if prompt logprobs are disabled for this request. List of all prompt logprobs, otherwise. """ plp = self.prompt_logprobs if plp: self.prompt_logprobs = [] return plp def _correct_decoded_token(self, idx: int, tokens: list[int]) -> str: assert self.tokenizer is not None, "self.tokenizer should not be None" # try with prev token id in same list if idx > 0: possible_decoded_token = self.tokenizer.decode(tokens[idx - 1 : idx + 1]) if not possible_decoded_token.endswith("�"): return possible_decoded_token # try with previous logprob token id if self.logprobs: latest_token_id = next(iter(self.logprobs[-1])) decode_ids = [latest_token_id] if idx > 0: decode_ids.extend(tokens[idx - 1 : idx + 1]) else: decode_ids.extend(tokens[idx : idx + 1]) possible_decoded_token = self.tokenizer.decode(decode_ids) if not possible_decoded_token.endswith("�"): return possible_decoded_token # by default return empty string return "" def _verify_tokens( self, decoded_tokens_list: list[str], tokens: list[int] ) -> list[str]: corrected_decoded_token_map = dict() for idx, text in enumerate(decoded_tokens_list): if text.endswith("�"): # utf-8 char at the end means it's a potential unfinished byte sequence # from byte fallback tokenization. corrected_decoded_token_map[idx] = self._correct_decoded_token( idx, tokens ) for idx, text in corrected_decoded_token_map.items(): decoded_tokens_list[idx] = text return decoded_tokens_list def update_from_output(self, output: EngineCoreOutput) -> None: if output.new_logprobs is not None: self._update_sample_logprobs(output.new_logprobs) if output.new_prompt_logprobs_tensors is not None: self._update_prompt_logprobs(output.new_prompt_logprobs_tensors)