# SPDX-License-Identifier: Apache-2.0 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project from array import array from itertools import chain, count from typing import Iterator, List, Optional, Tuple import torch from vllm import SamplingParams from vllm.model_executor.layers.sampler import SamplerOutput from vllm.sequence import (VLLM_INVALID_TOKEN_ID, VLLM_TOKEN_ID_ARRAY_TYPE, ExecuteModelRequest, SequenceData, SequenceGroupMetadata, get_all_seq_ids) from vllm.spec_decode.interfaces import (SpeculativeProposals, SpeculativeScorer, SpeculativeScores) from vllm.spec_decode.util import nvtx_range, split_batch_by_proposal_len SeqId = int TargetSeqId = int TokenId = int DEFAULT_SIMPLE_SAMPLING_PARAMS = SamplingParams() class BatchExpansionTop1Scorer(SpeculativeScorer): """Implements a speculative scorer that uses batch expansion to get probabilities of speculative tokens according to the scoring model. Batch expansion converts a list of sequences and multiple query positions to a new batch of sequences, each with a single query position. This allows for MQA-like scoring in speculative decoding without requiring an MQA kernel. It is strictly less efficient than MQA scoring. It only supports scoring the top1 proposal tokens of the proposer, instead of topk/tree. """ @nvtx_range("BatchExpansionTop1Scorer.score_proposals") def score_proposals( self, execute_model_req: ExecuteModelRequest, proposals: SpeculativeProposals, ) -> SpeculativeScores: """Score the proposed tokens via the scorer model. This converts each input sequence to a set of k+1 target sequences. The target sequences have the unique continuations to be scored and a unique sequence ID that is different from all input sequence ids. If a speculative sequence length would exceed the max model length, then no speculation is produced for that sequence. Args: execute_model_req: The execution request. proposals: The speculative proposals to score. Returns: SpeculativeScores: The scores of each speculative token, along with which sequences were ignored during scoring. """ # TODO(cade) perform this on GPU to remove blocking call. proposal_lens_list = proposals.proposal_lens.tolist() proposal_token_ids_list = proposals.proposal_token_ids.tolist() # Filter the list to ignore invalid proposals. proposal_token_ids_list_without_skips = [ proposals for proposals in proposal_token_ids_list if VLLM_INVALID_TOKEN_ID not in proposals ] (spec_indices, non_spec_indices, target_seq_group_metadata_list, num_scoring_tokens) = self._expand_batch( seq_group_metadata_list=execute_model_req.seq_group_metadata_list, proposal_token_ids_list=proposal_token_ids_list_without_skips, proposal_lens_list=proposal_lens_list, ) target_sampler_output = self._scorer_worker.execute_model( execute_model_req=execute_model_req.clone( seq_group_metadata_list=target_seq_group_metadata_list)) assert len(target_sampler_output) == 1, "expected single-step output" target_sampler_output = target_sampler_output[0] if not non_spec_indices: # All sequence groups in batch have spec decoding enabled return self._contract_batch_all_spec( target_sampler_output=target_sampler_output, proposals=proposals, ) else: # Batch has a mix of spec decode enabled and disabled seq groups return self._contract_batch( execute_model_req.seq_group_metadata_list, target_sampler_output=target_sampler_output, proposals=proposals, num_scoring_tokens=num_scoring_tokens, non_spec_indices=non_spec_indices, spec_indices=spec_indices, k=execute_model_req.num_lookahead_slots, ) def _expand_batch( self, seq_group_metadata_list: List[SequenceGroupMetadata], proposal_token_ids_list: List[List[TokenId]], proposal_lens_list: List[int], ) -> Tuple[List[int], List[int], List[SequenceGroupMetadata], int]: """Given the input sequences and potentially multiple corresponding proposal tokens, create a new batch where each sequence has a single query token. """ # vLLM currently only supports proposal lens equal to zero or the batch # proposal len. This adds some complexity (splitting the batch into spec # and non spec sequences) and should be removed in the future. It can be # done by supporting per-sequence proposal lens. (spec_seqs, spec_indices), (non_spec_seqs, non_spec_indices) = \ split_batch_by_proposal_len( seq_group_metadata_list, proposal_lens_list) spec_expanded_seqs = self._create_scoring_model_input( seq_group_metadata_list=spec_seqs, proposal_token_ids=proposal_token_ids_list, # NOTE: We determine the seq ids in the expanded batch using the # full seq_group_metadata_list, instead of only spec_seqs. target_seq_ids_iter=self._create_target_seq_id_iterator( seq_ids=get_all_seq_ids(seq_group_metadata_list)), ) num_scoring_tokens = len(spec_expanded_seqs) # Batch speculative and non-speculative (e.g. chunked prefill) requests # but make sure order is prefill|decode due to backend requirement. target_seq_group_metadata_list = non_spec_seqs + spec_expanded_seqs return (spec_indices, non_spec_indices, target_seq_group_metadata_list, num_scoring_tokens) def _contract_non_speculative( self, scores: SpeculativeScores, seq_group_metadata_list: List[SequenceGroupMetadata], non_spec_indices: List[int], non_spec_outputs: SpeculativeScores, has_prompt_log: bool) -> SpeculativeScores: """ Augment input `scores` with non-speculative requests outputs. This includes decode requests with speculation turned off, as well as prefill requests when `enable_chunked_prefill` is set. For the latter, prefills are further separated into terminal and non-terminal chunks (from which no token is sampled). """ if not non_spec_indices: return scores if has_prompt_log: # When prompt_logprobs is enabled, prefills yield output token # (and respective prob) in the last entry (prompt|out): # [.|.|.|prefill0_out|.|prefill1_out|decode0_out|..]. # With chunked prefill, non-terminal chunks have -1 on each # position: they're still picked, but they're discarded later. seq_meta = seq_group_metadata_list nospec_sizes = torch.tensor([ seq_meta[i].token_chunk_size if seq_meta[i].is_prompt else 1 for i in non_spec_indices ]) nospec_sampled_token_idxs = torch.cumsum(nospec_sizes, 0).add_(-1) else: # In this case only sampled tokens are returned, select all. nospec_sampled_token_idxs = list( range(len(non_spec_outputs.token_ids))) scores.token_ids[non_spec_indices, :1] = \ non_spec_outputs.token_ids[nospec_sampled_token_idxs].unsqueeze(1) scores.probs[non_spec_indices, :1, :] = \ non_spec_outputs.probs[nospec_sampled_token_idxs].unsqueeze(1) scores.logprobs[non_spec_indices, :1, :] = \ non_spec_outputs.logprobs[nospec_sampled_token_idxs].unsqueeze(1) if scores.hidden_states is not None: assert non_spec_outputs.hidden_states is not None scores.hidden_states[non_spec_indices, :1, :] = \ non_spec_outputs.hidden_states[nospec_sampled_token_idxs].unsqueeze(1) return scores def _contract_batch( self, contracted_seq_group_metadata_list: List[SequenceGroupMetadata], target_sampler_output: SamplerOutput, proposals: SpeculativeProposals, num_scoring_tokens: int, non_spec_indices: List[int], spec_indices: List[int], k: int) -> SpeculativeScores: """Contract the expanded batch back into its original size. This maps the scores of speculative tokens back to their original sequences. contracted_bs is the original batch size, and the batch size that the target_sampler_output will be contracted to. """ contracted_bs = len(contracted_seq_group_metadata_list) (target_token_ids, target_probs, target_logprobs, target_hidden_states, non_spec_target_token_ids, non_spec_target_probs, non_spec_target_logprobs, non_spec_target_hidden_states) = self._split_scoring_output( target_sampler_output, num_scoring_tokens) # Map distinct sequences used to score each token # of shape [batch_size * k + 1] back to [batch_size, k + 1]. expanded_batch_size, k = proposals.proposal_token_ids.shape # The number of tokens in the expanded batch used for speculation is # equal to the total expanded batch size minus the number of samples for # non-speculative sequences, prefill chunks with no out tokens included non_spec_expanded_bs = len(non_spec_indices) spec_expanded_bs = expanded_batch_size - non_spec_expanded_bs target_token_ids = target_token_ids.reshape(spec_expanded_bs, k + 1) target_probs = target_probs.reshape(*target_token_ids.shape, self._vocab_size) target_logprobs = target_logprobs.reshape(target_probs.shape) if target_hidden_states is not None: target_hidden_states = target_hidden_states.reshape( *target_token_ids.shape, target_hidden_states.shape[-1]) all_tokens = target_token_ids.new_full(size=(contracted_bs, k + 1), fill_value=-1) all_probs = target_probs.new_zeros(*all_tokens.shape, self._vocab_size) all_logprobs = target_logprobs.new_full(size=all_probs.shape, fill_value=-float("inf")) if target_sampler_output.hidden_states is not None: all_hidden_states = target_hidden_states.new_zeros( size=(contracted_bs, k + 1, target_hidden_states.shape[-1])) else: all_hidden_states = None has_prompt_log = any((sg.sampling_params.prompt_logprobs and sg.sampling_params.prompt_logprobs > 0) for sg in contracted_seq_group_metadata_list) # When prompt logprobs is enabled, lens of returned tensors go from # n_sampled (requests with do_sample=True) to n_prompt+n_prefills. # We adjust stride accordingly to get the generated tokens and # their probs, but pass on prompt_logprobs as is. prompt_logprobs = None if (not self._scorer_worker.model_runner.disable_logprobs\ and has_prompt_log): prompt_logprobs = [ o.prompt_logprobs for o in target_sampler_output.outputs ] elif not has_prompt_log: # When prompt logprobs are not to be returned, # we can ignore non-terminal chunks (no out token). non_spec_indices = [ idx for idx in non_spec_indices if contracted_seq_group_metadata_list[idx].do_sample ] # "Contract" speculative. if spec_indices: all_tokens[spec_indices] = target_token_ids all_probs[spec_indices] = target_probs all_logprobs[spec_indices] = target_logprobs if all_hidden_states is not None: all_hidden_states[spec_indices] = target_hidden_states spec_scores = SpeculativeScores(probs=all_probs, token_ids=all_tokens, logprobs=all_logprobs, hidden_states=all_hidden_states, prompt_logprobs=prompt_logprobs) non_spec_outputs = SpeculativeScores( probs=non_spec_target_probs, token_ids=non_spec_target_token_ids, logprobs=non_spec_target_logprobs, hidden_states=non_spec_target_hidden_states) # Contract remaining nonspec entries based on non_spec_indices, if any. return self._contract_non_speculative( spec_scores, contracted_seq_group_metadata_list, non_spec_indices, non_spec_outputs, has_prompt_log) def _contract_batch_all_spec( self, target_sampler_output: SamplerOutput, proposals: SpeculativeProposals, ) -> SpeculativeScores: """Contract the expanded batch back into its original size. This maps the scores of speculative tokens back to their original sequences. It assumes all sequences in the batch were previously expanded. """ # Map distinct sequences used to score each token # of shape [batch_size * k + 1] back to [batch_size, k + 1]. contracted_bs, k = proposals.proposal_token_ids.shape # Reshape tensors to original batch size target_token_ids = target_sampler_output.sampled_token_ids.reshape( contracted_bs, k + 1) target_probs = target_sampler_output.sampled_token_probs.reshape( *target_token_ids.shape, self._vocab_size) target_logprobs = target_sampler_output.logprobs.reshape( target_probs.shape) target_hidden_states = target_sampler_output.hidden_states if target_hidden_states is not None: target_hidden_states = target_hidden_states.reshape( *target_token_ids.shape, target_hidden_states.shape[-1]) return SpeculativeScores(probs=target_probs, token_ids=target_token_ids, logprobs=target_logprobs, hidden_states=target_hidden_states, prompt_logprobs=None) def _create_scoring_model_input( self, seq_group_metadata_list: List[SequenceGroupMetadata], proposal_token_ids: List[List[TokenId]], # shape: [batch_size, k] target_seq_ids_iter: Iterator[TargetSeqId], ) -> List[SequenceGroupMetadata]: """Given the original input sequences and proposed tokens from the draft model, create a list of target sequences that can be used for scoring. target_seq_ids_iter provides sequence ids for the expanded batch, fulfilling the requirement that no seq id in the expanded batch is equal to the seq id in the original batch. """ if not seq_group_metadata_list: return [] target_seq_group_metadata = list( chain.from_iterable( self._create_target_seq_group_metadata( seq_group_metadata, proposal_token_ids, i, target_seq_ids_iter, ) for i, seq_group_metadata in enumerate( seq_group_metadata_list))) return target_seq_group_metadata def _create_target_seq_group_metadata( self, input_seq_group_metadata: SequenceGroupMetadata, proposal_token_ids: List[List[TokenId]], # shape: [batch_size, k] batch_index: int, target_seq_ids_iter: Iterator[TargetSeqId], ) -> List[SequenceGroupMetadata]: """Given an input sequence group metadata and a list of draft tokens, create a list of target SequenceGroupMetadata, one for each token id that needs to be scored. Naive speculative decoding requires K target model scores, one for each draft model token. However one can add a bonus token such that if each token is accepted, then a final token may be sampled from the model. This function creates K+1 target SequenceGroupMetadata to take advantage of the bonus token. """ assert len(input_seq_group_metadata.seq_data) == 1, ( "Beam search " "not supported in speculative decoding") input_seq_id = next(iter(input_seq_group_metadata.seq_data.keys())) token_ids_to_score = self._get_token_ids_to_score( proposal_token_ids[batch_index]) sampling_params = input_seq_group_metadata.sampling_params target_seq_group_metadata_list: List[SequenceGroupMetadata] = [] for i, token_ids in enumerate(token_ids_to_score): target_seq_group_metadata_list.append( self._create_single_target_seq_group_metadata( input_seq_group_metadata, input_seq_id, next(target_seq_ids_iter), token_ids, sampling_params=sampling_params, )) return target_seq_group_metadata_list @staticmethod def _create_single_target_seq_group_metadata( seq_group_metadata: SequenceGroupMetadata, seq_id: SeqId, target_seq_id: TargetSeqId, token_ids: List[TokenId], sampling_params: SamplingParams, ) -> SequenceGroupMetadata: """Create a single target SequenceGroupMetadata. Args: seq_group_metadata: The metadata for the input sequence. seq_id: The input sequence ID. target_seq_id: The corresponding target sequence ID. token_ids: The list of token ids that are to be appended to the input sequence. """ seq_data = seq_group_metadata.seq_data[seq_id] prompt_token_ids = seq_data.prompt_token_ids_array new_output_token_ids = [*seq_data.get_output_token_ids(), *token_ids] mrope_position_delta = seq_data.mrope_position_delta new_seq_data_dict = { target_seq_id: SequenceData( prompt_token_ids, _output_token_ids=array(VLLM_TOKEN_ID_ARRAY_TYPE, new_output_token_ids), ), } # This is a hack. Technically, spec decoding should compute # num_lookahead slots at one shot, but instead, it expands the batch # and evaluate one by one right now. context_len is seq_len - 1 because # the kv cache is filled by a previous batch in the batch expansion. for data in new_seq_data_dict.values(): data.update_num_computed_tokens(data.get_len() - 1) data.mrope_position_delta = mrope_position_delta return SequenceGroupMetadata( request_id=seq_group_metadata.request_id, is_prompt=seq_group_metadata.is_prompt, seq_data=new_seq_data_dict, sampling_params=sampling_params, block_tables={ target_seq_id: seq_group_metadata.block_tables[seq_id], }, lora_request=None, token_chunk_size=1, ) @staticmethod def _split_scoring_output( sampler_output: SamplerOutput, num_scoring_tokens: int ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, Optional[torch.Tensor], torch.Tensor, torch.Tensor, torch.Tensor, Optional[torch.Tensor]]: """Split the target model output into speculative and non-speculative output. """ # vLLM currently only supports proposal lens equal to zero or the batch # proposal len. This adds some complexity (splitting the batch into spec # and non spec sequences) and should be removed in the future. It can be # done by supporting per-sequence proposal lens. # # First samples are non-speculative, latter samples are from speculative # scoring (prefill|decode order). split_sizes = (sampler_output.sampled_token_ids.numel() - num_scoring_tokens, num_scoring_tokens) (non_spec_probs, spec_probs) = sampler_output.sampled_token_probs.split(split_sizes) (non_spec_sampled_tokens, spec_sampled_tokens ) = sampler_output.sampled_token_ids.flatten().split(split_sizes) (non_spec_logprobs, spec_logprobs) = sampler_output.logprobs.split(split_sizes) if sampler_output.hidden_states is not None: (non_spec_hidden_states, spec_hidden_states ) = sampler_output.hidden_states.split(split_sizes) else: non_spec_hidden_states, spec_hidden_states = None, None return (spec_sampled_tokens, spec_probs, spec_logprobs, spec_hidden_states, non_spec_sampled_tokens, non_spec_probs, non_spec_logprobs, non_spec_hidden_states) @staticmethod def _create_target_seq_id_iterator( seq_ids: List[SeqId]) -> Iterator[TargetSeqId]: """Create an iterator for creating target sequence ids. Target sequence ids are distinct from sequence ids because we create a distinct target sequence id for each proposal token to be scored. This implementation increments a counter starting at 1 + max of all provided input sequence ids. """ return count(start=max(seq_ids) + 1) @staticmethod def _get_token_ids_to_score( full_spec_token_ids: List[TokenId] # shape: [k] ) -> List[List[TokenId]]: """Given an int tensor of proposal token ids, return a list of token ids that should be scored. Returns k+1 output lists. The additional one is used for generating the bonus token. Example: Input: [0, 1, 2, 3] (k=4) Output: (k+1 lists) [] [0] [0, 1] [0, 1, 2] [0, 1, 2, 3] """ empty_token_ids: List[TokenId] = [] token_ids_to_score = [empty_token_ids] token_ids_to_score.extend(full_spec_token_ids[:i + 1] for i in range(len(full_spec_token_ids))) return token_ids_to_score