from functools import cached_property from typing import Dict, List, Optional, Tuple import torch from loguru import logger from aphrodite.common.config import SchedulerConfig from aphrodite.common.sequence import (Logprob, SamplerOutput, SequenceGroupMetadata, SequenceGroupOutput, SequenceOutput) from aphrodite.modeling.layers.rejection import RejectionSampler from aphrodite.spec_decode.batch_expansion import BatchExpansionTop1Scorer from aphrodite.spec_decode.interfaces import (SpeculativeProposals, SpeculativeScorer, SpeculativeScores) from aphrodite.spec_decode.metrics import AsyncMetricsCollector from aphrodite.spec_decode.multi_step_worker import MultiStepWorker from aphrodite.spec_decode.ngram_worker import NGramWorker from aphrodite.spec_decode.util import (get_all_seq_ids, nvtx_range, split_batch_by_proposal_len) from aphrodite.task_handler.worker_base import (LoraNotSupportedWorkerBase, WorkerBase) class SpecDecodeWorker(LoraNotSupportedWorkerBase): """Worker which implements speculative decoding. Speculative decoding reduces decoding per-token latency by using a proposal method, such as a small draft model, to speculate ahead of a larger LLM. The probabilities of the speculative tokens are then determined by the larger LLM, after which some verification routine determines which (if any) of the speculative tokens are accepted by the larger LLM. The current implementation has the following limitations: * Only draft-model proposal is implemented (contributions for more forms are welcome!). * Only top-1 proposal and scoring are implemented. Tree-attention is left as future work. * Only lossless rejection sampling is supported. Contributions adding lossy verification routines are welcome (e.g. Medusa's typical acceptance). * All sequences in a batch must have the same proposal length, or zero. This can be improved by having per-sequence speculation in the future. * The scoring forward pass is done without an MQA kernel, which is suboptimal especially as the batch size, proposal length, and sequence lengths grow. Contributions to add a MQA scoring are welcome once correctness tests pass. """ @classmethod def create_worker( cls, scorer_worker: WorkerBase, speculative_config: SchedulerConfig, ) -> "SpecDecodeWorker": if speculative_config.ngram_prompt_lookup_max > 0: proposer_worker = NGramWorker( model_config=speculative_config.draft_model_config, parallel_config=speculative_config.draft_parallel_config, scheduler_config=scorer_worker.scheduler_config, device_config=scorer_worker.device_config, cache_config=scorer_worker.cache_config, local_rank=0, rank=0, distributed_init_method=scorer_worker.distributed_init_method, ) proposer_worker.set_ngram_window_size( speculative_config.ngram_prompt_lookup_min, speculative_config.ngram_prompt_lookup_max) else: proposer_worker = MultiStepWorker( model_config=speculative_config.draft_model_config, parallel_config=speculative_config.draft_parallel_config, scheduler_config=scorer_worker.scheduler_config, device_config=scorer_worker.device_config, cache_config=scorer_worker.cache_config, local_rank=0, rank=0, distributed_init_method=scorer_worker.distributed_init_method, lora_config=scorer_worker.lora_config, vision_language_config=scorer_worker.vision_language_config, is_driver_worker=True, ) return SpecDecodeWorker( proposer_worker, scorer_worker, # TODO: disable strict mode for speedup. rejection_sampler=RejectionSampler(strict_mode=True), ) def __init__( self, proposer_worker: WorkerBase, scorer_worker: WorkerBase, rejection_sampler: RejectionSampler, metrics_collector: Optional[AsyncMetricsCollector] = None, ): """ Create a SpecDecodeWorker. Args: proposer_worker: A worker that can produce speculative tokens for sequences. scorer_worker: A worker that produces probabilities of speculative tokens according to some base model. Typically a vanilla Aphrodite Worker. rejection_sampler: A Torch module used to perform modified rejection sampling for speculative decoding. metrics_collector: Helper class for collecting metrics; can be set for testing purposes. """ self.proposer_worker = proposer_worker self.scorer_worker = scorer_worker self.rejection_sampler = rejection_sampler self._metrics = AsyncMetricsCollector( rejection_sampler ) if metrics_collector is None else metrics_collector self.probs_dtype = self.rejection_sampler.probs_dtype self.token_id_dtype = self.rejection_sampler.token_id_dtype # Lazy initiazliation. self.scorer: SpeculativeScorer def init_device(self) -> None: """Initialize both scorer and proposer models. """ # The scorer worker model is initialized first in case the proposer # model has a smaller TP degree than the target worker. self.scorer_worker.init_device() self.proposer_worker.init_device() # NOTE: load_model is not part of the WorkerBase interface. self.scorer_worker.load_model() self.proposer_worker.load_model() self._metrics.init_gpu_tensors(self.rank) self.rejection_sampler.init_gpu_tensors(self.rank) self.scorer = BatchExpansionTop1Scorer( scorer_worker=self.scorer_worker, device=self.device, vocab_size=self._vocab_size) self._configure_model_sampler_for_spec_decode() def _configure_model_sampler_for_spec_decode(self): """Configure model sampler to emit GPU tensors. This allows spec decode to keep data on device without transferring to CPU and serializing, which significantly reduces overhead of rejection sampling. NOTE: This breaks abstraction boundaries pretty badly. The better design is to have the "move to CPU and serialize" sampling decision be done outside of the model/sampler; this way the "last-mile" worker object which interfaces with the scheduler can serialize and incur the performance hit as necessary. This allows us to run the worker several iterations in a row without incurring the "move to CPU and serialize" performance penalty. Since this requires a large change to Aphrodite, we defer it to later and temporarily accept this broken abstraction boundary. NOTE: This will require a special check if the proposer worker does not have a sampler (e.g. ngram speculation). """ (self.scorer_worker.model_runner.model.sampler.include_gpu_probs_tensor ) = True self.proposer_worker.set_include_gpu_probs_tensor() def determine_num_available_blocks(self) -> Tuple[int, int]: """Determine the number of cache blocks to use. This is done by profiling the scorer model (which is typically the larger of the two). Then the total memory which would be used by the scorer cache is divided evenly between the proposer and scorer model KV, such that the number of blocks is equal in both KV caches. """ num_gpu_blocks, num_cpu_blocks = ( self.scorer_worker.determine_num_available_blocks()) scorer_cache_block_size_bytes = ( self.scorer_worker.get_cache_block_size_bytes()) proposer_cache_block_size_bytes = ( self.proposer_worker.get_cache_block_size_bytes()) new_num_gpu_blocks = split_num_cache_blocks_evenly( scorer_cache_block_size_bytes, proposer_cache_block_size_bytes, num_gpu_blocks) return new_num_gpu_blocks, num_cpu_blocks def initialize_cache(self, num_gpu_blocks: int, num_cpu_blocks: int) -> None: """Initialize the cache engine of the scorer and proposer workers. """ self.scorer_worker.initialize_cache(num_gpu_blocks=num_gpu_blocks, num_cpu_blocks=num_cpu_blocks) self.proposer_worker.initialize_cache(num_gpu_blocks=num_gpu_blocks, num_cpu_blocks=num_cpu_blocks) @torch.inference_mode() def execute_model( self, seq_group_metadata_list: List[SequenceGroupMetadata], blocks_to_swap_in: Optional[Dict[int, int]], blocks_to_swap_out: Optional[Dict[int, int]], blocks_to_copy: Optional[Dict[int, List[int]]], num_lookahead_slots: int, ) -> List[SamplerOutput]: """Perform speculative decoding on the input batch. """ assert seq_group_metadata_list is not None, ( "speculative decoding " "requires non-None seq_group_metadata_list") logger.debug( f"spec_decode_worker.execute_model {num_lookahead_slots=}") # If no spec tokens, call the proposer and scorer workers normally. # Used for prefill. if num_lookahead_slots == 0 or len(seq_group_metadata_list) == 0: return self._run_no_spec( seq_group_metadata_list=seq_group_metadata_list, blocks_to_swap_in=blocks_to_swap_in, blocks_to_swap_out=blocks_to_swap_out, blocks_to_copy=blocks_to_copy, ) return self._run_speculative_decoding_step( seq_group_metadata_list=seq_group_metadata_list, blocks_to_swap_in=blocks_to_swap_in, blocks_to_swap_out=blocks_to_swap_out, blocks_to_copy=blocks_to_copy, k=num_lookahead_slots, ) @nvtx_range("spec_decode_worker._run_no_spec") def _run_no_spec( self, seq_group_metadata_list: List[SequenceGroupMetadata], blocks_to_swap_in: Optional[Dict[int, int]], blocks_to_swap_out: Optional[Dict[int, int]], blocks_to_copy: Optional[Dict[int, List[int]]], ) -> List[SamplerOutput]: """Run a prefill step, without any speculation. The input is sent to the proposer and scorer model so that the KV cache is consistent between the two. """ logger.debug("run proposer worker no spec") self.proposer_worker.execute_model( seq_group_metadata_list=seq_group_metadata_list, blocks_to_swap_in=blocks_to_swap_in, blocks_to_swap_out=blocks_to_swap_out, blocks_to_copy=blocks_to_copy, ) logger.debug("run target worker no spec") sampler_output = self.scorer_worker.execute_model( seq_group_metadata_list=seq_group_metadata_list, blocks_to_swap_in=blocks_to_swap_in, blocks_to_swap_out=blocks_to_swap_out, blocks_to_copy=blocks_to_copy, ) assert len(sampler_output) == 1 sampler_output = sampler_output[0] # Clear device tensors from sampler output. This reduces communication # overhead when the engine runs in a different process than the workers. sampler_output.probs = None sampler_output.sampled_tokens = None return [sampler_output] @nvtx_range("spec_decode_worker._run_speculative_decoding_step") def _run_speculative_decoding_step( self, seq_group_metadata_list: List[SequenceGroupMetadata], blocks_to_swap_in: Optional[Dict[int, int]], blocks_to_swap_out: Optional[Dict[int, int]], blocks_to_copy: Optional[Dict[int, List[int]]], k: int, ) -> List[SamplerOutput]: """Execute a single step of speculative decoding. This invokes the proposer worker to get k speculative tokens for each sequence, then scores each speculative token using the scoring worker. Returns a list of SamplerOutput, each containing a single token per sequence. """ logger.debug("get spec proposals") # Generate proposals using draft worker. assert blocks_to_swap_in is not None assert blocks_to_swap_out is not None assert blocks_to_copy is not None proposals = self.proposer_worker.get_spec_proposals( seq_group_metadata_list, blocks_to_swap_in, blocks_to_swap_out, blocks_to_copy, k) logger.debug("score proposals") proposal_scores = self.scorer.score_proposals( seq_group_metadata_list, blocks_to_swap_in, blocks_to_swap_out, blocks_to_copy, k, proposals, ) logger.debug("verify proposals") accepted_token_ids = self._verify_tokens(seq_group_metadata_list, proposal_scores, proposals, k) logger.debug("create output list") return self._create_output_sampler_list(seq_group_metadata_list, accepted_token_ids, k) @nvtx_range("spec_decode_worker._verify_tokens") def _verify_tokens( self, seq_group_metadata_list: List[SequenceGroupMetadata], proposal_scores: SpeculativeScores, proposals: SpeculativeProposals, max_proposal_len: int, ) -> torch.Tensor: """Determine which speculative tokens are accepted using the probabilities of each token according to the proposer and scorer models. """ proposal_lens_list = proposals.proposal_lens.tolist() # Aphrodite 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_indices = split_batch_by_proposal_len( seq_group_metadata_list, proposal_lens_list, select_proposal_len_zero=False) _, non_spec_indices = split_batch_by_proposal_len( seq_group_metadata_list, proposal_lens_list, select_proposal_len_zero=True) original_indices = spec_indices + non_spec_indices # Get probabilities of target model, excluding bonus token. proposal_verifier_probs = proposal_scores.probs[spec_indices, :-1] # Get non-speculative sampled tokens from target model. non_spec_token_ids = proposal_scores.token_ids[non_spec_indices] # Get bonus tokens from target model. bonus_token_ids = proposal_scores.token_ids[spec_indices, -1:] # Get probabilities according to proposal method. proposal_probs = proposals.proposal_probs[spec_indices] # Get proposed tokens. proposal_token_ids = proposals.proposal_token_ids[spec_indices] accepted_token_ids = self.rejection_sampler( target_probs=proposal_verifier_probs, bonus_token_ids=bonus_token_ids, draft_probs=proposal_probs, draft_token_ids=proposal_token_ids, ) # Append output tokens from non-speculative sequences to # the accepted token ids tensor. non_spec_token_ids = non_spec_token_ids.expand(-1, max_proposal_len + 1).clone() non_spec_token_ids[:, 1:] = -1 accepted_token_ids = torch.cat( [accepted_token_ids, non_spec_token_ids]) # Rearrange so that results are in the order of the original seq group # metadata. accepted_token_ids[original_indices] = accepted_token_ids.clone() return accepted_token_ids def _create_output_sampler_list( self, seq_group_metadata_list: List[SequenceGroupMetadata], accepted_token_ids: torch.Tensor, # shape: [batch_size, k+1] k: int, ) -> List[SamplerOutput]: """Given the accepted token ids, create a list of SamplerOutput. The output is padded with -1 tokens such that each sequence has the same number of outputs. """ seq_ids = get_all_seq_ids(seq_group_metadata_list) # shape: [k+1, batch_size] accepted_token_ids_by_step = accepted_token_ids.transpose(0, 1).tolist() sampler_output_list = [] for token_ids_by_step in accepted_token_ids_by_step: if all(token_id == -1 for token_id in token_ids_by_step): break step_output_token_ids = [] for token_id, seq_id in zip(token_ids_by_step, seq_ids): step_output_token_ids.append( SequenceGroupOutput( samples=[ SequenceOutput( parent_seq_id=seq_id, output_token=token_id, # TODO Add verifier logprobs. logprobs={token_id: Logprob(0.0)}, persistent_data={}, ) ], prompt_logprobs=None, )) sampler_output_list.append( SamplerOutput(outputs=step_output_token_ids)) maybe_rejsample_metrics = ( self._metrics.maybe_collect_rejsample_metrics(k)) if maybe_rejsample_metrics is not None: sampler_output_list[ 0].spec_decode_worker_metrics = maybe_rejsample_metrics return sampler_output_list @cached_property def _vocab_size(self) -> int: """Get the vocab size of the model and make sure it's consistent between draft and target workers. """ vocab_sizes = [ worker.vocab_size for worker in [self.proposer_worker, self.scorer_worker] ] assert all(vocab_sizes[0] == vocab_size for vocab_size in vocab_sizes) return vocab_sizes[0] @property def rank(self): return self.scorer_worker.rank @property def device(self): return self.scorer_worker.device def get_cache_block_size_bytes(self): """Return the size of a cache block in bytes. This function is only used to compose workers within a SpecDecodeWorker. We leave composing a SpecDecodeWorker within a SpecDecodeWorker undefined for now, although it could be implemented in the future. See https://arxiv.org/abs/2308.04623. """ raise NotImplementedError def split_num_cache_blocks_evenly(scorer_cache_block_size_bytes: int, proposer_cache_block_size_bytes: int, total_num_gpu_blocks: int) -> int: """Given total_num_gpu_blocks, the number of GPU blocks that could be allocate to the target model, this function calculates how many blocks should be given to the draft and target model. Note that usually the block size, in bytes, of each model is different, as it's a function of number of KV/layer, number of heads, and hidden dimension size. Since the target and draft models allocate the same number of blocks, we simply calculate the number of blocks where if allocated by both models, the total memory usage from KV cache is no larger than the number of blocks allocatable by the target model alone. """ new_num_gpu_blocks = int( total_num_gpu_blocks * scorer_cache_block_size_bytes / (proposer_cache_block_size_bytes + scorer_cache_block_size_bytes)) return new_num_gpu_blocks