"""Sequence and its related classes.""" import copy import enum import math from abc import ABC, abstractmethod from array import array from collections import defaultdict from dataclasses import dataclass, field from typing import TYPE_CHECKING, Dict, List, Optional, Set, Tuple, Union import torch from aphrodite.common.pooling_params import PoolingParams from aphrodite.common.sampling_params import SamplingParams from aphrodite.lora.request import LoRARequest from aphrodite.prompt_adapter.request import PromptAdapterRequest if TYPE_CHECKING: from aphrodite.inputs import LLMInputs from aphrodite.multimodal import MultiModalDataDict from aphrodite.spec_decode.metrics import SpecDecodeWorkerMetrics @dataclass class Logprob: """Infos for supporting OpenAI compatible logprobs and token ranks. Attributes: logprob: The logprob of chosen token rank: The vocab rank of chosen token (>=1) decoded_token: The decoded chosen token index """ logprob: float rank: Optional[int] = None decoded_token: Optional[str] = None # {token_id -> logprob} per each sequence group. None if the corresponding # sequence group doesn't require prompt logprob. PromptLogprobs = List[Optional[Dict[int, Logprob]]] # {token_id -> logprob} for each sequence group. SampleLogprobs = List[Dict[int, Logprob]] class SequenceStatus(enum.IntEnum): """Status of a sequence.""" WAITING = 0 RUNNING = 1 SWAPPED = 2 # Note: anything after SWAPPED (2) will be considered # as a finished status. FINISHED_STOPPED = 3 FINISHED_LENGTH_CAPPED = 4 FINISHED_ABORTED = 5 FINISHED_IGNORED = 6 @staticmethod def is_finished(status: "SequenceStatus") -> bool: return status > SequenceStatus.SWAPPED @staticmethod def get_finished_reason(status: "SequenceStatus") -> Union[str, None]: if status == SequenceStatus.FINISHED_STOPPED: finish_reason = "stop" elif status == SequenceStatus.FINISHED_LENGTH_CAPPED: finish_reason = "length" elif status == SequenceStatus.FINISHED_ABORTED: finish_reason = "abort" elif status == SequenceStatus.FINISHED_IGNORED: # The ignored sequences are the sequences whose prompt lengths # are longer than the model's length cap. Therefore, the stop # reason should also be "length" as in OpenAI API. finish_reason = "length" else: finish_reason = None return finish_reason class SequenceStage(enum.Enum): PREFILL = enum.auto() DECODE = enum.auto() @dataclass class RequestMetrics: """Metrics associated with a request. Attributes: arrival_time: The time when the request arrived. first_scheduled_time: The time when the request was first scheduled. first_token_time: The time when the first token was generated. time_in_queue: The time the request spent in the queue. finished_time: The time when the request was finished. """ arrival_time: float last_token_time: float first_scheduled_time: Optional[float] first_token_time: Optional[float] time_in_queue: Optional[float] finished_time: Optional[float] = None class SequenceData: """Data associated with a sequence. Args: prompt_token_ids: The token IDs of the prompt. output_token_ids: The token IDs of the output. Set to an empty list if None. Attributes: prompt_token_ids: The token IDs of the prompt. output_token_ids: The token IDs of the output. cumulative_logprob: The cumulative log probability of the output. """ def __init__( self, prompt_token_ids: List[int], output_token_ids: Optional[List[int]] = None, ) -> None: self._prompt_token_ids = array('l', prompt_token_ids) self._prompt_token_ids_tuple: Tuple[int, ...] = tuple(prompt_token_ids) self._output_token_ids = array( 'l', output_token_ids if output_token_ids is not None else []) self.cumulative_logprob = 0.0 # The number of tokens that are computed (that run against the model). self._num_computed_tokens = 0 self._stage: SequenceStage = SequenceStage.PREFILL self._update_cached_all_tokens() def _update_cached_all_tokens(self): self._cached_all_token_ids: List[int] = list(self._prompt_token_ids + self._output_token_ids) @property def prompt_token_ids(self) -> Tuple[int, ...]: return self._prompt_token_ids_tuple @prompt_token_ids.setter def prompt_token_ids(self, new_prompt_token_ids) -> None: self._prompt_token_ids = array('l', new_prompt_token_ids) self._prompt_token_ids_tuple = tuple(new_prompt_token_ids) self._update_cached_all_tokens() @property def prompt_token_ids_array(self) -> array: return self._prompt_token_ids @property def output_token_ids(self) -> Tuple[int, ...]: return tuple(self._output_token_ids) @output_token_ids.setter def output_token_ids(self, new_output_token_ids) -> None: self._output_token_ids = array('l', new_output_token_ids) self._update_cached_all_tokens() @property def output_token_ids_array(self) -> array: return self._output_token_ids def append_token_id(self, token_id: int, logprob: float) -> None: self._output_token_ids.append(token_id) self._cached_all_token_ids.append(token_id) self.cumulative_logprob += logprob def get_len(self) -> int: return len(self._output_token_ids) + len(self._prompt_token_ids) def get_prompt_len(self) -> int: return len(self._prompt_token_ids) def get_output_len(self) -> int: return len(self._output_token_ids) def get_token_ids(self) -> List[int]: return self._cached_all_token_ids def get_prefix_token_ids( self, num_tokens: int ) -> Tuple[Tuple[int, ...], Optional[Tuple[int, ...]]]: """Get prefix tokens, and make the return value hashable""" prompt_length = self.get_prompt_len() if num_tokens > prompt_length: return (self._prompt_token_ids_tuple, tuple(self._output_token_ids[:num_tokens - prompt_length])) else: return (self._prompt_token_ids_tuple[:num_tokens], None) def get_num_computed_tokens(self) -> int: """Return the number of prefill tokens that are already computed.""" return self._num_computed_tokens def update_num_computed_tokens(self, num_new_computed_tokens: int): """Update number of tokens computed so far.""" self._num_computed_tokens += num_new_computed_tokens assert self._num_computed_tokens <= self.get_len(), ( self._num_computed_tokens, self.get_len()) # If all tokens are computed, it means it is in decoding phase. if self.get_num_uncomputed_tokens() == 0: self._stage = SequenceStage.DECODE def reset_state_for_recompute(self) -> None: """Reset the number of computed tokens from this sequence. It is supposed to be called when a sequence needs to be started from the beginning again (e.g., sequence is preempted). """ self._num_computed_tokens = 0 self._stage = SequenceStage.PREFILL def get_num_uncomputed_tokens(self) -> int: """Return the number of prefill tokens that are not computed.""" # we use `get_len()` which includes prompt_len + output_len instead # of prompt_len here. This is because during recompute we need to # prefill for both prompt and output. return self.get_len() - self.get_num_computed_tokens() def get_last_token_id(self) -> int: if not self._output_token_ids: return self._prompt_token_ids[-1] return self._output_token_ids[-1] def get_prompt_token_ids(self) -> Tuple[int, ...]: return self.prompt_token_ids def get_output_token_ids(self) -> Tuple[int, ...]: return self.output_token_ids @property def stage(self) -> SequenceStage: return self._stage def __repr__(self) -> str: return (f"SequenceData(" f"prompt_token_ids={self._prompt_token_ids}, " f"output_token_ids={self._output_token_ids}, " f"cumulative_logprob={self.cumulative_logprob})") class Sequence: """Stores the data, status, and block information of a sequence. Args: seq_id: The ID of the sequence. inputs: The inputs of the sequence. block_size: The block size of the sequence. Should be the same as the block size used by the block manager and cache engine. lora_request: LoRA request. prompt_adapter_request: Prompt adapter request. """ def __init__( self, seq_id: int, inputs: "LLMInputs", block_size: int, eos_token_id: Optional[int] = None, lora_request: Optional[LoRARequest] = None, prompt_adapter_request: Optional[PromptAdapterRequest] = None ) -> None: self.seq_id = seq_id self.inputs = inputs self.block_size = block_size self.eos_token_id = eos_token_id self.lora_request = lora_request self.prompt_adapter_request = prompt_adapter_request self.data = SequenceData(self.prompt_token_ids) self.output_logprobs: SampleLogprobs = [] self.output_text = "" self.status = SequenceStatus.WAITING self.stop_reason: Union[int, str, None] = None # Used for incremental detokenization self.prefix_offset = 0 self.read_offset = 0 # Input + output tokens self.tokens: Optional[List[str]] = None @property def n_blocks(self) -> int: return math.ceil(self.get_len() / self.block_size) @property def prompt(self) -> Optional[str]: return self.inputs.get("prompt") @property def prompt_token_ids(self) -> List[int]: return self.inputs["prompt_token_ids"] @property def multi_modal_data(self) -> Optional["MultiModalDataDict"]: return self.inputs.get("multi_modal_data") @property def lora_int_id(self) -> int: return self.lora_request.lora_int_id if self.lora_request else 0 @property def prompt_adapter_id(self) -> int: return self.prompt_adapter_request.prompt_adapter_id \ if self.prompt_adapter_request else 0 def get_output_text_to_return(self, buffer_length: int): # We return the full output text if the sequence is finished. truncate = buffer_length and not self.is_finished() return self.output_text[:-buffer_length] if truncate else ( self.output_text) def hash_of_block(self, logical_idx: int) -> int: # TODO This can produce incorrect hash when block size > prompt size # Compute the number of tokens in the sequence # TODO: The current hashing function is O(L^2). We should optimize # this in the future. num_tokens = self.num_hashed_tokens_of_block(logical_idx) hashed_tokens = self.data.get_prefix_token_ids(num_tokens) return hash((hashed_tokens, self.lora_int_id)) def num_hashed_tokens_of_block(self, logical_idx: int): return logical_idx * self.block_size + self.block_size def reset_state_for_recompute(self): """Reset the sequence states for recomputation.""" self.data.reset_state_for_recompute() def append_token_id( self, token_id: int, logprobs: Dict[int, Logprob], ) -> None: assert token_id in logprobs self.output_logprobs.append(logprobs) self.data.append_token_id(token_id, logprobs[token_id].logprob) def get_len(self) -> int: return self.data.get_len() def get_prompt_len(self) -> int: return self.data.get_prompt_len() def get_output_len(self) -> int: return self.data.get_output_len() def get_token_ids(self) -> List[int]: return self.data.get_token_ids() def get_prompt_token_ids(self) -> Tuple[int, ...]: return self.data.get_prompt_token_ids() def get_last_token_id(self) -> int: return self.data.get_last_token_id() def get_output_token_ids(self) -> Tuple[int, ...]: return self.data.get_output_token_ids() def get_cumulative_logprob(self) -> float: return self.data.cumulative_logprob def get_beam_search_score(self, length_penalty: float = 1.0, seq_len: Optional[int] = None, eos_token_id: Optional[int] = None) -> float: """Calculate the beam search score with length penalty. Adapted from https://github.com/huggingface/transformers/blob/ccb92be23def445f2afdea94c31286f84b89eb5b/src/transformers/generation/beam_search.py#L938 """ if seq_len is None: seq_len = self.get_len() # NOTE: HF implementation does not count the EOS token # towards the length, we align with that here for testing. if (eos_token_id is not None and self.get_last_token_id() == eos_token_id): seq_len -= 1 return self.get_cumulative_logprob() / (seq_len**length_penalty) def is_finished(self) -> bool: return SequenceStatus.is_finished(self.status) def fork(self, new_seq_id: int) -> "Sequence": new_seq = copy.deepcopy(self) new_seq.seq_id = new_seq_id return new_seq def get_num_new_tokens(self) -> int: """Get the number of new tokens to be computed. Returns: The new number of tokens to be computed. I.e., 1 for decode, or the remaining prompt size for prefill. """ if self.data.stage == SequenceStage.DECODE: return 1 return self.data.get_num_uncomputed_tokens() def is_prefill(self) -> bool: return self.data.stage == SequenceStage.PREFILL def __repr__(self) -> str: return (f"Sequence(seq_id={self.seq_id}, " f"status={self.status.name}, " f"num_blocks={self.n_blocks}, ") class SequenceGroup: """A group of sequences that are generated from the same prompt. Args: request_id: The ID of the request. seqs: The list of sequences. sampling_params: The sampling parameters used to generate the outputs. arrival_time: The arrival time of the request. lora_request: LoRA request. embeddings: The embeddings vectors of the prompt of the sequence group for an embedding model. pooling_params: The pooling parameters used to generate the pooling for an embedding model. encoder_seq: Optional, the single encoder sequence. Should be None unless you are working with an encoder/decoder model. prompt_adapter_request: Prompt adapter request. """ def __init__( self, request_id: str, seqs: List[Sequence], arrival_time: float, sampling_params: Optional[SamplingParams] = None, lora_request: Optional[LoRARequest] = None, embeddings: Optional[List[float]] = None, pooling_params: Optional[PoolingParams] = None, encoder_seq: Optional[Sequence] = None, prompt_adapter_request: Optional[PromptAdapterRequest] = None, ) -> None: self.request_id = request_id self.seqs = seqs self.seqs_dict = {seq.seq_id: seq for seq in seqs} self.sampling_params = sampling_params self.metrics = RequestMetrics(arrival_time=arrival_time, last_token_time=arrival_time, first_scheduled_time=None, first_token_time=None, time_in_queue=None) self.lora_request = lora_request self.prompt_logprobs: Optional[PromptLogprobs] = None self.embeddings = embeddings self.pooling_params = pooling_params self.prompt_adapter_request = prompt_adapter_request self.encoder_seq = encoder_seq @property def prompt(self) -> Optional[str]: # All sequences in the group should have the same prompt. # We use the prompt of an arbitrary sequence. return self.seqs[0].prompt @property def prompt_token_ids(self) -> List[int]: # All sequences in the group should have the same prompt. # We use the prompt of an arbitrary sequence. return self.seqs[0].prompt_token_ids @property def multi_modal_data(self) -> "MultiModalDataDict": # All sequences in the group should have the same multi-modal data. # We use the multi-modal data of an arbitrary sequence. return self.seqs[0].multi_modal_data @property def lora_int_id(self) -> int: return self.lora_request.lora_int_id if self.lora_request else 0 @property def prompt_adapter_id(self) -> int: return self.prompt_adapter_request.prompt_adapter_id \ if self.prompt_adapter_request else 0 @property def prompt_adapter_num_virtual_tokens(self) -> int: return self.prompt_adapter_request.prompt_adapter_num_virtual_tokens\ if self.prompt_adapter_request else 0 def get_last_latency(self, now: float) -> Optional[float]: """Sets the last token time for Request level timings.""" # If still in prefill phase, raise Error. if self.is_prefill(): raise ValueError( "seq_group.get_last_latency() should not be called " "if the seq_group is in prefill phase.") # Otherwise return token latency. latency = now - self.metrics.last_token_time self.metrics.last_token_time = now return latency def maybe_set_first_token_time(self, time: float) -> None: """Sets the first token time for Request level timings.""" # NOTE: in a case where a sequence_group is swapped and # recomputed, the time between iterations is counted # in TPOT, rather than recalculating TTFT (since from the ) # POV of the user, there is simply a long generation delay. if (self.metrics.first_token_time is None and self.seqs[0].get_output_len() == 1): self.metrics.first_token_time = time def maybe_set_first_scheduled_time(self, time: float) -> None: """Sets the first scheduled time and time in queue for Request level timings.""" if self.metrics.first_scheduled_time is None: self.metrics.first_scheduled_time = time self.metrics.time_in_queue = time - self.metrics.arrival_time def set_finished_time(self, time: Optional[float]) -> None: """Sets the finished time for Request level timings.""" self.metrics.finished_time = time def get_max_num_running_seqs(self) -> int: """The maximum number of sequences running in parallel in the remaining lifetime of the request.""" if self.sampling_params and self.sampling_params.use_beam_search: # For beam search, maximally there will always be `best_of` beam # candidates running in the future. return self.sampling_params.best_of else: if (self.sampling_params and self.sampling_params.best_of > self.num_seqs()): # At prompt stage, the sequence group is not yet filled up # and only have one sequence running. However, in the # generation stage, we will have `best_of` sequences running. return self.sampling_params.best_of # At sampling stages, return the number of actual sequences # that are not finished yet. return self.num_unfinished_seqs() def get_seqs( self, status: Optional[SequenceStatus] = None, ) -> List[Sequence]: if status is None: return self.seqs return [seq for seq in self.seqs if seq.status == status] def is_encoder_decoder(self) -> bool: return self.encoder_seq is not None def get_encoder_seq(self) -> Optional[Sequence]: return self.encoder_seq def get_unfinished_seqs(self) -> List[Sequence]: return [seq for seq in self.seqs if not seq.is_finished()] def get_finished_seqs(self) -> List[Sequence]: return [seq for seq in self.seqs if seq.is_finished()] def update_num_computed_tokens(self, num_new_computed_tokens: int): """Update number of tokens computed so far.""" for seq in self.seqs: if not seq.is_finished(): seq.data.update_num_computed_tokens(num_new_computed_tokens) def get_num_uncomputed_tokens(self) -> int: num_uncomputed_tokens = 0 for seq in self.seqs: if not seq.is_finished(): num_uncomputed_tokens += seq.data.get_num_uncomputed_tokens() return num_uncomputed_tokens def num_seqs(self, status: Optional[SequenceStatus] = None) -> int: # Optimization. We don't need to call get_seqs if we don't need to # filter by states. if status is None: return len(self.seqs) return len(self.get_seqs(status)) def num_unfinished_seqs(self) -> int: return len(self.get_unfinished_seqs()) def num_finished_seqs(self) -> int: return len(self.get_finished_seqs()) def find(self, seq_id: int) -> Sequence: if seq_id not in self.seqs_dict: raise ValueError(f"Sequence {seq_id} not found.") return self.seqs_dict[seq_id] def add(self, seq: Sequence) -> None: if seq.seq_id in self.seqs_dict: raise ValueError(f"Sequence {seq.seq_id} already exists.") self.seqs_dict[seq.seq_id] = seq self.seqs.append(seq) def remove(self, seq_id: int) -> None: seq = self.seqs_dict.pop(seq_id, None) if seq is None: raise ValueError(f"Sequence {seq_id} not found.") self.seqs.remove(seq) def is_finished(self) -> bool: return all(seq.is_finished() for seq in self.seqs) def is_prefill(self) -> bool: # Every sequence should be in the same stage. return self.seqs[0].is_prefill() def __repr__(self) -> str: return (f"SequenceGroup(request_id={self.request_id}, " f"sampling_params={self.sampling_params}, " f"num_seqs={len(self.seqs)})") class SequenceGroupMetadata: """Metadata for a sequence group. Used to create `AttentionMetadata`. Args: request_id: The ID of the request. is_prompt: Whether the request is at prompt stage. seq_data: The sequence data. (Seq id -> sequence data) sampling_params: The sampling parameters used to generate the outputs. block_tables: The block tables. (Seq id -> list of physical block numbers) do_sample: True if sampling is required. Sampling is not required when e.g., prefill is chunked, and the current iteration only computes query tokens for prefill, we don't need sampling. token_chunk_size: The number of tokens to be processed (per sequence). None if chunking is not required. lora_request: LoRA request. computed_block_nums: The block numbers that are already computed, used in prefix caching. multi_modal_data: Multi modal data. encoder_seq_data: Optional sequence data for encoder prompt (SequenceGroup.encoder_seq). Should be None unless you are working with an encoder/decoder model. cross_block_table: Optional cross-attention block table associated with the encoder prompt (SequenceGroup.encoder_seq). Should be None unless you are working with an encoder/decoder model. prompt_adapter_request: Prompt Adapter request. """ def __init__( self, request_id: str, is_prompt: bool, seq_data: Dict[int, SequenceData], sampling_params: SamplingParams, block_tables: Dict[int, List[int]], do_sample: bool = True, pooling_params: Optional[PoolingParams] = None, token_chunk_size: Optional[int] = None, lora_request: Optional[LoRARequest] = None, computed_block_nums: Optional[List[int]] = None, multi_modal_data: Optional["MultiModalDataDict"] = None, encoder_seq_data: Optional[SequenceData] = None, cross_block_table: Optional[List[int]] = None, prompt_adapter_request: Optional[PromptAdapterRequest] = None, ) -> None: self.request_id = request_id self.is_prompt = is_prompt self.seq_data = seq_data self.sampling_params = sampling_params self.block_tables = block_tables self.pooling_params = pooling_params self.lora_request = lora_request self.prompt_adapter_request = prompt_adapter_request self.computed_block_nums = computed_block_nums self.multi_modal_data = multi_modal_data self.encoder_seq_data = encoder_seq_data self.cross_block_table = cross_block_table self._token_chunk_size = token_chunk_size self.do_sample = do_sample # The number of speculative tokens adopted in this request. # None means specuative decoding is not used. # Zero means speculative decoding is disabled for some reasons. # TODO: We should maintain this states out of the sequence group. self.num_speculative_tokens = None if self._token_chunk_size is None: if is_prompt: self._token_chunk_size = list(seq_data.values())[0].get_len() else: self._token_chunk_size = 1 @property def lora_int_id(self) -> int: return self.lora_request.lora_int_id if self.lora_request else 0 @property def prompt_adapter_id(self) -> int: return self.prompt_adapter_request.prompt_adapter_id \ if self.prompt_adapter_request else 0 @property def prompt_adapter_num_virtual_tokens(self) -> int: return self.prompt_adapter_request.prompt_adapter_num_virtual_tokens \ if self.prompt_adapter_request else 0 @property def token_chunk_size(self) -> int: """Return the number of tokens to be processed (chunk size).""" assert self._token_chunk_size is not None return self._token_chunk_size class SequenceOutput: """The model output associated with a sequence. Args: parent_seq_id: The ID of the parent sequence (for forking in beam search). output_token: The output token ID. logprobs: The logprobs of the output token. (Token id -> logP(x_i+1 | x_0, ..., x_i)) """ def __init__( self, parent_seq_id: int, output_token: int, logprobs: Dict[int, Logprob], ) -> None: self.parent_seq_id = parent_seq_id self.output_token = output_token self.logprobs = logprobs def __repr__(self) -> str: return (f"SequenceOutput(parent_seq_id={self.parent_seq_id}, " f"output_token={self.output_token}, " f"logprobs={self.logprobs})") def __eq__(self, other: object) -> bool: if not isinstance(other, SequenceOutput): raise NotImplementedError() equal = (self.parent_seq_id == other.parent_seq_id and self.output_token == other.output_token) log_probs_equal = other.logprobs == self.logprobs return equal and log_probs_equal class SequenceGroupOutput(ABC): """The base class for model outputs associated with a sequence group.""" @abstractmethod def __repr__(self) -> str: pass @abstractmethod def __eq__(self, other: object) -> bool: pass class CompletionSequenceGroupOutput(SequenceGroupOutput): """The model output associated with a completion sequence group.""" def __init__( self, samples: List[SequenceOutput], prompt_logprobs: Optional[PromptLogprobs], ) -> None: self.samples = samples # Prompt logprob for each prompt query token. self.prompt_logprobs = prompt_logprobs def __repr__(self) -> str: return (f"CompletionSequenceGroupOutput(samples={self.samples}, " f"prompt_logprobs={self.prompt_logprobs})") def __eq__(self, other: object) -> bool: if not isinstance(other, CompletionSequenceGroupOutput): raise NotImplementedError() return (self.samples == other.samples and self.prompt_logprobs == other.prompt_logprobs) class EmbeddingSequenceGroupOutput(SequenceGroupOutput): """The model output associated with an embedding sequence group.""" def __init__( self, embeddings: List[float], ) -> None: self.embeddings = embeddings def __repr__(self) -> str: return (f"EmbeddingSequenceGroupOutput(" f"embeddings_shape={len(self.embeddings)})") def __eq__(self, other: object) -> bool: if not isinstance(other, EmbeddingSequenceGroupOutput): raise NotImplementedError() return self.embeddings == other.embeddings @dataclass class IntermediateTensors: """For all pipeline stages except the last, we need to return the hidden states and residuals to be sent to the next stage. This data structure contains the hidden states and residuals for a request. """ tensors: Dict[str, torch.Tensor] def __getitem__(self, key: Union[str, slice]): if isinstance(key, str): return self.tensors[key] elif isinstance(key, slice): return self.__class__({k: v[key] for k, v in self.tensors.items()}) def __setitem__(self, key: str, value): self.tensors[key] = value def __len__(self): return len(self.tensors) def __eq__(self, other: object): return isinstance(other, self.__class__) and self def __repr__(self) -> str: return f"IntermediateTensors(tensors={self.tensors})" @dataclass class SamplerOutput: """For each sequence group, we generate a list of SequenceOutput object, each of which contains one possible candidate for the next token. This data structure implements methods, so it can be used like a list, but also has optional fields for device tensors. """ outputs: List[CompletionSequenceGroupOutput] # On-device tensor containing probabilities of each token. sampled_token_probs: Optional[torch.Tensor] = None # On-device tensor containing the logprobs of each token. logprobs: Optional["torch.Tensor"] = None # On-device tensor containing the sampled token ids. sampled_token_ids: Optional[torch.Tensor] = None # Spec decode metrics populated by workers. spec_decode_worker_metrics: Optional["SpecDecodeWorkerMetrics"] = None # Optional last hidden states from the model. hidden_states: Optional[torch.Tensor] = None def __getitem__(self, idx: int): return self.outputs[idx] def __setitem__(self, idx: int, value): self.outputs[idx] = value def __len__(self): return len(self.outputs) def __eq__(self, other: object): return isinstance(other, self.__class__) and self.outputs == other.outputs def __repr__(self) -> str: """Show the shape of a tensor instead of its values to reduce noise. """ sampled_token_probs_repr = ("None" if self.sampled_token_probs is None else self.sampled_token_probs.shape) sampled_token_ids_repr = ("None" if self.sampled_token_ids is None else self.sampled_token_ids.shape) return ( f"SamplerOutput(outputs={self.outputs}, " f"sampled_token_probs={sampled_token_probs_repr}, " f"sampled_token_ids={sampled_token_ids_repr}, " f"spec_decode_worker_metrics={self.spec_decode_worker_metrics})") @dataclass class PoolerOutput: """The output from a pooling operation in the embedding model.""" outputs: List[EmbeddingSequenceGroupOutput] spec_decode_worker_metrics: Optional["SpecDecodeWorkerMetrics"] = None def __getitem__(self, idx: int): return self.outputs[idx] def __setitem__(self, idx: int, value): self.outputs[idx] = value def __len__(self): return len(self.outputs) def __eq__(self, other: object): return isinstance(other, self.__class__) and self.outputs == other.outputs def get_all_seq_ids( seq_group_metadata_list: List[SequenceGroupMetadata]) -> List[int]: """Given a list of SequenceGroupMetadata, create a list of all sequence ids. """ return [seq_id for sg in seq_group_metadata_list for seq_id in sg.seq_data] def get_all_seq_ids_and_request_ids( seq_group_metadata_list: List[SequenceGroupMetadata] ) -> Tuple[List[int], Dict[str, Set[int]]]: """Given a list of SequenceGroupMetadata, create a list of all sequence ids. """ seq_ids: List[int] = [] request_id_seq_ids_mapping: Dict[str, Set[int]] = defaultdict(set) for sg in seq_group_metadata_list: for seq_id in sg.seq_data: seq_ids.append(seq_id) request_id_seq_ids_mapping[sg.request_id].add(seq_id) return seq_ids, request_id_seq_ids_mapping class HiddenStates: """Hidden states corresponding to in-progress sequences. Used in speculative decoding to pass hidden states from the target model to the proposer model in the subsequent step. seq_ids are the sequence ids of each entry of the batch dimension of the hidden_states tensor""" def __init__(self, seq_group_metadata_list: List[SequenceGroupMetadata], hidden_states: torch.Tensor): assert len(seq_group_metadata_list) == len(hidden_states) self.seq_ids: List[int] = get_all_seq_ids(seq_group_metadata_list) self.hidden_states: torch.Tensor = hidden_states def update(self, seq_group_metadata_list: List[SequenceGroupMetadata], hidden_states: torch.Tensor) -> None: """Update hidden states from target model invocation.""" assert len(seq_group_metadata_list) == len(hidden_states) self.seq_ids.extend(get_all_seq_ids(seq_group_metadata_list)) self.hidden_states = torch.cat([self.hidden_states, hidden_states]) def prune(self, seq_group_metadata_list: List[SequenceGroupMetadata]) -> None: """Prune to provided list of sequence ids.""" seq_ids = get_all_seq_ids(seq_group_metadata_list) if seq_ids != self.seq_ids: # Batch contents changed - prune removed sequences. index = [self.seq_ids.index(seq_id) for seq_id in seq_ids] self.hidden_states = self.hidden_states[index] self.seq_ids = seq_ids @dataclass class ExecuteModelRequest: """The model execution request, containing CPU metadata only. The LLM engine should create an instance of this class for each request batch.""" # The sequence group metadata list. seq_group_metadata_list: List[SequenceGroupMetadata] # Blocks to swap in. List of CPU -> GPU block number. blocks_to_swap_in: List[Tuple[int, int]] = field(default_factory=list) # Blocks to swap out. List of GPU -> CPU block number. blocks_to_swap_out: List[Tuple[int, int]] = field(default_factory=list) # Blocks to copy. Source to dest block. blocks_to_copy: List[Tuple[int, int]] = field(default_factory=list) # Virtual engine ID for pipeline parallel. virtual_engine: int = 0 # The number of slots for lookahead decoding. num_lookahead_slots: int = 0 # The number of requests in the running queue. running_queue_size: int = 0 # Optional hidden states from prior step. previous_hidden_states: Optional[HiddenStates] = None # The number of forward steps to run. num_steps: int = 1 # Finished request ids since last step. finished_requests_ids: List[str] = field(default_factory=list) def clone( self, seq_group_metadata_list: List[SequenceGroupMetadata] ) -> "ExecuteModelRequest": """Clone the request with a new sequence group metadata list.""" return ExecuteModelRequest( seq_group_metadata_list=seq_group_metadata_list, blocks_to_swap_in=self.blocks_to_swap_in.copy(), blocks_to_swap_out=self.blocks_to_swap_out.copy(), blocks_to_copy=self.blocks_to_copy.copy(), virtual_engine=self.virtual_engine, num_lookahead_slots=self.num_lookahead_slots, running_queue_size=self.running_queue_size, previous_hidden_states=self.previous_hidden_states, num_steps=self.num_steps, finished_requests_ids=self.finished_requests_ids, )