# # Copyright (c) 2025 Huawei Technologies Co., Ltd. All Rights Reserved. # Copyright 2023 The vLLM team. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # This file is a part of the vllm-ascend project. # Adapted from vllm-project/vllm/vllm/worker/gpu_input_batch.py # from dataclasses import dataclass from typing import Optional, cast import numpy as np import torch from typing_extensions import deprecated from vllm.lora.request import LoRARequest from vllm.multimodal.inputs import (MultiModalFeatureSpec, MultiModalKwargsItem, MultiModalKwargsItems, PlaceholderRange) from vllm.pooling_params import PoolingParams from vllm.sampling_params import SamplingParams, SamplingType from vllm.utils import swap_dict_values from vllm.v1.outputs import LogprobsTensors from vllm.v1.pool.metadata import PoolingMetadata from vllm.v1.sample.logits_processor import (BatchUpdateBuilder, LogitsProcessors, MoveDirectionality) from vllm.v1.sample.metadata import SamplingMetadata from vllm.v1.spec_decode.utils import is_spec_decode_unsupported from vllm.v1.utils import copy_slice from vllm_npu.worker.block_table import MultiGroupBlockTable @dataclass class CachedRequestState: req_id: str prompt_token_ids: list[int] sampling_params: Optional[SamplingParams] pooling_params: Optional[PoolingParams] generator: Optional[torch.Generator] block_ids: tuple[list[int], ...] num_computed_tokens: int output_token_ids: list[int] mrope_positions: Optional[torch.Tensor] = None mrope_position_delta: Optional[int] = None mm_features: Optional[list[MultiModalFeatureSpec]] = None # for back-compatibility, will be removed in next major release mm_kwargs: Optional[list[MultiModalKwargsItem]] = None mm_positions: Optional[list[PlaceholderRange]] = None mm_hashes: Optional[list[PlaceholderRange]] = None lora_request: Optional[LoRARequest] = None def __post_init__(self): self.num_prompt_tokens = len(self.prompt_token_ids) @property def num_tokens(self) -> int: return self.num_prompt_tokens + len(self.output_token_ids) # Temporary back-compatibility for plugins that define model runner @property @deprecated("`mm_inputs` is superseded by `mm_kwargs` and will be " "removed in v0.13. Please use `mm_kwargs` instead.") def mm_inputs(self) -> list[MultiModalKwargsItems]: assert self.mm_features is not None return [ MultiModalKwargsItems.from_seq([f.data]) for f in self.mm_features if f.data is not None ] def get_token_id(self, idx: int) -> int: if idx < self.num_prompt_tokens: return self.prompt_token_ids[idx] else: return self.output_token_ids[idx - self.num_prompt_tokens] class InputBatch: def __init__( self, max_num_reqs: int, max_model_len: int, max_num_batched_tokens: int, device: torch.device, pin_memory: bool, vocab_size: int, block_sizes: list[int], # The block_size of each kv cache group logitsprocs: Optional[LogitsProcessors] = None, is_spec_decode: bool = False, is_pooling_model: bool = False, num_speculative_tokens: int = 0, kernel_block_sizes: Optional[list[list[int]]] = None): self.is_pooling_model = is_pooling_model self.is_spec_decode = is_spec_decode self.max_num_reqs = max_num_reqs self.max_model_len = max_model_len self.max_num_batched_tokens = max_num_batched_tokens self.device = device self.pin_memory = pin_memory self.vocab_size = vocab_size self._req_ids: list[Optional[str]] = [] self.req_id_to_index: dict[str, int] = {} # TODO(woosuk): This buffer could be too large if max_model_len is big. # Find a way to reduce the CPU memory usage. # This buffer is not directly transferred to the NPU, so it does not # need to be pinned. self.token_ids_cpu_tensor = torch.zeros( (max_num_reqs, max_model_len), device="cpu", dtype=torch.int32, pin_memory=False, ) self.token_ids_cpu = self.token_ids_cpu_tensor.numpy() self.num_tokens = np.zeros(max_num_reqs, dtype=np.int32) self.num_tokens_no_spec = np.zeros(max_num_reqs, dtype=np.int32) self.num_prompt_tokens = np.zeros(max_num_reqs, dtype=np.int32) self.num_computed_tokens_cpu_tensor = torch.zeros( (max_num_reqs, ), device="cpu", dtype=torch.int32, pin_memory=pin_memory, ) self.num_computed_tokens_cpu = \ self.num_computed_tokens_cpu_tensor.numpy() # Block table. self.block_table = MultiGroupBlockTable( max_num_reqs=max_num_reqs, max_model_len=max_model_len, max_num_batched_tokens=max_num_batched_tokens, pin_memory=pin_memory, device=device, block_sizes=block_sizes, num_speculative_tokens=num_speculative_tokens, kernel_sizes=kernel_block_sizes) # Sampling-related. self.temperature = torch.empty((max_num_reqs, ), dtype=torch.float32, device=device) self.temperature_cpu_tensor = torch.empty((max_num_reqs, ), dtype=torch.float32, device="cpu", pin_memory=pin_memory) self.temperature_cpu = self.temperature_cpu_tensor.numpy() self.greedy_reqs: set[str] = set() self.random_reqs: set[str] = set() self.top_p = torch.empty((max_num_reqs, ), dtype=torch.float32, device=device) self.top_p_cpu_tensor = torch.empty((max_num_reqs, ), dtype=torch.float32, device="cpu", pin_memory=pin_memory) self.top_p_cpu = self.top_p_cpu_tensor.numpy() self.top_p_reqs: set[str] = set() self.top_k = torch.empty((max_num_reqs, ), dtype=torch.int32, device=device) self.top_k_cpu_tensor = torch.empty((max_num_reqs, ), dtype=torch.int32, device="cpu", pin_memory=pin_memory) self.top_k_cpu = self.top_k_cpu_tensor.numpy() self.top_k_reqs: set[str] = set() # IDs of requests which do not support spec decoding self.spec_decode_unsupported_reqs: set[str] = set() # Frequency penalty related data structures self.frequency_penalties = torch.empty((max_num_reqs, ), dtype=torch.float, device=device) self.frequency_penalties_cpu_tensor = torch.empty( (max_num_reqs, ), dtype=torch.float, device="cpu", pin_memory=pin_memory) self.frequency_penalties_cpu = \ self.frequency_penalties_cpu_tensor.numpy() self.frequency_penalties_reqs: set[str] = set() # Presence penalty related data structures self.presence_penalties = torch.empty((max_num_reqs, ), dtype=torch.float, device=device) self.presence_penalties_cpu_tensor = torch.empty((max_num_reqs, ), dtype=torch.float, device="cpu", pin_memory=pin_memory) self.presence_penalties_cpu = self.presence_penalties_cpu_tensor.numpy( ) self.presence_penalties_reqs: set[str] = set() # Repetition penalty related data structures self.repetition_penalties = torch.empty((max_num_reqs, ), dtype=torch.float, device=device) self.repetition_penalties_cpu_tensor = torch.empty( (max_num_reqs, ), dtype=torch.float, device="cpu", pin_memory=pin_memory) self.repetition_penalties_cpu = \ self.repetition_penalties_cpu_tensor.numpy() self.repetition_penalties_reqs: set[str] = set() # Speculative decoding self.num_accepted_tokens_cpu_tensor = torch.ones((max_num_reqs, ), dtype=torch.int64, device="cpu", pin_memory=pin_memory) self.num_accepted_tokens_cpu = \ self.num_accepted_tokens_cpu_tensor.numpy() # lora related self.request_lora_mapping = np.zeros((self.max_num_reqs, ), dtype=np.int32) self.lora_id_to_request_ids: dict[int, set[str]] = {} self.lora_id_to_lora_request: dict[int, LoRARequest] = {} # req_index -> generator # NOTE(woosuk): The indices of the requests that do not have their own # generator should not be included in the dictionary. self.generators: dict[int, torch.Generator] = {} self.num_logprobs: dict[str, int] = {} # NOTE(rob): num_prompt_logprobs only includes reqs # that are currently in the prefill phase. self.num_prompt_logprobs: dict[str, int] = {} # To accumulate prompt logprobs tensor chunks across prefill steps. self.in_progress_prompt_logprobs_cpu: dict[str, LogprobsTensors] = {} # Internal representation of per-step batch state changes, used for # reordering persistent batch and generating logitsprocs batch state # updates. Should reset each step. self.batch_update_builder = BatchUpdateBuilder() # TODO convert this to LogitsProcessor self.has_allowed_token_ids: set[str] = set() # NOTE(lufang): In the mask tensor, if the corresponding token allowed, # the value is False. Since we use masked_fill_ to set -inf. self.allowed_token_ids_mask: Optional[torch.Tensor] = None self.allowed_token_ids_mask_cpu_tensor: Optional[torch.Tensor] = None # req_index -> bad_words_token_ids self.bad_words_token_ids: dict[int, list[list[int]]] = {} self.logits_processing_needs_token_ids = np.zeros(max_num_reqs, dtype=bool) self.req_output_token_ids: list[Optional[list[int]]] = [] # Store provided logitsprocs. If none are provided, initialize empty # data structure self.logitsprocs = logitsprocs or LogitsProcessors() # This is updated each time the batch constituents change. self.sampling_metadata = self._make_sampling_metadata() self.pooling_params: dict[str, PoolingParams] = {} # Cached reference to the GPU tensor of previously sampled tokens self.prev_sampled_token_ids: Optional[torch.Tensor] = None self.prev_sampled_token_ids_invalid_indices: Optional[set[int]] = None self.prev_req_id_to_index: Optional[dict[str, int]] = None @property def req_ids(self) -> list[str]: # None elements should only be present transiently # while performing state updates to the batch. return cast(list[str], self._req_ids) def _register_add_request(self, request: "CachedRequestState") -> int: """Track add-request operations for logits processors. Not applicable to pooling models. """ # Detailed added request metadata is only required for non-pooling # models, to support logitsprocs assert request.sampling_params # Fill the next empty index if there is one. if (new_req_index := self.batch_update_builder.pop_removed()) is None: # Append to end otherwise. new_req_index = self.num_reqs assert new_req_index < self.max_num_reqs self.batch_update_builder.added.append( (new_req_index, request.sampling_params, request.prompt_token_ids, request.output_token_ids)) return new_req_index def add_request( self, request: "CachedRequestState", ) -> int: if not self.is_pooling_model: # New request index bookkeeping for autoregressive models. req_index = self._register_add_request(request) else: req_index = self.num_reqs req_id = request.req_id if req_index == len(self._req_ids): self._req_ids.append(req_id) self.req_output_token_ids.append(request.output_token_ids) else: self._req_ids[req_index] = req_id self.req_output_token_ids[req_index] = request.output_token_ids self.req_id_to_index[req_id] = req_index # Copy the prompt token ids and output token ids. num_prompt_tokens = len(request.prompt_token_ids) self.num_prompt_tokens[req_index] = num_prompt_tokens self.token_ids_cpu[ req_index, :num_prompt_tokens] = request.prompt_token_ids start_idx = num_prompt_tokens end_idx = start_idx + len(request.output_token_ids) self.token_ids_cpu[req_index, start_idx:end_idx] = request.output_token_ids # Number of token ids in token_ids_cpu. # NOTE(woosuk): This may include spec decode tokens. self.num_tokens[req_index] = request.num_tokens # Number of tokens without spec decode tokens. self.num_tokens_no_spec[req_index] = request.num_tokens self.num_computed_tokens_cpu[req_index] = request.num_computed_tokens self.block_table.add_row(request.block_ids, req_index) if sampling_params := request.sampling_params: if (self.is_spec_decode and is_spec_decode_unsupported(sampling_params)): self.spec_decode_unsupported_reqs.add(req_id) if sampling_params.sampling_type == SamplingType.GREEDY: # Avoid later division by zero. self.temperature_cpu[req_index] = -1.0 self.greedy_reqs.add(req_id) else: self.temperature_cpu[req_index] = sampling_params.temperature self.random_reqs.add(req_id) self.top_p_cpu[req_index] = sampling_params.top_p if sampling_params.top_p < 1: self.top_p_reqs.add(req_id) top_k = sampling_params.top_k if 0 < top_k < self.vocab_size: self.top_k_reqs.add(req_id) else: top_k = self.vocab_size self.top_k_cpu[req_index] = top_k self.frequency_penalties_cpu[ req_index] = sampling_params.frequency_penalty if sampling_params.frequency_penalty != 0.0: self.frequency_penalties_reqs.add(req_id) self.presence_penalties_cpu[ req_index] = sampling_params.presence_penalty if sampling_params.presence_penalty != 0.0: self.presence_penalties_reqs.add(req_id) self.repetition_penalties_cpu[ req_index] = sampling_params.repetition_penalty if sampling_params.repetition_penalty != 1.0: self.repetition_penalties_reqs.add(req_id) # NOTE(woosuk): self.generators should not include the requests that # do not have their own generator. if request.generator is not None: self.generators[req_index] = request.generator if sampling_params.logprobs is not None: self.num_logprobs[req_id] = (self.vocab_size if sampling_params.logprobs == -1 else sampling_params.logprobs) if sampling_params.prompt_logprobs is not None: self.num_prompt_logprobs[ req_id] = sampling_params.prompt_logprobs if sampling_params.allowed_token_ids: self.has_allowed_token_ids.add(req_id) if self.allowed_token_ids_mask_cpu_tensor is None: # Lazy allocation for this tensor, which can be large. # False means we don't fill with -inf. self.allowed_token_ids_mask = torch.zeros( self.max_num_reqs, self.vocab_size, dtype=torch.bool, device=self.device) self.allowed_token_ids_mask_cpu_tensor = torch.zeros( self.max_num_reqs, self.vocab_size, dtype=torch.bool, device="cpu") self.allowed_token_ids_mask_cpu_tensor[req_index] = True # False means we don't fill with -inf. self.allowed_token_ids_mask_cpu_tensor[req_index][ sampling_params.allowed_token_ids] = False if sampling_params.bad_words_token_ids: self.bad_words_token_ids[ req_index] = sampling_params.bad_words_token_ids elif pooling_params := request.pooling_params: self.pooling_params[req_id] = pooling_params self.logits_processing_needs_token_ids[req_index] = ( pooling_params.requires_token_ids) else: raise NotImplementedError(request) # Speculative decoding: by default 1 token is generated. self.num_accepted_tokens_cpu[req_index] = 1 # Add request lora ID if request.lora_request: lora_id = request.lora_request.lora_int_id if lora_id not in self.lora_id_to_request_ids: self.lora_id_to_request_ids[lora_id] = set() self.request_lora_mapping[req_index] = lora_id self.lora_id_to_request_ids[lora_id].add(request.req_id) self.lora_id_to_lora_request[lora_id] = request.lora_request else: # No LoRA self.request_lora_mapping[req_index] = 0 return req_index def remove_request(self, req_id: str) -> Optional[int]: """This method must always be followed by a call to condense(). Args: req_id: request to remove Returns: Removed request index, or `None` if `req_id` not recognized """ req_index = self.req_id_to_index.pop(req_id, None) if req_index is None: return None if not self.is_pooling_model: # Autoregressive models require bookkeeping of removed requests to # support logitsprocs. self.batch_update_builder.removed_append(req_index) self._req_ids[req_index] = None self.req_output_token_ids[req_index] = None self.greedy_reqs.discard(req_id) self.random_reqs.discard(req_id) self.top_p_reqs.discard(req_id) self.top_k_reqs.discard(req_id) self.spec_decode_unsupported_reqs.discard(req_id) self.frequency_penalties_reqs.discard(req_id) self.presence_penalties_reqs.discard(req_id) self.repetition_penalties_reqs.discard(req_id) self.generators.pop(req_index, None) self.num_logprobs.pop(req_id, None) self.num_prompt_logprobs.pop(req_id, None) self.in_progress_prompt_logprobs_cpu.pop(req_id, None) # LoRA lora_id = self.request_lora_mapping[req_index] if lora_id != 0: self.lora_id_to_request_ids[lora_id].discard(req_id) if len(self.lora_id_to_request_ids[lora_id]) == 0: self.lora_id_to_request_ids.pop(lora_id) self.lora_id_to_lora_request.pop(lora_id) self.request_lora_mapping[req_index] = 0 self.has_allowed_token_ids.discard(req_id) if self.allowed_token_ids_mask_cpu_tensor is not None: # False means we don't fill with -inf. self.allowed_token_ids_mask_cpu_tensor[req_index].fill_(False) self.bad_words_token_ids.pop(req_index, None) self.pooling_params.pop(req_id, None) return req_index def swap_states(self, i1: int, i2: int) -> None: # For autoregressive models, track detailed request reordering info # to support logitsprocs self.batch_update_builder.moved.append( (i1, i2, MoveDirectionality.SWAP)) old_id_i1 = self._req_ids[i1] old_id_i2 = self._req_ids[i2] self._req_ids[i1], self._req_ids[i2] =\ self._req_ids[i2], self._req_ids[i1] # noqa self.req_output_token_ids[i1], self.req_output_token_ids[i2] =\ self.req_output_token_ids[i2], self.req_output_token_ids[i1] assert old_id_i1 is not None and old_id_i2 is not None self.req_id_to_index[old_id_i1], self.req_id_to_index[old_id_i2] =\ self.req_id_to_index[old_id_i2], self.req_id_to_index[old_id_i1] self.num_tokens[i1], self.num_tokens[i2] =\ self.num_tokens[i2], self.num_tokens[i1] self.num_tokens_no_spec[i1], self.num_tokens_no_spec[i2] =\ self.num_tokens_no_spec[i2], self.num_tokens_no_spec[i1] self.num_prompt_tokens[i1], self.num_prompt_tokens[i2] =\ self.num_prompt_tokens[i2], self.num_prompt_tokens[i1] self.num_computed_tokens_cpu[i1], self.num_computed_tokens_cpu[i2] =\ self.num_computed_tokens_cpu[i2], self.num_computed_tokens_cpu[i1] self.temperature_cpu[i1], self.temperature_cpu[i2] =\ self.temperature_cpu[i2], self.temperature_cpu[i1] self.top_p_cpu[i1], self.top_p_cpu[i2] =\ self.top_p_cpu[i2], self.top_p_cpu[i1] self.top_k_cpu[i1], self.top_k_cpu[i2] =\ self.top_k_cpu[i2], self.top_k_cpu[i1] self.frequency_penalties_cpu[i1], self.frequency_penalties_cpu[i2] =\ self.frequency_penalties_cpu[i2], self.frequency_penalties_cpu[i1] self.presence_penalties_cpu[i1], self.presence_penalties_cpu[i2] =\ self.presence_penalties_cpu[i2], self.presence_penalties_cpu[i1] self.repetition_penalties_cpu[i1], self.repetition_penalties_cpu[i2] =\ self.repetition_penalties_cpu[i2], self.repetition_penalties_cpu[i1] self.num_accepted_tokens_cpu[i1], self.num_accepted_tokens_cpu[i2] =\ self.num_accepted_tokens_cpu[i2], self.num_accepted_tokens_cpu[i1] # NOTE: the following is unsafe # self.token_ids_cpu[i1, ...], self.token_ids_cpu[i2, ...], =\ # self.token_ids_cpu[i2, ...], self.token_ids_cpu[i1, ...] # instead, we need to temporiarily copy the data for one of the indices # TODO(lucas): optimize this by only copying valid indices tmp = self.token_ids_cpu[i1, ...].copy() self.token_ids_cpu[i1, ...] = self.token_ids_cpu[i2, ...] self.token_ids_cpu[i2, ...] = tmp swap_dict_values(self.generators, i1, i2) swap_dict_values(self.bad_words_token_ids, i1, i2) self.request_lora_mapping[i1], self.request_lora_mapping[i2] =\ self.request_lora_mapping[i2], self.request_lora_mapping[i1] if self.allowed_token_ids_mask_cpu_tensor is not None: self.allowed_token_ids_mask_cpu_tensor[i1], \ self.allowed_token_ids_mask_cpu_tensor[i2] =\ self.allowed_token_ids_mask_cpu_tensor[i2], \ self.allowed_token_ids_mask_cpu_tensor[i1] self.block_table.swap_row(i1, i2) def condense(self) -> None: """Slide non-empty requests down into lower, empty indices. Any consecutive empty indices at the very end of the list are not filled. Args: empty_req_indices: empty indices which may be filled. Returns: swaps: list of (from,to) swap tuples for moved requests empty_req_indices: indices not filled by condensation """ num_reqs = self.num_reqs if self.is_pooling_model: # Will be contiguous in pooling case, just trim the lists. del self._req_ids[num_reqs:] del self.req_output_token_ids[num_reqs:] return if not (empty_req_indices := self.batch_update_builder.removed): # All removed requests were replaced by added requests, or else no # requests were removed at all. No condense() needed return if num_reqs == 0: # The batched states are empty. self._req_ids.clear() self.req_output_token_ids.clear() return # NOTE(woosuk): This function assumes that the empty_req_indices # is sorted in descending order. last_req_index = num_reqs + len(empty_req_indices) - 1 while empty_req_indices: # Find the largest non-empty index. while last_req_index in empty_req_indices: last_req_index -= 1 # Find the smallest empty index. empty_index = self.batch_update_builder.peek_removed() assert empty_index is not None if empty_index >= last_req_index: break # Move active request down into empty request # index. self.batch_update_builder.pop_removed() # Autoregressive models require detailed tracking of condense # operations to support logitsprocs self.batch_update_builder.moved.append( (last_req_index, empty_index, MoveDirectionality.UNIDIRECTIONAL)) req_id = self._req_ids[last_req_index] output_token_ids = self.req_output_token_ids[last_req_index] assert req_id is not None self._req_ids[empty_index] = req_id self._req_ids[last_req_index] = None self.req_output_token_ids[empty_index] = output_token_ids self.req_output_token_ids[last_req_index] = None self.req_id_to_index[req_id] = empty_index num_tokens = self.num_tokens[last_req_index] self.token_ids_cpu[empty_index, :num_tokens] = self.token_ids_cpu[ last_req_index, :num_tokens] self.num_tokens[empty_index] = num_tokens self.num_tokens_no_spec[empty_index] = self.num_tokens_no_spec[ last_req_index] self.num_prompt_tokens[empty_index] = self.num_prompt_tokens[ last_req_index] self.num_computed_tokens_cpu[ empty_index] = self.num_computed_tokens_cpu[last_req_index] self.block_table.move_row(last_req_index, empty_index) self.temperature_cpu[empty_index] = self.temperature_cpu[ last_req_index] self.top_p_cpu[empty_index] = self.top_p_cpu[last_req_index] self.top_k_cpu[empty_index] = self.top_k_cpu[last_req_index] self.frequency_penalties_cpu[ empty_index] = self.frequency_penalties_cpu[last_req_index] self.presence_penalties_cpu[ empty_index] = self.presence_penalties_cpu[last_req_index] self.repetition_penalties_cpu[ empty_index] = self.repetition_penalties_cpu[last_req_index] self.num_accepted_tokens_cpu[ empty_index] = self.num_accepted_tokens_cpu[last_req_index] generator = self.generators.pop(last_req_index, None) if generator is not None: self.generators[empty_index] = generator self.request_lora_mapping[empty_index] = self.request_lora_mapping[ last_req_index] # TODO convert these to LogitsProcessors if self.allowed_token_ids_mask_cpu_tensor is not None: self.allowed_token_ids_mask_cpu_tensor[ empty_index] = self.allowed_token_ids_mask_cpu_tensor[ last_req_index] bad_words_token_ids = self.bad_words_token_ids.pop( last_req_index, None) if bad_words_token_ids is not None: self.bad_words_token_ids[empty_index] = bad_words_token_ids # Decrement last_req_index since it is now empty. last_req_index -= 1 # Trim lists to the batch size. del self._req_ids[num_reqs:] del self.req_output_token_ids[num_reqs:] def refresh_metadata(self): """Apply any batch updates to sampling metadata.""" if self.is_pooling_model: # Batch changes every step for pooling models. self.sampling_metadata = self._make_sampling_metadata() return # For non-pooling models - generate and apply logitsprocs update; # reset batch update tracking. # Update sampling metadata if batch state is changed. batch_update = self.batch_update_builder.get_and_reset(self.num_reqs) for logit_proc in self.logitsprocs.all: logit_proc.update_state(batch_update) if batch_update: self.sampling_metadata = self._make_sampling_metadata() def _make_sampling_metadata(self) -> SamplingMetadata: num_reqs = self.num_reqs if not self.all_greedy: temperature = copy_slice(self.temperature_cpu_tensor, self.temperature, num_reqs) else: temperature = None if not self.no_top_p: copy_slice(self.top_p_cpu_tensor, self.top_p, num_reqs) if not self.no_top_k: copy_slice(self.top_k_cpu_tensor, self.top_k, num_reqs) if not self.no_penalties: # Since syncing these tensors is expensive only copy them # if necessary i.e. if there are requests which require # penalties to be applied during sampling. copy_slice(self.frequency_penalties_cpu_tensor, self.frequency_penalties, num_reqs) copy_slice(self.presence_penalties_cpu_tensor, self.presence_penalties, num_reqs) copy_slice(self.repetition_penalties_cpu_tensor, self.repetition_penalties, num_reqs) needs_prompt_token_ids = ( not self.no_penalties or self.logits_processing_needs_token_ids[:num_reqs].any()) if needs_prompt_token_ids: # The prompt tokens are used only for applying penalties or # step pooling during the sampling/pooling process. # Hence copy these tensors only when there are requests which # need penalties/step_pooler to be applied. prompt_token_ids = self._make_prompt_token_ids_tensor() else: prompt_token_ids = None allowed_token_ids_mask: Optional[torch.Tensor] = None if not self.no_allowed_token_ids: assert self.allowed_token_ids_mask is not None copy_slice(self.allowed_token_ids_mask_cpu_tensor, self.allowed_token_ids_mask, num_reqs) allowed_token_ids_mask = self.allowed_token_ids_mask[:num_reqs] return SamplingMetadata( temperature=temperature, all_greedy=self.all_greedy, all_random=self.all_random, top_p=None if self.no_top_p else self.top_p[:num_reqs], top_k=None if self.no_top_k else self.top_k[:num_reqs], generators=self.generators, max_num_logprobs=self.max_num_logprobs, prompt_token_ids=prompt_token_ids, frequency_penalties=self.frequency_penalties[:num_reqs], presence_penalties=self.presence_penalties[:num_reqs], repetition_penalties=self.repetition_penalties[:num_reqs], output_token_ids=cast(list[list[int]], self.req_output_token_ids), no_penalties=self.no_penalties, allowed_token_ids_mask=allowed_token_ids_mask, bad_words_token_ids=self.bad_words_token_ids, logitsprocs=self.logitsprocs, ) @property def pooling_metadata(self) -> PoolingMetadata: if len(self.pooling_params) == 0: pooling_params = [] else: # Note, for now this assumes that all request in the batch # are either sampling or pooling requests assert len(self.req_ids) == len(self.pooling_params) pooling_params = [ self.pooling_params[req_id] for req_id in self.req_ids ] return PoolingMetadata( prompt_lens=torch.from_numpy( self.num_prompt_tokens[:self.num_reqs]), prompt_token_ids=self.sampling_metadata.prompt_token_ids, pooling_params=pooling_params, ) def _make_prompt_token_ids_tensor(self) -> torch.Tensor: max_prompt_len = self.num_prompt_tokens[:self.num_reqs].max() prompt_token_ids_cpu_tensor = torch.empty( (self.num_reqs, max_prompt_len), device="cpu", dtype=torch.int64, pin_memory=self.pin_memory, ) prompt_token_ids = prompt_token_ids_cpu_tensor.numpy() prompt_token_ids[:] = self.token_ids_cpu[:self. num_reqs, :max_prompt_len] # Use the value of vocab_size as a pad since we don't have a # token_id of this value. for i in range(self.num_reqs): prompt_token_ids[i, self.num_prompt_tokens[i]:] = self.vocab_size return prompt_token_ids_cpu_tensor.to(device=self.device, non_blocking=True) def make_lora_inputs( self, num_scheduled_tokens: np.ndarray ) -> tuple[tuple[int, ...], tuple[int, ...], set[LoRARequest]]: """ Given the num_scheduled_tokens for each request in the batch, return datastructures used to activate the current LoRAs. Returns: 1. prompt_lora_mapping: A tuple of size self.num_reqs where, prompt_lora_mapping[i] is the LoRA id to use for the ith prompt. 2. token_lora_mapping: A tuple of size np.sum(num_scheduled_tokens) where, token_lora_mapping[i] is the LoRA id to use for ith token. 3. lora_requests: Set of relevant LoRA requests. """ req_lora_mapping = self.request_lora_mapping[:self.num_reqs] prompt_lora_mapping = tuple(req_lora_mapping) token_lora_mapping = tuple( req_lora_mapping.repeat(num_scheduled_tokens)) active_lora_requests: set[LoRARequest] = set( self.lora_id_to_lora_request.values()) return prompt_lora_mapping, token_lora_mapping, active_lora_requests @property def num_reqs(self) -> int: return len(self.req_id_to_index) @property def all_greedy(self) -> bool: return len(self.random_reqs) == 0 @property def all_random(self) -> bool: return len(self.greedy_reqs) == 0 @property def no_top_p(self) -> bool: return len(self.top_p_reqs) == 0 @property def no_top_k(self) -> bool: return len(self.top_k_reqs) == 0 @property def no_penalties(self) -> bool: return (len(self.presence_penalties_reqs) == 0 and len(self.frequency_penalties_reqs) == 0 and len(self.repetition_penalties_reqs) == 0) @property def max_num_logprobs(self) -> Optional[int]: return max(self.num_logprobs.values()) if self.num_logprobs else None @property def no_prompt_logprob(self) -> bool: return not self.num_prompt_logprobs @property def no_allowed_token_ids(self) -> bool: return len(self.has_allowed_token_ids) == 0