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643 lines
27 KiB
Python
643 lines
27 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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# Datastructures defining an input batch
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from dataclasses import dataclass
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from typing import TYPE_CHECKING, Dict, List, Optional, Set, Tuple, cast
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import numpy as np
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import torch
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from vllm.lora.request import LoRARequest
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from vllm.multimodal import MultiModalKwargs
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from vllm.sampling_params import SamplingParams, SamplingType
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from vllm.v1.sample.metadata import SamplingMetadata
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from vllm.v1.utils import copy_slice
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from vllm.v1.worker.block_table import BlockTable
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_SAMPLING_EPS = 1e-5
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if TYPE_CHECKING:
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from vllm.multimodal.inputs import PlaceholderRange
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@dataclass
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class CachedRequestState:
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req_id: str
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prompt_token_ids: List[int]
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prompt: Optional[str]
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mm_inputs: List[MultiModalKwargs]
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mm_positions: List["PlaceholderRange"]
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sampling_params: SamplingParams
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generator: Optional[torch.Generator]
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block_ids: List[int]
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num_computed_tokens: int
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output_token_ids: List[int]
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mrope_positions: Optional[torch.Tensor] = None
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mrope_position_delta: Optional[int] = None
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lora_request: Optional[LoRARequest] = None
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@property
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def num_tokens(self) -> int:
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return len(self.prompt_token_ids) + len(self.output_token_ids)
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class InputBatch:
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def __init__(
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self,
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max_num_reqs: int,
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max_model_len: int,
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max_num_blocks_per_req: int,
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device: torch.device,
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pin_memory: bool,
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vocab_size: int,
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):
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self.max_num_reqs = max_num_reqs
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self.max_model_len = max_model_len
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self.max_num_blocks_per_req = max_num_blocks_per_req
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self.device = device
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self.pin_memory = pin_memory
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self.vocab_size = vocab_size
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self._req_ids: List[Optional[str]] = []
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self.req_id_to_index: Dict[str, int] = {}
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# TODO(woosuk): This buffer could be too large if max_model_len is big.
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# Find a way to reduce the CPU memory usage.
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# This buffer is not directly transferred to the GPU, so it does not
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# need to be pinned.
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self.token_ids_cpu_tensor = torch.zeros(
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(max_num_reqs, max_model_len),
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device="cpu",
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dtype=torch.int32,
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pin_memory=False,
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)
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self.token_ids_cpu = self.token_ids_cpu_tensor.numpy()
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self.num_tokens = np.zeros(max_num_reqs, dtype=np.int32)
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self.num_tokens_no_spec = np.zeros(max_num_reqs, dtype=np.int32)
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self.num_prompt_tokens = np.zeros(max_num_reqs, dtype=np.int32)
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self.num_computed_tokens_cpu_tensor = torch.zeros(
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(max_num_reqs, ),
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device="cpu",
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dtype=torch.int32,
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pin_memory=pin_memory,
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)
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self.num_computed_tokens_cpu = \
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self.num_computed_tokens_cpu_tensor.numpy()
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# Block table.
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self.block_table = BlockTable(
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max_num_reqs=max_num_reqs,
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max_model_len=max_model_len,
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max_num_blocks_per_req=max_num_blocks_per_req,
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pin_memory=pin_memory,
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device=device,
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)
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# Sampling-related.
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self.temperature = torch.empty((max_num_reqs, ),
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dtype=torch.float32,
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device=device)
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self.temperature_cpu_tensor = torch.empty((max_num_reqs, ),
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dtype=torch.float32,
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device="cpu",
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pin_memory=pin_memory)
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self.temperature_cpu = self.temperature_cpu_tensor.numpy()
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self.greedy_reqs: Set[str] = set()
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self.random_reqs: Set[str] = set()
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self.top_p = torch.empty((max_num_reqs, ),
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dtype=torch.float32,
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device=device)
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self.top_p_cpu_tensor = torch.empty((max_num_reqs, ),
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dtype=torch.float32,
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device="cpu",
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pin_memory=pin_memory)
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self.top_p_cpu = self.top_p_cpu_tensor.numpy()
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self.top_p_reqs: Set[str] = set()
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self.top_k = torch.empty((max_num_reqs, ),
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dtype=torch.int32,
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device=device)
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self.top_k_cpu_tensor = torch.empty((max_num_reqs, ),
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dtype=torch.int32,
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device="cpu",
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pin_memory=pin_memory)
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self.top_k_cpu = self.top_k_cpu_tensor.numpy()
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self.top_k_reqs: Set[str] = set()
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self.min_p = torch.empty((max_num_reqs, ),
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dtype=torch.float32,
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device=device)
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self.min_p_cpu_tensor = torch.empty((max_num_reqs, ),
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dtype=torch.float32,
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device="cpu",
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pin_memory=pin_memory)
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self.min_p_cpu = self.min_p_cpu_tensor.numpy()
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self.min_p_reqs: Set[str] = set()
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# Frequency penalty related data structures
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self.frequency_penalties = torch.empty((max_num_reqs, ),
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dtype=torch.float,
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device=device)
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self.frequency_penalties_cpu_tensor = torch.empty(
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(max_num_reqs, ),
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dtype=torch.float,
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device="cpu",
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pin_memory=pin_memory)
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self.frequency_penalties_cpu = \
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self.frequency_penalties_cpu_tensor.numpy()
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self.frequency_penalties_reqs: Set[str] = set()
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# Presence penalty related data structures
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self.presence_penalties = torch.empty((max_num_reqs, ),
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dtype=torch.float,
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device=device)
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self.presence_penalties_cpu_tensor = torch.empty((max_num_reqs, ),
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dtype=torch.float,
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device="cpu",
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pin_memory=pin_memory)
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self.presence_penalties_cpu = self.presence_penalties_cpu_tensor.numpy(
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)
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self.presence_penalties_reqs: Set[str] = set()
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# Repetition penalty related data structures
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self.repetition_penalties = torch.empty((max_num_reqs, ),
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dtype=torch.float,
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device=device)
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self.repetition_penalties_cpu_tensor = torch.empty(
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(max_num_reqs, ),
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dtype=torch.float,
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device="cpu",
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pin_memory=pin_memory)
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self.repetition_penalties_cpu = \
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self.repetition_penalties_cpu_tensor.numpy()
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self.repetition_penalties_reqs: Set[str] = set()
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# req_index -> (min_tokens, stop_token_ids)
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self.min_tokens: Dict[int, Tuple[int, Set[int]]] = {}
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# lora related
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self.request_lora_mapping = np.zeros((self.max_num_reqs, ),
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dtype=np.int32)
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self.lora_id_to_request_ids: Dict[int, Set[str]] = {}
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self.lora_id_to_lora_request: Dict[int, LoRARequest] = {}
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# req_index -> generator
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# NOTE(woosuk): The indices of the requests that do not have their own
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# generator should not be included in the dictionary.
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self.generators: Dict[int, torch.Generator] = {}
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self.num_logprobs: Dict[str, int] = {}
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# NOTE(rob): num_prompt_logprobs only includes reqs
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# that are currently in the prefill phase.
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self.num_prompt_logprobs: Dict[str, int] = {}
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self.logit_bias: List[Optional[Dict[int,
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float]]] = [None] * max_num_reqs
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self.has_allowed_token_ids: Set[str] = set()
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self.allowed_token_ids_mask: Optional[torch.Tensor] = None
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self.allowed_token_ids_mask_cpu_tensor: Optional[torch.Tensor] = None
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self.req_output_token_ids: List[Optional[List[int]]] = []
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# This is updated each time the batch constituents change.
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self.sampling_metadata = self._make_sampling_metadata()
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@property
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def req_ids(self) -> List[str]:
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# None elements should only be present transiently
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# while performing state updates to the batch.
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return cast(List[str], self._req_ids)
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def add_request(
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self,
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request: "CachedRequestState",
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req_index: Optional[int] = None,
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) -> None:
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if req_index is None:
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req_index = self.num_reqs
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assert req_index < self.max_num_reqs
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req_id = request.req_id
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if req_index == len(self._req_ids):
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self._req_ids.append(req_id)
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self.req_output_token_ids.append(request.output_token_ids)
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else:
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self._req_ids[req_index] = req_id
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self.req_output_token_ids[req_index] = request.output_token_ids
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self.req_id_to_index[req_id] = req_index
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# Copy the prompt token ids and output token ids.
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num_prompt_tokens = len(request.prompt_token_ids)
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self.num_prompt_tokens[req_index] = num_prompt_tokens
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self.token_ids_cpu[
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req_index, :num_prompt_tokens] = request.prompt_token_ids
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start_idx = num_prompt_tokens
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end_idx = start_idx + len(request.output_token_ids)
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self.token_ids_cpu[req_index,
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start_idx:end_idx] = request.output_token_ids
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# Number of token ids in token_ids_cpu.
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# NOTE(woosuk): This may include spec decode tokens.
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self.num_tokens[req_index] = request.num_tokens
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# Number of tokens without spec decode tokens.
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self.num_tokens_no_spec[req_index] = request.num_tokens
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self.num_computed_tokens_cpu[req_index] = request.num_computed_tokens
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self.block_table.add_row(req_index, request.block_ids)
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sampling_params = request.sampling_params
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if sampling_params.sampling_type == SamplingType.GREEDY:
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# Avoid later division by zero.
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self.temperature_cpu[req_index] = -1.0
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self.greedy_reqs.add(req_id)
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else:
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self.temperature_cpu[req_index] = sampling_params.temperature
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self.random_reqs.add(req_id)
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self.top_p_cpu[req_index] = sampling_params.top_p
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if sampling_params.top_p < 1:
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self.top_p_reqs.add(req_id)
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self.top_k_cpu[req_index] = sampling_params.top_k
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if sampling_params.top_k > 0:
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self.top_k_reqs.add(req_id)
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self.min_p_cpu[req_index] = sampling_params.min_p
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self.frequency_penalties_cpu[
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req_index] = sampling_params.frequency_penalty
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if sampling_params.min_p > _SAMPLING_EPS:
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self.min_p_reqs.add(req_id)
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if sampling_params.frequency_penalty != 0.0:
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self.frequency_penalties_reqs.add(req_id)
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self.presence_penalties_cpu[
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req_index] = sampling_params.presence_penalty
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if sampling_params.presence_penalty != 0.0:
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self.presence_penalties_reqs.add(req_id)
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self.repetition_penalties_cpu[
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req_index] = sampling_params.repetition_penalty
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if sampling_params.repetition_penalty != 1.0:
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self.repetition_penalties_reqs.add(req_id)
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if sampling_params.min_tokens:
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self.min_tokens[req_index] = (sampling_params.min_tokens,
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sampling_params.all_stop_token_ids)
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# NOTE(woosuk): self.generators should not include the requests that
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# do not have their own generator.
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if request.generator is not None:
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self.generators[req_index] = request.generator
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if sampling_params.logprobs is not None:
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self.num_logprobs[req_id] = sampling_params.logprobs
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if sampling_params.prompt_logprobs is not None:
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self.num_prompt_logprobs[req_id] = sampling_params.prompt_logprobs
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if sampling_params.logit_bias is not None:
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self.logit_bias[req_index] = sampling_params.logit_bias
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if sampling_params.allowed_token_ids:
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self.has_allowed_token_ids.add(req_id)
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if self.allowed_token_ids_mask_cpu_tensor is None:
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# Lazy allocation for this tensor, which can be large.
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self.allowed_token_ids_mask = torch.zeros(self.max_num_reqs,
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self.vocab_size,
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dtype=torch.bool,
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device=self.device)
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self.allowed_token_ids_mask_cpu_tensor = torch.zeros(
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self.max_num_reqs,
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self.vocab_size,
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dtype=torch.bool,
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device="cpu")
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self.allowed_token_ids_mask_cpu_tensor[req_index][
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sampling_params.allowed_token_ids] = True
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# Add request lora ID
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if request.lora_request:
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lora_id = request.lora_request.lora_int_id
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if lora_id not in self.lora_id_to_request_ids:
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self.lora_id_to_request_ids[lora_id] = set()
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self.request_lora_mapping[req_index] = lora_id
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self.lora_id_to_request_ids[lora_id].add(request.req_id)
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self.lora_id_to_lora_request[lora_id] = request.lora_request
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else:
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# No LoRA
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self.request_lora_mapping[req_index] = 0
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def remove_request(self, req_id: str) -> Optional[int]:
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"""This method must always be followed by a call to condense()."""
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req_index = self.req_id_to_index.pop(req_id, None)
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if req_index is None:
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return None
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self._req_ids[req_index] = None
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self.req_output_token_ids[req_index] = None
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self.greedy_reqs.discard(req_id)
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self.random_reqs.discard(req_id)
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self.top_p_reqs.discard(req_id)
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self.top_k_reqs.discard(req_id)
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self.min_p_reqs.discard(req_id)
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self.min_tokens.pop(req_index, None)
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self.frequency_penalties_reqs.discard(req_id)
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self.presence_penalties_reqs.discard(req_id)
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self.repetition_penalties_reqs.discard(req_id)
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self.generators.pop(req_index, None)
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self.num_logprobs.pop(req_id, None)
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self.num_prompt_logprobs.pop(req_id, None)
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# LoRA
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lora_id = self.request_lora_mapping[req_index]
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if lora_id != 0:
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self.lora_id_to_request_ids[lora_id].discard(req_id)
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if len(self.lora_id_to_request_ids[lora_id]) == 0:
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self.lora_id_to_request_ids.pop(lora_id)
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self.lora_id_to_lora_request.pop(lora_id)
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self.request_lora_mapping[req_index] = 0
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self.logit_bias[req_index] = None
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self.has_allowed_token_ids.discard(req_id)
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if self.allowed_token_ids_mask_cpu_tensor is not None:
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self.allowed_token_ids_mask_cpu_tensor[req_index].fill_(False)
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return req_index
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def swap_states(self, i1: int, i2: int) -> None:
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old_id_i1 = self._req_ids[i1]
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old_id_i2 = self._req_ids[i2]
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self._req_ids[i1], self._req_ids[i2] =\
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self._req_ids[i2], self._req_ids[i1] # noqa
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self.req_output_token_ids[i1], self.req_output_token_ids[i2] =\
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self.req_output_token_ids[i2], self.req_output_token_ids[i1]
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assert old_id_i1 is not None and old_id_i2 is not None
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self.req_id_to_index[old_id_i1], self.req_id_to_index[old_id_i2] =\
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self.req_id_to_index[old_id_i2], self.req_id_to_index[old_id_i1]
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self.num_tokens[i1], self.num_tokens[i2] =\
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self.num_tokens[i2], self.num_tokens[i1]
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self.token_ids_cpu[i1, ...], self.token_ids_cpu[i2, ...], =\
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self.token_ids_cpu[i2, ...], self.token_ids_cpu[i1, ...]
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self.num_tokens_no_spec[i1], self.num_tokens_no_spec[i2] =\
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self.num_tokens_no_spec[i2], self.num_tokens_no_spec[i1]
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self.num_prompt_tokens[i1], self.num_prompt_tokens[i2] =\
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self.num_prompt_tokens[i2], self.num_prompt_tokens[i1]
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self.num_computed_tokens_cpu[i1], self.num_computed_tokens_cpu[i2] =\
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self.num_computed_tokens_cpu[i2], self.num_computed_tokens_cpu[i1]
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self.temperature_cpu[i1], self.temperature_cpu[i2] =\
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self.temperature_cpu[i2], self.temperature_cpu[i1]
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self.top_p_cpu[i1], self.top_p_cpu[i2] =\
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self.top_p_cpu[i2], self.top_p_cpu[i1]
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self.top_k_cpu[i1], self.top_k_cpu[i2] =\
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self.top_k_cpu[i2], self.top_k_cpu[i1]
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self.frequency_penalties_cpu[i1], self.frequency_penalties_cpu[i2] =\
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self.frequency_penalties_cpu[i2], self.frequency_penalties_cpu[i1]
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self.presence_penalties_cpu[i1], self.presence_penalties_cpu[i2] =\
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self.presence_penalties_cpu[i2], self.presence_penalties_cpu[i1]
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self.repetition_penalties_cpu[i1], self.repetition_penalties_cpu[i2] =\
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self.repetition_penalties_cpu[i2], self.repetition_penalties_cpu[i1]
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self.min_p_cpu[i1], self.min_p_cpu[i2] =\
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self.min_p_cpu[i2], self.min_p_cpu[i1]
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g1 = self.generators.get(i1)
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g2 = self.generators.get(i2)
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if g1 is not None:
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self.generators[i2] = g1
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if g2 is not None:
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self.generators[i1] = g2
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t1 = self.min_tokens.get(i1)
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t2 = self.min_tokens.get(i2)
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if t1 is not None:
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self.min_tokens[i2] = t1
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if t2 is not None:
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self.min_tokens[i1] = t2
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self.request_lora_mapping[i1], self.request_lora_mapping[i2] =\
|
|
self.request_lora_mapping[i2], self.request_lora_mapping[i1]
|
|
self.logit_bias[i1], self.logit_bias[i2] =\
|
|
self.logit_bias[i2], self.logit_bias[i1]
|
|
self.block_table.swap_row(i1, i2)
|
|
|
|
def condense(self, empty_req_indices: List[int]) -> None:
|
|
num_reqs = self.num_reqs
|
|
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 = empty_req_indices.pop()
|
|
if empty_index >= last_req_index:
|
|
break
|
|
|
|
# Swap the states.
|
|
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.min_p_cpu[empty_index] = self.min_p_cpu[last_req_index]
|
|
generator = self.generators.pop(last_req_index, None)
|
|
if generator is not None:
|
|
self.generators[empty_index] = generator
|
|
|
|
min_token = self.min_tokens.pop(last_req_index, None)
|
|
if min_token is not None:
|
|
self.min_tokens[empty_index] = min_token
|
|
|
|
self.request_lora_mapping[empty_index] = self.request_lora_mapping[
|
|
last_req_index]
|
|
|
|
self.logit_bias[empty_index] = self.logit_bias[last_req_index]
|
|
|
|
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]
|
|
|
|
# Decrement last_req_index since it is now empty.
|
|
last_req_index -= 1
|
|
|
|
# Trim lists to the batch size.
|
|
del self._req_ids[self.num_reqs:]
|
|
del self.req_output_token_ids[self.num_reqs:]
|
|
|
|
def refresh_sampling_metadata(self):
|
|
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_min_p:
|
|
copy_slice(self.min_p_cpu_tensor, self.min_p, 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)
|
|
|
|
# The prompt tokens are used only for applying penalties during
|
|
# the sampling process. Hence copy these tensors only when
|
|
# there are requests which need penalties 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],
|
|
min_p=None if self.no_min_p else self.min_p[: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),
|
|
min_tokens=self.min_tokens,
|
|
no_penalties=self.no_penalties,
|
|
logit_bias=self.logit_bias[:num_reqs],
|
|
allowed_token_ids_mask=allowed_token_ids_mask,
|
|
)
|
|
|
|
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_min_p(self) -> bool:
|
|
return len(self.min_p_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
|