143 lines
4.4 KiB
Python
143 lines
4.4 KiB
Python
"""
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The definition of objects transfered between different
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processes (TokenizerManager, DetokenizerManager, Controller).
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"""
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import uuid
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from dataclasses import dataclass
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from typing import Dict, List, Optional, Union
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from sglang.srt.managers.controller.infer_batch import BaseFinishReason
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from sglang.srt.sampling_params import SamplingParams
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@dataclass
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class GenerateReqInput:
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# The input prompt
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text: Optional[Union[List[str], str]] = None
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# The token ids for text; one can either specify text or input_ids
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input_ids: Optional[Union[List[List[int]], List[int]]] = None
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# The image input
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image_data: Optional[Union[List[str], str]] = None
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# The sampling_params
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sampling_params: Union[List[Dict], Dict] = None
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# The request id
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rid: Optional[Union[List[str], str]] = None
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# Whether to return logprobs
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return_logprob: Optional[Union[List[bool], bool]] = None
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# The start location of the prompt for return_logprob
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logprob_start_len: Optional[Union[List[int], int]] = None
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# The number of top logprobs to return
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top_logprobs_num: Optional[Union[List[int], int]] = None
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# Whether to detokenize tokens in logprobs
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return_text_in_logprobs: bool = False
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# Whether to stream output
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stream: bool = False
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def post_init(self):
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if (self.text is None and self.input_ids is None) or (
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self.text is not None and self.input_ids is not None
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):
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raise ValueError("Either text or input_ids should be provided.")
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if self.text is not None:
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is_single = isinstance(self.text, str)
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else:
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is_single = isinstance(self.input_ids[0], int)
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self.is_single = is_single
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if is_single:
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if self.sampling_params is None:
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self.sampling_params = {}
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if self.rid is None:
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self.rid = uuid.uuid4().hex
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if self.return_logprob is None:
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self.return_logprob = False
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if self.logprob_start_len is None:
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self.logprob_start_len = 0
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if self.top_logprobs_num is None:
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self.top_logprobs_num = 0
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else:
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num = len(self.text) if self.text is not None else len(self.input_ids)
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if self.image_data is None:
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self.image_data = [None] * num
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elif not isinstance(self.image_data, list):
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self.image_data = [self.image_data] * num
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if self.sampling_params is None:
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self.sampling_params = [{}] * num
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elif not isinstance(self.sampling_params, list):
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self.sampling_params = [self.sampling_params] * num
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if self.rid is None:
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self.rid = [uuid.uuid4().hex for _ in range(num)]
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else:
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if not isinstance(self.rid, list):
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raise ValueError("The rid should be a list.")
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if self.return_logprob is None:
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self.return_logprob = [False] * num
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elif not isinstance(self.return_logprob, list):
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self.return_logprob = [self.return_logprob] * num
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if self.logprob_start_len is None:
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self.logprob_start_len = [0] * num
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elif not isinstance(self.logprob_start_len, list):
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self.logprob_start_len = [self.logprob_start_len] * num
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if self.top_logprobs_num is None:
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self.top_logprobs_num = [0] * num
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elif not isinstance(self.top_logprobs_num, list):
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self.top_logprobs_num = [self.top_logprobs_num] * num
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@dataclass
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class TokenizedGenerateReqInput:
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rid: str
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input_text: str
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input_ids: List[int]
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pixel_values: List[float]
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image_hash: int
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image_size: List[int]
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sampling_params: SamplingParams
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return_logprob: bool
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logprob_start_len: int
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top_logprobs_num: int
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stream: bool
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@dataclass
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class BatchTokenIDOut:
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rids: List[str]
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decoded_texts: List[str]
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surr_output_ids: List[List[int]]
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read_output_ids: List[List[int]]
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skip_special_tokens: List[bool]
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spaces_between_special_tokens: List[bool]
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meta_info: List[Dict]
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finished_reason: List[BaseFinishReason]
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@dataclass
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class BatchStrOut:
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rids: List[str]
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output_strs: List[str]
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meta_info: List[Dict]
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finished_reason: List[BaseFinishReason]
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@dataclass
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class FlushCacheReq:
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pass
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@dataclass
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class AbortReq:
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rid: str
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@dataclass
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class DetokenizeReqInput:
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input_ids: List[int]
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