358 lines
12 KiB
Python
358 lines
12 KiB
Python
"""
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Copyright 2023-2024 SGLang Team
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Licensed under the Apache License, Version 2.0 (the "License");
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you may not use this file except in compliance with the License.
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You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software
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distributed under the License is distributed on an "AS IS" BASIS,
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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See the License for the specific language governing permissions and
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limitations under the License.
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"""
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"""
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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 enum import Enum
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from typing import Dict, List, Optional, Union
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from sglang.srt.managers.schedule_batch import BaseFinishReason
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from sglang.srt.sampling.sampling_params import SamplingParams
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@dataclass
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class GenerateReqInput:
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# The input prompt. It can be a single prompt or a batch of prompts.
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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. It can be a file name, a url, or base64 encoded string.
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# See also python/sglang/srt/utils.py:load_image.
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image_data: Optional[Union[List[str], str]] = None
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# The sampling_params. See descriptions below.
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sampling_params: Optional[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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# If return logprobs, the start location in the prompt for returning logprobs.
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# By default, this value is "-1", which means it will only return logprobs for output tokens.
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logprob_start_len: Optional[Union[List[int], int]] = None
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# If return logprobs, the number of top logprobs to return at each position.
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top_logprobs_num: Optional[Union[List[int], int]] = None
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# Whether to detokenize tokens in text in the returned 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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# The modalities of the image data [image, multi-images, video]
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modalities: Optional[List[str]] = None
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# LoRA related
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lora_path: Optional[Union[List[Optional[str]], Optional[str]]] = None
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def normalize_batch_and_arguments(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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# Derive the batch size
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if self.text is not None:
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if isinstance(self.text, str):
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self.is_single = True
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self.batch_size = 1
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else:
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self.is_single = False
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self.batch_size = len(self.text)
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else:
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if isinstance(self.input_ids[0], int):
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self.is_single = True
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self.batch_size = 1
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else:
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self.is_single = False
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self.batch_size = len(self.input_ids)
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# Handle parallel sampling
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# When parallel sampling is used, we always treat the input as a batch.
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if self.sampling_params is None:
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self.parallel_sample_num = 1
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elif isinstance(self.sampling_params, dict):
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self.parallel_sample_num = self.sampling_params.get("n", 1)
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else: # isinstance(self.sampling_params, list):
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self.parallel_sample_num = self.sampling_params[0].get("n", 1)
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assert all(self.parallel_sample_num == sampling_params.get("n", 1) for sampling_params in self.sampling_params), (
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"The parallel_sample_num should be the same for all samples in sample params.")
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if self.parallel_sample_num > 1 and self.is_single:
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self.is_single = False
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if self.text is not None:
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self.text = [self.text]
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if self.input_ids is not None:
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self.input_ids = [self.input_ids]
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# Fill in default arguments
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if self.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 = -1
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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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if self.parallel_sample_num == 1:
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num = self.batch_size
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else:
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# Expand parallel_sample_num
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num = self.batch_size * self.parallel_sample_num
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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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elif isinstance(self.image_data, list):
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pass
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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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assert isinstance(self.rid, list), "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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else:
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assert self.parallel_sample_num == 1
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if self.logprob_start_len is None:
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self.logprob_start_len = [-1] * 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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else:
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assert self.parallel_sample_num == 1
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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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else:
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assert self.parallel_sample_num == 1
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def regenerate_rid(self):
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self.rid = uuid.uuid4().hex
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return self.rid
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def __getitem__(self, i):
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return GenerateReqInput(
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text=self.text[i] if self.text is not None else None,
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input_ids=self.input_ids[i] if self.input_ids is not None else None,
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image_data=self.image_data[i],
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sampling_params=self.sampling_params[i],
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rid=self.rid[i],
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return_logprob=self.return_logprob[i],
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logprob_start_len=self.logprob_start_len[i],
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top_logprobs_num=self.top_logprobs_num[i],
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return_text_in_logprobs=self.return_text_in_logprobs,
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stream=self.stream,
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modalities=self.modalities[i] if self.modalities else None,
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lora_path=self.lora_path[i] if self.lora_path is not None else None,
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)
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@dataclass
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class TokenizedGenerateReqInput:
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# The request id
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rid: str
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# The input text
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input_text: str
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# The input token ids
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input_ids: List[int]
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# The image input
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image_inputs: dict
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# The sampling parameters
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sampling_params: SamplingParams
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# Whether to return the logprobs
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return_logprob: bool
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# If return logprobs, the start location in the prompt for returning logprobs.
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logprob_start_len: int
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# If return logprobs, the number of top logprobs to return at each position.
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top_logprobs_num: int
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# Whether to stream output
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stream: bool
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# LoRA related
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lora_path: Optional[str] = None # None means just use the base model
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@dataclass
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class EmbeddingReqInput:
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# The input prompt. It can be a single prompt or a batch of prompts.
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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 request id.
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rid: Optional[Union[List[str], str]] = None
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# Dummy sampling params for compatibility
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sampling_params: Union[List[Dict], Dict] = None
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def normalize_batch_and_arguments(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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# Derive the batch size
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if self.text is not None:
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if isinstance(self.text, str):
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self.is_single = True
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self.batch_size = 1
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else:
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self.is_single = False
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self.batch_size = len(self.text)
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else:
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if isinstance(self.input_ids[0], int):
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self.is_single = True
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self.batch_size = 1
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else:
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self.is_single = False
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self.batch_size = len(self.input_ids)
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# Fill in default arguments
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if self.is_single:
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if self.rid is None:
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self.rid = uuid.uuid4().hex
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if self.sampling_params is None:
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self.sampling_params = {}
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self.sampling_params["max_new_tokens"] = 1
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else:
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if self.rid is None:
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self.rid = [uuid.uuid4().hex for _ in range(self.batch_size)]
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else:
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assert isinstance(self.rid, list), "The rid should be a list."
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if self.sampling_params is None:
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self.sampling_params = [{}] * self.batch_size
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for i in range(self.batch_size):
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self.sampling_params[i]["max_new_tokens"] = 1
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def regenerate_rid(self):
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self.rid = uuid.uuid4().hex
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return self.rid
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def __getitem__(self, i):
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return EmbeddingReqInput(
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text=self.text[i] if self.text is not None else None,
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input_ids=self.input_ids[i] if self.input_ids is not None else None,
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sampling_params=self.sampling_params[i],
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rid=self.rid[i],
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)
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@dataclass
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class TokenizedEmbeddingReqInput:
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# The request id
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rid: str
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# The input text
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input_text: str
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# The input token ids
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input_ids: List[int]
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# Dummy sampling params for compatibility
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sampling_params: SamplingParams
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@dataclass
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class BatchTokenIDOut:
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# The request id
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rids: List[str]
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# The version id to sync decode status with in detokenizer_manager
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vids: List[int]
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decoded_texts: List[str]
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decode_ids: List[int]
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read_offsets: List[int]
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# Only used when `--skip-tokenizer-init`
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output_ids: Optional[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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no_stop_trim: List[bool]
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@dataclass
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class BatchStrOut:
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# The request id
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rids: List[str]
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# The output decoded strings
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output_strs: List[str]
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# The meta info
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meta_info: List[Dict]
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# The finish reason
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finished_reason: List[BaseFinishReason]
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@dataclass
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class BatchEmbeddingOut:
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# The request id
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rids: List[str]
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# The output embedding
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embeddings: List[List[float]]
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# The meta info
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meta_info: List[Dict]
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# The finish reason
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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 UpdateWeightReqInput:
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# The model path with the new weights
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model_path: str
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# The format to load the weights
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load_format: Optional[str] = None
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@dataclass
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class UpdateWeightReqOutput:
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success: bool
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message: str
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@dataclass
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class AbortReq:
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# The request id
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rid: str
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class ProfileReq(Enum):
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START_PROFILE = 1
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STOP_PROFILE = 2
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@dataclass
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class GetMemPoolSizeReq:
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pass
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@dataclass
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class GetMemPoolSizeReqOutput:
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size: int
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