154 lines
5.8 KiB
Python
154 lines
5.8 KiB
Python
# 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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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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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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"""Sampling parameters for text generation."""
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from typing import Any, Dict, List, Optional, Union
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_SAMPLING_EPS = 1e-6
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class SamplingParams:
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"""
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The sampling parameters.
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See docs/backend/sampling_params.md or
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https://docs.sglang.ai/backend/sampling_params.html
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for the documentation.
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"""
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def __init__(
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self,
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max_new_tokens: int = 128,
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stop: Optional[Union[str, List[str]]] = None,
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stop_token_ids: Optional[List[int]] = None,
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temperature: float = 1.0,
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top_p: float = 1.0,
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top_k: int = -1,
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min_p: float = 0.0,
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frequency_penalty: float = 0.0,
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presence_penalty: float = 0.0,
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repetition_penalty: float = 1.0,
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min_new_tokens: int = 0,
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n: int = 1,
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json_schema: Optional[str] = None,
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regex: Optional[str] = None,
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ebnf: Optional[str] = None,
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structural_tag: Optional[str] = None,
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ignore_eos: bool = False,
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skip_special_tokens: bool = True,
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spaces_between_special_tokens: bool = True,
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no_stop_trim: bool = False,
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custom_params: Optional[Dict[str, Any]] = None,
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) -> None:
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self.max_new_tokens = max_new_tokens
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self.stop_strs = stop
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if stop_token_ids:
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self.stop_token_ids = set(stop_token_ids)
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else:
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self.stop_token_ids = None
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self.temperature = temperature
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self.top_p = top_p
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self.top_k = top_k
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self.min_p = min_p
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self.frequency_penalty = frequency_penalty
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self.presence_penalty = presence_penalty
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self.repetition_penalty = repetition_penalty
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self.min_new_tokens = min_new_tokens
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self.regex = regex
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self.n = n
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self.json_schema = json_schema
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self.ebnf = ebnf
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self.structural_tag = structural_tag
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self.ignore_eos = ignore_eos
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self.skip_special_tokens = skip_special_tokens
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self.spaces_between_special_tokens = spaces_between_special_tokens
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self.no_stop_trim = no_stop_trim
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self.custom_params = custom_params
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# Process some special cases
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if 0 <= self.temperature < _SAMPLING_EPS:
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# top_k = 1 means greedy sampling
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self.temperature = 1.0
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self.top_k = 1
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if self.top_k == -1:
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self.top_k = 1 << 30 # whole vocabulary
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def verify(self):
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if self.temperature < 0.0:
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raise ValueError(
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f"temperature must be non-negative, got {self.temperature}."
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)
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if not 0.0 < self.top_p <= 1.0:
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raise ValueError(f"top_p must be in (0, 1], got {self.top_p}.")
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if not 0.0 <= self.min_p <= 1.0:
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raise ValueError(f"min_p must be in [0, 1], got {self.min_p}.")
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if self.top_k < 1 or self.top_k == -1:
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raise ValueError(
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f"top_k must be -1 (disable) or at least 1, got {self.top_k}."
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)
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if not -2.0 <= self.frequency_penalty <= 2.0:
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raise ValueError(
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"frequency_penalty must be in [-2, 2], got "
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f"{self.frequency_penalty}."
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)
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if not -2.0 <= self.presence_penalty <= 2.0:
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raise ValueError(
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"presence_penalty must be in [-2, 2], got " f"{self.presence_penalty}."
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)
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if not 0.0 <= self.repetition_penalty <= 2.0:
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raise ValueError(
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"repetition_penalty must be in [0, 2], got "
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f"{self.repetition_penalty}."
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)
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if not 0 <= self.min_new_tokens:
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raise ValueError(
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f"min_new_tokens must be in [0, max_new_tokens], got "
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f"{self.min_new_tokens}."
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)
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if self.max_new_tokens is not None:
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if self.max_new_tokens < 0:
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raise ValueError(
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f"max_new_tokens must be at least 0, got {self.max_new_tokens}."
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)
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if not self.min_new_tokens <= self.max_new_tokens:
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raise ValueError(
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f"min_new_tokens must be in [0, max_new_tokens({self.max_new_tokens})], got "
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f"{self.min_new_tokens}."
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)
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grammars = [
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self.json_schema,
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self.regex,
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self.ebnf,
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] # since mutually exclusive, only one can be set
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if sum(x is not None for x in grammars) > 1:
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raise ValueError("Only one of regex, json_schema, or ebnf can be set.")
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def normalize(self, tokenizer):
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# Process stop strings
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if self.stop_strs is None:
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self.stop_strs = []
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self.stop_str_max_len = 0
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else:
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if isinstance(self.stop_strs, str):
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self.stop_strs = [self.stop_strs]
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stop_str_max_len = 0
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for stop_str in self.stop_strs:
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if tokenizer is not None:
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stop_str_ids = tokenizer.encode(stop_str, add_special_tokens=False)
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stop_str_max_len = max(stop_str_max_len, len(stop_str_ids))
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else:
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stop_str_max_len = max(stop_str_max_len, len(stop_str))
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self.stop_str_max_len = stop_str_max_len
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