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* Fastdeploy support min_p * add test_min_p * fix * min_p_sampling * update * delete vl_gpu_model_runner.py * fix * Align usage of min_p with vLLM * fix * modified unit test * fix test_min_sampling * pre-commit all files * fix * fix * fix * fix xpu_model_runner.py
220 lines
10 KiB
Python
220 lines
10 KiB
Python
"""
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# Copyright (c) 2025 PaddlePaddle Authors. All Rights Reserved.
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#
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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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from __future__ import annotations
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import random
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from dataclasses import dataclass, fields
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from typing import Any, List, Optional, Union
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@dataclass
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class SamplingParams:
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"""Sampling parameters for text generation.
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Overall, we follow the sampling parameters from the OpenAI text completion
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API (https://platform.openai.com/docs/api-reference/completions/create).
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In addition, we support beam search, which is not supported by OpenAI.
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Args:
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n: Number of output sequences to return for the given prompt.
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best_of: Number of output sequences that are generated from the prompt.
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From these `best_of` sequences, the top `n` sequences are returned.
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`best_of` must be greater than or equal to `n`. By default,
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`best_of` is set to `n`. Warning, this is only supported in V0.
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presence_penalty: Float that penalizes new tokens based on whether they
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appear in the generated text so far. Values > 0 encourage the model
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to use new tokens, while values < 0 encourage the model to repeat
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tokens.
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frequency_penalty: Float that penalizes new tokens based on their
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frequency in the generated text so far. Values > 0 encourage the
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model to use new tokens, while values < 0 encourage the model to
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repeat tokens.
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repetition_penalty: Float that penalizes new tokens based on whether
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they appear in the prompt and the generated text so far. Values > 1
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encourage the model to use new tokens, while values < 1 encourage
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the model to repeat tokens.
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temperature: Float that controls the randomness of the sampling. Lower
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values make the model more deterministic, while higher values make
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the model more random. Zero means greedy sampling.
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top_p: Float that controls the cumulative probability of the top tokens
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to consider. Must be in [0, 1]. Set to 1 to consider all tokens.
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top_k: Int that controls the number of top tokens to consider. Must be a positive integer.
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min_p: Float that represents the minimum probability for a token to be
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considered, relative to the probability of the most likely token.
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Must be in [0, 1]. Set to 0 to disable this.
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seed: Random seed to use for the generation.
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stop: list of strings that stop the generation when they are generated.
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The returned output will not contain the stop strings.
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stop_token_ids: list of tokens that stop the generation when they are
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generated. The returned output will contain the stop tokens unless
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the stop tokens are special tokens.
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bad_words: list of words that are not allowed to be generated.
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More precisely, only the last token of a corresponding
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token sequence is not allowed when the next generated token
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can complete the sequence.
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max_tokens: Maximum number of tokens to generate per output sequence.
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reasoning_max_tokens: Maximum number of tokens to generate for reasoning per output sequence.
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min_tokens: Minimum number of tokens to generate per output sequence
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before EOS or stop_token_ids can be generated
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logprobs: Number of log probabilities to return per output token.
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When set to None, no probability is returned. If set to a non-None
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value, the result includes the log probabilities of the specified
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number of most likely tokens, as well as the chosen tokens.
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Note that the implementation follows the OpenAI API: The API will
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always return the log probability of the sampled token, so there
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may be up to `logprobs+1` elements in the response.
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"""
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n: int = 1
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best_of: Optional[int] = None
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presence_penalty: float = None
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frequency_penalty: float = None
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repetition_penalty: float = None
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temperature: float = None
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top_p: float = None
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top_k: int = 0
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min_p: float = 0.0
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seed: Optional[int] = None
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stop: Optional[Union[str, List[str]]] = None
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stop_token_ids: Optional[Union[List[List[int]], List[int]]] = None
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max_tokens: Optional[int] = None
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reasoning_max_tokens: Optional[int] = None
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min_tokens: int = 1
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logprobs: Optional[int] = None
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bad_words: Optional[List[str]] = None
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@classmethod
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def from_dict(cls, req_dict: dict[str, Any]) -> SamplingParams:
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"""Create instance from command line arguments"""
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return cls(
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**{
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field.name: (req_dict[field.name] if field.name in req_dict else field.default)
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for field in fields(cls)
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}
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)
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@classmethod
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def from_optional(
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cls,
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n,
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best_of,
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presence_penalty,
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frequency_penalty,
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repetition_penalty,
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temperature,
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top_p,
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top_k,
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min_p,
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seed=None,
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stop=None,
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stop_token_ids=None,
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max_tokens=None,
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reasoning_max_tokens=None,
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min_tokens=1,
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logprobs=None,
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bad_words=None,
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) -> SamplingParams:
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"""Create instance from command line arguments"""
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return cls(
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n=1 if n is None else n,
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best_of=best_of,
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presence_penalty=(presence_penalty if presence_penalty is not None else 0.0),
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frequency_penalty=(frequency_penalty if frequency_penalty is not None else 0.0),
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repetition_penalty=(repetition_penalty if repetition_penalty is not None else 1.0),
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temperature=temperature if temperature is not None else 1.0,
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top_p=top_p,
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top_k=top_k if top_k is not None else 0,
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min_p=min_p if min_p is not None else 0.0,
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seed=seed,
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stop=stop,
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stop_token_ids=stop_token_ids,
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max_tokens=max_tokens if max_tokens is not None else 8192,
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reasoning_max_tokens=reasoning_max_tokens,
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min_tokens=min_tokens,
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logprobs=logprobs,
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bad_words=bad_words,
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)
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def __post_init__(self):
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if self.seed is None:
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self.seed = random.randint(0, 922337203685477580)
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if self.max_tokens is not None and self.reasoning_max_tokens is None:
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self.reasoning_max_tokens = max(int(self.max_tokens * 0.8), 1)
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self._verify_args()
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def _verify_args(self) -> None:
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if not isinstance(self.n, int):
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raise ValueError(f"n must be an int, but is of type {type(self.n)}")
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if self.n < 1:
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raise ValueError(f"n must be at least 1, got {self.n}.")
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if self.presence_penalty is not None and (not -2.0 <= self.presence_penalty <= 2.0):
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raise ValueError("presence_penalty must be in [-2, 2], got " f"{self.presence_penalty}.")
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if self.frequency_penalty is not None and (not -2.0 <= self.frequency_penalty <= 2.0):
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raise ValueError("frequency_penalty must be in [-2, 2], got " f"{self.frequency_penalty}.")
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if self.repetition_penalty is not None and self.repetition_penalty <= 0.0:
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raise ValueError("repetition_penalty must be greater than zero, got " f"{self.repetition_penalty}.")
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if self.temperature is not None and self.temperature < 0.0:
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raise ValueError(f"temperature must be non-negative, got {self.temperature}.")
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if self.top_p is not None and 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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# quietly accept -1 as disabled, but prefer 0
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if self.top_k < -1:
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raise ValueError(f"top_k must be 0 (disable), or at least 1, " f"got {self.top_k}.")
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if not isinstance(self.top_k, int):
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raise TypeError(f"top_k must be an integer, got {type(self.top_k).__name__}")
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if not 0.0 <= self.min_p <= 1.0:
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raise ValueError("min_p must be in [0,1],got f{self.min_p}")
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if self.max_tokens is not None and self.max_tokens < 1:
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raise ValueError(f"max_tokens must be at least 1, got {self.max_tokens}.")
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if self.reasoning_max_tokens is not None and self.reasoning_max_tokens > self.max_tokens:
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raise ValueError(f"reasoning_max_tokens must be less than max_tokens, got {self.reasoning_max_tokens}.")
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if self.min_tokens < 0:
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raise ValueError(f"min_tokens must be greater than or equal to 0, " f"got {self.min_tokens}.")
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if self.max_tokens is not None and self.min_tokens > self.max_tokens:
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raise ValueError(
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f"min_tokens must be less than or equal to " f"max_tokens={self.max_tokens}, got {self.min_tokens}."
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)
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if self.logprobs is not None and self.logprobs < 0:
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raise ValueError(f"logprobs must be non-negative, got {self.logprobs}.")
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if self.logprobs is not None and self.logprobs > 20:
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raise ValueError("Invalid value for 'top_logprobs': must be less than or equal to 20.")
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if not 0 <= self.seed <= 922337203685477580:
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raise ValueError("seed must be in [0, 922337203685477580], got " f"{self.seed}.")
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def update_from_tokenizer(self, tokenizer):
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"""
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# TODO: Implement stop tokens and bad words support
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# Currently stop tokens and bad words are not supported yet
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"""
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pass
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@dataclass
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class BeamSearchParams:
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"""Beam search parameters for text generation."""
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beam_width: int
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max_tokens: int
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ignore_eos: bool = False
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temperature: float = 0.0
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length_penalty: float = 1.0
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include_stop_str_in_output: bool = False
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