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wehub-resource-sync
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# Copyright (c) ModelScope Contributors. All rights reserved.
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import dataclasses
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import json
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from dataclasses import dataclass
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from datetime import datetime
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from typing import List, Literal, Optional
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from swift.utils import get_logger
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from .base_args import BaseArguments
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logger = get_logger()
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@dataclass
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class SamplingArguments(BaseArguments):
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"""A dataclass for configuring sampling parameters.
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Args:
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prm_model (Optional[str]): The type of the Process Reward Model (PRM). Can be a model ID (loaded via
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'transformers' engine) or a PRM key defined in a plugin for custom inference. Defaults to None.
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orm_model (Optional[str]): The type of the Outcome Reward Model (ORM). Typically a wildcard or test case,
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usually defined in a plugin. Defaults to None.
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sampler_type (Literal['sample', 'distill']): The type of sampling to perform. Supported types are 'sample' and
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'distill'. Defaults to 'sample'.
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sampler_engine (Literal['transformers', 'lmdeploy', 'vllm', 'no', 'client']): The inference engine for the
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sampling model. Supported options are 'transformers', 'lmdeploy', 'vllm', 'client', and 'no'.
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Defaults to 'transformers'.
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output_dir (str): The directory to save the output files. Defaults to 'sample_output'.
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output_file (Optional[str]): The name of the output file. If None, a timestamp will be used as the filename.
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The path should not be included, only the filename. Only the '.jsonl' format is supported. Defaults to
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None.
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resume (bool): Whether to resume file. Defaults to False.
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override_exist_file (bool): Whether to override the output file if it already exists. This is only effective
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when `output_file` is specified. Defaults to False.
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num_return_sequences (int): The number of raw sequences to return from sampling. Effective for the 'sample'
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`sampler_type`. Defaults to 64.
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num_sampling_batch_size (int): The batch size for each sampling iteration. Defaults to 1.
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num_sampling_batches (Optional[int]): The total number of batches to sample. Defaults to None.
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n_best_to_keep (int): The number of best sequences to keep after evaluation. Defaults to 5.
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data_range (List[int]): Specifies the data shard to process. A list of two integers `[shard_index,
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num_shards]`. For example, `[1, 3]` means the dataset is split into 3 shards and this process handles the
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second shard (0-indexed). Defaults to [].
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temperature (float): The temperature for sampling. Defaults to 1.0.
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prm_threshold (float): The threshold for the Process Reward Model (PRM). Results with a score below this
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threshold will be filtered out. Defaults to 0.0.
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easy_query_threshold (Optional[float]): For a single query, if the proportion of correctly sampled sequences
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(as evaluated by the ORM) is greater than this threshold, the query will be discarded. This prevents overly
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simple queries from appearing in the final results. Defaults to None, which disables this filter.
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engine_kwargs (Optional[str]): Additional arguments to pass to the `sampler_engine`, provided as a JSON string.
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For example: '{"cache_max_entry_count":0.7}'. Defaults to None.
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cache_files (List[str]): A list of cache files for a two-step sampling process to avoid OOM errors.
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Step 1: Set `prm_model`, and `orm_model` to None. All generated sequences are saved to a file.
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Step 2: Set `sampler_engine` to 'no' and provide the output file from Step 1 to `cache_files`.
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This run will perform PRM and ORM evaluation on the cached results.
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Note: The `--dataset` argument must still be provided, as IDs in the cache files are MD5 hashes of the
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original data and need to be linked.
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"""
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# rm models
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prm_model: Optional[str] = None
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orm_model: Optional[str] = None
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# sampler settings
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sampler_type: Literal['sample', 'distill'] = 'sample'
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sampler_engine: Literal['transformers', 'lmdeploy', 'vllm', 'no', 'client'] = 'transformers'
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output_dir: str = 'sample_output'
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output_file: Optional[str] = None
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resume: bool = False
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override_exist_file: bool = False
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num_return_sequences: int = 64
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num_sampling_batch_size: int = 1
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num_sampling_batches: Optional[int] = None
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n_best_to_keep: int = 5
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data_range: List[int] = dataclasses.field(default_factory=list)
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# generate settings
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temperature: float = 1.0
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prm_threshold: float = 0.0
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easy_query_threshold: Optional[float] = None
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# engine settings
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engine_kwargs: Optional[str] = None
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# Vanilla
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cache_files: List[str] = dataclasses.field(default_factory=list)
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def _init_model_info(self):
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if self.sampler_engine != 'client':
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return super()._init_model_info()
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else:
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self.model_info = None
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self.model_meta = None
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self.task_type = 'causal_lm'
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return
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def __post_init__(self):
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if self.sampler_engine == 'pt':
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self.sampler_engine = 'transformers' # compat swift3.x
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if self.output_file is None:
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now = datetime.now()
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formatted_time = now.strftime('%Y-%m-%d-%H-%M-%S')
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self.output_file = formatted_time + '.jsonl'
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logger.info(f'Setting output_file to {self.output_file}')
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else:
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if '/' in self.output_file or '\\' in self.output_file:
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raise ValueError(f'Please use a string prefix without directory to '
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f'`--output_file` but now is: {self.output_file}')
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self.padding_side = 'left'
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if self.engine_kwargs is not None:
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self.engine_kwargs = json.loads(self.engine_kwargs)
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else:
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self.engine_kwargs = {}
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super().__post_init__()
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if self.system is not None:
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self.system_message = [{
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'role': 'system',
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'content': self.system,
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}]
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else:
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self.system_message = []
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