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