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from dataclasses import dataclass, field |
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from typing import Optional |
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@dataclass |
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class TrainingArguments: |
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""" |
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Configuration for training model. |
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""" |
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model_ckpt: Optional[str] = field( |
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default="codeparrot/codeparrot", metadata={"help": "Model name or path of model to be trained."} |
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) |
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save_dir: Optional[str] = field( |
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default="./", metadata={"help": "Save dir where model repo is cloned and models updates are saved to."} |
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) |
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dataset_name_train: Optional[str] = field( |
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default="codeparrot/codeparrot-clean-train", metadata={"help": "Name or path of training dataset."} |
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) |
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dataset_name_valid: Optional[str] = field( |
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default="codeparrot/codeparrot-clean-valid", metadata={"help": "Name or path of validation dataset."} |
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) |
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train_batch_size: Optional[int] = field(default=2, metadata={"help": "Batch size for training."}) |
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valid_batch_size: Optional[int] = field(default=2, metadata={"help": "Batch size for evaluation."}) |
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weight_decay: Optional[float] = field(default=0.1, metadata={"help": "Value of weight decay."}) |
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shuffle_buffer: Optional[int] = field( |
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default=10000, metadata={"help": "Size of buffer used to shuffle streaming dataset."} |
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) |
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learning_rate: Optional[float] = field(default=2e-4, metadata={"help": "Learning rate fo training."}) |
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lr_scheduler_type: Optional[str] = field(default="cosine", metadata={"help": "Learning rate."}) |
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num_warmup_steps: Optional[int] = field( |
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default=750, metadata={"help": "Number of warmup steps in the learning rate schedule."} |
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) |
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gradient_accumulation_steps: Optional[int] = field( |
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default=16, metadata={"help": "Number of gradient accumulation steps."} |
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) |
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gradient_checkpointing: Optional[bool] = field( |
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default=True, metadata={"help": "Use gradient checkpointing to reduce memory footprint."} |
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) |
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max_train_steps: Optional[int] = field(default=50000, metadata={"help": "Maximum number of training steps."}) |
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max_eval_steps: Optional[int] = field( |
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default=-1, metadata={"help": "Maximum number of evaluation steps. If -1 the full dataset is evaluated."} |
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) |
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seq_length: Optional[int] = field(default=1024, metadata={"help": "Sequence lengths used for training."}) |
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seed: Optional[int] = field(default=1, metadata={"help": "Training seed."}) |
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save_checkpoint_steps: Optional[int] = field( |
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default=1024, |
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metadata={"help": "Interval to save checkpoints. Measured as number of forward passes not training steps."}, |
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) |
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resume_from_checkpoint: Optional[str] = field( |
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default=None, metadata={"help": "States path if the training should continue from a checkpoint folder."} |
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) |
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tokenized: Optional[bool] = field(default=False, metadata={"help": "If True the data is pretokenized."}) |
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@dataclass |
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class EvaluationArguments: |
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""" |
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Configuration for evaluating model. |
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""" |
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model_ckpt: Optional[str] = field( |
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default="codeparrot/codeparrot", metadata={"help": "Model name or path of model to be evaluated."} |
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) |
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dataset_name: Optional[str] = field( |
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default="codeparrot/codeparrot-clean-valid", metadata={"help": "Name or path of validation dataset."} |
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) |
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batch_size: Optional[int] = field(default=2, metadata={"help": "Batch size used for evaluation."}) |
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max_eval_steps: Optional[int] = field( |
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default=-1, metadata={"help": "Maximum number of evaluation steps. If -1 the full dataset is evaluated."} |
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) |
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seq_length: Optional[int] = field(default=1024, metadata={"help": "Length of sequences to be evaluated."}) |
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seed: Optional[int] = field(default=1, metadata={"help": "Random seed used for evaluation."}) |
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@dataclass |
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class HumanEvalArguments: |
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""" |
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Configuration for running evaluation on HumanEval dataset. |
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""" |
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model_ckpt: Optional[str] = field( |
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default="codeparrot/codeparrot", metadata={"help": "Model name or path of model to be evaluated."} |
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) |
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num_workers: Optional[int] = field(default=None, metadata={"help": "Number of workers used for code evaluation."}) |
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num_tasks: Optional[int] = field( |
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default=None, |
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metadata={"help": "The number of human-eval tasks to run. If not included all tasks are evaluated."}, |
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) |
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do_sample: Optional[bool] = field( |
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default=True, metadata={"help": "Sample from the language model's output distribution."} |
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) |
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temperature: Optional[float] = field(default=0.2, metadata={"help": "Sampling temperature used for generation."}) |
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max_new_tokens: Optional[int] = field(default=256, metadata={"help": "Maximum number of newly generated tokens."}) |
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top_k: Optional[int] = field(default=0, metadata={"help": "Top-k parameter used for generation."}) |
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top_p: Optional[float] = field(default=0.95, metadata={"help": "Top-p parameter used for nucleus sampling."}) |
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batch_size: Optional[int] = field(default=10, metadata={"help": "Number of generations to run in parallel."}) |
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n_samples: Optional[int] = field( |
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default=200, metadata={"help": "Number of completions to generate for each sample."} |
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) |
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seed: Optional[int] = field(default=1, metadata={"help": "Random seed used for evaluation."}) |
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output_file: Optional[str] = field( |
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default="eval_results.json", metadata={"help": "Random seed used for evaluation."} |
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) |
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HF_ALLOW_CODE_EVAL: Optional[str] = field( |
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default="0", metadata={"help": "Allow `code_eval` to execute Python code on machine"} |
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) |
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device_int: Optional[int] = field( |
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default=-1, |
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metadata={ |
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"help": ( |
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"Determine which device to run the `text-generation` Pipeline on. -1 is CPU and any zero or positive" |
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" number corresponds to which GPU device id to run on." |
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) |
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}, |
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) |
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@dataclass |
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class PreprocessingArguments: |
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""" |
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Configuration for preprocessing data. |
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""" |
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num_workers: Optional[int] = field( |
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default=None, |
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metadata={ |
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"help": "The number of CPU cores to use for parallel preprocessing. Default uses the maximum available." |
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}, |
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) |
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dataset_name: Optional[str] = field( |
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default="transformersbook/codeparrot", metadata={"help": "Folder or name of dataset to process."} |
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) |
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output_dir: Optional[str] = field( |
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default="codeparrot-clean", metadata={"help": "Folder to save processed processed dataset."} |
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) |
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samples_per_file: Optional[int] = field( |
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default=100_000, metadata={"help": "Number of files to save per JSON output file."} |
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) |
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text_column: Optional[str] = field(default="content", metadata={"help": "Column containing text data to process."}) |
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line_max: Optional[float] = field( |
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default=1000, metadata={"help": "Maximum line length in file, otherwise file is filtered."} |
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) |
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line_mean: Optional[float] = field( |
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default=100, metadata={"help": "Maximum mean line length in file, otherwise file is filtered."} |
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) |
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alpha_frac: Optional[float] = field( |
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default=0.25, metadata={"help": "Maximum fraction of non-alphanumeric characters, otherwise file is filtered."} |
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) |
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min_token_ratio: Optional[float] = field( |
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default=1.5, metadata={"help": "Minimum character token ratio for the file, otherwise file is filtered."} |
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) |
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filter_proba: Optional[float] = field( |
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default=0.7, metadata={"help": "Probability for filtering config, test and uncommon files."} |
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) |
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tokenizer: Optional[str] = field( |
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default="codeparrot/codeparrot", |
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metadata={"help": "Name or path to the tokenizer."}, |
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) |
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near_deduplication: Optional[bool] = field( |
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default=False, metadata={"help": "If True, near-duplicate samples are removed."} |
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) |
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jaccard_threshold: Optional[float] = field( |
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default=0.85, metadata={"help": "Jaccard threshold for near-duplicate samples."} |
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) |
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@dataclass |
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class TokenizerTrainingArguments: |
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""" |
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Configuration for tokenizer training. |
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""" |
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base_tokenizer: Optional[str] = field( |
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default="gpt2", metadata={"help": "Base tokenizer to build new tokenizer from."} |
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) |
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dataset_name: Optional[str] = field( |
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default="transformersbook/codeparrot-train", metadata={"help": "Dataset to train tokenizer on."} |
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) |
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text_column: Optional[str] = field(default="content", metadata={"help": "Column containing text data to process."}) |
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vocab_size: Optional[int] = field(default=200_000, metadata={"help": "Number of examples to train tokenizer on."}) |
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n_examples: Optional[int] = field( |
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default=32768, metadata={"help": "Number of examples to train the tokenizer on."} |
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) |
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tokenizer_name: Optional[str] = field(default="codeparrot", metadata={"help": "Name of new tokenizer."}) |
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push_to_hub: Optional[bool] = field(default=True, metadata={"help": "Push saved tokenizer to the hub."}) |
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@dataclass |
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class PretokenizationArguments: |
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""" |
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Configuration for data pretokenization. |
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""" |
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tokenizer_dir: Optional[str] = field( |
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default="codeparrot/codeparrot", metadata={"help": "Name or path to the tokenizer."} |
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) |
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dataset_name: Optional[str] = field( |
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default="codeparrot/codeparrot-clean-train", metadata={"help": "Name or path to the dataset to pretokenize."} |
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) |
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tokenized_data_repo: Optional[str] = field( |
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default="tokenized-codeparrot-train", metadata={"help": "Repo name of the pretokenized data."} |
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) |
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num_workers: Optional[int] = field(default=None, metadata={"help": "Number of workers used for code evaluation."}) |
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@dataclass |
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class InitializationArguments: |
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""" |
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Configuration for initializing new model. |
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""" |
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config_name: Optional[str] = field( |
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default="gpt2-large", metadata={"help": "Configuration to use for model initialization."} |
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) |
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tokenizer_name: Optional[str] = field( |
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default="codeparrot/codeparrot", metadata={"help": "Tokenizer attached to model."} |
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) |
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model_name: Optional[str] = field(default="codeparrot", metadata={"help": "Name of the created model."}) |
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push_to_hub: Optional[bool] = field(default=True, metadata={"help": "Push saved tokenizer to the hub."}) |
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