Add CodeParrot 🦜 codebase (#14536)
* add readme skeleton * update readme * add initialization script * add deduplication script * add codeparrot training script * add code generation evaluation * add validation loss script * add requirements * update readme * tweak readme * make style * add highlights to readme * add CLIs to scripts * add tokenizer training script * add docstring to constant length dataset * fix defaults in arguments * update readme with cli * move image to hub * tweaks of readme * fix cli commands * add author * explain env variables * fix formatting * Update examples/research_projects/codeparrot/README.md Co-authored-by: lewtun <lewis.c.tunstall@gmail.com> * Apply suggestions from code review Co-authored-by: lewtun <lewis.c.tunstall@gmail.com> * replace generic with gpt2 tokenizer Co-authored-by: lewtun <lewis.c.tunstall@gmail.com>
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Leandro von Werra
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175
examples/research_projects/codeparrot/scripts/arguments.py
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175
examples/research_projects/codeparrot/scripts/arguments.py
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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="lvwerra/codeparrot",
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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="./",
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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="lvwerra/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="lvwerra/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=1000, 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=50_000, 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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@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="lvwerra/codeparrot",
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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="lvwerra/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="lvwerra/codeparrot",
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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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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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@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="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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@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",
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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=200000, 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 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",
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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="lvwerra/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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