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"""Main tasks functionality.""" |
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import os |
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import sys |
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sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), |
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os.path.pardir))) |
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from megatron import get_args |
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import megatron.initialize |
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def get_tasks_args(parser): |
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"""Provide extra arguments required for tasks.""" |
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group = parser.add_argument_group(title='tasks') |
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group.add_argument('--task', type=str, required=True, |
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help='Task name.') |
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group.add_argument('--epochs', type=int, default=None, |
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help='Number of finetunning epochs. Zero results in ' |
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'evaluation only.') |
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group.add_argument('--pretrained_checkpoint', type=str, default=None, |
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help='Pretrained checkpoint used for finetunning.') |
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group.add_argument('--keep_last', action='store_true', |
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help='Keep the last batch (maybe incomplete) in' |
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'the data loader') |
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group.add_argument('--train_data', nargs='+', default=None, |
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help='Whitespace separated paths or corpora names ' |
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'for training.') |
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group.add_argument('--valid_data', nargs='*', default=None, |
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help='path(s) to the validation data.') |
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group.add_argument('--overlapping_eval', type=int, default=32, |
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help='Sliding window for overlapping evaluation.') |
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group.add_argument('--strict_lambada', action='store_true', |
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help='Use more difficult formulation of lambada.') |
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group.add_argument('--qa_data_dev', type=str, default=None, |
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help='Path to the QA dataset dev file.') |
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group.add_argument('--qa_data_test', type=str, default=None, |
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help='Path to the QA dataset test file.') |
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group.add_argument('--faiss_use_gpu', action='store_true', |
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help='Whether create the FaissMIPSIndex on GPU') |
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group.add_argument('--faiss_match', type=str, default='string', \ |
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choices=['regex', 'string'], help="Answer matching '\ |
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'logic type") |
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group.add_argument('--faiss_topk_retrievals', type=int, default=100, |
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help='Number of blocks to use as top-k during retrieval') |
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group.add_argument('--eval_micro_batch_size', type=int, default=None, |
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help='Eval Batch size per model instance (local batch ' |
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'size). Global batch size is local batch size ' |
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'times data parallel size.') |
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group.add_argument('--train_with_neg', action='store_true', |
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help='Whether to use negative examples during model training') |
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group.add_argument('--train_hard_neg', type=int, default=0, |
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help='Number of hard negative exmaples to use during ' |
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'training') |
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group.add_argument('--val_av_rank_hard_neg', type=int, default=30, |
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help='Av.rank validation: how many hard negatives to' |
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' take from each question pool') |
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group.add_argument('--val_av_rank_other_neg', type=int, default=30, |
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help='Av.rank validation: how many other negatives to' |
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' take from each question pool') |
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return parser |
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if __name__ == '__main__': |
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megatron.initialize.initialize_megatron(extra_args_provider=get_tasks_args) |
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args = get_args() |
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if args.num_layers_per_virtual_pipeline_stage is not None: |
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print("Interleaved pipeline schedule is not yet supported for downstream tasks.") |
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exit() |
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if args.task == 'RACE': |
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from race.finetune import main |
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elif args.task in ['MNLI', 'QQP']: |
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from glue.finetune import main |
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elif args.task in ['LAMBADA', 'WIKITEXT103']: |
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from zeroshot_gpt.evaluate import main |
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elif args.task in ['ICT-ZEROSHOT-NQ', 'RETRIEVER-EVAL']: |
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from orqa.evaluate_orqa import main |
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elif args.task in ['RET-FINETUNE-NQ']: |
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from orqa.supervised.finetune import main |
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else: |
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raise NotImplementedError('Task {} is not implemented.'.format( |
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args.task)) |
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main() |
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