Reformat source code with black.
This is the result of:
$ black --line-length 119 examples templates transformers utils hubconf.py setup.py
There's a lot of fairly long lines in the project. As a consequence, I'm
picking the longest widely accepted line length, 119 characters.
This is also Thomas' preference, because it allows for explicit variable
names, to make the code easier to understand.
This commit is contained in:
Aymeric Augustin
@@ -35,166 +35,200 @@ from lm_seqs_dataset import LmSeqsDataset
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MODEL_CLASSES = {
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'distilbert': (DistilBertConfig, DistilBertForMaskedLM, DistilBertTokenizer),
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'roberta': (RobertaConfig, RobertaForMaskedLM, RobertaTokenizer),
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'bert': (BertConfig, BertForMaskedLM, BertTokenizer),
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'gpt2': (GPT2Config, GPT2LMHeadModel, GPT2Tokenizer)
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"distilbert": (DistilBertConfig, DistilBertForMaskedLM, DistilBertTokenizer),
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"roberta": (RobertaConfig, RobertaForMaskedLM, RobertaTokenizer),
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"bert": (BertConfig, BertForMaskedLM, BertTokenizer),
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"gpt2": (GPT2Config, GPT2LMHeadModel, GPT2Tokenizer),
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}
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def sanity_checks(args):
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"""
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A bunch of args sanity checks to perform even starting...
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"""
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assert (args.mlm and args.alpha_mlm > 0.) or (not args.mlm and args.alpha_mlm == 0.)
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assert (args.alpha_mlm > 0. and args.alpha_clm == 0.) or (args.alpha_mlm == 0. and args.alpha_clm > 0.)
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assert (args.mlm and args.alpha_mlm > 0.0) or (not args.mlm and args.alpha_mlm == 0.0)
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assert (args.alpha_mlm > 0.0 and args.alpha_clm == 0.0) or (args.alpha_mlm == 0.0 and args.alpha_clm > 0.0)
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if args.mlm:
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assert os.path.isfile(args.token_counts)
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assert (args.student_type in ['roberta', 'distilbert']) and (args.teacher_type in ['roberta', 'bert'])
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assert (args.student_type in ["roberta", "distilbert"]) and (args.teacher_type in ["roberta", "bert"])
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else:
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assert (args.student_type in ['gpt2']) and (args.teacher_type in ['gpt2'])
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assert (args.student_type in ["gpt2"]) and (args.teacher_type in ["gpt2"])
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assert args.teacher_type == args.student_type or (args.student_type=='distilbert' and args.teacher_type=='bert')
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assert args.teacher_type == args.student_type or (
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args.student_type == "distilbert" and args.teacher_type == "bert"
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)
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assert os.path.isfile(args.student_config)
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if args.student_pretrained_weights is not None:
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assert os.path.isfile(args.student_pretrained_weights)
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if args.freeze_token_type_embds: assert args.student_type in ['roberta']
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if args.freeze_token_type_embds:
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assert args.student_type in ["roberta"]
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assert args.alpha_ce >= 0.0
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assert args.alpha_mlm >= 0.0
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assert args.alpha_clm >= 0.0
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assert args.alpha_mse >= 0.0
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assert args.alpha_cos >= 0.0
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assert args.alpha_ce + args.alpha_mlm + args.alpha_clm + args.alpha_mse + args.alpha_cos > 0.0
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assert args.alpha_ce >= 0.
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assert args.alpha_mlm >= 0.
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assert args.alpha_clm >= 0.
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assert args.alpha_mse >= 0.
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assert args.alpha_cos >= 0.
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assert args.alpha_ce + args.alpha_mlm + args.alpha_clm + args.alpha_mse + args.alpha_cos > 0.
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def freeze_pos_embeddings(student, args):
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if args.student_type == 'roberta':
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if args.student_type == "roberta":
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student.roberta.embeddings.position_embeddings.weight.requires_grad = False
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elif args.student_type == 'gpt2':
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elif args.student_type == "gpt2":
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student.transformer.wpe.weight.requires_grad = False
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def freeze_token_type_embeddings(student, args):
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if args.student_type == 'roberta':
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if args.student_type == "roberta":
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student.roberta.embeddings.token_type_embeddings.weight.requires_grad = False
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def main():
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parser = argparse.ArgumentParser(description="Training")
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parser.add_argument("--force", action='store_true',
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help="Overwrite dump_path if it already exists.")
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parser.add_argument("--force", action="store_true", help="Overwrite dump_path if it already exists.")
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parser.add_argument("--dump_path", type=str, required=True,
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help="The output directory (log, checkpoints, parameters, etc.)")
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parser.add_argument("--data_file", type=str, required=True,
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help="The binarized file (tokenized + tokens_to_ids) and grouped by sequence.")
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parser.add_argument(
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"--dump_path", type=str, required=True, help="The output directory (log, checkpoints, parameters, etc.)"
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)
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parser.add_argument(
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"--data_file",
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type=str,
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required=True,
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help="The binarized file (tokenized + tokens_to_ids) and grouped by sequence.",
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)
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parser.add_argument("--student_type", type=str, choices=["distilbert", "roberta", "gpt2"], required=True,
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help="The student type (DistilBERT, RoBERTa).")
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parser.add_argument("--student_config", type=str, required=True,
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help="Path to the student configuration.")
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parser.add_argument("--student_pretrained_weights", default=None, type=str,
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help="Load student initialization checkpoint.")
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parser.add_argument(
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"--student_type",
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type=str,
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choices=["distilbert", "roberta", "gpt2"],
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required=True,
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help="The student type (DistilBERT, RoBERTa).",
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)
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parser.add_argument("--student_config", type=str, required=True, help="Path to the student configuration.")
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parser.add_argument(
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"--student_pretrained_weights", default=None, type=str, help="Load student initialization checkpoint."
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)
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parser.add_argument("--teacher_type", choices=["bert", "roberta", "gpt2"], required=True,
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help="Teacher type (BERT, RoBERTa).")
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parser.add_argument("--teacher_name", type=str, required=True,
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help="The teacher model.")
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parser.add_argument(
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"--teacher_type", choices=["bert", "roberta", "gpt2"], required=True, help="Teacher type (BERT, RoBERTa)."
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)
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parser.add_argument("--teacher_name", type=str, required=True, help="The teacher model.")
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parser.add_argument("--temperature", default=2., type=float,
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help="Temperature for the softmax temperature.")
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parser.add_argument("--alpha_ce", default=0.5, type=float,
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help="Linear weight for the distillation loss. Must be >=0.")
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parser.add_argument("--alpha_mlm", default=0.0, type=float,
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help="Linear weight for the MLM loss. Must be >=0. Should be used in coonjunction with `mlm` flag.")
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parser.add_argument("--alpha_clm", default=0.5, type=float,
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help="Linear weight for the CLM loss. Must be >=0.")
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parser.add_argument("--alpha_mse", default=0.0, type=float,
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help="Linear weight of the MSE loss. Must be >=0.")
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parser.add_argument("--alpha_cos", default=0.0, type=float,
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help="Linear weight of the cosine embedding loss. Must be >=0.")
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parser.add_argument("--temperature", default=2.0, type=float, help="Temperature for the softmax temperature.")
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parser.add_argument(
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"--alpha_ce", default=0.5, type=float, help="Linear weight for the distillation loss. Must be >=0."
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)
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parser.add_argument(
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"--alpha_mlm",
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default=0.0,
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type=float,
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help="Linear weight for the MLM loss. Must be >=0. Should be used in coonjunction with `mlm` flag.",
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)
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parser.add_argument("--alpha_clm", default=0.5, type=float, help="Linear weight for the CLM loss. Must be >=0.")
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parser.add_argument("--alpha_mse", default=0.0, type=float, help="Linear weight of the MSE loss. Must be >=0.")
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parser.add_argument(
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"--alpha_cos", default=0.0, type=float, help="Linear weight of the cosine embedding loss. Must be >=0."
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)
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parser.add_argument("--mlm", action="store_true",
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help="The LM step: MLM or CLM. If `mlm` is True, the MLM is used over CLM.")
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parser.add_argument("--mlm_mask_prop", default=0.15, type=float,
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help="Proportion of tokens for which we need to make a prediction.")
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parser.add_argument("--word_mask", default=0.8, type=float,
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help="Proportion of tokens to mask out.")
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parser.add_argument("--word_keep", default=0.1, type=float,
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help="Proportion of tokens to keep.")
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parser.add_argument("--word_rand", default=0.1, type=float,
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help="Proportion of tokens to randomly replace.")
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parser.add_argument("--mlm_smoothing", default=0.7, type=float,
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help="Smoothing parameter to emphasize more rare tokens (see XLM, similar to word2vec).")
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parser.add_argument("--token_counts", type=str,
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help="The token counts in the data_file for MLM.")
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parser.add_argument(
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"--mlm", action="store_true", help="The LM step: MLM or CLM. If `mlm` is True, the MLM is used over CLM."
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)
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parser.add_argument(
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"--mlm_mask_prop",
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default=0.15,
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type=float,
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help="Proportion of tokens for which we need to make a prediction.",
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)
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parser.add_argument("--word_mask", default=0.8, type=float, help="Proportion of tokens to mask out.")
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parser.add_argument("--word_keep", default=0.1, type=float, help="Proportion of tokens to keep.")
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parser.add_argument("--word_rand", default=0.1, type=float, help="Proportion of tokens to randomly replace.")
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parser.add_argument(
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"--mlm_smoothing",
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default=0.7,
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type=float,
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help="Smoothing parameter to emphasize more rare tokens (see XLM, similar to word2vec).",
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)
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parser.add_argument("--token_counts", type=str, help="The token counts in the data_file for MLM.")
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parser.add_argument("--restrict_ce_to_mask", action='store_true',
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help="If true, compute the distilation loss only the [MLM] prediction distribution.")
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parser.add_argument("--freeze_pos_embs", action="store_true",
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help="Freeze positional embeddings during distillation. For student_type in ['roberta', 'gpt2'] only.")
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parser.add_argument("--freeze_token_type_embds", action="store_true",
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help="Freeze token type embeddings during distillation if existent. For student_type in ['roberta'] only.")
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parser.add_argument(
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"--restrict_ce_to_mask",
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action="store_true",
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help="If true, compute the distilation loss only the [MLM] prediction distribution.",
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)
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parser.add_argument(
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"--freeze_pos_embs",
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action="store_true",
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help="Freeze positional embeddings during distillation. For student_type in ['roberta', 'gpt2'] only.",
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)
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parser.add_argument(
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"--freeze_token_type_embds",
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action="store_true",
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help="Freeze token type embeddings during distillation if existent. For student_type in ['roberta'] only.",
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)
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parser.add_argument("--n_epoch", type=int, default=3,
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help="Number of pass on the whole dataset.")
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parser.add_argument("--batch_size", type=int, default=5,
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help="Batch size (for each process).")
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parser.add_argument("--group_by_size", action='store_false',
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help="If true, group sequences that have similar length into the same batch. Default is true.")
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parser.add_argument("--n_epoch", type=int, default=3, help="Number of pass on the whole dataset.")
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parser.add_argument("--batch_size", type=int, default=5, help="Batch size (for each process).")
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parser.add_argument(
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"--group_by_size",
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action="store_false",
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help="If true, group sequences that have similar length into the same batch. Default is true.",
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)
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parser.add_argument("--gradient_accumulation_steps", type=int, default=50,
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help="Gradient accumulation for larger training batches.")
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parser.add_argument("--warmup_prop", default=0.05, type=float,
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help="Linear warmup proportion.")
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parser.add_argument("--weight_decay", default=0.0, type=float,
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help="Weight deay if we apply some.")
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parser.add_argument("--learning_rate", default=5e-4, type=float,
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help="The initial learning rate for Adam.")
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parser.add_argument("--adam_epsilon", default=1e-6, type=float,
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help="Epsilon for Adam optimizer.")
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parser.add_argument("--max_grad_norm", default=5.0, type=float,
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help="Max gradient norm.")
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parser.add_argument("--initializer_range", default=0.02, type=float,
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help="Random initialization range.")
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parser.add_argument(
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"--gradient_accumulation_steps",
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type=int,
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default=50,
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help="Gradient accumulation for larger training batches.",
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)
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parser.add_argument("--warmup_prop", default=0.05, type=float, help="Linear warmup proportion.")
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parser.add_argument("--weight_decay", default=0.0, type=float, help="Weight deay if we apply some.")
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parser.add_argument("--learning_rate", default=5e-4, type=float, help="The initial learning rate for Adam.")
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parser.add_argument("--adam_epsilon", default=1e-6, type=float, help="Epsilon for Adam optimizer.")
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parser.add_argument("--max_grad_norm", default=5.0, type=float, help="Max gradient norm.")
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parser.add_argument("--initializer_range", default=0.02, type=float, help="Random initialization range.")
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parser.add_argument('--fp16', action='store_true',
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help="Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit")
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parser.add_argument('--fp16_opt_level', type=str, default='O1',
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help="For fp16: Apex AMP optimization level selected in ['O0', 'O1', 'O2', and 'O3']."
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"See details at https://nvidia.github.io/apex/amp.html")
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parser.add_argument("--n_gpu", type=int, default=1,
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help="Number of GPUs in the node.")
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parser.add_argument("--local_rank", type=int, default=-1,
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help="Distributed training - Local rank")
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parser.add_argument("--seed", type=int, default=56,
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help="Random seed")
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parser.add_argument(
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"--fp16",
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action="store_true",
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help="Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit",
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)
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parser.add_argument(
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"--fp16_opt_level",
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type=str,
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default="O1",
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help="For fp16: Apex AMP optimization level selected in ['O0', 'O1', 'O2', and 'O3']."
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"See details at https://nvidia.github.io/apex/amp.html",
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)
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parser.add_argument("--n_gpu", type=int, default=1, help="Number of GPUs in the node.")
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parser.add_argument("--local_rank", type=int, default=-1, help="Distributed training - Local rank")
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parser.add_argument("--seed", type=int, default=56, help="Random seed")
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parser.add_argument("--log_interval", type=int, default=500,
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help="Tensorboard logging interval.")
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parser.add_argument("--checkpoint_interval", type=int, default=4000,
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help="Checkpoint interval.")
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parser.add_argument("--log_interval", type=int, default=500, help="Tensorboard logging interval.")
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parser.add_argument("--checkpoint_interval", type=int, default=4000, help="Checkpoint interval.")
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args = parser.parse_args()
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sanity_checks(args)
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## ARGS ##
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init_gpu_params(args)
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set_seed(args)
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if args.is_master:
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if os.path.exists(args.dump_path):
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if not args.force:
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raise ValueError(f'Serialization dir {args.dump_path} already exists, but you have not precised wheter to overwrite it'
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'Use `--force` if you want to overwrite it')
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raise ValueError(
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f"Serialization dir {args.dump_path} already exists, but you have not precised wheter to overwrite it"
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"Use `--force` if you want to overwrite it"
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)
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else:
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shutil.rmtree(args.dump_path)
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if not os.path.exists(args.dump_path):
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os.makedirs(args.dump_path)
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logger.info(f'Experiment will be dumped and logged in {args.dump_path}')
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logger.info(f"Experiment will be dumped and logged in {args.dump_path}")
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### SAVE PARAMS ###
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logger.info(f'Param: {args}')
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with open(os.path.join(args.dump_path, 'parameters.json'), 'w') as f:
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logger.info(f"Param: {args}")
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with open(os.path.join(args.dump_path, "parameters.json"), "w") as f:
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json.dump(vars(args), f, indent=4)
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git_log(args.dump_path)
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@@ -207,58 +241,50 @@ def main():
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for tok_name, tok_symbol in tokenizer.special_tokens_map.items():
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idx = tokenizer.all_special_tokens.index(tok_symbol)
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special_tok_ids[tok_name] = tokenizer.all_special_ids[idx]
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logger.info(f'Special tokens {special_tok_ids}')
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logger.info(f"Special tokens {special_tok_ids}")
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args.special_tok_ids = special_tok_ids
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args.max_model_input_size = tokenizer.max_model_input_sizes[args.teacher_name]
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## DATA LOADER ##
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logger.info(f'Loading data from {args.data_file}')
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with open(args.data_file, 'rb') as fp:
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logger.info(f"Loading data from {args.data_file}")
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with open(args.data_file, "rb") as fp:
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data = pickle.load(fp)
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if args.mlm:
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logger.info(f'Loading token counts from {args.token_counts} (already pre-computed)')
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with open(args.token_counts, 'rb') as fp:
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logger.info(f"Loading token counts from {args.token_counts} (already pre-computed)")
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with open(args.token_counts, "rb") as fp:
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counts = pickle.load(fp)
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token_probs = np.maximum(counts, 1) ** -args.mlm_smoothing
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for idx in special_tok_ids.values():
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token_probs[idx] = 0. # do not predict special tokens
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token_probs[idx] = 0.0 # do not predict special tokens
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token_probs = torch.from_numpy(token_probs)
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else:
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token_probs = None
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train_lm_seq_dataset = LmSeqsDataset(params=args, data=data)
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logger.info(f'Data loader created.')
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logger.info(f"Data loader created.")
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## STUDENT ##
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logger.info(f'Loading student config from {args.student_config}')
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logger.info(f"Loading student config from {args.student_config}")
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stu_architecture_config = student_config_class.from_pretrained(args.student_config)
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stu_architecture_config.output_hidden_states = True
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if args.student_pretrained_weights is not None:
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logger.info(f'Loading pretrained weights from {args.student_pretrained_weights}')
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student = student_model_class.from_pretrained(args.student_pretrained_weights,
|
||||
config=stu_architecture_config)
|
||||
logger.info(f"Loading pretrained weights from {args.student_pretrained_weights}")
|
||||
student = student_model_class.from_pretrained(args.student_pretrained_weights, config=stu_architecture_config)
|
||||
else:
|
||||
student = student_model_class(stu_architecture_config)
|
||||
|
||||
|
||||
if args.n_gpu > 0:
|
||||
student.to(f'cuda:{args.local_rank}')
|
||||
logger.info(f'Student loaded.')
|
||||
|
||||
student.to(f"cuda:{args.local_rank}")
|
||||
logger.info(f"Student loaded.")
|
||||
|
||||
## TEACHER ##
|
||||
teacher = teacher_model_class.from_pretrained(args.teacher_name, output_hidden_states=True)
|
||||
if args.n_gpu > 0:
|
||||
teacher.to(f'cuda:{args.local_rank}')
|
||||
logger.info(f'Teacher loaded from {args.teacher_name}.')
|
||||
|
||||
teacher.to(f"cuda:{args.local_rank}")
|
||||
logger.info(f"Teacher loaded from {args.teacher_name}.")
|
||||
|
||||
## FREEZING ##
|
||||
if args.freeze_pos_embs:
|
||||
@@ -266,7 +292,6 @@ def main():
|
||||
if args.freeze_token_type_embds:
|
||||
freeze_token_type_embeddings(student, args)
|
||||
|
||||
|
||||
## SANITY CHECKS ##
|
||||
assert student.config.vocab_size == teacher.config.vocab_size
|
||||
assert student.config.hidden_size == teacher.config.hidden_size
|
||||
@@ -274,14 +299,11 @@ def main():
|
||||
if args.mlm:
|
||||
assert token_probs.size(0) == stu_architecture_config.vocab_size
|
||||
|
||||
|
||||
## DISTILLER ##
|
||||
torch.cuda.empty_cache()
|
||||
distiller = Distiller(params=args,
|
||||
dataset=train_lm_seq_dataset,
|
||||
token_probs=token_probs,
|
||||
student=student,
|
||||
teacher=teacher)
|
||||
distiller = Distiller(
|
||||
params=args, dataset=train_lm_seq_dataset, token_probs=token_probs, student=student, teacher=teacher
|
||||
)
|
||||
distiller.train()
|
||||
logger.info("Let's go get some drinks.")
|
||||
|
||||
|
||||
Reference in New Issue
Block a user