376 lines
14 KiB
Python
Executable File
376 lines
14 KiB
Python
Executable File
#!/usr/bin/env python
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# Copyright 2020 The HuggingFace Team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import logging
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import os
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import sys
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from dataclasses import dataclass, field
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from typing import Optional
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from seq2seq_trainer import Seq2SeqTrainer
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from seq2seq_training_args import Seq2SeqTrainingArguments
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import transformers
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from transformers import (
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AutoConfig,
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AutoModelForSeq2SeqLM,
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AutoTokenizer,
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HfArgumentParser,
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MBartTokenizer,
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MBartTokenizerFast,
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set_seed,
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)
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from transformers.trainer_utils import EvaluationStrategy, is_main_process
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from transformers.training_args import ParallelMode
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from utils import (
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Seq2SeqDataCollator,
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Seq2SeqDataset,
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assert_all_frozen,
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build_compute_metrics_fn,
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check_output_dir,
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freeze_embeds,
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freeze_params,
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lmap,
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save_json,
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use_task_specific_params,
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write_txt_file,
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)
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logger = logging.getLogger(__name__)
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@dataclass
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class ModelArguments:
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"""
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Arguments pertaining to which model/config/tokenizer we are going to fine-tune from.
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"""
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model_name_or_path: str = field(
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metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"}
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)
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config_name: Optional[str] = field(
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default=None, metadata={"help": "Pretrained config name or path if not the same as model_name"}
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)
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tokenizer_name: Optional[str] = field(
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default=None, metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"}
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)
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cache_dir: Optional[str] = field(
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default=None,
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metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"},
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)
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freeze_encoder: bool = field(default=False, metadata={"help": "Whether tp freeze the encoder."})
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freeze_embeds: bool = field(default=False, metadata={"help": "Whether to freeze the embeddings."})
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@dataclass
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class DataTrainingArguments:
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"""
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Arguments pertaining to what data we are going to input our model for training and eval.
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"""
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data_dir: str = field(
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metadata={"help": "The input data dir. Should contain the .tsv files (or other data files) for the task."}
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)
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task: Optional[str] = field(
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default="summarization",
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metadata={"help": "Task name, summarization (or summarization_{dataset} for pegasus) or translation"},
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)
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max_source_length: Optional[int] = field(
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default=1024,
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metadata={
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"help": (
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"The maximum total input sequence length after tokenization. Sequences longer "
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"than this will be truncated, sequences shorter will be padded."
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)
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},
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)
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max_target_length: Optional[int] = field(
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default=128,
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metadata={
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"help": (
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"The maximum total sequence length for target text after tokenization. Sequences longer "
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"than this will be truncated, sequences shorter will be padded."
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)
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},
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)
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val_max_target_length: Optional[int] = field(
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default=142,
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metadata={
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"help": (
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"The maximum total sequence length for validation target text after tokenization. Sequences longer "
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"than this will be truncated, sequences shorter will be padded. "
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"This argument is also used to override the ``max_length`` param of ``model.generate``, which is used "
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"during ``evaluate`` and ``predict``."
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)
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},
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)
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test_max_target_length: Optional[int] = field(
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default=142,
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metadata={
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"help": (
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"The maximum total sequence length for test target text after tokenization. Sequences longer "
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"than this will be truncated, sequences shorter will be padded."
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)
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},
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)
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n_train: Optional[int] = field(default=-1, metadata={"help": "# training examples. -1 means use all."})
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n_val: Optional[int] = field(default=-1, metadata={"help": "# validation examples. -1 means use all."})
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n_test: Optional[int] = field(default=-1, metadata={"help": "# test examples. -1 means use all."})
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src_lang: Optional[str] = field(default=None, metadata={"help": "Source language id for translation."})
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tgt_lang: Optional[str] = field(default=None, metadata={"help": "Target language id for translation."})
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eval_beams: Optional[int] = field(default=None, metadata={"help": "# num_beams to use for evaluation."})
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ignore_pad_token_for_loss: bool = field(
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default=True,
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metadata={"help": "If only pad tokens should be ignored. This assumes that `config.pad_token_id` is defined."},
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)
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def handle_metrics(split, metrics, output_dir):
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"""
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Log and save metrics
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Args:
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- split: one of train, val, test
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- metrics: metrics dict
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- output_dir: where to save the metrics
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"""
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logger.info(f"***** {split} metrics *****")
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for key in sorted(metrics.keys()):
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logger.info(f" {key} = {metrics[key]}")
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save_json(metrics, os.path.join(output_dir, f"{split}_results.json"))
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def main():
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# See all possible arguments in src/transformers/training_args.py
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# or by passing the --help flag to this script.
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# We now keep distinct sets of args, for a cleaner separation of concerns.
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parser = HfArgumentParser((ModelArguments, DataTrainingArguments, Seq2SeqTrainingArguments))
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if len(sys.argv) == 2 and sys.argv[1].endswith(".json"):
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# If we pass only one argument to the script and it's the path to a json file,
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# let's parse it to get our arguments.
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model_args, data_args, training_args = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]))
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else:
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model_args, data_args, training_args = parser.parse_args_into_dataclasses()
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check_output_dir(training_args)
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# Setup logging
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logging.basicConfig(
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format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
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datefmt="%m/%d/%Y %H:%M:%S",
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level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN,
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)
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logger.warning(
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"Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s",
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training_args.local_rank,
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training_args.device,
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training_args.n_gpu,
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bool(training_args.parallel_mode == ParallelMode.DISTRIBUTED),
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training_args.fp16,
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)
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transformers.utils.logging.enable_default_handler()
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transformers.utils.logging.enable_explicit_format()
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# Set the verbosity to info of the Transformers logger (on main process only):
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if is_main_process(training_args.local_rank):
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transformers.utils.logging.set_verbosity_info()
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logger.info("Training/evaluation parameters %s", training_args)
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# Set seed
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set_seed(training_args.seed)
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# Load pretrained model and tokenizer
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#
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# Distributed training:
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# The .from_pretrained methods guarantee that only one local process can concurrently
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# download model & vocab.
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config = AutoConfig.from_pretrained(
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model_args.config_name if model_args.config_name else model_args.model_name_or_path,
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cache_dir=model_args.cache_dir,
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)
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extra_model_params = ("encoder_layerdrop", "decoder_layerdrop", "dropout", "attention_dropout")
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for p in extra_model_params:
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if getattr(training_args, p, None):
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assert hasattr(config, p), f"({config.__class__.__name__}) doesn't have a `{p}` attribute"
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setattr(config, p, getattr(training_args, p))
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tokenizer = AutoTokenizer.from_pretrained(
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model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path,
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cache_dir=model_args.cache_dir,
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)
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model = AutoModelForSeq2SeqLM.from_pretrained(
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model_args.model_name_or_path,
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from_tf=".ckpt" in model_args.model_name_or_path,
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config=config,
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cache_dir=model_args.cache_dir,
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)
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# use task specific params
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use_task_specific_params(model, data_args.task)
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# set num_beams for evaluation
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if data_args.eval_beams is None:
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data_args.eval_beams = model.config.num_beams
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# set decoder_start_token_id for MBart
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if model.config.decoder_start_token_id is None and isinstance(tokenizer, (MBartTokenizer, MBartTokenizerFast)):
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assert (
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data_args.tgt_lang is not None and data_args.src_lang is not None
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), "mBart requires --tgt_lang and --src_lang"
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if isinstance(tokenizer, MBartTokenizer):
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model.config.decoder_start_token_id = tokenizer.lang_code_to_id[data_args.tgt_lang]
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else:
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model.config.decoder_start_token_id = tokenizer.convert_tokens_to_ids(data_args.tgt_lang)
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if model_args.freeze_embeds:
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freeze_embeds(model)
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if model_args.freeze_encoder:
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freeze_params(model.get_encoder())
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assert_all_frozen(model.get_encoder())
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dataset_class = Seq2SeqDataset
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# Get datasets
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train_dataset = (
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dataset_class(
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tokenizer,
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type_path="train",
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data_dir=data_args.data_dir,
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n_obs=data_args.n_train,
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max_target_length=data_args.max_target_length,
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max_source_length=data_args.max_source_length,
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prefix=model.config.prefix or "",
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)
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if training_args.do_train
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else None
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)
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eval_dataset = (
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dataset_class(
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tokenizer,
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type_path="val",
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data_dir=data_args.data_dir,
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n_obs=data_args.n_val,
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max_target_length=data_args.val_max_target_length,
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max_source_length=data_args.max_source_length,
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prefix=model.config.prefix or "",
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)
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if training_args.do_eval or training_args.eval_strategy != EvaluationStrategy.NO
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else None
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)
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test_dataset = (
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dataset_class(
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tokenizer,
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type_path="test",
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data_dir=data_args.data_dir,
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n_obs=data_args.n_test,
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max_target_length=data_args.test_max_target_length,
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max_source_length=data_args.max_source_length,
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prefix=model.config.prefix or "",
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)
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if training_args.do_predict
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else None
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)
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# Initialize our Trainer
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compute_metrics_fn = (
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build_compute_metrics_fn(data_args.task, tokenizer) if training_args.predict_with_generate else None
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)
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trainer = Seq2SeqTrainer(
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model=model,
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args=training_args,
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data_args=data_args,
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train_dataset=train_dataset,
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eval_dataset=eval_dataset,
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data_collator=Seq2SeqDataCollator(
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tokenizer, data_args, model.config.decoder_start_token_id, training_args.tpu_num_cores
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),
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compute_metrics=compute_metrics_fn,
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tokenizer=tokenizer,
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)
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all_metrics = {}
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# Training
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if training_args.do_train:
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logger.info("*** Train ***")
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train_result = trainer.train(
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model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path) else None
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)
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metrics = train_result.metrics
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metrics["train_n_objs"] = data_args.n_train
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trainer.save_model() # this also saves the tokenizer
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if trainer.is_world_process_zero():
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handle_metrics("train", metrics, training_args.output_dir)
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all_metrics.update(metrics)
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# Need to save the state, since Trainer.save_model saves only the tokenizer with the model
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trainer.state.save_to_json(os.path.join(training_args.output_dir, "trainer_state.json"))
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# For convenience, we also re-save the tokenizer to the same directory,
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# so that you can share your model easily on huggingface.co/models =)
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tokenizer.save_pretrained(training_args.output_dir)
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# Evaluation
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if training_args.do_eval:
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logger.info("*** Evaluate ***")
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metrics = trainer.evaluate(metric_key_prefix="val")
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metrics["val_n_objs"] = data_args.n_val
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metrics["val_loss"] = round(metrics["val_loss"], 4)
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if trainer.is_world_process_zero():
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handle_metrics("val", metrics, training_args.output_dir)
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all_metrics.update(metrics)
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if training_args.do_predict:
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logger.info("*** Predict ***")
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test_output = trainer.predict(test_dataset=test_dataset, metric_key_prefix="test")
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metrics = test_output.metrics
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metrics["test_n_objs"] = data_args.n_test
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if trainer.is_world_process_zero():
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metrics["test_loss"] = round(metrics["test_loss"], 4)
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handle_metrics("test", metrics, training_args.output_dir)
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all_metrics.update(metrics)
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if training_args.predict_with_generate:
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test_preds = tokenizer.batch_decode(
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test_output.predictions, skip_special_tokens=True, clean_up_tokenization_spaces=True
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)
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test_preds = lmap(str.strip, test_preds)
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write_txt_file(test_preds, os.path.join(training_args.output_dir, "test_generations.txt"))
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if trainer.is_world_process_zero():
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save_json(all_metrics, os.path.join(training_args.output_dir, "all_results.json"))
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return all_metrics
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def _mp_fn(index):
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# For xla_spawn (TPUs)
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main()
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if __name__ == "__main__":
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main()
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