mirror of
https://github.com/huggingface/transformers.git
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681 lines
28 KiB
Python
681 lines
28 KiB
Python
#!/usr/bin/env python
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# coding=utf-8
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# Copyright 2021 The HuggingFace Inc. 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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""" Fine-tuning a 🤗 Flax Transformers model on token classification tasks (NER, POS, CHUNKS)"""
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import logging
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import os
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import random
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import sys
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import time
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from dataclasses import dataclass, field
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from itertools import chain
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from pathlib import Path
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from typing import Any, Callable, Dict, Optional, Tuple
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import datasets
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import numpy as np
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from datasets import ClassLabel, load_dataset, load_metric
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from tqdm import tqdm
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import jax
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import jax.numpy as jnp
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import optax
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import transformers
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from flax import struct, traverse_util
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from flax.jax_utils import replicate, unreplicate
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from flax.metrics import tensorboard
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from flax.training import train_state
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from flax.training.common_utils import get_metrics, onehot, shard
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from huggingface_hub import Repository
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from transformers import (
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AutoConfig,
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AutoTokenizer,
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FlaxAutoModelForTokenClassification,
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HfArgumentParser,
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TrainingArguments,
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)
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from transformers.file_utils import get_full_repo_name
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from transformers.utils import check_min_version
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from transformers.utils.versions import require_version
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logger = logging.getLogger(__name__)
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# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
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check_min_version("4.13.0.dev0")
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require_version("datasets>=1.8.0", "To fix: pip install -r examples/pytorch/token-classification/requirements.txt")
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Array = Any
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Dataset = datasets.arrow_dataset.Dataset
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PRNGKey = Any
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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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model_revision: str = field(
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default="main",
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metadata={"help": "The specific model version to use (can be a branch name, tag name or commit id)."},
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)
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use_auth_token: bool = field(
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default=False,
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metadata={
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"help": "Will use the token generated when running `transformers-cli login` (necessary to use this script "
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"with private models)."
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},
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)
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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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task_name: Optional[str] = field(default="ner", metadata={"help": "The name of the task (ner, pos...)."})
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dataset_name: Optional[str] = field(
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default=None, metadata={"help": "The name of the dataset to use (via the datasets library)."}
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)
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dataset_config_name: Optional[str] = field(
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default=None, metadata={"help": "The configuration name of the dataset to use (via the datasets library)."}
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)
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train_file: Optional[str] = field(
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default=None, metadata={"help": "The input training data file (a csv or JSON file)."}
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)
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validation_file: Optional[str] = field(
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default=None,
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metadata={"help": "An optional input evaluation data file to evaluate on (a csv or JSON file)."},
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)
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test_file: Optional[str] = field(
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default=None,
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metadata={"help": "An optional input test data file to predict on (a csv or JSON file)."},
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)
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text_column_name: Optional[str] = field(
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default=None, metadata={"help": "The column name of text to input in the file (a csv or JSON file)."}
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)
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label_column_name: Optional[str] = field(
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default=None, metadata={"help": "The column name of label to input in the file (a csv or JSON file)."}
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)
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overwrite_cache: bool = field(
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default=False, metadata={"help": "Overwrite the cached training and evaluation sets"}
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)
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preprocessing_num_workers: Optional[int] = field(
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default=None,
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metadata={"help": "The number of processes to use for the preprocessing."},
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)
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max_seq_length: int = field(
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default=None,
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metadata={
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"help": "The maximum total input sequence length after tokenization. If set, 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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max_train_samples: Optional[int] = field(
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default=None,
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metadata={
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"help": "For debugging purposes or quicker training, truncate the number of training examples to this "
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"value if set."
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},
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)
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max_eval_samples: Optional[int] = field(
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default=None,
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metadata={
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"help": "For debugging purposes or quicker training, truncate the number of evaluation examples to this "
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"value if set."
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},
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)
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max_predict_samples: Optional[int] = field(
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default=None,
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metadata={
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"help": "For debugging purposes or quicker training, truncate the number of prediction examples to this "
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"value if set."
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},
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)
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label_all_tokens: bool = field(
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default=False,
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metadata={
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"help": "Whether to put the label for one word on all tokens of generated by that word or just on the "
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"one (in which case the other tokens will have a padding index)."
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},
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)
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return_entity_level_metrics: bool = field(
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default=False,
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metadata={"help": "Whether to return all the entity levels during evaluation or just the overall ones."},
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)
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def __post_init__(self):
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if self.dataset_name is None and self.train_file is None and self.validation_file is None:
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raise ValueError("Need either a dataset name or a training/validation file.")
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else:
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if self.train_file is not None:
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extension = self.train_file.split(".")[-1]
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assert extension in ["csv", "json"], "`train_file` should be a csv or a json file."
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if self.validation_file is not None:
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extension = self.validation_file.split(".")[-1]
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assert extension in ["csv", "json"], "`validation_file` should be a csv or a json file."
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self.task_name = self.task_name.lower()
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def create_train_state(
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model: FlaxAutoModelForTokenClassification,
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learning_rate_fn: Callable[[int], float],
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num_labels: int,
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training_args: TrainingArguments,
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) -> train_state.TrainState:
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"""Create initial training state."""
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class TrainState(train_state.TrainState):
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"""Train state with an Optax optimizer.
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The two functions below differ depending on whether the task is classification
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or regression.
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Args:
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logits_fn: Applied to last layer to obtain the logits.
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loss_fn: Function to compute the loss.
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"""
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logits_fn: Callable = struct.field(pytree_node=False)
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loss_fn: Callable = struct.field(pytree_node=False)
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# We use Optax's "masking" functionality to not apply weight decay
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# to bias and LayerNorm scale parameters. decay_mask_fn returns a
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# mask boolean with the same structure as the parameters.
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# The mask is True for parameters that should be decayed.
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# Note that this mask is specifically adapted for FlaxBERT-like models.
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# For other models, one should correct the layer norm parameter naming
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# accordingly.
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def decay_mask_fn(params):
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flat_params = traverse_util.flatten_dict(params)
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flat_mask = {path: (path[-1] != "bias" and path[-2:] != ("LayerNorm", "scale")) for path in flat_params}
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return traverse_util.unflatten_dict(flat_mask)
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tx = optax.adamw(
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learning_rate=learning_rate_fn,
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b1=training_args.adam_beta1,
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b2=training_args.adam_beta2,
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eps=training_args.adam_epsilon,
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weight_decay=training_args.weight_decay,
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mask=decay_mask_fn,
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)
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def cross_entropy_loss(logits, labels):
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xentropy = optax.softmax_cross_entropy(logits, onehot(labels, num_classes=num_labels))
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return jnp.mean(xentropy)
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return TrainState.create(
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apply_fn=model.__call__,
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params=model.params,
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tx=tx,
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logits_fn=lambda logits: logits.argmax(-1),
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loss_fn=cross_entropy_loss,
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)
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def create_learning_rate_fn(
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train_ds_size: int, train_batch_size: int, num_train_epochs: int, num_warmup_steps: int, learning_rate: float
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) -> Callable[[int], jnp.array]:
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"""Returns a linear warmup, linear_decay learning rate function."""
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steps_per_epoch = train_ds_size // train_batch_size
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num_train_steps = steps_per_epoch * num_train_epochs
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warmup_fn = optax.linear_schedule(init_value=0.0, end_value=learning_rate, transition_steps=num_warmup_steps)
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decay_fn = optax.linear_schedule(
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init_value=learning_rate, end_value=0, transition_steps=num_train_steps - num_warmup_steps
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)
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schedule_fn = optax.join_schedules(schedules=[warmup_fn, decay_fn], boundaries=[num_warmup_steps])
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return schedule_fn
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def train_data_collator(rng: PRNGKey, dataset: Dataset, batch_size: int):
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"""Returns shuffled batches of size `batch_size` from truncated `train dataset`, sharded over all local devices."""
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steps_per_epoch = len(dataset) // batch_size
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perms = jax.random.permutation(rng, len(dataset))
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perms = perms[: steps_per_epoch * batch_size] # Skip incomplete batch.
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perms = perms.reshape((steps_per_epoch, batch_size))
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for perm in perms:
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batch = dataset[perm]
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batch = {k: np.array(v) for k, v in batch.items()}
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batch = shard(batch)
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yield batch
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def eval_data_collator(dataset: Dataset, batch_size: int):
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"""Returns batches of size `batch_size` from `eval dataset`, sharded over all local devices."""
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for i in range(len(dataset) // batch_size):
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batch = dataset[i * batch_size : (i + 1) * batch_size]
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batch = {k: np.array(v) for k, v in batch.items()}
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batch = shard(batch)
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yield batch
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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, TrainingArguments))
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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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# Make one log on every process with the configuration for debugging.
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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,
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)
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# Setup logging, we only want one process per machine to log things on the screen.
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logger.setLevel(logging.INFO if jax.process_index() == 0 else logging.ERROR)
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if jax.process_index() == 0:
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datasets.utils.logging.set_verbosity_warning()
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transformers.utils.logging.set_verbosity_info()
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else:
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datasets.utils.logging.set_verbosity_error()
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transformers.utils.logging.set_verbosity_error()
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# Handle the repository creation
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if training_args.push_to_hub:
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if training_args.hub_model_id is None:
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repo_name = get_full_repo_name(
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Path(training_args.output_dir).absolute().name, token=training_args.hub_token
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)
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else:
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repo_name = training_args.hub_model_id
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repo = Repository(training_args.output_dir, clone_from=repo_name)
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# Get the datasets: you can either provide your own CSV/JSON/TXT training and evaluation files (see below)
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# or just provide the name of one of the public datasets for token classification task available on the hub at https://huggingface.co/datasets/
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# (the dataset will be downloaded automatically from the datasets Hub).
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#
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# For CSV/JSON files, this script will use the column called 'tokens' or the first column if no column called
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# 'tokens' is found. You can easily tweak this behavior (see below).
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#
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# In distributed training, the load_dataset function guarantee that only one local process can concurrently
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# download the dataset.
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if data_args.dataset_name is not None:
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# Downloading and loading a dataset from the hub.
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raw_datasets = load_dataset(
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data_args.dataset_name, data_args.dataset_config_name, cache_dir=model_args.cache_dir
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)
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else:
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# Loading the dataset from local csv or json file.
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data_files = {}
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if data_args.train_file is not None:
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data_files["train"] = data_args.train_file
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if data_args.validation_file is not None:
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data_files["validation"] = data_args.validation_file
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extension = (data_args.train_file if data_args.train_file is not None else data_args.valid_file).split(".")[-1]
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raw_datasets = load_dataset(extension, data_files=data_files, cache_dir=model_args.cache_dir)
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# See more about loading any type of standard or custom dataset at
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# https://huggingface.co/docs/datasets/loading_datasets.html.
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if raw_datasets["train"] is not None:
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column_names = raw_datasets["train"].column_names
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features = raw_datasets["train"].features
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else:
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column_names = raw_datasets["validation"].column_names
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features = raw_datasets["validation"].features
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if data_args.text_column_name is not None:
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text_column_name = data_args.text_column_name
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elif "tokens" in column_names:
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text_column_name = "tokens"
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else:
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text_column_name = column_names[0]
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if data_args.label_column_name is not None:
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label_column_name = data_args.label_column_name
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elif f"{data_args.task_name}_tags" in column_names:
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label_column_name = f"{data_args.task_name}_tags"
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else:
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label_column_name = column_names[1]
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# In the event the labels are not a `Sequence[ClassLabel]`, we will need to go through the dataset to get the
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# unique labels.
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def get_label_list(labels):
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unique_labels = set()
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for label in labels:
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unique_labels = unique_labels | set(label)
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label_list = list(unique_labels)
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label_list.sort()
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return label_list
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if isinstance(features[label_column_name].feature, ClassLabel):
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label_list = features[label_column_name].feature.names
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# No need to convert the labels since they are already ints.
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label_to_id = {i: i for i in range(len(label_list))}
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else:
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label_list = get_label_list(raw_datasets["train"][label_column_name])
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label_to_id = {l: i for i, l in enumerate(label_list)}
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num_labels = len(label_list)
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# Load pretrained model and tokenizer
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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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num_labels=num_labels,
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label2id=label_to_id,
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id2label={i: l for l, i in label_to_id.items()},
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finetuning_task=data_args.task_name,
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cache_dir=model_args.cache_dir,
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revision=model_args.model_revision,
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use_auth_token=True if model_args.use_auth_token else None,
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)
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tokenizer_name_or_path = model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path
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if config.model_type in {"gpt2", "roberta"}:
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tokenizer = AutoTokenizer.from_pretrained(
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tokenizer_name_or_path,
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cache_dir=model_args.cache_dir,
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revision=model_args.model_revision,
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use_auth_token=True if model_args.use_auth_token else None,
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add_prefix_space=True,
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)
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else:
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tokenizer = AutoTokenizer.from_pretrained(
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tokenizer_name_or_path,
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cache_dir=model_args.cache_dir,
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revision=model_args.model_revision,
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use_auth_token=True if model_args.use_auth_token else None,
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)
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model = FlaxAutoModelForTokenClassification.from_pretrained(
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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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revision=model_args.model_revision,
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use_auth_token=True if model_args.use_auth_token else None,
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)
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# Preprocessing the datasets
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# Tokenize all texts and align the labels with them.
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def tokenize_and_align_labels(examples):
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tokenized_inputs = tokenizer(
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examples[text_column_name],
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max_length=data_args.max_seq_length,
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padding="max_length",
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truncation=True,
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# We use this argument because the texts in our dataset are lists of words (with a label for each word).
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is_split_into_words=True,
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)
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labels = []
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for i, label in enumerate(examples[label_column_name]):
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word_ids = tokenized_inputs.word_ids(batch_index=i)
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previous_word_idx = None
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label_ids = []
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for word_idx in word_ids:
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# Special tokens have a word id that is None. We set the label to -100 so they are automatically
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# ignored in the loss function.
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if word_idx is None:
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label_ids.append(-100)
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# We set the label for the first token of each word.
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elif word_idx != previous_word_idx:
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label_ids.append(label_to_id[label[word_idx]])
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# For the other tokens in a word, we set the label to either the current label or -100, depending on
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# the label_all_tokens flag.
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else:
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label_ids.append(label_to_id[label[word_idx]] if data_args.label_all_tokens else -100)
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previous_word_idx = word_idx
|
|
|
|
labels.append(label_ids)
|
|
tokenized_inputs["labels"] = labels
|
|
return tokenized_inputs
|
|
|
|
processed_raw_datasets = raw_datasets.map(
|
|
tokenize_and_align_labels,
|
|
batched=True,
|
|
num_proc=data_args.preprocessing_num_workers,
|
|
load_from_cache_file=not data_args.overwrite_cache,
|
|
remove_columns=raw_datasets["train"].column_names,
|
|
desc="Running tokenizer on dataset",
|
|
)
|
|
|
|
train_dataset = processed_raw_datasets["train"]
|
|
eval_dataset = processed_raw_datasets["validation"]
|
|
|
|
# Log a few random samples from the training set:
|
|
for index in random.sample(range(len(train_dataset)), 3):
|
|
logger.info(f"Sample {index} of the training set: {train_dataset[index]}.")
|
|
|
|
# Define a summary writer
|
|
summary_writer = tensorboard.SummaryWriter(training_args.output_dir)
|
|
summary_writer.hparams({**training_args.to_dict(), **vars(model_args), **vars(data_args)})
|
|
|
|
def write_train_metric(summary_writer, train_metrics, train_time, step):
|
|
summary_writer.scalar("train_time", train_time, step)
|
|
|
|
train_metrics = get_metrics(train_metrics)
|
|
for key, vals in train_metrics.items():
|
|
tag = f"train_{key}"
|
|
for i, val in enumerate(vals):
|
|
summary_writer.scalar(tag, val, step - len(vals) + i + 1)
|
|
|
|
def write_eval_metric(summary_writer, eval_metrics, step):
|
|
for metric_name, value in eval_metrics.items():
|
|
summary_writer.scalar(f"eval_{metric_name}", value, step)
|
|
|
|
num_epochs = int(training_args.num_train_epochs)
|
|
rng = jax.random.PRNGKey(training_args.seed)
|
|
dropout_rngs = jax.random.split(rng, jax.local_device_count())
|
|
|
|
train_batch_size = training_args.per_device_train_batch_size * jax.local_device_count()
|
|
eval_batch_size = training_args.per_device_eval_batch_size * jax.local_device_count()
|
|
|
|
learning_rate_fn = create_learning_rate_fn(
|
|
len(train_dataset),
|
|
train_batch_size,
|
|
training_args.num_train_epochs,
|
|
training_args.warmup_steps,
|
|
training_args.learning_rate,
|
|
)
|
|
|
|
state = create_train_state(model, learning_rate_fn, num_labels=num_labels, training_args=training_args)
|
|
|
|
# define step functions
|
|
def train_step(
|
|
state: train_state.TrainState, batch: Dict[str, Array], dropout_rng: PRNGKey
|
|
) -> Tuple[train_state.TrainState, float]:
|
|
"""Trains model with an optimizer (both in `state`) on `batch`, returning a pair `(new_state, loss)`."""
|
|
dropout_rng, new_dropout_rng = jax.random.split(dropout_rng)
|
|
targets = batch.pop("labels")
|
|
|
|
def loss_fn(params):
|
|
logits = state.apply_fn(**batch, params=params, dropout_rng=dropout_rng, train=True)[0]
|
|
loss = state.loss_fn(logits, targets)
|
|
return loss
|
|
|
|
grad_fn = jax.value_and_grad(loss_fn)
|
|
loss, grad = grad_fn(state.params)
|
|
grad = jax.lax.pmean(grad, "batch")
|
|
new_state = state.apply_gradients(grads=grad)
|
|
metrics = jax.lax.pmean({"loss": loss, "learning_rate": learning_rate_fn(state.step)}, axis_name="batch")
|
|
return new_state, metrics, new_dropout_rng
|
|
|
|
p_train_step = jax.pmap(train_step, axis_name="batch", donate_argnums=(0,))
|
|
|
|
def eval_step(state, batch):
|
|
logits = state.apply_fn(**batch, params=state.params, train=False)[0]
|
|
return state.logits_fn(logits)
|
|
|
|
p_eval_step = jax.pmap(eval_step, axis_name="batch")
|
|
|
|
metric = load_metric("seqeval")
|
|
|
|
def get_labels(y_pred, y_true):
|
|
# Transform predictions and references tensos to numpy arrays
|
|
|
|
# Remove ignored index (special tokens)
|
|
true_predictions = [
|
|
[label_list[p] for (p, l) in zip(pred, gold_label) if l != -100]
|
|
for pred, gold_label in zip(y_pred, y_true)
|
|
]
|
|
true_labels = [
|
|
[label_list[l] for (p, l) in zip(pred, gold_label) if l != -100]
|
|
for pred, gold_label in zip(y_pred, y_true)
|
|
]
|
|
return true_predictions, true_labels
|
|
|
|
def compute_metrics():
|
|
results = metric.compute()
|
|
if data_args.return_entity_level_metrics:
|
|
# Unpack nested dictionaries
|
|
final_results = {}
|
|
for key, value in results.items():
|
|
if isinstance(value, dict):
|
|
for n, v in value.items():
|
|
final_results[f"{key}_{n}"] = v
|
|
else:
|
|
final_results[key] = value
|
|
return final_results
|
|
else:
|
|
return {
|
|
"precision": results["overall_precision"],
|
|
"recall": results["overall_recall"],
|
|
"f1": results["overall_f1"],
|
|
"accuracy": results["overall_accuracy"],
|
|
}
|
|
|
|
logger.info(f"===== Starting training ({num_epochs} epochs) =====")
|
|
train_time = 0
|
|
|
|
# make sure weights are replicated on each device
|
|
state = replicate(state)
|
|
|
|
train_time = 0
|
|
step_per_epoch = len(train_dataset) // train_batch_size
|
|
total_steps = step_per_epoch * num_epochs
|
|
epochs = tqdm(range(num_epochs), desc=f"Epoch ... (1/{num_epochs})", position=0)
|
|
for epoch in epochs:
|
|
|
|
train_start = time.time()
|
|
train_metrics = []
|
|
|
|
# Create sampling rng
|
|
rng, input_rng = jax.random.split(rng)
|
|
|
|
# train
|
|
for step, batch in enumerate(
|
|
tqdm(
|
|
train_data_collator(input_rng, train_dataset, train_batch_size),
|
|
total=step_per_epoch,
|
|
desc="Training...",
|
|
position=1,
|
|
)
|
|
):
|
|
state, train_metric, dropout_rngs = p_train_step(state, batch, dropout_rngs)
|
|
train_metrics.append(train_metric)
|
|
|
|
cur_step = (epoch * step_per_epoch) + (step + 1)
|
|
|
|
if cur_step % training_args.logging_steps == 0 and cur_step > 0:
|
|
# Save metrics
|
|
train_metric = unreplicate(train_metric)
|
|
train_time += time.time() - train_start
|
|
if jax.process_index() == 0:
|
|
write_train_metric(summary_writer, train_metrics, train_time, cur_step)
|
|
|
|
epochs.write(
|
|
f"Step... ({cur_step}/{total_steps} | Training Loss: {train_metric['loss']}, Learning Rate: {train_metric['learning_rate']})"
|
|
)
|
|
|
|
train_metrics = []
|
|
|
|
if cur_step % training_args.eval_steps == 0 and cur_step > 0:
|
|
|
|
eval_metrics = {}
|
|
# evaluate
|
|
for batch in tqdm(
|
|
eval_data_collator(eval_dataset, eval_batch_size),
|
|
total=len(eval_dataset) // eval_batch_size,
|
|
desc="Evaluating ...",
|
|
position=2,
|
|
):
|
|
labels = batch.pop("labels")
|
|
predictions = p_eval_step(state, batch)
|
|
predictions = np.array([pred for pred in chain(*predictions)])
|
|
labels = np.array([label for label in chain(*labels)])
|
|
labels[np.array(chain(*batch["attention_mask"])) == 0] = -100
|
|
preds, refs = get_labels(predictions, labels)
|
|
metric.add_batch(
|
|
predictions=preds,
|
|
references=refs,
|
|
)
|
|
|
|
# evaluate also on leftover examples (not divisible by batch_size)
|
|
num_leftover_samples = len(eval_dataset) % eval_batch_size
|
|
|
|
# make sure leftover batch is evaluated on one device
|
|
if num_leftover_samples > 0 and jax.process_index() == 0:
|
|
# take leftover samples
|
|
batch = eval_dataset[-num_leftover_samples:]
|
|
batch = {k: np.array(v) for k, v in batch.items()}
|
|
|
|
labels = batch.pop("labels")
|
|
predictions = eval_step(unreplicate(state), batch)
|
|
labels = np.array(labels)
|
|
labels[np.array(batch["attention_mask"]) == 0] = -100
|
|
preds, refs = get_labels(predictions, labels)
|
|
metric.add_batch(
|
|
predictions=preds,
|
|
references=refs,
|
|
)
|
|
|
|
eval_metrics = compute_metrics()
|
|
|
|
if data_args.return_entity_level_metrics:
|
|
logger.info(f"Step... ({cur_step}/{total_steps} | Validation metrics: {eval_metrics}")
|
|
else:
|
|
logger.info(
|
|
f"Step... ({cur_step}/{total_steps} | Validation f1: {eval_metrics['f1']}, Validation Acc: {eval_metrics['accuracy']})"
|
|
)
|
|
|
|
if jax.process_index() == 0:
|
|
write_eval_metric(summary_writer, eval_metrics, cur_step)
|
|
|
|
if (cur_step % training_args.save_steps == 0 and cur_step > 0) or (cur_step == total_steps):
|
|
# save checkpoint after each epoch and push checkpoint to the hub
|
|
if jax.process_index() == 0:
|
|
params = jax.device_get(unreplicate(state.params))
|
|
model.save_pretrained(training_args.output_dir, params=params)
|
|
tokenizer.save_pretrained(training_args.output_dir)
|
|
if training_args.push_to_hub:
|
|
repo.push_to_hub(commit_message=f"Saving weights and logs of step {cur_step}", blocking=False)
|
|
epochs.desc = f"Epoch ... {epoch + 1}/{num_epochs}"
|
|
|
|
|
|
if __name__ == "__main__":
|
|
main()
|