653 lines
28 KiB
Python
Executable File
653 lines
28 KiB
Python
Executable File
#!/usr/bin/env python
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# coding=utf-8
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# Copyright 2020 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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""" Finetuning the library models for sequence classification on GLUE."""
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# You can also adapt this script on your own text classification task. Pointers for this are left as comments.
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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 warnings
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from dataclasses import dataclass, field
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from typing import Optional
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import datasets
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import evaluate
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import numpy as np
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from datasets import load_dataset
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import transformers
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from transformers import (
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AutoConfig,
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AutoModelForSequenceClassification,
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AutoTokenizer,
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DataCollatorWithPadding,
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EvalPrediction,
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HfArgumentParser,
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PretrainedConfig,
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Trainer,
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TrainingArguments,
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default_data_collator,
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set_seed,
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)
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from transformers.trainer_utils import get_last_checkpoint
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from transformers.utils import check_min_version, send_example_telemetry
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from transformers.utils.versions import require_version
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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("4.38.0.dev0")
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require_version("datasets>=1.8.0", "To fix: pip install -r examples/pytorch/text-classification/requirements.txt")
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task_to_keys = {
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"cola": ("sentence", None),
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"mnli": ("premise", "hypothesis"),
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"mrpc": ("sentence1", "sentence2"),
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"qnli": ("question", "sentence"),
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"qqp": ("question1", "question2"),
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"rte": ("sentence1", "sentence2"),
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"sst2": ("sentence", None),
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"stsb": ("sentence1", "sentence2"),
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"wnli": ("sentence1", "sentence2"),
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}
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logger = logging.getLogger(__name__)
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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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Using `HfArgumentParser` we can turn this class
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into argparse arguments to be able to specify them on
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the command line.
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"""
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task_name: Optional[str] = field(
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default=None,
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metadata={"help": "The name of the task to train on: " + ", ".join(task_to_keys.keys())},
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)
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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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max_seq_length: 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 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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overwrite_cache: bool = field(
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default=False, metadata={"help": "Overwrite the cached preprocessed datasets or not."}
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)
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pad_to_max_length: bool = field(
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default=True,
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metadata={
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"help": (
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"Whether to pad all samples to `max_seq_length`. "
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"If False, will pad the samples dynamically when batching to the maximum length in the batch."
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)
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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": (
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"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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)
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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": (
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"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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)
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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": (
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"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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)
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train_file: Optional[str] = field(
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default=None, metadata={"help": "A csv or a json file containing the training data."}
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)
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validation_file: Optional[str] = field(
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default=None, metadata={"help": "A csv or a json file containing the validation data."}
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)
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test_file: Optional[str] = field(default=None, metadata={"help": "A csv or a json file containing the test data."})
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def __post_init__(self):
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if self.task_name is not None:
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self.task_name = self.task_name.lower()
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if self.task_name not in task_to_keys.keys():
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raise ValueError("Unknown task, you should pick one in " + ",".join(task_to_keys.keys()))
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elif self.dataset_name is not None:
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pass
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elif self.train_file is None or self.validation_file is None:
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raise ValueError("Need either a GLUE task, a training/validation file or a dataset name.")
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else:
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train_extension = self.train_file.split(".")[-1]
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assert train_extension in ["csv", "json"], "`train_file` should be a csv or a json file."
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validation_extension = self.validation_file.split(".")[-1]
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assert (
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validation_extension == train_extension
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), "`validation_file` should have the same extension (csv or json) as `train_file`."
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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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use_fast_tokenizer: bool = field(
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default=True,
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metadata={"help": "Whether to use one of the fast tokenizer (backed by the tokenizers library) or not."},
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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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token: str = field(
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default=None,
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metadata={
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"help": (
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"The token to use as HTTP bearer authorization for remote files. If not specified, will use the token "
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"generated when running `huggingface-cli login` (stored in `~/.huggingface`)."
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)
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},
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)
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use_auth_token: bool = field(
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default=None,
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metadata={
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"help": "The `use_auth_token` argument is deprecated and will be removed in v4.34. Please use `token` instead."
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},
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)
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trust_remote_code: bool = field(
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default=False,
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metadata={
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"help": (
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"Whether or not to allow for custom models defined on the Hub in their own modeling files. This option "
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"should only be set to `True` for repositories you trust and in which you have read the code, as it will "
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"execute code present on the Hub on your local machine."
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)
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},
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)
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ignore_mismatched_sizes: bool = field(
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default=False,
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metadata={"help": "Will enable to load a pretrained model whose head dimensions are different."},
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)
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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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if model_args.use_auth_token is not None:
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warnings.warn(
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"The `use_auth_token` argument is deprecated and will be removed in v4.34. Please use `token` instead.",
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FutureWarning,
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)
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if model_args.token is not None:
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raise ValueError("`token` and `use_auth_token` are both specified. Please set only the argument `token`.")
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model_args.token = model_args.use_auth_token
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# Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
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# information sent is the one passed as arguments along with your Python/PyTorch versions.
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send_example_telemetry("run_glue", model_args, data_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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handlers=[logging.StreamHandler(sys.stdout)],
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)
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if training_args.should_log:
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# The default of training_args.log_level is passive, so we set log level at info here to have that default.
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transformers.utils.logging.set_verbosity_info()
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log_level = training_args.get_process_log_level()
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logger.setLevel(log_level)
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datasets.utils.logging.set_verbosity(log_level)
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transformers.utils.logging.set_verbosity(log_level)
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transformers.utils.logging.enable_default_handler()
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transformers.utils.logging.enable_explicit_format()
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# Log on each process the small summary:
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logger.warning(
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f"Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}, "
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+ f"distributed training: {training_args.parallel_mode.value == 'distributed'}, 16-bits training: {training_args.fp16}"
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)
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logger.info(f"Training/evaluation parameters {training_args}")
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# Detecting last checkpoint.
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last_checkpoint = None
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if os.path.isdir(training_args.output_dir) and training_args.do_train and not training_args.overwrite_output_dir:
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last_checkpoint = get_last_checkpoint(training_args.output_dir)
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if last_checkpoint is None and len(os.listdir(training_args.output_dir)) > 0:
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raise ValueError(
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f"Output directory ({training_args.output_dir}) already exists and is not empty. "
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"Use --overwrite_output_dir to overcome."
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)
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elif last_checkpoint is not None and training_args.resume_from_checkpoint is None:
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logger.info(
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f"Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change "
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"the `--output_dir` or add `--overwrite_output_dir` to train from scratch."
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)
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# Set seed before initializing model.
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set_seed(training_args.seed)
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# Get the datasets: you can either provide your own CSV/JSON training and evaluation files (see below)
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# or specify a GLUE benchmark task (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 as labels the column called 'label' and as pair of sentences the
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# sentences in columns called 'sentence1' and 'sentence2' if such column exists or the first two columns not named
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# label if at least two columns are provided.
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#
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# If the CSVs/JSONs contain only one non-label column, the script does single sentence classification on this
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# single column. 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.task_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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"glue",
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data_args.task_name,
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cache_dir=model_args.cache_dir,
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token=model_args.token,
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)
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elif 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,
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data_args.dataset_config_name,
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cache_dir=model_args.cache_dir,
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token=model_args.token,
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)
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else:
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# Loading a dataset from your local files.
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# CSV/JSON training and evaluation files are needed.
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data_files = {"train": data_args.train_file, "validation": data_args.validation_file}
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# Get the test dataset: you can provide your own CSV/JSON test file (see below)
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# when you use `do_predict` without specifying a GLUE benchmark task.
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if training_args.do_predict:
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if data_args.test_file is not None:
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train_extension = data_args.train_file.split(".")[-1]
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test_extension = data_args.test_file.split(".")[-1]
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assert (
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test_extension == train_extension
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), "`test_file` should have the same extension (csv or json) as `train_file`."
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data_files["test"] = data_args.test_file
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else:
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raise ValueError("Need either a GLUE task or a test file for `do_predict`.")
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for key in data_files.keys():
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logger.info(f"load a local file for {key}: {data_files[key]}")
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if data_args.train_file.endswith(".csv"):
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# Loading a dataset from local csv files
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raw_datasets = load_dataset(
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"csv",
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data_files=data_files,
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cache_dir=model_args.cache_dir,
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token=model_args.token,
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)
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else:
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# Loading a dataset from local json files
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raw_datasets = load_dataset(
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"json",
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data_files=data_files,
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cache_dir=model_args.cache_dir,
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token=model_args.token,
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)
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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.
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# Labels
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if data_args.task_name is not None:
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is_regression = data_args.task_name == "stsb"
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if not is_regression:
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label_list = raw_datasets["train"].features["label"].names
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num_labels = len(label_list)
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else:
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num_labels = 1
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else:
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# Trying to have good defaults here, don't hesitate to tweak to your needs.
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is_regression = raw_datasets["train"].features["label"].dtype in ["float32", "float64"]
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if is_regression:
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num_labels = 1
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else:
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# A useful fast method:
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# https://huggingface.co/docs/datasets/package_reference/main_classes#datasets.Dataset.unique
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label_list = raw_datasets["train"].unique("label")
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label_list.sort() # Let's sort it for determinism
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num_labels = len(label_list)
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# Load pretrained model and tokenizer
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#
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# In distributed training, 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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num_labels=num_labels,
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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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token=model_args.token,
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trust_remote_code=model_args.trust_remote_code,
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)
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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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use_fast=model_args.use_fast_tokenizer,
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revision=model_args.model_revision,
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token=model_args.token,
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trust_remote_code=model_args.trust_remote_code,
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)
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model = AutoModelForSequenceClassification.from_pretrained(
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model_args.model_name_or_path,
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from_tf=bool(".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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revision=model_args.model_revision,
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token=model_args.token,
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trust_remote_code=model_args.trust_remote_code,
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ignore_mismatched_sizes=model_args.ignore_mismatched_sizes,
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)
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# Preprocessing the raw_datasets
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if data_args.task_name is not None:
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sentence1_key, sentence2_key = task_to_keys[data_args.task_name]
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else:
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# Again, we try to have some nice defaults but don't hesitate to tweak to your use case.
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non_label_column_names = [name for name in raw_datasets["train"].column_names if name != "label"]
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if "sentence1" in non_label_column_names and "sentence2" in non_label_column_names:
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sentence1_key, sentence2_key = "sentence1", "sentence2"
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else:
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if len(non_label_column_names) >= 2:
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sentence1_key, sentence2_key = non_label_column_names[:2]
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else:
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sentence1_key, sentence2_key = non_label_column_names[0], None
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# Padding strategy
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if data_args.pad_to_max_length:
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padding = "max_length"
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else:
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# We will pad later, dynamically at batch creation, to the max sequence length in each batch
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padding = False
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# Some models have set the order of the labels to use, so let's make sure we do use it.
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label_to_id = None
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if (
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model.config.label2id != PretrainedConfig(num_labels=num_labels).label2id
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and data_args.task_name is not None
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and not is_regression
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):
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# Some have all caps in their config, some don't.
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label_name_to_id = {k.lower(): v for k, v in model.config.label2id.items()}
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if sorted(label_name_to_id.keys()) == sorted(label_list):
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label_to_id = {i: int(label_name_to_id[label_list[i]]) for i in range(num_labels)}
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else:
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logger.warning(
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"Your model seems to have been trained with labels, but they don't match the dataset: ",
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f"model labels: {sorted(label_name_to_id.keys())}, dataset labels: {sorted(label_list)}."
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"\nIgnoring the model labels as a result.",
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)
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elif data_args.task_name is None and not is_regression:
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label_to_id = {v: i for i, v in enumerate(label_list)}
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if label_to_id is not None:
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model.config.label2id = label_to_id
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model.config.id2label = {id: label for label, id in config.label2id.items()}
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elif data_args.task_name is not None and not is_regression:
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model.config.label2id = {l: i for i, l in enumerate(label_list)}
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model.config.id2label = {id: label for label, id in config.label2id.items()}
|
|
|
|
if data_args.max_seq_length > tokenizer.model_max_length:
|
|
logger.warning(
|
|
f"The max_seq_length passed ({data_args.max_seq_length}) is larger than the maximum length for the "
|
|
f"model ({tokenizer.model_max_length}). Using max_seq_length={tokenizer.model_max_length}."
|
|
)
|
|
max_seq_length = min(data_args.max_seq_length, tokenizer.model_max_length)
|
|
|
|
def preprocess_function(examples):
|
|
# Tokenize the texts
|
|
args = (
|
|
(examples[sentence1_key],) if sentence2_key is None else (examples[sentence1_key], examples[sentence2_key])
|
|
)
|
|
result = tokenizer(*args, padding=padding, max_length=max_seq_length, truncation=True)
|
|
|
|
# Map labels to IDs (not necessary for GLUE tasks)
|
|
if label_to_id is not None and "label" in examples:
|
|
result["label"] = [(label_to_id[l] if l != -1 else -1) for l in examples["label"]]
|
|
return result
|
|
|
|
with training_args.main_process_first(desc="dataset map pre-processing"):
|
|
raw_datasets = raw_datasets.map(
|
|
preprocess_function,
|
|
batched=True,
|
|
load_from_cache_file=not data_args.overwrite_cache,
|
|
desc="Running tokenizer on dataset",
|
|
)
|
|
if training_args.do_train:
|
|
if "train" not in raw_datasets:
|
|
raise ValueError("--do_train requires a train dataset")
|
|
train_dataset = raw_datasets["train"]
|
|
if data_args.max_train_samples is not None:
|
|
max_train_samples = min(len(train_dataset), data_args.max_train_samples)
|
|
train_dataset = train_dataset.select(range(max_train_samples))
|
|
|
|
if training_args.do_eval:
|
|
if "validation" not in raw_datasets and "validation_matched" not in raw_datasets:
|
|
raise ValueError("--do_eval requires a validation dataset")
|
|
eval_dataset = raw_datasets["validation_matched" if data_args.task_name == "mnli" else "validation"]
|
|
if data_args.max_eval_samples is not None:
|
|
max_eval_samples = min(len(eval_dataset), data_args.max_eval_samples)
|
|
eval_dataset = eval_dataset.select(range(max_eval_samples))
|
|
|
|
if training_args.do_predict or data_args.task_name is not None or data_args.test_file is not None:
|
|
if "test" not in raw_datasets and "test_matched" not in raw_datasets:
|
|
raise ValueError("--do_predict requires a test dataset")
|
|
predict_dataset = raw_datasets["test_matched" if data_args.task_name == "mnli" else "test"]
|
|
if data_args.max_predict_samples is not None:
|
|
max_predict_samples = min(len(predict_dataset), data_args.max_predict_samples)
|
|
predict_dataset = predict_dataset.select(range(max_predict_samples))
|
|
|
|
# Log a few random samples from the training set:
|
|
if training_args.do_train:
|
|
for index in random.sample(range(len(train_dataset)), 3):
|
|
logger.info(f"Sample {index} of the training set: {train_dataset[index]}.")
|
|
|
|
# Get the metric function
|
|
if data_args.task_name is not None:
|
|
metric = evaluate.load("glue", data_args.task_name, cache_dir=model_args.cache_dir)
|
|
elif is_regression:
|
|
metric = evaluate.load("mse", cache_dir=model_args.cache_dir)
|
|
else:
|
|
metric = evaluate.load("accuracy", cache_dir=model_args.cache_dir)
|
|
|
|
# You can define your custom compute_metrics function. It takes an `EvalPrediction` object (a namedtuple with a
|
|
# predictions and label_ids field) and has to return a dictionary string to float.
|
|
def compute_metrics(p: EvalPrediction):
|
|
preds = p.predictions[0] if isinstance(p.predictions, tuple) else p.predictions
|
|
preds = np.squeeze(preds) if is_regression else np.argmax(preds, axis=1)
|
|
result = metric.compute(predictions=preds, references=p.label_ids)
|
|
if len(result) > 1:
|
|
result["combined_score"] = np.mean(list(result.values())).item()
|
|
return result
|
|
|
|
# Data collator will default to DataCollatorWithPadding when the tokenizer is passed to Trainer, so we change it if
|
|
# we already did the padding.
|
|
if data_args.pad_to_max_length:
|
|
data_collator = default_data_collator
|
|
elif training_args.fp16:
|
|
data_collator = DataCollatorWithPadding(tokenizer, pad_to_multiple_of=8)
|
|
else:
|
|
data_collator = None
|
|
|
|
# Initialize our Trainer
|
|
trainer = Trainer(
|
|
model=model,
|
|
args=training_args,
|
|
train_dataset=train_dataset if training_args.do_train else None,
|
|
eval_dataset=eval_dataset if training_args.do_eval else None,
|
|
compute_metrics=compute_metrics,
|
|
tokenizer=tokenizer,
|
|
data_collator=data_collator,
|
|
)
|
|
|
|
# Training
|
|
if training_args.do_train:
|
|
checkpoint = None
|
|
if training_args.resume_from_checkpoint is not None:
|
|
checkpoint = training_args.resume_from_checkpoint
|
|
elif last_checkpoint is not None:
|
|
checkpoint = last_checkpoint
|
|
train_result = trainer.train(resume_from_checkpoint=checkpoint)
|
|
metrics = train_result.metrics
|
|
max_train_samples = (
|
|
data_args.max_train_samples if data_args.max_train_samples is not None else len(train_dataset)
|
|
)
|
|
metrics["train_samples"] = min(max_train_samples, len(train_dataset))
|
|
|
|
trainer.save_model() # Saves the tokenizer too for easy upload
|
|
|
|
trainer.log_metrics("train", metrics)
|
|
trainer.save_metrics("train", metrics)
|
|
trainer.save_state()
|
|
|
|
# Evaluation
|
|
if training_args.do_eval:
|
|
logger.info("*** Evaluate ***")
|
|
|
|
# Loop to handle MNLI double evaluation (matched, mis-matched)
|
|
tasks = [data_args.task_name]
|
|
eval_datasets = [eval_dataset]
|
|
if data_args.task_name == "mnli":
|
|
tasks.append("mnli-mm")
|
|
valid_mm_dataset = raw_datasets["validation_mismatched"]
|
|
if data_args.max_eval_samples is not None:
|
|
max_eval_samples = min(len(valid_mm_dataset), data_args.max_eval_samples)
|
|
valid_mm_dataset = valid_mm_dataset.select(range(max_eval_samples))
|
|
eval_datasets.append(valid_mm_dataset)
|
|
combined = {}
|
|
|
|
for eval_dataset, task in zip(eval_datasets, tasks):
|
|
metrics = trainer.evaluate(eval_dataset=eval_dataset)
|
|
|
|
max_eval_samples = (
|
|
data_args.max_eval_samples if data_args.max_eval_samples is not None else len(eval_dataset)
|
|
)
|
|
metrics["eval_samples"] = min(max_eval_samples, len(eval_dataset))
|
|
|
|
if task == "mnli-mm":
|
|
metrics = {k + "_mm": v for k, v in metrics.items()}
|
|
if task is not None and "mnli" in task:
|
|
combined.update(metrics)
|
|
|
|
trainer.log_metrics("eval", metrics)
|
|
trainer.save_metrics("eval", combined if task is not None and "mnli" in task else metrics)
|
|
|
|
if training_args.do_predict:
|
|
logger.info("*** Predict ***")
|
|
|
|
# Loop to handle MNLI double evaluation (matched, mis-matched)
|
|
tasks = [data_args.task_name]
|
|
predict_datasets = [predict_dataset]
|
|
if data_args.task_name == "mnli":
|
|
tasks.append("mnli-mm")
|
|
predict_datasets.append(raw_datasets["test_mismatched"])
|
|
|
|
for predict_dataset, task in zip(predict_datasets, tasks):
|
|
# Removing the `label` columns because it contains -1 and Trainer won't like that.
|
|
predict_dataset = predict_dataset.remove_columns("label")
|
|
predictions = trainer.predict(predict_dataset, metric_key_prefix="predict").predictions
|
|
predictions = np.squeeze(predictions) if is_regression else np.argmax(predictions, axis=1)
|
|
|
|
output_predict_file = os.path.join(training_args.output_dir, f"predict_results_{task}.txt")
|
|
if trainer.is_world_process_zero():
|
|
with open(output_predict_file, "w") as writer:
|
|
logger.info(f"***** Predict results {task} *****")
|
|
writer.write("index\tprediction\n")
|
|
for index, item in enumerate(predictions):
|
|
if is_regression:
|
|
writer.write(f"{index}\t{item:3.3f}\n")
|
|
else:
|
|
item = label_list[item]
|
|
writer.write(f"{index}\t{item}\n")
|
|
|
|
kwargs = {"finetuned_from": model_args.model_name_or_path, "tasks": "text-classification"}
|
|
if data_args.task_name is not None:
|
|
kwargs["language"] = "en"
|
|
kwargs["dataset_tags"] = "glue"
|
|
kwargs["dataset_args"] = data_args.task_name
|
|
kwargs["dataset"] = f"GLUE {data_args.task_name.upper()}"
|
|
|
|
if training_args.push_to_hub:
|
|
trainer.push_to_hub(**kwargs)
|
|
else:
|
|
trainer.create_model_card(**kwargs)
|
|
|
|
|
|
def _mp_fn(index):
|
|
# For xla_spawn (TPUs)
|
|
main()
|
|
|
|
|
|
if __name__ == "__main__":
|
|
main()
|