初始化项目，由ModelHub XC社区提供模型
Model: flax-community/gpt2-medium-indonesian Source: Original Platform
This commit is contained in:
ModelHub XC
660
run_clm_flax.py
Executable file
660
run_clm_flax.py
Executable file
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#!/usr/bin/env python
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# coding=utf-8
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# Copyright 2021 The HuggingFace Team All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""
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Pre-training/Fine-tuning the library models for causal language modeling (GPT, GPT-2, CTRL, ...) on a text file or a dataset.
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Here is the full list of checkpoints on the hub that can be fine-tuned by this script:
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https://huggingface.co/models?filter=causal-lm
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"""
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# You can also adapt this script on your own causal language modeling task. Pointers for this are left as comments.
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import logging
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import math
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import os
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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 pathlib import Path
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from typing import Callable, Optional
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import jax
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import datasets
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from datasets import Dataset, load_dataset
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from tqdm import tqdm
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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 jax_utils, traverse_util
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from flax.jax_utils import unreplicate
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from flax.training import train_state
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from flax.training.common_utils import get_metrics, onehot, shard, shard_prng_key
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from transformers import (
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CONFIG_MAPPING,
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FLAX_MODEL_FOR_CAUSAL_LM_MAPPING,
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AutoConfig,
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AutoTokenizer,
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FlaxAutoModelForCausalLM,
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HfArgumentParser,
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TrainingArguments,
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is_tensorboard_available,
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)
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from transformers.testing_utils import CaptureLogger
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import wandb
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logger = logging.getLogger(__name__)
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MODEL_CONFIG_CLASSES = list(FLAX_MODEL_FOR_CAUSAL_LM_MAPPING.keys())
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MODEL_TYPES = tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)
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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, or train from scratch.
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"""
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model_name_or_path: Optional[str] = field(
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default=None,
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metadata={
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"help": "The model checkpoint for weights initialization."
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"Don't set if you want to train a model from scratch."
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},
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)
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model_type: Optional[str] = field(
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default=None,
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metadata={"help": "If training from scratch, pass a model type from the list: " + ", ".join(MODEL_TYPES)},
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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, metadata={"help": "Where do you want to store the pretrained models downloaded from s3"}
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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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dtype: Optional[str] = field(
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default="float32",
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metadata={
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"help": "Floating-point format in which the model weights should be initialized and trained. Choose one of `[float32, float16, bfloat16]`."
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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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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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dataset_data_dir: Optional[str] = field(
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default=None, metadata={"help": "The name of the data directory."}
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)
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train_file: Optional[str] = field(default=None, metadata={"help": "The input training data file (a text file)."})
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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 the perplexity on (a text file)."},
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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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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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validation_split_percentage: Optional[int] = field(
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default=5,
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metadata={
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"help": "The percentage of the train set used as validation set in case there's no validation split"
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},
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)
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block_size: Optional[int] = field(
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default=None,
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metadata={
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"help": "Optional input sequence length after tokenization. "
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"The training dataset will be truncated in block of this size for training. "
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"Default to the model max input length for single sentence inputs (take into account special tokens)."
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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 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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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", "txt"], "`train_file` should be a csv, a json or a txt 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", "txt"], "`validation_file` should be a csv, a json or a txt file."
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class TrainState(train_state.TrainState):
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dropout_rng: jnp.ndarray
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def replicate(self):
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return jax_utils.replicate(self).replace(dropout_rng=shard_prng_key(self.dropout_rng))
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def data_loader(rng: jax.random.PRNGKey, dataset: Dataset, batch_size: int, shuffle: bool = False):
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"""
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Returns batches of size `batch_size` from truncated `dataset`, sharded over all local devices.
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Shuffle batches if `shuffle` is `True`.
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"""
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steps_per_epoch = len(dataset) // batch_size
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if shuffle:
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batch_idx = jax.random.permutation(rng, len(dataset))
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else:
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batch_idx = jnp.arange(len(dataset))
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batch_idx = batch_idx[: steps_per_epoch * batch_size] # Skip incomplete batch.
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batch_idx = batch_idx.reshape((steps_per_epoch, batch_size))
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for idx in batch_idx:
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batch = dataset[idx]
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batch = {k: jnp.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 write_train_metric(summary_writer, train_metrics, train_time, step):
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summary_writer.scalar("train_time", train_time, step)
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train_metrics = get_metrics(train_metrics)
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for key, vals in train_metrics.items():
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tag = f"train_{key}"
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for i, val in enumerate(vals):
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summary_writer.scalar(tag, val, step - len(vals) + i + 1)
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def write_eval_metric(summary_writer, eval_metrics, step):
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for metric_name, value in eval_metrics.items():
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summary_writer.scalar(f"eval_{metric_name}", value, step)
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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 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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if jax.process_index() == 0:
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wandb.init(
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entity = os.getenv("WANDB_ENTITY", "indonesian-nlp"),
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project = os.getenv("WANDB_PROJECT", "huggingface"),
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sync_tensorboard =True
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)
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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 (
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os.path.exists(training_args.output_dir)
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and os.listdir(training_args.output_dir)
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and training_args.do_train
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and not training_args.overwrite_output_dir
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):
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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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# log your configs with wandb.config, accepts a dict
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if jax.process_index() == 0:
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wandb.config.update(training_args) # optional, log your configs
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wandb.config.update(model_args) # optional, log your configs
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wandb.config.update(data_args) # optional, log your configs
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wandb.config['test_log'] = 12345 # log additional things
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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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# Set the verbosity to info of the Transformers logger (on main process only):
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logger.info(f"Training/evaluation parameters {training_args}")
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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 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 'text' or the first column if no column called
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# 'text' 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 guarantees 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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dataset = load_dataset(
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data_args.dataset_name, data_args.dataset_config_name, data_dir=data_args.dataset_data_dir, cache_dir=model_args.cache_dir, keep_in_memory=False
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)
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if "validation" not in dataset.keys():
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dataset["validation"] = load_dataset(
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data_args.dataset_name,
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data_args.dataset_config_name,
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data_dir=data_args.dataset_data_dir,
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split=f"train[:{data_args.validation_split_percentage}%]",
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cache_dir=model_args.cache_dir,
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)
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dataset["train"] = load_dataset(
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data_args.dataset_name,
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data_args.dataset_config_name,
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data_dir=data_args.dataset_data_dir,
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split=f"train[{data_args.validation_split_percentage}%:]",
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cache_dir=model_args.cache_dir,
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)
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else:
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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.split(".")[-1]
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if extension == "txt":
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extension = "text"
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dataset = 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 (from files, python dict, pandas DataFrame, etc) at
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# https://huggingface.co/docs/datasets/loading_datasets.html.
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# Load pretrained model and tokenizer
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# Distributed training:
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# The .from_pretrained methods guarantee that only one local process can concurrently
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# download model & vocab.
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if model_args.config_name:
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config = AutoConfig.from_pretrained(model_args.config_name, cache_dir=model_args.cache_dir)
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elif model_args.model_name_or_path:
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config = AutoConfig.from_pretrained(model_args.model_name_or_path, cache_dir=model_args.cache_dir)
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else:
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config = CONFIG_MAPPING[model_args.model_type]()
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logger.warning("You are instantiating a new config instance from scratch.")
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if model_args.tokenizer_name:
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tokenizer = AutoTokenizer.from_pretrained(
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model_args.tokenizer_name, cache_dir=model_args.cache_dir, use_fast=model_args.use_fast_tokenizer
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)
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elif model_args.model_name_or_path:
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tokenizer = AutoTokenizer.from_pretrained(
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model_args.model_name_or_path, cache_dir=model_args.cache_dir, use_fast=model_args.use_fast_tokenizer
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)
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else:
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raise ValueError(
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"You are instantiating a new tokenizer from scratch. This is not supported by this script."
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"You can do it from another script, save it, and load it from here, using --tokenizer_name."
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)
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if model_args.model_name_or_path:
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model = FlaxAutoModelForCausalLM.from_pretrained(
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model_args.model_name_or_path, config=config, seed=training_args.seed, dtype=getattr(jnp, model_args.dtype)
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)
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else:
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model = FlaxAutoModelForCausalLM.from_config(
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config, seed=training_args.seed, dtype=getattr(jnp, model_args.dtype)
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)
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# Preprocessing the datasets.
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# First we tokenize all the texts.
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if training_args.do_train:
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column_names = dataset["train"].column_names
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else:
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column_names = dataset["validation"].column_names
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text_column_name = "text" if "text" in column_names else column_names[0]
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# since this will be pickled to avoid _LazyModule error in Hasher force logger loading before tokenize_function
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tok_logger = transformers.utils.logging.get_logger("transformers.tokenization_utils_base")
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def tokenize_function(examples):
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with CaptureLogger(tok_logger) as cl:
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output = tokenizer(examples[text_column_name])
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# clm input could be much much longer than block_size
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if "Token indices sequence length is longer than the" in cl.out:
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tok_logger.warning(
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"^^^^^^^^^^^^^^^^ Please ignore the warning above - this long input will be chunked into smaller bits before being passed to the model."
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)
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return output
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tokenized_datasets = dataset.map(
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tokenize_function,
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batched=True,
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||||
num_proc=data_args.preprocessing_num_workers,
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||||
remove_columns=column_names,
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||||
load_from_cache_file=not data_args.overwrite_cache,
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)
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||||
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||||
if data_args.block_size is None:
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||||
block_size = tokenizer.model_max_length
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||||
if block_size > config.max_position_embeddings:
|
||||
logger.warning(
|
||||
f"The tokenizer picked seems to have a very large `model_max_length` ({tokenizer.model_max_length}). "
|
||||
"Picking 1024 instead. You can change that default value by passing --block_size xxx."
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||||
)
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||||
block_size = 1024
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||||
else:
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||||
if data_args.block_size > tokenizer.model_max_length:
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||||
logger.warning(
|
||||
f"The block_size passed ({data_args.block_size}) is larger than the maximum length for the model"
|
||||
f"({tokenizer.model_max_length}). Using block_size={tokenizer.model_max_length}."
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||||
)
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||||
block_size = min(data_args.block_size, tokenizer.model_max_length)
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||||
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||||
# Main data processing function that will concatenate all texts from our dataset and generate chunks of block_size.
|
||||
def group_texts(examples):
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||||
# Concatenate all texts.
|
||||
concatenated_examples = {k: sum(examples[k], []) for k in examples.keys()}
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||||
total_length = len(concatenated_examples[list(examples.keys())[0]])
|
||||
# We drop the small remainder, we could add padding if the model supported it instead of this drop, you can
|
||||
# customize this part to your needs.
|
||||
if total_length >= block_size:
|
||||
total_length = (total_length // block_size) * block_size
|
||||
# Split by chunks of max_len.
|
||||
result = {
|
||||
k: [t[i : i + block_size] for i in range(0, total_length, block_size)]
|
||||
for k, t in concatenated_examples.items()
|
||||
}
|
||||
result["labels"] = result["input_ids"].copy()
|
||||
return result
|
||||
|
||||
# Note that with `batched=True`, this map processes 1,000 texts together, so group_texts throws away a remainder
|
||||
# for each of those groups of 1,000 texts. You can adjust that batch_size here but a higher value might be slower
|
||||
# to preprocess.
|
||||
#
|
||||
# To speed up this part, we use multiprocessing. See the documentation of the map method for more information:
|
||||
# https://huggingface.co/docs/datasets/package_reference/main_classes.html#datasets.Dataset.map
|
||||
|
||||
lm_datasets = tokenized_datasets.map(
|
||||
group_texts,
|
||||
batched=True,
|
||||
num_proc=data_args.preprocessing_num_workers,
|
||||
load_from_cache_file=not data_args.overwrite_cache,
|
||||
)
|
||||
|
||||
if training_args.do_train:
|
||||
if "train" not in tokenized_datasets:
|
||||
raise ValueError("--do_train requires a train dataset")
|
||||
train_dataset = lm_datasets["train"]
|
||||
if data_args.max_train_samples is not None:
|
||||
train_dataset = train_dataset.select(range(data_args.max_train_samples))
|
||||
|
||||
if training_args.do_eval:
|
||||
if "validation" not in tokenized_datasets:
|
||||
raise ValueError("--do_eval requires a validation dataset")
|
||||
eval_dataset = lm_datasets["validation"]
|
||||
if data_args.max_eval_samples is not None:
|
||||
eval_dataset = eval_dataset.select(range(data_args.max_eval_samples))
|
||||
|
||||
# Enable tensorboard only on the master node
|
||||
has_tensorboard = is_tensorboard_available()
|
||||
if has_tensorboard and jax.process_index() == 0:
|
||||
try:
|
||||
from flax.metrics.tensorboard import SummaryWriter
|
||||
|
||||
summary_writer = SummaryWriter(log_dir=Path(training_args.output_dir))
|
||||
except ImportError as ie:
|
||||
has_tensorboard = False
|
||||
logger.warning(
|
||||
f"Unable to display metrics through TensorBoard because some package are not installed: {ie}"
|
||||
)
|
||||
else:
|
||||
logger.warning(
|
||||
"Unable to display metrics through TensorBoard because the package is not installed: "
|
||||
"Please run pip install tensorboard to enable."
|
||||
)
|
||||
|
||||
# Initialize our training
|
||||
rng = jax.random.PRNGKey(training_args.seed)
|
||||
rng, dropout_rng = jax.random.split(rng)
|
||||
|
||||
# Store some constant
|
||||
num_epochs = int(training_args.num_train_epochs)
|
||||
train_batch_size = int(training_args.per_device_train_batch_size) * jax.device_count()
|
||||
eval_batch_size = int(training_args.per_device_eval_batch_size) * jax.device_count()
|
||||
steps_per_epoch = len(train_dataset) // train_batch_size
|
||||
total_train_steps = steps_per_epoch * num_epochs
|
||||
|
||||
# Create learning rate schedule
|
||||
linear_decay_lr_schedule_fn = create_learning_rate_fn(
|
||||
len(train_dataset),
|
||||
train_batch_size,
|
||||
training_args.num_train_epochs,
|
||||
training_args.warmup_steps,
|
||||
training_args.learning_rate,
|
||||
)
|
||||
|
||||
# We use Optax's "masking" functionality to not apply weight decay
|
||||
# to bias and LayerNorm scale parameters. decay_mask_fn returns a
|
||||
# mask boolean with the same structure as the parameters.
|
||||
# The mask is True for parameters that should be decayed.
|
||||
# Note that this mask is specifically adapted for FlaxGPT2.
|
||||
# For other models, one should correct the layer norm parameter naming
|
||||
# accordingly.
|
||||
def decay_mask_fn(params):
|
||||
flat_params = traverse_util.flatten_dict(params)
|
||||
flat_mask = {
|
||||
path: (path[-1] != "bias" and path[-2:] not in [("ln_1", "scale"), ("ln_2", "scale"), ("ln_f", "scale")])
|
||||
for path in flat_params
|
||||
}
|
||||
return traverse_util.unflatten_dict(flat_mask)
|
||||
|
||||
# create adam optimizer
|
||||
if training_args.adafactor:
|
||||
# We use the default parameters here to initialize adafactor,
|
||||
# For more details about the parameters please check https://github.com/deepmind/optax/blob/ed02befef9bf81cbbf236be3d2b0e032e9ed4a40/optax/_src/alias.py#L74
|
||||
optimizer = optax.adafactor(
|
||||
learning_rate=linear_decay_lr_schedule_fn,
|
||||
)
|
||||
else:
|
||||
optimizer = optax.adamw(
|
||||
learning_rate=linear_decay_lr_schedule_fn,
|
||||
b1=training_args.adam_beta1,
|
||||
b2=training_args.adam_beta2,
|
||||
eps=training_args.adam_epsilon,
|
||||
weight_decay=training_args.weight_decay,
|
||||
mask=decay_mask_fn,
|
||||
)
|
||||
|
||||
# Setup train state
|
||||
state = TrainState.create(apply_fn=model.__call__, params=model.params, tx=optimizer, dropout_rng=dropout_rng)
|
||||
|
||||
def loss_fn(logits, labels):
|
||||
shift_logits = logits[..., :-1, :]
|
||||
shift_labels = labels[..., 1:]
|
||||
loss = optax.softmax_cross_entropy(shift_logits, onehot(shift_labels, shift_logits.shape[-1]))
|
||||
return loss.mean()
|
||||
|
||||
# Define gradient update step fn
|
||||
def train_step(state, batch):
|
||||
dropout_rng, new_dropout_rng = jax.random.split(state.dropout_rng)
|
||||
|
||||
def compute_loss(params):
|
||||
labels = batch.pop("labels")
|
||||
logits = state.apply_fn(**batch, params=params, dropout_rng=dropout_rng, train=True)[0]
|
||||
loss = loss_fn(logits, labels)
|
||||
return loss
|
||||
|
||||
grad_fn = jax.value_and_grad(compute_loss)
|
||||
loss, grad = grad_fn(state.params)
|
||||
grad = jax.lax.pmean(grad, "batch")
|
||||
|
||||
new_state = state.apply_gradients(grads=grad, dropout_rng=new_dropout_rng)
|
||||
|
||||
metrics = {"loss": loss, "learning_rate": linear_decay_lr_schedule_fn(state.step)}
|
||||
metrics = jax.lax.pmean(metrics, axis_name="batch")
|
||||
|
||||
return new_state, metrics
|
||||
|
||||
# Define eval fn
|
||||
def eval_step(params, batch):
|
||||
labels = batch.pop("labels")
|
||||
logits = model(**batch, params=params, train=False)[0]
|
||||
loss = loss_fn(logits, labels)
|
||||
|
||||
# summarize metrics
|
||||
metrics = {"loss": loss}
|
||||
metrics = jax.lax.pmean(metrics, axis_name="batch")
|
||||
return metrics
|
||||
|
||||
# Create parallel version of the train and eval step
|
||||
p_train_step = jax.pmap(train_step, "batch", donate_argnums=(0,))
|
||||
p_eval_step = jax.pmap(eval_step, "batch")
|
||||
|
||||
# Replicate the train state on each device
|
||||
state = state.replicate()
|
||||
|
||||
logger.info("***** Running training *****")
|
||||
logger.info(f" Num examples = {len(train_dataset)}")
|
||||
logger.info(f" Num Epochs = {num_epochs}")
|
||||
logger.info(f" Instantaneous batch size per device = {training_args.per_device_train_batch_size}")
|
||||
logger.info(f" Total train batch size (w. parallel & distributed) = {train_batch_size}")
|
||||
logger.info(f" Total optimization steps = {total_train_steps}")
|
||||
|
||||
train_time = 0
|
||||
train_metrics = []
|
||||
epochs = tqdm(range(num_epochs), desc=f"Epoch ... (1/{num_epochs})", position=0)
|
||||
for epoch in epochs:
|
||||
# ======================== Training ================================
|
||||
train_start = time.time()
|
||||
|
||||
# Create sampling rng
|
||||
rng, input_rng = jax.random.split(rng)
|
||||
|
||||
# Generate an epoch by shuffling sampling indices from the train dataset
|
||||
train_loader = data_loader(input_rng, train_dataset, train_batch_size, shuffle=True)
|
||||
steps_per_epoch = len(train_dataset) // train_batch_size
|
||||
# train
|
||||
for step in tqdm(range(steps_per_epoch), desc="Training...", position=1, leave=False):
|
||||
batch = next(train_loader)
|
||||
state, train_metric = p_train_step(state, batch)
|
||||
train_metrics.append(train_metric)
|
||||
|
||||
cur_step = epoch * (len(train_dataset) // train_batch_size) + step
|
||||
|
||||
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 has_tensorboard and jax.process_index() == 0:
|
||||
write_train_metric(summary_writer, train_metrics, train_time, cur_step)
|
||||
|
||||
epochs.write(
|
||||
f"Step... ({cur_step} | Loss: {train_metric['loss'].mean()}, Learning Rate: {train_metric['learning_rate'].mean()})"
|
||||
)
|
||||
|
||||
train_metrics = []
|
||||
|
||||
if cur_step % training_args.eval_steps == 0 and cur_step > 0:
|
||||
# ======================== Evaluating ==============================
|
||||
eval_metrics = []
|
||||
eval_loader = data_loader(input_rng, eval_dataset, eval_batch_size)
|
||||
eval_steps = len(eval_dataset) // eval_batch_size
|
||||
for _ in tqdm(range(eval_steps), desc="Evaluating...", position=2, leave=False):
|
||||
# Model forward
|
||||
batch = next(eval_loader)
|
||||
metrics = p_eval_step(state.params, batch)
|
||||
eval_metrics.append(metrics)
|
||||
|
||||
# normalize eval metrics
|
||||
eval_metrics = get_metrics(eval_metrics)
|
||||
eval_metrics = jax.tree_map(jnp.mean, eval_metrics)
|
||||
|
||||
try:
|
||||
eval_metrics["perplexity"] = math.exp(eval_metrics["loss"])
|
||||
except OverflowError:
|
||||
eval_metrics["perplexity"] = float("inf")
|
||||
|
||||
# Print metrics and update progress bar
|
||||
desc = f"Step... ({cur_step} | Eval Loss: {eval_metrics['loss']} | Eval Perplexity: {eval_metrics['perplexity']})"
|
||||
epochs.write(desc)
|
||||
epochs.desc = desc
|
||||
|
||||
# Save metrics
|
||||
if has_tensorboard and 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:
|
||||
# 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,
|
||||
push_to_hub=training_args.push_to_hub,
|
||||
commit_message=f"Saving weights and logs of step {cur_step}",
|
||||
)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
Reference in New Issue
Block a user