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* updates run_clm_streaming_flax.py * adds apps dataset loading and reindent scripts * checkpoint Co-authored-by: arampacha <aruthart@gmail.com>
637 lines
26 KiB
Python
Executable File
637 lines
26 KiB
Python
Executable File
#!/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 GPTNeo model for causal language modeling on a text file or a dataset using model parallelism.
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"""
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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 datasets
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import numpy as np
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from datasets import Dataset, load_dataset
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from tqdm import tqdm
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import jax
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import jax.numpy as jnp
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import optax
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import transformers
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from flax.core.frozen_dict import freeze, unfreeze
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from flax.training.common_utils import onehot, stack_forest
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from jax.experimental.maps import mesh
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from jax.experimental.pjit import pjit
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from partitions import set_partitions
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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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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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from_pt: Optional[bool] = field(
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default=False,
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metadata={"help": "Whether the model weights should be converted from pytorch."},
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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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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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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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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 = stack_forest(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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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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# 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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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, 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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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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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 config and tokenizer
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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 tokenizer.pad_token is None:
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tokenizer.pad_token = tokenizer.eos_token
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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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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:
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logger.warning(
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f"The tokenizer picked seems to have a very large `model_max_length` ({tokenizer.model_max_length}). "
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"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(
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f"The block_size passed ({data_args.block_size}) is larger than the maximum length for the model"
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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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def tokenize_function(examples):
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toks = tokenizer(examples["question"],
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examples["answer"],
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max_length=block_size,
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padding="max_length",
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truncation=True,
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return_token_type_ids=True,
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# return_tensors="np",
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)
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labels = toks["input_ids"].copy()
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toks["labels"] = labels
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return toks
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lm_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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if training_args.do_train:
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if "train" not in lm_datasets:
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raise ValueError("--do_train requires a train dataset")
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train_dataset = lm_datasets["train"]
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if data_args.max_train_samples is not None:
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train_dataset = train_dataset.select(range(data_args.max_train_samples))
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if training_args.do_eval:
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if "validation" not in lm_datasets:
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raise ValueError("--do_eval requires a validation dataset")
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eval_dataset = lm_datasets["validation"]
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if data_args.max_eval_samples is not None:
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eval_dataset = eval_dataset.select(range(data_args.max_eval_samples))
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# Enable tensorboard only on the master node
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has_tensorboard = is_tensorboard_available()
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if has_tensorboard and jax.process_index() == 0:
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try:
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from flax.metrics.tensorboard import SummaryWriter
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summary_writer = SummaryWriter(log_dir=Path(training_args.output_dir))
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except ImportError as ie:
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has_tensorboard = False
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logger.warning(
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f"Unable to display metrics through TensorBoard because some package are not installed: {ie}"
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)
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else:
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logger.warning(
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"Unable to display metrics through TensorBoard because the package is not installed: "
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"Please run pip install tensorboard to enable."
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)
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# Initialize our training
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rng = jax.random.PRNGKey(training_args.seed)
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rng, dropout_rng = jax.random.split(rng)
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# Store some constant
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num_epochs = int(training_args.num_train_epochs)
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train_batch_size = int(training_args.per_device_train_batch_size) * jax.device_count()
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eval_batch_size = int(training_args.per_device_eval_batch_size) * jax.device_count()
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steps_per_epoch = len(train_dataset) // train_batch_size
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total_train_steps = steps_per_epoch * num_epochs
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# TODO: weights should be initialized in pjitted fun, this won't work for REALLY large models
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# TODO: when loading from pre-trained model we need to make sure the vocab is divisible by num_partitions
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# GPT2's vocab is odd, we need to resize it for fine-tuning
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model = FlaxAutoModelForCausalLM.from_pretrained(
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model_args.model_name_or_path, seed=training_args.seed, dtype=getattr(jnp, model_args.dtype), from_pt=model_args.from_pt,
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)
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# Create learning rate schedule
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linear_decay_lr_schedule_fn = create_learning_rate_fn(
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len(train_dataset),
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train_batch_size,
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training_args.num_train_epochs,
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training_args.warmup_steps,
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training_args.learning_rate,
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)
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if training_args.adafactor:
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optimizer = optax.adafactor(
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learning_rate=linear_decay_lr_schedule_fn,
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)
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else:
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optimizer = optax.adamw(
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learning_rate=linear_decay_lr_schedule_fn,
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b1=training_args.adam_beta1,
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b2=training_args.adam_beta2,
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eps=training_args.adam_epsilon,
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weight_decay=training_args.weight_decay,
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)
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def get_initial_state(params):
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state = optimizer.init(params)
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return tuple(state), params
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# Get PartitionSpec for model params
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param_spec = set_partitions(unfreeze(model.params))
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# Get the PyTree for opt_state, we don't actually initialize the opt_state yet.
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params_shapes = jax.tree_map(lambda x: x.shape, model.params)
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state_shapes = jax.eval_shape(get_initial_state, params_shapes)
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|
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# get PartitionSpec for opt_state, this is very specific to adamw
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# TODO: optax returns different state for different optimizers, how can we handle this generically ?
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# or maybe we don't since in our examples we just use adamw or adafactor
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def get_opt_spec(x):
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if isinstance(x, dict):
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return param_spec
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return None
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|
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opt_state_spec, param_spec = jax.tree_map(
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get_opt_spec, state_shapes, is_leaf=lambda x: isinstance(x, (dict, optax.EmptyState))
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)
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|
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# pjit the get_initial_state function to shard params and init
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# optimizer state in sharded way
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p_get_initial_state = pjit(
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get_initial_state,
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in_axis_resources=None,
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out_axis_resources=(opt_state_spec, param_spec),
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|
)
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|
|
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# hack: move the inital params to CPU to free up device memory
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# TODO: allow loading weights on CPU in pre-trained model
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model.params = jax.tree_map(lambda x: np.asarray(x), model.params)
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|
|
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# mesh defination
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mesh_devices = np.array(jax.devices()).reshape(1, jax.local_device_count())
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|
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# actually initialize the opt_state
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with mesh(mesh_devices, ("dp", "mp")):
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opt_state, params = p_get_initial_state(freeze(model.params))
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|
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# cross-entropy with z loss
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def loss_fn(logits, labels, z_loss=0):
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shift_logits = logits[..., :-1, :]
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shift_labels = labels[..., 1:]
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|
|
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shift_labels = onehot(shift_labels, shift_logits.shape[-1])
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|
|
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shift_logits = shift_logits - jax.lax.stop_gradient(shift_logits.max(axis=-1, keepdims=True))
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log_z = jnp.log(jnp.sum(jnp.exp(shift_logits), axis=-1, keepdims=True))
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log_softmax = shift_logits - log_z
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loss = -jnp.sum(shift_labels * log_softmax, axis=-1)
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|
|
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loss += (1e-4 * jnp.square(log_z.squeeze(-1))) * z_loss
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|
|
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return loss.mean()
|
|
|
|
# Define gradient update step fn
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|
# TODO: try to use TrainState instead of passing params and opt_state individually
|
|
def train_step(params, opt_state, dropout_rng, batch, step):
|
|
dropout_rng, new_dropout_rng = jax.random.split(dropout_rng)
|
|
|
|
def compute_loss(params):
|
|
labels = batch.pop("labels")
|
|
# TODO: mask question in loss_func
|
|
token_type_ids = batch.pop('token_type_ids')
|
|
logits = model(**batch, params=params, dropout_rng=dropout_rng, train=True)[0]
|
|
loss = loss_fn(logits, labels, z_loss=1.0)
|
|
return loss
|
|
|
|
grad_fn = jax.value_and_grad(compute_loss)
|
|
loss, grads = grad_fn(params)
|
|
|
|
updates, new_opt_state = optimizer.update(grads, opt_state, params)
|
|
new_params = optax.apply_updates(params, updates)
|
|
|
|
metrics = {"loss": loss, "learning_rate": linear_decay_lr_schedule_fn(step)}
|
|
return new_params, tuple(new_opt_state), new_dropout_rng, metrics, step + 1
|
|
|
|
# Define eval fn
|
|
def eval_step(input_ids, labels, params):
|
|
logits = model(input_ids=input_ids, params=params, train=False)[0]
|
|
loss = loss_fn(logits, labels)
|
|
# metrics
|
|
return {"loss": loss}
|
|
|
|
p_train_step = pjit(
|
|
train_step,
|
|
in_axis_resources=(param_spec, opt_state_spec, None, None, None),
|
|
out_axis_resources=(param_spec, opt_state_spec, None, None, None),
|
|
donate_argnums=(0, 1),
|
|
)
|
|
|
|
p_eval_step = pjit(
|
|
eval_step,
|
|
in_axis_resources=(None, None, param_spec),
|
|
out_axis_resources=None,
|
|
)
|
|
|
|
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)
|
|
global_step = 0
|
|
# we are not doing 2D parallelism (yet!), this just does model parallelism
|
|
with mesh(mesh_devices, ("dp", "mp")):
|
|
for _ 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_metrics = []
|
|
train_loader = data_loader(input_rng, train_dataset, train_batch_size, shuffle=True)
|
|
steps_per_epoch = len(train_dataset) // train_batch_size
|
|
|
|
# train
|
|
for _ in tqdm(range(steps_per_epoch), desc="Training...", position=1, leave=False):
|
|
batch = next(train_loader)
|
|
|
|
params, opt_state, dropout_rng, train_metric, global_step = p_train_step(
|
|
params,
|
|
opt_state,
|
|
dropout_rng,
|
|
batch,
|
|
global_step,
|
|
)
|
|
train_metrics.append(train_metric)
|
|
|
|
cur_step = global_step
|
|
|
|
if cur_step % training_args.logging_steps == 0 and cur_step > 0:
|
|
# Save metrics
|
|
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']}, Learning Rate: {train_metric['learning_rate']})"
|
|
)
|
|
|
|
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):
|
|
batch = next(eval_loader)
|
|
metrics = p_eval_step(batch["input_ids"], batch["labels"], params)
|
|
eval_metrics.append(metrics)
|
|
|
|
# normalize eval metrics
|
|
eval_metrics = stack_forest(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")
|
|
|
|
logger.info(
|
|
f"Step... ({cur_step} | Eval loss: {eval_metrics['loss']} | Eval Perplexity: {eval_metrics['perplexity']}"
|
|
)
|
|
|
|
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(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()
|