From 15b498f3b89a071736466ac0bf6ac92932353fdf Mon Sep 17 00:00:00 2001 From: Suraj Patil Date: Fri, 11 Jun 2021 15:16:20 +0530 Subject: [PATCH] Flax CLM script (#12023) * first draft * max_seq_length => block_size * fix arg names * fix typos * fix loss calculation * add max examples, fix train eval steps, metrics * optimizer mask * fix perpelexity, metric logging * fix logging * data_collator = > data_loader * refactor loss_fn * support single GPU * pass distributed to write_metric * fix jitting * fix single device training * fix single device metrics * close inner progress bars once finished * add overwrite_cache arg * ifx dataset caching issue * add more logs * few small fixes, * address nicholas suggestions * fix docstr * address patricks suggestions * make flake happy * pass new new_dropout_rng to apply_gradients * reset train metrics after every epoc * remove distributed logis, small fixes --- .../flax/language-modeling/run_clm_flax.py | 614 ++++++++++++++++++ 1 file changed, 614 insertions(+) create mode 100644 examples/flax/language-modeling/run_clm_flax.py diff --git a/examples/flax/language-modeling/run_clm_flax.py b/examples/flax/language-modeling/run_clm_flax.py new file mode 100644 index 0000000000..9d04922754 --- /dev/null +++ b/examples/flax/language-modeling/run_clm_flax.py @@ -0,0 +1,614 @@ +#!/usr/bin/env python +# coding=utf-8 +# Copyright 2021 The HuggingFace Team All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +""" +Pre-training/Fine-tuning the library models for causal language modeling (GPT, GPT-2, CTRL, ...) on a text file or a dataset. + +Here is the full list of checkpoints on the hub that can be fine-tuned by this script: +https://huggingface.co/models?filter=causal-lm +""" +# You can also adapt this script on your own causal language modeling task. Pointers for this are left as comments. + +import logging +import math +import os +import sys +import time +from dataclasses import dataclass, field +from pathlib import Path +from typing import Callable, Optional + +import datasets +from datasets import Dataset, load_dataset +from tqdm import tqdm + +import jax +import jax.numpy as jnp +import optax +import transformers +from flax import jax_utils, traverse_util +from flax.jax_utils import unreplicate +from flax.training import train_state +from flax.training.common_utils import get_metrics, onehot, shard, shard_prng_key +from transformers import ( + CONFIG_MAPPING, + FLAX_MODEL_FOR_CAUSAL_LM_MAPPING, + AutoConfig, + AutoTokenizer, + FlaxAutoModelForCausalLM, + HfArgumentParser, + TrainingArguments, + is_tensorboard_available, +) +from transformers.testing_utils import CaptureLogger + + +logger = logging.getLogger(__name__) + +# Cache the result +has_tensorboard = is_tensorboard_available() +if has_tensorboard: + try: + from flax.metrics.tensorboard import SummaryWriter + except ImportError as ie: + has_tensorboard = False + print(f"Unable to display metrics through TensorBoard because some package are not installed: {ie}") + +else: + print( + "Unable to display metrics through TensorBoard because the package is not installed: " + "Please run pip install tensorboard to enable." + ) + + +MODEL_CONFIG_CLASSES = list(FLAX_MODEL_FOR_CAUSAL_LM_MAPPING.keys()) +MODEL_TYPES = tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES) + + +@dataclass +class ModelArguments: + """ + Arguments pertaining to which model/config/tokenizer we are going to fine-tune, or train from scratch. + """ + + model_name_or_path: Optional[str] = field( + default=None, + metadata={ + "help": "The model checkpoint for weights initialization." + "Don't set if you want to train a model from scratch." + }, + ) + model_type: Optional[str] = field( + default=None, + metadata={"help": "If training from scratch, pass a model type from the list: " + ", ".join(MODEL_TYPES)}, + ) + config_name: Optional[str] = field( + default=None, metadata={"help": "Pretrained config name or path if not the same as model_name"} + ) + tokenizer_name: Optional[str] = field( + default=None, metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"} + ) + cache_dir: Optional[str] = field( + default=None, metadata={"help": "Where do you want to store the pretrained models downloaded from s3"} + ) + use_fast_tokenizer: bool = field( + default=True, + metadata={"help": "Whether to use one of the fast tokenizer (backed by the tokenizers library) or not."}, + ) + dtype: Optional[str] = field( + default="float32", + metadata={ + "help": "Floating-point format in which the model weights should be initialized and trained. Choose one of `[float32, float16, bfloat16]`." + }, + ) + + +@dataclass +class DataTrainingArguments: + """ + Arguments pertaining to what data we are going to input our model for training and eval. + """ + + dataset_name: Optional[str] = field( + default=None, metadata={"help": "The name of the dataset to use (via the datasets library)."} + ) + dataset_config_name: Optional[str] = field( + default=None, metadata={"help": "The configuration name of the dataset to use (via the datasets library)."} + ) + train_file: Optional[str] = field(default=None, metadata={"help": "The input training data file (a text file)."}) + validation_file: Optional[str] = field( + default=None, + metadata={"help": "An optional input evaluation data file to evaluate the perplexity on (a text file)."}, + ) + max_train_samples: Optional[int] = field( + default=None, + metadata={ + "help": "For debugging purposes or quicker training, truncate the number of training examples to this " + "value if set." + }, + ) + max_eval_samples: Optional[int] = field( + default=None, + metadata={ + "help": "For debugging purposes or quicker training, truncate the number of evaluation examples to this " + "value if set." + }, + ) + overwrite_cache: bool = field( + default=False, metadata={"help": "Overwrite the cached training and evaluation sets"} + ) + validation_split_percentage: Optional[int] = field( + default=5, + metadata={ + "help": "The percentage of the train set used as validation set in case there's no validation split" + }, + ) + block_size: Optional[int] = field( + default=None, + metadata={ + "help": "Optional input sequence length after tokenization. " + "The training dataset will be truncated in block of this size for training. " + "Default to the model max input length for single sentence inputs (take into account special tokens)." + }, + ) + overwrite_cache: bool = field( + default=False, metadata={"help": "Overwrite the cached training and evaluation sets"} + ) + preprocessing_num_workers: Optional[int] = field( + default=None, + metadata={"help": "The number of processes to use for the preprocessing."}, + ) + + def __post_init__(self): + if self.dataset_name is None and self.train_file is None and self.validation_file is None: + raise ValueError("Need either a dataset name or a training/validation file.") + else: + if self.train_file is not None: + extension = self.train_file.split(".")[-1] + assert extension in ["csv", "json", "txt"], "`train_file` should be a csv, a json or a txt file." + if self.validation_file is not None: + extension = self.validation_file.split(".")[-1] + assert extension in ["csv", "json", "txt"], "`validation_file` should be a csv, a json or a txt file." + + +class TrainState(train_state.TrainState): + dropout_rng: jnp.ndarray + + def replicate(self): + return jax_utils.replicate(self).replace(dropout_rng=shard_prng_key(self.dropout_rng)) + + +def data_loader(rng: jax.random.PRNGKey, dataset: Dataset, batch_size: int, shuffle: bool = False): + """ + Returns batches of size `batch_size` from truncated `dataset`, sharded over all local devices. + Shuffle batches if `shuffle` is `True`. + """ + steps_per_epoch = len(dataset) // batch_size + + if shuffle: + batch_idx = jax.random.permutation(rng, len(dataset)) + else: + batch_idx = jnp.arange(len(dataset)) + + batch_idx = batch_idx[: steps_per_epoch * batch_size] # Skip incomplete batch. + batch_idx = batch_idx.reshape((steps_per_epoch, batch_size)) + + for idx in batch_idx: + batch = dataset[idx] + batch = {k: jnp.array(v) for k, v in batch.items()} + + batch = shard(batch) + + yield batch + + +def write_metric(summary_writer, train_metrics, eval_metrics, train_time, step): + summary_writer.scalar("train_time", train_time, step) + + train_metrics = get_metrics(train_metrics) + for key, vals in train_metrics.items(): + tag = f"train_{key}" + for i, val in enumerate(vals): + summary_writer.scalar(tag, val, step - len(vals) + i + 1) + + for metric_name, value in eval_metrics.items(): + summary_writer.scalar(f"eval_{metric_name}", value, step) + + +def create_learning_rate_fn( + train_ds_size: int, train_batch_size: int, num_train_epochs: int, num_warmup_steps: int, learning_rate: float +) -> Callable[[int], jnp.array]: + """Returns a linear warmup, linear_decay learning rate function.""" + steps_per_epoch = train_ds_size // train_batch_size + num_train_steps = steps_per_epoch * num_train_epochs + warmup_fn = optax.linear_schedule(init_value=0.0, end_value=learning_rate, transition_steps=num_warmup_steps) + decay_fn = optax.linear_schedule( + init_value=learning_rate, end_value=0, transition_steps=num_train_steps - num_warmup_steps + ) + schedule_fn = optax.join_schedules(schedules=[warmup_fn, decay_fn], boundaries=[num_warmup_steps]) + return schedule_fn + + +def main(): + # See all possible arguments in src/transformers/training_args.py + # or by passing the --help flag to this script. + # We now keep distinct sets of args, for a cleaner separation of concerns. + + parser = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)) + if len(sys.argv) == 2 and sys.argv[1].endswith(".json"): + # If we pass only one argument to the script and it's the path to a json file, + # let's parse it to get our arguments. + model_args, data_args, training_args = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1])) + else: + model_args, data_args, training_args = parser.parse_args_into_dataclasses() + + if ( + os.path.exists(training_args.output_dir) + and os.listdir(training_args.output_dir) + and training_args.do_train + and not training_args.overwrite_output_dir + ): + raise ValueError( + f"Output directory ({training_args.output_dir}) already exists and is not empty." + "Use --overwrite_output_dir to overcome." + ) + + # Make one log on every process with the configuration for debugging. + logging.basicConfig( + format="%(asctime)s - %(levelname)s - %(name)s - %(message)s", + datefmt="%m/%d/%Y %H:%M:%S", + level=logging.INFO, + ) + # Setup logging, we only want one process per machine to log things on the screen. + logger.setLevel(logging.INFO if jax.process_index() == 0 else logging.ERROR) + if jax.process_index() == 0: + datasets.utils.logging.set_verbosity_warning() + transformers.utils.logging.set_verbosity_info() + else: + datasets.utils.logging.set_verbosity_error() + transformers.utils.logging.set_verbosity_error() + + # Set the verbosity to info of the Transformers logger (on main process only): + logger.info(f"Training/evaluation parameters {training_args}") + + # Get the datasets: you can either provide your own CSV/JSON/TXT training and evaluation files (see below) + # or just provide the name of one of the public datasets available on the hub at https://huggingface.co/datasets/ + # (the dataset will be downloaded automatically from the datasets Hub). + # + # For CSV/JSON files, this script will use the column called 'text' or the first column if no column called + # 'text' is found. You can easily tweak this behavior (see below). + # + # In distributed training, the load_dataset function guarantees that only one local process can concurrently + # download the dataset. + if data_args.dataset_name is not None: + # Downloading and loading a dataset from the hub. + dataset = load_dataset( + data_args.dataset_name, data_args.dataset_config_name, cache_dir=model_args.cache_dir, keep_in_memory=False + ) + + if "validation" not in dataset.keys(): + dataset["validation"] = load_dataset( + data_args.dataset_name, + data_args.dataset_config_name, + split=f"train[:{data_args.validation_split_percentage}%]", + cache_dir=model_args.cache_dir, + ) + dataset["train"] = load_dataset( + data_args.dataset_name, + data_args.dataset_config_name, + split=f"train[{data_args.validation_split_percentage}%:]", + cache_dir=model_args.cache_dir, + ) + else: + data_files = {} + if data_args.train_file is not None: + data_files["train"] = data_args.train_file + if data_args.validation_file is not None: + data_files["validation"] = data_args.validation_file + extension = data_args.train_file.split(".")[-1] + if extension == "txt": + extension = "text" + dataset = load_dataset(extension, data_files=data_files, cache_dir=model_args.cache_dir) + # See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at + # https://huggingface.co/docs/datasets/loading_datasets.html. + + # Load pretrained model and tokenizer + + # Distributed training: + # The .from_pretrained methods guarantee that only one local process can concurrently + # download model & vocab. + if model_args.config_name: + config = AutoConfig.from_pretrained(model_args.config_name, cache_dir=model_args.cache_dir) + elif model_args.model_name_or_path: + config = AutoConfig.from_pretrained(model_args.model_name_or_path, cache_dir=model_args.cache_dir) + else: + config = CONFIG_MAPPING[model_args.model_type]() + logger.warning("You are instantiating a new config instance from scratch.") + + if model_args.tokenizer_name: + tokenizer = AutoTokenizer.from_pretrained( + model_args.tokenizer_name, cache_dir=model_args.cache_dir, use_fast=model_args.use_fast_tokenizer + ) + elif model_args.model_name_or_path: + tokenizer = AutoTokenizer.from_pretrained( + model_args.model_name_or_path, cache_dir=model_args.cache_dir, use_fast=model_args.use_fast_tokenizer + ) + else: + raise ValueError( + "You are instantiating a new tokenizer from scratch. This is not supported by this script." + "You can do it from another script, save it, and load it from here, using --tokenizer_name." + ) + + if model_args.model_name_or_path: + model = FlaxAutoModelForCausalLM.from_pretrained( + model_args.model_name_or_path, config=config, seed=training_args.seed, dtype=getattr(jnp, model_args.dtype) + ) + else: + model = FlaxAutoModelForCausalLM.from_config( + config, seed=training_args.seed, dtype=getattr(jnp, model_args.dtype) + ) + + # Preprocessing the datasets. + # First we tokenize all the texts. + if training_args.do_train: + column_names = dataset["train"].column_names + else: + column_names = dataset["validation"].column_names + text_column_name = "text" if "text" in column_names else column_names[0] + + # since this will be pickled to avoid _LazyModule error in Hasher force logger loading before tokenize_function + tok_logger = transformers.utils.logging.get_logger("transformers.tokenization_utils_base") + + def tokenize_function(examples): + with CaptureLogger(tok_logger) as cl: + output = tokenizer(examples[text_column_name]) + # clm input could be much much longer than block_size + if "Token indices sequence length is longer than the" in cl.out: + tok_logger.warning( + "^^^^^^^^^^^^^^^^ Please ignore the warning above - this long input will be chunked into smaller bits before being passed to the model." + ) + return output + + tokenized_datasets = dataset.map( + tokenize_function, + batched=True, + num_proc=data_args.preprocessing_num_workers, + remove_columns=column_names, + load_from_cache_file=not data_args.overwrite_cache, + ) + + if data_args.block_size is None: + block_size = tokenizer.model_max_length + 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." + ) + block_size = 1024 + else: + if data_args.block_size > tokenizer.model_max_length: + 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}." + ) + block_size = min(data_args.block_size, tokenizer.model_max_length) + + # Main data processing function that will concatenate all texts from our dataset and generate chunks of block_size. + def group_texts(examples): + # Concatenate all texts. + concatenated_examples = {k: sum(examples[k], []) for k in examples.keys()} + 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. + 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 + if has_tensorboard and jax.process_index() == 0: + summary_writer = SummaryWriter(log_dir=Path(training_args.output_dir).joinpath("logs").as_posix()) + + # 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. + def decay_mask_fn(params): + flat_params = traverse_util.flatten_dict(params) + flat_mask = {path: (path[-1] != "bias" and path[-2:] != ("LayerNorm", "scale")) for path in flat_params} + return traverse_util.unflatten_dict(flat_mask) + + # create adam optimizer + adamw = 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=adamw, 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 + 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) + train_metrics = [] + + # 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 _ 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) + + train_time += time.time() - train_start + + train_metric = unreplicate(train_metric) + + epochs.write( + f"Epoch... ({epoch + 1}/{num_epochs} | Loss: {train_metric['loss']}, Learning Rate: {train_metric['learning_rate']})" + ) + + # ======================== 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"Epoch... ({epoch + 1}/{num_epochs} | 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: + cur_step = epoch * (len(train_dataset) // train_batch_size) + write_metric(summary_writer, train_metrics, eval_metrics, train_time, cur_step) + + # save last checkpoint + if jax.process_index() == 0: + params = jax.device_get(unreplicate(state.params)) + model.save_pretrained(training_args.output_dir, params=params) + + +if __name__ == "__main__": + main()