run_flax_glue.py

#!/usr/bin/env python
# coding=utf-8
# Copyright 2021 The HuggingFace Inc. 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.
""" Finetuning a 🤗 Flax Transformers model for sequence classification on GLUE."""
import argparse
import logging
import os
import random
import time
from itertools import chain
from typing import Any, Callable, Dict, Tuple

import datasets
from datasets import load_dataset, load_metric

import jax
import jax.numpy as jnp
import optax
import transformers
from flax import struct, traverse_util
from flax.jax_utils import replicate, unreplicate
from flax.metrics import tensorboard
from flax.training import train_state
from flax.training.common_utils import get_metrics, onehot, shard
from transformers import AutoConfig, AutoTokenizer, FlaxAutoModelForSequenceClassification, PretrainedConfig


logger = logging.getLogger(__name__)

Array = Any
Dataset = datasets.arrow_dataset.Dataset
PRNGKey = Any


task_to_keys = {
    "cola": ("sentence", None),
    "mnli": ("premise", "hypothesis"),
    "mrpc": ("sentence1", "sentence2"),
    "qnli": ("question", "sentence"),
    "qqp": ("question1", "question2"),
    "rte": ("sentence1", "sentence2"),
    "sst2": ("sentence", None),
    "stsb": ("sentence1", "sentence2"),
    "wnli": ("sentence1", "sentence2"),
}


def parse_args():
    parser = argparse.ArgumentParser(description="Finetune a transformers model on a text classification task")
    parser.add_argument(
        "--task_name",
        type=str,
        default=None,
        help="The name of the glue task to train on.",
        choices=list(task_to_keys.keys()),
    )
    parser.add_argument(
        "--train_file", type=str, default=None, help="A csv or a json file containing the training data."
    )
    parser.add_argument(
        "--validation_file", type=str, default=None, help="A csv or a json file containing the validation data."
    )
    parser.add_argument(
        "--max_length",
        type=int,
        default=128,
        help=(
            "The maximum total input sequence length after tokenization. Sequences longer than this will be truncated,"
            " sequences shorter will be padded."
        ),
    )
    parser.add_argument(
        "--model_name_or_path",
        type=str,
        help="Path to pretrained model or model identifier from huggingface.co/models.",
        required=True,
    )
    parser.add_argument(
        "--use_slow_tokenizer",
        action="store_true",
        help="If passed, will use a slow tokenizer (not backed by the 🤗 Tokenizers library).",
    )
    parser.add_argument(
        "--per_device_train_batch_size",
        type=int,
        default=8,
        help="Batch size (per device) for the training dataloader.",
    )
    parser.add_argument(
        "--per_device_eval_batch_size",
        type=int,
        default=8,
        help="Batch size (per device) for the evaluation dataloader.",
    )
    parser.add_argument(
        "--learning_rate",
        type=float,
        default=5e-5,
        help="Initial learning rate (after the potential warmup period) to use.",
    )
    parser.add_argument("--weight_decay", type=float, default=0.0, help="Weight decay to use.")
    parser.add_argument("--num_train_epochs", type=int, default=3, help="Total number of training epochs to perform.")
    parser.add_argument(
        "--max_train_steps",
        type=int,
        default=None,
        help="Total number of training steps to perform. If provided, overrides num_train_epochs.",
    )
    parser.add_argument(
        "--num_warmup_steps", type=int, default=0, help="Number of steps for the warmup in the lr scheduler."
    )
    parser.add_argument("--output_dir", type=str, default=None, help="Where to store the final model.")
    parser.add_argument("--seed", type=int, default=3, help="A seed for reproducible training.")
    args = parser.parse_args()

    # Sanity checks
    if args.task_name is None and args.train_file is None and args.validation_file is None:
        raise ValueError("Need either a task name or a training/validation file.")
    else:
        if args.train_file is not None:
            extension = args.train_file.split(".")[-1]
            assert extension in ["csv", "json"], "`train_file` should be a csv or a json file."
        if args.validation_file is not None:
            extension = args.validation_file.split(".")[-1]
            assert extension in ["csv", "json"], "`validation_file` should be a csv or a json file."

    if args.output_dir is not None:
        os.makedirs(args.output_dir, exist_ok=True)

    return args


def create_train_state(
    model: FlaxAutoModelForSequenceClassification,
    learning_rate_fn: Callable[[int], float],
    is_regression: bool,
    num_labels: int,
    weight_decay: float,
) -> train_state.TrainState:
    """Create initial training state."""

    class TrainState(train_state.TrainState):
        """Train state with an Optax optimizer.

        The two functions below differ depending on whether the task is classification
        or regression.

        Args:
          logits_fn: Applied to last layer to obtain the logits.
          loss_fn: Function to compute the loss.
        """

        logits_fn: Callable = struct.field(pytree_node=False)
        loss_fn: Callable = struct.field(pytree_node=False)

    # 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)

    tx = optax.adamw(
        learning_rate=learning_rate_fn, b1=0.9, b2=0.999, eps=1e-6, weight_decay=weight_decay, mask=decay_mask_fn
    )

    if is_regression:

        def mse_loss(logits, labels):
            return jnp.mean((logits[..., 0] - labels) ** 2)

        return TrainState.create(
            apply_fn=model.__call__,
            params=model.params,
            tx=tx,
            logits_fn=lambda logits: logits[..., 0],
            loss_fn=mse_loss,
        )
    else:  # Classification.

        def cross_entropy_loss(logits, labels):
            xentropy = optax.softmax_cross_entropy(logits, onehot(labels, num_classes=num_labels))
            return jnp.mean(xentropy)

        return TrainState.create(
            apply_fn=model.__call__,
            params=model.params,
            tx=tx,
            logits_fn=lambda logits: logits.argmax(-1),
            loss_fn=cross_entropy_loss,
        )


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 glue_train_data_collator(rng: PRNGKey, dataset: Dataset, batch_size: int):
    """Returns shuffled batches of size `batch_size` from truncated `train dataset`, sharded over all local devices."""
    steps_per_epoch = len(dataset) // batch_size
    perms = jax.random.permutation(rng, len(dataset))
    perms = perms[: steps_per_epoch * batch_size]  # Skip incomplete batch.
    perms = perms.reshape((steps_per_epoch, batch_size))

    for perm in perms:
        batch = dataset[perm]
        batch = {k: jnp.array(v) for k, v in batch.items()}
        batch = shard(batch)

        yield batch


def glue_eval_data_collator(dataset: Dataset, batch_size: int):
    """Returns batches of size `batch_size` from `eval dataset`, sharded over all local devices."""
    for i in range(len(dataset) // batch_size):
        batch = dataset[i * batch_size : (i + 1) * batch_size]
        batch = {k: jnp.array(v) for k, v in batch.items()}
        batch = shard(batch)

        yield batch


def main():
    args = parse_args()

    # 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()

    # Get the datasets: you can either provide your own CSV/JSON training and evaluation files (see below)
    # or specify a GLUE benchmark task (the dataset will be downloaded automatically from the datasets Hub).

    # For CSV/JSON files, this script will use as labels the column called 'label' and as pair of sentences the
    # sentences in columns called 'sentence1' and 'sentence2' if such column exists or the first two columns not named
    # label if at least two columns are provided.

    # If the CSVs/JSONs contain only one non-label column, the script does single sentence classification on this
    # single column. You can easily tweak this behavior (see below)

    # In distributed training, the load_dataset function guarantee that only one local process can concurrently
    # download the dataset.
    if args.task_name is not None:
        # Downloading and loading a dataset from the hub.
        raw_datasets = load_dataset("glue", args.task_name)
    else:
        # Loading the dataset from local csv or json file.
        data_files = {}
        if args.train_file is not None:
            data_files["train"] = args.train_file
        if args.validation_file is not None:
            data_files["validation"] = args.validation_file
        extension = (args.train_file if args.train_file is not None else args.valid_file).split(".")[-1]
        raw_datasets = load_dataset(extension, data_files=data_files)
    # See more about loading any type of standard or custom dataset at
    # https://huggingface.co/docs/datasets/loading_datasets.html.

    # Labels
    if args.task_name is not None:
        is_regression = args.task_name == "stsb"
        if not is_regression:
            label_list = raw_datasets["train"].features["label"].names
            num_labels = len(label_list)
        else:
            num_labels = 1
    else:
        # Trying to have good defaults here, don't hesitate to tweak to your needs.
        is_regression = raw_datasets["train"].features["label"].dtype in ["float32", "float64"]
        if is_regression:
            num_labels = 1
        else:
            # A useful fast method:
            # https://huggingface.co/docs/datasets/package_reference/main_classes.html#datasets.Dataset.unique
            label_list = raw_datasets["train"].unique("label")
            label_list.sort()  # Let's sort it for determinism
            num_labels = len(label_list)

    # Load pretrained model and tokenizer
    config = AutoConfig.from_pretrained(args.model_name_or_path, num_labels=num_labels, finetuning_task=args.task_name)
    tokenizer = AutoTokenizer.from_pretrained(args.model_name_or_path, use_fast=not args.use_slow_tokenizer)
    model = FlaxAutoModelForSequenceClassification.from_pretrained(args.model_name_or_path, config=config)

    # Preprocessing the datasets
    if args.task_name is not None:
        sentence1_key, sentence2_key = task_to_keys[args.task_name]
    else:
        # Again, we try to have some nice defaults but don't hesitate to tweak to your use case.
        non_label_column_names = [name for name in raw_datasets["train"].column_names if name != "label"]
        if "sentence1" in non_label_column_names and "sentence2" in non_label_column_names:
            sentence1_key, sentence2_key = "sentence1", "sentence2"
        else:
            if len(non_label_column_names) >= 2:
                sentence1_key, sentence2_key = non_label_column_names[:2]
            else:
                sentence1_key, sentence2_key = non_label_column_names[0], None

    # Some models have set the order of the labels to use, so let's make sure we do use it.
    label_to_id = None
    if (
        model.config.label2id != PretrainedConfig(num_labels=num_labels).label2id
        and args.task_name is not None
        and not is_regression
    ):
        # Some have all caps in their config, some don't.
        label_name_to_id = {k.lower(): v for k, v in model.config.label2id.items()}
        if list(sorted(label_name_to_id.keys())) == list(sorted(label_list)):
            logger.info(
                f"The configuration of the model provided the following label correspondence: {label_name_to_id}. "
                "Using it!"
            )
            label_to_id = {i: label_name_to_id[label_list[i]] for i in range(num_labels)}
        else:
            logger.warning(
                "Your model seems to have been trained with labels, but they don't match the dataset: ",
                f"model labels: {list(sorted(label_name_to_id.keys()))}, dataset labels: {list(sorted(label_list))}."
                "\nIgnoring the model labels as a result.",
            )
    elif args.task_name is None:
        label_to_id = {v: i for i, v in enumerate(label_list)}

    def preprocess_function(examples):
        # Tokenize the texts
        texts = (
            (examples[sentence1_key],) if sentence2_key is None else (examples[sentence1_key], examples[sentence2_key])
        )
        result = tokenizer(*texts, padding="max_length", max_length=args.max_length, truncation=True)

        if "label" in examples:
            if label_to_id is not None:
                # Map labels to IDs (not necessary for GLUE tasks)
                result["labels"] = [label_to_id[l] for l in examples["label"]]
            else:
                # In all cases, rename the column to labels because the model will expect that.
                result["labels"] = examples["label"]
        return result

    processed_datasets = raw_datasets.map(
        preprocess_function, batched=True, remove_columns=raw_datasets["train"].column_names
    )

    train_dataset = processed_datasets["train"]
    eval_dataset = processed_datasets["validation_matched" if args.task_name == "mnli" else "validation"]

    # Log a few random samples from the training set:
    for index in random.sample(range(len(train_dataset)), 3):
        logger.info(f"Sample {index} of the training set: {train_dataset[index]}.")

    # Define a summary writer
    summary_writer = tensorboard.SummaryWriter(args.output_dir)
    summary_writer.hparams(vars(args))

    def write_metric(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)

    num_epochs = int(args.num_train_epochs)
    rng = jax.random.PRNGKey(args.seed)
    dropout_rngs = jax.random.split(rng, jax.local_device_count())

    train_batch_size = args.per_device_train_batch_size * jax.local_device_count()
    eval_batch_size = args.per_device_eval_batch_size * jax.local_device_count()

    learning_rate_fn = create_learning_rate_fn(
        len(train_dataset), train_batch_size, args.num_train_epochs, args.num_warmup_steps, args.learning_rate
    )

    state = create_train_state(
        model, learning_rate_fn, is_regression, num_labels=num_labels, weight_decay=args.weight_decay
    )

    # define step functions
    def train_step(
        state: train_state.TrainState, batch: Dict[str, Array], dropout_rng: PRNGKey
    ) -> Tuple[train_state.TrainState, float]:
        """Trains model with an optimizer (both in `state`) on `batch`, returning a pair `(new_state, loss)`."""
        dropout_rng, new_dropout_rng = jax.random.split(dropout_rng)
        targets = batch.pop("labels")

        def loss_fn(params):
            logits = state.apply_fn(**batch, params=params, dropout_rng=dropout_rng, train=True)[0]
            loss = state.loss_fn(logits, targets)
            return loss

        grad_fn = jax.value_and_grad(loss_fn)
        loss, grad = grad_fn(state.params)
        grad = jax.lax.pmean(grad, "batch")
        new_state = state.apply_gradients(grads=grad)
        metrics = jax.lax.pmean({"loss": loss, "learning_rate": learning_rate_fn(state.step)}, axis_name="batch")
        return new_state, metrics, new_dropout_rng

    p_train_step = jax.pmap(train_step, axis_name="batch", donate_argnums=(0,))

    def eval_step(state, batch):
        logits = state.apply_fn(**batch, params=state.params, train=False)[0]
        return state.logits_fn(logits)

    p_eval_step = jax.pmap(eval_step, axis_name="batch")

    if args.task_name is not None:
        metric = load_metric("glue", args.task_name)
    else:
        metric = load_metric("accuracy")

    logger.info(f"===== Starting training ({num_epochs} epochs) =====")
    train_time = 0

    # make sure weights are replicated on each device
    state = replicate(state)

    for epoch in range(1, num_epochs + 1):
        logger.info(f"Epoch {epoch}")
        logger.info("  Training...")

        train_start = time.time()
        train_metrics = []
        rng, input_rng = jax.random.split(rng)

        # train
        for batch in glue_train_data_collator(input_rng, train_dataset, train_batch_size):
            state, metrics, dropout_rngs = p_train_step(state, batch, dropout_rngs)
            train_metrics.append(metrics)
        train_time += time.time() - train_start
        logger.info(f"    Done! Training metrics: {unreplicate(metrics)}")

        logger.info("  Evaluating...")

        # evaluate
        for batch in glue_eval_data_collator(eval_dataset, eval_batch_size):
            labels = batch.pop("labels")
            predictions = p_eval_step(state, batch)
            metric.add_batch(predictions=chain(*predictions), references=chain(*labels))

        # evaluate also on leftover examples (not divisible by batch_size)
        num_leftover_samples = len(eval_dataset) % eval_batch_size

        # make sure leftover batch is evaluated on one device
        if num_leftover_samples > 0 and jax.process_index() == 0:
            # take leftover samples
            batch = eval_dataset[-num_leftover_samples:]
            batch = {k: jnp.array(v) for k, v in batch.items()}

            labels = batch.pop("labels")
            predictions = eval_step(unreplicate(state), batch)
            metric.add_batch(predictions=predictions, references=labels)

        eval_metric = metric.compute()
        logger.info(f"    Done! Eval metrics: {eval_metric}")

        cur_step = epoch * (len(train_dataset) // train_batch_size)
        write_metric(train_metrics, eval_metric, train_time, cur_step)

    # save last checkpoint
    if jax.process_index() == 0:
        params = jax.device_get(jax.tree_map(lambda x: x[0], state.params))
        model.save_pretrained(args.output_dir, params=params)


if __name__ == "__main__":
    main()