transformers/examples/pytorch/speech-pretraining/run_wav2vec2_pretraining_no_trainer.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

""" Pre-Training a 🤗 Wav2Vec2 model on unlabeled audio data """

import argparse
import math
import os
from dataclasses import dataclass
from pathlib import Path
from typing import Dict, List, Optional, Union

import datasets
import torch
from accelerate import Accelerator
from accelerate.logging import get_logger
from datasets import DatasetDict, concatenate_datasets, load_dataset
from huggingface_hub import Repository, create_repo
from torch.utils.data.dataloader import DataLoader
from tqdm.auto import tqdm

import transformers
from transformers import (
    AdamW,
    SchedulerType,
    Wav2Vec2Config,
    Wav2Vec2FeatureExtractor,
    Wav2Vec2ForPreTraining,
    get_scheduler,
    is_wandb_available,
    set_seed,
)
from transformers.models.wav2vec2.modeling_wav2vec2 import _compute_mask_indices, _sample_negative_indices
from transformers.utils import get_full_repo_name, send_example_telemetry


logger = get_logger(__name__)


def parse_args():
    parser = argparse.ArgumentParser(description="Finetune a transformers model on a text classification task")
    parser.add_argument(
        "--dataset_name",
        type=str,
        default=None,
        help="The name of the dataset to use (via the datasets library).",
    )
    parser.add_argument(
        "--dataset_config_names",
        nargs="+",
        type=str,
        required=True,
        help="The configuration names of the dataset to use (via the datasets library).",
    )
    parser.add_argument(
        "--dataset_split_names",
        nargs="+",
        type=str,
        required=True,
        help="The names of the training data set splits to use (via the datasets library).",
    )
    parser.add_argument(
        "--preprocessing_num_workers",
        type=int,
        default=None,
        help="The number of processes to use for the preprocessing.",
    )
    parser.add_argument(
        "--overwrite_cache", action="store_true", help="Overwrite the cached training and evaluation sets"
    )
    parser.add_argument(
        "--preprocessing_only",
        action="store_true",
        help="Only run the preprocessing script to be cached for future use",
    )
    parser.add_argument(
        "--cache_dir",
        type=str,
        default=None,
        help="Where do you want to store the pretrained models downloaded from huggingface.co",
    )
    parser.add_argument(
        "--validation_split_percentage",
        type=int,
        default=1,
        help="Percentage of training data that should be used for validation if no validation is present in dataset.",
    )
    parser.add_argument(
        "--logging_steps",
        type=int,
        default=500,
        help="Number of steps between each logging",
    )
    parser.add_argument(
        "--saving_steps",
        type=int,
        default=500,
        help="Number of steps between each logging",
    )
    parser.add_argument(
        "--audio_column_name",
        type=str,
        default="audio",
        help="Column in the dataset that contains speech file path. Defaults to 'audio'",
    )
    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(
        "--config_name",
        type=str,
        default=None,
        help="Pretrained config name or path if not the same as model_name",
    )
    parser.add_argument(
        "--train_cache_file_name",
        type=str,
        default=None,
        help="Path to the train cached file name",
    )
    parser.add_argument(
        "--validation_cache_file_name",
        type=str,
        default=None,
        help="Path to the validation cached file name",
    )
    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(
        "--gradient_accumulation_steps",
        type=int,
        default=1,
        help="Number of updates steps to accumulate before performing a backward/update pass.",
    )
    parser.add_argument(
        "--gradient_checkpointing",
        action="store_true",
        help="If True, use gradient checkpointing to save memory at the expense of slower backward pass.",
    )
    parser.add_argument(
        "--lr_scheduler_type",
        type=SchedulerType,
        default="linear",
        help="The scheduler type to use.",
        choices=["linear", "cosine", "cosine_with_restarts", "polynomial", "constant", "constant_with_warmup"],
    )
    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=0, help="A seed for reproducible training.")
    parser.add_argument(
        "--max_gumbel_temperature",
        type=float,
        default=2.0,
        help="Maximum temperature for gumbel softmax.",
    )
    parser.add_argument(
        "--min_gumbel_temperature",
        type=float,
        default=0.5,
        help="Minimum temperature for gumbel softmax.",
    )
    parser.add_argument(
        "--gumbel_temperature_decay", type=float, default=0.999995, help="Decay of gumbel temperature during training."
    )
    parser.add_argument(
        "--max_duration_in_seconds",
        type=float,
        default=5.0,
        help="Filter out audio files that are longer than `max_duration_in_seconds` seconds",
    )
    parser.add_argument(
        "--min_duration_in_seconds",
        type=float,
        default=3.0,
        help="Filter out audio files that are shorter than `min_duration_in_seconds` seconds",
    )
    parser.add_argument(
        "--pad_to_multiple_of",
        type=int,
        default=None,
        help=(
            "If set will pad the sequence to a multiple of the provided value. This is especially useful to enable the"
            " use of Tensor Cores on NVIDIA hardware with compute capability >= 7.5 (Volta)."
        ),
    )
    parser.add_argument(
        "--adam_beta1",
        type=float,
        default=0.9,
        help="Beta1 for AdamW optimizer",
    )
    parser.add_argument(
        "--adam_beta2",
        type=float,
        default=0.999,
        help="Beta2 for AdamW optimizer",
    )
    parser.add_argument(
        "--adam_epsilon",
        type=float,
        default=1e-8,
        help="Epsilon for AdamW optimizer",
    )
    parser.add_argument("--push_to_hub", action="store_true", help="Whether or not to push the model to the Hub.")
    parser.add_argument(
        "--hub_model_id", type=str, help="The name of the repository to keep in sync with the local `output_dir`."
    )
    parser.add_argument("--hub_token", type=str, help="The token to use to push to the Model Hub.")
    parser.add_argument(
        "--mask_time_prob",
        type=float,
        default=None,
        help=(
            "Percentage (between 0 and 1) of all feature vectors along the time axis which will be masked in the"
            " contrastive task. If omitted, will pull value from model config."
        ),
    )
    parser.add_argument(
        "--mask_time_length",
        type=int,
        default=None,
        help=(
            "Length of each vector mask span to mask along the time axis in the contrastive task."
            " If omitted, will pull value from model config."
        ),
    )
    args = parser.parse_args()

    if args.push_to_hub:
        assert args.output_dir is not None, "Need an `output_dir` to create a repo when `--push_to_hub` is passed."

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

    return args


@dataclass
class DataCollatorForWav2Vec2Pretraining:
    """
    Data collator that will dynamically pad the inputs received and prepare masked indices
    for self-supervised pretraining.

    Args:
        model (:class:`~transformers.Wav2Vec2ForPreTraining`):
            The Wav2Vec2 model used for pretraining. The data collator needs to have access
            to config and ``_get_feat_extract_output_lengths`` function for correct padding.
        feature_extractor (:class:`~transformers.Wav2Vec2FeatureExtractor`):
            The processor used for proccessing the data.
        padding (:obj:`bool`, :obj:`str` or :class:`~transformers.tokenization_utils_base.PaddingStrategy`, `optional`, defaults to :obj:`True`):
            Select a strategy to pad the returned sequences (according to the model's padding side and padding index)
            among:
            * :obj:`True` or :obj:`'longest'`: Pad to the longest sequence in the batch (or no padding if only a single
              sequence if provided).
            * :obj:`'max_length'`: Pad to a maximum length specified with the argument :obj:`max_length` or to the
              maximum acceptable input length for the model if that argument is not provided.
            * :obj:`False` or :obj:`'do_not_pad'` (default): No padding (i.e., can output a batch with sequences of
              different lengths).
        max_length (:obj:`int`, `optional`):
            Maximum length of the ``input_values`` of the returned list and optionally padding length (see above).
        pad_to_multiple_of (:obj:`int`, `optional`):
            If set will pad the sequence to a multiple of the provided value.
            This is especially useful to enable the use of Tensor Cores on NVIDIA hardware with compute capability >=
            7.5 (Volta).
        mask_time_prob (:obj:`float`, `optional`, defaults to :obj:`0.65`):
            Percentage (between 0 and 1) of all feature vectors along the time axis which will be masked for the contrastive task.
            Note that overlap between masked sequences may decrease the actual percentage of masked vectors.
            The default value is taken from the original wav2vec 2.0 article (https://arxiv.org/abs/2006.11477),
            and results in about 49 percent of each sequence being masked on average.
        mask_time_length (:obj:`int`, `optional`, defaults to :obj:`10`):
            Length of each vector mask span to mask along the time axis in the contrastive task. The default value
            originates from the original wav2vec 2.0 article and corresponds to the ``M`` variable mentioned there.
    """

    model: Wav2Vec2ForPreTraining
    feature_extractor: Wav2Vec2FeatureExtractor
    padding: Union[bool, str] = "longest"
    pad_to_multiple_of: Optional[int] = None
    mask_time_prob: Optional[float] = 0.65
    mask_time_length: Optional[int] = 10

    def __call__(self, features: List[Dict[str, Union[List[int], torch.Tensor]]]) -> Dict[str, torch.Tensor]:
        # reformat list to dict and set to pytorch format
        batch = self.feature_extractor.pad(
            features,
            padding=self.padding,
            pad_to_multiple_of=self.pad_to_multiple_of,
            return_tensors="pt",
        )

        device = batch["input_values"].device
        batch_size = batch["input_values"].shape[0]

        mask_indices_seq_length = self.model._get_feat_extract_output_lengths(batch["input_values"].shape[-1])
        # make sure masked sequence length is a Python scalar
        mask_indices_seq_length = int(mask_indices_seq_length)

        # make sure that no loss is computed on padded inputs
        if batch.get("attention_mask") is not None:
            # compute real output lengths according to convolution formula
            batch["sub_attention_mask"] = self.model._get_feature_vector_attention_mask(
                mask_indices_seq_length, batch["attention_mask"]
            )

        features_shape = (batch_size, mask_indices_seq_length)

        # sample randomly masked indices
        mask_time_indices = _compute_mask_indices(
            features_shape,
            self.mask_time_prob,
            self.mask_time_length,
            attention_mask=batch.get("sub_attention_mask"),
        )

        # sample negative indices
        sampled_negative_indices = _sample_negative_indices(
            features_shape,
            self.model.config.num_negatives,
            mask_time_indices=mask_time_indices,
        )
        batch["mask_time_indices"] = torch.tensor(mask_time_indices, dtype=torch.long, device=device)
        batch["sampled_negative_indices"] = torch.tensor(sampled_negative_indices, dtype=torch.long, device=device)

        return batch


def multiply_grads(params, c):
    """Multiplies grads by a constant *c*."""
    for p in params:
        if p.grad is not None:
            if torch.is_tensor(c):
                c = c.to(p.grad.device)
            p.grad.data.mul_(c)


def get_grad_norm(params, scale=1):
    """Compute grad norm given a gradient scale."""
    total_norm = 0.0
    for p in params:
        if p.grad is not None:
            param_norm = (p.grad.detach().data / scale).norm(2)
            total_norm += param_norm.item() ** 2
    total_norm = total_norm**0.5
    return total_norm


def main():
    # See all possible arguments in src/transformers/args.py
    # or by passing the --help flag to this script.
    # We now keep distinct sets of args, for a cleaner separation of concerns.
    args = parse_args()

    # Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
    # information sent is the one passed as arguments along with your Python/PyTorch versions.
    send_example_telemetry("run_wav2vec2_pretraining_no_trainer", args)

    # Initialize the accelerator. We will let the accelerator handle device placement for us in this example.
    accelerator = Accelerator()
    logger.info(accelerator.state, main_process_only=False)
    if accelerator.is_local_main_process:
        datasets.utils.logging.set_verbosity_warning()
        transformers.utils.logging.set_verbosity_info()

        # set up weights and biases if available
        if is_wandb_available():
            import wandb

            wandb.init(project=args.output_dir.split("/")[-1])
    else:
        datasets.utils.logging.set_verbosity_error()
        transformers.utils.logging.set_verbosity_error()

    # If passed along, set the training seed now.
    if args.seed is not None:
        set_seed(args.seed)

    # Handle the repository creation
    if accelerator.is_main_process:
        if args.push_to_hub and not args.preprocessing_only:
            if args.hub_model_id is None:
                repo_name = get_full_repo_name(Path(args.output_dir).name, token=args.hub_token)
            else:
                repo_name = args.hub_model_id
            create_repo(repo_name, exist_ok=True, token=args.hub_token)
            repo = Repository(args.output_dir, clone_from=repo_name, token=args.hub_token)
        elif args.output_dir is not None:
            os.makedirs(args.output_dir, exist_ok=True)
    accelerator.wait_for_everyone()

    # 1. Download and create train, validation dataset
    # We load all dataset configuration and datset split pairs passed in
    # ``args.dataset_config_names`` and ``args.dataset_split_names``
    datasets_splits = []
    for dataset_config_name, train_split_name in zip(args.dataset_config_names, args.dataset_split_names):
        # load dataset
        dataset_split = load_dataset(
            args.dataset_name,
            dataset_config_name,
            split=train_split_name,
            cache_dir=args.cache_dir,
        )
        datasets_splits.append(dataset_split)

    # Next, we concatenate all configurations and splits into a single training dataset
    raw_datasets = DatasetDict()
    if len(datasets_splits) > 1:
        raw_datasets["train"] = concatenate_datasets(datasets_splits).shuffle(seed=args.seed)
    else:
        raw_datasets["train"] = datasets_splits[0]

    # Take ``args.validation_split_percentage`` from the training dataset for the validation_split_percentage
    num_validation_samples = raw_datasets["train"].num_rows * args.validation_split_percentage // 100

    if num_validation_samples == 0:
        raise ValueError(
            "`args.validation_split_percentage` is less than a single sample "
            f"for {len(raw_datasets['train'])} training samples. Increase "
            "`args.num_validation_split_percentage`. "
        )

    raw_datasets["validation"] = raw_datasets["train"].select(range(num_validation_samples))
    raw_datasets["train"] = raw_datasets["train"].select(range(num_validation_samples, raw_datasets["train"].num_rows))

    # 2. Now we preprocess the datasets including loading the audio, resampling and normalization
    # Thankfully, `datasets` takes care of automatically loading and resampling the audio,
    # so that we just need to set the correct target sampling rate and normalize the input
    # via the `feature_extractor`
    feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained(args.model_name_or_path)

    # make sure that dataset decodes audio with correct sampling rate
    raw_datasets = raw_datasets.cast_column(
        args.audio_column_name, datasets.features.Audio(sampling_rate=feature_extractor.sampling_rate)
    )

    # only normalized-inputs-training is supported
    if not feature_extractor.do_normalize:
        raise ValueError(
            "Training is only supported for normalized inputs. Make sure ``feature_extractor.do_normalize == True``"
        )

    # set max & min audio length in number of samples
    max_length = int(args.max_duration_in_seconds * feature_extractor.sampling_rate)
    min_length = int(args.min_duration_in_seconds * feature_extractor.sampling_rate)

    def prepare_dataset(batch):
        sample = batch[args.audio_column_name]

        inputs = feature_extractor(
            sample["array"], sampling_rate=sample["sampling_rate"], max_length=max_length, truncation=True
        )
        batch["input_values"] = inputs.input_values[0]
        batch["input_length"] = len(inputs.input_values[0])

        return batch

    # load via mapped files via path
    cache_file_names = None
    if args.train_cache_file_name is not None:
        cache_file_names = {"train": args.train_cache_file_name, "validation": args.validation_cache_file_name}

    # load audio files into numpy arrays
    with accelerator.main_process_first():
        vectorized_datasets = raw_datasets.map(
            prepare_dataset,
            num_proc=args.preprocessing_num_workers,
            remove_columns=raw_datasets["train"].column_names,
            cache_file_names=cache_file_names,
        )

        if min_length > 0.0:
            vectorized_datasets = vectorized_datasets.filter(
                lambda x: x > min_length,
                num_proc=args.preprocessing_num_workers,
                input_columns=["input_length"],
            )

        vectorized_datasets = vectorized_datasets.remove_columns("input_length")

    # for large datasets it is advised to run the preprocessing on a
    # single machine first with ``args.preprocessing_only`` since there will mostly likely
    # be a timeout when running the script in distributed mode.
    # In a second step ``args.preprocessing_only`` can then be set to `False` to load the
    # cached dataset
    if args.preprocessing_only:
        return

    # 3. Load model
    config = Wav2Vec2Config.from_pretrained(args.model_name_or_path)

    # pretraining is only supported for "newer" stable layer norm architecture
    # apply_spec_augment has to be True, mask_feature_prob has to be 0.0
    if not config.do_stable_layer_norm or config.feat_extract_norm != "layer":
        raise ValueError(
            "PreTraining is only supported for ``config.do_stable_layer_norm=True`` and"
            " ``config.feat_extract_norm='layer'"
        )

    # initialize random model
    model = Wav2Vec2ForPreTraining(config)

    # Activate gradient checkpointing if needed
    if args.gradient_checkpointing:
        model.gradient_checkpointing_enable()

    # 4. Define data collator, optimizer and scheduler

    mask_time_prob = config.mask_time_prob if args.mask_time_prob is None else args.mask_time_prob
    mask_time_length = config.mask_time_length if args.mask_time_length is None else args.mask_time_length

    data_collator = DataCollatorForWav2Vec2Pretraining(
        model=model,
        feature_extractor=feature_extractor,
        pad_to_multiple_of=args.pad_to_multiple_of,
        mask_time_prob=mask_time_prob,
        mask_time_length=mask_time_length,
    )
    train_dataloader = DataLoader(
        vectorized_datasets["train"],
        shuffle=True,
        collate_fn=data_collator,
        batch_size=args.per_device_train_batch_size,
    )
    eval_dataloader = DataLoader(
        vectorized_datasets["validation"], collate_fn=data_collator, batch_size=args.per_device_eval_batch_size
    )

    # Optimizer
    optimizer = AdamW(
        list(model.parameters()),
        lr=args.learning_rate,
        betas=[args.adam_beta1, args.adam_beta2],
        eps=args.adam_epsilon,
    )

    # Prepare everything with our `accelerator`.
    model, optimizer, train_dataloader, eval_dataloader = accelerator.prepare(
        model, optimizer, train_dataloader, eval_dataloader
    )

    # Scheduler and math around the number of training steps.
    num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)

    if args.max_train_steps is None:
        args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch

    lr_scheduler = get_scheduler(
        name=args.lr_scheduler_type,
        optimizer=optimizer,
        num_warmup_steps=args.num_warmup_steps,
        num_training_steps=args.max_train_steps,
    )

    # Afterwards we recalculate our number of training epochs
    args.num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch)

    # 5. Train
    total_batch_size = args.per_device_train_batch_size * accelerator.num_processes * args.gradient_accumulation_steps

    logger.info("***** Running training *****")
    logger.info(f"  Num examples = {len(vectorized_datasets['train'])}")
    logger.info(f"  Num Epochs = {args.num_train_epochs}")
    logger.info(f"  Instantaneous batch size per device = {args.per_device_train_batch_size}")
    logger.info(f"  Total train batch size (w. parallel, distributed & accumulation) = {total_batch_size}")
    logger.info(f"  Gradient Accumulation steps = {args.gradient_accumulation_steps}")
    logger.info(f"  Total optimization steps = {args.max_train_steps}")
    completed_steps = 0
    starting_epoch = 0

    # Only show the progress bar once on each machine.
    progress_bar = tqdm(range(args.max_train_steps), disable=not accelerator.is_local_main_process)
    completed_steps = 0
    starting_epoch = 0
    for epoch in range(starting_epoch, args.num_train_epochs):
        model.train()
        for step, batch in enumerate(train_dataloader):
            # compute num of losses
            num_losses = batch["mask_time_indices"].sum()
            sub_attention_mask = batch.pop("sub_attention_mask", None)
            sub_attention_mask = (
                sub_attention_mask if sub_attention_mask is not None else torch.ones_like(batch["mask_time_indices"])
            )
            percent_masked = num_losses / sub_attention_mask.sum()

            # forward
            outputs = model(**batch)

            # divide loss by gradient accumulation steps since gradients
            # are accumulated for multiple backward passes in PyTorch
            loss = outputs.loss / args.gradient_accumulation_steps
            accelerator.backward(loss)

            # make sure that `num_losses` is summed for distributed training
            # and average gradients over losses of all devices
            if accelerator.state.num_processes > 1:
                num_losses = accelerator.gather_for_metrics(num_losses).sum()
                gradient_multiplier = accelerator.state.num_processes / num_losses
                multiply_grads(model.module.parameters(), gradient_multiplier)
            else:
                multiply_grads(model.parameters(), 1 / num_losses)

            # update step
            if (step + 1) % args.gradient_accumulation_steps == 0 or step == len(train_dataloader) - 1:
                # compute grad norm for monitoring
                scale = (
                    accelerator.scaler._scale.item()
                    if hasattr(accelerator, "scaler") and accelerator.scaler is not None
                    else 1
                )
                if accelerator.state.num_processes > 1:
                    grad_norm = get_grad_norm(model.module.parameters(), scale)
                else:
                    grad_norm = get_grad_norm(model.parameters(), scale)

                # update parameters
                optimizer.step()
                optimizer.zero_grad()

                if not accelerator.optimizer_step_was_skipped:
                    lr_scheduler.step()
                elif accelerator.is_local_main_process:
                    progress_bar.write(
                        f"Gradients have overflown - skipping update step... Updating gradient scale to {scale}..."
                    )

                # update gumbel temperature
                gumbel_temperature = max(
                    args.max_gumbel_temperature * args.gumbel_temperature_decay**completed_steps,
                    args.min_gumbel_temperature,
                )
                if hasattr(model, "module"):
                    model.module.set_gumbel_temperature(gumbel_temperature)
                else:
                    model.set_gumbel_temperature(gumbel_temperature)

                progress_bar.update(1)
                completed_steps += 1

            # 6. Log all results
            if (step + 1) % (args.gradient_accumulation_steps * args.logging_steps) == 0:
                loss.detach()
                outputs.contrastive_loss.detach()
                outputs.diversity_loss.detach()

                if accelerator.state.num_processes > 1:
                    loss = accelerator.gather_for_metrics(loss).sum()
                    outputs.contrastive_loss = accelerator.gather_for_metrics(outputs.contrastive_loss).sum()
                    outputs.diversity_loss = accelerator.gather_for_metrics(outputs.diversity_loss).sum()
                    percent_masked = accelerator.gather_for_metrics(percent_masked).sum()

                train_logs = {
                    "loss": (loss * args.gradient_accumulation_steps) / num_losses,
                    "constrast_loss": outputs.contrastive_loss / num_losses,
                    "div_loss": outputs.diversity_loss / num_losses,
                    "%_mask_idx": percent_masked / accelerator.num_processes,
                    "ppl": outputs.codevector_perplexity,
                    "lr": torch.tensor(optimizer.param_groups[0]["lr"]),
                    "temp": torch.tensor(gumbel_temperature),
                    "grad_norm": torch.tensor(grad_norm),
                }
                log_str = ""
                for k, v in train_logs.items():
                    log_str += "| {}: {:.3e}".format(k, v.item())

                if accelerator.is_local_main_process:
                    progress_bar.write(log_str)
                    if is_wandb_available():
                        wandb.log(train_logs)

            # save model every `args.saving_steps` steps
            if (step + 1) % (args.gradient_accumulation_steps * args.saving_steps) == 0:
                if (args.push_to_hub and epoch < args.num_train_epochs - 1) or args.output_dir is not None:
                    accelerator.wait_for_everyone()
                    unwrapped_model = accelerator.unwrap_model(model)
                    unwrapped_model.save_pretrained(
                        args.output_dir, is_main_process=accelerator.is_main_process, save_function=accelerator.save
                    )

                if (args.push_to_hub and epoch < args.num_train_epochs - 1) and accelerator.is_main_process:
                    repo.push_to_hub(
                        commit_message=f"Training in progress step {completed_steps}",
                        blocking=False,
                        auto_lfs_prune=True,
                    )

            # if completed steps > `args.max_train_steps` stop
            if completed_steps >= args.max_train_steps:
                break

        # 7. Validate!
        model.eval()

        # init logs
        val_logs = {
            "val_loss": 0,
            "val_contrastive_loss": 0,
            "val_diversity_loss": 0,
            "val_num_losses": 0,
        }
        for step, batch in enumerate(eval_dataloader):
            with torch.no_grad():
                batch.pop("sub_attention_mask", None)
                outputs = model(**batch)

            val_logs["val_loss"] += outputs.loss
            val_logs["val_contrastive_loss"] += outputs.contrastive_loss
            val_logs["val_diversity_loss"] += outputs.diversity_loss
            val_logs["val_num_losses"] += batch["mask_time_indices"].sum()

        # sum over devices in multi-processing
        if accelerator.num_processes > 1:
            val_logs = {k: accelerator.gather_for_metrics(v).sum() for k, v in val_logs.items()}

        val_logs = {k: v / val_logs["val_num_losses"] for k, v in val_logs.items()}

        log_str = ""
        for k, v in val_logs.items():
            log_str += "| {}: {:.3e}".format(k, v.item())

        if accelerator.is_local_main_process:
            progress_bar.write(log_str)
            if is_wandb_available():
                wandb.log(val_logs)

        if args.output_dir is not None:
            accelerator.wait_for_everyone()
            unwrapped_model = accelerator.unwrap_model(model)
            unwrapped_model.save_pretrained(
                args.output_dir, is_main_process=accelerator.is_main_process, save_function=accelerator.save
            )
            if accelerator.is_main_process:
                if args.push_to_hub:
                    repo.push_to_hub(commit_message="End of training", auto_lfs_prune=True)


if __name__ == "__main__":
    main()