changed README

README.md

@@ -15,28 +15,29 @@ Currently the model is training. It is expected that it should be finished by th
 
  The following setting were used in training:
 ```bash
- ./run_t5_mlm_flax.py \
+./run_t5_mlm_flax_streaming.py \
     --output_dir="./" \
     --model_type="t5" \
     --config_name="./" \
     --tokenizer_name="./" \
-    --train_file /mnt/disks/flaxdisk/corpus/norwegian_colossal_corpus_train.json \
-    --validation_file /mnt/disks/flaxdisk/corpus/norwegian_colossal_corpus_validation.json \
-    --max_seq_length="128" \
+    --dataset_name="pere/norwegian_colossal_corpus_v2_short100k" \
+    --max_seq_length="512" \
     --weight_decay="0.01" \
-    --per_device_train_batch_size="128" \
-    --per_device_eval_batch_size="128" \
+    --per_device_train_batch_size="32" \
+    --per_device_eval_batch_size="32" \
     --learning_rate="8e-3" \
-    --warmup_steps="2000" \
+    --warmup_steps="5000" \
     --overwrite_output_dir \
     --cache_dir /mnt/disks/flaxdisk/cache/ \
-    --num_train_epochs="3" \
+    --num_train_epochs="5" \
     --adam_beta1="0.9" \
     --adam_beta2="0.98" \
-    --logging_steps="100" \
-    --save_steps="2500" \
-    --eval_steps="2500" \
+    --logging_steps="500" \
+    --num_train_steps="1000000" \
+    --num_eval_samples="5000" \
+    --save_steps="5000" \
+    --eval_steps="5000" \
     --preprocessing_num_workers 96 \
     --adafactor \
     --push_to_hub
- ```
+ ```

events.out.tfevents.1628276093.t1v-n-1a0a7c50-w-0.1590501.3.v2

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:1c0d3a5c92f2fe2957520468ab05b91b066470122ff1601cb094a5d4a9269a0a
+size 40

run_streaming.sh

@@ -16,7 +16,7 @@
     --adam_beta1="0.9" \
     --adam_beta2="0.98" \
     --logging_steps="500" \
-    --num_train_steps="500000" \
+    --num_train_steps="1000000" \
     --num_eval_samples="5000" \
     --save_steps="5000" \
     --eval_steps="5000" \

run_streaming_fix.sh

@@ -1,26 +0,0 @@
-./run_t5_mlm_flax_streaming.py \
-    --output_dir="./" \
-    --model_type="t5" \
-    --config_name="./" \
-    --tokenizer_name="./" \
-    --dataset_name="pere/norwegian_colossal_corpus_v2_short100k" \
-    --max_seq_length="512" \
-    --weight_decay="0.01" \
-    --per_device_train_batch_size="32" \
-    --per_device_eval_batch_size="32" \
-    --learning_rate="8e-3" \
-    --warmup_steps="5000" \
-    --overwrite_output_dir \
-    --cache_dir /mnt/disks/flaxdisk/cache/ \
-    --num_train_epochs="5" \
-    --adam_beta1="0.9" \
-    --adam_beta2="0.98" \
-    --logging_steps="500" \
-    --num_train_steps="1000000" \
-    --num_eval_samples="5000" \
-    --save_steps="5000" \
-    --eval_steps="5000" \
-    --preprocessing_num_workers 96 \
-    --adafactor \
-    --push_to_hub
-

run_t5_mlm_flax_streaming.py

@@ -227,7 +227,7 @@ class FlaxDataCollatorForT5MLM:
     decoder_start_token_id: int
 
     def __call__(self, examples: Dict[str, np.ndarray]) -> BatchEncoding:
-        
+
         batch = BatchEncoding(
             {k: np.array(examples[k]) for k in examples.keys()}
         )
@@ -332,7 +332,8 @@ class FlaxDataCollatorForT5MLM:
             np.random.shuffle(mask_indices)
             first_in_segment = np.pad(mask_indices, [[1, 0]])
             segment_id = np.cumsum(first_in_segment)
-            segment_length = np.asarray(jax.ops.segment_sum(np.ones_like(segment_id), segment_id))
+            # count length of sub segments assuming that list is sorted
+            _, segment_length = np.unique(segment_id, return_counts=True)
             return segment_length
 
         noise_span_lengths = _random_segmentation(num_noise_tokens, num_noise_spans)
@@ -458,8 +459,8 @@ if __name__ == "__main__":
     if data_args.dataset_name is not None:
         # Downloading and loading a dataset from the hub.
         datasets = load_dataset(
-            data_args.dataset_name, 
-            data_args.dataset_config_name, 
+            data_args.dataset_name,
+            data_args.dataset_config_name,
             cache_dir=model_args.cache_dir,
             streaming=True,
             split="train"
@@ -472,15 +473,15 @@ if __name__ == "__main__":
 
     if model_args.tokenizer_name:
         tokenizer = T5TokenizerFast.from_pretrained(
-            model_args.tokenizer_name, 
-            cache_dir=model_args.cache_dir, 
+            model_args.tokenizer_name,
+            cache_dir=model_args.cache_dir,
             use_fast=model_args.use_fast_tokenizer,
             use_auth_token=model_args.auth_token
         )
     elif model_args.model_name_or_path:
         tokenizer = T5TokenizerFast.from_pretrained(
-            model_args.model_name_or_path, 
-            cache_dir=model_args.cache_dir, 
+            model_args.model_name_or_path,
+            cache_dir=model_args.cache_dir,
             use_fast=model_args.use_fast_tokenizer,
             use_auth_token=model_args.auth_token
         )
@@ -759,3 +760,4 @@ if __name__ == "__main__":
 
         # update tqdm bar
         steps.update(1)
+

run_t5_mlm_flax_streaming_fix.py

@@ -1,763 +0,0 @@
-#!/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.
-"""
-Pretraining with T5-like span-masked language modeling on a streaming dataset.
-Here is the full list of checkpoints on the hub that can be pretrained by this script:
-https://huggingface.co/models?filter=t5
-"""
-import logging
-import os
-import sys
-import time
-from collections import defaultdict
-from dataclasses import dataclass, field
-from pathlib import Path
-from typing import Dict, Optional
-
-import datasets
-import numpy as np
-from datasets import load_dataset
-from tqdm import tqdm
-
-import flax
-import jax
-import jax.numpy as jnp
-import optax
-from flax import jax_utils, traverse_util
-from flax.training import train_state
-from flax.training.common_utils import get_metrics, onehot, shard
-from transformers import (
-    CONFIG_MAPPING,
-    FLAX_MODEL_FOR_MASKED_LM_MAPPING,
-    BatchEncoding,
-    FlaxT5ForConditionalGeneration,
-    HfArgumentParser,
-    PreTrainedTokenizerBase,
-    T5Config,
-    T5TokenizerFast,
-    TrainingArguments,
-    is_tensorboard_available,
-    set_seed,
-)
-from transformers.models.t5.modeling_flax_t5 import shift_tokens_right
-
-#if datasets.__version__ <= "1.8.0":
-#    raise ValueError("Make sure to upgrade `datasets` to a version >= 1.9.0 to use dataset streaming")
-
-MODEL_CONFIG_CLASSES = list(FLAX_MODEL_FOR_MASKED_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]`."
-        },
-    )
-    auth_token: Optional[str] = field(
-        default=None,
-        metadata={
-            "help": "Auth token for private repositories on the Huggingface Hub"
-        }
-    )
-
-
-@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)."}
-    )
-    max_seq_length: Optional[int] = field(
-        default=None,
-        metadata={
-            "help": "The maximum total input sequence length after tokenization and masking. Sequences longer than this will be truncated. Default to the max input length of the model."
-        },
-    )
-    preprocessing_num_workers: Optional[int] = field(
-        default=None,
-        metadata={"help": "The number of processes to use for the preprocessing."},
-    )
-    mlm_probability: float = field(
-        default=0.15, metadata={"help": "Ratio of tokens to mask for span masked language modeling loss"}
-    )
-    mean_noise_span_length: float = field(
-        default=3.0,
-        metadata={"help": "Mean span length of masked tokens"},
-    )
-    text_column_name: str = field(
-        default="text", metadata={"help": "The name of the column to retrieve the training text."}
-    )
-    shuffle_buffer_size: int = field(
-        default=10000, metadata={"help": "The number of examples to pre-load for shuffling."}
-    )
-    num_train_steps: int = field(default=50000, metadata={"help": "The number of training steps."})
-    num_eval_samples: int = field(default=50000, metadata={"help": "The number of samples to be used for evaluation"})
-
-    def __post_init__(self):
-        if self.dataset_name is None:
-            raise ValueError("Need a dataset name for streaming.")
-
-
-def compute_input_and_target_lengths(inputs_length, noise_density, mean_noise_span_length):
-    """This function is copy of `random_spans_helper <https://github.com/google-research/text-to-text-transfer-transformer/blob/84f8bcc14b5f2c03de51bd3587609ba8f6bbd1cd/t5/data/preprocessors.py#L2466>`__ .
-    Training parameters to avoid padding with random_spans_noise_mask.
-    When training a model with random_spans_noise_mask, we would like to set the other
-    training hyperparmeters in a way that avoids padding.
-    This function helps us compute these hyperparameters.
-    We assume that each noise span in the input is replaced by extra_tokens_per_span_inputs sentinel tokens,
-    and each non-noise span in the targets is replaced by extra_tokens_per_span_targets sentinel tokens.
-    This function tells us the required number of tokens in the raw example (for split_tokens())
-    as well as the length of the encoded targets. Note that this function assumes
-    the inputs and targets will have EOS appended and includes that in the reported length.
-    Args:
-        inputs_length: an integer - desired length of the tokenized inputs sequence
-        noise_density: a float
-        mean_noise_span_length: a float
-    Returns:
-        tokens_length: length of original text in tokens
-        targets_length: an integer - length in tokens of encoded targets sequence
-    """
-
-    def _tokens_length_to_inputs_length_targets_length(tokens_length):
-        num_noise_tokens = int(round(tokens_length * noise_density))
-        num_nonnoise_tokens = tokens_length - num_noise_tokens
-        num_noise_spans = int(round(num_noise_tokens / mean_noise_span_length))
-        # inputs contain all nonnoise tokens, sentinels for all noise spans
-        # and one EOS token.
-        _input_length = num_nonnoise_tokens + num_noise_spans + 1
-        _output_length = num_noise_tokens + num_noise_spans + 1
-        return _input_length, _output_length
-
-    tokens_length = inputs_length
-
-    while _tokens_length_to_inputs_length_targets_length(tokens_length + 1)[0] <= inputs_length:
-        tokens_length += 1
-
-    inputs_length, targets_length = _tokens_length_to_inputs_length_targets_length(tokens_length)
-
-    # minor hack to get the targets length to be equal to inputs length
-    # which is more likely to have been set to a nice round number.
-    if noise_density == 0.5 and targets_length > inputs_length:
-        tokens_length -= 1
-        targets_length -= 1
-    return tokens_length, targets_length
-
-
-@flax.struct.dataclass
-class FlaxDataCollatorForT5MLM:
-    """
-    Data collator used for T5 span-masked language modeling.
-    It is made sure that after masking the inputs are of length `data_args.max_seq_length` and targets are also of fixed length.
-    For more information on how T5 span-masked language modeling works, one can take a look
-    at the `official paper <https://arxiv.org/pdf/1910.10683.pdf>`__
-    or the `official code for preprocessing <https://github.com/google-research/text-to-text-transfer-transformer/blob/master/t5/data/preprocessors.py>`__ .
-    Args:
-        tokenizer (:class:`~transformers.PreTrainedTokenizer` or :class:`~transformers.PreTrainedTokenizerFast`):
-            The tokenizer used for encoding the data.
-        noise_density (:obj:`float`):
-            The probability with which to (randomly) mask tokens in the input.
-        mean_noise_span_length (:obj:`float`):
-            The average span length of the masked tokens.
-        input_length (:obj:`int`):
-            The expected input length after masking.
-        target_length (:obj:`int`):
-            The expected target length after masking.
-        pad_token_id: (:obj:`int`):
-            The pad token id of the model
-        decoder_start_token_id: (:obj:`int):
-            The decoder start token id of the model
-    """
-
-    tokenizer: PreTrainedTokenizerBase
-    noise_density: float
-    mean_noise_span_length: float
-    input_length: int
-    target_length: int
-    pad_token_id: int
-    decoder_start_token_id: int
-
-    def __call__(self, examples: Dict[str, np.ndarray]) -> BatchEncoding:
-
-        batch = BatchEncoding(
-            {k: np.array(examples[k]) for k in examples.keys()}
-        )
-        input_ids = batch['input_ids']
-        batch_size, expandend_input_length = input_ids.shape
-
-        mask_indices = np.asarray([self.random_spans_noise_mask(expandend_input_length) for i in range(batch_size)])
-        labels_mask = ~mask_indices
-
-        input_ids_sentinel = self.create_sentinel_ids(mask_indices.astype(np.int8))
-        labels_sentinel = self.create_sentinel_ids(labels_mask.astype(np.int8))
-
-        batch["input_ids"] = self.filter_input_ids(input_ids, input_ids_sentinel)
-        batch["labels"] = self.filter_input_ids(input_ids, labels_sentinel)
-
-        if batch["input_ids"].shape[-1] != self.input_length:
-            raise ValueError(
-                f"`input_ids` are incorrectly preprocessed. `input_ids` length is {batch['input_ids'].shape[-1]}, but should be {self.target_length}."
-            )
-
-        if batch["labels"].shape[-1] != self.target_length:
-            raise ValueError(
-                f"`labels` are incorrectly preprocessed. `labels` length is {batch['labels'].shape[-1]}, but should be {self.target_length}."
-            )
-
-        # to check that tokens are correctly proprocessed, one can run `self.tokenizer.batch_decode(input_ids)` and `self.tokenizer.batch_decode(labels)` here...
-        batch["decoder_input_ids"] = shift_tokens_right(
-            batch["labels"], self.pad_token_id, self.decoder_start_token_id
-        )
-
-        return batch
-
-    def create_sentinel_ids(self, mask_indices):
-        """
-        Sentinel ids creation given the indices that should be masked.
-        The start indices of each mask are replaced by the sentinel ids in increasing
-        order. Consecutive mask indices to be deleted are replaced with `-1`.
-        """
-        start_indices = mask_indices - np.roll(mask_indices, 1, axis=-1) * mask_indices
-        start_indices[:, 0] = mask_indices[:, 0]
-
-        sentinel_ids = np.where(start_indices != 0, np.cumsum(start_indices, axis=-1), start_indices)
-        sentinel_ids = np.where(sentinel_ids != 0, (sentinel_ids + self.tokenizer.vocab_size - 1), 0)
-        sentinel_ids -= mask_indices - start_indices
-
-        return sentinel_ids
-
-    def filter_input_ids(self, input_ids, sentinel_ids):
-        """
-        Puts sentinel mask on `input_ids` and fuse consecutive mask tokens into a single mask token by deleting.
-        This will reduce the sequence length from `expanded_inputs_length` to `input_length`.
-        """
-        batch_size = input_ids.shape[0]
-
-        input_ids_full = np.where(sentinel_ids != 0, sentinel_ids, input_ids)
-        input_ids = input_ids_full[input_ids_full > 0].reshape((batch_size, -1))
-        input_ids = np.concatenate(
-            [input_ids, np.full((batch_size, 1), self.tokenizer.eos_token_id, dtype=np.int32)], axis=-1
-        )
-        return input_ids
-
-    def random_spans_noise_mask(self, length):
-
-        """This function is copy of `random_spans_helper <https://github.com/google-research/text-to-text-transfer-transformer/blob/84f8bcc14b5f2c03de51bd3587609ba8f6bbd1cd/t5/data/preprocessors.py#L2682>`__ .
-        Noise mask consisting of random spans of noise tokens.
-        The number of noise tokens and the number of noise spans and non-noise spans
-        are determined deterministically as follows:
-        num_noise_tokens = round(length * noise_density)
-        num_nonnoise_spans = num_noise_spans = round(num_noise_tokens / mean_noise_span_length)
-        Spans alternate between non-noise and noise, beginning with non-noise.
-        Subject to the above restrictions, all masks are equally likely.
-        Args:
-            length: an int32 scalar (length of the incoming token sequence)
-            noise_density: a float - approximate density of output mask
-            mean_noise_span_length: a number
-        Returns:
-            a boolean tensor with shape [length]
-        """
-
-        orig_length = length
-
-        num_noise_tokens = int(np.round(length * self.noise_density))
-        # avoid degeneracy by ensuring positive numbers of noise and nonnoise tokens.
-        num_noise_tokens = min(max(num_noise_tokens, 1), length - 1)
-        num_noise_spans = int(np.round(num_noise_tokens / self.mean_noise_span_length))
-
-        # avoid degeneracy by ensuring positive number of noise spans
-        num_noise_spans = max(num_noise_spans, 1)
-        num_nonnoise_tokens = length - num_noise_tokens
-
-        # pick the lengths of the noise spans and the non-noise spans
-        def _random_segmentation(num_items, num_segments):
-            """Partition a sequence of items randomly into non-empty segments.
-            Args:
-                num_items: an integer scalar > 0
-                num_segments: an integer scalar in [1, num_items]
-            Returns:
-                a Tensor with shape [num_segments] containing positive integers that add
-                up to num_items
-            """
-            mask_indices = np.arange(num_items - 1) < (num_segments - 1)
-            np.random.shuffle(mask_indices)
-            first_in_segment = np.pad(mask_indices, [[1, 0]])
-            segment_id = np.cumsum(first_in_segment)
-            # count length of sub segments assuming that list is sorted
-            _, segment_length = np.unique(segment_id, return_counts=True)
-            return segment_length
-
-        noise_span_lengths = _random_segmentation(num_noise_tokens, num_noise_spans)
-        nonnoise_span_lengths = _random_segmentation(num_nonnoise_tokens, num_noise_spans)
-
-        interleaved_span_lengths = np.reshape(
-            np.stack([nonnoise_span_lengths, noise_span_lengths], axis=1), [num_noise_spans * 2]
-        )
-        span_starts = np.cumsum(interleaved_span_lengths)[:-1]
-        span_start_indicator = np.zeros((length,), dtype=np.int8)
-        span_start_indicator[span_starts] = True
-        span_num = np.cumsum(span_start_indicator)
-        is_noise = np.equal(span_num % 2, 1)
-
-        return is_noise[:orig_length]
-
-
-def generate_batch_splits(samples_idx: jnp.ndarray, batch_size: int) -> jnp.ndarray:
-    num_samples = len(samples_idx)
-    samples_to_remove = num_samples % batch_size
-
-    if samples_to_remove != 0:
-        samples_idx = samples_idx[:-samples_to_remove]
-    sections_split = num_samples // batch_size
-    batch_idx = np.split(samples_idx, sections_split)
-    return batch_idx
-
-
-def advance_iter_and_group_samples(train_iterator, num_samples, max_seq_length):
-    """
-    The training iterator is advanced so that after groupifying the samples,
-    `num_samples` of length `max_seq_length` are returned.
-    """
-    num_total_tokens = max_seq_length * num_samples
-    samples = defaultdict(list)
-
-    i = 0
-    while i < num_total_tokens:
-        tokenized_samples = next(train_iterator)
-        i += len(tokenized_samples["input_ids"])
-
-        # concatenate tokenized samples to list
-        samples = {k: samples[k] + tokenized_samples[k] for k in tokenized_samples.keys()}
-
-    # Concatenated tokens are split to lists of length `max_seq_length`.
-    # Note that remainedr of % max_seq_length are thrown away.
-    def group_texts(examples):
-        result = {
-            k: [t[i : i + max_seq_length] for i in range(0, num_total_tokens, max_seq_length)]
-            for k, t in examples.items()
-        }
-        return result
-
-    grouped_samples = group_texts(samples)
-    return grouped_samples
-
-
-def write_train_metric(summary_writer, train_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)
-
-
-def write_eval_metric(summary_writer, eval_metrics, step):
-    for metric_name, value in eval_metrics.items():
-        summary_writer.scalar(f"eval_{metric_name}", value, step)
-
-
-if __name__ == "__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."
-        )
-
-    # Setup logging
-    logging.basicConfig(
-        format="%(asctime)s - %(levelname)s - %(name)s -   %(message)s",
-        level="INFO",
-        datefmt="[%X]",
-    )
-
-    # Log on each process the small summary:
-    logger = logging.getLogger(__name__)
-    #logger.warning(
-    #    f"Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}"
-    #    + f"distributed training: {bool(training_args.local_rank != -1)}, 16-bits training: {training_args.fp16}"
-    #)
-
-    # Set the verbosity to info of the Transformers logger (on main process only):
-    logger.info(f"Training/evaluation parameters {training_args}")
-
-    # Set seed before initializing model.
-    set_seed(training_args.seed)
-
-    # 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).
-    if data_args.dataset_name is not None:
-        # Downloading and loading a dataset from the hub.
-        datasets = load_dataset(
-            data_args.dataset_name,
-            data_args.dataset_config_name,
-            cache_dir=model_args.cache_dir,
-            streaming=True,
-            split="train"
-        )
-
-    # 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
-
-    if model_args.tokenizer_name:
-        tokenizer = T5TokenizerFast.from_pretrained(
-            model_args.tokenizer_name,
-            cache_dir=model_args.cache_dir,
-            use_fast=model_args.use_fast_tokenizer,
-            use_auth_token=model_args.auth_token
-        )
-    elif model_args.model_name_or_path:
-        tokenizer = T5TokenizerFast.from_pretrained(
-            model_args.model_name_or_path,
-            cache_dir=model_args.cache_dir,
-            use_fast=model_args.use_fast_tokenizer,
-            use_auth_token=model_args.auth_token
-        )
-    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.config_name:
-        config = T5Config.from_pretrained(
-            model_args.config_name, cache_dir=model_args.cache_dir, vocab_size=len(tokenizer)
-        )
-    elif model_args.model_name_or_path:
-        config = T5Config.from_pretrained(
-            model_args.model_name_or_path, cache_dir=model_args.cache_dir, vocab_size=len(tokenizer)
-        )
-    else:
-        config = CONFIG_MAPPING[model_args.model_type]()
-        logger.warning("You are instantiating a new config instance from scratch.")
-
-    # Preprocessing the datasets.
-    # First we tokenize all the texts.
-
-    max_seq_length = min(data_args.max_seq_length, tokenizer.model_max_length)
-
-    # Otherwise, we tokenize every text, then concatenate them together before splitting them in smaller parts.
-    # Since we make sure that all sequences are of the same length, no attention_mask is needed.
-    def tokenize_function(examples):
-        return tokenizer(examples[data_args.text_column_name], return_attention_mask=False)
-
-    tokenized_datasets = datasets.map(
-        tokenize_function,
-        batched=True
-    )
-
-    # T5-like span masked language modeling will fuse consecutively masked tokens to a single sentinel token.
-    # To ensure that the input length is `max_seq_length`, we need to increase the maximum length
-    # according to `mlm_probability` and `mean_noise_span_length`. We can also define the label length accordingly.
-    expanded_inputs_length, targets_length = compute_input_and_target_lengths(
-        inputs_length=max_seq_length,
-        noise_density=data_args.mlm_probability,
-        mean_noise_span_length=data_args.mean_noise_span_length,
-    )
-
-    shuffle_seed = training_args.seed
-    tokenized_datasets = tokenized_datasets.shuffle(buffer_size=data_args.shuffle_buffer_size, seed=shuffle_seed)
-
-    # Enable tensorboard only on the master node
-    has_tensorboard = is_tensorboard_available()
-    if has_tensorboard and jax.process_index() == 0:
-        try:
-            from flax.metrics.tensorboard import SummaryWriter
-
-            summary_writer = SummaryWriter(log_dir=Path(training_args.output_dir))
-        except ImportError as ie:
-            has_tensorboard = False
-            logger.warning(
-                f"Unable to display metrics through TensorBoard because some package are not installed: {ie}"
-            )
-    else:
-        logger.warning(
-            "Unable to display metrics through TensorBoard because the package is not installed: "
-            "Please run pip install tensorboard to enable."
-        )
-
-    # Initialize our training
-    rng = jax.random.PRNGKey(training_args.seed)
-    dropout_rngs = jax.random.split(rng, jax.local_device_count())
-
-    model = FlaxT5ForConditionalGeneration(config, seed=training_args.seed, dtype=getattr(jnp, model_args.dtype))
-
-    #if model_args.model_name_or_path:
-    #    model = FlaxT5ForConditionalGeneration.from_pretrained(
-    #        model_args.model_name_or_path, config=config, seed=training_args.seed, dtype=getattr(jnp, model_args.dtype)
-    #    )
-    #else:
-    #    model = FlaxT5ForConditionalGeneration.from_pretrained(
-    #        config, seed=training_args.seed, dtype=getattr(jnp, model_args.dtype)
-    #    )
-
-
-    # Data collator
-    # This one will take care of randomly masking the tokens.
-    data_collator = FlaxDataCollatorForT5MLM(
-        tokenizer=tokenizer,
-        noise_density=data_args.mlm_probability,
-        mean_noise_span_length=data_args.mean_noise_span_length,
-        input_length=max_seq_length,
-        target_length=targets_length,
-        pad_token_id=model.config.pad_token_id,
-        decoder_start_token_id=model.config.decoder_start_token_id,
-    )
-
-    # 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()
-
-    num_train_steps = data_args.num_train_steps
-
-    # Create learning rate schedule
-    warmup_fn = optax.linear_schedule(
-        init_value=0.0, end_value=training_args.learning_rate, transition_steps=training_args.warmup_steps
-    )
-    decay_fn = optax.linear_schedule(
-        init_value=training_args.learning_rate,
-        end_value=0,
-        transition_steps=num_train_steps - training_args.warmup_steps,
-    )
-    linear_decay_lr_schedule_fn = optax.join_schedules(
-        schedules=[warmup_fn, decay_fn], boundaries=[training_args.warmup_steps]
-    )
-
-    # 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:] not in [("layer_norm", "scale"), ("final_layer_norm", "scale")])
-            for path in flat_params
-        }
-        return traverse_util.unflatten_dict(flat_mask)
-
-    # create adam optimizer
-    if training_args.adafactor:
-        # We use the default parameters here to initialize adafactor,
-        # For more details about the parameters please check https://github.com/deepmind/optax/blob/ed02befef9bf81cbbf236be3d2b0e032e9ed4a40/optax/_src/alias.py#L74
-        optimizer = optax.adafactor(
-            learning_rate=linear_decay_lr_schedule_fn,
-        )
-    else:
-        optimizer = optax.adamw(
-            learning_rate=linear_decay_lr_schedule_fn,
-            b1=training_args.adam_beta1,
-            b2=training_args.adam_beta2,
-            weight_decay=training_args.weight_decay,
-            mask=decay_mask_fn,
-        )
-
-    # Setup train state
-    state = train_state.TrainState.create(apply_fn=model.__call__, params=model.params, tx=optimizer)
-
-    # Define gradient update step fn
-    def train_step(state, batch, dropout_rng):
-        dropout_rng, new_dropout_rng = jax.random.split(dropout_rng)
-
-        def loss_fn(params):
-            labels = batch.pop("labels")
-
-            logits = state.apply_fn(**batch, params=params, dropout_rng=dropout_rng, train=True)[0]
-
-            # compute loss
-            loss = optax.softmax_cross_entropy(logits, onehot(labels, logits.shape[-1])).mean()
-
-            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": linear_decay_lr_schedule_fn(state.step)}, axis_name="batch"
-        )
-
-        return new_state, metrics, new_dropout_rng
-
-    # Create parallel version of the train step
-    p_train_step = jax.pmap(train_step, "batch", donate_argnums=(0,))
-
-    # Define eval fn
-    def eval_step(params, batch):
-        labels = batch.pop("labels")
-
-        logits = model(**batch, params=params, train=False)[0]
-
-        # compute loss
-        loss = optax.softmax_cross_entropy(logits, onehot(labels, logits.shape[-1]))
-
-        # compute accuracy
-        accuracy = jnp.equal(jnp.argmax(logits, axis=-1), labels)
-
-        # summarize metrics
-        metrics = {"loss": loss.mean(), "accuracy": accuracy.mean()}
-        metrics = jax.lax.pmean(metrics, axis_name="batch")
-
-        return metrics
-
-    p_eval_step = jax.pmap(eval_step, "batch", donate_argnums=(0,))
-
-    # Replicate the train state on each device
-    state = jax_utils.replicate(state)
-
-    train_time = 0
-    train_start = time.time()
-    train_metrics = []
-    eval_metrics = []
-
-    training_iter = iter(tokenized_datasets)
-
-    eval_samples = advance_iter_and_group_samples(training_iter, data_args.num_eval_samples, expanded_inputs_length)
-
-    steps = tqdm(range(num_train_steps), desc="Training...", position=0)
-    for step in range(num_train_steps):
-        # ======================== Training ================================
-        try:
-            samples = advance_iter_and_group_samples(training_iter, train_batch_size, expanded_inputs_length)
-        except StopIteration:
-            # Once the end of the dataset stream is reached, the training iterator
-            # is reinitialized and reshuffled and a new eval dataset is randomely chosen.
-            shuffle_seed += 1
-            tokenized_datasets.set_epoch(shuffle_seed)
-
-            training_iter = iter(tokenized_datasets)
-
-            eval_dataset = advance_iter_and_group_samples(training_iter, data_args.num_eval_samples, expanded_inputs_length)
-            samples = advance_iter_and_group_samples(training_iter, train_batch_size, expanded_inputs_length)
-
-        # Model forward
-        model_inputs = data_collator(samples)
-        model_inputs = shard(model_inputs.data)
-        state, train_metric, dropout_rngs = p_train_step(state, model_inputs, dropout_rngs)
-        train_metrics.append(train_metric)
-
-        if step % training_args.logging_steps == 0 and step > 0:
-            train_metric = jax_utils.unreplicate(train_metric)
-            steps.write(
-                f"Step... ({step} | Loss: {train_metric['loss'].mean()}, Learning Rate: {train_metric['learning_rate'].mean()})"
-            )
-            train_time += time.time() - train_start
-            if has_tensorboard and jax.process_index() == 0:
-                write_train_metric(summary_writer, train_metrics, train_time, step)
-            train_metrics = []
-        # ======================== Evaluating ==============================
-        if step % training_args.eval_steps == 0 and step > 0:
-            eval_samples_idx = jnp.arange(data_args.num_eval_samples)
-            eval_batch_idx = generate_batch_splits(eval_samples_idx, eval_batch_size)
-
-            for i, batch_idx in enumerate(tqdm(eval_batch_idx, desc="Evaluating ...", position=1)):
-                # process input samples
-                batch_eval_samples = {k: [v[idx] for idx in batch_idx] for k, v in eval_samples.items()}
-                model_inputs = data_collator(batch_eval_samples)
-
-                # Model forward
-                model_inputs = shard(model_inputs.data)
-                metrics = p_eval_step(state.params, model_inputs)
-                eval_metrics.append(metrics)
-
-            # normalize eval metrics
-            eval_metrics = get_metrics(eval_metrics)
-            eval_metrics = jax.tree_map(jnp.sum, eval_metrics)
-
-            # Update progress bar
-            steps.desc = f"Step... ({step + 1}/{num_train_steps} | Loss: {eval_metrics['loss']}, Acc: {eval_metrics['accuracy']})"
-
-            if has_tensorboard and jax.process_index() == 0:
-                write_eval_metric(summary_writer, eval_metrics, step)
-            eval_metrics = []
-
-            if step % training_args.save_steps == 0 and step > 0:
-            # save checkpoint after each save_steps and push checkpoint to the hub
-                if jax.process_index() == 0:
-                    params = jax.device_get(jax.tree_map(lambda x: x[0], state.params))
-                    model.save_pretrained(
-                        training_args.output_dir,
-                        params=params,
-                        push_to_hub=training_args.push_to_hub,
-                        commit_message=f"Saving weights and logs of step {step+1}",
-                    )
-                    tokenizer.save_pretrained(
-                        training_args.output_dir
-                    )
-
-        # update tqdm bar
-        steps.update(1)
-