Jax out of vram using pmap

[Samuel-Fipps]

When running a training script by google, pmap seems to be causing a out of memory error. It tries to allocate a couple of TB's of memory when I'm just increasing the batch size. I have never use jax before but I was able to pin down the location where it goes wrong.

Here is the code for the training script.
Line 870 is where the pmap is created
and line 927 is where it is used and errors out.

I put the error after the code

`
#!/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 the library models for T5-like span-masked language modeling on a text file or a 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 json
import logging
import math
import os
import sys
import time
import copy
from dataclasses import asdict, dataclass, field
from jax.experimental.maps import xmap

You can also adapt this script on your own masked language modeling task. Pointers for this are left as comments.

from enum import Enum
from itertools import chain
from pathlib import Path
from typing import Dict, List, Optional

import numpy as np
from datasets import load_dataset
from tqdm import tqdm
import torch.cuda
import torch
import flax
import jax
import jax.numpy as jnp
import optax
from flax import jax_utils, traverse_util
from flax.jax_utils import pad_shard_unpad
from flax.training import train_state
from flax.training.common_utils import get_metrics, onehot, shard
from huggingface_hub import Repository
from transformers import (
CONFIG_MAPPING,
FLAX_MODEL_FOR_MASKED_LM_MAPPING,
AutoTokenizer,
BatchEncoding,
FlaxT5ForConditionalGeneration,
HfArgumentParser,
PreTrainedTokenizerBase,
T5Config,
is_tensorboard_available,
set_seed,
)
from transformers.models.t5.modeling_flax_t5 import shift_tokens_right
from transformers.utils import get_full_repo_name, send_example_telemetry

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 TrainingArguments:
output_dir: str = field(
metadata={"help": "The output directory where the model predictions and checkpoints will be written."},
)
overwrite_output_dir: bool = field(
default=False,
metadata={
"help": (
"Overwrite the content of the output directory. "
"Use this to continue training if output_dir points to a checkpoint directory."
)
},
)
do_train: bool = field(default=False, metadata={"help": "Whether to run training."})
do_eval: bool = field(default=False, metadata={"help": "Whether to run eval on the dev set."})
per_device_train_batch_size: int = field(
default=8, metadata={"help": "Batch size per GPU/TPU core/CPU for training."}
)
per_device_eval_batch_size: int = field(
default=8, metadata={"help": "Batch size per GPU/TPU core/CPU for evaluation."}
)
learning_rate: float = field(default=5e-5, metadata={"help": "The initial learning rate for AdamW."})
weight_decay: float = field(default=0.0, metadata={"help": "Weight decay for AdamW if we apply some."})
adam_beta1: float = field(default=0.9, metadata={"help": "Beta1 for AdamW optimizer"})
adam_beta2: float = field(default=0.999, metadata={"help": "Beta2 for AdamW optimizer"})
adam_epsilon: float = field(default=1e-8, metadata={"help": "Epsilon for AdamW optimizer."})
adafactor: bool = field(default=False, metadata={"help": "Whether or not to replace AdamW by Adafactor."})
num_train_epochs: float = field(default=3.0, metadata={"help": "Total number of training epochs to perform."})
warmup_steps: int = field(default=0, metadata={"help": "Linear warmup over warmup_steps."})
logging_steps: int = field(default=500, metadata={"help": "Log every X updates steps."})
save_steps: int = field(default=500, metadata={"help": "Save checkpoint every X updates steps."})
eval_steps: int = field(default=None, metadata={"help": "Run an evaluation every X steps."})
seed: int = field(default=42, metadata={"help": "Random seed that will be set at the beginning of training."})
push_to_hub: bool = field(
default=False, metadata={"help": "Whether or not to upload the trained model to the model hub after training."}
)
hub_model_id: str = field(
default=None, metadata={"help": "The name of the repository to keep in sync with the local output_dir."}
)
hub_token: str = field(default=None, metadata={"help": "The token to use to push to the Model Hub."})

def __post_init__(self):
    if self.output_dir is not None:
        self.output_dir = os.path.expanduser(self.output_dir)

def to_dict(self):
    """
    Serializes this instance while replace `Enum` by their values (for JSON serialization support). It obfuscates
    the token values by removing their value.
    """
    d = asdict(self)
    for k, v in d.items():
        if isinstance(v, Enum):
            d[k] = v.value
        if isinstance(v, list) and len(v) > 0 and isinstance(v[0], Enum):
            d[k] = [x.value for x in v]
        if k.endswith("_token"):
            d[k] = f"<{k.upper()}>"
    return d

@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]`."
        )
    },
)
use_auth_token: bool = field(
    default=False,
    metadata={
        "help": (
            "Will use the token generated when running `huggingface-cli login` (necessary to use this script "
            "with private models)."
        )
    },
)

@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)."},
)
train_ref_file: Optional[str] = field(
    default=None,
    metadata={"help": "An optional input train ref data file for whole word masking in Chinese."},
)
validation_ref_file: Optional[str] = field(
    default=None,
    metadata={"help": "An optional input validation ref data file for whole word masking in Chinese."},
)
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"
    },
)
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"},
)

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."

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: List[Dict[str, np.ndarray]]) -> BatchEncoding:

    # convert list to dict and tensorize input
    batch = BatchEncoding(
        {k: np.array([examples[i][k] for i in range(len(examples))]) for k, v in examples[0].items()}
    )

    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"
            f" 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"
            f" {self.target_length}."
        )

    # to check that tokens are correctly preprocessed, 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, (len(self.tokenizer) - sentinel_ids), 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 tokens and sentinel tokens are >= 0, tokens < 0 are
    # masked tokens coming after sentinel tokens and should be removed
    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: np.ndarray, batch_size: int, drop_last=True) -> np.ndarray:
"""Generate batches of data for a specified batch size from sample indices. If the dataset size is not divisible by
the batch size and drop_last is True, the last incomplete batch is dropped. Else, it is returned."""
num_samples = len(samples_idx)
if drop_last:
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
samples_idx = samples_idx.reshape((sections_split, batch_size))
else:
sections_split = math.ceil(num_samples / batch_size)
samples_idx = np.array_split(samples_idx, sections_split)
return samples_idx

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)

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()

# 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_t5_mlm", model_args, data_args, framework="flax")

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=logging.INFO,
    datefmt="[%X]",
)

# Log on each process the small summary:
logger = logging.getLogger(__name__)

# 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)

# Handle the repository creation
if training_args.push_to_hub:
    if training_args.hub_model_id is None:
        repo_name = get_full_repo_name(
            Path(training_args.output_dir).absolute().name, token=training_args.hub_token
        )
    else:
        repo_name = training_args.hub_model_id
    repo = Repository(training_args.output_dir, clone_from=repo_name)

# 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,
        use_auth_token=True if model_args.use_auth_token else None,
    )

    if "validation" not in datasets.keys():
        datasets["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,
            use_auth_token=True if model_args.use_auth_token else None,
        )
        datasets["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,
            use_auth_token=True if model_args.use_auth_token else None,
        )
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"
    datasets = load_dataset(
        extension,
        data_files=data_files,
        cache_dir=model_args.cache_dir,
        use_auth_token=True if model_args.use_auth_token else None,
    )

    if "validation" not in datasets.keys():
        datasets["validation"] = load_dataset(
            extension,
            data_files=data_files,
            split=f"train[:{data_args.validation_split_percentage}%]",
            cache_dir=model_args.cache_dir,
            use_auth_token=True if model_args.use_auth_token else None,
        )
        datasets["train"] = load_dataset(
            extension,
            data_files=data_files,
            split=f"train[{data_args.validation_split_percentage}%:]",
            cache_dir=model_args.cache_dir,
            use_auth_token=True if model_args.use_auth_token else None,
        )
# 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 = AutoTokenizer.from_pretrained(
        model_args.tokenizer_name,
        cache_dir=model_args.cache_dir,
        use_fast=model_args.use_fast_tokenizer,
        use_auth_token=True if model_args.use_auth_token else None,
    )
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,
        use_auth_token=True if model_args.use_auth_token else None,
    )
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),
        use_auth_token=True if model_args.use_auth_token else None,
    )
elif model_args.model_name_or_path:
    config = T5Config.from_pretrained(
        model_args.model_name_or_path,
        cache_dir=model_args.cache_dir,
        use_auth_token=True if model_args.use_auth_token else None,
    )
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.
if training_args.do_train:
    column_names = datasets["train"].column_names
else:
    column_names = datasets["validation"].column_names
text_column_name = "text" if "text" in column_names else column_names[0]

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[text_column_name], return_attention_mask=False)

tokenized_datasets = datasets.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,
)

# 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,
)

# Main data processing function that will concatenate all texts from our dataset and generate chunks of expanded_inputs_length.
def group_texts(examples):
    # Concatenate all texts.
    concatenated_examples = {k: list(chain(*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.
    if total_length >= expanded_inputs_length:
        total_length = (total_length // expanded_inputs_length) * expanded_inputs_length
    # Split by chunks of max_len.
    result = {
        k: [t[i : i + expanded_inputs_length] for i in range(0, total_length, expanded_inputs_length)]
        for k, t in concatenated_examples.items()
    }
    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
tokenized_datasets = tokenized_datasets.map(
    group_texts,
    batched=True,
    num_proc=data_args.preprocessing_num_workers,
    load_from_cache_file=not data_args.overwrite_cache,
)

# 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


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),
        use_auth_token=True if model_args.use_auth_token else None,
        
    )

    
else:
    config.vocab_size = len(tokenizer)
    model = FlaxT5ForConditionalGeneration(
        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,
)

# junk i added
holder = tokenized_datasets["train"][0:4048]
holder = torch.Tensor(holder['input_ids'])
holder2 = copy.deepcopy(holder)
holder = holder.cuda(0)
holder2 = holder2.cuda(1)

rng = jax.random.PRNGKey(training_args.seed)
dropout_rngs = jax.random.split(rng, jax.local_device_count())

# 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()
per_device_eval_batch_size = int(training_args.per_device_eval_batch_size)
eval_batch_size = per_device_eval_batch_size * jax.device_count()

num_train_steps = len(tokenized_datasets["train"]) // train_batch_size * num_epochs

num_of_hosts = jax.process_count()
current_host_idx = jax.process_index()

# 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)
    # find out all LayerNorm parameters
    layer_norm_candidates = ["layernorm", "layer_norm", "ln"]
    layer_norm_named_params = set(
        [
            layer[-2:]
            for layer_norm_name in layer_norm_candidates
            for layer in flat_params.keys()
            if layer_norm_name in "".join(layer).lower()
        ]
    )
    flat_mask = {path: (path[-1] != "bias" and path[-2:] not in layer_norm_named_params) 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")

        # here is model(inputs) functions 
        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,), backend="gpu")
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,), backend="gpu")

# Replicate the train state on each device
state = jax_utils.replicate(state)

del holder, holder2

train_time = 0
epochs = tqdm(range(num_epochs), desc="Epoch ... ", position=0)
for epoch in epochs:
    # ======================== Training ================================
    train_start = time.time()
    train_metrics = []

    # Create sampling rng
    rng, input_rng = jax.random.split(rng)

    # Generate an epoch by shuffling sampling indices from the train dataset
    num_train_samples = len(tokenized_datasets["train"])
    # Avoid using jax.numpy here in case of TPU training
    train_samples_idx = np.random.permutation(np.arange(num_train_samples))
    train_batch_idx = generate_batch_splits(train_samples_idx, train_batch_size)
    count = 0
    # Gather the indexes for creating the batch and do a training step
    for step, batch_idx in enumerate(tqdm(train_batch_idx, desc="Training...", position=1)):
        samples = [tokenized_datasets["train"][int(idx)] for idx in batch_idx]
        model_inputs = data_collator(samples)

        local_host_model_inputs = {
            key: np.split(model_inputs.data[key], num_of_hosts, axis=0)[current_host_idx]
            for key, value in model_inputs.data.items()
        }
        
        

        model_inputs = shard(local_host_model_inputs)
    
        state, train_metric, dropout_rngs = p_train_step(state, model_inputs, dropout_rngs)

        train_metrics.append(train_metric)   

        cur_step = epoch * (num_train_samples // train_batch_size) + step  

        if cur_step % training_args.logging_steps == 0 and cur_step > 0:



            # Save metrics
            train_metric = jax_utils.unreplicate(train_metric)
            train_time += time.time() - train_start
            if has_tensorboard and jax.process_index() == 0:
                write_train_metric(summary_writer, train_metrics, train_time, cur_step)

            epochs.write(
                f"Step... ({cur_step} | Loss: {train_metric['loss'].mean()}, Learning Rate:"
                f" {train_metric['learning_rate'].mean()})"
            )

            train_metrics = []

        if cur_step % training_args.eval_steps == 0 and cur_step > 0:
            
            # ======================== Evaluating ==============================
            num_eval_samples = len(tokenized_datasets["validation"])
            # Avoid using jax.numpy here in case of TPU training
            eval_samples_idx = np.arange(num_eval_samples)
            eval_batch_idx = generate_batch_splits(eval_samples_idx, eval_batch_size, drop_last=False)

            eval_metrics = []
            for i, batch_idx in enumerate(tqdm(eval_batch_idx, desc="Evaluating ...", position=2)):
                samples = [tokenized_datasets["validation"][int(idx)] for idx in batch_idx]
                model_inputs = data_collator(samples)

                # Model forward
                metrics = pad_shard_unpad(p_eval_step, static_return=True)(
                    state.params, model_inputs.data, min_device_batch=per_device_eval_batch_size
                )
                eval_metrics.append(metrics)

            # get eval metrics
            eval_metrics = get_metrics(eval_metrics)
            eval_metrics = jax.tree_map(jnp.mean, eval_metrics)

            # Update progress bar
            epochs.write(f"Step... ({cur_step} | Loss: {eval_metrics['loss']}, Acc: {eval_metrics['accuracy']})")

            # Save metrics
            if has_tensorboard and jax.process_index() == 0:
                write_eval_metric(summary_writer, eval_metrics, cur_step)

        if cur_step % training_args.save_steps == 0 and cur_step > 0:
            # save checkpoint after each epoch and push checkpoint to the hub
            if jax.process_index() == 0:
                params = jax.device_get(jax.tree_map(lambda x: x[0], state.params))
                model.save_pretrained(training_args.output_dir, params=params)
                tokenizer.save_pretrained(training_args.output_dir)
                if training_args.push_to_hub:
                    repo.push_to_hub(commit_message=f"Saving weights and logs of step {cur_step}", blocking=False)

# Eval after training
if training_args.do_eval:
    num_eval_samples = len(tokenized_datasets["validation"])
    # Avoid using jax.numpy here in case of TPU training
    eval_samples_idx = np.arange(num_eval_samples)
    eval_batch_idx = generate_batch_splits(eval_samples_idx, eval_batch_size, drop_last=False)

    eval_metrics = []
    for i, batch_idx in enumerate(tqdm(eval_batch_idx, desc="Evaluating ...", position=2)):
        samples = [tokenized_datasets["validation"][int(idx)] for idx in batch_idx]
        model_inputs = data_collator(samples)

        # Model forward
        metrics = pad_shard_unpad(p_eval_step, static_return=True)(
            state.params, model_inputs.data, min_device_batch=per_device_eval_batch_size
        )
        eval_metrics.append(metrics)

    # get eval metrics
    eval_metrics = get_metrics(eval_metrics)
    eval_metrics = jax.tree_map(lambda metric: jnp.mean(metric).item(), eval_metrics)

    if jax.process_index() == 0:
        eval_metrics = {f"eval_{metric_name}": value for metric_name, value in eval_metrics.items()}
        path = os.path.join(training_args.output_dir, "eval_results.json")
        with open(path, "w") as f:
            json.dump(eval_metrics, f, indent=4, sort_keys=True)

if name == "main":
main()
`

Here is the error:
`
022-08-29 21:36:02.173212: W external/org_tensorflow/tensorflow/core/common_runtime/bfc_allocator.cc:479] Allocator (GPU_1_bfc) ran out of memory trying to allocate 2.83TiB (rounded to 3109218748160)requested by op | 0/185 [00:00<?, ?it/s]
2022-08-29 21:36:02.175722: W external/org_tensorflow/tensorflow/core/common_runtime/bfc_allocator.cc:491] ***********************************************************************_____________________________
2022-08-29 21:36:02.191908: E external/org_tensorflow/tensorflow/compiler/xla/pjrt/pjrt_stream_executor_client.cc:2130] Execution of replica 1 failed: RESOURCE_EXHAUSTED: Out of memory while trying to allocate 3109218748016 bytes.
BufferAssignment OOM Debugging.
BufferAssignment stats:
parameter allocation: 14.14GiB
constant allocation: 1.2KiB
maybe_live_out allocation: 14.14GiB
preallocated temp allocation: 2.83TiB
preallocated temp fragmentation: 222.80MiB (0.01%)
total allocation: 2.84TiB
total fragmentation: 223.14MiB (0.01%)
Peak buffers:
Buffer 1:
Size: 16.00GiB
Operator: op_name="pmap(train_step)/jit(main)/jvp(FlaxT5ForConditionalGenerationModule)/encoder/block/7/layer/1/DenseReluDense/wi/dot_general[dimension_numbers=(((2,), (0,)), ((), ())) precision=None preferred_element_type=None]" source_file="/home/samuel/.local/lib/python3.8/site-packages/flax/linen/linear.py" source_line=196
XLA Label: custom-call
Shape: f32[262144,16384]
==========================

Buffer 2:
	Size: 16.00GiB
	XLA Label: fusion
	Shape: f32[262144,16384]
	==========================

Buffer 3:
	Size: 16.00GiB
	Operator: op_name="pmap(train_step)/jit(main)/jvp(FlaxT5ForConditionalGenerationModule)/encoder/block/6/layer/1/DenseReluDense/wi/dot_general[dimension_numbers=(((2,), (0,)), ((), ())) precision=None preferred_element_type=None]" source_file="/home/samuel/.local/lib/python3.8/site-packages/flax/linen/linear.py" source_line=196
	XLA Label: custom-call
	Shape: f32[262144,16384]
	==========================

Buffer 4:
	Size: 16.00GiB
	XLA Label: fusion
	Shape: f32[262144,16384]
	==========================

Buffer 5:
	Size: 16.00GiB
	Operator: op_name="pmap(train_step)/jit(main)/jvp(FlaxT5ForConditionalGenerationModule)/encoder/block/5/layer/1/DenseReluDense/wi/dot_general[dimension_numbers=(((2,), (0,)), ((), ())) precision=None preferred_element_type=None]" source_file="/home/samuel/.local/lib/python3.8/site-packages/flax/linen/linear.py" source_line=196
	XLA Label: custom-call
	Shape: f32[262144,16384]
	==========================

Buffer 6:
	Size: 16.00GiB
	XLA Label: fusion
	Shape: f32[262144,16384]
	==========================

Buffer 7:
	Size: 16.00GiB
	Operator: op_name="pmap(train_step)/jit(main)/jvp(FlaxT5ForConditionalGenerationModule)/encoder/block/4/layer/1/DenseReluDense/wi/dot_general[dimension_numbers=(((2,), (0,)), ((), ())) precision=None preferred_element_type=None]" source_file="/home/samuel/.local/lib/python3.8/site-packages/flax/linen/linear.py" source_line=196
	XLA Label: custom-call
	Shape: f32[262144,16384]
	==========================

Buffer 8:
	Size: 16.00GiB
	XLA Label: fusion
	Shape: f32[262144,16384]
	==========================

Buffer 9:
	Size: 16.00GiB
	Operator: op_name="pmap(train_step)/jit(main)/jvp(FlaxT5ForConditionalGenerationModule)/encoder/block/3/layer/1/DenseReluDense/wi/dot_general[dimension_numbers=(((2,), (0,)), ((), ())) precision=None preferred_element_type=None]" source_file="/home/samuel/.local/lib/python3.8/site-packages/flax/linen/linear.py" source_line=196
	XLA Label: custom-call
	Shape: f32[262144,16384]
	==========================

Buffer 10:
	Size: 16.00GiB
	XLA Label: fusion
	Shape: f32[262144,16384]
	==========================

Buffer 11:
	Size: 16.00GiB
	Operator: op_name="pmap(train_step)/jit(main)/jvp(FlaxT5ForConditionalGenerationModule)/encoder/block/2/layer/1/DenseReluDense/wi/dot_general[dimension_numbers=(((2,), (0,)), ((), ())) precision=None preferred_element_type=None]" source_file="/home/samuel/.local/lib/python3.8/site-packages/flax/linen/linear.py" source_line=196
	XLA Label: custom-call
	Shape: f32[262144,16384]
	==========================

Buffer 12:
	Size: 16.00GiB
	XLA Label: fusion
	Shape: f32[262144,16384]
	==========================

Buffer 13:
	Size: 16.00GiB
	Operator: op_name="pmap(train_step)/jit(main)/jvp(FlaxT5ForConditionalGenerationModule)/encoder/block/1/layer/1/DenseReluDense/wi/dot_general[dimension_numbers=(((2,), (0,)), ((), ())) precision=None preferred_element_type=None]" source_file="/home/samuel/.local/lib/python3.8/site-packages/flax/linen/linear.py" source_line=196
	XLA Label: custom-call
	Shape: f32[262144,16384]
	==========================

Buffer 14:
	Size: 16.00GiB
	XLA Label: fusion
	Shape: f32[262144,16384]
	==========================

Buffer 15:
	Size: 16.00GiB
	Operator: op_name="pmap(train_step)/jit(main)/jvp(FlaxT5ForConditionalGenerationModule)/encoder/block/0/layer/1/DenseReluDense/wi/dot_general[dimension_numbers=(((2,), (0,)), ((), ())) precision=None preferred_element_type=None]" source_file="/home/samuel/.local/lib/python3.8/site-packages/flax/linen/linear.py" source_line=196
	XLA Label: custom-call
	Shape: f32[262144,16384]
	==========================

2022-08-29 21:36:02.264641: W external/org_tensorflow/tensorflow/core/common_runtime/bfc_allocator.cc:479] Allocator (GPU_0_bfc) ran out of memory trying to allocate 2.83TiB (rounded to 3109218748160)requested by op
2022-08-29 21:36:02.266129: W external/org_tensorflow/tensorflow/core/common_runtime/bfc_allocator.cc:491] ********************************************************************________________________________
2022-08-29 21:36:02.282608: E external/org_tensorflow/tensorflow/compiler/xla/pjrt/pjrt_stream_executor_client.cc:2130] Execution of replica 0 failed: RESOURCE_EXHAUSTED: Out of memory while trying to allocate 3109218748016 bytes.
BufferAssignment OOM Debugging.
BufferAssignment stats:
parameter allocation: 14.14GiB
constant allocation: 1.2KiB
maybe_live_out allocation: 14.14GiB
preallocated temp allocation: 2.83TiB
preallocated temp fragmentation: 222.80MiB (0.01%)
total allocation: 2.84TiB
total fragmentation: 223.14MiB (0.01%)
Peak buffers:
Buffer 1:
Size: 16.00GiB
Operator: op_name="pmap(train_step)/jit(main)/jvp(FlaxT5ForConditionalGenerationModule)/encoder/block/7/layer/1/DenseReluDense/wi/dot_general[dimension_numbers=(((2,), (0,)), ((), ())) precision=None preferred_element_type=None]" source_file="/home/samuel/.local/lib/python3.8/site-packages/flax/linen/linear.py" source_line=196
XLA Label: custom-call
Shape: f32[262144,16384]
==========================

Buffer 2:
	Size: 16.00GiB
	XLA Label: fusion
	Shape: f32[262144,16384]
	==========================

Buffer 3:
	Size: 16.00GiB
	Operator: op_name="pmap(train_step)/jit(main)/jvp(FlaxT5ForConditionalGenerationModule)/encoder/block/6/layer/1/DenseReluDense/wi/dot_general[dimension_numbers=(((2,), (0,)), ((), ())) precision=None preferred_element_type=None]" source_file="/home/samuel/.local/lib/python3.8/site-packages/flax/linen/linear.py" source_line=196
	XLA Label: custom-call
	Shape: f32[262144,16384]
	==========================

Buffer 4:
	Size: 16.00GiB
	XLA Label: fusion
	Shape: f32[262144,16384]
	==========================

Buffer 5:
	Size: 16.00GiB
	Operator: op_name="pmap(train_step)/jit(main)/jvp(FlaxT5ForConditionalGenerationModule)/encoder/block/5/layer/1/DenseReluDense/wi/dot_general[dimension_numbers=(((2,), (0,)), ((), ())) precision=None preferred_element_type=None]" source_file="/home/samuel/.local/lib/python3.8/site-packages/flax/linen/linear.py" source_line=196
	XLA Label: custom-call
	Shape: f32[262144,16384]
	==========================

Buffer 6:
	Size: 16.00GiB
	XLA Label: fusion
	Shape: f32[262144,16384]
	==========================

Buffer 7:
	Size: 16.00GiB
	Operator: op_name="pmap(train_step)/jit(main)/jvp(FlaxT5ForConditionalGenerationModule)/encoder/block/4/layer/1/DenseReluDense/wi/dot_general[dimension_numbers=(((2,), (0,)), ((), ())) precision=None preferred_element_type=None]" source_file="/home/samuel/.local/lib/python3.8/site-packages/flax/linen/linear.py" source_line=196
	XLA Label: custom-call
	Shape: f32[262144,16384]
	==========================

Buffer 8:
	Size: 16.00GiB
	XLA Label: fusion
	Shape: f32[262144,16384]
	==========================

Buffer 9:
	Size: 16.00GiB
	Operator: op_name="pmap(train_step)/jit(main)/jvp(FlaxT5ForConditionalGenerationModule)/encoder/block/3/layer/1/DenseReluDense/wi/dot_general[dimension_numbers=(((2,), (0,)), ((), ())) precision=None preferred_element_type=None]" source_file="/home/samuel/.local/lib/python3.8/site-packages/flax/linen/linear.py" source_line=196
	XLA Label: custom-call
	Shape: f32[262144,16384]
	==========================

Buffer 10:
	Size: 16.00GiB
	XLA Label: fusion
	Shape: f32[262144,16384]
	==========================

Buffer 11:
	Size: 16.00GiB
	Operator: op_name="pmap(train_step)/jit(main)/jvp(FlaxT5ForConditionalGenerationModule)/encoder/block/2/layer/1/DenseReluDense/wi/dot_general[dimension_numbers=(((2,), (0,)), ((), ())) precision=None preferred_element_type=None]" source_file="/home/samuel/.local/lib/python3.8/site-packages/flax/linen/linear.py" source_line=196
	XLA Label: custom-call
	Shape: f32[262144,16384]
	==========================

Buffer 12:
	Size: 16.00GiB
	XLA Label: fusion
	Shape: f32[262144,16384]
	==========================

Buffer 13:
	Size: 16.00GiB
	Operator: op_name="pmap(train_step)/jit(main)/jvp(FlaxT5ForConditionalGenerationModule)/encoder/block/1/layer/1/DenseReluDense/wi/dot_general[dimension_numbers=(((2,), (0,)), ((), ())) precision=None preferred_element_type=None]" source_file="/home/samuel/.local/lib/python3.8/site-packages/flax/linen/linear.py" source_line=196
	XLA Label: custom-call
	Shape: f32[262144,16384]
	==========================

Buffer 14:
	Size: 16.00GiB
	XLA Label: fusion
	Shape: f32[262144,16384]
	==========================

Buffer 15:
	Size: 16.00GiB
	Operator: op_name="pmap(train_step)/jit(main)/jvp(FlaxT5ForConditionalGenerationModule)/encoder/block/0/layer/1/DenseReluDense/wi/dot_general[dimension_numbers=(((2,), (0,)), ((), ())) precision=None preferred_element_type=None]" source_file="/home/samuel/.local/lib/python3.8/site-packages/flax/linen/linear.py" source_line=196
	XLA Label: custom-call
	Shape: f32[262144,16384]
	==========================

Training...: 0%| | 0/185 [04:00<?, ?it/s]
Epoch ... : 0%| | 0/1 [04:00<?, ?it/s]
Traceback (most recent call last):
File "scripttraining.py", line 1030, in
if name == "main":
File "scripttraining.py", line 938, in main

File "/home/samuel/.local/lib/python3.8/site-packages/jax/_src/traceback_util.py", line 162, in reraise_with_filtered_traceback
return fun(*args, **kwargs)
File "/home/samuel/.local/lib/python3.8/site-packages/jax/src/api.py", line 2156, in cache_miss
out_tree, out_flat = f_pmapped(*args, **kwargs)
File "/home/samuel/.local/lib/python3.8/site-packages/jax/_src/api.py", line 2032, in pmap_f
out = pxla.xla_pmap(
File "/home/samuel/.local/lib/python3.8/site-packages/jax/core.py", line 2040, in bind
return map_bind(self, fun, *args, **params)
File "/home/samuel/.local/lib/python3.8/site-packages/jax/core.py", line 2072, in map_bind
outs = primitive.process(top_trace, fun, tracers, params)
File "/home/samuel/.local/lib/python3.8/site-packages/jax/core.py", line 2043, in process
return trace.process_map(self, fun, tracers, params)
File "/home/samuel/.local/lib/python3.8/site-packages/jax/core.py", line 687, in process_call
return primitive.impl(f, *tracers, **params)
File "/home/samuel/.local/lib/python3.8/site-packages/jax/interpreters/pxla.py", line 919, in xla_pmap_impl
return compiled_fun(*args)
File "/home/samuel/.local/lib/python3.8/site-packages/jax/_src/profiler.py", line 294, in wrapper
return func(*args, **kwargs)
File "/home/samuel/.local/lib/python3.8/site-packages/jax/interpreters/pxla.py", line 1742, in call
out_bufs = self.xla_executable.execute_sharded_on_local_devices(
jax._src.traceback_util.UnfilteredStackTrace: jaxlib.xla_extension.XlaRuntimeError: RESOURCE_EXHAUSTED: Out of memory while trying to allocate 3109218748016 bytes.
BufferAssignment OOM Debugging.
BufferAssignment stats:
parameter allocation: 14.14GiB
constant allocation: 1.2KiB
maybe_live_out allocation: 14.14GiB
preallocated temp allocation: 2.83TiB
preallocated temp fragmentation: 222.80MiB (0.01%)
total allocation: 2.84TiB
total fragmentation: 223.14MiB (0.01%)
Peak buffers:
Buffer 1:
Size: 16.00GiB
Operator: op_name="pmap(train_step)/jit(main)/jvp(FlaxT5ForConditionalGenerationModule)/encoder/block/7/layer/1/DenseReluDense/wi/dot_general[dimension_numbers=(((2,), (0,)), ((), ())) precision=None preferred_element_type=None]" source_file="/home/samuel/.local/lib/python3.8/site-packages/flax/linen/linear.py" source_line=196
XLA Label: custom-call
Shape: f32[262144,16384]
==========================

Buffer 2:
	Size: 16.00GiB
	XLA Label: fusion
	Shape: f32[262144,16384]
	==========================

Buffer 3:
	Size: 16.00GiB
	Operator: op_name="pmap(train_step)/jit(main)/jvp(FlaxT5ForConditionalGenerationModule)/encoder/block/6/layer/1/DenseReluDense/wi/dot_general[dimension_numbers=(((2,), (0,)), ((), ())) precision=None preferred_element_type=None]" source_file="/home/samuel/.local/lib/python3.8/site-packages/flax/linen/linear.py" source_line=196
	XLA Label: custom-call
	Shape: f32[262144,16384]
	==========================

Buffer 4:
	Size: 16.00GiB
	XLA Label: fusion
	Shape: f32[262144,16384]
	==========================

Buffer 5:
	Size: 16.00GiB
	Operator: op_name="pmap(train_step)/jit(main)/jvp(FlaxT5ForConditionalGenerationModule)/encoder/block/5/layer/1/DenseReluDense/wi/dot_general[dimension_numbers=(((2,), (0,)), ((), ())) precision=None preferred_element_type=None]" source_file="/home/samuel/.local/lib/python3.8/site-packages/flax/linen/linear.py" source_line=196
	XLA Label: custom-call
	Shape: f32[262144,16384]
	==========================

Buffer 6:
	Size: 16.00GiB
	XLA Label: fusion
	Shape: f32[262144,16384]
	==========================

Buffer 7:
	Size: 16.00GiB
	Operator: op_name="pmap(train_step)/jit(main)/jvp(FlaxT5ForConditionalGenerationModule)/encoder/block/4/layer/1/DenseReluDense/wi/dot_general[dimension_numbers=(((2,), (0,)), ((), ())) precision=None preferred_element_type=None]" source_file="/home/samuel/.local/lib/python3.8/site-packages/flax/linen/linear.py" source_line=196
	XLA Label: custom-call
	Shape: f32[262144,16384]
	==========================

Buffer 8:
	Size: 16.00GiB
	XLA Label: fusion
	Shape: f32[262144,16384]
	==========================

Buffer 9:
	Size: 16.00GiB
	Operator: op_name="pmap(train_step)/jit(main)/jvp(FlaxT5ForConditionalGenerationModule)/encoder/block/3/layer/1/DenseReluDense/wi/dot_general[dimension_numbers=(((2,), (0,)), ((), ())) precision=None preferred_element_type=None]" source_file="/home/samuel/.local/lib/python3.8/site-packages/flax/linen/linear.py" source_line=196
	XLA Label: custom-call
	Shape: f32[262144,16384]
	==========================

Buffer 10:
	Size: 16.00GiB
	XLA Label: fusion
	Shape: f32[262144,16384]
	==========================

Buffer 11:
	Size: 16.00GiB
	Operator: op_name="pmap(train_step)/jit(main)/jvp(FlaxT5ForConditionalGenerationModule)/encoder/block/2/layer/1/DenseReluDense/wi/dot_general[dimension_numbers=(((2,), (0,)), ((), ())) precision=None preferred_element_type=None]" source_file="/home/samuel/.local/lib/python3.8/site-packages/flax/linen/linear.py" source_line=196
	XLA Label: custom-call
	Shape: f32[262144,16384]
	==========================

Buffer 12:
	Size: 16.00GiB
	XLA Label: fusion
	Shape: f32[262144,16384]
	==========================

Buffer 13:
	Size: 16.00GiB
	Operator: op_name="pmap(train_step)/jit(main)/jvp(FlaxT5ForConditionalGenerationModule)/encoder/block/1/layer/1/DenseReluDense/wi/dot_general[dimension_numbers=(((2,), (0,)), ((), ())) precision=None preferred_element_type=None]" source_file="/home/samuel/.local/lib/python3.8/site-packages/flax/linen/linear.py" source_line=196
	XLA Label: custom-call
	Shape: f32[262144,16384]
	==========================

Buffer 14:
	Size: 16.00GiB
	XLA Label: fusion
	Shape: f32[262144,16384]
	==========================

Buffer 15:
	Size: 16.00GiB
	Operator: op_name="pmap(train_step)/jit(main)/jvp(FlaxT5ForConditionalGenerationModule)/encoder/block/0/layer/1/DenseReluDense/wi/dot_general[dimension_numbers=(((2,), (0,)), ((), ())) precision=None preferred_element_type=None]" source_file="/home/samuel/.local/lib/python3.8/site-packages/flax/linen/linear.py" source_line=196
	XLA Label: custom-call
	Shape: f32[262144,16384]
	==========================

: while running replica 0 and partition 0 of a replicated computation (other replicas may have failed as well).

The stack trace below excludes JAX-internal frames.
The preceding is the original exception that occurred, unmodified.

---

The above exception was the direct cause of the following exception:

Traceback (most recent call last):
File "scripttraining.py", line 1030, in
if name == "main":
File "scripttraining.py", line 938, in main

jaxlib.xla_extension.XlaRuntimeError: RESOURCE_EXHAUSTED: Out of memory while trying to allocate 3109218748016 bytes.
BufferAssignment OOM Debugging.
BufferAssignment stats:
parameter allocation: 14.14GiB
constant allocation: 1.2KiB
maybe_live_out allocation: 14.14GiB
preallocated temp allocation: 2.83TiB
preallocated temp fragmentation: 222.80MiB (0.01%)
total allocation: 2.84TiB
total fragmentation: 223.14MiB (0.01%)
Peak buffers:
Buffer 1:
Size: 16.00GiB
Operator: op_name="pmap(train_step)/jit(main)/jvp(FlaxT5ForConditionalGenerationModule)/encoder/block/7/layer/1/DenseReluDense/wi/dot_general[dimension_numbers=(((2,), (0,)), ((), ())) precision=None preferred_element_type=None]" source_file="/home/samuel/.local/lib/python3.8/site-packages/flax/linen/linear.py" source_line=196
XLA Label: custom-call
Shape: f32[262144,16384]
==========================

Buffer 2:
	Size: 16.00GiB
	XLA Label: fusion
	Shape: f32[262144,16384]
	==========================

Buffer 3:
	Size: 16.00GiB
	Operator: op_name="pmap(train_step)/jit(main)/jvp(FlaxT5ForConditionalGenerationModule)/encoder/block/6/layer/1/DenseReluDense/wi/dot_general[dimension_numbers=(((2,), (0,)), ((), ())) precision=None preferred_element_type=None]" source_file="/home/samuel/.local/lib/python3.8/site-packages/flax/linen/linear.py" source_line=196
	XLA Label: custom-call
	Shape: f32[262144,16384]
	==========================

Buffer 4:
	Size: 16.00GiB
	XLA Label: fusion
	Shape: f32[262144,16384]
	==========================

Buffer 5:
	Size: 16.00GiB
	Operator: op_name="pmap(train_step)/jit(main)/jvp(FlaxT5ForConditionalGenerationModule)/encoder/block/5/layer/1/DenseReluDense/wi/dot_general[dimension_numbers=(((2,), (0,)), ((), ())) precision=None preferred_element_type=None]" source_file="/home/samuel/.local/lib/python3.8/site-packages/flax/linen/linear.py" source_line=196
	XLA Label: custom-call
	Shape: f32[262144,16384]
	==========================

Buffer 6:
	Size: 16.00GiB
	XLA Label: fusion
	Shape: f32[262144,16384]
	==========================

Buffer 7:
	Size: 16.00GiB
	Operator: op_name="pmap(train_step)/jit(main)/jvp(FlaxT5ForConditionalGenerationModule)/encoder/block/4/layer/1/DenseReluDense/wi/dot_general[dimension_numbers=(((2,), (0,)), ((), ())) precision=None preferred_element_type=None]" source_file="/home/samuel/.local/lib/python3.8/site-packages/flax/linen/linear.py" source_line=196
	XLA Label: custom-call
	Shape: f32[262144,16384]
	==========================

Buffer 8:
	Size: 16.00GiB
	XLA Label: fusion
	Shape: f32[262144,16384]
	==========================

Buffer 9:
	Size: 16.00GiB
	Operator: op_name="pmap(train_step)/jit(main)/jvp(FlaxT5ForConditionalGenerationModule)/encoder/block/3/layer/1/DenseReluDense/wi/dot_general[dimension_numbers=(((2,), (0,)), ((), ())) precision=None preferred_element_type=None]" source_file="/home/samuel/.local/lib/python3.8/site-packages/flax/linen/linear.py" source_line=196
	XLA Label: custom-call
	Shape: f32[262144,16384]
	==========================

Buffer 10:
	Size: 16.00GiB
	XLA Label: fusion
	Shape: f32[262144,16384]
	==========================

Buffer 11:
	Size: 16.00GiB
	Operator: op_name="pmap(train_step)/jit(main)/jvp(FlaxT5ForConditionalGenerationModule)/encoder/block/2/layer/1/DenseReluDense/wi/dot_general[dimension_numbers=(((2,), (0,)), ((), ())) precision=None preferred_element_type=None]" source_file="/home/samuel/.local/lib/python3.8/site-packages/flax/linen/linear.py" source_line=196
	XLA Label: custom-call
	Shape: f32[262144,16384]
	==========================

Buffer 12:
	Size: 16.00GiB
	XLA Label: fusion
	Shape: f32[262144,16384]
	==========================

Buffer 13:
	Size: 16.00GiB
	Operator: op_name="pmap(train_step)/jit(main)/jvp(FlaxT5ForConditionalGenerationModule)/encoder/block/1/layer/1/DenseReluDense/wi/dot_general[dimension_numbers=(((2,), (0,)), ((), ())) precision=None preferred_element_type=None]" source_file="/home/samuel/.local/lib/python3.8/site-packages/flax/linen/linear.py" source_line=196
	XLA Label: custom-call
	Shape: f32[262144,16384]
	==========================

Buffer 14:
	Size: 16.00GiB
	XLA Label: fusion
	Shape: f32[262144,16384]
	==========================

Buffer 15:
	Size: 16.00GiB
	Operator: op_name="pmap(train_step)/jit(main)/jvp(FlaxT5ForConditionalGenerationModule)/encoder/block/0/layer/1/DenseReluDense/wi/dot_general[dimension_numbers=(((2,), (0,)), ((), ())) precision=None preferred_element_type=None]" source_file="/home/samuel/.local/lib/python3.8/site-packages/flax/linen/linear.py" source_line=196
	XLA Label: custom-call
	Shape: f32[262144,16384]
	==========================

: while running replica 0 and partition 0 of a replicated computation (other replicas may have failed as well).

`