Saving weights and logs of step 2500

events.out.tfevents.1626027152.t1v-n-934dd7d5-w-0.42551.3.v2

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+oid sha256:6014b2439019a454ff299b5d33dd83d9f8e5dfa5508901303ac09b2ac318a8e1
+size 367914

flax_model.msgpack

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+version https://git-lfs.github.com/spec/v1
+oid sha256:064a2c476ff8b960a62039cf6ee5ad1450f4e7848dfad669dddf51d21c496847
+size 497764120

run.sh

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+#!/usr/bin/env bash
+python run_clm_flax.py \
+    --output_dir="${MODEL_DIR}" \
+    --model_type="gpt2" \
+    --config_name="${MODEL_DIR}" \
+    --tokenizer_name="${MODEL_DIR}" \
+    --dataset_name="mc4" \
+    --dataset_config_name="bn" \
+    --do_train --do_eval \
+    --block_size="512" \
+    --per_device_train_batch_size="64" \
+    --per_device_eval_batch_size="64" \
+    --learning_rate="5e-3" --warmup_steps="1000" \
+    --adam_beta1="0.9" --adam_beta2="0.98" --weight_decay="0.01" \
+    --overwrite_output_dir \
+    --num_train_epochs="50" \
+    --logging_steps="500" \
+    --save_steps="2500" \
+    --eval_steps="2500" \
+    --preprocessing_num_workers="90" \
+    --push_to_hub

run_2.sh

@@ -0,0 +1,22 @@
+#!/usr/bin/env bash
+python run_clm_flax_v2.py \
+    --output_dir="${MODEL_DIR}" \
+    --model_type="gpt2" \
+    --config_name="${MODEL_DIR}" \
+    --tokenizer_name="${MODEL_DIR}" \
+    --dataset_name="mc4" \
+    --dataset_config_name="bn" \
+    --do_train --do_eval \
+    --block_size="512" \
+    --per_device_train_batch_size="64" \
+    --per_device_eval_batch_size="64" \
+    --learning_rate="5e-3" --warmup_steps="1000" \
+    --adam_beta1="0.9" --adam_beta2="0.98" --weight_decay="0.01" \
+    --overwrite_output_dir \
+    --max_steps="100000" \
+    --decay_steps="100000" \
+    --logging_steps="50" \
+    --save_steps="50" \
+    --eval_steps="50" \
+    --max_eval_samples 100 \
+    --push_to_hub

run_clm_flax_v2.py

@@ -0,0 +1,823 @@
+#!/usr/bin/env python
+# coding=utf-8
+# Copyright 2021 The HuggingFace Team All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""
+Pre-training/Fine-tuning the library models for causal language modeling (GPT, GPT-2, CTRL, ...) on a text file or a dataset.
+Here is the full list of checkpoints on the hub that can be fine-tuned by this script:
+https://huggingface.co/models?filter=causal-lm
+"""
+# You can also adapt this script on your own causal language modeling task. Pointers for this are left as comments.
+
+from ast import Str
+import logging
+import math
+import os
+import sys
+import time
+from dataclasses import dataclass, field
+from pathlib import Path
+from typing import Callable, Optional
+import json
+import shutil
+from collections import defaultdict
+from flax import training
+import numpy as np
+import datasets
+from datasets import Dataset, load_dataset
+from tqdm import tqdm
+
+import jax
+import jax.profiler
+import jax.numpy as jnp
+import optax
+import transformers
+from flax import jax_utils, traverse_util
+from flax.jax_utils import unreplicate
+from flax.training import train_state
+from flax.training.common_utils import get_metrics, onehot, shard, shard_prng_key
+from flax.training.checkpoints import save_checkpoint, restore_checkpoint
+from flax.serialization import to_bytes, from_bytes
+from transformers import (
+    CONFIG_MAPPING,
+    FLAX_MODEL_FOR_CAUSAL_LM_MAPPING,
+    AutoConfig,
+    AutoTokenizer,
+    FlaxAutoModelForCausalLM,
+    HfArgumentParser,
+    TrainingArguments,
+    is_tensorboard_available,
+)
+from transformers.testing_utils import CaptureLogger
+
+from importlib.util import find_spec
+from utils import PrefetchDataloader, make_batch
+
+logger = logging.getLogger(__name__)
+
+MODEL_CONFIG_CLASSES = list(FLAX_MODEL_FOR_CAUSAL_LM_MAPPING.keys())
+MODEL_TYPES = tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)
+
+
+@dataclass
+class ModelArguments:
+    """
+    Arguments pertaining to which model/config/tokenizer we are going to fine-tune, or train from scratch.
+    """
+
+    model_name_or_path: Optional[str] = field(
+        default=None,
+        metadata={
+            "help": "The model checkpoint for weights initialization."
+            "Don't set if you want to train a model from scratch."
+        },
+    )
+    model_type: Optional[str] = field(
+        default=None,
+        metadata={"help": "If training from scratch, pass a model type from the list: " + ", ".join(MODEL_TYPES)},
+    )
+    config_name: Optional[str] = field(
+        default=None, metadata={"help": "Pretrained config name or path if not the same as model_name"}
+    )
+    tokenizer_name: Optional[str] = field(
+        default=None, metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"}
+    )
+    cache_dir: Optional[str] = field(
+        default=None, metadata={"help": "Where do you want to store the pretrained models downloaded from s3"}
+    )
+    use_fast_tokenizer: bool = field(
+        default=True,
+        metadata={"help": "Whether to use one of the fast tokenizer (backed by the tokenizers library) or not."},
+    )
+    dtype: Optional[str] = field(
+        default="float32",
+        metadata={
+            "help": "Floating-point format in which the model weights should be initialized and trained. Choose one of `[float32, float16, bfloat16]`."
+        },
+    )
+    save_optimizer: Optional[bool] = field(
+        default=True,
+        metadata={"help": "Whether to store full train state including optimizer."},
+    )
+    repo_path_or_name: Optional[str] = field(
+        default=None,
+        metadata={"help": "Path to the modelhub repo directory"},
+    )
+    repo_url: Optional[str] = field(
+        default=None,
+        metadata={"help": "URL of the modelhub repo"},
+    )
+    decay_steps: int = field(default=None, metadata={"help":"Number of steps from peak to final learning rate"})
+
+@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)."},
+    )
+    data_dir: Optional[str] = field(default=None, metadata={"help": "Path to data directory."})
+    max_train_samples: Optional[int] = field(
+        default=None,
+        metadata={
+            "help": "For debugging purposes or quicker training, truncate the number of training examples to this "
+            "value if set."
+        },
+    )
+    max_eval_samples: Optional[int] = field(
+        default=None,
+        metadata={
+            "help": "For debugging purposes or quicker training, truncate the number of evaluation examples to this "
+            "value if set."
+        },
+    )
+    overwrite_cache: bool = field(
+        default=False, metadata={"help": "Overwrite the cached training and evaluation sets"}
+    )
+    validation_split_percentage: Optional[int] = field(
+        default=5,
+        metadata={
+            "help": "The percentage of the train set used as validation set in case there's no validation split"
+        },
+    )
+    block_size: Optional[int] = field(
+        default=None,
+        metadata={
+            "help": "Optional input sequence length after tokenization. "
+            "The training dataset will be truncated in block of this size for training. "
+            "Default to the model max input length for single sentence inputs (take into account special tokens)."
+        },
+    )
+    overwrite_cache: bool = field(
+        default=False, metadata={"help": "Overwrite the cached training and evaluation sets"}
+    )
+    preprocessing_num_workers: Optional[int] = field(
+        default=None,
+        metadata={"help": "The number of processes to use for the preprocessing."},
+    )
+    text_column_name: Optional[str] = field(
+            default='text',
+            metadata={"help": "Column containing main text data."},
+        )
+    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"})
+    prefetch_buffer: int = field(default=8, metadata={"help": "The number of batches to prefetch for loading"})
+
+    def __post_init__(self):
+        if self.dataset_name is None and self.train_file is None and self.validation_file is None:
+            raise ValueError("Need either a dataset name or a training/validation file.")
+        else:
+            if self.train_file is not None:
+                extension = self.train_file.split(".")[-1]
+                assert extension in ["csv", "json", "txt"], "`train_file` should be a csv, a json or a txt file."
+            if self.validation_file is not None:
+                extension = self.validation_file.split(".")[-1]
+                assert extension in ["csv", "json", "txt"], "`validation_file` should be a csv, a json or a txt file."
+
+
+class TrainState(train_state.TrainState):
+    dropout_rng: jnp.ndarray
+
+    def replicate(self):
+        return jax_utils.replicate(self).replace(dropout_rng=shard_prng_key(self.dropout_rng))
+
+# the below functions are not used now, probably to be removed
+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 data_loader(rng: jax.random.PRNGKey, dataset: Dataset, batch_size: int, shuffle: bool = False):
+    """
+    Returns batches of size `batch_size` from truncated `dataset`, sharded over all local devices.
+    Shuffle batches if `shuffle` is `True`.
+    """
+    steps_per_epoch = len(dataset) // batch_size
+
+    if shuffle:
+        batch_idx = jax.random.permutation(rng, len(dataset))
+    else:
+        batch_idx = jnp.arange(len(dataset))
+
+    batch_idx = batch_idx[: steps_per_epoch * batch_size]  # Skip incomplete batch.
+    batch_idx = batch_idx.reshape((steps_per_epoch, batch_size))
+
+    for idx in batch_idx:
+        batch = dataset[idx]
+        batch = {k: jnp.array(v) for k, v in batch.items()}
+
+        batch = shard(batch)
+
+        yield batch
+
+
+def write_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 create_learning_rate_fn(
+    num_train_steps: int, train_batch_size: int, num_warmup_steps: int, learning_rate: float
+) -> Callable[[int], jnp.array]:
+    """Returns a linear warmup, linear_decay learning rate function."""
+    warmup_fn = optax.linear_schedule(init_value=0.0, end_value=learning_rate, transition_steps=num_warmup_steps)
+    decay_fn = optax.linear_schedule(
+        init_value=learning_rate, end_value=0, transition_steps=num_train_steps - num_warmup_steps
+    )
+    schedule_fn = optax.join_schedules(schedules=[warmup_fn, decay_fn], boundaries=[num_warmup_steps])
+    return schedule_fn
+def gpt3_schedule(warmup_steps,
+                  total_steps,
+                  peak_lr,
+                  end_lr):
+    def sch(step):
+        warmup_pct = jnp.clip(step, 0, warmup_steps) / warmup_steps
+        anneal_pct = jnp.clip(step - warmup_steps, 0, total_steps) / total_steps
+
+        return warmup_pct * peak_lr - (peak_lr - end_lr) * (1 - jnp.cos(jnp.pi * anneal_pct)) / 2
+
+    return sch
+
+# utils
+def mb_item(x):
+    return x.item() if hasattr(x, "item") else x
+
+#checkpoint functions
+def save_model_checkpoint(model, save_dir, state, with_opt=True, push_to_hub=False):
+    """
+    If `push_to_hub` is True, will save to `save_dir`. Otherwise will save to `save_dir/ckpt-{step}`.
+    """
+    state = jax_utils.unreplicate(state)
+    logger.info(f"SAVING CHECKPOINT IN {save_dir}...")
+    if not push_to_hub:
+        save_dir = f"{save_dir}/ckpt-{mb_item(state.step)-1}"
+    model.save_pretrained(
+        save_dir,
+        params=state.params,
+        push_to_hub=push_to_hub,
+        commit_message=f"Saving weights and logs at step {mb_item(state.step)-1}",
+    )
+    if with_opt:
+        with open(os.path.join(save_dir, "opt_state.msgpack"), "wb") as f:
+            f.write(to_bytes(state.opt_state))
+        with open(os.path.join(save_dir, "training_state.json"), "w") as f:
+            json.dump({"step": state.step.item()}, f)
+    logger.info("checkpoint saved")
+        
+def restore_model_checkpoint(save_dir, state):
+    logger.info(f"RESTORING CHECKPOINT FROM {save_dir}...")
+    with open(os.path.join(save_dir, "flax_model.msgpack"), "rb") as f:
+        params = from_bytes(state.params, f.read())
+
+    with open(os.path.join(save_dir, "opt_state.msgpack"), "rb") as f:
+        opt_state = from_bytes(state.opt_state, f.read())
+
+    with open(os.path.join(save_dir, "training_state.json"), "r") as f:
+        training_state = json.load(f)
+    step = training_state["step"]
+
+    logger.info("checkpoint restored")
+    return state.replace(step=step, params=params, opt_state=opt_state), step
+
+def rotate_checkpoints(ckpt_dir:str, save_total_limit:int):
+    "Removes older checkpoints so that `save_total_limit` checkpoints are kept"
+    # TODO: what to remove is decided using step number only, we might want to improve that
+    ckpts = [str(x) for x in Path(ckpt_dir).glob("ckpt-*")]
+    # sort checkpoints by step
+    ckpts_sorted = sorted(ckpts, key=lambda x: int(x.split('-')[-1]))
+    ckpts_to_delete = ckpts_sorted[:-save_total_limit]
+    for ckpt in ckpts_to_delete:
+        logger.info(f"Deleting older checkpoint [{ckpt}] due to save_total_limit ({save_total_limit})")
+        shutil.rmtree(ckpt)
+
+def main():
+    # See all possible arguments in src/transformers/training_args.py
+    # or by passing the --help flag to this script.
+    # We now keep distinct sets of args, for a cleaner separation of concerns.
+
+    parser = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments))
+    if len(sys.argv) == 2 and sys.argv[1].endswith(".json"):
+        # If we pass only one argument to the script and it's the path to a json file,
+        # let's parse it to get our arguments.
+        model_args, data_args, training_args = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]))
+    else:
+        model_args, data_args, training_args = parser.parse_args_into_dataclasses()
+
+    if (
+        os.path.exists(training_args.output_dir)
+        and os.listdir(training_args.output_dir)
+        and training_args.do_train
+        and not training_args.overwrite_output_dir
+    ):
+        raise ValueError(
+            f"Output directory ({training_args.output_dir}) already exists and is not empty."
+            "Use --overwrite_output_dir to overcome."
+        )
+
+    # Make one log on every process with the configuration for debugging.
+    logging.basicConfig(
+        format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
+        datefmt="%m/%d/%Y %H:%M:%S",
+        level=logging.INFO,
+    )
+    # Setup logging, we only want one process per machine to log things on the screen.
+    logger.setLevel(logging.INFO if jax.process_index() == 0 else logging.ERROR)
+    if jax.process_index() == 0:
+        datasets.utils.logging.set_verbosity_warning()
+        transformers.utils.logging.set_verbosity_info()
+    else:
+        datasets.utils.logging.set_verbosity_error()
+        transformers.utils.logging.set_verbosity_error()
+
+    # Set the verbosity to info of the Transformers logger (on main process only):
+    logger.info(f"Training/evaluation parameters {training_args}")
+
+    #  Get the datasets: you can either provide your own CSV/JSON/TXT training and evaluation files (see below)
+    # or just provide the name of one of the public datasets available on the hub at https://huggingface.co/datasets/
+    # (the dataset will be downloaded automatically from the datasets Hub).
+    #
+    # For CSV/JSON files, this script will use the column called 'text' or the first column if no column called
+    # 'text' is found. You can easily tweak this behavior (see below).
+    #
+    # In distributed training, the load_dataset function guarantees that only one local process can concurrently
+    # download the dataset.
+    if data_args.dataset_name is not None:
+        # Downloading and loading a dataset from the hub.
+        train_dataset = load_dataset(
+            data_args.dataset_name,
+            data_args.dataset_config_name,
+            cache_dir=model_args.cache_dir, 
+            streaming=True,
+            split="train"
+        )
+        eval_dataset = load_dataset(
+            data_args.dataset_name,
+            data_args.dataset_config_name,
+            cache_dir=model_args.cache_dir, 
+            streaming=True,
+            split="validation"
+        )
+        
+    # See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at
+    # https://huggingface.co/docs/datasets/loading_datasets.html.
+
+    # Load pretrained model and tokenizer
+
+    # Distributed training:
+    # The .from_pretrained methods guarantee that only one local process can concurrently
+    # download model & vocab.
+    if model_args.config_name:
+        config = AutoConfig.from_pretrained(model_args.config_name, cache_dir=model_args.cache_dir)
+    elif model_args.model_name_or_path:
+        config = AutoConfig.from_pretrained(model_args.model_name_or_path, cache_dir=model_args.cache_dir)
+    else:
+        config = CONFIG_MAPPING[model_args.model_type]()
+        logger.warning("You are instantiating a new config instance from scratch.")
+
+    if model_args.tokenizer_name:
+        tokenizer = AutoTokenizer.from_pretrained(
+            model_args.tokenizer_name, cache_dir=model_args.cache_dir, use_fast=model_args.use_fast_tokenizer
+        )
+    elif model_args.model_name_or_path:
+        tokenizer = AutoTokenizer.from_pretrained(
+            model_args.model_name_or_path, cache_dir=model_args.cache_dir, use_fast=model_args.use_fast_tokenizer
+        )
+    else:
+        raise ValueError(
+            "You are instantiating a new tokenizer from scratch. This is not supported by this script."
+            "You can do it from another script, save it, and load it from here, using --tokenizer_name."
+        )
+
+    if model_args.model_name_or_path:
+        model = FlaxAutoModelForCausalLM.from_pretrained(
+            model_args.model_name_or_path, config=config, seed=training_args.seed, dtype=getattr(jnp, model_args.dtype)
+        )
+    else:
+        model = FlaxAutoModelForCausalLM.from_config(
+            config, seed=training_args.seed, dtype=getattr(jnp, model_args.dtype)
+        )
+
+    # Preprocessing the datasets.
+    # First we tokenize all the texts.
+    # column_names = eval_dataset.column_names
+    text_column_name = data_args.text_column_name # if data_args.text_column_name in column_names else column_names[0]
+
+    # since this will be pickled to avoid _LazyModule error in Hasher force logger loading before tokenize_function
+    tok_logger = transformers.utils.logging.get_logger("transformers.tokenization_utils_base")
+
+    def tokenize_function(examples):
+        with CaptureLogger(tok_logger) as cl:
+            output = tokenizer(examples[text_column_name])
+        # clm input could be much much longer than block_size
+        if "Token indices sequence length is longer than the" in cl.out:
+            tok_logger.warning(
+                "^^^^^^^^^^^^^^^^ Please ignore the warning above - this long input will be chunked into smaller bits before being passed to the model."
+            )
+        return output
+
+    tokenized_dataset = train_dataset.map(
+        tokenize_function,
+        batched=True,
+    )
+    tokenized_eval_dataset = eval_dataset.map(
+        tokenize_function,
+        batched=True,
+        # remove_columns=column_names,
+        # num_proc=data_args.preprocessing_num_workers,
+        # load_from_cache_file=not data_args.overwrite_cache,
+    )
+
+    if data_args.block_size is None:
+        block_size = tokenizer.model_max_length
+        if block_size > config.max_position_embeddings:
+            logger.warning(
+                f"The tokenizer picked seems to have a very large `model_max_length` ({tokenizer.model_max_length}). "
+                "Picking 1024 instead. You can change that default value by passing --block_size xxx."
+            )
+            block_size = 1024
+    else:
+        if data_args.block_size > tokenizer.model_max_length:
+            logger.warning(
+                f"The block_size passed ({data_args.block_size}) is larger than the maximum length for the model"
+                f"({tokenizer.model_max_length}). Using block_size={tokenizer.model_max_length}."
+            )
+        block_size = min(data_args.block_size, tokenizer.model_max_length)
+
+    # # Main data processing function that will concatenate all texts from our dataset and generate chunks of block_size.
+    def group_texts(examples):
+        # Concatenate all texts.
+        concatenated_examples = {k: sum(examples[k], []) for k in examples.keys()}
+        total_length = len(concatenated_examples[list(examples.keys())[0]])
+        # We drop the small remainder, we could add padding if the model supported it instead of this drop, you can
+        # customize this part to your needs.
+        total_length = (total_length // block_size) * block_size
+        # Split by chunks of max_len.
+        result = {
+            k: [t[i : i + block_size] for i in range(0, total_length, block_size)]
+            for k, t in concatenated_examples.items()
+        }
+        result["labels"] = result["input_ids"].copy()
+        return result
+
+    # Note that with `batched=True`, this map processes 1,000 texts together, so group_texts throws away a remainder
+    # for each of those groups of 1,000 texts. You can adjust that batch_size here but a higher value might be slower
+    # to preprocess.
+    #
+    # To speed up this part, we use multiprocessing. See the documentation of the map method for more information:
+    # https://huggingface.co/docs/datasets/package_reference/main_classes.html#datasets.Dataset.map
+
+    shuffle_seed = training_args.seed
+    # if training_args.do_train:
+    #     if "train" not in tokenized_dataset:
+    #         raise ValueError("--do_train requires a train dataset")
+    #     train_dataset = tokenized_dataset
+    #     if data_args.max_train_samples is not None:
+    #         train_dataset = train_dataset.take(range(data_args.max_train_samples))
+    #     train_dataset = train_dataset.shuffle(buffer_size=data_args.shuffle_buffer_size, seed=shuffle_seed)
+    #     train_iter = iter(train_dataset)
+
+    
+    train_loader = PrefetchDataloader(
+        tokenized_dataset, 
+        training_args.max_steps * training_args.gradient_accumulation_steps, 
+        int(training_args.per_device_train_batch_size) * jax.device_count(),
+        block_size,
+        prefetch_buffer=data_args.prefetch_buffer,
+        seed=shuffle_seed
+    )
+    # evaluation data is not in streaming mode
+    # if training_args.do_eval:
+    #     eval_dataset = tokenized_eval_dataset.map(
+    #         group_texts,
+    #         batched=True,
+    #         num_proc=data_args.preprocessing_num_workers,
+    #         load_from_cache_file=not data_args.overwrite_cache,
+    #     )
+    #     if data_args.max_eval_samples is not None:
+    #         eval_dataset = eval_dataset.select(range(data_args.max_eval_samples))
+    
+    # Enable tensorboard only on the master node
+    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."
+        )
+    
+    # enable wandb tracking
+    has_wandb = find_spec("wandb") is not None 
+    if jax.process_index() == 0 and has_wandb and ("wandb" in training_args.report_to):
+        try:
+            import wandb
+            wandb.init(
+                name=training_args.run_name,
+                entity="wandb", 
+                project="hf-flax-gpt-neo-copilot",
+                sync_tensorboard=True
+            )
+            wandb.config.update(training_args)
+            wandb.config.update(model_args)
+            wandb.config.update(data_args)
+        except ImportError as e:
+            print(e)
+            has_wandb = False
+    
+
+    # Initialize our training
+    rng = jax.random.PRNGKey(training_args.seed)
+    rng, dropout_rng = jax.random.split(rng)
+
+    # Store some constant
+    num_epochs = int(training_args.num_train_epochs)
+    train_batch_size = int(training_args.per_device_train_batch_size) * jax.device_count() * training_args.gradient_accumulation_steps
+    eval_batch_size = int(training_args.per_device_eval_batch_size) * jax.device_count()
+    total_train_steps = training_args.max_steps * training_args.gradient_accumulation_steps
+
+    # Create learning rate schedule
+    gpt3_schedule_fn = gpt3_schedule(
+        training_args.warmup_steps,
+        model_args.decay_steps,
+        training_args.learning_rate,
+        training_args.learning_rate / 10.
+    )
+
+    # 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.
+    # Note that this mask is specifically adapted for FlaxGPT2.
+    # For other models, one should correct the layer norm parameter naming
+    # accordingly.
+    def decay_mask_fn(params):
+        flat_params = traverse_util.flatten_dict(params)
+        flat_mask = {
+            path: (path[-1] != "bias" and path[-2:] not in [("ln_1", "scale"), ("ln_2", "scale"), ("ln_f", "scale")])
+            for path in flat_params
+        }
+        return traverse_util.unflatten_dict(flat_mask)
+
+    # create 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=gpt3_schedule_fn,
+        )
+    else:
+        optimizer = optax.adamw(
+            learning_rate=gpt3_schedule_fn,
+            b1=training_args.adam_beta1,
+            b2=training_args.adam_beta2,
+            eps=training_args.adam_epsilon,
+            weight_decay=training_args.weight_decay,
+            mask=decay_mask_fn,
+        )
+    if training_args.gradient_accumulation_steps > 1:
+        optimizer = optax.MultiSteps(optimizer, training_args.gradient_accumulation_steps)
+    grad_accum_steps = training_args.gradient_accumulation_steps
+
+    # Setup train state
+    state = TrainState.create(apply_fn=model.__call__, params=model.params, tx=optimizer, dropout_rng=dropout_rng)
+    
+    if training_args.resume_from_checkpoint:
+        state = restore_checkpoint(training_args.resume_from_checkpoint, state)
+        resume_step = mb_item(state.step)
+    else:
+        resume_step = 0
+
+    def loss_fn(logits, labels):
+        shift_logits = logits[..., :-1, :]
+        shift_labels = labels[..., 1:]
+        loss = optax.softmax_cross_entropy(shift_logits, onehot(shift_labels, shift_logits.shape[-1]))
+        return loss.mean()
+
+    # Define gradient update step fn
+    def train_step(state, batch):
+        dropout_rng, new_dropout_rng = jax.random.split(state.dropout_rng)
+
+        def compute_loss(params):
+            labels = batch.pop("labels")
+            logits = state.apply_fn(**batch, params=params, dropout_rng=dropout_rng, train=True)[0]
+            loss = loss_fn(logits, labels)
+            return loss
+
+        grad_fn = jax.value_and_grad(compute_loss)
+        loss, grad = grad_fn(state.params)
+        grad = jax.lax.pmean(grad, "batch")
+
+        new_state = state.apply_gradients(grads=grad, dropout_rng=new_dropout_rng)
+
+        metrics = {"loss": loss, "learning_rate": gpt3_schedule_fn(state.step // grad_accum_steps)}
+        metrics = jax.lax.pmean(metrics, axis_name="batch")
+
+        return new_state, metrics
+
+    # Define eval fn
+    def eval_step(params, batch):
+        labels = batch.pop("labels")
+        logits = model(**batch, params=params, train=False)[0]
+        loss = loss_fn(logits, labels)
+
+        # summarize metrics
+        metrics = {"loss": loss}
+        metrics = jax.lax.pmean(metrics, axis_name="batch")
+        return metrics
+
+    # Create parallel version of the train and eval step
+    p_train_step = jax.pmap(train_step, "batch", donate_argnums=(0,))
+    p_eval_step = jax.pmap(eval_step, "batch")
+
+    # Replicate the train state on each device
+    state = state.replicate()
+
+    logger.info("***** Running training *****")
+    logger.info(f"  Instantaneous batch size per device = {training_args.per_device_train_batch_size}")
+    logger.info(f"  Total train batch size (w. parallel, distributed and grad_accum) = {train_batch_size}")
+    logger.info(f"  Total optimization steps = {training_args.max_steps}")
+
+    if not training_args.skip_memory_metrics:
+        server = jax.profiler.start_server(9999)
+
+    train_time = 0
+    train_metrics = []
+    # TODO: figure out training duration
+    steps = tqdm(range(training_args.max_steps), position=0, initial=resume_step)
+    for step in range(total_train_steps):
+        # ======================== Training ================================
+        train_start = time.time()
+        rng, input_rng = jax.random.split(rng)
+        
+        cur_step = step
+        # skip to the step from which we are resuming
+        if cur_step < resume_step:
+            continue
+        
+        # using advance_iter_and_group_samples seem to make training slower
+        # samples = advance_iter_and_group_samples(iter(tokenized_dataset), int(training_args.per_device_train_batch_size) * jax.device_count(), block_size)
+        # batch = shard(make_batch(samples))
+        batch = shard(next(train_loader))
+        # logger.info(f"{batch['input_ids'].shape}")
+        state, train_metric = p_train_step(state, batch)
+        train_metrics.append(train_metric)
+        if step % grad_accum_steps == 0:
+            steps.update(1)
+
+        if cur_step % (training_args.logging_steps * grad_accum_steps)== 0 and cur_step > 0:
+            # Save metrics
+            train_metric = 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)
+            if has_wandb and jax.process_index() == 0 and ("wandb" in training_args.report_to):
+                # TODO: add accumulation of metrics
+                _metrics = {k if k=="learning_rate" else f"train_{k}":mb_item(v.mean()) for k, v in train_metric.items()}
+                wandb.log({"training_step":cur_step, **_metrics}, commit=True)
+
+            steps.write(
+                f"Step... ({cur_step} | Loss: {train_metric['loss'].mean()}, Learning Rate: {train_metric['learning_rate'].mean()})"
+            )
+
+            train_metrics = []
+
+        if cur_step % (training_args.eval_steps * grad_accum_steps) == 0 and cur_step > 0 and training_args.do_eval:
+            # ======================== Evaluating ==============================
+            eval_metrics = []
+            eval_steps = data_args.max_eval_samples # len(eval_dataset) // eval_batch_size
+            # eval_loader = data_loader(input_rng, eval_dataset, eval_batch_size)
+            eval_loader = PrefetchDataloader(
+                tokenized_eval_dataset, 
+                eval_steps,
+                eval_batch_size,
+                block_size,
+                prefetch_buffer=data_args.prefetch_buffer,
+                shuffle=False,
+            )
+            for _ in tqdm(range(eval_steps), desc="Evaluating...", position=2, leave=False):
+                # Model forward
+                batch = shard(next(eval_loader))
+                metrics = p_eval_step(state.params, batch)
+                eval_metrics.append(metrics)
+
+            # normalize eval metrics
+            eval_metrics = get_metrics(eval_metrics)
+            eval_metrics = jax.tree_map(jnp.mean, eval_metrics)
+
+            try:
+                eval_metrics["perplexity"] = math.exp(eval_metrics["loss"])
+            except OverflowError:
+                eval_metrics["perplexity"] = float("inf")
+            # TODO: this needs to be closed properly
+            eval_loader.terminate()
+            # Print metrics and update progress bar
+            desc = f"Step... ({cur_step} | Eval Loss: {eval_metrics['loss']} | Eval Perplexity: {eval_metrics['perplexity']})"
+            steps.write(desc)
+            steps.desc = desc
+
+            # Save metrics
+            if has_tensorboard and jax.process_index() == 0:
+                # cur_step = epoch * (len(train_dataset) // train_batch_size)
+                write_eval_metric(summary_writer, eval_metrics, cur_step)
+            if has_wandb and jax.process_index() == 0 and ("wandb" in training_args.report_to):
+                _metrics = {f"eval_{k}":mb_item(v) for k, v in eval_metrics.items()}
+                wandb.log({"eval_step":cur_step, **_metrics})
+
+        if cur_step % (training_args.save_steps * grad_accum_steps) == 0 and cur_step > 0:
+            # save checkpoint after each epoch and push checkpoint to the hub
+            if jax.process_index() == 0:
+                print("*********", training_args.push_to_hub)
+                save_model_checkpoint(model, training_args.output_dir, state, with_opt=False,
+                                      push_to_hub=training_args.push_to_hub)
+                if model_args.save_optimizer:
+                    # this saves full state including optimizer
+                    save_checkpoint(training_args.output_dir, jax_utils.unreplicate(state), cur_step, keep=training_args.save_total_limit, overwrite=False)
+                if training_args.save_total_limit is not None:
+                    rotate_checkpoints(training_args.output_dir, training_args.save_total_limit)
+    
+    train_loader.terminate()
+    # save model after training is over
+    save_model_checkpoint(model, training_args.output_dir, state, with_opt=False,
+                    push_to_hub=training_args.push_to_hub)
+
+
+
+
+if __name__ == "__main__":
+    main()

utils.py

@@ -0,0 +1,122 @@
+import numpy as np
+import threading
+import queue
+import multiprocessing
+from collections import defaultdict
+import jax
+import jax.numpy as jnp
+
+
+
+def make_batch(samples):
+    batch = {k:jnp.array(v) for k,v in samples.items()}
+    batch['labels'] = batch['input_ids'].copy()
+    return batch
+
+class PrefetchDataloaderTread(threading.Thread):
+    "Prefetch dataloader for IterableDataset"
+    def __init__(self, dataset, max_steps, batch_size, sequence_length, prefetch_buffer=1, shuffle=True, shuffle_buffer=1000, seed=0):
+        super().__init__(daemon=True)
+        self.max_steps = max_steps
+        self.bs = batch_size
+        self.seq_len = sequence_length
+        self.max_length = batch_size * sequence_length
+        self.prefetch_buffer = prefetch_buffer
+        self.shuffle = shuffle
+        self.shuffle_buffer = shuffle_buffer
+        self.seed = seed
+        self.dataset = dataset
+        if shuffle:
+            shuffled_dataset = dataset.shuffle(shuffle_buffer, seed=self.seed)
+            self.seed += 1
+            self.ds_iter = iter(shuffled_dataset)
+        else:
+            self.ds_iter = iter(dataset)
+        self.queue = queue.Queue(prefetch_buffer)
+        self.rem = defaultdict(list)
+        self.start()
+        
+    def __next__(self):
+        batch = self.queue.get()
+        return batch
+        
+    def run(self):
+        i = 0
+        while True and i < self.max_steps:
+            i += 1
+            # prepair next batch
+            sample = self.rem.copy()
+            l = len(sample["input_ids"])
+            max_length = self.max_length
+            while l < max_length:
+                next_sample = next(self.ds_iter)
+                l += len(next_sample["input_ids"])
+                sample = {k:sample[k]+next_sample[k] for k in next_sample.keys()}
+            
+            self.rem = {k:v[max_length:] for k,v in sample.items()}
+            sample = {k:v[:max_length] for k,v in sample.items()}
+            # regroup to shape [bs x seq_len]
+            samples = {k:np.array([v[i*self.seq_len:(i+1)*self.seq_len] for i in range(self.bs)]) for k,v in sample.items()}
+            
+            self.queue.put(make_batch(samples))
+        self.queue.put(None)
+    
+    def __iter__(self):
+        return self
+
+
+class PrefetchDataloader(multiprocessing.Process):
+    "Prefetch dataloader for IterableDataset"
+    def __init__(self, dataset, max_steps, batch_size, sequence_length, prefetch_buffer=1, shuffle=True, shuffle_buffer=1000, seed=0):
+        super().__init__(daemon=True)
+        self.max_steps = max_steps
+        self.bs = batch_size
+        self.seq_len = sequence_length
+        self.max_length = batch_size * sequence_length
+        self.prefetch_buffer = prefetch_buffer
+        self.shuffle = shuffle
+        self.shuffle_buffer = shuffle_buffer
+        self.seed = seed
+        self.dataset = dataset
+        self.make_iter()
+        self.queue = multiprocessing.Queue(prefetch_buffer)
+        self.rem = defaultdict(list)
+        self.start()
+
+    def make_iter(self):
+        if self.shuffle:
+            shuffled_dataset = self.dataset.shuffle(self.shuffle_buffer, seed=self.seed)
+            self.seed += 1
+            self.ds_iter = iter(shuffled_dataset)
+        else:
+            self.ds_iter = iter(self.dataset)
+
+    def __next__(self):
+        return make_batch(self.queue.get())
+        
+    def run(self):
+        i = 0
+        while True and i < self.max_steps:
+            # prepair next batch
+            sample = self.rem.copy()
+            l = len(sample["input_ids"])
+            max_length = self.max_length
+            while l < max_length:
+                try:
+                    next_sample = next(self.ds_iter)
+                except StopIteration:
+                    # reset generator if a pass through dataset is completed
+                    self.make_iter()
+                l += len(next_sample["input_ids"])
+                sample = {k:sample[k]+next_sample[k] for k in next_sample.keys()}
+            
+            self.rem = {k:v[max_length:] for k,v in sample.items()}
+            sample = {k:v[:max_length] for k,v in sample.items()}
+            # regroup to shape [bs x seq_len]
+            samples = {k:np.array([v[i*self.seq_len:(i+1)*self.seq_len] for i in range(self.bs)]) for k,v in sample.items()}
+            
+            self.queue.put(samples)
+        self.queue.put(None)
+    
+    def __iter__(self):
+        return self