removing all files

LICENSE

@@ -1,21 +0,0 @@
-MIT License
-
-Copyright (c) 2021 Dan Saattrup Nielsen
-
-Permission is hereby granted, free of charge, to any person obtaining a copy
-of this software and associated documentation files (the "Software"), to deal
-in the Software without restriction, including without limitation the rights
-to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
-copies of the Software, and to permit persons to whom the Software is
-furnished to do so, subject to the following conditions:
-
-The above copyright notice and this permission notice shall be included in all
-copies or substantial portions of the Software.
-
-THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
-IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
-FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
-AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
-LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
-OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
-SOFTWARE.

README.md

@@ -1,21 +0,0 @@
----
-language: da
-license: CC-BY 4.0
-tags:
-- danish
-- roberta
-pipeline_tag: fill-mask
-widget:
-- text: "På biblioteket kan du låne en <mask>."
----
-
-
-# Danish Roberta Base - MC4
-
-## Description
-
-This is a sample reference model for Flax/Jax training using only on the MC4. It is trained for roughly three day on a TPU v3-8. Training procedure...
-
----
-## Description
-My description

config.json

@@ -1,27 +0,0 @@
-{
-  "_name_or_path": "./",
-  "architectures": [
-    "RobertaForMaskedLM"
-  ],
-  "attention_probs_dropout_prob": 0.1,
-  "bos_token_id": 0,
-  "eos_token_id": 2,
-  "gradient_checkpointing": false,
-  "hidden_act": "gelu",
-  "hidden_dropout_prob": 0.1,
-  "hidden_size": 768,
-  "initializer_range": 0.02,
-  "intermediate_size": 3072,
-  "layer_norm_eps": 1e-05,
-  "max_position_embeddings": 514,
-  "model_type": "roberta",
-  "num_attention_heads": 12,
-  "num_hidden_layers": 12,
-  "pad_token_id": 1,
-  "position_embedding_type": "absolute",
-  "torch_dtype": "float32",
-  "transformers_version": "4.9.0.dev0",
-  "type_vocab_size": 1,
-  "use_cache": true,
-  "vocab_size": 50265
-}

continue_run_mlm_flax_stream.sh

@@ -1,26 +0,0 @@
-export MODEL_DIR=/home/Z6HJB/roberta-base-danish/roberta-base-danish/
-
-source /home/Z6HJB/test/bin/activate
-
-python3 ./src/run_mlm_flax_stream.py \
-    --model_name_or_path="${MODEL_DIR}" \
-    --output_dir="${MODEL_DIR}" \
-    --tokenizer_name="${MODEL_DIR}" \
-    --dataset_name="mc4" \
-    --dataset_config_name="unshuffled_deduplicated_en" \
-    --max_seq_length="514" \
-    --per_device_train_batch_size="32" \
-    --per_device_eval_batch_size="32" \
-    --learning_rate="3e-4" \
-    --warmup_steps="1000" \
-    --overwrite_output_dir \
-    --adam_beta1="0.9" \
-    --adam_beta2="0.98" \
-    --num_train_steps="100000" \
-    --num_eval_samples="5000" \
-    --save_steps="1000" \
-    --logging_steps="250" \
-    --eval_steps="1000" \
-    #--push_to_hub \
-    #--config_name="${MODEL_DIR}" \
-    #--model_type="roberta" \

events.out.tfevents.1625976092.t1v-n-ba9840ed-w-0.105113.3.v2

@@ -1,3 +0,0 @@
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events.out.tfevents.1625999454.t1v-n-ba9840ed-w-0.129483.3.v2

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flax_model.msgpack

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-version https://git-lfs.github.com/spec/v1
-oid sha256:c221eb636b60017750c5a40abee0d1578ad3425f7b8460a9d80ee59e0ae13b8b
-size 498796983

makefile

@@ -1,18 +0,0 @@
-train:
-	python3 ./src/run_mlm_flax.py \
-		--output_dir="." \
-		--model_type="roberta" \
-		--config_name="." \
-		--tokenizer_name="." \
-		--max_seq_length="128" \
-		--weight_decay="0.01" \
-		--per_device_train_batch_size="128" \
-		--per_device_eval_batch_size="128" \
-		--learning_rate="3e-4" \
-		--warmup_steps="1000" \
-		--overwrite_output_dir \
-		--pad_to_max_length \
-		--num_train_epochs="18" \
-		--adam_beta1="0.9" \
-		--adam_beta2="0.98" \
-		--push_to_hub

md_logs/train_tokenizer.md

@@ -1,70 +0,0 @@
-# Setting up a Google Cloud TPU VM for training a tokenizer
-
-## TPU VM Configurations
-To start off follow the guide from the Flax/JAX community week 2021 [here](https://github.com/huggingface/transformers/tree/master/examples/research_projects/jax-projects#how-to-setup-tpu-vm), but **NOTE** modify all the `pip` commands to `pip3`. 
-
-Some might encounter this error message: 
-```
- Building wheel for jax (setup.py) ... error
-  ERROR: Command errored out with exit status 1:
-   command: /home/patrick/patrick/bin/python3 -u -c 'import sys, setuptools, tokenize; sys.argv[0] = '"'"'/tmp/pip-install-lwseckn1/jax/setup.py'"'"'; __file__='"'"'/tmp/pip-install-lwseckn1/jax/setup.py'"'"';f=getattr(tokenize, '"'"'open'"'"', open)(__file__);code=f.read().replace('"'"'\r\n'"'"', '"'"'\n'"'"');f.close();exec(compile(code, __file__, '"'"'exec'"'"'))' bdist_wheel -d /tmp/pip-wheel-pydotzlo
-       cwd: /tmp/pip-install-lwseckn1/jax/
-  Complete output (6 lines):
-  usage: setup.py [global_opts] cmd1 [cmd1_opts] [cmd2 [cmd2_opts] ...]
-     or: setup.py --help [cmd1 cmd2 ...]
-     or: setup.py --help-commands
-     or: setup.py cmd --help
-  
-  error: invalid command 'bdist_wheel'
-  ----------------------------------------
-  ERROR: Failed building wheel for jax
-```
-
-If encountering the error message run the following commands:
-```
-pip3 install --upgrade clu
-pip3 install "jax[tpu]>=0.2.16" -f https://storage.googleapis.com/jax-releases/libtpu_releases.html
-```
-
-Then give your user sudo rights:
-```bash
-chmod a+rwx /tmp/*
-chmod a+rwx /tmp/tpu_logs/*  # Just to be sure ;-)
-```
-
-Afterwards you can verify the installation by either running the following script:
-
-```python
-from transformers import FlaxRobertaModel, RobertaTokenizerFast
-from datasets import load_dataset
-import jax
-
-dataset = load_dataset('oscar', "unshuffled_deduplicated_en", split='train', streaming=True)
-
-dummy_input = next(iter(dataset))["text"]
-
-tokenizer = RobertaTokenizerFast.from_pretrained("roberta-base")
-input_ids = tokenizer(dummy_input, return_tensors="np").input_ids[:, :10]
-
-model = FlaxRobertaModel.from_pretrained("julien-c/dummy-unknown")
-
-# run a forward pass, should return an object `FlaxBaseModelOutputWithPooling`
-model(input_ids)
-```
-
-Or by running the following `python` commands:
-```python
-import jax
-jax.devices()
-```
-
-## Training the tokenizer
-To train the tokenizer run the `train_tokenizer.py` script:
-```bash
-python3 train_tokenizer.py
-```
-
-### Problems while developing the script:
--  Loading the '*mc4*' dataset using the  `load_dataset()` from HugginFace's dataset package `datasets` was not able to load multiple language in one line of code, as otherwise specified [here](https://huggingface.co/datasets/mc4). It was thus chosen to load each language and concatenate them.
-- Furthermore, it seems that even though you predefine a subset-split using the `split` argument, the entire dataset still needs to be downloaded. 
-- Some bug occured when downloading the danish dataset, and we then had to force a redownload to mitigate the bug, and make the VM download it. 

pytorch_model.bin

@@ -1,3 +0,0 @@
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-oid sha256:aedd97cdaaa3ac8422b20c4a391097d2af86308bd130045bc630d0ef222b183d
-size 498858859

run_mlm_flax_stream.sh

@@ -1,26 +0,0 @@
-export MODEL_DIR=/home/Z6HJB/roberta-base-danish/roberta-base-danish/
-
-source /home/Z6HJB/test/bin/activate
-
-python3 ./src/run_mlm_flax_stream.py \
-    --config_name="${MODEL_DIR}" \
-    --output_dir="${MODEL_DIR}" \
-    --tokenizer_name="${MODEL_DIR}" \
-    --model_type="roberta" \
-    --dataset_name="mc4" \
-    --dataset_config_name="unshuffled_deduplicated_en" \
-    --max_seq_length="128" \
-    --per_device_train_batch_size="128" \
-    --per_device_eval_batch_size="128" \
-    --learning_rate="3e-4" \
-    --warmup_steps="1000" \
-    --overwrite_output_dir \
-    --adam_beta1="0.9" \
-    --adam_beta2="0.98" \
-    --num_train_steps="300000" \
-    --num_eval_samples="5000" \
-    --save_steps="1000" \
-    --logging_steps="250" \
-    --eval_steps="1000" \
-    #--push_to_hub \ currently results in this error: ValueError: If not specifying `clone_from`, you need to pass Repository a valid git clone.
-    #--model_name_or_path="${MODEL_DIR}" \ used to continue pretrained

special_tokens_map.json

@@ -1 +0,0 @@
-{"bos_token": "<s>", "eos_token": "</s>", "unk_token": "<unk>", "sep_token": "</s>", "pad_token": "<pad>", "cls_token": "<s>", "mask_token": {"content": "<mask>", "single_word": false, "lstrip": true, "rstrip": false, "normalized": false}}

src/config.py

@@ -1,9 +0,0 @@
-'''Create the configuration for the model'''
-
-from transformers import RobertaConfig
-from .utils import model_dir
-
-# Currently it merely copies the `roberta-base` config, but we can change this
-# of course
-config = RobertaConfig.from_pretrained("roberta-base")
-config.save_pretrained(model_dir)

src/danish_run_mlm_flax_stream.py

@@ -1,635 +0,0 @@
-#!/usr/bin/env python
-# coding=utf-8
-# Copyright 2021 The HuggingFace Team All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-"""
-Fine-tuning the library models for masked language modeling (BERT, ALBERT, RoBERTa...) with whole word masking 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=masked-lm
-"""
-import logging
-import os
-import sys
-import time
-from collections import defaultdict
-from dataclasses import dataclass, field
-
-# You can also adapt this script on your own masked language modeling task. Pointers for this are left as comments.
-from pathlib import Path
-from typing import Dict, List, Optional, Tuple
-
-import datasets
-import numpy as np
-from datasets import load_dataset, interleave_datasets
-from tqdm import tqdm
-
-import flax
-import jax
-import jax.numpy as jnp
-import optax
-from flax import jax_utils, traverse_util
-from flax.training import train_state
-from flax.training.common_utils import get_metrics, onehot, shard
-from transformers import (
-    CONFIG_MAPPING,
-    FLAX_MODEL_FOR_MASKED_LM_MAPPING,
-    AutoConfig,
-    AutoTokenizer,
-    FlaxAutoModelForMaskedLM,
-    HfArgumentParser,
-    PreTrainedTokenizerBase,
-    TensorType,
-    TrainingArguments,
-    is_tensorboard_available,
-    set_seed,
-)
-
-
-if datasets.__version__ <= "1.8.0":
-    raise ValueError("Make sure to upgrade `datasets` to a version >= 1.9.0 to use dataset streaming")
-
-
-MODEL_CONFIG_CLASSES = list(FLAX_MODEL_FOR_MASKED_LM_MAPPING.keys())
-MODEL_TYPES = tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)
-
-
-@dataclass
-class ModelArguments:
-    """
-    Arguments pertaining to which model/config/tokenizer we are going to fine-tune, or train from scratch.
-    """
-
-    model_name_or_path: Optional[str] = field(
-        default=None,
-        metadata={
-            "help": "The model checkpoint for weights initialization."
-            "Don't set if you want to train a model from scratch."
-        },
-    )
-    model_type: Optional[str] = field(
-        default=None,
-        metadata={"help": "If training from scratch, pass a model type from the list: " + ", ".join(MODEL_TYPES)},
-    )
-    config_name: Optional[str] = field(
-        default=None, metadata={"help": "Pretrained config name or path if not the same as model_name"}
-    )
-    tokenizer_name: Optional[str] = field(
-        default=None, metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"}
-    )
-    cache_dir: Optional[str] = field(
-        default=None, metadata={"help": "Where do you want to store the pretrained models downloaded from s3"}
-    )
-    use_fast_tokenizer: bool = field(
-        default=True,
-        metadata={"help": "Whether to use one of the fast tokenizer (backed by the tokenizers library) or not."},
-    )
-    dtype: Optional[str] = field(
-        default="float32",
-        metadata={
-            "help": "Floating-point format in which the model weights should be initialized and trained. Choose one of `[float32, float16, bfloat16]`."
-        },
-    )
-
-
-@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. 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 masked language modeling loss"}
-    )
-    pad_to_max_length: bool = field(
-        default=False,
-        metadata={
-            "help": "Whether to pad all samples to `max_seq_length`. "
-            "If False, will pad the samples dynamically when batching to the maximum length in the batch."
-        },
-    )
-    line_by_line: bool = field(
-        default=False,
-        metadata={"help": "Whether distinct lines of text in the dataset are to be handled as distinct sequences."},
-    )
-    text_column_name: str = field(
-        default="text", metadata={"help": "The name of the column to retrieve the training text."}
-    )
-    shuffle_buffer_size: int = field(
-        default=10000, metadata={"help": "The number of examples to pre-load for shuffling."}
-    )
-    num_train_steps: int = field(default=50000, metadata={"help": "The number of training steps."})
-    num_eval_samples: int = field(default=50000, metadata={"help": "The number of samples to be used for evaluation"})
-
-    def __post_init__(self):
-        if self.dataset_name is None 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."
-
-
-@flax.struct.dataclass
-class FlaxDataCollatorForLanguageModeling:
-    """
-    Data collator used for language modeling. Inputs are dynamically padded to the maximum length of a batch if they
-    are not all of the same length.
-    Args:
-        tokenizer (:class:`~transformers.PreTrainedTokenizer` or :class:`~transformers.PreTrainedTokenizerFast`):
-            The tokenizer used for encoding the data.
-        mlm_probability (:obj:`float`, `optional`, defaults to 0.15):
-            The probability with which to (randomly) mask tokens in the input.
-    .. note::
-        For best performance, this data collator should be used with a dataset having items that are dictionaries or
-        BatchEncoding, with the :obj:`"special_tokens_mask"` key, as returned by a
-        :class:`~transformers.PreTrainedTokenizer` or a :class:`~transformers.PreTrainedTokenizerFast` with the
-        argument :obj:`return_special_tokens_mask=True`.
-    """
-
-    tokenizer: PreTrainedTokenizerBase
-    mlm_probability: float = 0.15
-
-    def __post_init__(self):
-        if self.tokenizer.mask_token is None:
-            raise ValueError(
-                "This tokenizer does not have a mask token which is necessary for masked language modeling. "
-                "You should pass `mlm=False` to train on causal language modeling instead."
-            )
-
-    def __call__(self, examples: List[Dict[str, np.ndarray]]) -> Dict[str, np.ndarray]:
-        # Handle dict or lists with proper padding and conversion to tensor.
-        batch = self.tokenizer.pad(examples, return_tensors=TensorType.NUMPY)
-
-        # If special token mask has been preprocessed, pop it from the dict.
-        special_tokens_mask = batch.pop("special_tokens_mask", None)
-
-        batch["input_ids"], batch["labels"] = self.mask_tokens(
-            batch["input_ids"], special_tokens_mask=special_tokens_mask
-        )
-        return batch
-
-    def mask_tokens(
-        self, inputs: np.ndarray, special_tokens_mask: Optional[np.ndarray]
-    ) -> Tuple[jnp.ndarray, jnp.ndarray]:
-        """
-        Prepare masked tokens inputs/labels for masked language modeling: 80% MASK, 10% random, 10% original.
-        """
-        labels = inputs.copy()
-        # We sample a few tokens in each sequence for MLM training (with probability `self.mlm_probability`)
-        probability_matrix = np.full(labels.shape, self.mlm_probability)
-        special_tokens_mask = special_tokens_mask.astype("bool")
-
-        probability_matrix[special_tokens_mask] = 0.0
-        masked_indices = np.random.binomial(1, probability_matrix).astype("bool")
-        labels[~masked_indices] = -100  # We only compute loss on masked tokens
-
-        # 80% of the time, we replace masked input tokens with tokenizer.mask_token ([MASK])
-        indices_replaced = np.random.binomial(1, np.full(labels.shape, 0.8)).astype("bool") & masked_indices
-        inputs[indices_replaced] = self.tokenizer.convert_tokens_to_ids(self.tokenizer.mask_token)
-
-        # 10% of the time, we replace masked input tokens with random word
-        indices_random = np.random.binomial(1, np.full(labels.shape, 0.5)).astype("bool")
-        indices_random &= masked_indices & ~indices_replaced
-
-        random_words = np.random.randint(self.tokenizer.vocab_size, size=labels.shape, dtype="i4")
-        inputs[indices_random] = random_words[indices_random]
-
-        # The rest of the time (10% of the time) we keep the masked input tokens unchanged
-        return inputs, labels
-
-
-def generate_batch_splits(samples_idx: jnp.ndarray, batch_size: int) -> jnp.ndarray:
-    num_samples = len(samples_idx)
-    samples_to_remove = num_samples % batch_size
-
-    if samples_to_remove != 0:
-        samples_idx = samples_idx[:-samples_to_remove]
-    sections_split = num_samples // batch_size
-    batch_idx = np.split(samples_idx, sections_split)
-    return batch_idx
-
-
-def advance_iter_and_group_samples(train_iterator, num_samples, max_seq_length):
-    """
-    The training iterator is advanced so that after groupifying the samples,
-    `num_samples` of length `max_seq_length` are returned.
-    """
-    num_total_tokens = max_seq_length * num_samples
-    samples = defaultdict(list)
-
-    i = 0
-    while i < num_total_tokens:
-        tokenized_samples = next(train_iterator)
-        i += len(tokenized_samples["input_ids"])
-
-        # concatenate tokenized samples to list
-        samples = {k: samples[k] + tokenized_samples[k] for k in tokenized_samples.keys()}
-
-    # Concatenated tokens are split to lists of length `max_seq_length`.
-    # Note that remainedr of % max_seq_length are thrown away.
-    def group_texts(examples):
-        result = {
-            k: [t[i : i + max_seq_length] for i in range(0, num_total_tokens, max_seq_length)]
-            for k, t in examples.items()
-        }
-        return result
-
-    grouped_samples = group_texts(samples)
-    return grouped_samples
-
-
-def write_train_metric(summary_writer, train_metrics, train_time, step):
-    summary_writer.scalar("train_time", train_time, step)
-
-    train_metrics = get_metrics(train_metrics)
-    for key, vals in train_metrics.items():
-        tag = f"train_{key}"
-        for i, val in enumerate(vals):
-            summary_writer.scalar(tag, val, step - len(vals) + i + 1)
-
-
-def write_eval_metric(summary_writer, eval_metrics, step):
-    for metric_name, value in eval_metrics.items():
-        summary_writer.scalar(f"eval_{metric_name}", value, step)
-
-
-if __name__ == "__main__":
-    # See all possible arguments in src/transformers/training_args.py
-    # or by passing the --help flag to this script.
-    # We now keep distinct sets of args, for a cleaner separation of concerns.
-
-    parser = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments))
-    if len(sys.argv) == 2 and sys.argv[1].endswith(".json"):
-        # If we pass only one argument to the script and it's the path to a json file,
-        # let's parse it to get our arguments.
-        model_args, data_args, training_args = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]))
-    else:
-        model_args, data_args, training_args = parser.parse_args_into_dataclasses()
-
-    if (
-        os.path.exists(training_args.output_dir)
-        and os.listdir(training_args.output_dir)
-        and training_args.do_train
-        and not training_args.overwrite_output_dir
-    ):
-        raise ValueError(
-            f"Output directory ({training_args.output_dir}) already exists and is not empty."
-            "Use --overwrite_output_dir to overcome."
-        )
-
-    # Setup logging
-    logging.basicConfig(
-        format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
-        level="INFO",
-        datefmt="[%X]",
-    )
-
-    # Log on each process the small summary:
-    logger = logging.getLogger(__name__)
-    logger.warning(
-        f"Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}"
-        + f"distributed training: {bool(training_args.local_rank != -1)}, 16-bits training: {training_args.fp16}"
-    )
-
-    # Set the verbosity to info of the Transformers logger (on main process only):
-    logger.info(f"Training/evaluation parameters {training_args}")
-
-    # Set seed before initializing model.
-    set_seed(training_args.seed)
-
-    # Get the datasets: you can either provide your own CSV/JSON/TXT training and evaluation files (see below)
-    # or just provide the name of one of the public datasets available on the hub at https://huggingface.co/datasets/
-    # (the dataset will be downloaded automatically from the datasets Hub).
-    #
-    # For CSV/JSON files, this script will use the column called 'text' or the first column if no column called
-    # 'text' is found. You can easily tweak this behavior (see below).
-    if data_args.dataset_name is not None:
-        # Downloading and loading a dataset from the hub.
-        # dataset = load_dataset(
-        #     data_args.dataset_name,
-        #     data_args.dataset_config_name,
-        #     cache_dir=model_args.cache_dir,
-        #     streaming=True,
-        #     split="train",
-        # )
-
-        dataset = load_dataset("mc4", "da", split="train", streaming=True)
-        # norwegian_dataset = load_dataset("mc4", "no", split="train", streaming=True)
-        # swedish_dataset = load_dataset("mc4", "sv", split="train", streaming=True)
-
-        # danish_dataset_subset = danish_dataset.take(int(24.1e6))
-        # norwegian_dataset_subset = norwegian_dataset.take(int(24.1e6))
-        # swedish_dataset_subset = swedish_dataset.take(int(24.1e6))
-
-        # dataset = interleave_datasets(
-        #     [danish_dataset_subset, norwegian_dataset_subset, swedish_dataset_subset], probabilities=[0.34, 0.33, 0.33]
-        # )
-
-    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:
-        print(f"Setting config from path: {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."
-        )
-
-    # Otherwise, we tokenize every text, then concatenate them together before splitting them in smaller parts.
-    # We use `return_special_tokens_mask=True` because DataCollatorForLanguageModeling (see below) is more
-    # efficient when it receives the `special_tokens_mask`.
-    def tokenize_function(examples):
-        return tokenizer(examples[data_args.text_column_name], return_special_tokens_mask=True)
-
-    tokenized_datasets = dataset.map(
-        tokenize_function,
-        batched=True,
-    )
-
-    shuffle_seed = training_args.seed
-    tokenized_datasets = tokenized_datasets.shuffle(buffer_size=data_args.shuffle_buffer_size, seed=shuffle_seed)
-
-    has_tensorboard = is_tensorboard_available()
-    if has_tensorboard and jax.process_index() == 0:
-        try:
-            from flax.metrics.tensorboard import SummaryWriter
-        except ImportError as ie:
-            has_tensorboard = False
-            logger.warning(
-                f"Unable to display metrics through TensorBoard because some package are not installed: {ie}"
-            )
-
-        summary_writer = SummaryWriter(log_dir=Path(training_args.output_dir))
-
-    # Data collator
-    # This one will take care of randomly masking the tokens.
-    data_collator = FlaxDataCollatorForLanguageModeling(tokenizer=tokenizer, mlm_probability=data_args.mlm_probability)
-
-    # Initialize our training
-    rng = jax.random.PRNGKey(training_args.seed)
-    dropout_rngs = jax.random.split(rng, jax.local_device_count())
-
-    if model_args.model_name_or_path:
-        model = FlaxAutoModelForMaskedLM.from_pretrained(
-            model_args.model_name_or_path, config=config, seed=training_args.seed, dtype=getattr(jnp, model_args.dtype)
-        )
-    else:
-        model = FlaxAutoModelForMaskedLM.from_config(
-            config, seed=training_args.seed, dtype=getattr(jnp, model_args.dtype)
-        )
-
-    # Store some constant
-    num_epochs = int(training_args.num_train_epochs)
-    train_batch_size = int(training_args.per_device_train_batch_size) * jax.device_count()
-    eval_batch_size = int(training_args.per_device_eval_batch_size) * jax.device_count()
-
-    # define number steps per stream epoch
-    num_train_steps = data_args.num_train_steps
-
-    # Create learning rate schedule
-    warmup_fn = optax.linear_schedule(
-        init_value=0.0, end_value=training_args.learning_rate, transition_steps=training_args.warmup_steps
-    )
-    decay_fn = optax.linear_schedule(
-        init_value=training_args.learning_rate,
-        end_value=0,
-        transition_steps=num_train_steps - training_args.warmup_steps,
-    )
-    linear_decay_lr_schedule_fn = optax.join_schedules(
-        schedules=[warmup_fn, decay_fn], boundaries=[training_args.warmup_steps]
-    )
-
-    # We use Optax's "masking" functionality to not apply weight decay
-    # to bias and LayerNorm scale parameters. decay_mask_fn returns a
-    # mask boolean with the same structure as the parameters.
-    # The mask is True for parameters that should be decayed.
-    # Note that this mask is specifically adapted for FlaxBERT-like models.
-    # 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:] != ("LayerNorm", "scale")) for path in flat_params}
-        return traverse_util.unflatten_dict(flat_mask)
-
-    # create adam optimizer
-    adamw = optax.adamw(
-        learning_rate=linear_decay_lr_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,
-    )
-
-    # Setup train state
-    state = train_state.TrainState.create(apply_fn=model.__call__, params=model.params, tx=adamw)
-
-    # Define gradient update step fn
-    def train_step(state, batch, dropout_rng):
-        dropout_rng, new_dropout_rng = jax.random.split(dropout_rng)
-
-        def loss_fn(params):
-            labels = batch.pop("labels")
-
-            logits = state.apply_fn(**batch, params=params, dropout_rng=dropout_rng, train=True)[0]
-
-            # compute loss, ignore padded input tokens
-            label_mask = jnp.where(labels > 0, 1.0, 0.0)
-            loss = optax.softmax_cross_entropy(logits, onehot(labels, logits.shape[-1])) * label_mask
-
-            # take average
-            loss = loss.sum() / label_mask.sum()
-
-            return loss
-
-        grad_fn = jax.value_and_grad(loss_fn)
-        loss, grad = grad_fn(state.params)
-        grad = jax.lax.pmean(grad, "batch")
-        new_state = state.apply_gradients(grads=grad)
-
-        metrics = jax.lax.pmean(
-            {"loss": loss, "learning_rate": linear_decay_lr_schedule_fn(state.step)}, axis_name="batch"
-        )
-
-        return new_state, metrics, new_dropout_rng
-
-    # Create parallel version of the train step
-    p_train_step = jax.pmap(train_step, "batch", donate_argnums=(0,))
-
-    # Define eval fn
-    def eval_step(params, batch):
-        labels = batch.pop("labels")
-
-        logits = model(**batch, params=params, train=False)[0]
-
-        # compute loss, ignore padded input tokens
-        label_mask = jnp.where(labels > 0, 1.0, 0.0)
-        loss = optax.softmax_cross_entropy(logits, onehot(labels, logits.shape[-1])) * label_mask
-
-        # compute accuracy
-        accuracy = jnp.equal(jnp.argmax(logits, axis=-1), labels) * label_mask
-
-        # summarize metrics
-        metrics = {"loss": loss.sum(), "accuracy": accuracy.sum(), "normalizer": label_mask.sum()}
-        metrics = jax.lax.psum(metrics, axis_name="batch")
-
-        return metrics
-
-    p_eval_step = jax.pmap(eval_step, "batch", donate_argnums=(0,))
-
-    # Replicate the train state on each device
-    state = jax_utils.replicate(state)
-
-    train_time = 0
-    train_start = time.time()
-    train_metrics = []
-    eval_metrics = []
-
-    training_iter = iter(tokenized_datasets)
-
-    max_seq_length = min(data_args.max_seq_length, tokenizer.model_max_length)
-    eval_samples = advance_iter_and_group_samples(training_iter, data_args.num_eval_samples, max_seq_length)
-
-    steps = tqdm(range(num_train_steps), desc="Training...", position=0)
-    for step in range(num_train_steps):
-        # ======================== Training ================================
-        try:
-            samples = advance_iter_and_group_samples(training_iter, train_batch_size, max_seq_length)
-        except StopIteration:
-            # Once the end of the dataset stream is reached, the training iterator
-            # is reinitialized and reshuffled and a new eval dataset is randomely chosen.
-            shuffle_seed += 1
-            tokenized_datasets.set_epoch(shuffle_seed)
-
-            training_iter = iter(tokenized_datasets)
-
-            eval_dataset = advance_iter_and_group_samples(training_iter, data_args.num_eval_samples, max_seq_length)
-            samples = advance_iter_and_group_samples(training_iter, train_batch_size, max_seq_length)
-
-        # process input samples
-        model_inputs = data_collator(samples)
-
-        # Model forward
-        model_inputs = shard(model_inputs.data)
-        state, train_metric, dropout_rngs = p_train_step(state, model_inputs, dropout_rngs)
-
-        train_metrics.append(train_metric)
-
-        if step % training_args.logging_steps == 0 and step > 0:
-            steps.write(
-                f"Step... ({step} | Loss: {train_metric['loss'].mean()}, Learning Rate: {train_metric['learning_rate'].mean()})"
-            )
-            train_time += time.time() - train_start
-            if has_tensorboard and jax.process_index() == 0:
-                write_train_metric(summary_writer, train_metrics, train_time, step)
-            train_metrics = []
-
-        # ======================== Evaluating ==============================
-        if step % training_args.eval_steps == 0 and step > 0:
-            eval_samples_idx = jnp.arange(data_args.num_eval_samples)
-            eval_batch_idx = generate_batch_splits(eval_samples_idx, eval_batch_size)
-
-            for i, batch_idx in enumerate(tqdm(eval_batch_idx, desc="Evaluating ...", position=1)):
-                # process input samples
-                batch_eval_samples = {k: [v[idx] for idx in batch_idx] for k, v in eval_samples.items()}
-                model_inputs = data_collator(batch_eval_samples)
-
-                # Model forward
-                model_inputs = shard(model_inputs.data)
-                metrics = p_eval_step(state.params, model_inputs)
-                eval_metrics.append(metrics)
-
-            # normalize eval metrics
-            eval_metrics = get_metrics(eval_metrics)
-            eval_metrics = jax.tree_map(jnp.sum, eval_metrics)
-            eval_normalizer = eval_metrics.pop("normalizer")
-            eval_metrics = jax.tree_map(lambda x: x / eval_normalizer, eval_metrics)
-
-            # Update progress bar
-            steps.desc = f"Step... ({step + 1}/{num_train_steps} | Loss: {eval_metrics['loss']}, Acc: {eval_metrics['accuracy']})"
-
-            if has_tensorboard and jax.process_index() == 0:
-                write_eval_metric(summary_writer, eval_metrics, step)
-            eval_metrics = []
-
-            # Saving at each save_step
-            if step % training_args.save_steps == 0 and 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,
-                        push_to_hub=training_args.push_to_hub,
-                        commit_message=f"Saving weights and logs of step {step+1}",
-                    )
-
-        # update tqdm bar
-        steps.update(1)

src/gigaword.py

@@ -1 +0,0 @@
-'''Functions and classes for loading/streaming the relevant GigaWord datasets'''

src/mc4.py

@@ -1 +0,0 @@
-'''Functions and classes for loading/streaming the relevant mC4 datasets'''

src/scandi_run_mlm_flax.py

@@ -1,681 +0,0 @@
-#!/usr/bin/env python
-# coding=utf-8
-# Copyright 2021 The HuggingFace Team All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-"""
-Fine-tuning the library models for masked language modeling (BERT, ALBERT, RoBERTa...) with whole word masking 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=masked-lm
-"""
-import logging
-import os
-import sys
-import time
-from dataclasses import dataclass, field
-
-# You can also adapt this script on your own masked language modeling task. Pointers for this are left as comments.
-from pathlib import Path
-from typing import Dict, List, Optional, Tuple
-
-import numpy as np
-from datasets import load_dataset, concatenate_datasets, interleave_datasets
-from tqdm import tqdm
-
-import flax
-import jax
-import jax.numpy as jnp
-import optax
-from flax import jax_utils, traverse_util
-from flax.training import train_state
-from flax.training.common_utils import get_metrics, onehot, shard
-from transformers import (
-    CONFIG_MAPPING,
-    FLAX_MODEL_FOR_MASKED_LM_MAPPING,
-    AutoConfig,
-    AutoTokenizer,
-    FlaxAutoModelForMaskedLM,
-    HfArgumentParser,
-    PreTrainedTokenizerBase,
-    TensorType,
-    TrainingArguments,
-    is_tensorboard_available,
-    set_seed,
-)
-
-
-# Cache the result
-has_tensorboard = is_tensorboard_available()
-if has_tensorboard:
-    try:
-        from flax.metrics.tensorboard import SummaryWriter
-    except ImportError as ie:
-        has_tensorboard = False
-        print(f"Unable to display metrics through TensorBoard because some package are not installed: {ie}")
-
-else:
-    print(
-        "Unable to display metrics through TensorBoard because the package is not installed: "
-        "Please run pip install tensorboard to enable."
-    )
-
-
-MODEL_CONFIG_CLASSES = list(FLAX_MODEL_FOR_MASKED_LM_MAPPING.keys())
-MODEL_TYPES = tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)
-
-
-@dataclass
-class ModelArguments:
-    """
-    Arguments pertaining to which model/config/tokenizer we are going to fine-tune, or train from scratch.
-    """
-
-    model_name_or_path: Optional[str] = field(
-        default=None,
-        metadata={
-            "help": "The model checkpoint for weights initialization."
-            "Don't set if you want to train a model from scratch."
-        },
-    )
-    model_type: Optional[str] = field(
-        default=None,
-        metadata={"help": "If training from scratch, pass a model type from the list: " + ", ".join(MODEL_TYPES)},
-    )
-    config_name: Optional[str] = field(
-        default=None, metadata={"help": "Pretrained config name or path if not the same as model_name"}
-    )
-    tokenizer_name: Optional[str] = field(
-        default=None, metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"}
-    )
-    cache_dir: Optional[str] = field(
-        default=None, metadata={"help": "Where do you want to store the pretrained models downloaded from s3"}
-    )
-    use_fast_tokenizer: bool = field(
-        default=True,
-        metadata={"help": "Whether to use one of the fast tokenizer (backed by the tokenizers library) or not."},
-    )
-    dtype: Optional[str] = field(
-        default="float32",
-        metadata={
-            "help": "Floating-point format in which the model weights should be initialized and trained. Choose one of `[float32, float16, bfloat16]`."
-        },
-    )
-
-
-@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. 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 masked language modeling loss"}
-    )
-    pad_to_max_length: bool = field(
-        default=False,
-        metadata={
-            "help": "Whether to pad all samples to `max_seq_length`. "
-            "If False, will pad the samples dynamically when batching to the maximum length in the batch."
-        },
-    )
-    line_by_line: bool = field(
-        default=False,
-        metadata={"help": "Whether distinct lines of text in the dataset are to be handled as distinct sequences."},
-    )
-
-    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."
-
-
-@flax.struct.dataclass
-class FlaxDataCollatorForLanguageModeling:
-    """
-    Data collator used for language modeling. Inputs are dynamically padded to the maximum length of a batch if they
-    are not all of the same length.
-
-    Args:
-        tokenizer (:class:`~transformers.PreTrainedTokenizer` or :class:`~transformers.PreTrainedTokenizerFast`):
-            The tokenizer used for encoding the data.
-        mlm_probability (:obj:`float`, `optional`, defaults to 0.15):
-            The probability with which to (randomly) mask tokens in the input.
-
-    .. note::
-
-        For best performance, this data collator should be used with a dataset having items that are dictionaries or
-        BatchEncoding, with the :obj:`"special_tokens_mask"` key, as returned by a
-        :class:`~transformers.PreTrainedTokenizer` or a :class:`~transformers.PreTrainedTokenizerFast` with the
-        argument :obj:`return_special_tokens_mask=True`.
-    """
-
-    tokenizer: PreTrainedTokenizerBase
-    mlm_probability: float = 0.15
-
-    def __post_init__(self):
-        if self.tokenizer.mask_token is None:
-            raise ValueError(
-                "This tokenizer does not have a mask token which is necessary for masked language modeling. "
-                "You should pass `mlm=False` to train on causal language modeling instead."
-            )
-
-    def __call__(self, examples: List[Dict[str, np.ndarray]], pad_to_multiple_of: int) -> Dict[str, np.ndarray]:
-        # Handle dict or lists with proper padding and conversion to tensor.
-        batch = self.tokenizer.pad(examples, pad_to_multiple_of=pad_to_multiple_of, return_tensors=TensorType.NUMPY)
-
-        # If special token mask has been preprocessed, pop it from the dict.
-        special_tokens_mask = batch.pop("special_tokens_mask", None)
-
-        batch["input_ids"], batch["labels"] = self.mask_tokens(
-            batch["input_ids"], special_tokens_mask=special_tokens_mask
-        )
-        return batch
-
-    def mask_tokens(
-        self, inputs: np.ndarray, special_tokens_mask: Optional[np.ndarray]
-    ) -> Tuple[jnp.ndarray, jnp.ndarray]:
-        """
-        Prepare masked tokens inputs/labels for masked language modeling: 80% MASK, 10% random, 10% original.
-        """
-        labels = inputs.copy()
-        # We sample a few tokens in each sequence for MLM training (with probability `self.mlm_probability`)
-        probability_matrix = np.full(labels.shape, self.mlm_probability)
-        special_tokens_mask = special_tokens_mask.astype("bool")
-
-        probability_matrix[special_tokens_mask] = 0.0
-        masked_indices = np.random.binomial(1, probability_matrix).astype("bool")
-        labels[~masked_indices] = -100  # We only compute loss on masked tokens
-
-        # 80% of the time, we replace masked input tokens with tokenizer.mask_token ([MASK])
-        indices_replaced = np.random.binomial(1, np.full(labels.shape, 0.8)).astype("bool") & masked_indices
-        inputs[indices_replaced] = self.tokenizer.convert_tokens_to_ids(self.tokenizer.mask_token)
-
-        # 10% of the time, we replace masked input tokens with random word
-        indices_random = np.random.binomial(1, np.full(labels.shape, 0.5)).astype("bool")
-        indices_random &= masked_indices & ~indices_replaced
-
-        random_words = np.random.randint(self.tokenizer.vocab_size, size=labels.shape, dtype="i4")
-        inputs[indices_random] = random_words[indices_random]
-
-        # The rest of the time (10% of the time) we keep the masked input tokens unchanged
-        return inputs, labels
-
-
-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 write_metric(summary_writer, train_metrics, eval_metrics, train_time, step):
-    summary_writer.scalar("train_time", train_time, step)
-
-    train_metrics = get_metrics(train_metrics)
-    for key, vals in train_metrics.items():
-        tag = f"train_{key}"
-        for i, val in enumerate(vals):
-            summary_writer.scalar(tag, val, step - len(vals) + i + 1)
-
-    for metric_name, value in eval_metrics.items():
-        summary_writer.scalar(f"eval_{metric_name}", value, step)
-
-
-if __name__ == "__main__":
-    # See all possible arguments in src/transformers/training_args.py
-    # or by passing the --help flag to this script.
-    # We now keep distinct sets of args, for a cleaner separation of concerns.
-
-    parser = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments))
-    if len(sys.argv) == 2 and sys.argv[1].endswith(".json"):
-        # If we pass only one argument to the script and it's the path to a json file,
-        # let's parse it to get our arguments.
-        model_args, data_args, training_args = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]))
-    else:
-        model_args, data_args, training_args = parser.parse_args_into_dataclasses()
-
-    if (
-        os.path.exists(training_args.output_dir)
-        and os.listdir(training_args.output_dir)
-        and training_args.do_train
-        and not training_args.overwrite_output_dir
-    ):
-        raise ValueError(
-            f"Output directory ({training_args.output_dir}) already exists and is not empty."
-            "Use --overwrite_output_dir to overcome."
-        )
-
-    # Setup logging
-    logging.basicConfig(
-        format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
-        level="NOTSET",
-        datefmt="[%X]",
-    )
-
-    # Log on each process the small summary:
-    logger = logging.getLogger(__name__)
-    logger.warning(
-        f"Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}"
-        + f"distributed training: {bool(training_args.local_rank != -1)}, 16-bits training: {training_args.fp16}"
-    )
-
-    # Set the verbosity to info of the Transformers logger (on main process only):
-    logger.info(f"Training/evaluation parameters {training_args}")
-
-    # Set seed before initializing model.
-    set_seed(training_args.seed)
-
-    # Get the datasets: you can either provide your own CSV/JSON/TXT training and evaluation files (see below)
-    # or just provide the name of one of the public datasets available on the hub at https://huggingface.co/datasets/
-    # (the dataset will be downloaded automatically from the datasets Hub).
-    #
-    # For CSV/JSON files, this script will use the column called 'text' or the first column if no column called
-    # 'text' is found. You can easily tweak this behavior (see below).
-    #
-    # 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.
-    # datasets = load_dataset(data_args.dataset_name, data_args.dataset_config_name, cache_dir=model_args.cache_dir)
-
-    # # Downloading the scandinavian datasets and concatenating them
-    # danish_dataset = load_dataset("mc4", "da", split="train[:24100000]")  # , download_mode="force_redownload")
-    # norwegian_dataset = load_dataset("mc4", "no", split="train[:24100000]")  # , download_mode="force_redownload")
-    # swedish_dataset = load_dataset("mc4", "sv", split="train[:24100000]")  # , download_mode="force_redownload")
-    # all_datasets = concatenate_datasets([danish_dataset, norwegian_dataset, swedish_dataset])
-    # datasets = all_datasets.shuffle()
-    # datasets = datasets.select(range(1000))
-    # datasets = datasets.train_test_split(test_size=0.01)
-    # datasets["validation"] = datasets["test"]
-
-    # Downloading the scandinavian datasets and interleaving them
-    danish_dataset = load_dataset('mc4', 'da', split="train[:24100000]", streaming=True)
-    norwegian_dataset = load_dataset('mc4', 'no', split="train[:24100000]", streaming=True)
-    swedish_dataset = load_dataset('mc4', 'sv', split="train[:24100000]", streaming=True)
-
-    dataset = interleave_datasets([danish_dataset , norwegian_dataset , swedish_dataset], probabilities=[0.33, 0.33, 0.33])
-    dataset = dataset.train_test_split(test_size=0.01)
-    dataset["validation"] = dataset["test"]
-    
-
-    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,
-        )
-        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,
-        )
-    # 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)
-    # 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."
-        )
-
-    # 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)
-
-    if data_args.line_by_line:
-        # When using line_by_line, we just tokenize each nonempty line.
-        padding = "max_length" if data_args.pad_to_max_length else False
-
-        def tokenize_function(examples):
-            # Remove empty lines
-            examples = [line for line in examples if len(line) > 0 and not line.isspace()]
-            return tokenizer(
-                examples,
-                return_special_tokens_mask=True,
-                padding=padding,
-                truncation=True,
-                max_length=max_seq_length,
-            )
-
-        tokenized_datasets = datasets.map(
-            tokenize_function,
-            input_columns=[text_column_name],
-            batched=True,
-            num_proc=data_args.preprocessing_num_workers,
-            remove_columns=column_names,
-            load_from_cache_file=not data_args.overwrite_cache,
-        )
-
-    else:
-        # Otherwise, we tokenize every text, then concatenate them together before splitting them in smaller parts.
-        # We use `return_special_tokens_mask=True` because DataCollatorForLanguageModeling (see below) is more
-        # efficient when it receives the `special_tokens_mask`.
-        def tokenize_function(examples):
-            return tokenizer(examples[text_column_name], return_special_tokens_mask=True)
-
-        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,
-        )
-
-        # Main data processing function that will concatenate all texts from our dataset and generate chunks of
-        # max_seq_length.
-        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 // max_seq_length) * max_seq_length
-            # Split by chunks of max_len.
-            result = {
-                k: [t[i : i + max_seq_length] for i in range(0, total_length, max_seq_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
-    if has_tensorboard and jax.process_index() == 0:
-        summary_writer = SummaryWriter(log_dir=Path(training_args.output_dir))
-
-    # Data collator
-    # This one will take care of randomly masking the tokens.
-    data_collator = FlaxDataCollatorForLanguageModeling(tokenizer=tokenizer, mlm_probability=data_args.mlm_probability)
-
-    # Initialize our training
-    rng = jax.random.PRNGKey(training_args.seed)
-    dropout_rngs = jax.random.split(rng, jax.local_device_count())
-
-    model = FlaxAutoModelForMaskedLM.from_config(config, seed=training_args.seed, dtype=getattr(jnp, model_args.dtype))
-
-    # Store some constant
-    num_epochs = int(training_args.num_train_epochs)
-    train_batch_size = int(training_args.per_device_train_batch_size) * jax.device_count()
-    eval_batch_size = int(training_args.per_device_eval_batch_size) * jax.device_count()
-
-    num_train_steps = len(tokenized_datasets["train"]) // train_batch_size * num_epochs
-
-    # 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.
-    # Note that this mask is specifically adapted for FlaxBERT-like models.
-    # 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:] != ("LayerNorm", "scale")) for path in flat_params}
-        return traverse_util.unflatten_dict(flat_mask)
-
-    # create adam optimizer
-    adamw = optax.adamw(
-        learning_rate=linear_decay_lr_schedule_fn,
-        b1=training_args.adam_beta1,
-        b2=training_args.adam_beta2,
-        eps=1e-8,
-        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=adamw)
-
-    # Define gradient update step fn
-    def train_step(state, batch, dropout_rng):
-        dropout_rng, new_dropout_rng = jax.random.split(dropout_rng)
-
-        def loss_fn(params):
-            labels = batch.pop("labels")
-
-            logits = state.apply_fn(**batch, params=params, dropout_rng=dropout_rng, train=True)[0]
-
-            # compute loss, ignore padded input tokens
-            label_mask = jnp.where(labels > 0, 1.0, 0.0)
-            loss = optax.softmax_cross_entropy(logits, onehot(labels, logits.shape[-1])) * label_mask
-
-            # take average
-            loss = loss.sum() / label_mask.sum()
-
-            return loss
-
-        grad_fn = jax.value_and_grad(loss_fn)
-        loss, grad = grad_fn(state.params)
-        grad = jax.lax.pmean(grad, "batch")
-        new_state = state.apply_gradients(grads=grad)
-
-        metrics = jax.lax.pmean(
-            {"loss": loss, "learning_rate": linear_decay_lr_schedule_fn(state.step)}, axis_name="batch"
-        )
-
-        return new_state, metrics, new_dropout_rng
-
-    # Create parallel version of the train step
-    p_train_step = jax.pmap(train_step, "batch", donate_argnums=(0,))
-
-    # Define eval fn
-    def eval_step(params, batch):
-        labels = batch.pop("labels")
-
-        logits = model(**batch, params=params, train=False)[0]
-
-        # compute loss, ignore padded input tokens
-        label_mask = jnp.where(labels > 0, 1.0, 0.0)
-        loss = optax.softmax_cross_entropy(logits, onehot(labels, logits.shape[-1])) * label_mask
-
-        # compute accuracy
-        accuracy = jnp.equal(jnp.argmax(logits, axis=-1), labels) * label_mask
-
-        # summarize metrics
-        metrics = {"loss": loss.sum(), "accuracy": accuracy.sum(), "normalizer": label_mask.sum()}
-        metrics = jax.lax.psum(metrics, axis_name="batch")
-
-        return metrics
-
-    p_eval_step = jax.pmap(eval_step, "batch", donate_argnums=(0,))
-
-    # Replicate the train state on each device
-    state = jax_utils.replicate(state)
-
-    train_time = 0
-    epochs = tqdm(range(num_epochs), desc=f"Epoch ... (1/{num_epochs})", 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"])
-        train_samples_idx = jax.random.permutation(input_rng, jnp.arange(num_train_samples))
-        train_batch_idx = generate_batch_splits(train_samples_idx, train_batch_size)
-
-        # Gather the indexes for creating the batch and do a training step
-        for i, 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, pad_to_multiple_of=16)
-
-            # Model forward
-            model_inputs = shard(model_inputs.data)
-            state, train_metric, dropout_rngs = p_train_step(state, model_inputs, dropout_rngs)
-            train_metrics.append(train_metric)
-
-        train_time += time.time() - train_start
-
-        epochs.write(
-            f"Epoch... ({epoch + 1}/{num_epochs} | Loss: {train_metric['loss']}, Learning Rate: {train_metric['learning_rate']})"
-        )
-
-        # ======================== Evaluating ==============================
-        num_eval_samples = len(tokenized_datasets["validation"])
-        eval_samples_idx = jnp.arange(num_eval_samples)
-        eval_batch_idx = generate_batch_splits(eval_samples_idx, eval_batch_size)
-
-        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, pad_to_multiple_of=16)
-
-            # Model forward
-            model_inputs = shard(model_inputs.data)
-            metrics = p_eval_step(state.params, model_inputs)
-            eval_metrics.append(metrics)
-
-        # normalize eval metrics
-        eval_metrics = get_metrics(eval_metrics)
-        eval_metrics = jax.tree_map(jnp.sum, eval_metrics)
-        eval_normalizer = eval_metrics.pop("normalizer")
-        eval_metrics = jax.tree_map(lambda x: x / eval_normalizer, eval_metrics)
-
-        # Update progress bar
-        epochs.desc = (
-            f"Epoch... ({epoch + 1}/{num_epochs} | Loss: {eval_metrics['loss']}, Acc: {eval_metrics['accuracy']})"
-        )
-
-        # Save metrics
-        if has_tensorboard and jax.process_index() == 0:
-            cur_step = epoch * (len(tokenized_datasets["train"]) // train_batch_size)
-            write_metric(summary_writer, train_metrics, eval_metrics, train_time, cur_step)
-
-        # 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,
-                push_to_hub=training_args.push_to_hub,
-                commit_message=f"Saving weights and logs of epoch {epoch+1}",
-            )
-
-# python3 ./roberta-large-scandi/src/scandi_run_mlm_flax.py --output_dir="./roberta-large-scandi/runs" --model_type="roberta" --config_name="${MODEL_DIR}" --tokenizer_name="${MODEL_DIR}" --dataset_name="mc4" --max_seq_length="128" --weight_decay="0.01" --per_device_train_batch_size="128" --per_device_eval_batch_size="128"  --learning_rate="3e-4" --warmup_steps="1000" --overwrite_output_dir --pad_to_max_length --num_train_epochs="10" --adam_beta1="0.9" --adam_beta2="0.98"

src/tokenizer.py

@@ -1,27 +0,0 @@
-'''Training script for tokenizer'''
-
-from datasets import load_dataset
-from tokenizers import trainers, Tokenizer, normalizers, ByteLevelBPETokenizer
-from .utils import model_dir
-
-# load dataset
-dataset = load_dataset("oscar", "unshuffled_deduplicated_no", split="train")
-
-# Instantiate tokenizer
-tokenizer = ByteLevelBPETokenizer()
-
-def batch_iterator(batch_size=1000):
-    for i in range(0, len(dataset), batch_size):
-        yield dataset[i: i + batch_size]["text"]
-
-# Customized training
-tokenizer.train_from_iterator(
-    batch_iterator(),
-    vocab_size=50265,
-    min_frequency=2,
-    special_tokens=["<s>", "<pad>", "</s>", "<unk>", "<mask>"]
-)
-
-# Save files to disk
-tokenizer_path = model_dir / 'tokenizer.json'
-tokenizer.save(str(tokenizer_path))

src/train_tokenizer.py

@@ -1,44 +0,0 @@
-from datasets import load_dataset, concatenate_datasets
-from tokenizers import trainers, Tokenizer, normalizers, ByteLevelBPETokenizer
-
-model_dir = "./scandinavian"  # ${MODEL_DIR}
-
-# load dataset
-# dataset = load_dataset("oscar", "unshuffled_deduplicated_no", split="train")
-# mc4_subset_with_five_languages = load_dataset("mc4", languages=["en", "fr", "es", "de", "zh"])
-# yoruba_dataset = load_dataset("mc4", "yo", split="train[0:10]")
-# yoruba_dataset2 = load_dataset("mc4", "yo", split="train[10:20]")
-
-danish_dataset = load_dataset("mc4", "da")  # , download_mode="force_redownload")
-norwegian_dataset = load_dataset("mc4", "no")  # , download_mode="force_redownload")
-swedish_dataset = load_dataset("mc4", "sv")  # , download_mode="force_redownload")
-
-# all_datasets = concatenate_datasets([yoruba_dataset, yoruba_dataset2])
-all_datasets = concatenate_datasets([danish_dataset, norwegian_dataset, swedish_dataset])
-all_datasets = all_datasets.shuffle()
-
-# Instantiate tokenizer
-tokenizer = ByteLevelBPETokenizer()
-
-
-def batch_iterator(batch_size=1000):
-    for i in range(0, len(all_datasets), batch_size):
-        yield all_datasets[i : i + batch_size]["text"]
-
-
-# Customized training
-tokenizer.train_from_iterator(
-    batch_iterator(),
-    vocab_size=50265,
-    min_frequency=2,
-    special_tokens=[
-        "<s>",
-        "<pad>",
-        "</s>",
-        "<unk>",
-        "<mask>",
-    ],
-)
-
-# Save files to disk
-tokenizer.save(f"{model_dir}/tokenizer.json")

src/utils.py

@@ -1,7 +0,0 @@
-'''Utility functions and variables used in other scripts'''
-
-from pathlib import Path
-
-
-root_dir = Path(__file__).parent.parent
-model_dir = '' # TODO: Needs to be set

tokenizer_config.json

@@ -1 +0,0 @@
-{"unk_token": "<unk>", "bos_token": "<s>", "eos_token": "</s>", "add_prefix_space": false, "errors": "replace", "sep_token": "</s>", "cls_token": "<s>", "pad_token": "<pad>", "mask_token": "<mask>", "special_tokens_map_file": null, "name_or_path": "./", "tokenizer_class": "RobertaTokenizer"}