From 3909d7f139affb79f811155ef53868996039a5fd Mon Sep 17 00:00:00 2001
From: "Duong A. Nguyen" <38061659+duongna21@users.noreply.github.com>
Date: Mon, 1 Aug 2022 23:06:30 +0700
Subject: [PATCH] Add Flax BART pretraining script (#18297)
* add bart pretraining flax script
* fixup
* add bart pretraining flax script
* add BART to README
* add BART to README
* add BART to README
* add BART to README
* add BART to README
* add bos eos document
* Update README.md
* Update README.md
* Update examples/flax/language-modeling/run_bart_dlm_flax.py
Co-authored-by: Sanchit Gandhi <93869735+sanchit-gandhi@users.noreply.github.com>
* final
* final
* final
* remove use_auth_token ing from_config
Co-authored-by: Sanchit Gandhi <93869735+sanchit-gandhi@users.noreply.github.com>
---
examples/flax/language-modeling/README.md | 92 ++
.../language-modeling/run_bart_dlm_flax.py | 964 ++++++++++++++++++
.../flax/language-modeling/run_mlm_flax.py | 1 -
.../flax/language-modeling/run_t5_mlm_flax.py | 3 +-
4 files changed, 1057 insertions(+), 3 deletions(-)
create mode 100644 examples/flax/language-modeling/run_bart_dlm_flax.py
diff --git a/examples/flax/language-modeling/README.md b/examples/flax/language-modeling/README.md
index 79f6011788..5b83ed0654 100644
--- a/examples/flax/language-modeling/README.md
+++ b/examples/flax/language-modeling/README.md
@@ -338,6 +338,98 @@ of 2.36 and 57.0 respectively after 3 epochs on a single TPUv3-8.
This should take around 4.5 hours.
Training statistics can be accessed on directly on the 🤗 [hub](https://huggingface.co/patrickvonplaten/t5-base-norwegian/tensorboard)
+## BART: Denoising language modeling
+
+In the following, we demonstrate how to train a BART model
+using denoising language modeling objective as introduced in [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://arxiv.org/abs/1910.13461).
+More specifically, we demonstrate how JAX/Flax can be leveraged
+to pre-train [**`bart-base`**](https://huggingface.co/facebook/bart-base)
+in Norwegian on a single TPUv3-8 pod.
+
+The example script uses the 🤗 Datasets library. You can easily customize them to your needs if you need extra processing on your datasets.
+
+To setup all relevant files for training, let's create a directory.
+
+```bash
+mkdir ./norwegian-roberta-base
+```
+
+### Train tokenizer
+In the first step, we train a tokenizer to efficiently process the text input for the model. Similar to how it is shown in [How to train a new language model from scratch using Transformers and Tokenizers](https://huggingface.co/blog/how-to-train), we use a **`ByteLevelBPETokenizer`**.
+The tokenizer is trained on the complete Norwegian dataset of OSCAR
+and consequently saved in the cloned model directory.
+This can take up to 10 minutes depending on your hardware ☕.
+
+```python
+from datasets import load_dataset
+from tokenizers import trainers, Tokenizer, normalizers, ByteLevelBPETokenizer
+
+# 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=[
+ "",
+ "",
+ "",
+ "",
+ "",
+])
+
+# Save files to disk
+tokenizer.save("./norwegian-bart-base/tokenizer.json")
+```
+
+### Create configuration
+
+Next, we create the model's configuration file. This is as simple
+as loading and storing [`**facebook/bart-base**`](https://huggingface.co/facebook/bart-base)
+in the local model folder:
+
+```python
+from transformers import BartConfig
+config = BartConfig.from_pretrained("facebook/bart-base", vocab_size=50265)
+config.save_pretrained("./norwegian-bart-base")
+```
+
+Great, we have set up our model repository. During training, we will automatically
+push the training logs and model weights to the repo.
+
+### Train model
+
+Next we can run the example script to pretrain the model:
+
+```bash
+python run_bart_dlm_flax.py \
+ --output_dir="./norwegian-bart-base" \
+ --config_name="./norwegian-bart-base" \
+ --tokenizer_name="./norwegian-bart-base" \
+ --dataset_name="oscar" \
+ --dataset_config_name="unshuffled_deduplicated_no" \
+ --max_seq_length="1024" \
+ --per_device_train_batch_size="32" \
+ --per_device_eval_batch_size="32" \
+ --learning_rate="1e-4" \
+ --warmup_steps="2000" \
+ --overwrite_output_dir \
+ --logging_steps="500" \
+ --save_steps="2000" \
+ --eval_steps="2000" \
+ --push_to_hub
+```
+
+Training should converge at a loss and accuracy
+of 1.36 and 0.77 respectively after 3 epochs on a single TPUv3-8.
+This should take less than 6 hours.
+Training statistics can be accessed on [tfhub.dev](https://tensorboard.dev/experiment/Maw62QlaSXWS0MOf2V2lbg/).
+
## Runtime evaluation
We also ran masked language modeling using PyTorch/XLA on a TPUv3-8, and PyTorch on 8 V100 GPUs. We report the
diff --git a/examples/flax/language-modeling/run_bart_dlm_flax.py b/examples/flax/language-modeling/run_bart_dlm_flax.py
new file mode 100644
index 0000000000..5c8bf1bbc4
--- /dev/null
+++ b/examples/flax/language-modeling/run_bart_dlm_flax.py
@@ -0,0 +1,964 @@
+#!/usr/bin/env python
+# coding=utf-8
+# Copyright 2021 The HuggingFace Team All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""
+Pretraining the library models for denoising language modeling on a text file or a dataset.
+Here is the full list of checkpoints on the hub that can be pretrained by this script:
+https://huggingface.co/models?filter=bart
+"""
+# You can also adapt this script on your own denoising language modeling task. Pointers for this are left as comments.
+
+import json
+import logging
+import math
+import os
+import sys
+import time
+from dataclasses import asdict, dataclass, field
+from enum import Enum
+from itertools import chain
+from pathlib import Path
+from typing import Dict, List, Optional
+
+import nltk
+import numpy as np
+from datasets import load_dataset
+from tqdm import tqdm
+
+import flax
+import jax
+import jax.numpy as jnp
+import optax
+from flax import jax_utils, traverse_util
+from flax.jax_utils import pad_shard_unpad
+from flax.training import train_state
+from flax.training.common_utils import get_metrics, onehot, shard
+from huggingface_hub import Repository
+from transformers import (
+ CONFIG_MAPPING,
+ FLAX_MODEL_FOR_MASKED_LM_MAPPING,
+ AutoTokenizer,
+ BartConfig,
+ BatchEncoding,
+ FlaxBartForConditionalGeneration,
+ HfArgumentParser,
+ PreTrainedTokenizerBase,
+ is_tensorboard_available,
+ set_seed,
+)
+from transformers.models.bart.modeling_flax_bart import shift_tokens_right
+from transformers.utils import get_full_repo_name, send_example_telemetry
+
+
+MODEL_CONFIG_CLASSES = list(FLAX_MODEL_FOR_MASKED_LM_MAPPING.keys())
+MODEL_TYPES = tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)
+
+
+@dataclass
+class TrainingArguments:
+ output_dir: str = field(
+ metadata={"help": "The output directory where the model predictions and checkpoints will be written."},
+ )
+ overwrite_output_dir: bool = field(
+ default=False,
+ metadata={
+ "help": (
+ "Overwrite the content of the output directory. "
+ "Use this to continue training if output_dir points to a checkpoint directory."
+ )
+ },
+ )
+ do_train: bool = field(default=False, metadata={"help": "Whether to run training."})
+ do_eval: bool = field(default=False, metadata={"help": "Whether to run eval on the dev set."})
+ per_device_train_batch_size: int = field(
+ default=8, metadata={"help": "Batch size per GPU/TPU core/CPU for training."}
+ )
+ per_device_eval_batch_size: int = field(
+ default=8, metadata={"help": "Batch size per GPU/TPU core/CPU for evaluation."}
+ )
+ learning_rate: float = field(default=5e-5, metadata={"help": "The initial learning rate for AdamW."})
+ weight_decay: float = field(default=0.0, metadata={"help": "Weight decay for AdamW if we apply some."})
+ adam_beta1: float = field(default=0.9, metadata={"help": "Beta1 for AdamW optimizer"})
+ adam_beta2: float = field(default=0.999, metadata={"help": "Beta2 for AdamW optimizer"})
+ adam_epsilon: float = field(default=1e-8, metadata={"help": "Epsilon for AdamW optimizer."})
+ adafactor: bool = field(default=False, metadata={"help": "Whether or not to replace AdamW by Adafactor."})
+ num_train_epochs: float = field(default=3.0, metadata={"help": "Total number of training epochs to perform."})
+ warmup_steps: int = field(default=0, metadata={"help": "Linear warmup over warmup_steps."})
+ logging_steps: int = field(default=500, metadata={"help": "Log every X updates steps."})
+ save_steps: int = field(default=500, metadata={"help": "Save checkpoint every X updates steps."})
+ eval_steps: int = field(default=None, metadata={"help": "Run an evaluation every X steps."})
+ seed: int = field(default=42, metadata={"help": "Random seed that will be set at the beginning of training."})
+ push_to_hub: bool = field(
+ default=False, metadata={"help": "Whether or not to upload the trained model to the model hub after training."}
+ )
+ hub_model_id: str = field(
+ default=None, metadata={"help": "The name of the repository to keep in sync with the local `output_dir`."}
+ )
+ hub_token: str = field(default=None, metadata={"help": "The token to use to push to the Model Hub."})
+
+ def __post_init__(self):
+ if self.output_dir is not None:
+ self.output_dir = os.path.expanduser(self.output_dir)
+
+ def to_dict(self):
+ """
+ Serializes this instance while replace `Enum` by their values (for JSON serialization support). It obfuscates
+ the token values by removing their value.
+ """
+ d = asdict(self)
+ for k, v in d.items():
+ if isinstance(v, Enum):
+ d[k] = v.value
+ if isinstance(v, list) and len(v) > 0 and isinstance(v[0], Enum):
+ d[k] = [x.value for x in v]
+ if k.endswith("_token"):
+ d[k] = f"<{k.upper()}>"
+ return d
+
+
+@dataclass
+class ModelArguments:
+ """
+ Arguments pertaining to which model/config/tokenizer we are going to fine-tune, or train from scratch.
+ """
+
+ model_name_or_path: Optional[str] = field(
+ default=None,
+ metadata={
+ "help": (
+ "The model checkpoint for weights initialization.Don't set if you want to train a model from scratch."
+ )
+ },
+ )
+ model_type: Optional[str] = field(
+ default=None,
+ metadata={"help": "If training from scratch, pass a model type from the list: " + ", ".join(MODEL_TYPES)},
+ )
+ config_name: Optional[str] = field(
+ default=None, metadata={"help": "Pretrained config name or path if not the same as model_name"}
+ )
+ tokenizer_name: Optional[str] = field(
+ default=None, metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"}
+ )
+ cache_dir: Optional[str] = field(
+ default=None, metadata={"help": "Where do you want to store the pretrained models downloaded from s3"}
+ )
+ use_fast_tokenizer: bool = field(
+ default=True,
+ metadata={"help": "Whether to use one of the fast tokenizer (backed by the tokenizers library) or not."},
+ )
+ dtype: Optional[str] = field(
+ default="float32",
+ metadata={
+ "help": (
+ "Floating-point format in which the model weights should be initialized and trained. Choose one of"
+ " `[float32, float16, bfloat16]`."
+ )
+ },
+ )
+ use_auth_token: bool = field(
+ default=False,
+ metadata={
+ "help": (
+ "Will use the token generated when running `transformers-cli login` (necessary to use this script "
+ "with private models)."
+ )
+ },
+ )
+
+
+@dataclass
+class DataTrainingArguments:
+ """
+ Arguments pertaining to what data we are going to input our model for training and eval.
+ """
+
+ dataset_name: Optional[str] = field(
+ default=None, metadata={"help": "The name of the dataset to use (via the datasets library)."}
+ )
+ dataset_config_name: Optional[str] = field(
+ default=None, metadata={"help": "The configuration name of the dataset to use (via the datasets library)."}
+ )
+ train_file: Optional[str] = field(default=None, metadata={"help": "The input training data file (a text file)."})
+ validation_file: Optional[str] = field(
+ default=None,
+ metadata={"help": "An optional input evaluation data file to evaluate the perplexity on (a text file)."},
+ )
+ train_ref_file: Optional[str] = field(
+ default=None,
+ metadata={"help": "An optional input train ref data file for whole word masking in Chinese."},
+ )
+ validation_ref_file: Optional[str] = field(
+ default=None,
+ metadata={"help": "An optional input validation ref data file for whole word masking in Chinese."},
+ )
+ overwrite_cache: bool = field(
+ default=False, metadata={"help": "Overwrite the cached training and evaluation sets"}
+ )
+ validation_split_percentage: Optional[int] = field(
+ default=5,
+ metadata={
+ "help": "The percentage of the train set used as validation set in case there's no validation split"
+ },
+ )
+ max_seq_length: Optional[int] = field(
+ default=None,
+ metadata={
+ "help": (
+ "The maximum total input sequence length after tokenization and masking. Sequences longer than this"
+ " will be truncated. Default to the max input length of the model."
+ )
+ },
+ )
+ preprocessing_num_workers: Optional[int] = field(
+ default=None,
+ metadata={"help": "The number of processes to use for the preprocessing."},
+ )
+ mlm_probability: float = field(
+ default=0.3, metadata={"help": "Ratio of tokens to mask for span masked language modeling loss"}
+ )
+ permute_sentence_ratio: float = field(
+ default=1.0, metadata={"help": "Ratio of sentences to be permuted in each document"}
+ )
+ poisson_lambda: float = field(
+ default=3.0, metadata={"help": "Mean of Poisson distribution used to generate span-lengths to be masked"}
+ )
+
+ 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 FlaxDataCollatorForBartDenoisingLM:
+ """
+ Data collator used for BART denoising language modeling. The code is largely copied from
+ ``__.
+ For more information on how BART denoising language modeling works, one can take a look
+ at the `official paper `__
+ or the `official code for preprocessing `__ .
+ Args:
+ tokenizer (:class:`~transformers.PreTrainedTokenizer` or :class:`~transformers.PreTrainedTokenizerFast`):
+ The tokenizer used for encoding the data
+ mask_ratio (:obj:`float`):
+ The probability with which to (randomly) mask tokens in the input
+ poisson_lambda (:obj:`float`):
+ Mean parameter of Poisson distribution used to generate span-lengths to be masked
+ permute_sentence_ratio (:obj:`float`):
+ Ratio of sentences to be permuted in each document
+ decoder_start_token_id: (:obj:`int):
+ The decoder start token id of the model
+ """
+
+ tokenizer: PreTrainedTokenizerBase
+ decoder_start_token_id: int
+ mask_ratio: float = 0.3
+ poisson_lambda: float = 3.0
+ permute_sentence_ratio: float = 1.0
+
+ def __post_init__(self):
+ if self.tokenizer.mask_token is None or self.tokenizer.eos_token is None:
+ raise ValueError(
+ "This tokenizer does not have a mask token or eos token token which is necessary for denoising"
+ " language modeling. "
+ )
+
+ def __call__(self, examples: List[Dict[str, List[int]]]) -> BatchEncoding:
+ # convert list to dict and tensorize input
+ batch = BatchEncoding(
+ {k: np.array([examples[i][k] for i in range(len(examples))]) for k, v in examples[0].items()}
+ )
+ batch["labels"] = batch["input_ids"].copy()
+ batch["decoder_input_ids"] = shift_tokens_right(
+ batch["labels"], self.tokenizer.pad_token_id, self.decoder_start_token_id
+ )
+ # permuting sentences
+ do_permute = False
+ if self.permute_sentence_ratio > 0.0:
+ batch["input_ids"] = self.permute_sentences(batch["input_ids"])
+ do_permute = True
+
+ # masking span of tokens (text infilling in the paper)
+ if self.mask_ratio:
+ batch["input_ids"], batch["labels"] = self.span_mask_tokens(
+ batch["input_ids"], batch["labels"], do_permute
+ )
+
+ # ignore pad tokens
+ batch["attention_mask"] = (batch["input_ids"] != self.tokenizer.pad_token_id).astype(int)
+ batch["decoder_attention_mask"] = (batch["decoder_input_ids"] != self.tokenizer.pad_token_id).astype(int)
+ return batch
+
+ def permute_sentences(self, input_ids):
+ """
+ Shuffle sentences in each document.
+ """
+ results = input_ids.copy()
+
+ # find end locations of sentences
+ end_sentence_mask = input_ids == self.tokenizer.pad_token_id
+ sentence_ends = np.argwhere(end_sentence_mask)
+ sentence_ends[:, 1] += 1
+ example_has_multiple_sentences, num_sentences = np.unique(sentence_ends[:, 0], return_counts=True)
+ num_sentences_map = {sent_idx: count for sent_idx, count in zip(example_has_multiple_sentences, num_sentences)}
+
+ num_to_permute = np.ceil(num_sentences * self.permute_sentence_ratio).astype(int)
+ num_to_permute_map = {
+ sent_idx: count for sent_idx, count in zip(example_has_multiple_sentences, num_to_permute)
+ }
+
+ sentence_ends = np.split(sentence_ends[:, 1], np.unique(sentence_ends[:, 0], return_index=True)[1][1:])
+ sentence_ends_map = {sent_idx: count for sent_idx, count in zip(example_has_multiple_sentences, sentence_ends)}
+
+ for i in range(input_ids.shape[0]):
+ if i not in example_has_multiple_sentences:
+ continue
+ substitutions = np.random.permutation(num_sentences_map[i])[: num_to_permute_map[i]]
+ ordering = np.arange(0, num_sentences_map[i])
+ ordering[substitutions] = substitutions[np.random.permutation(num_to_permute_map[i])]
+
+ # write shuffled sentences into results
+ index = 0
+ for j in ordering:
+ sentence = input_ids[i, (sentence_ends_map[i][j - 1] if j > 0 else 0) : sentence_ends_map[i][j]]
+ results[i, index : index + sentence.shape[0]] = sentence
+ index += sentence.shape[0]
+ return results
+
+ def span_mask_tokens(self, input_ids, labels, do_permute):
+ """
+ Sampling text spans with span lengths drawn from a Poisson distribution and masking them.
+ """
+ special_tokens_mask_labels = [
+ self.tokenizer.get_special_tokens_mask(val, already_has_special_tokens=True) for val in labels.tolist()
+ ]
+ special_tokens_mask_inputs = [
+ self.tokenizer.get_special_tokens_mask(val, already_has_special_tokens=True) for val in input_ids.tolist()
+ ]
+ special_tokens_mask_labels = np.array(special_tokens_mask_labels, dtype=bool)
+ special_tokens_mask_inputs = np.array(special_tokens_mask_inputs, dtype=bool)
+
+ # determine how many tokens we need to mask in total
+ is_token_mask = ~(input_ids == self.tokenizer.pad_token_id) & ~special_tokens_mask_inputs
+ num_tokens_to_mask = int(math.ceil(is_token_mask.astype(float).sum() * self.mask_ratio))
+ if num_tokens_to_mask == 0:
+ return input_ids, labels
+
+ # generate a sufficient number of span lengths
+ span_lengths = np.random.poisson(lam=self.poisson_lambda, size=(num_tokens_to_mask,))
+ while np.cumsum(span_lengths, 0)[-1] < num_tokens_to_mask:
+ span_lengths = np.concatenate(
+ [span_lengths, np.random.poisson(lam=self.poisson_lambda, size=(num_tokens_to_mask,))]
+ )
+
+ # remove all spans of length 0
+ # note that BART inserts additional mask tokens where length == 0,
+ # which we do not implement for now as it adds additional complexity
+ span_lengths = span_lengths[span_lengths > 0]
+
+ # trim to about num_tokens_to_mask tokens
+ cutoff_idx = np.argmin(np.abs(np.cumsum(span_lengths, 0) - num_tokens_to_mask)) + 1
+ span_lengths = span_lengths[:cutoff_idx]
+
+ # randomly choose starting positions for masking
+ token_indices = np.argwhere(is_token_mask == 1)
+ span_starts = np.random.permutation(token_indices.shape[0])[: span_lengths.shape[0]]
+ # prepare mask
+ masked_indices = np.array(token_indices[span_starts])
+ mask = np.full_like(input_ids, fill_value=False)
+
+ # mask starting positions
+ for mi in masked_indices:
+ mask[tuple(mi)] = True
+ span_lengths -= 1
+
+ # fill up spans
+ max_index = input_ids.shape[1] - 1
+ remaining = (span_lengths > 0) & (masked_indices[:, 1] < max_index)
+ while np.any(remaining):
+ masked_indices[remaining, 1] += 1
+ for mi in masked_indices:
+ mask[tuple(mi)] = True
+ span_lengths -= 1
+ remaining = (span_lengths > 0) & (masked_indices[:, 1] < max_index)
+
+ # place the mask tokens
+ mask[np.where(special_tokens_mask_inputs)] = False
+ input_ids[np.where(mask)] = self.tokenizer.mask_token_id
+ if not do_permute:
+ labels[np.where(mask == 0)] = -100
+ else:
+ labels[np.where(special_tokens_mask_labels)] = -100
+
+ # remove mask tokens that are not starts of spans
+ to_remove = (mask == 1) & np.roll((mask == 1), 1, 1)
+ new_input_ids = np.full_like(input_ids, fill_value=self.tokenizer.pad_token_id)
+ for i, example in enumerate(input_ids):
+ new_example = example[~to_remove[i]]
+ new_input_ids[i, : new_example.shape[0]] = new_example
+
+ return new_input_ids, labels
+
+
+def generate_batch_splits(samples_idx: np.ndarray, batch_size: int, drop_last=True) -> np.ndarray:
+ """Generate batches of data for a specified batch size from sample indices. If the dataset size is not divisible by
+ the batch size and `drop_last` is `True`, the last incomplete batch is dropped. Else, it is returned."""
+ num_samples = len(samples_idx)
+ if drop_last:
+ samples_to_remove = num_samples % batch_size
+ if samples_to_remove != 0:
+ samples_idx = samples_idx[:-samples_to_remove]
+ sections_split = num_samples // batch_size
+ samples_idx = samples_idx.reshape((sections_split, batch_size))
+ else:
+ sections_split = math.ceil(num_samples / batch_size)
+ samples_idx = np.array_split(samples_idx, sections_split)
+ return samples_idx
+
+
+def write_train_metric(summary_writer, train_metrics, train_time, step):
+ summary_writer.scalar("train_time", train_time, step)
+
+ train_metrics = get_metrics(train_metrics)
+ for key, vals in train_metrics.items():
+ tag = f"train_{key}"
+ for i, val in enumerate(vals):
+ summary_writer.scalar(tag, val, step - len(vals) + i + 1)
+
+
+def write_eval_metric(summary_writer, eval_metrics, step):
+ for metric_name, value in eval_metrics.items():
+ summary_writer.scalar(f"eval_{metric_name}", value, step)
+
+
+def main():
+ # See all possible arguments in src/transformers/training_args.py
+ # or by passing the --help flag to this script.
+ # We now keep distinct sets of args, for a cleaner separation of concerns.
+
+ parser = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments))
+ if len(sys.argv) == 2 and sys.argv[1].endswith(".json"):
+ # If we pass only one argument to the script and it's the path to a json file,
+ # let's parse it to get our arguments.
+ model_args, data_args, training_args = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]))
+ else:
+ model_args, data_args, training_args = parser.parse_args_into_dataclasses()
+
+ # Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
+ # information sent is the one passed as arguments along with your Python/PyTorch versions.
+ send_example_telemetry("run_bart_dlm", model_args, data_args, framework="flax")
+
+ if (
+ os.path.exists(training_args.output_dir)
+ and os.listdir(training_args.output_dir)
+ and training_args.do_train
+ and not training_args.overwrite_output_dir
+ ):
+ raise ValueError(
+ f"Output directory ({training_args.output_dir}) already exists and is not empty."
+ "Use --overwrite_output_dir to overcome."
+ )
+
+ # Setup logging
+ logging.basicConfig(
+ format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
+ level=logging.INFO,
+ datefmt="[%X]",
+ )
+
+ # Log on each process the small summary:
+ logger = logging.getLogger(__name__)
+
+ # Set the verbosity to info of the Transformers logger (on main process only):
+ logger.info(f"Training/evaluation parameters {training_args}")
+
+ # Set seed before initializing model.
+ set_seed(training_args.seed)
+
+ # Handle the repository creation
+ if training_args.push_to_hub:
+ if training_args.hub_model_id is None:
+ repo_name = get_full_repo_name(
+ Path(training_args.output_dir).absolute().name, token=training_args.hub_token
+ )
+ else:
+ repo_name = training_args.hub_model_id
+ repo = Repository(training_args.output_dir, clone_from=repo_name)
+
+ # Get the datasets: you can either provide your own CSV/JSON/TXT training and evaluation files (see below)
+ # or just provide the name of one of the public datasets available on the hub at https://huggingface.co/datasets/
+ # (the dataset will be downloaded automatically from the datasets Hub).
+ #
+ # For CSV/JSON files, this script will use the column called 'text' or the first column if no column called
+ # 'text' is found. You can easily tweak this behavior (see below).
+ if data_args.dataset_name is not None:
+ # Downloading and loading a dataset from the hub.
+ datasets = load_dataset(
+ data_args.dataset_name,
+ data_args.dataset_config_name,
+ cache_dir=model_args.cache_dir,
+ use_auth_token=True if model_args.use_auth_token else None,
+ )
+
+ if "validation" not in datasets.keys():
+ datasets["validation"] = load_dataset(
+ data_args.dataset_name,
+ data_args.dataset_config_name,
+ split=f"train[:{data_args.validation_split_percentage}%]",
+ cache_dir=model_args.cache_dir,
+ use_auth_token=True if model_args.use_auth_token else None,
+ )
+ datasets["train"] = load_dataset(
+ data_args.dataset_name,
+ data_args.dataset_config_name,
+ split=f"train[{data_args.validation_split_percentage}%:]",
+ cache_dir=model_args.cache_dir,
+ use_auth_token=True if model_args.use_auth_token else None,
+ )
+ else:
+ data_files = {}
+ if data_args.train_file is not None:
+ data_files["train"] = data_args.train_file
+ if data_args.validation_file is not None:
+ data_files["validation"] = data_args.validation_file
+ extension = data_args.train_file.split(".")[-1]
+ if extension == "txt":
+ extension = "text"
+ datasets = load_dataset(
+ extension,
+ data_files=data_files,
+ cache_dir=model_args.cache_dir,
+ use_auth_token=True if model_args.use_auth_token else None,
+ )
+
+ if "validation" not in datasets.keys():
+ datasets["validation"] = load_dataset(
+ extension,
+ data_files=data_files,
+ split=f"train[:{data_args.validation_split_percentage}%]",
+ cache_dir=model_args.cache_dir,
+ use_auth_token=True if model_args.use_auth_token else None,
+ )
+ datasets["train"] = load_dataset(
+ extension,
+ data_files=data_files,
+ split=f"train[{data_args.validation_split_percentage}%:]",
+ cache_dir=model_args.cache_dir,
+ use_auth_token=True if model_args.use_auth_token else None,
+ )
+ # See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at
+ # https://huggingface.co/docs/datasets/loading_datasets.html.
+
+ # Load pretrained model and tokenizer
+
+ if model_args.tokenizer_name:
+ tokenizer = AutoTokenizer.from_pretrained(
+ model_args.tokenizer_name,
+ cache_dir=model_args.cache_dir,
+ use_fast=model_args.use_fast_tokenizer,
+ use_auth_token=True if model_args.use_auth_token else None,
+ )
+ elif model_args.model_name_or_path:
+ tokenizer = AutoTokenizer.from_pretrained(
+ model_args.model_name_or_path,
+ cache_dir=model_args.cache_dir,
+ use_fast=model_args.use_fast_tokenizer,
+ use_auth_token=True if model_args.use_auth_token else None,
+ )
+ else:
+ raise ValueError(
+ "You are instantiating a new tokenizer from scratch. This is not supported by this script."
+ "You can do it from another script, save it, and load it from here, using --tokenizer_name."
+ )
+
+ if model_args.config_name:
+ config = BartConfig.from_pretrained(
+ model_args.config_name,
+ cache_dir=model_args.cache_dir,
+ vocab_size=len(tokenizer),
+ use_auth_token=True if model_args.use_auth_token else None,
+ )
+ elif model_args.model_name_or_path:
+ config = BartConfig.from_pretrained(
+ model_args.model_name_or_path,
+ cache_dir=model_args.cache_dir,
+ use_auth_token=True if model_args.use_auth_token else None,
+ )
+ else:
+ config = CONFIG_MAPPING[model_args.model_type]()
+ logger.warning("You are instantiating a new config instance from scratch.")
+
+ # Preprocessing the datasets.
+ # First we tokenize all the texts.
+ if training_args.do_train:
+ column_names = datasets["train"].column_names
+ else:
+ column_names = datasets["validation"].column_names
+ text_column_name = "text" if "text" in column_names else column_names[0]
+
+ max_seq_length = min(data_args.max_seq_length, tokenizer.model_max_length)
+
+ # Use Punkt Sentence Tokenizer to divide a document into a list of sentences
+ nltk.download("punkt")
+ sentence_tokenizer = nltk.data.load("tokenizers/punkt/english.pickle")
+
+ def sentence_split_function(example):
+ sents = sentence_tokenizer.tokenize(example["text"])
+ # use pad token as end of sentence indicator
+ new_text = tokenizer.bos_token + f"{tokenizer.pad_token}".join(sents) + tokenizer.eos_token
+ return {"text": new_text}
+
+ split_datasets = datasets.map(
+ sentence_split_function,
+ batched=False,
+ num_proc=data_args.preprocessing_num_workers,
+ remove_columns=column_names,
+ load_from_cache_file=not data_args.overwrite_cache,
+ )
+
+ # Tokenize every text, then concatenate them together before splitting them in smaller parts.
+ # Since we make sure that all sequences are of the same length, no attention_mask is needed.
+ def tokenize_function(examples):
+ return tokenizer(examples[text_column_name], add_special_tokens=False, return_attention_mask=False)
+
+ tokenized_datasets = split_datasets.map(
+ tokenize_function,
+ batched=True,
+ num_proc=data_args.preprocessing_num_workers,
+ remove_columns=text_column_name,
+ 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: list(chain(*examples[k])) for k in examples.keys()}
+ total_length = len(concatenated_examples[list(examples.keys())[0]])
+ # We drop the small remainder, we could add padding if the model supported it instead of this drop, you can
+ # customize this part to your needs.
+ if total_length >= max_seq_length:
+ 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
+ has_tensorboard = is_tensorboard_available()
+ if has_tensorboard and jax.process_index() == 0:
+ try:
+ from flax.metrics.tensorboard import SummaryWriter
+
+ summary_writer = SummaryWriter(log_dir=Path(training_args.output_dir))
+ except ImportError as ie:
+ has_tensorboard = False
+ logger.warning(
+ f"Unable to display metrics through TensorBoard because some package are not installed: {ie}"
+ )
+ else:
+ logger.warning(
+ "Unable to display metrics through TensorBoard because the package is not installed: "
+ "Please run pip install tensorboard to enable."
+ )
+
+ # Initialize our training
+ rng = jax.random.PRNGKey(training_args.seed)
+ dropout_rngs = jax.random.split(rng, jax.local_device_count())
+
+ if model_args.model_name_or_path:
+ model = FlaxBartForConditionalGeneration.from_pretrained(
+ model_args.model_name_or_path,
+ config=config,
+ seed=training_args.seed,
+ dtype=getattr(jnp, model_args.dtype),
+ use_auth_token=True if model_args.use_auth_token else None,
+ )
+ else:
+ config.vocab_size = len(tokenizer)
+ model = FlaxBartForConditionalGeneration(
+ config,
+ seed=training_args.seed,
+ dtype=getattr(jnp, model_args.dtype),
+ )
+
+ # Data collator
+ # This one will take care of randomly masking the tokens and permuting the sentences.
+ data_collator = FlaxDataCollatorForBartDenoisingLM(
+ tokenizer=tokenizer,
+ decoder_start_token_id=model.config.decoder_start_token_id,
+ mask_ratio=data_args.mlm_probability,
+ poisson_lambda=data_args.poisson_lambda,
+ permute_sentence_ratio=data_args.permute_sentence_ratio,
+ )
+
+ # Store some constant
+ num_epochs = int(training_args.num_train_epochs)
+ train_batch_size = int(training_args.per_device_train_batch_size) * jax.device_count()
+ per_device_eval_batch_size = int(training_args.per_device_eval_batch_size)
+ eval_batch_size = per_device_eval_batch_size * jax.device_count()
+
+ num_train_steps = len(tokenized_datasets["train"]) // train_batch_size * num_epochs
+
+ # Create learning rate schedule
+ warmup_fn = optax.linear_schedule(
+ init_value=0.0, end_value=training_args.learning_rate, transition_steps=training_args.warmup_steps
+ )
+ decay_fn = optax.linear_schedule(
+ init_value=training_args.learning_rate,
+ end_value=0,
+ transition_steps=num_train_steps - training_args.warmup_steps,
+ )
+ linear_decay_lr_schedule_fn = optax.join_schedules(
+ schedules=[warmup_fn, decay_fn], boundaries=[training_args.warmup_steps]
+ )
+
+ # We use Optax's "masking" functionality to not apply weight decay
+ # to bias and LayerNorm scale parameters. decay_mask_fn returns a
+ # mask boolean with the same structure as the parameters.
+ # The mask is True for parameters that should be decayed.
+ def decay_mask_fn(params):
+ flat_params = traverse_util.flatten_dict(params)
+ # find out all LayerNorm parameters
+ layer_norm_candidates = ["layernorm", "layer_norm", "ln"]
+ layer_norm_named_params = set(
+ [
+ layer[-2:]
+ for layer_norm_name in layer_norm_candidates
+ for layer in flat_params.keys()
+ if layer_norm_name in "".join(layer).lower()
+ ]
+ )
+ flat_mask = {path: (path[-1] != "bias" and path[-2:] not in layer_norm_named_params) for path in flat_params}
+ return traverse_util.unflatten_dict(flat_mask)
+
+ # create adam optimizer
+ if training_args.adafactor:
+ # We use the default parameters here to initialize adafactor,
+ # For more details about the parameters please check https://github.com/deepmind/optax/blob/ed02befef9bf81cbbf236be3d2b0e032e9ed4a40/optax/_src/alias.py#L74
+ optimizer = optax.adafactor(
+ learning_rate=linear_decay_lr_schedule_fn,
+ )
+ else:
+ optimizer = optax.adamw(
+ learning_rate=linear_decay_lr_schedule_fn,
+ b1=training_args.adam_beta1,
+ b2=training_args.adam_beta2,
+ weight_decay=training_args.weight_decay,
+ mask=decay_mask_fn,
+ )
+
+ # Setup train state
+ state = train_state.TrainState.create(apply_fn=model.__call__, params=model.params, tx=optimizer)
+
+ # Define gradient update step fn
+ def train_step(state, batch, dropout_rng):
+ dropout_rng, new_dropout_rng = jax.random.split(dropout_rng)
+
+ def loss_fn(params):
+ labels = batch.pop("labels")
+
+ logits = state.apply_fn(**batch, params=params, dropout_rng=dropout_rng, train=True)[0]
+
+ # compute loss, ignore padded input tokens and special 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 and special 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="Epoch ... ", position=0)
+ for epoch in epochs:
+ # ======================== Training ================================
+ train_start = time.time()
+ train_metrics = []
+
+ # Create sampling rng
+ rng, input_rng = jax.random.split(rng)
+
+ # Generate an epoch by shuffling sampling indices from the train dataset
+ num_train_samples = len(tokenized_datasets["train"])
+ # Avoid using jax.numpy here in case of TPU training
+ train_samples_idx = np.random.permutation(np.arange(num_train_samples))
+ train_batch_idx = generate_batch_splits(train_samples_idx, train_batch_size)
+
+ # Gather the indexes for creating the batch and do a training step
+ for step, batch_idx in enumerate(tqdm(train_batch_idx, desc="Training...", position=1)):
+ samples = [tokenized_datasets["train"][int(idx)] for idx in batch_idx]
+ model_inputs = data_collator(samples)
+
+ # 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)
+
+ cur_step = epoch * (num_train_samples // train_batch_size) + step
+
+ if cur_step % training_args.logging_steps == 0 and cur_step > 0:
+ # Save metrics
+ train_metric = jax_utils.unreplicate(train_metric)
+ train_time += time.time() - train_start
+ if has_tensorboard and jax.process_index() == 0:
+ write_train_metric(summary_writer, train_metrics, train_time, cur_step)
+
+ epochs.write(
+ f"Step... ({cur_step} | Loss: {train_metric['loss']}, Learning Rate:"
+ f" {train_metric['learning_rate']})"
+ )
+
+ train_metrics = []
+
+ if cur_step % training_args.eval_steps == 0 and cur_step > 0:
+ # ======================== Evaluating ==============================
+ num_eval_samples = len(tokenized_datasets["validation"])
+ # Avoid using jax.numpy here in case of TPU training
+ eval_samples_idx = np.arange(num_eval_samples)
+ eval_batch_idx = generate_batch_splits(eval_samples_idx, eval_batch_size, drop_last=False)
+
+ eval_metrics = []
+ for i, batch_idx in enumerate(tqdm(eval_batch_idx, desc="Evaluating ...", position=2)):
+ samples = [tokenized_datasets["validation"][int(idx)] for idx in batch_idx]
+ model_inputs = data_collator(samples)
+
+ # Model forward
+ metrics = pad_shard_unpad(p_eval_step, static_return=True)(
+ state.params, model_inputs.data, min_device_batch=per_device_eval_batch_size
+ )
+ eval_metrics.append(metrics)
+
+ # 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"Step... ({cur_step} | Loss: {eval_metrics['loss']}, Acc: {eval_metrics['accuracy']})"
+
+ # Save metrics
+ if has_tensorboard and jax.process_index() == 0:
+ write_eval_metric(summary_writer, eval_metrics, cur_step)
+
+ if cur_step % training_args.save_steps == 0 and cur_step > 0:
+ # save checkpoint after each epoch and push checkpoint to the hub
+ if jax.process_index() == 0:
+ params = jax.device_get(jax.tree_map(lambda x: x[0], state.params))
+ model.save_pretrained(training_args.output_dir, params=params)
+ tokenizer.save_pretrained(training_args.output_dir)
+ if training_args.push_to_hub:
+ repo.push_to_hub(commit_message=f"Saving weights and logs of step {cur_step}", blocking=False)
+
+ # Eval after training
+ if training_args.do_eval:
+ num_eval_samples = len(tokenized_datasets["validation"])
+ # Avoid using jax.numpy here in case of TPU training
+ eval_samples_idx = np.arange(num_eval_samples)
+ eval_batch_idx = generate_batch_splits(eval_samples_idx, eval_batch_size, drop_last=False)
+
+ eval_metrics = []
+ for _, batch_idx in enumerate(tqdm(eval_batch_idx, desc="Evaluating ...", position=2)):
+ samples = [tokenized_datasets["validation"][int(idx)] for idx in batch_idx]
+ model_inputs = data_collator(samples)
+
+ # Model forward
+ metrics = pad_shard_unpad(p_eval_step, static_return=True)(
+ state.params, model_inputs.data, min_device_batch=per_device_eval_batch_size
+ )
+ eval_metrics.append(metrics)
+
+ # normalize eval metrics
+ eval_metrics = get_metrics(eval_metrics)
+ eval_metrics = jax.tree_map(lambda metric: jnp.sum(metric).item(), eval_metrics)
+ eval_normalizer = eval_metrics.pop("normalizer")
+ eval_metrics = jax.tree_map(lambda x: x / eval_normalizer, eval_metrics)
+
+ try:
+ perplexity = math.exp(eval_metrics["loss"])
+ except OverflowError:
+ perplexity = float("inf")
+ eval_metrics["perplexity"] = perplexity
+
+ if jax.process_index() == 0:
+ eval_metrics = {f"eval_{metric_name}": value for metric_name, value in eval_metrics.items()}
+ path = os.path.join(training_args.output_dir, "eval_results.json")
+ with open(path, "w") as f:
+ json.dump(eval_metrics, f, indent=4, sort_keys=True)
+
+
+if __name__ == "__main__":
+ main()
diff --git a/examples/flax/language-modeling/run_mlm_flax.py b/examples/flax/language-modeling/run_mlm_flax.py
index 4b0c8c803b..f3f3c324ec 100755
--- a/examples/flax/language-modeling/run_mlm_flax.py
+++ b/examples/flax/language-modeling/run_mlm_flax.py
@@ -638,7 +638,6 @@ def main():
config,
seed=training_args.seed,
dtype=getattr(jnp, model_args.dtype),
- use_auth_token=True if model_args.use_auth_token else None,
)
# Store some constant
diff --git a/examples/flax/language-modeling/run_t5_mlm_flax.py b/examples/flax/language-modeling/run_t5_mlm_flax.py
index e0943ffdfb..a2906c4108 100755
--- a/examples/flax/language-modeling/run_t5_mlm_flax.py
+++ b/examples/flax/language-modeling/run_t5_mlm_flax.py
@@ -327,7 +327,7 @@ class FlaxDataCollatorForT5MLM:
pad_token_id: int
decoder_start_token_id: int
- def __call__(self, examples: List[Dict[str, np.ndarray]]) -> Dict[str, np.ndarray]:
+ def __call__(self, examples: List[Dict[str, np.ndarray]]) -> BatchEncoding:
# convert list to dict and tensorize input
batch = BatchEncoding(
@@ -746,7 +746,6 @@ def main():
config,
seed=training_args.seed,
dtype=getattr(jnp, model_args.dtype),
- use_auth_token=True if model_args.use_auth_token else None,
)
# Data collator