Saving weights and logs of epoch 0

config.json

@@ -1,4 +1,5 @@
 {
+  "_name_or_path": "./",
   "architectures": [
     "SpeechEncoderDecoderModel"
   ],

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run_flax_speech_recognition_seq2seq.py

@@ -1,897 +0,0 @@
-#!/usr/bin/env python
-# coding=utf-8
-# Copyright 2022 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 Flax library models for sequence to sequence speech recognition.
-"""
-# You can also adapt this script on your own sequence to sequence task. Pointers for this are left as comments.
-
-import logging
-import os
-import sys
-import time
-from dataclasses import field
-from functools import partial
-from pathlib import Path
-from typing import Any, Callable, Dict, List, Optional, Union
-
-import datasets
-import numpy as np
-from datasets import DatasetDict, load_dataset, load_metric
-from tqdm import tqdm
-
-import flax
-import jax
-import jax.numpy as jnp
-import optax
-import transformers
-from flax import jax_utils, traverse_util
-from flax.jax_utils import unreplicate
-from flax.training import train_state
-from flax.training.common_utils import get_metrics, onehot, shard, shard_prng_key
-from huggingface_hub import Repository
-from transformers import (
-    AutoConfig,
-    AutoFeatureExtractor,
-    AutoProcessor,
-    AutoTokenizer,
-    FlaxAutoModelForSpeechSeq2Seq,
-    HfArgumentParser,
-    Seq2SeqTrainingArguments,
-    is_tensorboard_available,
-)
-from transformers.file_utils import get_full_repo_name
-from transformers.trainer_utils import get_last_checkpoint, is_main_process
-from transformers.utils import check_min_version
-from transformers.utils.versions import require_version
-
-
-# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
-check_min_version("4.17.0.dev0")
-
-require_version("datasets>=1.18.0", "To fix: pip install -r examples/pytorch/speech-recognition/requirements.txt")
-
-logger = logging.getLogger(__name__)
-
-
-@flax.struct.dataclass
-class ModelArguments:
-    """
-    Arguments pertaining to which model/config/tokenizer we are going to fine-tune from.
-    """
-
-    model_name_or_path: str = field(
-        metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"}
-    )
-    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"}
-    )
-    feature_extractor_name: Optional[str] = field(
-        default=None, metadata={"help": "feature extractor name or path if not the same as model_name"}
-    )
-    cache_dir: Optional[str] = field(
-        default=None,
-        metadata={"help": "Where to store the pretrained models downloaded from huggingface.co"},
-    )
-    use_fast_tokenizer: bool = field(
-        default=True,
-        metadata={"help": "Whether to use one of the fast tokenizer (backed by the tokenizers library) or not."},
-    )
-    model_revision: str = field(
-        default="main",
-        metadata={"help": "The specific model version to use (can be a branch name, tag name or commit id)."},
-    )
-    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)."
-        },
-    )
-    freeze_feature_encoder: bool = field(
-        default=True, metadata={"help": "Whether to freeze the feature encoder layers of the model."}
-    )
-
-
-@flax.struct.dataclass
-class DataTrainingArguments:
-    """
-    Arguments pertaining to what data we are going to input our model for training and eval.
-    """
-
-    dataset_name: 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)."}
-    )
-    text_column: Optional[str] = field(
-        default=None,
-        metadata={"help": "The name of the column in the datasets containing the full texts (for summarization)."},
-    )
-    overwrite_cache: bool = field(
-        default=False, metadata={"help": "Overwrite the cached training and evaluation sets"}
-    )
-    preprocessing_num_workers: Optional[int] = field(
-        default=None,
-        metadata={"help": "The number of processes to use for the preprocessing."},
-    )
-    max_train_samples: Optional[int] = field(
-        default=None,
-        metadata={
-            "help": "For debugging purposes or quicker training, truncate the number of training examples to this "
-            "value if set."
-        },
-    )
-    max_eval_samples: Optional[int] = field(
-        default=None,
-        metadata={
-            "help": "For debugging purposes or quicker training, truncate the number of evaluation examples to this "
-            "value if set."
-        },
-    )
-    audio_column_name: str = field(
-        default="audio",
-        metadata={"help": "The name of the dataset column containing the audio data. Defaults to 'audio'"},
-    )
-    text_column_name: str = field(
-        default="text",
-        metadata={"help": "The name of the dataset column containing the text data. Defaults to 'text'"},
-    )
-    max_duration_in_seconds: float = field(
-        default=20.0,
-        metadata={
-            "help": "Truncate audio files that are longer than `max_duration_in_seconds` seconds to 'max_duration_in_seconds`"
-        },
-    )
-    min_duration_in_seconds: float = field(
-        default=0.0, metadata={"help": "Filter audio files that are shorter than `min_duration_in_seconds` seconds"}
-    )
-    max_target_length: Optional[int] = field(
-        default=128,
-        metadata={
-            "help": "The maximum total sequence length for target text after tokenization. Sequences longer "
-            "than this will be truncated, sequences shorter will be padded."
-        },
-    )
-    min_target_length: Optional[int] = field(
-        default=0,
-        metadata={
-            "help": "The minimum total sequence length for target text after tokenization. Sequences shorter "
-            "than this will be filtered."
-        },
-    )
-    pad_input_to_multiple_of: Optional[int] = field(
-        default=None,
-        metadata={
-            "help": "If set will pad the input sequence to a multiple of the provided value. This is important to avoid triggering recompilations on TPU"
-        },
-    )
-    pad_target_to_multiple_of: Optional[int] = field(
-        default=None,
-        metadata={
-            "help": "If set will pad the target sequence to a multiple of the provided value. This is important to avoid triggering recompilations on TPU"
-        },
-    )
-    preprocessing_only: bool = field(
-        default=False,
-        metadata={
-            "help": "Whether to only do data preprocessing and skip training. "
-            "This is especially useful when data preprocessing errors out in distributed training due to timeout. "
-            "In this case, one should run the preprocessing in a non-distributed setup with `preprocessing_only=True` "
-            "so that the cached datasets can consequently be loaded in distributed training"
-        },
-    )
-    train_split_name: str = field(
-        default="train",
-        metadata={
-            "help": "The name of the training data set split to use (via the datasets library). Defaults to 'train'"
-        },
-    )
-    eval_split_name: str = field(
-        default="test",
-        metadata={
-            "help": "The name of the training data set split to use (via the datasets library). Defaults to 'train'"
-        },
-    )
-    do_lower_case: bool = field(
-        default=True,
-        metadata={"help": "Whether the target text should be lower cased."},
-    )
-
-
-class TrainState(train_state.TrainState):
-    dropout_rng: jnp.ndarray
-
-    def replicate(self):
-        return jax_utils.replicate(self).replace(dropout_rng=shard_prng_key(self.dropout_rng))
-
-
-def shift_tokens_right(label_ids: np.array, decoder_start_token_id: int) -> np.ndarray:
-    """
-    Shift label ids one token to the right.
-    """
-    shifted_label_ids = np.zeros_like(label_ids)
-    shifted_label_ids[:, 1:] = label_ids[:, :-1]
-    shifted_label_ids[:, 0] = decoder_start_token_id
-
-    return shifted_label_ids
-
-
-@flax.struct.dataclass
-class FlaxDataCollatorSpeechSeq2SeqWithPadding:
-    """
-    Data collator that will dynamically pad the inputs received.
-    Args:
-        processor ([`Wav2Vec2Processor`])
-            The processor used for proccessing the data.
-        decoder_start_token_id (`int`)
-            The begin-of-sentence of the decoder.
-        input_padding (:obj:`bool`, :obj:`str` or :class:`~transformers.tokenization_utils_base.PaddingStrategy`, `optional`, defaults to :obj:`True`):
-            Select a strategy to pad the returned input sequences (according to the model's padding side and padding index)
-            among:
-            * :obj:`True` or :obj:`'longest'`: Pad to the longest sequence in the batch (or no padding if only a single
-              sequence if provided).
-            * :obj:`'max_length'`: Pad to a maximum length specified with the argument :obj:`max_length` or to the
-              maximum acceptable input length for the model if that argument is not provided.
-            * :obj:`False` or :obj:`'do_not_pad'` (default): No padding (i.e., can output a batch with sequences of
-              different lengths).
-        target_padding (:obj:`bool`, :obj:`str` or :class:`~transformers.tokenization_utils_base.PaddingStrategy`, `optional`, defaults to :obj:`True`):
-            Select a strategy to pad the returned target sequences (according to the model's padding side and padding index).
-            See above for details.
-        max_input_length (:obj:`float`, `optional`):
-            Maximum length of the ``input_values`` of the returned list and optionally padding length (see above).
-        max_target_length (:obj:`int`, `optional`):
-            Maximum length of the ``labels`` of the returned list and optionally padding length (see above).
-        pad_input_to_multiple_of (:obj:`int`, `optional`):
-            If set will pad the input sequence to a multiple of the provided value.
-            This is especially useful to enable the use of Tensor Cores on NVIDIA hardware with compute capability >=
-            7.5 (Volta).
-        pad_target_to_multiple_of (:obj:`int`, `optional`):
-            If set will pad the target sequence to a multiple of the provided value.
-            This is especially useful to enable the use of Tensor Cores on NVIDIA hardware with compute capability >=
-            7.5 (Volta).
-    """
-
-    processor: Any
-    decoder_start_token_id: int
-    input_padding: Union[bool, str] = "max_length"
-    target_padding: Union[bool, str] = "max_length"
-    max_input_length: Optional[float] = None
-    max_target_length: Optional[int] = None
-    pad_input_to_multiple_of: Optional[int] = None
-    pad_target_to_multiple_of: Optional[int] = None
-
-    def __call__(self, features: List[Dict[str, Union[List[int], np.ndarray]]]) -> Dict[str, np.ndarray]:
-        # split inputs and labels since they have to be of different lengths and need
-        # different padding methods
-        input_features = [{"input_values": feature["input_values"]} for feature in features]
-        label_features = [{"input_ids": feature["labels"]} for feature in features]
-
-        # reformat list to dict and set to pytorch format
-        batch = self.processor.feature_extractor.pad(
-            input_features,
-            max_length=self.max_input_length,
-            padding=self.input_padding,
-            pad_to_multiple_of=self.pad_input_to_multiple_of,
-            return_tensors="np",
-        )
-
-        labels_batch = self.processor.tokenizer.pad(
-            label_features,
-            max_length=self.max_target_length,
-            padding=self.target_padding,
-            pad_to_multiple_of=self.pad_target_to_multiple_of,
-            return_tensors="np",
-        )
-
-        # if bos token is appended in previous tokenization step,
-        # cut bos token here as it's append later anyways
-        labels = labels_batch["input_ids"]
-        if (labels[:, 0] == self.decoder_start_token_id).all().item():
-            labels = labels[:, 1:]
-            labels_batch.attention_mask = labels_batch.attention_mask[:, 1:]
-
-        decoder_input_ids = shift_tokens_right(labels, self.decoder_start_token_id)
-
-        # replace padding with -100 to ignore loss correctly
-        labels = np.ma.array(labels, mask=np.not_equal(labels_batch.attention_mask, 1))
-        labels = labels.filled(fill_value=-100)
-
-        batch["inputs"] = batch.pop("input_values")
-        batch["labels"] = labels
-        batch["decoder_input_ids"] = decoder_input_ids
-        # decoder_attention_mask known to give issues with nan's
-        # remove decoder_attention_mask as an arg for the time being - handled by the causal mask in XXXForCausalLM
-        # batch["decoder_attention_mask"] = labels_batch.attention_mask
-
-        return batch
-
-
-def write_train_metric(summary_writer, train_metrics, train_time, step):
-    summary_writer.scalar("train_time", train_time, step)
-
-    train_metrics = get_metrics(train_metrics)
-    for key, vals in train_metrics.items():
-        tag = f"train_{key}"
-        for i, val in enumerate(vals):
-            summary_writer.scalar(tag, val, step - len(vals) + i + 1)
-
-
-def write_eval_metric(summary_writer, eval_metrics, step):
-    for metric_name, value in eval_metrics.items():
-        summary_writer.scalar(f"eval_{metric_name}", value, step)
-
-
-def create_learning_rate_fn(
-    train_ds_size: int, train_batch_size: int, num_train_epochs: int, num_warmup_steps: int, learning_rate: float
-) -> Callable[[int], jnp.array]:
-    """Returns a linear warmup, linear_decay learning rate function."""
-    steps_per_epoch = train_ds_size // train_batch_size
-    num_train_steps = steps_per_epoch * num_train_epochs
-    warmup_fn = optax.linear_schedule(init_value=0.0, end_value=learning_rate, transition_steps=num_warmup_steps)
-    decay_fn = optax.linear_schedule(
-        init_value=learning_rate, end_value=0, transition_steps=num_train_steps - num_warmup_steps
-    )
-    schedule_fn = optax.join_schedules(schedules=[warmup_fn, decay_fn], boundaries=[num_warmup_steps])
-    return schedule_fn
-
-
-def 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 main():
-    # 1. Parse input arguments
-    # 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, Seq2SeqTrainingArguments))
-
-    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()
-
-    # 2. Setup logging
-    logging.basicConfig(
-        format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
-        datefmt="%m/%d/%Y %H:%M:%S",
-        handlers=[logging.StreamHandler(sys.stdout)],
-    )
-    # We only want one process per machine to log things on the screen.
-    logger.setLevel(logging.INFO if jax.process_index() == 0 else logging.ERROR)
-    if jax.process_index() == 0:
-        datasets.utils.logging.set_verbosity_warning()
-        transformers.utils.logging.set_verbosity_info()
-    else:
-        datasets.utils.logging.set_verbosity_error()
-        transformers.utils.logging.set_verbosity_error()
-
-    # Log on each process the small summary:
-    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):
-    if is_main_process(training_args.local_rank):
-        transformers.utils.logging.set_verbosity_info()
-    logger.info("Training/evaluation parameters %s", training_args)
-
-    logger.info(f"JAX devices: {jax.device_count()}")
-
-    # 3. Detecting last checkpoint and eventually continue from last checkpoint
-    last_checkpoint = None
-    if os.path.isdir(training_args.output_dir) and training_args.do_train and not training_args.overwrite_output_dir:
-        last_checkpoint = get_last_checkpoint(training_args.output_dir)
-        if last_checkpoint is None and len(os.listdir(training_args.output_dir)) > 0:
-            raise ValueError(
-                f"Output directory ({training_args.output_dir}) already exists and is not empty. "
-                "Use --overwrite_output_dir to overcome."
-            )
-        elif last_checkpoint is not None and training_args.resume_from_checkpoint is None:
-            logger.info(
-                f"Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change "
-                "the `--output_dir` or add `--overwrite_output_dir` to train from scratch."
-            )
-
-    # 4. Load dataset
-    raw_datasets = DatasetDict()
-
-    if training_args.do_train:
-        raw_datasets["train"] = load_dataset(
-            data_args.dataset_name, data_args.dataset_config_name, split=data_args.train_split_name
-        )
-
-    if training_args.do_eval:
-        raw_datasets["eval"] = load_dataset(
-            data_args.dataset_name, data_args.dataset_config_name, split=data_args.eval_split_name
-        )
-
-    if data_args.audio_column_name not in next(iter(raw_datasets.values())).column_names:
-        raise ValueError(
-            f"--audio_column_name '{data_args.audio_column_name}' not found in dataset '{data_args.dataset_name}'. "
-            "Make sure to set `--audio_column_name` to the correct audio column - one of "
-            f"{', '.join(next(iter(raw_datasets.values())).column_names)}."
-        )
-
-    if data_args.text_column_name not in next(iter(raw_datasets.values())).column_names:
-        raise ValueError(
-            f"--text_column_name {data_args.text_column_name} not found in dataset '{data_args.dataset_name}'. "
-            "Make sure to set `--text_column_name` to the correct text column - one of "
-            f"{', '.join(next(iter(raw_datasets.values())).column_names)}."
-        )
-
-    # 5. Load pretrained model, tokenizer, and feature extractor
-    #
-    # Distributed training:
-    # The .from_pretrained methods guarantee that only one local process can concurrently
-    config = AutoConfig.from_pretrained(
-        model_args.config_name if model_args.config_name else model_args.model_name_or_path,
-        cache_dir=model_args.cache_dir,
-        revision=model_args.model_revision,
-        use_auth_token=True if model_args.use_auth_token else None,
-    )
-
-    feature_extractor = AutoFeatureExtractor.from_pretrained(
-        model_args.feature_extractor_name if model_args.feature_extractor_name else model_args.model_name_or_path,
-        cache_dir=model_args.cache_dir,
-        revision=model_args.model_revision,
-        use_auth_token=True if model_args.use_auth_token else None,
-    )
-    tokenizer = AutoTokenizer.from_pretrained(
-        model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path,
-        cache_dir=model_args.cache_dir,
-        use_fast=model_args.use_fast_tokenizer,
-        revision=model_args.model_revision,
-        use_auth_token=True if model_args.use_auth_token else None,
-    )
-    model = FlaxAutoModelForSpeechSeq2Seq.from_pretrained(
-        model_args.model_name_or_path,
-        config=config,
-        cache_dir=model_args.cache_dir,
-        revision=model_args.model_revision,
-        use_auth_token=True if model_args.use_auth_token else None,
-    )
-
-    if model.config.decoder_start_token_id is None:
-        raise ValueError("Make sure that `config.decoder_start_token_id` is correctly defined")
-
-    # 6. Resample speech dataset if necessary
-    dataset_sampling_rate = next(iter(raw_datasets.values())).features[data_args.audio_column_name].sampling_rate
-    if dataset_sampling_rate != feature_extractor.sampling_rate:
-        raw_datasets = raw_datasets.cast_column(
-            data_args.audio_column_name, datasets.features.Audio(sampling_rate=feature_extractor.sampling_rate)
-        )
-
-    # 7. Preprocessing the datasets.
-    # We need to read the audio files as arrays and tokenize the targets.
-    max_input_length = int(data_args.max_duration_in_seconds * feature_extractor.sampling_rate)
-    min_input_length = int(data_args.min_duration_in_seconds * feature_extractor.sampling_rate)
-    max_target_length = data_args.max_target_length
-    min_target_length = data_args.min_target_length
-    pad_input_to_multiple_of = data_args.pad_input_to_multiple_of
-    pad_target_to_multiple_of = data_args.pad_target_to_multiple_of
-    audio_column_name = data_args.audio_column_name
-    num_workers = data_args.preprocessing_num_workers
-    text_column_name = data_args.text_column_name
-    model_input_name = feature_extractor.model_input_names[0]
-    do_lower_case = data_args.do_lower_case
-
-    if data_args.max_train_samples is not None:
-        raw_datasets["train"] = raw_datasets["train"].select(range(data_args.max_train_samples))
-
-    if data_args.max_eval_samples is not None:
-        raw_datasets["eval"] = raw_datasets["eval"].select(range(data_args.max_eval_samples))
-
-    def prepare_dataset(batch):
-        # process audio
-        sample = batch[audio_column_name]
-        inputs = feature_extractor(sample["array"], sampling_rate=sample["sampling_rate"])
-        # process audio length
-        batch[model_input_name] = inputs.input_values[0]
-        batch["input_length"] = len(batch["input_values"])
-
-        # process targets
-        input_str = batch[text_column_name].lower() if do_lower_case else batch[text_column_name]
-        batch["labels"] = tokenizer(input_str).input_ids
-        batch["labels_length"] = len(batch["labels"])
-        return batch
-
-    with training_args.main_process_first(desc="dataset map pre-processing"):
-        vectorized_datasets = raw_datasets.map(
-            prepare_dataset,
-            remove_columns=next(iter(raw_datasets.values())).column_names,
-            num_proc=data_args.preprocessing_num_workers,
-            desc="preprocess train dataset",
-        )
-
-    # filter data with inputs shorter than min_input_length or longer than
-    # max_input_length
-    def is_audio_in_length_range(length):
-        return length > min_input_length and length < max_input_length
-
-    vectorized_datasets = vectorized_datasets.filter(
-        is_audio_in_length_range,
-        num_proc=num_workers,
-        input_columns=["input_length"],
-    )
-
-    # filter data with targets shorter than min_target_length or longer than
-    # max_target_length
-    def is_labels_in_length_range(length):
-        return length > min_target_length and length < max_target_length
-
-    vectorized_datasets = vectorized_datasets.filter(
-        is_labels_in_length_range,
-        num_proc=num_workers,
-        input_columns=["labels_length"],
-    )
-
-    # for large datasets it is advised to run the preprocessing on a
-    # single machine first with `args.preprocessing_only` since there will mostly likely
-    # be a timeout when running the script in distributed mode.
-    # In a second step `args.preprocessing_only` can then be set to `False` to load the
-    # cached dataset
-    if data_args.preprocessing_only:
-        cache = {k: v.cache_files for k, v in vectorized_datasets.items()}
-        logger.info(f"Data preprocessing finished. Files cached at {cache}.")
-        return
-
-    # 8. Load Metric
-    metric = load_metric("wer")
-
-    def compute_metrics(pred_ids: List[List[int]], label_ids: List[List[int]]):
-        padded_ids = np.where(np.asarray(label_ids) == -100, tokenizer.pad_token_id, np.asarray(label_ids))
-
-        pred_str = tokenizer.batch_decode(pred_ids, skip_special_tokens=True)
-        # we do not want to group tokens when computing the metrics
-        label_str = tokenizer.batch_decode(padded_ids, skip_special_tokens=True)
-
-        wer = metric.compute(predictions=pred_str, references=label_str)
-
-        return {"wer": wer}
-
-    # 9. Create a single speech processor
-    if is_main_process(training_args.local_rank):
-        # save feature extractor, tokenizer and config
-        feature_extractor.save_pretrained(training_args.output_dir)
-        tokenizer.save_pretrained(training_args.output_dir)
-        config.save_pretrained(training_args.output_dir)
-
-    processor = AutoProcessor.from_pretrained(training_args.output_dir)
-
-    data_collator = FlaxDataCollatorSpeechSeq2SeqWithPadding(
-        processor=processor,
-        decoder_start_token_id=model.config.decoder_start_token_id,
-        input_padding="max_length",
-        target_padding="max_length",
-        max_input_length=max_input_length,
-        max_target_length=max_target_length,
-        pad_input_to_multiple_of=pad_input_to_multiple_of,
-        pad_target_to_multiple_of=pad_target_to_multiple_of,
-    )
-
-    # 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."
-        )
-
-    # 10. 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)
-
-    # 11. Initialize our training
-    rng = jax.random.PRNGKey(training_args.seed)
-    rng, dropout_rng = jax.random.split(rng)
-
-    # Store some constant
-    num_epochs = int(training_args.num_train_epochs)
-    train_batch_size = int(training_args.per_device_train_batch_size) * jax.device_count()
-    eval_batch_size = int(training_args.per_device_eval_batch_size) * jax.device_count()
-    steps_per_epoch = len(vectorized_datasets["train"]) // train_batch_size
-    total_train_steps = steps_per_epoch * num_epochs
-    gradient_accumulation_steps = int(training_args.gradient_accumulation_steps)
-
-    # Create learning rate schedule
-    linear_decay_lr_schedule_fn = create_learning_rate_fn(
-        len(vectorized_datasets["train"]),
-        train_batch_size,
-        training_args.num_train_epochs,
-        training_args.warmup_steps,
-        training_args.learning_rate,
-    )
-
-    # 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 FlaxBart.
-    # For FlaxT5, one should correct the layer norm parameter naming
-    # accordingly - see `run_t5_mlm_flax.py` e.g.
-    # TODO: check param dictionary of encoder and decoder match the layer_norm_params list
-    def decay_mask_fn(params):
-        flat_params = traverse_util.flatten_dict(params)
-        layer_norm_params = [
-            (name, "scale") for name in ["self_attn_layer_norm", "layernorm_embedding", "final_layer_norm"]
-        ]
-        flat_mask = {path: (path[-1] != "bias" and path[-2:] not in layer_norm_params) 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,
-    )
-
-    # augment adam optimizer to facilitate gradient accumulation (ignore for now)
-    # optim = optax.chain(adamw, optax.apply_every(gradient_accumulation_steps))
-
-    # Setup train state
-    state = TrainState.create(apply_fn=model.__call__, params=model.params, tx=adamw, dropout_rng=dropout_rng)
-
-    # label smoothed cross entropy
-    def loss_fn(logits, labels, label_smoothing_factor=0.0):
-        """
-        The label smoothing implementation is adapted from Flax's official example:
-        https://github.com/google/flax/blob/87a211135c6a377c8f29048a1cac3840e38b9da4/examples/wmt/train.py#L104
-        """
-        vocab_size = logits.shape[-1]
-        confidence = 1.0 - label_smoothing_factor
-        low_confidence = (1.0 - confidence) / (vocab_size - 1)
-        normalizing_constant = -(
-            confidence * jnp.log(confidence) + (vocab_size - 1) * low_confidence * jnp.log(low_confidence + 1e-20)
-        )
-        soft_labels = onehot(labels, vocab_size, on_value=confidence, off_value=low_confidence)
-
-        loss = optax.softmax_cross_entropy(logits, soft_labels)
-        loss = loss - normalizing_constant
-
-        # ignore padded tokens from loss, i.e. where labels are not set to -100
-        padding = labels > 0
-        loss = loss * padding
-        loss = loss.sum() / padding.sum()
-        return loss
-
-    # Define gradient update step fn
-    def train_step(state, batch, label_smoothing_factor=0.0):
-        dropout_rng, new_dropout_rng = jax.random.split(state.dropout_rng)
-
-        def compute_loss(params):
-            labels = batch.pop("labels")
-            outputs = state.apply_fn(
-                **batch,
-                params=params,
-                dropout_rng=dropout_rng,
-                freeze_feature_encoder=model_args.freeze_feature_encoder,
-                return_dict=True,
-                output_attentions=True,
-                output_hidden_states=True,
-                train=True,
-            )
-            encoder_hidden_states = jnp.asarray(outputs.encoder_hidden_states)
-            encoder_outputs = outputs.encoder_last_hidden_state
-            decoder_hidden_states = jnp.asarray(outputs.decoder_hidden_states)
-            logits = outputs.logits
-
-            # check for nan in inputs by taking l2-norm over inputs
-            # a single nan in the inputs will return a nan when normed
-            logs = {"inputs": jnp.linalg.norm(batch["inputs"])}
-
-            # check for nan in encoder_hidden_states, encoder_outputs
-            logs["encoder_hidden_states"] = jnp.linalg.norm(
-                encoder_hidden_states.reshape(-1, encoder_hidden_states.shape[0]), axis=0
-            )
-            logs["encoder_outputs"] = jnp.linalg.norm(encoder_outputs)
-
-            # check for nan in decoder_hidden_states, decoder_outputs (logits)
-            logs["decoder_hidden_states"] = jnp.linalg.norm(
-                decoder_hidden_states.reshape(-1, decoder_hidden_states.shape[0]), axis=0
-            )
-            logs["logits"] = jnp.linalg.norm(logits)
-
-            loss = loss_fn(logits, labels, label_smoothing_factor)
-            # normalize loss over gradient accumulation steps (ignore for now)
-            # loss = loss / gradient_accumulation_steps
-            return loss, logs
-
-        grad_fn = jax.value_and_grad(compute_loss, has_aux=True)
-        (loss, logs), grad = grad_fn(state.params)
-        # TODO: compute loss correctly over pmapped axis
-        grad = jax.lax.pmean(grad, "batch")
-
-        # compute gradient norm for monitoring
-        # (re-introduce when no nan's on forward pass, currently meaningless)
-        # grad_norm = jnp.linalg.norm(jax.tree_util.tree_leaves(jax.tree_map(jnp.linalg.norm, grad)))
-
-        new_state = state.apply_gradients(grads=grad, dropout_rng=new_dropout_rng)
-
-        # don't log learning-rate and grad-norm until forward pass returns real-valued numbers
-        metrics = {"loss": loss}
-        metrics.update(logs)
-        metrics = jax.lax.pmean(metrics, axis_name="batch")
-
-        return new_state, metrics
-
-    # Define eval fn
-    def eval_step(params, batch, label_smoothing_factor=0.0):
-        labels = batch.pop("labels")
-        logits = model(**batch, params=params, train=False)[0]
-        loss = loss_fn(logits, labels, label_smoothing_factor)
-
-        # summarize metrics
-        metrics = {"loss": loss}
-        metrics = jax.lax.pmean(metrics, axis_name="batch")
-        return metrics
-
-    # Define generation function
-    gen_kwargs = {"max_length": training_args.generation_max_length, "num_beams": training_args.generation_num_beams}
-
-    def generate_step(params, batch):
-        model.params = params
-        output_ids = model.generate(batch["inputs"], **gen_kwargs)
-        return output_ids.sequences
-
-    # Create parallel version of the train and eval step
-    p_train_step = jax.pmap(
-        partial(train_step, label_smoothing_factor=training_args.label_smoothing_factor), "batch", donate_argnums=(0,)
-    )
-    p_eval_step = jax.pmap(partial(eval_step, label_smoothing_factor=training_args.label_smoothing_factor), "batch")
-    p_generate_step = jax.pmap(generate_step, "batch")
-
-    # Replicate the train state on each device
-    state = state.replicate()
-
-    logger.info("***** Running training *****")
-    logger.info(f"  Num examples = {len(vectorized_datasets['train'])}")
-    logger.info(f"  Num Epochs = {num_epochs}")
-    logger.info(f"  Instantaneous batch size per device = {training_args.per_device_train_batch_size}")
-    logger.info(f"  Total train batch size (w. parallel & distributed) = {train_batch_size}")
-    logger.info(f"  Total optimization steps = {total_train_steps}")
-
-    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()
-
-        # Create sampling rng
-        rng, input_rng = jax.random.split(rng)
-        train_metrics = []
-
-        # Generate an epoch by shuffling sampling indices from the train dataset
-        num_train_samples = len(vectorized_datasets["train"])
-        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 = [vectorized_datasets["train"][int(idx)] for idx in batch_idx]
-            batch = data_collator(samples)
-            batch = shard(batch.data)
-            state, train_metric = p_train_step(state, batch)
-            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)
-
-                # Log everything
-                metric_desc = " ".join([f"{key}: {value} |" for key, value in train_metric.items()])
-                epochs.write(f"Step... ({cur_step}) | {metric_desc}")
-
-                train_metrics = []
-
-        # epochs.write(
-        #    f"Epoch... ({epoch + 1}/{num_epochs} | Loss: {train_metric['loss']}, Learning Rate: {train_metric['learning_rate']})"
-        # )
-
-        continue
-        # ======================== Evaluating ==============================
-        eval_metrics = []
-        eval_preds = []
-        eval_labels = []
-
-        num_eval_samples = len(vectorized_datasets["eval"])
-        eval_samples_idx = jnp.arange(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=2)):
-            samples = [vectorized_datasets["eval"][int(idx)] for idx in batch_idx]
-            batch = data_collator(samples)
-            batch = shard(batch.data)
-            labels = batch["labels"]
-
-            metrics = p_eval_step(state.params, batch)
-            eval_metrics.append(metrics)
-
-            # generation
-            if training_args.predict_with_generate:
-                generated_ids = p_generate_step(state.params, batch)
-                eval_preds.extend(jax.device_get(generated_ids.reshape(-1, gen_kwargs["max_length"])))
-                eval_labels.extend(jax.device_get(labels.reshape(-1, labels.shape[-1])))
-
-        # normalize eval metrics
-        eval_metrics = get_metrics(eval_metrics)
-        eval_metrics = jax.tree_map(jnp.mean, eval_metrics)
-
-        # compute WER metric
-        wer_desc = ""
-        if training_args.predict_with_generate:
-            wer_metric = compute_metrics(eval_preds, eval_labels)
-            eval_metrics.update(wer_metric)
-            wer_desc = " ".join([f"Eval {key}: {value} |" for key, value in wer_metric.items()])
-
-        # Print metrics and update progress bar
-        desc = f"Epoch... ({epoch + 1}/{num_epochs} | Eval Loss: {eval_metrics['loss']} | {wer_desc})"
-        epochs.write(desc)
-        epochs.desc = desc
-
-        # Save metrics
-        if has_tensorboard and jax.process_index() == 0:
-            cur_step = epoch * (len(vectorized_datasets["train"]) // train_batch_size)
-            write_eval_metric(summary_writer, eval_metrics, 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)
-            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 epoch {epoch}", blocking=False)
-
-
-if __name__ == "__main__":
-    main()

run_flax_speech_recognition_seq2seq.py

@@ -0,0 +1 @@
+/home/sanchitgandhi/transformers/examples/flax/speech-recognition/run_flax_speech_recognition_seq2seq.py

run_librispeech.sh

@@ -1,5 +1,5 @@
 #!/usr/bin/env bash
-JAX_DEFAULT_MATMUL_PRECISION=float32 python run_flax_speech_recognition_seq2seq.py \
+python run_flax_speech_recognition_seq2seq.py \
         --dataset_name="librispeech_asr" \
         --model_name_or_path="./" \
         --dataset_config_name="clean" \
@@ -10,11 +10,11 @@ JAX_DEFAULT_MATMUL_PRECISION=float32 python run_flax_speech_recognition_seq2seq.
         --length_column_name="input_length" \
         --overwrite_output_dir \
         --num_train_epochs="1" \
-        --per_device_train_batch_size="2" \
-        --per_device_eval_batch_size="2" \
+        --per_device_train_batch_size="4" \
+        --per_device_eval_batch_size="4" \
         --logging_steps="1" \
-        --max_duration_in_seconds="10" \
-        --max_target_length="32" \
+        --max_duration_in_seconds="15" \
+        --max_target_length="64" \
         --generation_max_length="40" \
         --generation_num_beams="1" \
         --learning_rate="3e-4" \
@@ -25,5 +25,7 @@ JAX_DEFAULT_MATMUL_PRECISION=float32 python run_flax_speech_recognition_seq2seq.
         --predict_with_generate \
         --do_lower_case \
         --do_eval \
-        --do_train 
+        --do_train \
+        --push_to_hub \
+        --use_auth_token	
 

tokenizer_config.json

@@ -1 +1 @@
-{"errors": "replace", "bos_token": "<s>", "eos_token": "</s>", "sep_token": "</s>", "cls_token": "<s>", "unk_token": "<unk>", "pad_token": "<pad>", "mask_token": "<mask>", "add_prefix_space": false, "trim_offsets": true, "model_max_length": 1024, "special_tokens_map_file": null, "name_or_path": "facebook/bart-large-cnn", "tokenizer_class": "BartTokenizer"}
+{"errors": "replace", "bos_token": "<s>", "eos_token": "</s>", "sep_token": "</s>", "cls_token": "<s>", "unk_token": "<unk>", "pad_token": "<pad>", "mask_token": "<mask>", "add_prefix_space": false, "trim_offsets": true, "model_max_length": 1024, "special_tokens_map_file": null, "name_or_path": "./", "tokenizer_class": "BartTokenizer"}