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[Flax Examples] Seq2Seq ASR Fine-Tuning Script (#21764)

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* from seq2seq speech

* [Flax] Example script for speech seq2seq

* tests and fixes

* make style

* fix: label padding tokens

* fix: label padding tokens over list

* update ln names for Whisper

* try datasets iter loader

* create readme and append results

* style

* make style

* adjust lr

* use pt dataloader

* make fast

* pin gen max len

* finish

* add pt to requirements for test

* fix pt -> torch

* add accelerate
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Sanchit Gandhi
2023-09-29 16:42:58 +01:00
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seqeval
tensorboard
evaluate >= 0.2.0
torch
accelerate

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<!---
Copyright 2023 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.
-->
# Automatic Speech Recognition - Flax Examples
## Sequence to Sequence
The script [`run_flax_speech_recognition_seq2seq.py`](https://github.com/huggingface/transformers/blob/main/examples/flax/speech-recognition/run_flax_speech_recognition_seq2seq.py)
can be used to fine-tune any [Flax Speech Sequence-to-Sequence Model](https://huggingface.co/docs/transformers/main/en/model_doc/auto#transformers.FlaxAutoModelForSpeechSeq2Seq)
for automatic speech recognition on one of the [official speech recognition datasets](https://huggingface.co/datasets?task_ids=task_ids:automatic-speech-recognition)
or a custom dataset. This includes the Whisper model from OpenAI, or a warm-started Speech-Encoder-Decoder Model,
an example for which is included below.
### Whisper Model
We can load all components of the Whisper model directly from the pretrained checkpoint, including the pretrained model
weights, feature extractor and tokenizer. We simply have to specify the id of fine-tuning dataset and the necessary
training hyperparameters.
The following example shows how to fine-tune the [Whisper small](https://huggingface.co/openai/whisper-small) checkpoint
on the Hindi subset of the [Common Voice 13](https://huggingface.co/datasets/mozilla-foundation/common_voice_13_0) dataset.
Note that before running this script you must accept the dataset's [terms of use](https://huggingface.co/datasets/mozilla-foundation/common_voice_13_0)
and register your Hugging Face Hub token on your device by running `huggingface-hub login`.
```bash
python run_flax_speech_recognition_seq2seq.py \
--model_name_or_path="openai/whisper-small" \
--dataset_name="mozilla-foundation/common_voice_13_0" \
--dataset_config_name="hi" \
--language="hindi" \
--train_split_name="train+validation" \
--eval_split_name="test" \
--output_dir="./whisper-small-hi-flax" \
--per_device_train_batch_size="16" \
--per_device_eval_batch_size="16" \
--num_train_epochs="10" \
--learning_rate="1e-4" \
--warmup_steps="500" \
--logging_steps="25" \
--generation_max_length="40" \
--preprocessing_num_workers="32" \
--dataloader_num_workers="32" \
--max_duration_in_seconds="30" \
--text_column_name="sentence" \
--overwrite_output_dir \
--do_train \
--do_eval \
--predict_with_generate \
--push_to_hub \
--use_auth_token
```
On a TPU v4-8, training should take approximately 25 minutes, with a final cross-entropy loss of 0.02 and word error
rate of **34%**. See the checkpoint [sanchit-gandhi/whisper-small-hi-flax](https://huggingface.co/sanchit-gandhi/whisper-small-hi-flax)
for an example training run.

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datasets[audio]>=2.14.0
jax>=0.3.6
jaxlib>=0.3.6
flax>=0.4.1
optax>=0.0.8
torch>=1.9.0
jiwer
evaluate

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#!/usr/bin/env python
# coding=utf-8
# Copyright 2023 The HuggingFace Inc. 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 evaluate
import flax
import jax
import jax.numpy as jnp
import numpy as np
import optax
from datasets import DatasetDict, load_dataset
from flax import jax_utils, traverse_util
from flax.jax_utils import pad_shard_unpad, 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, create_repo
from torch.utils.data import DataLoader
from tqdm import tqdm
import transformers
from transformers import (
AutoConfig,
AutoFeatureExtractor,
AutoProcessor,
AutoTokenizer,
FlaxAutoModelForSpeechSeq2Seq,
HfArgumentParser,
Seq2SeqTrainingArguments,
is_tensorboard_available,
)
from transformers.file_utils import get_full_repo_name
from transformers.utils import check_min_version, send_example_telemetry
from transformers.utils.versions import require_version
# Will error if the minimal version of Transformers is not installed. Remove at your own risk.
check_min_version("4.32.0.dev0")
require_version("datasets>=2.14.0", "To fix: pip install -r examples/flax/speech-recogintion/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)."
},
)
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]`."
)
},
)
num_beams: Optional[int] = field(
default=None,
metadata={
"help": (
"Number of beams to use for evaluation. This argument will be passed to `model.generate`, "
"which is used during evaluation."
)
},
)
@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)."},
)
dataset_cache_dir: Optional[str] = field(
default=None, metadata={"help": "Path to cache directory for saving and loading datasets"}
)
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": "Filter audio files that are longer than `max_duration_in_seconds` 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_label_length: float = field(
default=128,
metadata={"help": "Truncate transcriptions that are longer `max_eval_length` tokens."},
)
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. If unspecified, will default to padding the inputs to max length."
},
)
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. If unspecified, will default to padding the targets to max length."
},
)
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="validation",
metadata={
"help": "The name of the evaluation data set split to use (via the datasets library). Defaults to 'validation'"
},
)
do_lower_case: bool = field(
default=True,
metadata={"help": "Whether the target text should be lower cased."},
)
language: str = field(
default=None,
metadata={
"help": (
"Language for multilingual fine-tuning. This argument should be set for multilingual fine-tuning "
"only. For English speech recognition, it should be set to `None`."
)
},
)
task: str = field(
default="transcribe",
metadata={"help": "Task, either `transcribe` for speech recognition or `translate` for speech translation."},
)
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 (:obj: `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] = "longest"
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
model_input_name = self.processor.model_input_names[0]
# dataloader returns a list of features which we convert to a dict
input_features = {model_input_name: [feature[model_input_name] 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 correctly when computing the loss
labels = np.ma.array(labels, mask=np.not_equal(labels_batch.attention_mask, 1))
labels = labels.filled(fill_value=-100)
batch["labels"] = labels
batch["decoder_input_ids"] = decoder_input_ids
return batch
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 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)
def create_learning_rate_fn(
num_train_steps: int, num_warmup_steps: int, learning_rate: float
) -> Callable[[int], jnp.array]:
"""Returns a linear warmup, linear_decay learning rate function."""
warmup_fn = optax.linear_schedule(init_value=0.0, end_value=learning_rate, transition_steps=num_warmup_steps)
decay_fn = optax.linear_schedule(
init_value=learning_rate, end_value=0, transition_steps=num_train_steps - num_warmup_steps
)
schedule_fn = optax.join_schedules(schedules=[warmup_fn, decay_fn], boundaries=[num_warmup_steps])
return schedule_fn
def 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()
# 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 JAX/Flax versions.
send_example_telemetry("run_speech_recognition_seq2seq", model_args, data_args, framework="flax")
# 2. Setup logging
# Make one log on every process with the configuration for debugging.
logging.basicConfig(
format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
datefmt="%m/%d/%Y %H:%M:%S",
handlers=[logging.StreamHandler(sys.stdout)],
)
# Set the verbosity to info of the Transformers logger.
# 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()
logger.info("Training/evaluation parameters %s", training_args)
# Check the output dir is valid
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."
)
# 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
create_repo(repo_name, exist_ok=True, token=training_args.hub_token)
repo = Repository(training_args.output_dir, clone_from=repo_name, token=training_args.hub_token)
# 3. 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,
cache_dir=data_args.dataset_cache_dir,
token=True if model_args.use_auth_token else None,
)
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,
cache_dir=data_args.dataset_cache_dir,
token=True if model_args.use_auth_token else None,
)
if not training_args.do_train and not training_args.do_eval:
raise ValueError(
"Cannot not train and not do evaluation. At least one of training or evaluation has to be performed."
)
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
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,
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,
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,
token=True if model_args.use_auth_token else None,
)
model = FlaxAutoModelForSpeechSeq2Seq.from_pretrained(
model_args.model_name_or_path,
config=config,
dtype=getattr(jnp, model_args.dtype),
cache_dir=model_args.cache_dir,
revision=model_args.model_revision,
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: `datasets` takes care of automatically loading and resampling the audio,
# so we just need to set the correct target 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_label_length = (
data_args.max_label_length if data_args.max_label_length is not None else model.config.max_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 training_args.do_train and data_args.max_train_samples is not None:
raw_datasets["train"] = raw_datasets["train"].select(range(data_args.max_train_samples))
if training_args.do_eval and data_args.max_eval_samples is not None:
raw_datasets["eval"] = raw_datasets["eval"].select(range(data_args.max_eval_samples))
if data_args.language is not None:
# We only need to set the task id when the language is specified (i.e. in a multilingual setting)
tokenizer.set_prefix_tokens(language=data_args.language, task=data_args.task)
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.get(model_input_name)[0]
batch["input_length"] = len(sample["array"])
# 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
return batch
vectorized_datasets = raw_datasets.map(
prepare_dataset,
remove_columns=next(iter(raw_datasets.values())).column_names,
num_proc=num_workers,
desc="preprocess train dataset",
)
# filter training data with inputs longer than max_input_length
def is_audio_in_length_range(length):
return min_input_length < length < max_input_length
vectorized_datasets = vectorized_datasets.filter(
is_audio_in_length_range,
num_proc=num_workers,
input_columns=["input_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 = evaluate.load("wer")
def compute_metrics(preds, labels):
# replace padded labels by the padding token
for idx in range(len(labels)):
labels[idx][labels[idx] == -100] = tokenizer.pad_token_id
pred_str = tokenizer.batch_decode(preds, skip_special_tokens=True)
# we do not want to group tokens when computing the metrics
label_str = tokenizer.batch_decode(labels, skip_special_tokens=True)
wer = metric.compute(predictions=pred_str, references=label_str)
return {"wer": wer}
# 9. 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="longest",
target_padding="longest",
max_target_length=max_label_length,
pad_input_to_multiple_of=pad_input_to_multiple_of,
pad_target_to_multiple_of=pad_target_to_multiple_of if pad_target_to_multiple_of else max_label_length,
)
# 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)
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()
per_device_eval_batch_size = int(training_args.per_device_eval_batch_size)
eval_batch_size = 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
# Create learning rate schedule
linear_decay_lr_schedule_fn = create_learning_rate_fn(
len(vectorized_datasets["train"]),
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.
def decay_mask_fn(params):
flat_params = traverse_util.flatten_dict(params)
# find out all LayerNorm parameters
layer_norm_candidates = ["layer_norm", "self_attn_layer_norm", "final_layer_norm", "encoder_attn_layer_norm"]
layer_norm_named_params = {
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
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 = 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_mask = labels >= 0
loss = loss * padding_mask
loss = loss.sum()
num_labels = padding_mask.sum()
return loss, num_labels
# 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")
logits = state.apply_fn(**batch, params=params, dropout_rng=dropout_rng, train=True)[0]
loss, num_labels = loss_fn(logits, labels, label_smoothing_factor)
return loss, num_labels
grad_fn = jax.value_and_grad(compute_loss, has_aux=True)
(loss, num_labels), grad = grad_fn(state.params)
num_labels = jax.lax.psum(num_labels, "batch")
# true loss = total loss / total samples
loss = jax.lax.psum(loss, "batch")
loss = jax.tree_util.tree_map(lambda x: x / num_labels, loss)
# true grad = total grad / total samples
grad = jax.lax.psum(grad, "batch")
grad = jax.tree_util.tree_map(lambda x: x / num_labels, grad)
new_state = state.apply_gradients(grads=grad, dropout_rng=new_dropout_rng)
metrics = {"loss": loss, "learning_rate": linear_decay_lr_schedule_fn(state.step)}
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, num_labels = loss_fn(logits, labels, label_smoothing_factor)
num_labels = jax.lax.psum(num_labels, "batch")
# true loss = total loss / total samples
loss = jax.lax.psum(loss, "batch")
loss = jax.tree_util.tree_map(lambda x: x / num_labels, loss)
metrics = {"loss": loss}
return metrics
# Define generation function
num_beams = model_args.num_beams if model_args.num_beams is not None else model.config.num_beams
gen_kwargs = {"max_length": max_label_length, "num_beams": num_beams}
def generate_step(params, batch):
model.params = params
output_ids = model.generate(batch[model_input_name], attention_mask=batch.get("attention_mask"), **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()
train_metrics = []
# Generate an epoch by shuffling sampling indices from the train dataset and create a data loader
vectorized_datasets["train"] = vectorized_datasets["train"].shuffle(training_args.seed)
train_loader = DataLoader(
vectorized_datasets["train"],
batch_size=train_batch_size,
drop_last=True,
collate_fn=data_collator,
num_workers=training_args.dataloader_num_workers,
)
# train
for batch in tqdm(train_loader, desc="Training...", position=1, leave=False):
batch = shard(batch.data)
state, train_metric = p_train_step(state, batch)
train_metrics.append(train_metric)
train_time += time.time() - train_start
train_metric = unreplicate(train_metric)
epochs.write(
f"Epoch... ({epoch + 1}/{num_epochs} | Loss: {train_metric['loss']}, Learning Rate:"
f" {train_metric['learning_rate']})"
)
# ======================== Evaluating ==============================
eval_metrics = []
eval_preds = []
eval_labels = []
eval_loader = DataLoader(
vectorized_datasets["eval"],
batch_size=eval_batch_size,
drop_last=False,
collate_fn=data_collator,
num_workers=training_args.dataloader_num_workers,
)
for batch in tqdm(eval_loader, desc="Evaluating...", position=2, leave=False):
# Model forward
labels = batch["labels"]
metrics = pad_shard_unpad(p_eval_step, static_return=True)(
state.params, batch.data, min_device_batch=per_device_eval_batch_size
)
eval_metrics.append(metrics)
# generation
if training_args.predict_with_generate:
generated_ids = pad_shard_unpad(p_generate_step)(state.params, batch.data)
eval_preds.extend(jax.device_get(generated_ids.reshape(-1, gen_kwargs["max_length"])))
eval_labels.extend(labels)
# normalize eval metrics
eval_metrics = get_metrics(eval_metrics)
eval_metrics = jax.tree_util.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_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_util.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()

31
examples/flax/test_flax_examples.py
View File

@@ -32,6 +32,7 @@ SRC_DIRS = [
"summarization",
"token-classification",
"question-answering",
"speech-recognition",
]
]
sys.path.extend(SRC_DIRS)
@@ -41,6 +42,7 @@ if SRC_DIRS is not None:
import run_clm_flax
import run_flax_glue
import run_flax_ner
import run_flax_speech_recognition_seq2seq
import run_mlm_flax
import run_qa
import run_summarization_flax
@@ -252,3 +254,32 @@ class ExamplesTests(TestCasePlus):
result = get_results(tmp_dir)
self.assertGreaterEqual(result["eval_f1"], 30)
self.assertGreaterEqual(result["eval_exact"], 30)
@slow
def test_run_flax_speech_recognition_seq2seq(self):
tmp_dir = self.get_auto_remove_tmp_dir()
testargs = f"""
run_flax_speech_recognition_seq2seq.py
--model_name_or_path openai/whisper-tiny.en
--dataset_name hf-internal-testing/librispeech_asr_dummy
--dataset_config clean
--train_split_name validation
--eval_split_name validation
--output_dir {tmp_dir}
--overwrite_output_dir
--num_train_epochs=2
--max_train_samples 10
--max_eval_samples 10
--warmup_steps=8
--do_train
--do_eval
--learning_rate=2e-4
--per_device_train_batch_size=2
--per_device_eval_batch_size=1
--predict_with_generate
""".split()
with patch.object(sys, "argv", testargs):
run_flax_speech_recognition_seq2seq.main()
result = get_results(tmp_dir, split="eval")
self.assertLessEqual(result["eval_wer"], 0.05)