def main(argv):
if len(argv) > 1:
raise app.UsageError('Too many command-line arguments.')
# Make sure tf does not allocate gpu memory.
tf.config.experimental.set_visible_devices([], 'GPU')
batch_size = FLAGS.batch_size
learning_rate = FLAGS.learning_rate
num_train_steps = FLAGS.num_train_steps
eval_freq = FLAGS.eval_frequency
random_seed = FLAGS.random_seed
if not FLAGS.dev:
raise app.UsageError('Please provide path to dev set.')
if not FLAGS.train:
raise app.UsageError('Please provide path to training set.')
if batch_size % jax.device_count() > 0:
raise ValueError(
'Batch size must be divisible by the number of devices')
device_batch_size = batch_size // jax.device_count()
if jax.host_id() == 0:
train_summary_writer = tensorboard.SummaryWriter(
os.path.join(FLAGS.model_dir, FLAGS.experiment + '_train'))
eval_summary_writer = tensorboard.SummaryWriter(
os.path.join(FLAGS.model_dir, FLAGS.experiment + '_eval'))
# create the training and development dataset
vocabs = input_pipeline.create_vocabs(FLAGS.train)
config = models.TransformerConfig(vocab_size=len(vocabs['forms']),
output_vocab_size=len(vocabs['xpos']),
max_len=FLAGS.max_length)
attributes_input = [input_pipeline.CoNLLAttributes.FORM]
attributes_target = [input_pipeline.CoNLLAttributes.XPOS]
train_ds = input_pipeline.sentence_dataset_dict(FLAGS.train,
vocabs,
attributes_input,
attributes_target,
batch_size=batch_size,
bucket_size=config.max_len)
train_iter = iter(train_ds)
eval_ds = input_pipeline.sentence_dataset_dict(FLAGS.dev,
vocabs,
attributes_input,
attributes_target,
batch_size=batch_size,
bucket_size=config.max_len,
repeat=1)
model = models.Transformer(config)
rng = random.PRNGKey(random_seed)
rng, init_rng = random.split(rng)
# call a jitted initialization function to get the initial parameter tree
@jax.jit
def initialize_variables(init_rng):
init_batch = jnp.ones((config.max_len, 1), jnp.float32)
init_variables = model.init(init_rng, inputs=init_batch, train=False)
return init_variables
init_variables = initialize_variables(init_rng)
optimizer_def = optim.Adam(learning_rate,
beta1=0.9,
beta2=0.98,
eps=1e-9,
weight_decay=1e-1)
optimizer = optimizer_def.create(init_variables['params'])
optimizer = jax_utils.replicate(optimizer)
learning_rate_fn = create_learning_rate_scheduler(
base_learning_rate=learning_rate)
p_train_step = jax.pmap(functools.partial(
train_step, model=model, learning_rate_fn=learning_rate_fn),
axis_name='batch')
def eval_step(params, batch):
"""Calculate evaluation metrics on a batch."""
inputs, targets = batch['inputs'], batch['targets']
weights = jnp.where(targets > 0, 1.0, 0.0)
logits = model.apply({'params': params}, inputs=inputs, train=False)
return compute_metrics(logits, targets, weights)
p_eval_step = jax.pmap(eval_step, axis_name='batch')
# We init the first set of dropout PRNG keys, but update it afterwards inside
# the main pmap'd training update for performance.
dropout_rngs = random.split(rng, jax.local_device_count())
metrics_all = []
tick = time.time()
best_dev_score = 0
for step, batch in zip(range(num_train_steps), train_iter):
batch = common_utils.shard(jax.tree_map(lambda x: x._numpy(), batch)) # pylint: disable=protected-access
optimizer, metrics, dropout_rngs = p_train_step(
optimizer, batch, dropout_rng=dropout_rngs)
metrics_all.append(metrics)
if (step + 1) % eval_freq == 0:
metrics_all = common_utils.get_metrics(metrics_all)
lr = metrics_all.pop('learning_rate').mean()
metrics_sums = jax.tree_map(jnp.sum, metrics_all)
denominator = metrics_sums.pop('denominator')
summary = jax.tree_map(lambda x: x / denominator, metrics_sums) # pylint: disable=cell-var-from-loop
summary['learning_rate'] = lr
logging.info('train in step: %d, loss: %.4f', step,
summary['loss'])
if jax.host_id() == 0:
tock = time.time()
steps_per_sec = eval_freq / (tock - tick)
tick = tock
train_summary_writer.scalar('steps per second', steps_per_sec,
step)
for key, val in summary.items():
train_summary_writer.scalar(key, val, step)
train_summary_writer.flush()
metrics_all = [
] # reset metric accumulation for next evaluation cycle.
eval_metrics = []
eval_iter = iter(eval_ds)
for eval_batch in eval_iter:
eval_batch = jax.tree_map(lambda x: x._numpy(), eval_batch) # pylint: disable=protected-access
# Handle final odd-sized batch by padding instead of dropping it.
cur_pred_batch_size = eval_batch['inputs'].shape[0]
if cur_pred_batch_size != batch_size:
# pad up to batch size
eval_batch = jax.tree_map(
lambda x: pad_examples(x, batch_size), eval_batch)
eval_batch = common_utils.shard(eval_batch)
metrics = p_eval_step(optimizer.target, eval_batch)
eval_metrics.append(metrics)
eval_metrics = common_utils.get_metrics(eval_metrics)
eval_metrics_sums = jax.tree_map(jnp.sum, eval_metrics)
eval_denominator = eval_metrics_sums.pop('denominator')
eval_summary = jax.tree_map(
lambda x: x / eval_denominator, # pylint: disable=cell-var-from-loop
eval_metrics_sums)
logging.info('eval in step: %d, loss: %.4f, accuracy: %.4f', step,
eval_summary['loss'], eval_summary['accuracy'])
if best_dev_score < eval_summary['accuracy']:
best_dev_score = eval_summary['accuracy']
# TODO: save model.
eval_summary['best_dev_score'] = best_dev_score
logging.info('best development model score %.4f', best_dev_score)
if jax.host_id() == 0:
for key, val in eval_summary.items():
eval_summary_writer.scalar(key, val, step)
eval_summary_writer.flush()
def main(argv):
if len(argv) > 1:
raise app.UsageError('Too many command-line arguments.')
if FLAGS.jax_backend_target:
jax.config.FLAGS.jax_xla_backend = 'tpu_driver'
jax.config.FLAGS.jax_backend_target = FLAGS.jax_backend_target
# This seems to be necessary even when importing TF2?
tf.enable_v2_behavior()
# Number of local devices for this host.
n_devices = jax.local_device_count()
if jax.host_id() == 0:
tf.io.gfile.makedirs(FLAGS.model_dir)
train_summary_writer = tensorboard.SummaryWriter(
os.path.join(FLAGS.model_dir, 'train'))
eval_summary_writer = tensorboard.SummaryWriter(
os.path.join(FLAGS.model_dir, 'eval'))
if FLAGS.batch_size % n_devices:
raise ValueError(
'Batch size must be divisible by the number of devices')
vocab_path = FLAGS.vocab_path
if vocab_path is None:
vocab_path = os.path.join(FLAGS.model_dir, 'sentencepiece_model')
tf.io.gfile.makedirs(os.path.split(vocab_path)[0])
# Load Dataset
# ---------------------------------------------------------------------------
logging.info('Initializing dataset.')
train_ds, eval_ds, predict_ds, encoder = input_pipeline.get_wmt_datasets(
n_devices=n_devices,
dataset_name=FLAGS.dataset_name,
eval_dataset_name=FLAGS.eval_dataset_name,
shard_idx=jax.host_id(),
shard_count=jax.host_count(),
data_dir=FLAGS.data_dir,
vocab_path=vocab_path,
target_vocab_size=FLAGS.vocab_size,
batch_size=FLAGS.batch_size,
max_length=FLAGS.max_target_length,
max_eval_length=FLAGS.max_eval_target_length)
train_iter = iter(train_ds)
vocab_size = int(encoder.vocab_size())
eos_id = decode.EOS_ID # Default Sentencepiece EOS token.
def decode_tokens(toks):
valid_toks = toks[:np.argmax(toks == eos_id) + 1].astype(np.int32)
return encoder.detokenize(valid_toks).numpy().decode('utf-8')
logging.info('Initializing model, optimizer, and step functions.')
# Build Model and Optimizer
# ---------------------------------------------------------------------------
train_config = models.TransformerConfig(
vocab_size=vocab_size,
output_vocab_size=vocab_size,
share_embeddings=FLAGS.share_embeddings,
logits_via_embedding=FLAGS.logits_via_embedding,
dtype=jnp.bfloat16 if FLAGS.use_bfloat16 else jnp.float32,
emb_dim=FLAGS.emb_dim,
num_heads=FLAGS.num_heads,
num_layers=FLAGS.num_layers,
qkv_dim=FLAGS.qkv_dim,
mlp_dim=FLAGS.mlp_dim,
max_len=max(FLAGS.max_target_length, FLAGS.max_eval_target_length),
dropout_rate=FLAGS.dropout_rate,
attention_dropout_rate=FLAGS.attention_dropout_rate,
deterministic=False,
decode=False,
kernel_init=nn.initializers.xavier_uniform(),
bias_init=nn.initializers.normal(stddev=1e-6))
eval_config = train_config.replace(deterministic=True)
predict_config = train_config.replace(deterministic=True, decode=True)
start_step = 0
rng = random.PRNGKey(FLAGS.random_seed)
rng, init_rng = random.split(rng)
input_shape = (FLAGS.batch_size, FLAGS.max_target_length)
target_shape = (FLAGS.batch_size, FLAGS.max_target_length)
# call a jitted initialization function to get the initial parameter tree
@jax.jit
def initialize_variables(rng):
return models.Transformer(eval_config).init(
rng, jnp.ones(input_shape, jnp.float32),
jnp.ones(target_shape, jnp.float32))
initial_variables = initialize_variables(init_rng)
# apply an optimizer to this tree
optimizer_def = optim.Adam(FLAGS.learning_rate,
beta1=0.9,
beta2=0.98,
eps=1e-9,
weight_decay=FLAGS.weight_decay)
optimizer = optimizer_def.create(initial_variables['param'])
# We access model params only from optimizer below via optimizer.target.
del initial_variables
if FLAGS.restore_checkpoints:
# Restore unreplicated optimizer + model state from last checkpoint.
optimizer = checkpoints.restore_checkpoint(FLAGS.model_dir, optimizer)
# Grab last step.
start_step = int(optimizer.state.step)
# Replicate optimizer.
optimizer = jax_utils.replicate(optimizer)
learning_rate_fn = create_learning_rate_scheduler(
base_learning_rate=FLAGS.learning_rate,
warmup_steps=FLAGS.warmup_steps)
# compile multidevice versions of train/eval/predict step and cache init fn.
p_train_step = jax.pmap(functools.partial(
train_step,
config=train_config,
learning_rate_fn=learning_rate_fn,
label_smoothing=FLAGS.label_smoothing),
axis_name='batch',
donate_argnums=(0, ))
p_eval_step = jax.pmap(functools.partial(
eval_step, config=eval_config, label_smoothing=FLAGS.label_smoothing),
axis_name='batch')
p_init_cache = jax.pmap(functools.partial(
initialize_cache,
max_decode_len=FLAGS.max_predict_length,
config=predict_config),
axis_name='batch')
p_pred_step = jax.pmap(
functools.partial(predict_step,
config=predict_config,
beam_size=FLAGS.beam_size),
axis_name='batch',
static_broadcasted_argnums=(3,
4)) # eos token, max_length are constant
# Main Train Loop
# ---------------------------------------------------------------------------
# We init the first set of dropout PRNG keys, but update it afterwards inside
# the main pmap'd training update for performance.
dropout_rngs = random.split(rng, n_devices)
logging.info('Starting training loop.')
metrics_all = []
t_loop_start = time.time()
for step, batch in zip(range(start_step, FLAGS.num_train_steps),
train_iter):
# Shard data to devices and do a training step.
batch = common_utils.shard(jax.tree_map(lambda x: x._numpy(), batch)) # pylint: disable=protected-access
optimizer, metrics, dropout_rngs = p_train_step(
optimizer, batch, dropout_rng=dropout_rngs)
metrics_all.append(metrics)
# Save a checkpoint on one host after every checkpoint_freq steps.
if (FLAGS.save_checkpoints and step % FLAGS.checkpoint_freq == 0
and step > 0 and jax.host_id() == 0):
checkpoints.save_checkpoint(FLAGS.model_dir,
jax_utils.unreplicate(optimizer), step)
# Periodic metric handling.
if step % FLAGS.eval_frequency != 0 and step > 0:
continue
# Training Metrics
logging.info('Gathering training metrics.')
metrics_all = common_utils.get_metrics(metrics_all)
lr = metrics_all.pop('learning_rate').mean()
metrics_sums = jax.tree_map(jnp.sum, metrics_all)
denominator = metrics_sums.pop('denominator')
summary = jax.tree_map(lambda x: x / denominator, metrics_sums) # pylint: disable=cell-var-from-loop
summary['learning_rate'] = lr
steps_per_eval = FLAGS.eval_frequency if step != 0 else 1
steps_per_sec = steps_per_eval / (time.time() - t_loop_start)
t_loop_start = time.time()
if jax.host_id() == 0:
train_summary_writer.scalar('steps per second', steps_per_sec,
step)
for key, val in summary.items():
train_summary_writer.scalar(key, val, step)
train_summary_writer.flush()
metrics_all = []
logging.info('train in step: %d, loss: %.4f', step, summary['loss'])
# Eval Metrics
logging.info('Gathering evaluation metrics.')
t_eval_start = time.time()
eval_metrics = []
eval_iter = iter(eval_ds)
for _, eval_batch in zip(range(FLAGS.num_eval_steps), eval_iter):
eval_batch = jax.tree_map(lambda x: x._numpy(), eval_batch) # pylint: disable=protected-access
eval_batch = common_utils.shard(eval_batch)
metrics = p_eval_step(optimizer.target, eval_batch)
eval_metrics.append(metrics)
eval_metrics = common_utils.get_metrics(eval_metrics)
eval_metrics_sums = jax.tree_map(jnp.sum, eval_metrics)
eval_denominator = eval_metrics_sums.pop('denominator')
eval_summary = jax.tree_map(
lambda x: x / eval_denominator, # pylint: disable=cell-var-from-loop
eval_metrics_sums)
if jax.host_id() == 0:
for key, val in eval_summary.items():
eval_summary_writer.scalar(key, val, step)
eval_summary_writer.flush()
logging.info('eval in step: %d, loss: %.4f', step,
eval_summary['loss'])
logging.info('eval time: %.4f s step %d',
time.time() - t_eval_start, step)
# Translation and BLEU Score.
logging.info('Translating evaluation dataset.')
t_inference_start = time.time()
predict_iter = iter(predict_ds)
sources, references, predictions = [], [], []
for _, pred_batch in enumerate(predict_iter):
pred_batch = jax.tree_map(lambda x: x._numpy(), pred_batch) # pylint: disable=protected-access
# Handle final odd-sized batch by padding instead of dropping it.
cur_pred_batch_size = pred_batch['inputs'].shape[0]
if cur_pred_batch_size % n_devices:
padded_size = int(
np.ceil(cur_pred_batch_size / n_devices) * n_devices)
pred_batch = jax.tree_map(
lambda x: pad_examples(x, padded_size), pred_batch) # pylint: disable=cell-var-from-loop
pred_batch = common_utils.shard(pred_batch)
cache = p_init_cache(pred_batch['inputs'])
predicted = p_pred_step(pred_batch['inputs'], optimizer.target,
cache, eos_id, FLAGS.max_predict_length)
predicted = tohost(predicted)
inputs = tohost(pred_batch['inputs'])
targets = tohost(pred_batch['targets'])
# Iterate through non-padding examples of batch.
for i, s in enumerate(predicted[:cur_pred_batch_size]):
sources.append(decode_tokens(inputs[i]))
references.append(decode_tokens(targets[i]))
predictions.append(decode_tokens(s))
logging.info('Translation: %d predictions %d references %d sources.',
len(predictions), len(references), len(sources))
logging.info('Translation time: %.4f s step %d.',
time.time() - t_inference_start, step)
# Calculate BLEU score for translated eval corpus against reference.
bleu_matches = bleu.bleu_partial(references, predictions)
all_bleu_matches = per_host_sum_pmap(bleu_matches)
bleu_score = bleu.complete_bleu(*all_bleu_matches)
# Save translation samples for tensorboard.
exemplars = ''
for n in np.random.choice(np.arange(len(predictions)), 8):
exemplars += f'{sources[n]}\n\n{references[n]}\n\n{predictions[n]}\n\n'
if jax.host_id() == 0:
eval_summary_writer.scalar('bleu', bleu_score, step)
eval_summary_writer.text('samples', exemplars, step)
eval_summary_writer.flush()
logging.info('Translation BLEU Score %.4f', bleu_score)
def main():
# See all possible arguments in src/transformers/training_args.py
# or by passing the --help flag to this script.
# We now keep distinct sets of args, for a cleaner separation of concerns.
parser = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments))
if len(sys.argv) == 2 and sys.argv[1].endswith(".json"):
# If we pass only one argument to the script and it's the path to a json file,
# let's parse it to get our arguments.
model_args, data_args, training_args = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]))
else:
model_args, data_args, training_args = parser.parse_args_into_dataclasses()
if (
os.path.exists(training_args.output_dir)
and os.listdir(training_args.output_dir)
and training_args.do_train
and not training_args.overwrite_output_dir
):
raise ValueError(
f"Output directory ({training_args.output_dir}) already exists and is not empty."
"Use --overwrite_output_dir to overcome."
)
# Make one log on every process with the configuration for debugging.
logging.basicConfig(
format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
datefmt="%m/%d/%Y %H:%M:%S",
level=logging.INFO,
)
# Setup logging, we only want one process per machine to log things on the screen.
logger.setLevel(logging.INFO if jax.process_index() == 0 else logging.ERROR)
if jax.process_index() == 0:
datasets.utils.logging.set_verbosity_warning()
transformers.utils.logging.set_verbosity_info()
else:
datasets.utils.logging.set_verbosity_error()
transformers.utils.logging.set_verbosity_error()
# Set the verbosity to info of the Transformers logger (on main process only):
logger.info(f"Training/evaluation parameters {training_args}")
# Set seed before initializing model.
set_seed(training_args.seed)
# Get the datasets: you can either provide your own CSV/JSON/TXT training and evaluation files (see below)
# or just provide the name of one of the public datasets available on the hub at https://huggingface.co/datasets/
# (the dataset will be downloaded automatically from the datasets Hub).
#
# For CSV/JSON files, this script will use the column called 'text' or the first column if no column called
# 'text' is found. You can easily tweak this behavior (see below).
#
# In distributed training, the load_dataset function guarantees that only one local process can concurrently
# download the dataset.
if data_args.dataset_name is not None:
# Downloading and loading a dataset from the hub.
dataset = load_dataset(
data_args.dataset_name, data_args.dataset_config_name, cache_dir=model_args.cache_dir, keep_in_memory=False
)
if "validation" not in dataset.keys():
dataset["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,
)
dataset["train"] = load_dataset(
data_args.dataset_name,
data_args.dataset_config_name,
split=f"train[{data_args.validation_split_percentage}%:]",
cache_dir=model_args.cache_dir,
)
else:
data_files = {}
if data_args.train_file is not None:
data_files["train"] = data_args.train_file
if data_args.validation_file is not None:
data_files["validation"] = data_args.validation_file
extension = data_args.train_file.split(".")[-1]
if extension == "txt":
extension = "text"
dataset = load_dataset(extension, data_files=data_files, cache_dir=model_args.cache_dir)
if "validation" not in dataset.keys():
dataset["validation"] = load_dataset(
extension,
keep_linebreaks=data_args.keep_linebreaks,
data_files=data_files,
split=f"train[:{data_args.validation_split_percentage}%]",
cache_dir=model_args.cache_dir,
)
dataset["train"] = load_dataset(
extension,
keep_linebreaks=data_args.keep_linebreaks,
data_files=data_files,
split=f"train[{data_args.validation_split_percentage}%:]",
cache_dir=model_args.cache_dir,
)
# See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at
# https://huggingface.co/docs/datasets/loading_datasets.html.
# Load pretrained model and tokenizer
# Distributed training:
# The .from_pretrained methods guarantee that only one local process can concurrently
# download model & vocab.
if model_args.config_name:
config = AutoConfig.from_pretrained(model_args.config_name, cache_dir=model_args.cache_dir)
elif model_args.model_name_or_path:
config = AutoConfig.from_pretrained(model_args.model_name_or_path, cache_dir=model_args.cache_dir)
else:
config = CONFIG_MAPPING[model_args.model_type]()
logger.warning("You are instantiating a new config instance from scratch.")
if model_args.tokenizer_name:
tokenizer = AutoTokenizer.from_pretrained(
model_args.tokenizer_name, cache_dir=model_args.cache_dir, use_fast=model_args.use_fast_tokenizer
)
elif model_args.model_name_or_path:
tokenizer = AutoTokenizer.from_pretrained(
model_args.model_name_or_path, cache_dir=model_args.cache_dir, use_fast=model_args.use_fast_tokenizer
)
else:
raise ValueError(
"You are instantiating a new tokenizer from scratch. This is not supported by this script."
"You can do it from another script, save it, and load it from here, using --tokenizer_name."
)
if model_args.model_name_or_path:
model = FlaxAutoModelForCausalLM.from_pretrained(
model_args.model_name_or_path, config=config, seed=training_args.seed, dtype=getattr(jnp, model_args.dtype)
)
else:
model = FlaxAutoModelForCausalLM.from_config(
config, seed=training_args.seed, dtype=getattr(jnp, model_args.dtype)
)
# Preprocessing the datasets.
# First we tokenize all the texts.
if training_args.do_train:
column_names = dataset["train"].column_names
else:
column_names = dataset["validation"].column_names
text_column_name = "text" if "text" in column_names else column_names[0]
# since this will be pickled to avoid _LazyModule error in Hasher force logger loading before tokenize_function
tok_logger = transformers.utils.logging.get_logger("transformers.tokenization_utils_base")
def tokenize_function(examples):
with CaptureLogger(tok_logger) as cl:
output = tokenizer(examples[text_column_name])
# clm input could be much much longer than block_size
if "Token indices sequence length is longer than the" in cl.out:
tok_logger.warning(
"^^^^^^^^^^^^^^^^ Please ignore the warning above - this long input will be chunked into smaller bits before being passed to the model."
)
return output
tokenized_datasets = dataset.map(
tokenize_function,
batched=True,
num_proc=data_args.preprocessing_num_workers,
remove_columns=column_names,
load_from_cache_file=not data_args.overwrite_cache,
)
if data_args.block_size is None:
block_size = tokenizer.model_max_length
if block_size > config.max_position_embeddings:
logger.warning(
f"The tokenizer picked seems to have a very large `model_max_length` ({tokenizer.model_max_length}). "
"Picking 1024 instead. You can change that default value by passing --block_size xxx."
)
block_size = 1024
else:
if data_args.block_size > tokenizer.model_max_length:
logger.warning(
f"The block_size passed ({data_args.block_size}) is larger than the maximum length for the model"
f"({tokenizer.model_max_length}). Using block_size={tokenizer.model_max_length}."
)
block_size = min(data_args.block_size, tokenizer.model_max_length)
# Main data processing function that will concatenate all texts from our dataset and generate chunks of block_size.
def group_texts(examples):
# Concatenate all texts.
concatenated_examples = {k: sum(examples[k], []) for k in examples.keys()}
total_length = len(concatenated_examples[list(examples.keys())[0]])
# We drop the small remainder, we could add padding if the model supported it instead of this drop, you can
# customize this part to your needs.
if total_length >= block_size:
total_length = (total_length // block_size) * block_size
# Split by chunks of max_len.
result = {
k: [t[i : i + block_size] for i in range(0, total_length, block_size)]
for k, t in concatenated_examples.items()
}
result["labels"] = result["input_ids"].copy()
return result
# Note that with `batched=True`, this map processes 1,000 texts together, so group_texts throws away a remainder
# for each of those groups of 1,000 texts. You can adjust that batch_size here but a higher value might be slower
# to preprocess.
#
# To speed up this part, we use multiprocessing. See the documentation of the map method for more information:
# https://huggingface.co/docs/datasets/package_reference/main_classes.html#datasets.Dataset.map
lm_datasets = tokenized_datasets.map(
group_texts,
batched=True,
num_proc=data_args.preprocessing_num_workers,
load_from_cache_file=not data_args.overwrite_cache,
)
if training_args.do_train:
if "train" not in tokenized_datasets:
raise ValueError("--do_train requires a train dataset")
train_dataset = lm_datasets["train"]
if data_args.max_train_samples is not None:
train_dataset = train_dataset.select(range(data_args.max_train_samples))
if training_args.do_eval:
if "validation" not in tokenized_datasets:
raise ValueError("--do_eval requires a validation dataset")
eval_dataset = lm_datasets["validation"]
if data_args.max_eval_samples is not None:
eval_dataset = eval_dataset.select(range(data_args.max_eval_samples))
# Enable tensorboard only on the master node
has_tensorboard = is_tensorboard_available()
if has_tensorboard and jax.process_index() == 0:
try:
from flax.metrics.tensorboard import SummaryWriter
summary_writer = SummaryWriter(log_dir=Path(training_args.output_dir))
except ImportError as ie:
has_tensorboard = False
logger.warning(
f"Unable to display metrics through TensorBoard because some package are not installed: {ie}"
)
else:
logger.warning(
"Unable to display metrics through TensorBoard because the package is not installed: "
"Please run pip install tensorboard to enable."
)
# 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(train_dataset) // 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(train_dataset),
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 FlaxGPT2.
# For other models, one should correct the layer norm parameter naming
# accordingly.
def decay_mask_fn(params):
flat_params = traverse_util.flatten_dict(params)
flat_mask = {
path: (path[-1] != "bias" and path[-2:] not in [("ln_1", "scale"), ("ln_2", "scale"), ("ln_f", "scale")])
for path in flat_params
}
return traverse_util.unflatten_dict(flat_mask)
# create 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,
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=optimizer, dropout_rng=dropout_rng)
def loss_fn(logits, labels):
shift_logits = logits[..., :-1, :]
shift_labels = labels[..., 1:]
loss = optax.softmax_cross_entropy(shift_logits, onehot(shift_labels, shift_logits.shape[-1]))
return loss.mean()
# Define gradient update step fn
def train_step(state, batch):
dropout_rng, new_dropout_rng = jax.random.split(state.dropout_rng)
def compute_loss(params):
labels = batch.pop("labels")
logits = state.apply_fn(**batch, params=params, dropout_rng=dropout_rng, train=True)[0]
loss = loss_fn(logits, labels)
return loss
grad_fn = jax.value_and_grad(compute_loss)
loss, grad = grad_fn(state.params)
grad = jax.lax.pmean(grad, "batch")
new_state = state.apply_gradients(grads=grad, dropout_rng=new_dropout_rng)
metrics = {"loss": loss, "learning_rate": linear_decay_lr_schedule_fn(state.step)}
metrics = jax.lax.pmean(metrics, axis_name="batch")
return new_state, metrics
# Define eval fn
def eval_step(params, batch):
labels = batch.pop("labels")
logits = model(**batch, params=params, train=False)[0]
loss = loss_fn(logits, labels)
# summarize metrics
metrics = {"loss": loss}
metrics = jax.lax.pmean(metrics, axis_name="batch")
return metrics
# Create parallel version of the train and eval step
p_train_step = jax.pmap(train_step, "batch", donate_argnums=(0,))
p_eval_step = jax.pmap(eval_step, "batch")
# Replicate the train state on each device
state = state.replicate()
logger.info("***** Running training *****")
logger.info(f" Num examples = {len(train_dataset)}")
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
train_metrics = []
epochs = tqdm(range(num_epochs), desc="Epoch ... ", position=0)
for epoch in epochs:
# ======================== Training ================================
train_start = time.time()
# Create sampling rng
rng, input_rng = jax.random.split(rng)
# Generate an epoch by shuffling sampling indices from the train dataset
train_loader = data_loader(input_rng, train_dataset, train_batch_size, shuffle=True)
steps_per_epoch = len(train_dataset) // train_batch_size
# train
for step in tqdm(range(steps_per_epoch), desc="Training...", position=1, leave=False):
batch = next(train_loader)
batch = shard(batch)
state, train_metric = p_train_step(state, batch)
train_metrics.append(train_metric)
cur_step = epoch * (len(train_dataset) // train_batch_size) + step
if cur_step % training_args.logging_steps == 0 and cur_step > 0:
# Save metrics
train_metric = unreplicate(train_metric)
train_time += time.time() - train_start
if has_tensorboard and jax.process_index() == 0:
write_train_metric(summary_writer, train_metrics, train_time, cur_step)
epochs.write(
f"Step... ({cur_step} | Loss: {train_metric['loss'].mean()}, Learning Rate: {train_metric['learning_rate'].mean()})"
)
train_metrics = []
if cur_step % training_args.eval_steps == 0 and cur_step > 0:
# ======================== Evaluating ==============================
eval_metrics = []
eval_loader = data_loader(input_rng, eval_dataset, eval_batch_size)
eval_steps = len(eval_dataset) // eval_batch_size
for _ in tqdm(range(eval_steps), desc="Evaluating...", position=2, leave=False):
# Model forward
batch = next(eval_loader)
batch = shard(batch)
metrics = p_eval_step(state.params, batch)
eval_metrics.append(metrics)
# normalize eval metrics
eval_metrics = get_metrics(eval_metrics)
eval_metrics = jax.tree_map(jnp.mean, eval_metrics)
try:
eval_metrics["perplexity"] = math.exp(eval_metrics["loss"])
except OverflowError:
eval_metrics["perplexity"] = float("inf")
# Print metrics and update progress bar
desc = f"Step... ({cur_step} | Eval Loss: {eval_metrics['loss']} | Eval Perplexity: {eval_metrics['perplexity']})"
epochs.write(desc)
epochs.desc = desc
# 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(unreplicate(state.params))
model.save_pretrained(
training_args.output_dir,
params=params,
push_to_hub=training_args.push_to_hub,
commit_message=f"Saving weights and logs of step {cur_step}",
)
def do_prediction(p_init_cache,
p_pred_step,
dataset,
optimizer,
beam_size,
verbose=False):
"""Runs the model on a dataset."""
total_acc = 0
total_denominator = 0
pred_accs = collections.defaultdict(int)
pred_denominators = collections.defaultdict(int)
program_lengths = collections.defaultdict(list)
ios, targets, predictions = [], [], []
for batches in dataset.as_numpy_iterator():
inputs, outputs, programs = common_utils.shard(batches)
cache = (p_init_cache(inputs, outputs, programs)
if not slow_decode else None)
predicted = p_pred_step(optimizer.target, inputs, outputs, cache,
beam_size)
predicted = train_lib.tohost(predicted)
inputs, outputs, programs = map(train_lib.tohost,
(inputs, outputs, programs))
for i, beams in enumerate(predicted):
inps, outs = decode_io(inputs[i], outputs[i])
p, p_score = train_lib.eval_predicted(beams,
inps,
outs,
parse_beam_fn=decode_program)
# Split by length of program.
num_expressions = len(decode_program(programs[i]).expressions)
program = programs[i]
program_length = len(program[:np.argmax(program == eos_token)])
program_lengths[num_expressions].append(program_length)
pred_denominators[num_expressions] += 1
total_denominator += 1
if p_score >= len(inps):
pred_accs[num_expressions] += 1
total_acc += 1
ios.append(' ; '.join(map(str, zip(inps, outs))))
targets.append(decode_program(programs[i]).to_string())
try:
predictions.append(p.to_string())
except: # pylint: disable=bare-except
predictions.append('')
if verbose:
logging.info('IOs: %s', ios[-1])
logging.info('Target: %s', targets[-1])
logging.info('Top of beam:')
for index, beam in enumerate(beams[:-5:-1]):
try:
decoded_program = decode_program(beam).to_string()
except: # pylint: disable=bare-except
decoded_program = 'Did not compile'
logging.info('index: %s\n decoded: %s\n tokens: %s',
index, decoded_program, beam)
if verbose:
logging.info('Total: %s/%s = %s%%', total_acc, total_denominator,
100 * total_acc / total_denominator)
return total_acc, total_denominator, pred_accs, pred_denominators
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))
model_args, data_args, training_args = parser.parse_args_into_dataclasses()
configure_logger(model_args, training_args)
# 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)
if "validation" not in datasets.keys():
# make sure only "validation" and "train" keys remain"
datasets = DatasetDict()
datasets["validation"] = load_dataset(
data_args.dataset_name,
data_args.dataset_config_name,
split=
f"{data_args.train_split_name}[:{data_args.validation_split_percentage}%]",
cache_dir=model_args.cache_dir,
)
datasets["train"] = load_dataset(
data_args.dataset_name,
data_args.dataset_config_name,
split=
f"{data_args.train_split_name}[{data_args.validation_split_percentage}%:]",
cache_dir=model_args.cache_dir,
)
else:
# make sure only "validation" and "train" keys remain"
datasets = DatasetDict()
datasets["validation"] = load_dataset(
data_args.dataset_name,
data_args.dataset_config_name,
split="validation",
cache_dir=model_args.cache_dir,
)
datasets["train"] = load_dataset(
data_args.dataset_name,
data_args.dataset_config_name,
split=f"{data_args.train_split_name}",
cache_dir=model_args.cache_dir,
)
# only normalized-inputs-training is supported
feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained(
model_args.model_name_or_path,
cache_dir=model_args.cache_dir,
do_normalize=True)
def prepare_dataset(batch):
# check that all files have the correct sampling rate
batch["speech"], _ = librosa.load(batch[data_args.speech_file_column],
sr=feature_extractor.sampling_rate)
return batch
# load audio files into numpy arrays
vectorized_datasets = datasets.map(
prepare_dataset,
num_proc=data_args.preprocessing_num_workers,
remove_columns=datasets["train"].column_names)
# filter audio files that are too long
vectorized_datasets = vectorized_datasets.filter(lambda data: len(data[
"speech"]) < int(data_args.max_duration_in_seconds * feature_extractor.
sampling_rate))
def normalize(batch):
return feature_extractor(batch["speech"],
sampling_rate=feature_extractor.sampling_rate)
# normalize and transform to `BatchFeatures`
vectorized_datasets = vectorized_datasets.map(
normalize,
batched=True,
num_proc=data_args.preprocessing_num_workers,
load_from_cache_file=not data_args.overwrite_cache,
remove_columns=vectorized_datasets["train"].column_names,
)
# pretraining is only supported for "newer" stable layer norm architecture
# apply_spec_augment has to be True, mask_feature_prob has to be 0.0
config = Wav2Vec2Config.from_pretrained(
model_args.model_name_or_path,
cache_dir=model_args.cache_dir,
gradient_checkpointing=model_args.gradient_checkpointing,
)
if not config.do_stable_layer_norm or config.feat_extract_norm != "layer":
raise ValueError(
"PreTraining is only supported for ``config.do_stable_layer_norm=True`` and ``config.feat_extract_norm='layer'"
)
model = FlaxWav2Vec2ForPreTraining(config,
seed=training_args.seed,
dtype=getattr(jnp, model_args.dtype))
data_collator = FlaxDataCollatorForWav2Vec2Pretraining(
model=model,
feature_extractor=feature_extractor,
pad_to_multiple_of=data_args.pad_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.")
# Initialize our training
rng = jax.random.PRNGKey(training_args.seed)
dropout_rngs = jax.random.split(rng, jax.local_device_count())
gumbel_rngs = jax.random.split(rng, jax.local_device_count())
num_epochs = int(training_args.num_train_epochs)
train_batch_size = int(
training_args.per_device_train_batch_size) * jax.device_count()
eval_batch_size = int(
training_args.per_device_eval_batch_size) * jax.device_count()
num_train_steps = len(
vectorized_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)
flat_mask = {
path: (path[-1] != "bias"
and path[-2:] not in [("layer_norm", "scale"),
("final_layer_norm", "scale")])
for path in flat_params
}
return traverse_util.unflatten_dict(flat_mask)
# create adam optimizer
adamw = optax.adamw(
learning_rate=linear_decay_lr_schedule_fn,
b1=training_args.adam_beta1,
b2=training_args.adam_beta2,
eps=training_args.adam_epsilon,
weight_decay=training_args.weight_decay,
mask=decay_mask_fn,
)
# Setup train state and define training hyper-parameters
state = train_state.TrainState.create(apply_fn=model.__call__,
params=model.params,
tx=adamw)
num_negatives = model.config.num_negatives
contrastive_logits_temperature = model.config.contrastive_logits_temperature
num_codevectors = model.config.num_codevectors_per_group * model.config.num_codevector_groups
diversity_loss_weight = model.config.diversity_loss_weight
# Define gradient update step fn
def train_step(state, batch, dropout_rng, gumbel_rng):
dropout_rng, new_dropout_rng = jax.random.split(dropout_rng)
gumbel_rng, new_gumbel_rng = jax.random.split(gumbel_rng)
def loss_fn(params):
negative_indices = batch.pop("sampled_negative_indices")
gumbel_temperature = jnp.clip(
model_args.max_gumbel_temperature *
model_args.gumbel_temperature_decay**state.step,
a_min=model_args.min_gumbel_temperature,
)
outputs = state.apply_fn(
**batch,
gumbel_temperature=gumbel_temperature,
params=params,
dropout_rng=dropout_rng,
gumbel_rng=gumbel_rng,
train=True,
)
contrastive_loss = compute_contrastive_loss(
outputs.projected_quantized_states,
outputs.projected_states,
negative_indices,
batch["mask_time_indices"],
contrastive_logits_temperature,
num_negatives,
)
diversity_loss = (num_codevectors -
outputs.codevector_perplexity) / num_codevectors
loss = contrastive_loss + diversity_loss_weight * diversity_loss
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, new_gumbel_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):
negative_indices = batch.pop("sampled_negative_indices")
outputs = model(**batch, params=params, train=False)
contrastive_loss = compute_contrastive_loss(
outputs.projected_quantized_states,
outputs.projected_states,
negative_indices,
batch["mask_time_indices"],
contrastive_logits_temperature,
num_negatives,
)
diversity_loss = (num_codevectors -
outputs.codevector_perplexity) / num_codevectors
loss = contrastive_loss + diversity_loss_weight * diversity_loss
# summarize metrics
metrics = {
"loss": loss.mean(),
"codevector_perplexity": outputs.codevector_perplexity
}
metrics = jax.lax.pmean(metrics, axis_name="batch")
return metrics
p_eval_step = jax.pmap(eval_step, "batch", donate_argnums=(0, ))
# Replicate the train state on each device
state = jax_utils.replicate(state)
train_time = 0
train_metrics = []
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)
# Generate an epoch by shuffling sampling indices from the train dataset
num_train_samples = len(vectorized_datasets["train"])
train_samples_idx = jax.random.permutation(
input_rng, jnp.arange(num_train_samples))
train_batch_idx = generate_batch_splits(train_samples_idx,
train_batch_size)
# Gather the indexes for creating the batch and do a training step
for step, batch_idx in enumerate(
tqdm(train_batch_idx, desc="Training...", position=1)):
samples = [
vectorized_datasets["train"][int(idx)] for idx in batch_idx
]
model_inputs = data_collator(samples)
model_inputs = shard(model_inputs.data)
# Model forward
state, train_metric, dropout_rngs, gumbel_rngs = p_train_step(
state, model_inputs, dropout_rngs, gumbel_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'].mean()}, Learning Rate: {train_metric['learning_rate'].mean()})"
)
train_metrics = []
# ======================== Evaluating ==============================
num_eval_samples = len(vectorized_datasets["validation"])
eval_samples_idx = jnp.arange(num_eval_samples)
eval_batch_idx = generate_batch_splits(eval_samples_idx,
eval_batch_size)
eval_metrics = []
for i, batch_idx in enumerate(
tqdm(eval_batch_idx, desc="Evaluating ...", position=2)):
samples = [
vectorized_datasets["validation"][int(idx)]
for idx in batch_idx
]
model_inputs = data_collator(samples)
# Model forward
model_inputs = shard(model_inputs.data)
metrics = p_eval_step(state.params, model_inputs)
eval_metrics.append(metrics)
# get eval metrics
eval_metrics = get_metrics(eval_metrics)
eval_metrics = jax.tree_map(jnp.mean, eval_metrics)
# Update progress bar
epochs.write(
f"Epoch... ({epoch + 1}/{num_epochs} | Loss: {eval_metrics['loss']}, Perplexity: {eval_metrics['codevector_perplexity']})"
)
# 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,
push_to_hub=training_args.push_to_hub)
def main():
# See all possible arguments in src/transformers/training_args.py
# or by passing the --help flag to this script.
# We now keep distinct sets of args, for a cleaner separation of concerns.
parser = HfArgumentParser(
(ModelArguments, DataTrainingArguments, TrainingArguments))
if len(sys.argv) == 2 and sys.argv[1].endswith(".json"):
# If we pass only one argument to the script and it's the path to a json file,
# let's parse it to get our arguments.
model_args, data_args, training_args = parser.parse_json_file(
json_file=os.path.abspath(sys.argv[1]))
else:
model_args, data_args, training_args = parser.parse_args_into_dataclasses(
)
if (os.path.exists(training_args.output_dir)
and os.listdir(training_args.output_dir) and training_args.do_train
and not training_args.overwrite_output_dir):
raise ValueError(
f"Output directory ({training_args.output_dir}) already exists and is not empty."
"Use --overwrite_output_dir to overcome.")
# Make one log on every process with the configuration for debugging.
logging.basicConfig(
format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
datefmt="%m/%d/%Y %H:%M:%S",
level=logging.INFO,
)
# Setup logging, we only want one process per machine to log things on the screen.
logger.setLevel(logging.INFO if jax.process_index() ==
0 else logging.ERROR)
if jax.process_index() == 0:
transformers.utils.logging.set_verbosity_info()
else:
transformers.utils.logging.set_verbosity_error()
# Set the verbosity to info of the Transformers logger (on main process only):
logger.info(f"Training/evaluation parameters {training_args}")
# set seed for random transforms and torch dataloaders
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)
# Initialize datasets and pre-processing transforms
# We use torchvision here for faster pre-processing
# Note that here we are using some default pre-processing, for maximum accuray
# one should tune this part and carefully select what transformations to use.
normalize = transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])
train_dataset = torchvision.datasets.ImageFolder(
data_args.train_dir,
transforms.Compose([
transforms.RandomResizedCrop(data_args.image_size),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
normalize,
]),
)
eval_dataset = torchvision.datasets.ImageFolder(
data_args.validation_dir,
transforms.Compose([
transforms.Resize(data_args.image_size),
transforms.CenterCrop(data_args.image_size),
transforms.ToTensor(),
normalize,
]),
)
# Load pretrained model and tokenizer
if model_args.config_name:
config = AutoConfig.from_pretrained(
model_args.config_name,
num_labels=len(train_dataset.classes),
image_size=data_args.image_size,
cache_dir=model_args.cache_dir,
use_auth_token=True if model_args.use_auth_token else None,
)
elif model_args.model_name_or_path:
config = AutoConfig.from_pretrained(
model_args.model_name_or_path,
num_labels=len(train_dataset.classes),
image_size=data_args.image_size,
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.")
if model_args.model_name_or_path:
model = FlaxAutoModelForImageClassification.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:
model = FlaxAutoModelForImageClassification.from_config(
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
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(train_dataset) // train_batch_size
total_train_steps = steps_per_epoch * num_epochs
def collate_fn(examples):
pixel_values = torch.stack([example[0] for example in examples])
labels = torch.tensor([example[1] for example in examples])
batch = {"pixel_values": pixel_values, "labels": labels}
batch = {k: v.numpy() for k, v in batch.items()}
return batch
# Create data loaders
train_loader = torch.utils.data.DataLoader(
train_dataset,
batch_size=train_batch_size,
shuffle=True,
num_workers=data_args.preprocessing_num_workers,
persistent_workers=True,
drop_last=True,
collate_fn=collate_fn,
)
eval_loader = torch.utils.data.DataLoader(
eval_dataset,
batch_size=eval_batch_size,
shuffle=False,
num_workers=data_args.preprocessing_num_workers,
persistent_workers=True,
drop_last=True,
collate_fn=collate_fn,
)
# 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)
# Create learning rate schedule
linear_decay_lr_schedule_fn = create_learning_rate_fn(
len(train_dataset),
train_batch_size,
training_args.num_train_epochs,
training_args.warmup_steps,
training_args.learning_rate,
)
# 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,
)
# Setup train state
state = TrainState.create(apply_fn=model.__call__,
params=model.params,
tx=adamw,
dropout_rng=dropout_rng)
def loss_fn(logits, labels):
loss = optax.softmax_cross_entropy(logits,
onehot(labels, logits.shape[-1]))
return loss.mean()
# Define gradient update step fn
def train_step(state, batch):
dropout_rng, new_dropout_rng = jax.random.split(state.dropout_rng)
def compute_loss(params):
labels = batch.pop("labels")
logits = state.apply_fn(**batch,
params=params,
dropout_rng=dropout_rng,
train=True)[0]
loss = loss_fn(logits, labels)
return loss
grad_fn = jax.value_and_grad(compute_loss)
loss, grad = grad_fn(state.params)
grad = jax.lax.pmean(grad, "batch")
new_state = state.apply_gradients(grads=grad,
dropout_rng=new_dropout_rng)
metrics = {
"loss": loss,
"learning_rate": linear_decay_lr_schedule_fn(state.step)
}
metrics = jax.lax.pmean(metrics, axis_name="batch")
return new_state, metrics
# Define eval fn
def eval_step(params, batch):
labels = batch.pop("labels")
logits = model(**batch, params=params, train=False)[0]
loss = loss_fn(logits, labels)
# summarize metrics
accuracy = (jnp.argmax(logits, axis=-1) == labels).mean()
metrics = {"loss": loss, "accuracy": accuracy}
metrics = jax.lax.pmean(metrics, axis_name="batch")
return metrics
# Create parallel version of the train and eval step
p_train_step = jax.pmap(train_step, "batch", donate_argnums=(0, ))
p_eval_step = jax.pmap(eval_step, "batch")
# Replicate the train state on each device
state = state.replicate()
logger.info("***** Running training *****")
logger.info(f" Num examples = {len(train_dataset)}")
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 = []
steps_per_epoch = len(train_dataset) // train_batch_size
train_step_progress_bar = tqdm(total=steps_per_epoch,
desc="Training...",
position=1,
leave=False)
# train
for batch in train_loader:
batch = shard(batch)
state, train_metric = p_train_step(state, batch)
train_metrics.append(train_metric)
train_step_progress_bar.update(1)
train_time += time.time() - train_start
train_metric = unreplicate(train_metric)
train_step_progress_bar.close()
epochs.write(
f"Epoch... ({epoch + 1}/{num_epochs} | Loss: {train_metric['loss']}, Learning Rate: {train_metric['learning_rate']})"
)
# ======================== Evaluating ==============================
eval_metrics = []
eval_steps = len(eval_dataset) // eval_batch_size
eval_step_progress_bar = tqdm(total=eval_steps,
desc="Evaluating...",
position=2,
leave=False)
for batch in eval_loader:
# Model forward
batch = shard(batch)
metrics = p_eval_step(state.params, batch)
eval_metrics.append(metrics)
eval_step_progress_bar.update(1)
# normalize eval metrics
eval_metrics = get_metrics(eval_metrics)
eval_metrics = jax.tree_map(jnp.mean, eval_metrics)
# Print metrics and update progress bar
eval_step_progress_bar.close()
desc = (
f"Epoch... ({epoch + 1}/{num_epochs} | Eval Loss: {round(eval_metrics['loss'].item(), 4)} | "
f"Eval Accuracy: {round(eval_metrics['accuracy'].item(), 4)})")
epochs.write(desc)
epochs.desc = desc
# Save metrics
if has_tensorboard and jax.process_index() == 0:
cur_step = epoch * (len(train_dataset) // train_batch_size)
write_metric(summary_writer, train_metrics, eval_metrics,
train_time, cur_step)
# save checkpoint after each epoch and push checkpoint to the hub
if jax.process_index() == 0:
params = jax.device_get(jax.tree_map(lambda x: x[0], state.params))
model.save_pretrained(training_args.output_dir, params=params)
if training_args.push_to_hub:
repo.push_to_hub(
commit_message=f"Saving weights and logs of epoch {epoch}",
blocking=False)
def translate_and_calculate_bleu(*,
p_pred_step,
p_init_cache,
target,
predict_ds,
decode_tokens,
max_predict_length,
num_eval_steps=1000,
decode_file=''):
"""Translates the `predict_ds` and calculates the BLEU score."""
n_devices = jax.local_device_count()
logging.info('Translating evaluation dataset.')
sources, references, predictions = [], [], []
for counter, pred_batch in zip(range(num_eval_steps), predict_ds):
print(counter)
start_batch = time.time()
pred_batch = jax.tree_map(lambda x: x._numpy(), pred_batch) # pylint: disable=protected-access
# Handle final odd-sized batch by padding instead of dropping it.
cur_pred_batch_size = pred_batch['inputs'].shape[0]
if cur_pred_batch_size % n_devices:
padded_size = int(
np.ceil(cur_pred_batch_size / n_devices) * n_devices)
pred_batch = jax.tree_map(
lambda x: pad_examples(x, padded_size), # pylint: disable=cell-var-from-loop
pred_batch)
pred_batch = common_utils.shard(pred_batch)
end_batch_proc = time.time()
cache = p_init_cache(pred_batch['inputs'])
predicted, _ = p_pred_step(pred_batch['inputs'], target, cache,
decode.EOS_ID, max_predict_length)
end_p_step = time.time()
predicted = tohost(predicted)
inputs = tohost(pred_batch['inputs'])
targets = tohost(pred_batch['targets'])
# Iterate through non-padding examples of batch.
for i, s in enumerate(predicted[:cur_pred_batch_size]):
sources.append(decode_tokens(inputs[i]))
references.append(decode_tokens(targets[i]))
predictions.append(decode_tokens(s))
end = time.time()
print('data proc', end_batch_proc - start_batch)
print('pstep', end_p_step - end_batch_proc)
print('last part', end - end_p_step)
logging.info('Translation: %d predictions %d references %d sources.',
len(predictions), len(references), len(sources))
# Calculate BLEU score for translated eval corpus against reference.
bleu_matches = bleu.bleu_partial(references, predictions)
all_bleu_matches = per_host_sum_pmap(bleu_matches)
bleu_score = bleu.complete_bleu(*all_bleu_matches)
# Save translation samples for tensorboard.
exemplars = ''
for n in np.random.choice(np.arange(len(predictions)), 8):
exemplars += f'{sources[n]}\n\n{references[n]}\n\n{predictions[n]}\n\n'
if decode_file:
with tf.io.gfile.GFile(decode_file, 'w') as f:
writer = csv.writer(f)
for val in zip(sources, references, predictions):
writer.writerow(val)
return exemplars, bleu_score
def train_and_evaluate(config: ml_collections.ConfigDict, workdir: str):
"""Runs a training and evaluation loop.
Args:
config: Configuration to use.
workdir: Working directory for checkpoints and TF summaries. If this
contains checkpoint training will be resumed from the latest checkpoint.
"""
tf.io.gfile.makedirs(workdir)
vocab_path = config.vocab_path
if vocab_path is None:
vocab_path = os.path.join(workdir, "sentencepiece_model")
tf.io.gfile.makedirs(os.path.split(vocab_path)[0])
# Load Dataset
# ---------------------------------------------------------------------------
logging.info("Initializing dataset.")
train_ds, eval_ds, predict_ds, encoder = input_pipeline.get_wmt_datasets(
n_devices=jax.local_device_count(),
config=config,
vocab_path=vocab_path)
train_iter = iter(train_ds)
vocab_size = int(encoder.vocab_size())
eos_id = decode.EOS_ID # Default Sentencepiece EOS token.
def decode_tokens(toks):
valid_toks = toks[:np.argmax(toks == eos_id) + 1].astype(np.int32)
return encoder.detokenize(valid_toks).numpy().decode("utf-8")
if config.num_predict_steps > 0:
predict_ds = predict_ds.take(config.num_predict_steps)
logging.info("Initializing model, optimizer, and step functions.")
# Build Model and Optimizer
# ---------------------------------------------------------------------------
train_config = models.TransformerConfig(
vocab_size=vocab_size,
output_vocab_size=vocab_size,
share_embeddings=config.share_embeddings,
logits_via_embedding=config.logits_via_embedding,
dtype=jnp.bfloat16 if config.use_bfloat16 else jnp.float32,
emb_dim=config.emb_dim,
num_heads=config.num_heads,
num_layers=config.num_layers,
qkv_dim=config.qkv_dim,
mlp_dim=config.mlp_dim,
max_len=max(config.max_target_length, config.max_eval_target_length),
dropout_rate=config.dropout_rate,
attention_dropout_rate=config.attention_dropout_rate,
deterministic=False,
decode=False,
kernel_init=nn.initializers.xavier_uniform(),
bias_init=nn.initializers.normal(stddev=1e-6))
eval_config = train_config.replace(deterministic=True)
predict_config = train_config.replace(deterministic=True, decode=True)
start_step = 0
rng = jax.random.PRNGKey(config.seed)
rng, init_rng = jax.random.split(rng)
input_shape = (config.per_device_batch_size, config.max_target_length)
target_shape = (config.per_device_batch_size, config.max_target_length)
m = models.Transformer(eval_config)
initial_variables = jax.jit(m.init)(init_rng,
jnp.ones(input_shape, jnp.float32),
jnp.ones(target_shape, jnp.float32))
# apply an optimizer to this tree
optimizer_def = optim.Adam(config.learning_rate,
beta1=0.9,
beta2=0.98,
eps=1e-9,
weight_decay=config.weight_decay)
optimizer = optimizer_def.create(initial_variables["params"])
# We access model params only from optimizer below via optimizer.target.
del initial_variables
if config.restore_checkpoints:
# Restore unreplicated optimizer + model state from last checkpoint.
optimizer = checkpoints.restore_checkpoint(workdir, optimizer)
# Grab last step.
start_step = int(optimizer.state.step)
writer = metric_writers.create_default_writer(
workdir, just_logging=jax.host_id() > 0)
if start_step == 1:
writer.write_hparams(dict(config))
# Replicate optimizer.
optimizer = jax_utils.replicate(optimizer)
learning_rate_fn = create_learning_rate_scheduler(
base_learning_rate=config.learning_rate,
warmup_steps=config.warmup_steps)
# compile multidevice versions of train/eval/predict step and cache init fn.
p_train_step = jax.pmap(functools.partial(
train_step,
config=train_config,
learning_rate_fn=learning_rate_fn,
label_smoothing=config.label_smoothing),
axis_name="batch",
donate_argnums=(0, )) # pytype: disable=wrong-arg-types
p_eval_step = jax.pmap(functools.partial(
eval_step, config=eval_config, label_smoothing=config.label_smoothing),
axis_name="batch")
p_init_cache = jax.pmap(functools.partial(
initialize_cache,
max_decode_len=config.max_predict_length,
config=predict_config),
axis_name="batch")
p_pred_step = jax.pmap(
functools.partial(predict_step,
config=predict_config,
beam_size=config.beam_size),
axis_name="batch",
static_broadcasted_argnums=(3,
4)) # eos token, max_length are constant
# Main Train Loop
# ---------------------------------------------------------------------------
# We init the first set of dropout PRNG keys, but update it afterwards inside
# the main pmap"d training update for performance.
dropout_rngs = jax.random.split(rng, jax.local_device_count())
del rng
logging.info("Starting training loop.")
hooks = []
report_progress = periodic_actions.ReportProgress(
num_train_steps=config.num_train_steps, writer=writer)
if jax.host_id() == 0:
hooks += [
report_progress,
periodic_actions.Profile(logdir=workdir, num_profile_steps=5)
]
train_metrics = []
with metric_writers.ensure_flushes(writer):
for step in range(start_step, config.num_train_steps):
is_last_step = step == config.num_train_steps - 1
# Shard data to devices and do a training step.
with jax.profiler.StepTraceContext("train", step_num=step):
batch = common_utils.shard(
jax.tree_map(np.asarray, next(train_iter)))
optimizer, metrics = p_train_step(optimizer,
batch,
dropout_rng=dropout_rngs)
train_metrics.append(metrics)
# Quick indication that training is happening.
logging.log_first_n(logging.INFO, "Finished training step %d.", 5,
step)
for h in hooks:
h(step)
# Periodic metric handling.
if step % config.eval_every_steps == 0 or is_last_step:
with report_progress.timed("training_metrics"):
logging.info("Gathering training metrics.")
train_metrics = common_utils.get_metrics(train_metrics)
lr = train_metrics.pop("learning_rate").mean()
metrics_sums = jax.tree_map(jnp.sum, train_metrics)
denominator = metrics_sums.pop("denominator")
summary = jax.tree_map(lambda x: x / denominator,
metrics_sums) # pylint: disable=cell-var-from-loop
summary["learning_rate"] = lr
summary = {"train_" + k: v for k, v in summary.items()}
writer.write_scalars(step, summary)
train_metrics = []
with report_progress.timed("eval"):
eval_results = evaluate(
p_eval_step=p_eval_step,
target=optimizer.target,
eval_ds=eval_ds,
num_eval_steps=config.num_eval_steps)
writer.write_scalars(
step,
{"eval_" + k: v
for k, v in eval_results.items()})
with report_progress.timed("translate_and_bleu"):
exemplars, bleu_score = translate_and_calculate_bleu(
p_pred_step=p_pred_step,
p_init_cache=p_init_cache,
target=optimizer.target,
predict_ds=predict_ds,
decode_tokens=decode_tokens,
max_predict_length=config.max_predict_length)
writer.write_scalars(step, {"bleu": bleu_score})
writer.write_texts(step, {"samples": exemplars})
# Save a checkpoint on one host after every checkpoint_freq steps.
save_checkpoint = step % config.checkpoint_every_steps == 0 or is_last_step
if config.save_checkpoints and save_checkpoint and jax.host_id(
) == 0:
with report_progress.timed("checkpoint"):
checkpoints.save_checkpoint(
workdir, jax_utils.unreplicate(optimizer), step)
def load_data(batches):
data_dict = common_utils.shard(batches)
return data_dict['inputs'], data_dict['outputs'], data_dict['target']
def test_train(self):
tf.enable_v2_behavior()
tf.random.set_seed(0)
np.random.seed(0)
random.seed(0)
dataset_filepattern = os.path.join(
os.path.dirname(__file__),
'tasks/robust_fill/dataset/test_dataset/program_tasks.tf_records-*'
)
print('dataset_filepattern = {}'.format(dataset_filepattern))
batch_size = 4
num_strings_per_task = 4
max_characters = 10
max_program_length = 15
# Build token tables.
id_char_table = {i + 1: char for (i, char) in enumerate(dsl.CHARACTER)}
char_id_table = {char: id for id, char in id_char_table.items()}
_, token_id_table = dsl_tokens.build_token_tables()
io_vocab_size = len(char_id_table) + 1 # For padding.
program_vocab_size = len(token_id_table) + 1
bos_token = token_id_table[dsl.BOS]
# Load dataset.
dataset = input_pipeline.create_dataset_from_tf_record(
dataset_filepattern, token_id_table, char_id_table)
dataset = dataset.padded_batch(batch_size,
padded_shapes=((num_strings_per_task,
max_characters),
(num_strings_per_task,
max_characters),
(max_program_length, )),
drop_remainder=True)
dataset_iter = dataset.repeat().as_numpy_iterator()
train_config = models.TransformerConfig(
vocab_size=io_vocab_size,
output_vocab_size=program_vocab_size,
shift=True,
emb_dim=32,
num_heads=4,
num_layers=2,
qkv_dim=32,
mlp_dim=32,
max_len=max(max_characters, max_program_length),
deterministic=False,
decode=False,
bos_token=bos_token)
eval_config = train_config.replace(deterministic=True)
rng = jax.random.PRNGKey(0)
rng, init_rng = jax.random.split(rng)
m = models.ProgramTransformer(eval_config)
initial_variables = jax.jit(m.init)(
init_rng,
jnp.ones((batch_size, num_strings_per_task, max_characters),
jnp.float32),
jnp.ones((batch_size, num_strings_per_task, max_characters),
jnp.float32),
jnp.ones((batch_size, max_program_length), jnp.float32))
optimizer_def = optim.Adam(1e-2,
beta1=0.9,
beta2=0.98,
eps=1e-9,
weight_decay=0.1)
optimizer = optimizer_def.create(initial_variables['params'])
del initial_variables # Don't keep a copy of the initial model.
optimizer = jax_utils.replicate(optimizer)
learning_rate_fn = train_lib.create_learning_rate_scheduler(
base_learning_rate=1e-2)
p_train_step = jax.pmap(functools.partial(
train_lib.train_step,
learning_rate_fn=learning_rate_fn,
config=train_config),
axis_name='batch')
p_eval_step = jax.pmap(functools.partial(train_lib.eval_step,
config=eval_config),
axis_name='batch')
# Training loop.
start_step = 0
rngs = jax.random.split(rng, jax.local_device_count())
del rng
for _ in range(start_step, 1000):
inputs, outputs, programs = common_utils.shard(next(dataset_iter))
optimizer, _, rngs = p_train_step(optimizer,
inputs,
outputs,
programs,
train_rng=rngs)
# Evaluation.
eval_metrics = []
for batches in dataset.as_numpy_iterator():
inputs, outputs, programs = common_utils.shard(batches)
metrics = p_eval_step(optimizer.target, inputs, outputs, programs)
eval_metrics.append(metrics)
eval_metrics = common_utils.get_metrics(eval_metrics)
eval_metrics_sums = jax.tree_map(jnp.sum, eval_metrics)
eval_denominator = eval_metrics_sums.pop('denominator')
eval_summary = jax.tree_map(
lambda x: x / eval_denominator, # pylint: disable=cell-var-from-loop
eval_metrics_sums)
if jax.host_id() == 0:
self.assertGreater(eval_summary['accuracy'], 0.1)
def compute_is_scores(filename):
"""Compute IS scores for training data."""
# Make sure tf does not allocate gpu memory.
tf.config.experimental.set_visible_devices([], 'GPU')
if FLAGS.jax_backend_target:
jax.config.FLAGS.jax_xla_backend = 'tpu_driver'
jax.config.FLAGS.jax_backend_target = FLAGS.jax_backend_target
# Number of local devices for this host.
n_devices = jax.local_device_count()
if jax.host_id() == 0:
tf.io.gfile.makedirs(FLAGS.model_dir)
if FLAGS.batch_size % n_devices:
raise ValueError('Batch size must be divisible by the number of devices')
vocab_path = FLAGS.vocab_path
if vocab_path is None:
vocab_path = os.path.join(FLAGS.model_dir, 'sentencepiece_model')
tf.io.gfile.makedirs(os.path.split(vocab_path)[0])
# Load Dataset
print('Loading data')
logging.info('Initializing dataset.')
train_ds, encoder = input_pipeline.get_wmt_is_datasets(
n_devices=n_devices,
dataset_name=FLAGS.dataset_name,
shard_idx=jax.host_id(),
shard_count=jax.host_count(),
data_dir=FLAGS.data_dir,
vocab_path=vocab_path,
target_vocab_size=FLAGS.vocab_size,
batch_size=FLAGS.batch_size,
max_length=FLAGS.max_target_length,
paracrawl_size=FLAGS.paracrawl_size)
print('Datasets created')
train_iter = iter(train_ds)
vocab_size = int(encoder.vocab_size())
eos_id = decode.EOS_ID # Default Sentencepiece EOS token.
print('data iterators created')
logging.info('Initializing model, optimizer, and step functions.')
# Build Model and Optimizer
# ---------------------------------------------------------------------------
eval_config = models.TransformerConfig(
vocab_size=vocab_size,
output_vocab_size=vocab_size,
share_embeddings=FLAGS.share_embeddings,
logits_via_embedding=FLAGS.logits_via_embedding,
dtype=jnp.bfloat16 if FLAGS.use_bfloat16 else jnp.float32,
emb_dim=FLAGS.emb_dim,
num_heads=FLAGS.num_heads,
num_layers=FLAGS.num_layers,
qkv_dim=FLAGS.qkv_dim,
mlp_dim=FLAGS.mlp_dim,
max_len=max(FLAGS.max_target_length, FLAGS.max_eval_target_length),
dropout_rate=FLAGS.dropout_rate,
attention_dropout_rate=FLAGS.attention_dropout_rate,
deterministic=True,
decode=False,
kernel_init=nn.initializers.xavier_uniform(),
bias_init=nn.initializers.normal(stddev=1e-6))
start_step = 0
rng = jax.random.PRNGKey(FLAGS.random_seed)
rng, init_rng = jax.random.split(rng)
# It's possible that is supposed to be per device batch size
input_shape = (FLAGS.batch_size, FLAGS.max_target_length)
target_shape = (FLAGS.batch_size, FLAGS.max_target_length)
m = models.Transformer(eval_config)
initial_variables = jax.jit(m.init)(init_rng,
jnp.ones(input_shape, jnp.float32),
jnp.ones(target_shape, jnp.float32))
# apply an optimizer to this tree
optimizer_def = optim.Adam(
FLAGS.learning_rate,
beta1=0.9,
beta2=0.98,
eps=1e-9,
weight_decay=FLAGS.weight_decay)
optimizer = optimizer_def.create(initial_variables['params'])
# We access model params only from optimizer below via optimizer.target.
del initial_variables
if FLAGS.restore_checkpoints:
logging.info('Restoring checkpoint.')
# If we have a pretrained model, use that. Else, just continue where leftoff
model_path = FLAGS.pretrained_model_dir if FLAGS.pretrained_model_dir else FLAGS.model_dir
# When loading a checkpoint trained with adapters (ie. frozen weights)
# restoring from the base optimizer fails. We catch this error and create
# the optimizer with frozen weights.
try:
optimizer = checkpoints.restore_checkpoint(model_path, optimizer)
# Grab last step.
start_step = int(optimizer.state.step)
except ValueError:
adapter = optim.ModelParamTraversal(lambda path, _: FLAGS.adapter in path)
optimizer = optimizer_def.create(optimizer.target, focus=adapter)
optimizer = checkpoints.restore_checkpoint(model_path, optimizer)
start_step = optimizer.state[0].step
else:
raise RuntimeError('Must restore checkpoint for IS')
if FLAGS.adapter != NONE and not isinstance(optimizer, optim.MultiOptimizer):
adapter = optim.ModelParamTraversal(lambda path, _: FLAGS.adapter in path)
optimizer = optimizer_def.create(optimizer.target, focus=adapter)
# Replicate optimizer.
optimizer = jax_utils.replicate(optimizer)
p_eval_step = jax.pmap(
functools.partial(
eval_for_is_step,
config=eval_config),
axis_name='batch')
logging.info('Start scoring loop.')
metrics_all = []
t_loop_start = time.time()
# Eval Metrics
logging.info('Gathering evaluation metrics.')
t_eval_start = time.time()
save_file = FLAGS.is_save_path + '/' + filename + '-lengths.txt'
length_fp = tf.io.gfile.GFile(save_file, 'w')
lengths_writer = csv.writer(length_fp)
save_file = FLAGS.is_save_path + '/' + filename + '.txt'
with tf.io.gfile.GFile(save_file, 'w') as fp:
writer = csv.writer(fp)
for batch_idx, eval_batch in enumerate(train_iter):
eval_batch = jax.tree_map(lambda x: x._numpy(), eval_batch) # pylint: disable=protected-access
cur_pred_batch_size = eval_batch['inputs'].shape[0]
if cur_pred_batch_size % n_devices:
padded_size = int(
np.ceil(cur_pred_batch_size / n_devices) * n_devices)
eval_batch = jax.tree_map(
lambda x: common.pad_examples(x, padded_size), eval_batch) # pylint: disable=cell-var-from-loop
eval_batch = common_utils.shard(eval_batch)
losses, lengths = p_eval_step(optimizer.target, eval_batch)
if jax.host_id() == 0:
losses = common.tohost(losses)
lengths = common.tohost(lengths)
if cur_pred_batch_size % n_devices:
writer.writerow(losses[:cur_pred_batch_size])
lengths_writer.writerow(lengths[:cur_pred_batch_size])
else:
writer.writerow(losses)
lengths_writer.writerow(lengths)
if batch_idx % 500 == 0:
print('Batch', batch_idx)
print(time.time() - t_loop_start)
length_fp.close()
def evaluate_batch(self, batch):
"""Computes metrics for given [batch x length] sequences."""
batch = common_utils.shard(batch)
metrics = self._p_eval_step(self._optimizer.target, batch)
return metrics
def main(argv):
if len(argv) > 1:
raise app.UsageError('Too many command-line arguments.')
# Make sure tf does not allocate gpu memory.
tf.config.experimental.set_visible_devices([], 'GPU')
if FLAGS.jax_backend_target:
jax.config.FLAGS.jax_xla_backend = 'tpu_driver'
jax.config.FLAGS.jax_backend_target = FLAGS.jax_backend_target
# Number of local devices for this host.
n_devices = jax.local_device_count()
if jax.process_index() == 0:
tf.io.gfile.makedirs(FLAGS.model_dir)
if FLAGS.batch_size % n_devices:
raise ValueError(
'Batch size must be divisible by the number of devices')
vocab_path = FLAGS.vocab_path
if vocab_path is None:
vocab_path = os.path.join(FLAGS.model_dir, 'sentencepiece_model')
tf.io.gfile.makedirs(os.path.split(vocab_path)[0])
# Load Dataset
# ---------------------------------------------------------------------------
logging.info('Initializing dataset.')
if FLAGS.dynamic:
train_ds_mgr, eval_ds, predict_ds, encoder = input_pipeline.get_dynamic_datasets(
dataset_name=FLAGS.dataset_name,
eval_dataset_name=FLAGS.eval_dataset_name,
shard_idx=jax.process_index(),
shard_count=jax.process_count(),
data_dir=FLAGS.data_dir,
vocab_path=FLAGS.vocab_path,
target_vocab_size=FLAGS.vocab_size,
batch_size=FLAGS.batch_size,
max_length=FLAGS.max_target_length,
max_eval_length=FLAGS.max_eval_target_length,
paracrawl_size=FLAGS.paracrawl_size,
is_scores_path=FLAGS.is_scores_path,
num_buckets=FLAGS.num_data_buckets)
if FLAGS.static:
weights = np.array([float(w) for w in FLAGS.static.split(',')])
assert len(weights) == FLAGS.num_data_buckets
train_ds = train_ds_mgr.sampled_dataset(weights)
FLAGS.dynamic = False
else:
init_dist = np.zeros(FLAGS.num_data_buckets)
if FLAGS.data_selection_size < FLAGS.num_data_buckets:
init_dist[range(FLAGS.data_selection_size)] = 1.0
train_ds = train_ds_mgr.sampled_dataset(init_dist)
else:
train_ds = build_split(train_ds_mgr, 1.0)
else:
train_ds, eval_ds, predict_ds, encoder = input_pipeline.get_wmt_datasets(
dataset_name=FLAGS.dataset_name,
eval_dataset_name=FLAGS.eval_dataset_name,
shard_idx=jax.process_index(),
shard_count=jax.process_count(),
data_dir=FLAGS.data_dir,
vocab_path=vocab_path,
target_vocab_size=FLAGS.vocab_size,
batch_size=FLAGS.batch_size,
max_length=FLAGS.max_target_length,
max_eval_length=FLAGS.max_eval_target_length,
paracrawl_size=FLAGS.paracrawl_size,
is_scores_path=FLAGS.is_scores_path,
num_to_keep=FLAGS.data_selection_size,
pseudo_path=FLAGS.pseudo_path,
repeat_count=FLAGS.repeat_count,
newscommentary_size=FLAGS.newscommentary_size,
split_tokenizer=FLAGS.split_tokenizer,
sample_size=FLAGS.sample_size,
newscomment_sample_ratio=FLAGS.newscomment_sample_ratio)
if FLAGS.aux_eval_dataset:
aux_datasets = []
aux_names = FLAGS.aux_eval_dataset.split(',')
for name in aux_names:
_, aux_eval_ds, _, _ = input_pipeline.get_wmt_datasets(
dataset_name=name,
eval_dataset_name=None,
shard_idx=jax.process_index(),
shard_count=jax.process_count(),
data_dir=FLAGS.data_dir,
vocab_path=vocab_path,
target_vocab_size=FLAGS.vocab_size,
batch_size=FLAGS.batch_size,
max_length=FLAGS.max_target_length,
max_eval_length=FLAGS.max_eval_target_length,
paracrawl_size=FLAGS.paracrawl_size,
is_scores_path=FLAGS.is_scores_path,
num_to_keep=FLAGS.data_selection_size,
pseudo_path=FLAGS.pseudo_path,
repeat_count=FLAGS.repeat_count,
newscommentary_size=FLAGS.newscommentary_size)
aux_datasets.append(aux_eval_ds)
train_iter = iter(train_ds)
vocab_size = int(encoder.vocab_size())
eos_id = decode.EOS_ID # Default Sentencepiece EOS token.
def decode_tokens(toks):
valid_toks = toks[:np.argmax(toks == eos_id) + 1].astype(np.int32)
return encoder.detokenize(valid_toks).numpy().decode('utf-8')
logging.info('Initializing model, optimizer, and step functions.')
# Build Model and Optimizer
# ---------------------------------------------------------------------------
train_config = models.TransformerConfig(
vocab_size=vocab_size,
output_vocab_size=vocab_size,
share_embeddings=FLAGS.share_embeddings,
logits_via_embedding=FLAGS.logits_via_embedding,
dtype=jnp.bfloat16 if FLAGS.use_bfloat16 else jnp.float32,
emb_dim=FLAGS.emb_dim,
num_heads=FLAGS.num_heads,
num_layers=FLAGS.num_layers,
qkv_dim=FLAGS.qkv_dim,
mlp_dim=FLAGS.mlp_dim,
max_len=max(FLAGS.max_target_length, FLAGS.max_eval_target_length),
dropout_rate=FLAGS.dropout_rate,
attention_dropout_rate=FLAGS.attention_dropout_rate,
deterministic=False,
decode=False,
kernel_init=nn.initializers.xavier_uniform(),
bias_init=nn.initializers.normal(stddev=1e-6))
eval_config = train_config.replace(deterministic=True)
predict_config = train_config.replace(deterministic=True, decode=True)
start_step = 0
rng = jax.random.PRNGKey(FLAGS.random_seed)
rng, init_rng = jax.random.split(rng)
# It's possible that is supposed to be per device batch size
input_shape = (FLAGS.batch_size, FLAGS.max_target_length)
target_shape = (FLAGS.batch_size, FLAGS.max_target_length)
m = models.Transformer(eval_config)
initial_variables = jax.jit(m.init)(init_rng,
jnp.ones(input_shape, jnp.float32),
jnp.ones(target_shape, jnp.float32))
# apply an optimizer to this tree
optimizer_def = optim.Adam(FLAGS.learning_rate,
beta1=0.9,
beta2=0.98,
eps=1e-9,
weight_decay=FLAGS.weight_decay)
optimizer = optimizer_def.create(initial_variables['params'])
# We access model params only from optimizer below via optimizer.target.
del initial_variables
if FLAGS.restore_checkpoints:
logging.info('Restoring checkpoint.')
# If we have a pretrained model, use that. Else, just continue where leftoff
model_path = FLAGS.pretrained_model_dir if FLAGS.pretrained_model_dir else FLAGS.model_dir
optimizer = checkpoints.restore_checkpoint(model_path, optimizer)
# Grab last step.
start_step = int(optimizer.state.step)
if FLAGS.adapter != train_util.NONE:
adapter = optim.ModelParamTraversal(
lambda path, _: FLAGS.adapter in path)
optimizer = optimizer_def.create(optimizer.target, focus=adapter)
writer = metric_writers.create_default_writer(
FLAGS.model_dir, just_logging=jax.process_index() > 0)
flag_key = [
k for k in FLAGS.flags_by_module_dict().keys() if 'wmt.par' in k
]
if flag_key:
flag_key = flag_key[0]
local_flags = {
f.name: f.value
for f in FLAGS.flags_by_module_dict()[flag_key]
}
writer.write_hparams(local_flags)
# Replicate optimizer.
optimizer = jax_utils.replicate(optimizer)
if FLAGS.adapter != train_util.NONE:
learning_rate_fn = common.create_learning_rate_scheduler(
factors='constant',
base_learning_rate=FLAGS.learning_rate,
warmup_steps=FLAGS.warmup_steps)
else:
learning_rate_fn = common.create_learning_rate_scheduler(
base_learning_rate=FLAGS.learning_rate,
warmup_steps=FLAGS.warmup_steps,
steps_per_cycle=FLAGS.steps_per_cycle,
init_step=start_step,
finetune_lr=FLAGS.finetune_lr)
# compile multidevice versions of train/eval/predict step and cache init fn.
p_train_step = jax.pmap(functools.partial(
train_util.train_step,
config=train_config,
learning_rate_fn=learning_rate_fn,
label_smoothing=FLAGS.label_smoothing),
axis_name='batch',
donate_argnums=(0, )) # pytype: disable=wrong-arg-types
p_eval_step = jax.pmap(functools.partial(train_util.eval_step,
config=eval_config),
axis_name='batch')
p_init_cache = jax.pmap(functools.partial(
train_util.initialize_cache,
max_decode_len=FLAGS.max_predict_length,
config=predict_config),
axis_name='batch')
p_pred_step = jax.pmap(
functools.partial(train_util.predict_step,
config=predict_config,
beam_size=FLAGS.beam_size),
axis_name='batch',
static_broadcasted_argnums=(3,
4)) # eos token, max_length are constant
p_get_diag_grads = jax.pmap(functools.partial(train_util.get_diag_grads,
config=eval_config),
axis_name='batch')
p_get_bucket_score = jax.pmap(functools.partial(get_diag_score,
strategy=FLAGS.strategy),
axis_name='batch')
# Main Train Loop
# ---------------------------------------------------------------------------
# We init the first set of dropout PRNG keys, but update it afterwards inside
# the main pmap"d training update for performance.
dropout_rngs = jax.random.split(rng, jax.local_device_count())
del rng
logging.info('Starting training loop.')
hooks = []
report_progress = periodic_actions.ReportProgress(
num_train_steps=FLAGS.num_train_steps, writer=writer)
if jax.process_index() == 0:
hooks += [
report_progress,
periodic_actions.Profile(logdir=FLAGS.model_dir,
num_profile_steps=5)
]
train_metrics = []
total_steps = start_step + FLAGS.num_train_steps
if FLAGS.eval_only:
total_steps = start_step + 1
best_eval_loss = 1000
curr_eval_loss = 1000
with metric_writers.ensure_flushes(writer):
for step in range(start_step, total_steps):
is_last_step = step == total_steps - 1
if FLAGS.dynamic and ((step - start_step) % FLAGS.resample_freq
== 0):
# Dynamic macro: use gradient alignment to score different ratios
# of top k vs bottom N-k bins
if FLAGS.macro:
train_iter = get_macro_distribution(
p_get_diag_grads, p_get_bucket_score, aux_eval_ds,
train_ds_mgr, optimizer, eval_ds)
else:
# Use gradient alignment to score bins
# take the top k bins and sample uniformly from them.
raw_distribution = get_new_distribution(
p_get_diag_grads, p_get_bucket_score, aux_eval_ds,
train_ds_mgr, optimizer, eval_ds)
logging.info(raw_distribution)
selected = np.argsort(
raw_distribution)[::-1][:FLAGS.data_selection_size]
new_distribution = np.zeros(100)
new_distribution[selected] = 1.0
logging.info(new_distribution)
train_ds = train_ds_mgr.sampled_dataset(new_distribution)
train_iter = iter(train_ds)
# Shard data to devices and do a training step.
if not FLAGS.eval_only:
logging.info('Doing Training.')
with jax.profiler.StepTraceAnnotation('train', step_num=step):
try:
batch = common_utils.shard(
jax.tree_map(np.asarray, next(train_iter)))
optimizer, metrics = p_train_step(
optimizer, batch, dropout_rng=dropout_rngs)
train_metrics.append(metrics)
except StopIteration:
is_last_step = True
# Quick indication that training is happening.
logging.log_first_n(logging.INFO, 'Finished training step %d.', 5,
step)
for h in hooks:
h(step)
# Periodic metric handling.
if (step - start_step) % FLAGS.eval_frequency == 0 or is_last_step:
if not FLAGS.eval_only:
with report_progress.timed('training_metrics'):
logging.info('Gathering training metrics.')
train_metrics = common_utils.get_metrics(train_metrics)
lr = train_metrics.pop('learning_rate').mean()
metrics_sums = jax.tree_map(jnp.sum, train_metrics)
denominator = metrics_sums.pop('denominator')
summary = jax.tree_map(lambda x: x / denominator,
metrics_sums) # pylint: disable=cell-var-from-loop
summary['learning_rate'] = lr
summary = {'train_' + k: v for k, v in summary.items()}
writer.write_scalars(step, summary)
train_metrics = []
if FLAGS.eval_only:
p_eval_per_pos_step = jax.pmap(functools.partial(
train_util.eval_per_pos_step, config=eval_config),
axis_name='batch')
# Get per example loss
loss_filename = FLAGS.model_dir + '/test_losses.csv'
train_util.write_per_example_losses(
p_eval_step=p_eval_per_pos_step,
target=optimizer.target,
eval_ds=eval_ds,
num_eval_steps=FLAGS.num_eval_steps,
loss_filename=loss_filename)
else:
with report_progress.timed('eval'):
eval_results = train_util.evaluate(
p_eval_step=p_eval_step,
target=optimizer.target,
eval_ds=eval_ds,
num_eval_steps=FLAGS.num_eval_steps)
curr_eval_loss = eval_results['loss']
writer.write_scalars(
step,
{'eval_' + k: v
for k, v in eval_results.items()})
if FLAGS.aux_eval_dataset:
for aux_i, aux_eval_ds in enumerate(aux_datasets):
with report_progress.timed('aux_eval'):
eval_results = train_util.evaluate(
p_eval_step=p_eval_step,
target=optimizer.target,
eval_ds=aux_eval_ds,
num_eval_steps=FLAGS.num_eval_steps)
writer.write_scalars(
step, {
'aux' + str(aux_i) + '_eval_' + k: v
for k, v in eval_results.items()
})
if FLAGS.compute_bleu:
with report_progress.timed('translate_and_bleu'):
decode_file = FLAGS.model_dir + '/decodes.csv'
exemplars, bleu_score = train_util.translate_and_calculate_bleu(
p_pred_step=p_pred_step,
p_init_cache=p_init_cache,
target=optimizer.target,
predict_ds=predict_ds,
decode_tokens=decode_tokens,
max_predict_length=FLAGS.max_predict_length,
num_eval_steps=FLAGS.num_eval_steps,
decode_file=decode_file if FLAGS.eval_only else '')
writer.write_scalars(step, {'bleu': bleu_score})
writer.write_texts(step, {'samples': exemplars})
# Save a checkpoint on one host after every checkpoint_freq steps.
save_checkpoint = ((step - start_step) % FLAGS.checkpoint_freq == 0
or is_last_step)
if FLAGS.save_checkpoints and save_checkpoint and jax.process_index(
) == 0:
if curr_eval_loss < best_eval_loss: # only save better checkpoints
best_eval_loss = curr_eval_loss
with report_progress.timed('checkpoint'):
checkpoints.save_checkpoint(
FLAGS.model_dir,
jax_utils.unreplicate(optimizer),
step,
keep=FLAGS.chkpts_to_keep,
overwrite=True)
if is_last_step:
break
def train_and_evaluate(config, workdir, vocab_filepath):
"""Runs a training and evaluation loop.
Args:
config: Model and training configuration.
workdir: Working directory for checkpoints and Tensorboard summaries. If
this contains a checkpoint, training will be resumed from the latest
checkpoint.
vocab_filepath: Absolute path to SentencePiece vocab model.
Raises:
ValueError: If training or eval batch sizes won't fit number of processes
and devices, or config is underspecified.
"""
n_processes = jax.process_count() # Number of processes
n_devices = jax.local_device_count() # Number of local devices per process
if config.train_batch_size % (n_processes * n_devices) > 0:
raise ValueError(
"Training batch size must be divisible by the total number of devices, "
"but training batch size = %d, while total number of devices = %d "
"(%d processes, each with %d devices)" %
(config.train_batch_size, n_processes * n_devices, n_processes,
n_devices))
if config.eval_batch_size % (n_processes * n_devices) > 0:
raise ValueError(
"Eval batch size must be divisible by the total number of devices, "
"but eval batch size = %d, while total number of devices = %d "
"(%d processes, each with %d devices)" %
(config.eval_batch_size, n_processes * n_devices, n_processes,
n_devices))
per_process_train_batch_size = config.train_batch_size // n_processes
per_process_eval_batch_size = config.eval_batch_size // n_processes
if jax.process_index() == 0:
train_summary_writer = tensorboard.SummaryWriter(
os.path.join(workdir, "train"))
eval_summary_writer = tensorboard.SummaryWriter(
os.path.join(workdir, "eval"))
else:
train_summary_writer = None
eval_summary_writer = None
rng = random.PRNGKey(config.seed)
rng, init_rng = random.split(rng)
ds_info = tfds.builder(config.dataset_name).info
num_train_examples = ds_info.splits[tfds.Split.TRAIN].num_examples
num_train_steps = int(num_train_examples * config.num_train_epochs //
config.train_batch_size)
num_warmup_steps = int(config.warmup_proportion * num_train_steps)
# Round up evaluation frequency to power of 10.
eval_frequency = int(
math.ceil(config.eval_proportion * num_train_steps / 10)) * 10
is_regression_task = config.dataset_name == "glue/stsb"
num_classes = (1 if is_regression_task else
ds_info.features["label"].num_classes)
tokenizer = spm.SentencePieceProcessor()
tokenizer.Load(vocab_filepath)
with config.unlocked():
config.vocab_size = tokenizer.GetPieceSize()
frozen_config = ml_collections.FrozenConfigDict(config)
model = models.SequenceClassificationModel(config=frozen_config,
n_classes=num_classes)
params = _init_params(model, init_rng, config)
optimizer = _create_adam_optimizer(config.learning_rate, params)
# In case current job restarts, ensure that we continue from where we left
# off.
optimizer = checkpoints.restore_checkpoint(workdir, optimizer)
start_step = int(optimizer.state.step)
# Otherwise, try to restore optimizer and model state from config checkpoint.
if (start_step == 0 and "init_checkpoint_dir" in config
and config.init_checkpoint_dir):
optimizer = _restore_pretrained_model(optimizer, params, config)
# We access model state only from optimizer via optimizer.target.
del params
optimizer = jax_utils.replicate(optimizer)
if is_regression_task:
compute_stats = functools.partial(_compute_regression_stats,
model=model,
pad_id=tokenizer.pad_id())
else:
compute_stats = functools.partial(_compute_classification_stats,
model=model,
pad_id=tokenizer.pad_id())
learning_rate_fn = train_utils.create_learning_rate_scheduler(
factors="constant * linear_warmup * linear_decay",
base_learning_rate=config.learning_rate,
warmup_steps=num_warmup_steps,
decay_steps=num_train_steps - num_warmup_steps,
)
glue_inputs = functools.partial(input_pipeline.glue_inputs,
dataset_name=config.dataset_name,
max_seq_length=config.max_seq_length,
tokenizer=tokenizer)
train_ds = glue_inputs(split=tfds.Split.TRAIN,
batch_size=per_process_train_batch_size,
training=True)
train_iter = iter(train_ds)
if config.dataset_name == "glue/mnli":
# MNLI contains two validation and test datasets.
split_suffixes = ["_matched", "_mismatched"]
else:
split_suffixes = [""]
# We init the first set of dropout PRNG keys, but update it afterwards inside
# the main pmap'd training update for performance.
rngs = random.split(rng, n_devices)
loss_and_metrics_fn = functools.partial(_compute_loss_and_metrics,
model=model,
pad_id=tokenizer.pad_id())
p_train_step = jax.pmap(functools.partial(
train_utils.train_step,
loss_and_metrics_fn=loss_and_metrics_fn,
learning_rate_fn=learning_rate_fn),
axis_name="batch")
p_eval_step = jax.pmap(functools.partial(train_utils.eval_step,
metric_fn=compute_stats),
axis_name="batch")
eval_metrics_fn = _create_eval_metrics_fn(config.dataset_name,
is_regression_task)
train_metrics = []
logging.info("Starting training loop.")
logging.info("====================")
for step in range(start_step, num_train_steps):
with jax.profiler.StepTraceContext("train", step_num=step):
train_batch = next(train_iter)
train_batch = common_utils.shard(train_batch)
optimizer, train_step_metrics, rngs = p_train_step(optimizer,
train_batch,
rng=rngs)
train_metrics.append(train_step_metrics)
if ((step > 0 and config.save_checkpoints_steps
and step % config.save_checkpoints_steps == 0)
or step == num_train_steps - 1) and jax.process_index() == 0:
# Save un-replicated optimizer and model state.
checkpoints.save_checkpoint(workdir,
jax_utils.unreplicate(optimizer),
step,
keep=2)
# Periodic metric handling.
if step % eval_frequency != 0 and step < num_train_steps - 1:
continue
logging.info("Gathering training metrics at step: %d", step)
train_metrics = common_utils.get_metrics(train_metrics)
train_summary = {
"loss":
jnp.sum(train_metrics["loss"]) /
jnp.sum(train_metrics["num_labels"]),
"learning_rate":
learning_rate_fn(step)
}
if not is_regression_task:
train_summary["accuracy"] = jnp.sum(
train_metrics["correct_predictions"]) / jnp.sum(
train_metrics["num_labels"])
if jax.process_index() == 0:
assert train_summary_writer
for key, val in train_summary.items():
train_summary_writer.scalar(key, val, step)
train_summary_writer.flush()
# Reset metric accumulation for next evaluation cycle.
train_metrics = []
logging.info("Gathering validation metrics at step: %d", step)
for split_suffix in split_suffixes:
eval_ds = glue_inputs(split=tfds.Split.VALIDATION + split_suffix,
batch_size=per_process_eval_batch_size,
training=False)
all_stats = []
for _, eval_batch in zip(range(config.max_num_eval_steps),
eval_ds):
all_stats.append(
_evaluate(p_eval_step, optimizer.target, eval_batch,
n_devices))
flat_stats = {}
for k in all_stats[
0]: # All batches of output stats are the same size
flat_stats[k] = np.concatenate([stat[k] for stat in all_stats],
axis=0)
eval_summary = eval_metrics_fn(flat_stats)
if jax.process_index() == 0:
assert eval_summary_writer
for key, val in eval_summary.items():
eval_summary_writer.scalar(f"{key}{split_suffix}", val,
step)
eval_summary_writer.flush()
def main():
parser = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments))
if len(sys.argv) == 2 and sys.argv[1].endswith(".json"):
# If we pass only one argument to the script and it's the path to a json file,
# let's parse it to get our arguments.
model_args, data_args, training_args = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]))
else:
model_args, data_args, training_args = parser.parse_args_into_dataclasses()
if (
os.path.exists(training_args.output_dir)
and os.listdir(training_args.output_dir)
and training_args.do_train
and not training_args.overwrite_output_dir
):
raise ValueError(
f"Output directory ({training_args.output_dir}) already exists and is not empty."
"Use --overwrite_output_dir to overcome."
)
# Make one log on every process with the configuration for debugging.
logging.basicConfig(
format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
datefmt="%m/%d/%Y %H:%M:%S",
level=logging.INFO,
)
# Setup logging, we only want one process per machine to log things on the screen.
logger.setLevel(logging.INFO if jax.process_index() == 0 else logging.ERROR)
if jax.process_index() == 0:
transformers.utils.logging.set_verbosity_info()
else:
transformers.utils.logging.set_verbosity_error()
# Set the verbosity to info of the Transformers logger (on main process only):
logger.info(f"Training/evaluation parameters {training_args}")
if model_args.tokenizer_name:
tokenizer = AutoTokenizer.from_pretrained(
model_args.tokenizer_name, cache_dir=model_args.cache_dir, use_fast=model_args.use_fast_tokenizer
)
elif model_args.text_model_name_or_path:
tokenizer = AutoTokenizer.from_pretrained(
model_args.text_model_name_or_path, cache_dir=model_args.cache_dir, use_fast=model_args.use_fast_tokenizer
)
else:
raise ValueError(
"You are instantiating a new tokenizer from scratch. This is not supported by this script."
"You can do it from another script, save it, and load it from here, using --tokenizer_name."
)
model = FlaxHybridCLIP.from_text_vision_pretrained(
model_args.text_model_name_or_path,
model_args.vision_model_name_or_path,
seed=training_args.seed,
dtype=getattr(jnp, model_args.dtype),
text_from_pt=model_args.from_pt,
vision_from_pt=model_args.from_pt,
)
config = model.config
# set seed for torch dataloaders
set_seed(training_args.seed)
# Initialize torchvision transforms and jit them for faster processing
preprocess = Transform(config.vision_config.image_size)
preprocess = torch.jit.script(preprocess)
# Initialize the image-text dataset
train_dataset = ImageTextDataset(
data_args.data_dir,
data_args.train_file,
captions_per_image=2,
transform=preprocess,
)
eval_dataset = ImageTextDataset(
data_args.data_dir,
data_args.validation_file,
captions_per_image=1,
transform=preprocess,
)
# 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(train_dataset) // train_batch_size
total_train_steps = steps_per_epoch * num_epochs
# Use collate function to tokenizer the text and convert the processed images to numpy
def collate_fn(examples):
pixel_values = torch.stack([example[0] for example in examples]).permute(0, 2, 3, 1).numpy()
captions = [example[1] for example in examples]
inputs = tokenizer(
captions, max_length=data_args.max_seq_length, padding="max_length", truncation=True, return_tensors="np"
)
batch = {
"pixel_values": pixel_values,
"input_ids": inputs["input_ids"],
"attention_mask": inputs["attention_mask"],
}
return batch
# Create data loaders
train_loader = torch.utils.data.DataLoader(
train_dataset,
batch_size=train_batch_size,
shuffle=True,
num_workers=data_args.preprocessing_num_workers,
persistent_workers=True,
drop_last=True,
collate_fn=collate_fn,
)
eval_loader = torch.utils.data.DataLoader(
eval_dataset,
batch_size=eval_batch_size,
shuffle=False,
num_workers=data_args.preprocessing_num_workers,
persistent_workers=True,
drop_last=True,
collate_fn=collate_fn,
)
# Enable tensorboard only on the master node
if has_tensorboard and jax.process_index() == 0:
summary_writer = SummaryWriter(log_dir=Path(training_args.output_dir).joinpath("logs").as_posix())
# Initialize our training
rng = jax.random.PRNGKey(training_args.seed)
rng, dropout_rng = jax.random.split(rng)
# Create learning rate schedule
linear_decay_lr_schedule_fn = create_learning_rate_fn(
len(train_dataset),
train_batch_size,
training_args.num_train_epochs,
training_args.warmup_steps,
training_args.learning_rate,
)
# 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,
)
# Setup train state
state = TrainState.create(apply_fn=model.__call__, params=model.params, tx=adamw, dropout_rng=dropout_rng)
def cross_entropy(logits, axis):
logprobs = jax.nn.log_softmax(logits, axis=axis)
nll = jnp.diag(logprobs)
ce = -jnp.mean(nll)
return ce
def clip_loss(similarity):
loss = (cross_entropy(similarity, axis=0) + cross_entropy(similarity, axis=1)) / 2
return loss
# Define gradient update step fn
def train_step(state, batch):
dropout_rng, new_dropout_rng = jax.random.split(state.dropout_rng)
def compute_loss(params):
logits = state.apply_fn(**batch, params=params, dropout_rng=dropout_rng, train=True)[0]
loss = clip_loss(logits)
return loss
grad_fn = jax.value_and_grad(compute_loss)
loss, grad = grad_fn(state.params)
grad = jax.lax.pmean(grad, "batch")
new_state = state.apply_gradients(grads=grad, dropout_rng=new_dropout_rng)
metrics = {"loss": loss, "learning_rate": linear_decay_lr_schedule_fn(state.step)}
metrics = jax.lax.pmean(metrics, axis_name="batch")
return new_state, metrics
# Define eval fn
def eval_step(params, batch):
logits = model(**batch, params=params, train=False)[0]
loss = clip_loss(logits)
# summarize metrics
metrics = {"loss": loss}
metrics = jax.lax.pmean(metrics, axis_name="batch")
return metrics
# Create parallel version of the train and eval step
p_train_step = jax.pmap(train_step, "batch", donate_argnums=(0,))
p_eval_step = jax.pmap(eval_step, "batch")
# Replicate the train state on each device
state = state.replicate()
logger.info("***** Running training *****")
logger.info(f" Num examples = {len(train_dataset)}")
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
# Create sampling rng
rng, input_rng = jax.random.split(rng)
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 = []
steps_per_epoch = len(train_dataset) // train_batch_size
train_step_progress_bar = tqdm(total=steps_per_epoch, desc="Training...", position=1, leave=False)
# train
for batch in train_loader:
batch = shard(batch)
state, train_metric = p_train_step(state, batch)
train_metrics.append(train_metric)
train_step_progress_bar.update(1)
train_time += time.time() - train_start
train_metric = unreplicate(train_metric)
train_step_progress_bar.close()
epochs.write(
f"Epoch... ({epoch + 1}/{num_epochs} | Loss: {train_metric['loss']}, Learning Rate: {train_metric['learning_rate']})"
)
# ======================== Evaluating ==============================
eval_metrics = []
eval_steps = len(eval_dataset) // eval_batch_size
eval_step_progress_bar = tqdm(total=eval_steps, desc="Evaluating...", position=2, leave=False)
for batch in eval_loader:
# Model forward
batch = shard(batch)
metrics = p_eval_step(state.params, batch)
eval_metrics.append(metrics)
eval_step_progress_bar.update(1)
# normalize eval metrics
eval_metrics = get_metrics(eval_metrics)
eval_metrics = jax.tree_map(jnp.mean, eval_metrics)
# Print metrics and update progress bar
eval_step_progress_bar.close()
desc = f"Epoch... ({epoch + 1}/{num_epochs} | Eval Loss: {eval_metrics['loss']})"
epochs.write(desc)
epochs.desc = desc
# Save metrics
if has_tensorboard and jax.process_index() == 0:
cur_step = epoch * (len(train_dataset) // 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(unreplicate(state.params))
model.save_pretrained(
training_args.output_dir,
params=params,
push_to_hub=training_args.push_to_hub,
commit_message=f"Saving weights and logs of epoch {epoch+1}",
)
def main():
# See all possible arguments in src/transformers/training_args.py
# or by passing the --help flag to this script.
# We now keep distinct sets of args, for a cleaner separation of concerns.
parser = HfArgumentParser(
(ModelArguments, DataTrainingArguments, TrainingArguments))
if len(sys.argv) == 2 and sys.argv[1].endswith(".json"):
# If we pass only one argument to the script and it's the path to a json file,
# let's parse it to get our arguments.
model_args, data_args, training_args = parser.parse_json_file(
json_file=os.path.abspath(sys.argv[1]))
else:
model_args, data_args, training_args = parser.parse_args_into_dataclasses(
)
if (os.path.exists(training_args.output_dir)
and os.listdir(training_args.output_dir) and training_args.do_train
and not training_args.overwrite_output_dir):
raise ValueError(
f"Output directory ({training_args.output_dir}) already exists and is not empty."
"Use --overwrite_output_dir to overcome.")
# 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)
if "validation" not in datasets.keys():
datasets["validation"] = load_dataset(
data_args.dataset_name,
data_args.dataset_config_name,
split=f"train[:{data_args.validation_split_percentage}%]",
cache_dir=model_args.cache_dir,
)
datasets["train"] = load_dataset(
data_args.dataset_name,
data_args.dataset_config_name,
split=f"train[{data_args.validation_split_percentage}%:]",
cache_dir=model_args.cache_dir,
)
else:
data_files = {}
if data_args.train_file is not None:
data_files["train"] = data_args.train_file
if data_args.validation_file is not None:
data_files["validation"] = data_args.validation_file
extension = data_args.train_file.split(".")[-1]
if extension == "txt":
extension = "text"
datasets = load_dataset(extension,
data_files=data_files,
cache_dir=model_args.cache_dir)
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,
)
datasets["train"] = load_dataset(
extension,
data_files=data_files,
split=f"train[{data_args.validation_split_percentage}%:]",
cache_dir=model_args.cache_dir,
)
# See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at
# https://huggingface.co/docs/datasets/loading_datasets.html.
# Load pretrained model and tokenizer
if model_args.tokenizer_name:
tokenizer = AutoTokenizer.from_pretrained(
model_args.tokenizer_name,
cache_dir=model_args.cache_dir,
use_fast=model_args.use_fast_tokenizer)
elif model_args.model_name_or_path:
tokenizer = AutoTokenizer.from_pretrained(
model_args.model_name_or_path,
cache_dir=model_args.cache_dir,
use_fast=model_args.use_fast_tokenizer)
else:
raise ValueError(
"You are instantiating a new tokenizer from scratch. This is not supported by this script."
"You can do it from another script, save it, and load it from here, using --tokenizer_name."
)
if model_args.config_name:
config = T5Config.from_pretrained(model_args.config_name,
cache_dir=model_args.cache_dir,
vocab_size=len(tokenizer))
elif model_args.model_name_or_path:
config = T5Config.from_pretrained(model_args.model_name_or_path,
cache_dir=model_args.cache_dir)
else:
config = CONFIG_MAPPING[model_args.model_type]()
logger.warning(
"You are instantiating a new config instance from scratch.")
# 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)
# Otherwise, we 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],
return_attention_mask=False)
tokenized_datasets = datasets.map(
tokenize_function,
batched=True,
num_proc=data_args.preprocessing_num_workers,
remove_columns=column_names,
load_from_cache_file=not data_args.overwrite_cache,
)
# T5-like span masked language modeling will fuse consecutively masked tokens to a single sentinel token.
# To ensure that the input length is `max_seq_length`, we need to increase the maximum length
# according to `mlm_probability` and `mean_noise_span_length`. We can also define the label length accordingly.
expanded_inputs_length, targets_length = compute_input_and_target_lengths(
inputs_length=max_seq_length,
noise_density=data_args.mlm_probability,
mean_noise_span_length=data_args.mean_noise_span_length,
)
# Main data processing function that will concatenate all texts from our dataset and generate chunks of expanded_inputs_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 >= expanded_inputs_length:
total_length = (total_length //
expanded_inputs_length) * expanded_inputs_length
# Split by chunks of max_len.
result = {
k: [
t[i:i + expanded_inputs_length]
for i in range(0, total_length, expanded_inputs_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 = FlaxT5ForConditionalGeneration.from_pretrained(
model_args.model_name_or_path,
config=config,
seed=training_args.seed,
dtype=getattr(jnp, model_args.dtype))
else:
config.vocab_size = len(tokenizer)
model = FlaxT5ForConditionalGeneration(config,
seed=training_args.seed,
dtype=getattr(
jnp, model_args.dtype))
# Data collator
# This one will take care of randomly masking the tokens.
data_collator = FlaxDataCollatorForT5MLM(
tokenizer=tokenizer,
noise_density=data_args.mlm_probability,
mean_noise_span_length=data_args.mean_noise_span_length,
input_length=max_seq_length,
target_length=targets_length,
pad_token_id=model.config.pad_token_id,
decoder_start_token_id=model.config.decoder_start_token_id,
)
# Store some constant
num_epochs = int(training_args.num_train_epochs)
train_batch_size = int(
training_args.per_device_train_batch_size) * jax.device_count()
eval_batch_size = int(
training_args.per_device_eval_batch_size) * jax.device_count()
num_train_steps = len(
tokenized_datasets["train"]) // train_batch_size * num_epochs
# Create learning rate schedule
warmup_fn = optax.linear_schedule(
init_value=0.0,
end_value=training_args.learning_rate,
transition_steps=training_args.warmup_steps)
decay_fn = optax.linear_schedule(
init_value=training_args.learning_rate,
end_value=0,
transition_steps=num_train_steps - training_args.warmup_steps,
)
linear_decay_lr_schedule_fn = optax.join_schedules(
schedules=[warmup_fn, decay_fn],
boundaries=[training_args.warmup_steps])
# We use Optax's "masking" functionality to not apply weight decay
# to bias and LayerNorm scale parameters. decay_mask_fn returns a
# mask boolean with the same structure as the parameters.
# The mask is True for parameters that should be decayed.
def decay_mask_fn(params):
flat_params = traverse_util.flatten_dict(params)
flat_mask = {
path: (path[-1] != "bias"
and path[-2:] not in [("layer_norm", "scale"),
("final_layer_norm", "scale")])
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
loss = optax.softmax_cross_entropy(
logits, onehot(labels, logits.shape[-1])).mean()
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
loss = optax.softmax_cross_entropy(logits,
onehot(labels, logits.shape[-1]))
# compute accuracy
accuracy = jnp.equal(jnp.argmax(logits, axis=-1), labels)
# summarize metrics
metrics = {"loss": loss.mean(), "accuracy": accuracy.mean()}
metrics = jax.lax.pmean(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"])
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'].mean()}, Learning Rate: {train_metric['learning_rate'].mean()})"
)
train_metrics = []
if cur_step % training_args.eval_steps == 0 and cur_step > 0:
# ======================== Evaluating ==============================
num_eval_samples = len(tokenized_datasets["validation"])
eval_samples_idx = jnp.arange(num_eval_samples)
eval_batch_idx = generate_batch_splits(eval_samples_idx,
eval_batch_size)
eval_metrics = []
for i, batch_idx in enumerate(
tqdm(eval_batch_idx, desc="Evaluating ...",
position=2)):
samples = [
tokenized_datasets["validation"][int(idx)]
for idx in batch_idx
]
model_inputs = data_collator(samples)
# Model forward
model_inputs = shard(model_inputs.data)
metrics = p_eval_step(state.params, model_inputs)
eval_metrics.append(metrics)
# get eval metrics
eval_metrics = get_metrics(eval_metrics)
eval_metrics = jax.tree_map(jnp.mean, eval_metrics)
# Update progress bar
epochs.write(
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"])
eval_samples_idx = jnp.arange(num_eval_samples)
eval_batch_idx = generate_batch_splits(eval_samples_idx,
eval_batch_size)
eval_metrics = []
for i, batch_idx in enumerate(
tqdm(eval_batch_idx, desc="Evaluating ...", position=2)):
samples = [
tokenized_datasets["validation"][int(idx)] for idx in batch_idx
]
model_inputs = data_collator(samples)
# Model forward
model_inputs = shard(model_inputs.data)
metrics = p_eval_step(state.params, model_inputs)
eval_metrics.append(metrics)
# get eval metrics
eval_metrics = get_metrics(eval_metrics)
eval_metrics = jax.tree_map(lambda metric: jnp.mean(metric).item(),
eval_metrics)
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)
num_train_samples = len(tokenized_datasets["train"])
train_samples_idx = jax.random.permutation(
input_rng, jnp.arange(num_train_samples))
train_batch_idx = generate_batch_splits(train_samples_idx,
train_batch_size)
# Gather the indexes for creating the batch and do a training step
for 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, pad_to_multiple_of=16)
# Model forward
model_inputs = shard(model_inputs.data)
state, train_metric, dropout_rngs = p_train_step(
state, model_inputs, dropout_rngs)
train_metrics.append(train_metric)
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(
def train_and_evaluate(config, workdir, vocab_filepath):
"""Runs a training and evaluation loop.
Args:
config: Model and training configuration.
workdir: Working directory for checkpoints and Tensorboard summaries. If
this contains a checkpoint, training will be resumed from the latest
checkpoint.
vocab_filepath: Absolute path to SentencePiece vocab model.
Raises:
ValueError: If training or eval batch sizes won't fit number of processes
and devices, or config is underspecified.
"""
n_processes = jax.process_count() # Number of processes
n_devices = jax.local_device_count() # Number of local devices per process
if config.train_batch_size % (n_processes * n_devices) > 0:
raise ValueError(
"Training batch size must be divisible by the total number of devices, "
"but training batch size = %d, while total number of devices = %d "
"(%d processes, each with %d devices)" %
(config.train_batch_size, n_processes * n_devices, n_processes,
n_devices))
if config.eval_batch_size % (n_processes * n_devices) > 0:
raise ValueError(
"Eval batch size must be divisible by the total number of devices, "
"but eval batch size = %d, while total number of devices = %d "
"(%d processes, each with %d devices)" %
(config.eval_batch_size, n_processes * n_devices, n_processes,
n_devices))
per_process_train_batch_size = config.train_batch_size // n_processes
per_process_eval_batch_size = config.eval_batch_size // n_processes
if jax.process_index() == 0:
train_summary_writer = tensorboard.SummaryWriter(
os.path.join(workdir, "train"))
eval_summary_writer = tensorboard.SummaryWriter(
os.path.join(workdir, "eval"))
else:
train_summary_writer = None
eval_summary_writer = None
rng = random.PRNGKey(config.seed)
rng, init_rng = random.split(rng)
tokenizer = spm.SentencePieceProcessor()
tokenizer.Load(vocab_filepath)
tokenizer.SetEncodeExtraOptions("")
# Note: [CLS] and [SEP] will be added by the data pipeline, not the tokenizer.
with config.unlocked():
config.vocab_size = tokenizer.GetPieceSize()
frozen_config = ml_collections.FrozenConfigDict(config)
model = models.PreTrainingModel(config=frozen_config,
random_seed=config.seed)
params = _init_params(model, init_rng, frozen_config)
optimizer = _create_adam_optimizer(config.learning_rate, params)
# We access model state only from optimizer via optimizer.target.
del params
# In case current job restarts, ensure that we continue from where we left
# off.
optimizer = checkpoints.restore_checkpoint(workdir, optimizer)
start_step = int(optimizer.state.step)
# Otherwise, try to restore optimizer and model state from config checkpoint.
if start_step == 0 and "init_checkpoint_dir" in config and config.init_checkpoint_dir:
optimizer = checkpoints.restore_checkpoint(config.init_checkpoint_dir,
optimizer)
optimizer = jax_utils.replicate(optimizer)
learning_rate_fn = train_utils.create_learning_rate_scheduler(
factors="constant * linear_warmup * linear_decay",
base_learning_rate=config.learning_rate,
warmup_steps=config.num_warmup_steps,
decay_steps=config.num_train_steps - config.num_warmup_steps,
)
c4_masked_lm_inputs = functools.partial(
input_pipeline.c4_masked_lm_inputs,
tokenizer=tokenizer,
max_seq_length=config.max_seq_length,
max_predictions_per_seq=config.max_predictions_per_seq,
masking_rate=config.masking_rate,
mask_token_proportion=config.mask_token_proportion,
random_token_proportion=config.random_token_proportion)
train_ds = c4_masked_lm_inputs(batch_size=per_process_train_batch_size)
train_iter = iter(train_ds)
eval_ds = c4_masked_lm_inputs(batch_size=per_process_eval_batch_size)
# We init the first set of dropout PRNG keys, but update it afterwards inside
# the main pmap'd training update for performance.
rngs = random.split(rng, n_devices)
loss_and_metrics_fn = functools.partial(_compute_loss_and_metrics,
model=model,
pad_id=tokenizer.pad_id())
p_train_step = jax.pmap(functools.partial(
train_utils.train_step,
loss_and_metrics_fn=loss_and_metrics_fn,
learning_rate_fn=learning_rate_fn,
clipped_grad_norm=config.clipped_grad_norm),
axis_name="batch")
metric_fn = functools.partial(_compute_eval_stats,
model=model,
pad_id=tokenizer.pad_id())
p_eval_step = jax.pmap(functools.partial(train_utils.eval_step,
metric_fn=metric_fn),
axis_name="batch")
train_metrics = []
logging.info("Starting training loop.")
logging.info("====================")
for step in range(start_step, config.num_train_steps):
with jax.profiler.StepTraceContext("train", step_num=step):
train_batch = next(train_iter)
train_batch = common_utils.shard(train_batch)
optimizer, train_step_metrics, rngs = p_train_step(optimizer,
train_batch,
rng=rngs)
train_metrics.append(train_step_metrics)
if (step > 0 and config.save_checkpoints_steps
and step % config.save_checkpoints_steps == 0
and jax.process_index() == 0):
# Save un-replicated optimizer + model state.
checkpoints.save_checkpoint(workdir,
jax_utils.unreplicate(optimizer),
step,
keep=2)
# Periodic metric handling.
if step % config.eval_frequency != 0 and step > 0:
continue
logging.info("Gathering training metrics at step: %d", step)
train_metrics = common_utils.get_metrics(train_metrics)
train_summary = _compute_loss_and_accuracy_metrics(train_metrics)
# Add training specific metrics.
train_summary["unclipped_grad_l2_norm"] = jnp.sqrt(
jnp.sum(train_metrics["unclipped_grad_l2_sum"]))
train_summary["clipped_grad_l2_norm"] = jnp.sqrt(
jnp.sum(train_metrics["clipped_grad_l2_sum"]))
train_summary["learning_rate"] = learning_rate_fn(step)
if jax.process_index() == 0:
assert train_summary_writer
for key, val in train_summary.items():
train_summary_writer.scalar(key, val, step)
train_summary_writer.flush()
# Reset metric accumulation for next training evaluation cycle.
train_metrics = []
logging.info("Gathering evaluation metrics at step: %d", step)
all_stats = []
for _, eval_batch in zip(range(config.max_num_eval_steps), eval_ds):
eval_batch = common_utils.shard(eval_batch)
all_stats.append(p_eval_step(optimizer.target, eval_batch))
flat_stats = {}
for k in all_stats[0]:
flat_stats[k] = np.concatenate([stats[k] for stats in all_stats],
axis=0)
eval_summary = _compute_loss_and_accuracy_metrics(flat_stats)
if jax.process_index() == 0:
assert eval_summary_writer
for key, val in eval_summary.items():
eval_summary_writer.scalar(key, val, step)
eval_summary_writer.flush()
def main(_):
tf.enable_v2_behavior()
tf.random.set_seed(FLAGS.seed)
np.random.seed(FLAGS.seed)
random.seed(FLAGS.seed)
if not gfile.isdir(FLAGS.save_dir):
gfile.mkdir(FLAGS.save_dir)
hparam_str_dict = dict(seed=FLAGS.seed, lr=FLAGS.lr)
# Get hyperparmaters
if FLAGS.xm_parameters:
for key, value in json.loads(FLAGS.xm_parameters).items():
if key not in hparam_str_dict:
hparam_str_dict[key] = value
hparam_str = ','.join([
'%s=%s' % (k, str(hparam_str_dict[k]))
for k in sorted(hparam_str_dict.keys())
])
# Number of local devices for this host.
n_devices = jax.local_device_count()
if jax.host_id() == 0:
summary_writer = tensorboard.SummaryWriter(
os.path.join(FLAGS.save_dir, 'tb', hparam_str))
batch_size = FLAGS.per_device_batch_size * n_devices
io_shape = (FLAGS.per_device_batch_size, FLAGS.num_strings_per_task,
FLAGS.max_characters)
program_shape = (FLAGS.per_device_batch_size, FLAGS.max_program_length)
# Setup DSL
# ---------------------------------------------------------------------------
# Build token tables.
id_char_table = {i + 1: char for (i, char) in enumerate(dsl.CHARACTER)}
char_id_table = {char: id for id, char in id_char_table.items()}
id_token_table, token_id_table = dsl_tokens.build_token_tables()
io_vocab_size = len(char_id_table) + 1 # For padding.
program_vocab_size = len(token_id_table) + 1
bos_token = token_id_table[dsl.BOS]
eos_token = token_id_table[dsl.EOS]
# Parse io and program token sequences (for eval).
def decode_io(inputs, outputs):
"""Decode io examples tokens."""
def decode_str(s):
"""Decode string tokens."""
return ''.join([id_char_table[c_id] for c_id in s if c_id > 0])
inps, outs = [], []
for inp, out in zip(inputs, outputs):
inps.append(decode_str(inp))
outs.append(decode_str(out))
return inps, outs
def decode_program(program):
"""Decode program tokens."""
program = program[:np.argmax(program == eos_token) + 1].astype(
np.int32)
program = program[program != bos_token]
try:
return dsl.decode_program(program, id_token_table)
except: # pylint: disable=bare-except
return None # Program does not compile.
# Load Dataset
# ---------------------------------------------------------------------------
logging.info('Initializing dataset.')
if not FLAGS.dataset_filepattern:
raise ValueError('Must specify filepattern to dataset.')
# Training dataset.
dataset = input_pipeline.create_dataset_from_tf_record(
FLAGS.dataset_filepattern, token_id_table, char_id_table)
dataset = dataset.padded_batch(batch_size,
padded_shapes=(io_shape[1:], io_shape[1:],
program_shape[1:]),
drop_remainder=True)
# Split evaluation and training.
eval_ds = dataset.take(FLAGS.num_eval_steps)
# Decrease batch of predict dataset to handle beam search.
predict_ds = eval_ds.unbatch().padded_batch(
int(np.ceil(batch_size / 10)),
padded_shapes=(io_shape[1:], io_shape[1:], program_shape[1:]))
train_ds = dataset.skip(FLAGS.num_eval_steps).repeat()
train_iter = train_ds.as_numpy_iterator()
# Build Model and Optimizer
# ---------------------------------------------------------------------------
train_config = base_models.TransformerConfig(
vocab_size=io_vocab_size,
output_vocab_size=program_vocab_size,
shift=True,
emb_dim=FLAGS.embedding_dim,
num_heads=FLAGS.num_heads,
num_layers=FLAGS.num_layers,
qkv_dim=FLAGS.embedding_dim,
mlp_dim=FLAGS.hidden_dim,
max_len=max(FLAGS.max_characters, FLAGS.max_program_length),
deterministic=False,
decode=False,
bos_token=bos_token)
eval_config = train_config.replace(deterministic=True)
predict_config = train_config.replace(shift=False,
deterministic=True,
decode=not FLAGS.slow_decode)
rng = jax.random.PRNGKey(FLAGS.seed)
rng = jax.random.fold_in(rng, jax.host_id())
rng, init_rng = jax.random.split(rng)
m = models.DecomposeAttentionTransformer(eval_config)
initial_variables = jax.jit(m.init)(init_rng,
jnp.ones(io_shape, jnp.float32),
jnp.ones(io_shape, jnp.float32),
jnp.ones(program_shape, jnp.float32))
optimizer_def = optim.Adam(FLAGS.lr,
beta1=0.9,
beta2=0.98,
eps=1e-9,
weight_decay=FLAGS.weight_decay)
optimizer = optimizer_def.create(initial_variables['params'])
del initial_variables # Don't keep a copy of the initial model.
start_step = 0
if FLAGS.restore_checkpoints:
# Restore unreplicated optimizer + model state from last checkpoint.
optimizer = checkpoints.restore_checkpoint(
os.path.join(FLAGS.save_dir, 'checkpoints', hparam_str), optimizer)
# Grab last step.
start_step = int(optimizer.state.step)
logging.info('Found model checkpointed at step %d.', start_step)
# Replicate optimizer.
optimizer = jax_utils.replicate(optimizer)
# TODO(jxihong): Implement fast decoding.
assert FLAGS.slow_decode, 'Fast decoding is not implemented yet.'
learning_rate_fn = create_learning_rate_scheduler(
base_learning_rate=FLAGS.lr)
p_train_step = jax.pmap(functools.partial(
train_step, learning_rate_fn=learning_rate_fn, config=train_config),
axis_name='batch')
p_eval_step = jax.pmap(functools.partial(eval_step,
eos_token=eos_token,
config=eval_config),
axis_name='batch')
p_init_cache = jax.pmap(functools.partial(
initialize_cache,
max_decode_len=FLAGS.max_program_length,
config=predict_config),
axis_name='batch')
p_pred_step = jax.pmap(functools.partial(
predict_step,
eos_token=eos_token,
max_decode_len=FLAGS.max_program_length,
config=predict_config,
slow_decode=FLAGS.slow_decode),
axis_name='batch',
static_broadcasted_argnums=(4, ))
# Main Train Loop
# ---------------------------------------------------------------------------
train_rngs = jax.random.split(rng, jax.local_device_count())
del rng
metrics_all = []
tick = time.time()
for step in range(start_step, FLAGS.num_train_steps):
inputs, outputs, programs = common_utils.shard(next(train_iter))
optimizer, metrics, train_rngs = p_train_step(optimizer,
inputs,
outputs,
programs,
train_rng=train_rngs)
metrics_all.append(metrics)
is_last_step = step == FLAGS.num_train_steps - 1
# Save a Checkpoint
if (step % FLAGS.checkpoint_freq == 0 and step > 0) or is_last_step:
if jax.host_id() == 0:
# Save unreplicated optimizer + model state.
checkpoints.save_checkpoint(
os.path.join(FLAGS.save_dir, 'checkpoints', hparam_str),
jax_utils.unreplicate(optimizer), step)
# Periodic metric handling.
if not step or (step % FLAGS.log_freq != 0 and not is_last_step):
continue
logging.info('Gathering training metrics.')
# Training Metrics
metrics_all = common_utils.get_metrics(metrics_all)
lr = metrics_all.pop('learning_rate').mean()
metrics_sums = jax.tree_map(jnp.sum, metrics_all)
denominator = metrics_sums.pop('denominator')
summary = jax.tree_map(
lambda x: x / denominator, # pylint: disable=cell-var-from-loop
metrics_sums)
summary['learning_rate'] = lr
# Calculate (clipped) perplexity after averaging log-perplexities:
summary['perplexity'] = jnp.clip(jnp.exp(summary['loss']), a_max=1.0e4)
if jax.host_id() == 0:
logging.info('Train in step: %d, loss: %.4f', step,
summary['loss'])
tock = time.time()
steps_per_sec = FLAGS.log_freq / (tock - tick)
tick = tock
summary_writer.scalar('train/steps per second', steps_per_sec,
step)
for key, val in summary.items():
summary_writer.scalar('train/' + key, val, step)
summary_writer.flush()
# Reset metric accumulation for next evaluation cycle.
metrics_all = []
# Evaluation Metrics
logging.info('Gathering evaluation metrics.')
t_evaluation_start = time.time()
eval_metrics = []
for batches in eval_ds.as_numpy_iterator():
inputs, outputs, programs = common_utils.shard(batches)
metrics = p_eval_step(optimizer.target, inputs, outputs, programs)
eval_metrics.append(metrics)
eval_metrics = common_utils.get_metrics(eval_metrics)
eval_metrics_sums = jax.tree_map(jnp.sum, eval_metrics)
eval_denominator = eval_metrics_sums.pop('denominator')
eval_summary = jax.tree_map(
lambda x: x / eval_denominator, # pylint: disable=cell-var-from-loop
eval_metrics_sums)
if jax.host_id() == 0:
logging.info('Evaluation time: %.4f s step %d, loss: %.4f.',
time.time() - t_evaluation_start, step,
eval_summary['loss'])
for key, val in eval_summary.items():
summary_writer.scalar('eval/' + key, val, step)
summary_writer.flush()
# Beam search metrics.
logging.info('Gathering beam search metrics.')
for beam_size in [1, 10, 12, 24, 48, 96]:
t_inference_start = time.time()
pred_acc = 0
pred_denominator = 0
ios, targets, predictions = [], [], []
for batches in predict_ds.as_numpy_iterator():
pred_batch = batches
# Handle final odd-sized batch by padding instead of dropping it.
cur_pred_batch_size = pred_batch[0].shape[0]
if cur_pred_batch_size % n_devices:
padded_size = int(
np.ceil(cur_pred_batch_size / n_devices) * n_devices)
# pylint: disable=cell-var-from-loop
pred_batch = jax.tree_map(
lambda x: pad_examples(x, padded_size), pred_batch)
inputs, outputs, programs = common_utils.shard(pred_batch)
cache = (p_init_cache(inputs, outputs, programs)
if not FLAGS.slow_decode else None)
predicted = p_pred_step(optimizer.target, inputs, outputs,
cache, beam_size)
predicted = tohost(predicted)
inputs, outputs, programs = map(tohost,
(inputs, outputs, programs))
pred_denominator += programs.shape[0]
for i, beams in enumerate(predicted):
inps, outs = decode_io(inputs[i], outputs[i])
p, p_score = eval_predicted(beams,
inps,
outs,
parse_beam_fn=decode_program)
if p_score >= len(inps):
pred_acc += 1
ios.append(' ; '.join(map(str, zip(inps, outs))))
targets.append(decode_program(programs[i]).to_string())
try:
predictions.append(p.to_string())
except: # pylint: disable=bare-except
predictions.append('')
logging.info('ios: %s', ios[-1])
logging.info('target: %s', targets[-1])
beams_log = []
for beam in beams:
try:
beams_log.append(decode_program(beam).to_string())
except: # pylint: disable=bare-except
beams_log.append('None')
logging.info('predicted beam: %s', '\n'.join(beams_log))
all_pred_acc, all_pred_denominator = per_host_sum_pmap(
jax.tree_map(np.array, (pred_acc, pred_denominator)))
# Record beam search results as text summaries.
message = []
for n in np.random.choice(np.arange(len(predictions)), 8):
text = (f'ios: {ios[n]}\n\ntarget: {targets[n]}\n\n'
f'predicted: {predictions[n]}\n\n')
message.append(text)
# Write to tensorboard.
if jax.host_id() == 0:
slow_or_fast = 'slow' if FLAGS.slow_decode else 'fast'
logging.info(
'Prediction time, %s (beam %d): %.4f s, step %d, score %.4f',
slow_or_fast, beam_size,
time.time() - t_inference_start, step,
all_pred_acc / all_pred_denominator)
summary_writer.scalar(
'predict-{}/score-{}'.format(slow_or_fast, beam_size),
all_pred_acc / all_pred_denominator, step)
summary_writer.text('samples-{}'.format(beam_size),
'\n------\n'.join(message), step)
summary_writer.flush()
def main(argv):
if len(argv) > 1:
raise app.UsageError('Too many command-line arguments.')
tf.enable_v2_behavior()
config = FLAGS.config
logging.info('===========Config Dict============')
logging.info(config)
batch_size = config.batch_size
learning_rate = config.learning_rate
num_train_steps = config.num_train_steps
num_eval_steps = config.num_eval_steps
eval_freq = config.eval_frequency
random_seed = config.random_seed
model_type = config.model_type
model_kwargs = (config.model_kwargs.to_dict()
if 'model_kwargs' in config else {})
if jax.host_id() == 0:
summary_writer = tensorboard.SummaryWriter(
os.path.join(FLAGS.model_dir, 'summary'))
if batch_size % jax.device_count() > 0:
raise ValueError(
'Batch size must be divisible by the number of devices')
train_ds, eval_ds, test_ds, encoder = input_pipeline.get_datasets(
n_devices=jax.local_device_count(),
task_name=FLAGS.task_name,
data_dir=FLAGS.data_dir,
batch_size=batch_size,
max_length=config.max_length)
vocab_size = encoder.vocab_size
train_ds = train_ds.repeat()
train_iter = iter(train_ds)
max_length = config.max_length
input_shape = (batch_size, max_length)
model_kwargs.update({
'vocab_size': vocab_size,
'emb_dim': config.emb_dim,
'num_heads': config.num_heads,
'num_layers': config.num_layers,
'qkv_dim': config.qkv_dim,
'mlp_dim': config.mlp_dim,
'max_len': config.max_length,
'classifier': True,
'num_classes': 10
})
rng = random.PRNGKey(random_seed)
rng = jax.random.fold_in(rng, jax.host_id())
rng, init_rng = random.split(rng)
# We init the first set of dropout PRNG keys, but update it afterwards inside
# the main pmap'd training update for performance.
dropout_rngs = random.split(rng, jax.local_device_count())
if model_type == 'transformer':
model = create_model(init_rng, transformer.TransformerEncoder,
input_shape, model_kwargs)
else:
raise ValueError('Model type not supported')
optimizer = create_optimizer(model, learning_rate)
del model # Don't keep a copy of the initial model.
start_step = 0
if config.restore_checkpoints:
# Restore unreplicated optimizer + model state from last checkpoint.
optimizer = checkpoints.restore_checkpoint(FLAGS.model_dir, optimizer)
# Grab last step.
start_step = int(optimizer.state.step)
# Replicate optimizer.
optimizer = jax_utils.replicate(optimizer)
learning_rate_fn = train_utils.create_learning_rate_scheduler(
base_learning_rate=learning_rate)
p_train_step = jax.pmap(functools.partial(
train_step, learning_rate_fn=learning_rate_fn),
axis_name='batch')
p_eval_step = jax.pmap(eval_step, axis_name='batch')
# p_pred_step = jax.pmap(predict_step, axis_name='batch')
metrics_all = []
tick = time.time()
for step, batch in zip(range(start_step, num_train_steps), train_iter):
batch = common_utils.shard(jax.tree_map(lambda x: x._numpy(), batch)) # pylint: disable=protected-access
optimizer, metrics, dropout_rngs = p_train_step(
optimizer, batch, dropout_rng=dropout_rngs)
metrics_all.append(metrics)
logging.info('train in step: %d', step)
# Save a Checkpoint
if ((step % config.checkpoint_freq == 0 and step > 0)
or step == num_train_steps - 1):
if jax.host_id() == 0 and config.save_checkpoints:
# Save unreplicated optimizer + model state.
checkpoints.save_checkpoint(FLAGS.model_dir,
jax_utils.unreplicate(optimizer),
step)
# Periodic metric handling.
if step % eval_freq == 0 and step > 0:
metrics_all = common_utils.get_metrics(metrics_all)
lr = metrics_all.pop('learning_rate').mean()
metrics_sums = jax.tree_map(jnp.sum, metrics_all)
denominator = metrics_sums.pop('denominator')
summary = jax.tree_map(lambda x: x / denominator, metrics_sums) # pylint: disable=cell-var-from-loop
summary['learning_rate'] = lr
# Calculate (clipped) perplexity after averaging log-perplexities:
summary['perplexity'] = jnp.clip(jnp.exp(summary['loss']),
a_max=1.0e4)
logging.info('train in step: %d, loss: %.4f', step,
summary['loss'])
if jax.host_id() == 0:
tock = time.time()
steps_per_sec = eval_freq / (tock - tick)
tick = tock
summary_writer.scalar('steps per second', steps_per_sec, step)
for key, val in summary.items():
summary_writer.scalar(f'train_{key}', val, step)
summary_writer.flush()
# Reset metric accumulation for next evaluation cycle.
metrics_all = []
# Eval Metrics
eval_metrics = []
eval_iter = iter(eval_ds)
if num_eval_steps == -1:
num_iter = itertools.repeat(1)
else:
num_iter = range(num_eval_steps)
for _, eval_batch in zip(num_iter, eval_iter):
# pylint: disable=protected-access
eval_batch = common_utils.shard(
jax.tree_map(lambda x: x._numpy(), eval_batch))
# pylint: enable=protected-access
metrics = p_eval_step(optimizer.target, eval_batch)
eval_metrics.append(metrics)
eval_metrics = common_utils.get_metrics(eval_metrics)
eval_metrics_sums = jax.tree_map(jnp.sum, eval_metrics)
eval_denominator = eval_metrics_sums.pop('denominator')
eval_summary = jax.tree_map(
lambda x: x / eval_denominator, # pylint: disable=cell-var-from-loop
eval_metrics_sums)
# Calculate (clipped) perplexity after averaging log-perplexities:
eval_summary['perplexity'] = jnp.clip(jnp.exp(
eval_summary['loss']),
a_max=1.0e4)
logging.info('eval in step: %d, loss: %.4f, acc: %.4f', step,
eval_summary['loss'], eval_summary['accuracy'])
if jax.host_id() == 0:
for key, val in eval_summary.items():
summary_writer.scalar(f'eval_{key}', val, step)
summary_writer.flush()
def main(argv):
if len(argv) > 1:
raise app.UsageError('Too many command-line arguments.')
tf.enable_v2_behavior()
batch_size = FLAGS.batch_size
learning_rate = FLAGS.learning_rate
num_train_steps = FLAGS.num_train_steps
num_eval_steps = FLAGS.num_eval_steps
eval_freq = FLAGS.eval_frequency
max_length = FLAGS.max_length
random_seed = FLAGS.random_seed
if not FLAGS.dev:
raise app.UsageError('Please provide path to dev set.')
if not FLAGS.train:
raise app.UsageError('Please provide path to training set.')
parameter_path = os.path.join(FLAGS.model_dir,
FLAGS.experiment + '.params')
if jax.host_id() == 0:
train_summary_writer = tensorboard.SummaryWriter(
os.path.join(FLAGS.model_dir, FLAGS.experiment + '_train'))
eval_summary_writer = tensorboard.SummaryWriter(
os.path.join(FLAGS.model_dir, FLAGS.experiment + '_eval'))
if batch_size % jax.device_count() > 0:
raise ValueError(
'Batch size must be divisible by the number of devices')
device_batch_size = batch_size // jax.device_count()
# create the training and development dataset
vocabs = input_pipeline.create_vocabs(FLAGS.train)
attributes_input = [input_pipeline.CoNLLAttributes.FORM]
attributes_target = [input_pipeline.CoNLLAttributes.XPOS]
train_ds = input_pipeline.sentence_dataset_dict(FLAGS.train,
vocabs,
attributes_input,
attributes_target,
batch_size=batch_size,
bucket_size=max_length)
eval_ds = input_pipeline.sentence_dataset_dict(FLAGS.dev,
vocabs,
attributes_input,
attributes_target,
batch_size=batch_size,
bucket_size=max_length,
repeat=1)
train_iter = iter(train_ds)
bs = device_batch_size * jax.device_count()
rng = random.PRNGKey(random_seed)
rng, init_rng = random.split(rng)
input_shape = (bs, max_length)
transformer_kwargs = {
'vocab_size': len(vocabs['forms']),
'output_vocab_size': len(vocabs['xpos']),
'emb_dim': 512,
'num_heads': 8,
'num_layers': 6,
'qkv_dim': 512,
'mlp_dim': 2048,
'max_len': max_length,
}
model = create_model(init_rng, tuple(input_shape), transformer_kwargs)
optimizer = create_optimizer(model, learning_rate)
del model # don't keep a copy of the initial model
learning_rate_fn = create_learning_rate_scheduler(
base_learning_rate=learning_rate)
p_train_step = jax.pmap(functools.partial(
train_step, learning_rate_fn=learning_rate_fn),
axis_name='batch')
p_eval_step = jax.pmap(eval_step, axis_name='batch')
# We init the first set of dropout PRNG keys, but update it afterwards inside
# the main pmap'd training update for performance.
dropout_rngs = random.split(rng, jax.local_device_count())
metrics_all = []
tick = time.time()
best_dev_score = 0
for step, batch in zip(range(num_train_steps), train_iter):
batch = common_utils.shard(jax.tree_map(lambda x: x._numpy(), batch)) # pylint: disable=protected-access
optimizer, metrics, dropout_rngs = p_train_step(
optimizer, batch, dropout_rng=dropout_rngs)
metrics_all.append(metrics)
if (step + 1) % eval_freq == 0:
metrics_all = common_utils.get_metrics(metrics_all)
lr = metrics_all.pop('learning_rate').mean()
metrics_sums = jax.tree_map(jnp.sum, metrics_all)
denominator = metrics_sums.pop('denominator')
summary = jax.tree_map(lambda x: x / denominator, metrics_sums) # pylint: disable=cell-var-from-loop
summary['learning_rate'] = lr
# Calculate (clipped) perplexity after averaging log-perplexities:
summary['perplexity'] = jnp.clip(jnp.exp(summary['loss']),
a_max=1.0e4)
logging.info('train in step: %d, loss: %.4f', step,
summary['loss'])
if jax.host_id() == 0:
tock = time.time()
steps_per_sec = eval_freq / (tock - tick)
tick = tock
train_summary_writer.scalar('steps per second', steps_per_sec,
step)
for key, val in summary.items():
train_summary_writer.scalar(key, val, step)
train_summary_writer.flush()
# reset metric accumulation for next evaluation cycle.
metrics_all = []
eval_metrics = []
eval_iter = iter(eval_ds)
if num_eval_steps == -1:
num_iter = itertools.repeat(1)
else:
num_iter = range(num_eval_steps)
for _, eval_batch in zip(num_iter, eval_iter):
eval_batch = jax.tree_map(lambda x: x._numpy(), eval_batch) # pylint: disable=protected-access
# Handle final odd-sized batch by padding instead of dropping it.
cur_pred_batch_size = eval_batch['inputs'].shape[0]
if cur_pred_batch_size != batch_size:
logging.info('Uneven batch size %d.', cur_pred_batch_size)
eval_batch = jax.tree_map(
lambda x: pad_examples(x, batch_size), eval_batch)
eval_batch = common_utils.shard(eval_batch)
metrics = p_eval_step(optimizer.target, eval_batch)
eval_metrics.append(metrics)
eval_metrics = common_utils.get_metrics(eval_metrics)
eval_metrics_sums = jax.tree_map(jnp.sum, eval_metrics)
eval_denominator = eval_metrics_sums.pop('denominator')
eval_summary = jax.tree_map(
lambda x: x / eval_denominator, # pylint: disable=cell-var-from-loop
eval_metrics_sums)
# Calculate (clipped) perplexity after averaging log-perplexities:
eval_summary['perplexity'] = jnp.clip(jnp.exp(
eval_summary['loss']),
a_max=1.0e4)
logging.info('eval in step: %d, loss: %.4f, accuracy: %.4f', step,
eval_summary['loss'], eval_summary['accuracy'])
if best_dev_score < eval_summary['accuracy']:
best_dev_score = eval_summary['accuracy']
# TODO: save model.
eval_summary['best_dev_score'] = best_dev_score
logging.info('best development model score %.4f', best_dev_score)
if jax.host_id() == 0:
for key, val in eval_summary.items():
eval_summary_writer.scalar(key, val, step)
eval_summary_writer.flush()
def train_loop(config, dropout_rngs, eval_ds, eval_freq, num_eval_steps,
num_train_steps, optimizer, state, p_eval_step, p_train_step,
start_step, train_iter, summary_writer):
"""Training loop.
Args:
config: experiment config.
dropout_rngs: float array; Jax PRNG key.
eval_ds: tf.dataset; Evaluation dataset.
eval_freq: int; Evaluation frequency;
num_eval_steps: int; Number of evaluation steps.
num_train_steps: int; Number of training steps.
optimizer: flax optimizer.
state: model state, e.g. batch statistics.
p_eval_step: fn; Pmapped evaluation step function.
p_train_step: fn; Pmapped train step function.
start_step: int; global training step.
train_iter: iter(tf.dataset); Training data iterator.
summary_writer: tensorflow summary writer.
Returns:
optimizer, global training step
"""
metrics_all = []
tick = time.time()
logging.info('Starting training')
logging.info('====================')
step = 0
for step, batch in zip(range(start_step, num_train_steps), train_iter):
batch = common_utils.shard(jax.tree_map(lambda x: x._numpy(), batch)) # pylint: disable=protected-access
optimizer, state, metrics, dropout_rngs = p_train_step(
optimizer, state, batch, dropout_rng=dropout_rngs)
metrics_all.append(metrics)
# Save a Checkpoint
if ((step % config.checkpoint_freq == 0 and step > 0)
or step == num_train_steps - 1):
if jax.process_index() == 0 and config.save_checkpoints:
# Save unreplicated optimizer + model state.
checkpoints.save_checkpoint(FLAGS.model_dir,
(jax_utils.unreplicate(optimizer),
jax_utils.unreplicate(state)),
step)
# Periodic metric handling.
if step % eval_freq == 0 and step > 0:
metrics_all = common_utils.get_metrics(metrics_all)
lr = metrics_all.pop('learning_rate').mean()
metrics_sums = jax.tree_map(jnp.sum, metrics_all)
denominator = metrics_sums.pop('denominator')
summary = jax.tree_map(lambda x: x / denominator, metrics_sums) # pylint: disable=cell-var-from-loop
summary['learning_rate'] = lr
# Calculate (clipped) perplexity after averaging log-perplexities:
logging.info('train in step: %d, loss: %.4f, acc: %.4f', step,
summary['loss'], summary['accuracy'])
if jax.process_index() == 0:
tock = time.time()
steps_per_sec = eval_freq / (tock - tick)
tick = tock
summary_writer.scalar('examples_per_second',
steps_per_sec * config.batch_size, step)
for key, val in summary.items():
summary_writer.scalar(f'train_{key}', val, step)
summary_writer.flush()
# Reset metric accumulation for next evaluation cycle.
metrics_all = []
# Eval Metrics
eval_metrics = []
eval_iter = iter(eval_ds)
if num_eval_steps == -1:
num_iter = itertools.repeat(1)
else:
num_iter = range(num_eval_steps)
for _, eval_batch in zip(num_iter, eval_iter):
# pylint: disable=protected-access
eval_batch = common_utils.shard(
jax.tree_map(lambda x: x._numpy(), eval_batch))
# pylint: enable=protected-access
metrics = p_eval_step(optimizer.target, state, eval_batch)
eval_metrics.append(metrics)
eval_metrics = common_utils.get_metrics(eval_metrics)
eval_metrics_sums = jax.tree_map(jnp.sum, eval_metrics)
eval_denominator = eval_metrics_sums.pop('denominator')
eval_summary = jax.tree_map(
lambda x: x / eval_denominator, # pylint: disable=cell-var-from-loop
eval_metrics_sums)
logging.info('eval in step: %d, loss: %.4f, acc: %.4f', step,
eval_summary['loss'], eval_summary['accuracy'])
if jax.process_index() == 0:
for key, val in eval_summary.items():
summary_writer.scalar(f'val_{key}', val, step)
summary_writer.flush()
return optimizer, state, step
def main(argv):
global BLEU_THRESHOLD_REACHED
if len(argv) > 1:
raise app.UsageError('Too many command-line arguments.')
init_mllogger()
mllogger.event('cache_clear')
mllogger.start('init_start')
mllogger.event('submission_org', 'Google')
mllogger.event('submission_platform',
'TPUv3-{}'.format(jax.device_count()))
mllogger.event('submission_division', 'closed')
mllogger.event('submission_status', 'research')
mllogger.event('submission_benchmark', 'transformer')
mllogger.event('train_samples', input_pipeline.N_TRAIN)
mllogger.event('eval_samples', input_pipeline.N_EVAL)
tf.enable_v2_behavior()
# Use hardware RNG for bernoulli randoms in dropout mask creation.
if FLAGS.hardware_rng:
models.set_hardware_bernoulli()
num_partitions = FLAGS.num_partitions
batch_size = FLAGS.batch_size
if batch_size is None:
batch_size = min(16 * jax.device_count() // num_partitions, 2048)
mllogger.event('global_batch_size', batch_size)
num_eval_steps = FLAGS.num_eval_steps
max_target_length = FLAGS.max_target_length
max_eval_target_length = FLAGS.max_eval_target_length
max_length = max(max_target_length, max_eval_target_length)
mllogger.event('max_sequence_length',
max_length,
metadata={'method': 'discard'})
if FLAGS.random_seed is not None:
seed = FLAGS.random_seed
else:
seed = np.int32(time.time() if jax.host_id() == 0 else 0)
seed = per_host_sum_pmap(seed)
mllogger.event('seed', int(seed))
steps_per_epoch = int(math.ceil(input_pipeline.N_TRAIN / batch_size))
logging.info('steps per epoch: %d', steps_per_epoch)
num_replicas = jax.local_device_count() // num_partitions
device_train_input_shape = (batch_size //
(num_replicas * jax.host_count()),
max_target_length)
# This is per-host; in principle 64/replica or more should fit
eval_batch_size = min(
32 * num_replicas,
int(
math.ceil(input_pipeline.N_EVAL /
(num_replicas * jax.host_count()))) * num_replicas)
logging.info('eval batch size: %d', eval_batch_size)
pred_batches = int(
math.ceil(input_pipeline.N_EVAL /
(jax.host_count() * eval_batch_size)))
logging.info('pred batches: %d', pred_batches)
broadcast = functools.partial(_broadcast,
num_replicas=num_replicas,
num_partitions=num_partitions)
if jax.host_id() == 0:
train_summary_writer = tensorboard.SummaryWriter(
os.path.join(FLAGS.model_dir, 'train'))
eval_summary_writer = tensorboard.SummaryWriter(
os.path.join(FLAGS.model_dir, 'eval'))
else:
train_summary_writer = None
eval_summary_writer = None
# Write summaries in background thread to avoid blocking on device sync
summary_thread = thread.ThreadPoolExecutor(1, 'summary')
if FLAGS.infeed:
# Infeed is currently synchronous, so do it in a background thread too
infeed_pool = thread.ThreadPoolExecutor(jax.local_device_count(),
'infeed')
def maybe_start_xprof(seconds):
if jax.host_id() == 0 and FLAGS.xprof:
xprof = xprof_session.XprofSession()
xprof.start_session('REDACTED', True, 2)
def sleep_and_end_xprof():
time.sleep(seconds)
logging.info(
'Xprof URL: %s',
xprof.end_session_and_get_url(
tag=
'flax transformer, {} devices, {}-way, batch {} per replica'
.format(jax.device_count(), num_partitions,
device_train_input_shape[0])))
thread.ThreadPoolExecutor(1, 'xprof').submit(sleep_and_end_xprof)
# MLPerf 2020 WMT en-de dataset uses a custom T2T dataset:
# Shared 32K subword tokenization
# 256-length packed training examples from WMT17
# 97-length unpacked evaluation examples from WMT14
train_keys = [
'inputs', 'targets', 'inputs_position', 'targets_position',
'inputs_segmentation', 'targets_segmentation'
]
encoder = mlperf_encoder.SubwordTextEncoder(filename=FLAGS.vocab_path)
input_encoder = encoder
target_encoder = encoder
vocab_size = input_encoder.vocab_size
output_vocab_size = target_encoder.vocab_size
input_shape = (batch_size, max_target_length)
target_shape = (batch_size, max_target_length)
transformer_kwargs = {
'vocab_size': vocab_size,
'output_vocab_size': output_vocab_size,
'emb_dim': 1024,
'num_heads': 16,
'num_layers': 6,
'qkv_dim': 1024,
'mlp_dim': 4096,
'max_len': max_length,
'share_embeddings': FLAGS.share_embeddings,
'logits_via_embedding': FLAGS.logits_via_embedding,
'num_partitions': num_partitions,
}
rng = random.PRNGKey(seed)
rng, init_rng = random.split(rng)
model, cache_def = create_model(init_rng, tuple(input_shape),
tuple(target_shape), transformer_kwargs)
mllogger.event('opt_name', 'adam')
if batch_size < 1024:
learning_rate = 4.0 # 0.0625
warmup_steps = 1000
beta1 = 0.9
beta2 = 0.98
if batch_size < 2048:
learning_rate = 2.0
warmup_steps = 500 # ??
beta1 = 0.9 # ??
beta2 = 0.98 # ??
else:
learning_rate = 3.3092157691415953
warmup_steps = 664
beta1 = 0.9086575725261137
beta2 = 0.9198719118104947
epsilon = 1e-9
if FLAGS.learning_rate is not None:
learning_rate = FLAGS.learning_rate
mllogger.event('opt_adam_beta_1', beta1)
mllogger.event('opt_adam_beta_2', beta2)
mllogger.event('opt_adam_epsilon', epsilon)
optimizer_def = optim.Adam(learning_rate,
beta1=beta1,
beta2=beta2,
eps=epsilon,
weight_decay=FLAGS.weight_decay)
optimizer = optimizer_def.create(model)
del model # don't keep a copy of the initial model
# Build parameter partition annotations for preserving partitions from train
# to eval.
partition_rules = [
(('encoder', 'posembed_input'), partitions.empty_dict),
(('decoder', 'posembed_targets'), partitions.empty_dict),
(('embedding', ), partitions.spec(num_partitions, 1)),
((r'LayerNorm_\d+', '(bias|scale)'), None),
((r'encoder(decoder)?_norm', '(bias|scale)'), None),
((r'MultiHeadDotProductAttention_\d+', '(query|key|value)', 'kernel'),
partitions.spec(1, num_partitions, 1)),
((r'MultiHeadDotProductAttention_\d+', 'out', 'kernel'),
partitions.spec(num_partitions, 1, 1)),
((r'MlpBlock_\d+', r'Dense_\d+', 'bias'), None),
((r'MlpBlock_\d+', 'Dense_0', 'kernel'),
partitions.spec(1, num_partitions)),
((r'MlpBlock_\d+', 'Dense_1', 'kernel'),
partitions.spec(num_partitions, 1)),
(('state', 'step'), None),
]
optimizer_partitions = optimizer.restore_state(
partitions.set_partitions(partition_rules, optimizer.state_dict()))
optimizer = broadcast(optimizer)
empty_metrics = broadcast({'loss': 0.0, 'accuracy': 0, 'denominator': 0})
learning_rate_fn = create_learning_rate_scheduler(
base_learning_rate=learning_rate,
warmup_steps=warmup_steps,
hidden_size=transformer_kwargs['qkv_dim'])
p_train_step = jax.pmap(functools.partial(
train_step, learning_rate_fn=learning_rate_fn),
axis_name='batch',
in_axes=(None, 0, 0, 0))
if num_partitions > 1:
sharded_predict_step = sharded_jit(
predict_step,
in_parts=(None, optimizer_partitions.target, None),
out_parts=None)
else:
sharded_predict_step = predict_step
if FLAGS.extra_eval_metrics:
p_eval_step = jax.pmap(eval_step, axis_name='batch', in_axes=(None, 0))
p_pred_step = jax.pmap(sharded_predict_step,
axis_name='batch',
in_axes=(0, None, None))
p_allreduce_metrics = jax.pmap(functools.partial(lax.psum,
axis_name='batch'),
axis_name='batch')
def device_train_loop_cond(args):
_, _, _, _, step, epoch = args
return step // steps_per_epoch == epoch
def device_train_loop_body(args):
optimizer, dropout_rngs, metrics, token, step, epoch = args
input_data, token = lax.infeed(token,
shape=tuple([
jax.ShapedArray(
device_train_input_shape,
jnp.int32) for _ in train_keys
]))
batch = {k: v for k, v in zip(train_keys, input_data)}
optimizer, metrics, dropout_rngs = train_step(optimizer,
batch,
metrics,
learning_rate_fn,
dropout_rng=dropout_rngs)
step += 1
return optimizer, dropout_rngs, metrics, token, step, epoch
def device_train_loop(optimizer, dropout_rngs, metrics, step, epoch):
token = lax.create_token(step)
optimizer, dropout_rngs, metrics, _, step, _ = lax.while_loop(
device_train_loop_cond, device_train_loop_body,
(optimizer, dropout_rngs, metrics, token, step, epoch))
return optimizer, dropout_rngs, metrics, step
if num_partitions > 1:
device_train_loop = sharded_jit(device_train_loop,
in_parts=(optimizer_partitions, None,
None, None, None),
out_parts=(optimizer_partitions, None,
None, None))
p_train_epoch = jax.pmap(device_train_loop,
axis_name='batch',
in_axes=(None, 0, 0, None, None))
p_allreduce_metrics_train = functools.partial(lax.psum, axis_name='batch')
if num_partitions > 1:
p_allreduce_metrics_train = sharded_jit(p_allreduce_metrics_train,
in_parts=None,
out_parts=None,
num_partitions=num_partitions)
p_allreduce_metrics_train = jax.pmap(p_allreduce_metrics_train,
axis_name='batch')
# Precompile all needed computations with fake data so as not to include
# compilation time in MLPerf metrics.
if FLAGS.precompile:
logging.info('precompiling step/epoch functions')
if FLAGS.infeed:
# the device training loop condition will immediately be false, but
# the optimizer tree will be resharded here
optimizer, *_ = p_train_epoch(unbroadcast(optimizer),
random.split(rng, num_replicas),
empty_metrics,
jnp.array(0, dtype=jnp.int32), 1)
else:
metrics = empty_metrics
train_input_shape = (num_replicas, batch_size // num_replicas,
input_pipeline.MAX_TRAIN_LEN)
fake_batch = {
k: jnp.ones(train_input_shape, jnp.int32)
for k in train_keys
}
p_train_step(unbroadcast(optimizer),
fake_batch,
metrics,
dropout_rng=random.split(rng, num_replicas))
eval_input_shape = (num_replicas, eval_batch_size // num_replicas,
input_pipeline.MAX_EVAL_LEN)
fake_eval_batch = {
'inputs': jnp.ones(eval_input_shape, jnp.int32),
'targets': jnp.ones(eval_input_shape, jnp.int32),
}
if FLAGS.extra_eval_metrics:
p_eval_step(unbroadcast(optimizer.target), fake_eval_batch)
fake_cache = cache_def.initialize_cache(
(eval_input_shape[1], FLAGS.max_predict_length))
maybe_start_xprof(20)
p_pred_step(fake_eval_batch['inputs'], unbroadcast(optimizer.target),
fake_cache)
time.sleep(20)
sync_devices()
fake_bleu_1 = np.zeros((4, ), dtype=np.int32)
fake_bleu_2 = np.zeros((), dtype=np.int32)
per_host_sum_pmap((fake_bleu_1, fake_bleu_1, fake_bleu_2, fake_bleu_2))
sync_devices()
p_allreduce_metrics_train(empty_metrics)
sync_devices()
logging.info('finished precompiling step/epoch functions')
# We init the first set of dropout PRNG keys, but update it afterwards inside
# the main pmap'd training update for performance.
dropout_rngs = random.split(rng, num_replicas)
# Record time-0 metrics for proper tensorboard plot x-axis scaling.
if jax.host_id() == 0:
if FLAGS.compute_train_metrics:
train_summary_writer.scalar('loss', 9.999, 0)
train_summary_writer.scalar('accuracy', 0.0, 0)
train_summary_writer.flush()
eval_summary_writer.scalar('bleu', 0.0, 0)
eval_summary_writer.flush()
train_ds = input_pipeline.get_wmt_dataset(batch_size=batch_size //
jax.host_count(),
train=True)
eval_ds = input_pipeline.get_wmt_dataset(batch_size=eval_batch_size,
train=False)
train_iter = iter(train_ds)
eval_iter = iter(eval_ds)
local_devices = jax.local_devices()
maybe_start_xprof(max(30, 60 / (jax.device_count() / 2048)))
host_step, device_step = 0, broadcast(0)
gc.disable()
mllogger.end('init_stop')
if jax.host_id() == 0:
mllogger.start('run_start')
for epoch in range(FLAGS.num_epochs):
if jax.host_id() == 0 and not BLEU_THRESHOLD_REACHED:
mllogger.start('block_start',
metadata={
'first_epoch_num': epoch + 1,
'epoch_count': 1
})
metrics = empty_metrics
if FLAGS.infeed:
optimizer, dropout_rngs, metrics, device_step = p_train_epoch(
unbroadcast(optimizer), dropout_rngs, metrics,
unbroadcast(device_step), epoch)
while int(host_step // steps_per_epoch) == epoch:
# pylint: disable=protected-access
batch = jax.tree_map(lambda x: x._numpy(), next(train_iter))
# Shard data to devices and do a training step.
batch = jax.tree_map(
lambda x: x.reshape((num_replicas, -1) + x.shape[1:]), batch)
if FLAGS.infeed:
for i, device in enumerate(local_devices):
replica_id = i // num_partitions
input_tuple = tuple(
[batch[k][replica_id] for k in train_keys])
assert input_tuple[0].shape == device_train_input_shape, (
'infeed shape error %s != %s' %
(input_tuple[0].shape, device_train_input_shape))
assert input_tuple[0].dtype == jnp.int32, (
'infeed dtype error %s != %s' %
(input_tuple[0].dtype, jnp.int32))
infeed_pool.submit(
functools.partial(device.transfer_to_infeed,
input_tuple))
else:
optimizer, metrics, dropout_rngs = p_train_step(
unbroadcast(optimizer),
batch,
metrics,
dropout_rng=dropout_rngs)
host_step += 1
if FLAGS.compute_train_metrics:
metrics = p_allreduce_metrics_train(metrics)
# Schedule training metric handling.
summary_thread.submit(
functools.partial(write_train_summary, metrics,
train_summary_writer, host_step))
# Optional, extra evaluation metrics.
if FLAGS.extra_eval_metrics:
eval_metrics = []
eval_iter = iter(eval_ds)
for _, eval_batch in zip(range(num_eval_steps), eval_iter):
eval_batch = common_utils.shard(eval_batch)
metrics = p_eval_step(unbroadcast(optimizer.target),
eval_batch)
eval_metrics.append(metrics)
eval_metrics = p_allreduce_metrics(eval_metrics)
# Schedule metric summarization/logging.
summary_thread.submit(
functools.partial(write_eval_summary, eval_metrics,
eval_summary_writer, host_step))
# Translation and BLEU Score.
all_predicted, all_targets, all_bs = [], [], []
for i in range(pred_batches):
# pylint: disable=protected-access
pred_batch = jax.tree_map(lambda x: x._numpy(), next(eval_iter))
logging.info('Predicting on input of shape %s.',
str(pred_batch['inputs'].shape))
# Handle final odd-sized batch by padding instead of dropping it.
cur_pred_batch_size = pred_batch['inputs'].shape[0]
if cur_pred_batch_size != eval_batch_size:
logging.info('Translation: uneven batch size %d.',
cur_pred_batch_size)
pred_batch = jax.tree_map(
lambda x: pad_examples(x, eval_batch_size), pred_batch)
pred_batch = jax.tree_map(
lambda x: x.reshape((num_replicas, -1) + x.shape[1:]),
pred_batch)
per_device_batchsize = pred_batch['inputs'].shape[1]
cache = cache_def.initialize_cache(
(per_device_batchsize, FLAGS.max_predict_length))
all_predicted.append(
p_pred_step(pred_batch['inputs'],
unbroadcast(optimizer.target), cache))
all_targets.append(pred_batch['targets'])
all_bs.append(cur_pred_batch_size)
# Schedule BLEU calculation and summarization/logging.
# We use the ICI as part of BLEU score computation, so we call this from the
# main thread so the BLEU pmap runs before the next train epoch pmap
write_predict_summary(all_predicted, all_targets, all_bs,
target_encoder, eval_summary_writer, epoch,
host_step, summary_thread)
# Wait until computations are done before exiting
sync_devices()
if jax.host_id() == 0:
summary_thread.shutdown()
if not BLEU_THRESHOLD_REACHED:
mllogger.end('run_stop', metadata={'status': 'aborted'})
training_samples_idx = jax.random.permutation(
training_rng, jnp.arange(nb_training_samples))
training_batch_idx = generate_batch_splits(training_samples_idx,
batch_size)
# Gather the indexes for creating the batch and do a training step
for batch_idx in tqdm(training_batch_idx,
desc="Training...",
position=1):
samples = [
tokenized_datasets["train"][int(idx)] for idx in batch_idx
]
model_inputs = data_collator(samples, pad_to_multiple_of=16)
# Model forward
model_inputs = common_utils.shard(model_inputs.data)
loss, optimizer, dropout_rngs = p_training_step(
optimizer, model_inputs, dropout_rngs)
epochs.write(f"Loss: {loss}")
# ======================== Evaluating ==============================
nb_eval_samples = len(tokenized_datasets["validation"])
eval_samples_idx = jnp.arange(nb_eval_samples)
eval_batch_idx = generate_batch_splits(eval_samples_idx,
eval_batch_size)
eval_metrics = []
for i, batch_idx in enumerate(
tqdm(eval_batch_idx, desc="Evaluating ...", position=2)):
samples = [
def main(argv):
if len(argv) > 1:
raise app.UsageError('Too many command-line arguments.')
# This seems to be necessary even when importing TF2?
tf.enable_v2_behavior()
# Number of local devices for this host.
n_devices = jax.local_device_count()
if jax.host_id() == 0:
train_summary_writer = tensorboard.SummaryWriter(
os.path.join(FLAGS.model_dir, 'train'))
eval_summary_writer = tensorboard.SummaryWriter(
os.path.join(FLAGS.model_dir, 'eval'))
if FLAGS.batch_size % n_devices:
raise ValueError('Batch size must be divisible by the number of devices')
vocab_path = FLAGS.vocab_path
if vocab_path is None:
# Since the subword vocab file can take some time to generate,
# by default save and retrieve from parent directory containing model runs.
vocab_path = os.path.join(
os.path.join(*os.path.split(FLAGS.model_dir)[:1]),
'subwords.vocab')
# Load Dataset.
train_ds, eval_ds, predict_ds, encoder = input_pipeline.get_wmt_datasets(
n_devices=n_devices,
dataset_name=FLAGS.dataset_name,
data_dir=FLAGS.data_dir,
vocab_path=vocab_path,
batch_size=FLAGS.batch_size,
max_target_length=FLAGS.max_target_length,
max_eval_target_length=FLAGS.max_eval_target_length)
vocab_size = encoder.vocab_size + 1
eos_token = encoder.vocab_size
def decode_tokens(toks):
return encoder.decode(toks - eos_token * (toks == eos_token))
train_iter = iter(train_ds)
# Build Model and Optimizer.
transformer_kwargs = {
'vocab_size': vocab_size,
'output_vocab_size': vocab_size,
'emb_dim': 1024,
'num_heads': 16,
'num_layers': 6,
'qkv_dim': 1024,
'mlp_dim': 4096,
'max_len': max(FLAGS.max_target_length, FLAGS.max_eval_target_length),
'share_embeddings': FLAGS.share_embeddings,
'logits_via_embedding': FLAGS.logits_via_embedding,
}
start_step = 0
rng = random.PRNGKey(FLAGS.random_seed)
rng, init_rng = random.split(rng)
input_shape = (FLAGS.batch_size, FLAGS.max_target_length)
target_shape = (FLAGS.batch_size, FLAGS.max_target_length)
model, cache_def = create_model(init_rng,
input_shape,
target_shape,
transformer_kwargs)
optimizer = create_optimizer(model,
FLAGS.learning_rate,
FLAGS.weight_decay)
# We access model only from optimizer below via optimizer.target.
del model
if FLAGS.restore_checkpoints:
# Restore unreplicated optimizer + model state from last checkpoint.
optimizer = checkpoints.restore_checkpoint(FLAGS.model_dir, optimizer)
# Grab last step.
start_step = int(optimizer.state.step)
# Replicate optimizer over local devices.
optimizer = jax_utils.replicate(optimizer)
learning_rate_fn = create_learning_rate_scheduler(
base_learning_rate=FLAGS.learning_rate,
warmup_steps=FLAGS.warmup_steps)
p_train_step = jax.pmap(
functools.partial(
train_step,
learning_rate_fn=learning_rate_fn,
label_smoothing=FLAGS.label_smoothing,
use_bfloat16=FLAGS.use_bfloat16),
axis_name='batch')
p_eval_step = jax.pmap(
functools.partial(
eval_step,
label_smoothing=FLAGS.label_smoothing,
use_bfloat16=FLAGS.use_bfloat16),
axis_name='batch')
p_pred_step = jax.pmap(
functools.partial(
predict_step,
use_bfloat16=FLAGS.use_bfloat16),
axis_name='batch',
static_broadcasted_argnums=(3, 4)) # eos token, max_length are constant
# We init the first set of dropout PRNG keys, but update it afterwards inside
# the main pmap'd training update for performance.
dropout_rngs = random.split(rng, n_devices)
metrics_all = []
t_loop_start = time.time()
for step, batch in zip(range(start_step, FLAGS.num_train_steps), train_iter):
# Shard data to devices and do a training step.
batch = common_utils.shard(jax.tree_map(lambda x: x._numpy(), batch)) # pylint: disable=protected-access
optimizer, metrics, dropout_rngs = p_train_step(
optimizer, batch, dropout_rng=dropout_rngs)
metrics_all.append(metrics)
# Save a checkpoint on one host after every checkpoint_freq steps.
if (FLAGS.save_checkpoints
and step % FLAGS.checkpoint_freq == 0 and step > 0
and jax.host_id() == 0):
checkpoints.save_checkpoint(FLAGS.model_dir,
jax_utils.unreplicate(optimizer),
step)
# Periodic metric handling below.
if step % FLAGS.eval_frequency != 0:
continue
# Training metrics.
metrics_all = common_utils.get_metrics(metrics_all)
lr = metrics_all.pop('learning_rate').mean()
metrics_sums = jax.tree_map(jnp.sum, metrics_all)
denominator = metrics_sums.pop('denominator')
summary = jax.tree_map(lambda x: x / denominator, metrics_sums) # pylint: disable=cell-var-from-loop
summary['learning_rate'] = lr
logging.info('train in step: %d, loss: %.4f', step, summary['loss'])
steps_per_eval = FLAGS.eval_frequency if step != 0 else 1
steps_per_sec = steps_per_eval / (time.time() - t_loop_start)
t_loop_start = time.time()
if jax.host_id() == 0:
train_summary_writer.scalar('steps per second', steps_per_sec, step)
for key, val in summary.items():
train_summary_writer.scalar(key, val, step)
train_summary_writer.flush()
metrics_all = []
# Eval metrics.
t_eval_start = time.time()
eval_metrics = []
eval_iter = iter(eval_ds)
for _, eval_batch in zip(range(FLAGS.num_eval_steps), eval_iter):
eval_batch = jax.tree_map(lambda x: x._numpy(), eval_batch) # pylint: disable=protected-access
eval_batch = common_utils.shard(eval_batch)
metrics = p_eval_step(optimizer.target, eval_batch)
eval_metrics.append(metrics)
eval_metrics = common_utils.get_metrics(eval_metrics)
eval_metrics_sums = jax.tree_map(jnp.sum, eval_metrics)
eval_denominator = eval_metrics_sums.pop('denominator')
eval_summary = jax.tree_map(
lambda x: x / eval_denominator, # pylint: disable=cell-var-from-loop
eval_metrics_sums)
logging.info('eval in step: %d, loss: %.4f', step, eval_summary['loss'])
if jax.host_id() == 0:
for key, val in eval_summary.items():
eval_summary_writer.scalar(key, val, step)
eval_summary_writer.flush()
logging.info('eval time: %.4f s step %d', time.time()-t_eval_start, step)
# Translation and BLEU score.
t_inference_start = time.time()
predict_iter = iter(predict_ds)
sources, references, predictions = [], [], []
for _, pred_batch in enumerate(predict_iter):
pred_batch = jax.tree_map(lambda x: x._numpy(), pred_batch) # pylint: disable=protected-access
# Handle final odd-sized batch by padding instead of dropping it.
cur_pred_batch_size = pred_batch['inputs'].shape[0]
if cur_pred_batch_size % n_devices:
logging.info('Translation: uneven batch size %d.',
cur_pred_batch_size)
padded_size = int(
np.ceil(cur_pred_batch_size / n_devices) * n_devices)
pred_batch = jax.tree_map(
lambda x: pad_examples(x, padded_size), pred_batch) # pylint: disable=cell-var-from-loop
pred_batch = common_utils.shard(pred_batch)
per_device_batch_size = pred_batch['inputs'].shape[1]
cache = jax_utils.replicate(
cache_def.initialize_cache((per_device_batch_size,
FLAGS.max_predict_length)))
predicted = p_pred_step(pred_batch['inputs'],
optimizer.target,
cache,
eos_token,
FLAGS.max_predict_length)
predicted = tohost(predicted)
inputs = tohost(pred_batch['inputs'])
targets = tohost(pred_batch['targets'])
# Iterate through non-padding examples of batch.
for i, s in enumerate(predicted[:cur_pred_batch_size]):
sources.append(decode_tokens(inputs[i]))
references.append(decode_tokens(targets[i]))
# TODO(levskaya): debug very rare initial 0-token predictions.
try:
predictions.append(decode_tokens(s))
except ValueError:
logging.error('bad predicted tokens: %s', s)
predictions.append('Wir haben technische Schwierigkeiten.')
logging.info('inference time: %.4f s step %d.',
time.time()-t_inference_start, step)
logging.info('Translation: %d predictions %d references %d sources.',
len(predictions), len(references), len(sources))
# Calculate BLEU score for translated eval corpus against reference.
bleu_score = bleu.bleu_local(references, predictions)
sacrebleu_score = sacrebleu.corpus_bleu(predictions, [references]).score
# Save translation samples for tensorboard.
exemplars = ''
for n in np.random.choice(np.arange(len(predictions)), 8):
exemplars += f'{sources[n]}\n\n{references[n]}\n\n{predictions[n]}\n\n'
if jax.host_id() == 0:
eval_summary_writer.scalar('bleu', bleu_score, step)
eval_summary_writer.scalar('sacrebleu', sacrebleu_score, step)
eval_summary_writer.text('samples', exemplars, step)
eval_summary_writer.flush()
