def testBasicCollective(self):
local_devices = list(jax.local_devices())
if len(local_devices) < 4:
raise SkipTest("Test requires at least 4 local devices")
def f(a, b):
return lax.psum(a * 2, 'a'), b * 4
devices = np.array(local_devices[:4]).reshape((2, 2))
with mesh(devices, ('x', 'y')):
fm = xmap(f,
in_axes=[A({
'a': 0,
'b': 1
}), A({'c': 0})],
out_axes=[A({'b': 0}), A({'c': 0})],
schedule=[
('a', 'x'),
('b', 'y'),
('c', 'x'),
('a', 'vectorize'),
('b', 'vectorize'),
])
ashape = (16, 8, 5)
a = jnp.arange(np.prod(ashape)).reshape(ashape)
bshape = (2, 7)
b = jnp.arange(np.prod(bshape)).reshape(bshape)
c, d = fm(a, b)
self.assertAllClose(c, (a * 2).sum(0))
self.assertAllClose(d, b * 4)
def testBasic(self):
local_devices = list(jax.local_devices())
if len(local_devices) < 4:
raise SkipTest("Test requires at least 4 local devices")
def f(a, b):
return a * 2, b * 4
devices = np.array(local_devices[:4]).reshape((2, 2))
with mesh(devices, ('x', 'y')):
fm = xmap(f,
in_axes=[{
0: 'a',
1: 'b'
}, ['c', ...]],
out_axes=[{
0: 'a',
1: 'b'
}, ['c', ...]],
axis_resources={
'a': 'x',
'b': 'y',
'c': 'x'
})
ashape = (16, 8, 5)
a = jnp.arange(np.prod(ashape)).reshape(ashape)
bshape = (2, 7)
b = jnp.arange(np.prod(bshape)).reshape(bshape)
c, d = fm(a, b)
self.assertAllClose(c, a * 2)
self.assertAllClose(d, b * 4)
def f(x):
with mesh(np.empty((), dtype=np.object), ()):
@partial(xmap, in_axes={0: 'b'}, out_axes={0: 'b'})
def h(x):
return x
return h(x)
def new_f(*args, **kwargs):
axis_names, shape = unzip2(named_shape)
size = np.prod(shape)
local_devices = list(jax.local_devices())
if len(local_devices) < size:
raise SkipTest(f"Test requires {size} local devices")
mesh_devices = np.array(local_devices[:size]).reshape(shape)
with mesh(mesh_devices, axis_names):
return f(*args, **kwargs)
def testCaching(self):
def f(x):
assert should_be_tracing
return jnp.sin(x) * 2
x = np.arange(16).reshape(4, 4)
devices = np.array(list(jax.local_devices())[:4])
if devices.size < 4:
raise unittest.SkipTest("Test requires 4 devices")
devices = devices.reshape((2, 2))
with mesh(devices, ('x', 'y')):
should_be_tracing = True
pjit(f, in_axis_resources=P(('x', 'y')), out_axis_resources=None)(x)
should_be_tracing = False
pjit(f, in_axis_resources=P(('x', 'y')), out_axis_resources=None)(x)
# Re-create the mesh to make sure that has no influence on caching
with mesh(devices, ('x', 'y')):
should_be_tracing = False
pjit(f, in_axis_resources=P(('x', 'y')), out_axis_resources=None)(x)
def testCaching(self):
def f(x):
assert python_should_be_executing
return x * 2
devices = np.array(jax.local_devices()[:2])
if devices.size < 2:
raise SkipTest("Test requires 2 devices")
x = np.arange(8).reshape((2, 2, 2))
with mesh(devices, ('x',)):
python_should_be_executing = True
xmap(f, in_axes=['a', ...], out_axes=['a', ...],
axis_resources={'a': 'x'})(x)
python_should_be_executing = False
xmap(f, in_axes=['a', ...], out_axes=['a', ...],
axis_resources={'a': 'x'})(x)
with mesh(devices, ('x',)):
python_should_be_executing = False
xmap(f, in_axes=['a', ...], out_axes=['a', ...],
axis_resources={'a': 'x'})(x)
def f(x):
with mesh(np.array([jax.local_devices()[0]]), ('x')):
@partial(pjit,
in_axis_resources=P('x'),
out_axis_resources=None)
def h(x):
return x
return h(x)
def with_mesh(named_shape: MeshSpec) -> Generator[None, None, None]:
"""Test utility for setting up meshes given mesh data from `schedules`."""
# This is similar to the `with_mesh` function above, but isn't a decorator.
axis_names, shape = unzip2(named_shape)
size = prod(shape)
local_devices = list(jax.local_devices())
if len(local_devices) < size:
raise SkipTest(f"Test requires {size} local devices")
mesh_devices = np.array(local_devices[:size]).reshape(shape)
with mesh(mesh_devices, axis_names):
yield
def f(x):
with mesh(np.empty((), dtype=np.object), ()):
@partial(xmap,
in_axes=A({'b': 0}),
out_axes=A({'b': 0}),
schedule=[('b', 'vectorize')])
def h(x):
return x
return h(x)
def test_from_gda_duplicates(self):
global_mesh = create_global_mesh((1, 2), ('x', 'y'))
global_input_shape = (8, 2)
mesh_axes = ['x', 'y']
input_gda = create_gda(global_input_shape, global_mesh, mesh_axes)
# It's occasionally possible to end up with two FROM_GDA singletons (e.g. if
# pickling in_axis_resources and sending to other processes). Make sure this
# this doesn't cause an error to avoid user confusion.
from_gda_dup = pjit_lib._FromGsdaSingleton()
with mesh(global_mesh.devices, global_mesh.axis_names):
pjit(lambda x: x, in_axis_resources=from_gda_dup, out_axis_resources=None)(
input_gda)
def _pjit(inp):
if isinstance(inp, GlobalDeviceArray):
if inp.is_fully_replicated:
return inp.local_data(0).to_py()
global_mesh = inp._global_mesh
in_axis_resources = FROM_GDA
else:
# DA/SDA/np.array will be sharded based on global_mesh.local_mesh.
# Shape of local_mesh will always be (1, local_device_count())
devices = np.array(jax.devices()).reshape(jax.process_count(),
jax.local_device_count())
global_mesh = maps.Mesh(devices, ('processes', 'local_devices'))
in_axis_resources = P('processes')
if inp.ndim == 0 or not titled:
inp = np.expand_dims(inp, axis=0)
with maps.mesh(global_mesh.devices, global_mesh.axis_names):
out = pjit(lambda x: x,
in_axis_resources=in_axis_resources,
out_axis_resources=None)(inp)
return out.local_data(0).to_py()
def test_no_recompilation_due_to_in_axis_resources(self):
global_mesh = create_global_mesh((1, 2), ('x', 'y'))
global_input_shape = (8, 2)
mesh_axes = P(None,)
input_gda = create_gda(global_input_shape, global_mesh, mesh_axes)
with jax._src.config.parallel_functions_output_gda(True):
@partial(pjit, in_axis_resources=mesh_axes, out_axis_resources=mesh_axes)
def f(x):
return x
with mesh(global_mesh.devices, global_mesh.axis_names):
out_gda = f(input_gda)
self.assertEqual(out_gda._mesh_axes, ())
before_cache = pjit_lib._pjit_lower.cache_info()
f(out_gda)
after_cache = pjit_lib._pjit_lower.cache_info()
self.assertNotEqual(id(before_cache), id(after_cache))
self.assertEqual(before_cache.hits + 1, after_cache.hits)
self.assertEqual(before_cache.misses, after_cache.misses)
def testInfeed(self):
devices = np.array(jax.local_devices())
nr_devices = len(devices)
shape = (nr_devices * 3, nr_devices * 5)
def f_for_jit(x):
token = lax.create_token(x)
(y, ), token = lax.infeed(token,
shape=(jax.ShapedArray(
x.shape, np.float32), ))
(z, ), token = lax.infeed(token,
shape=(jax.ShapedArray(
x.shape, np.float32), ))
(w, ), token = lax.infeed(token,
shape=(jax.ShapedArray(
x.shape, np.float32), ))
return x + y + z + w
x = np.arange(np.prod(shape), dtype=np.float32).reshape(shape)
y = x * 2.
z = x * 3.
w = x * 4.
# Transfer data to infeed before executing the function. For GPUs, the
# execution of the compiled function is blocking, so transferring data
# to infeed before executing ensures that the execution does not deadlock
# waiting for the infeed data.
logging.info('Transfering to infeed for the jit call')
d = devices[0]
d.transfer_to_infeed((y, ))
d.transfer_to_infeed((z, ))
d.transfer_to_infeed((w, ))
# JIT
logging.info('Making jit call')
res0 = jax.jit(f_for_jit)(x)
self.assertAllClose(res0, x + y + z + w, check_dtypes=True)
# PJIT
def f_for_pjit(x):
token = lax.create_token(x)
# A replicated infeed
(y, ), token = lax.infeed(token,
shape=(jax.ShapedArray(
x.shape, np.float32), ),
partitions=(None, ))
# An infeed sharded on first axis
(z, ), token = lax.infeed(token,
shape=(jax.ShapedArray(
x.shape, np.float32), ),
partitions=(P(nr_devices, 1), ))
# An infeed sharded on second axis
(w, ), token = lax.infeed(token,
shape=(jax.ShapedArray(
x.shape, np.float32), ),
partitions=(P(1, nr_devices), ))
return x + y + z + w
logging.info('Transfering to infeed for the pjit call')
for didx, d in enumerate(devices):
# Transfer the whole array to all devices for replicated.
d.transfer_to_infeed((y, ))
# For sharded infeed, transfer only the needed slices to each device.
d.transfer_to_infeed((z[3 * didx:3 * didx + 3, :]))
d.transfer_to_infeed((w[:, 5 * didx:5 * didx + 5], ))
with mesh(devices, ['d']):
logging.info('Making pjit call')
res = pjit(f_for_pjit,
in_axis_resources=(P('d'), ),
out_axis_resources=P('d'))(x)
self.assertAllClose(res0, res, check_dtypes=True)
def dispatch():
with mesh(devices, ['d']):
logging.info('Making pjit call')
pjit(f, in_axis_resources=(P('d'),), out_axis_resources=P('d'))(x)
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}")
# Get the datasets: you can either provide your own CSV/JSON/TXT training and evaluation files (see below)
# or just provide the name of one of the public datasets available on the hub at https://huggingface.co/datasets/
# (the dataset will be downloaded automatically from the datasets Hub).
#
# For CSV/JSON files, this script will use the column called 'text' or the first column if no column called
# 'text' is found. You can easily tweak this behavior (see below).
if data_args.dataset_name is not None:
# Downloading and loading a dataset from the hub.
dataset = load_dataset(
data_args.dataset_name, data_args.dataset_config_name, cache_dir=model_args.cache_dir, 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)
# 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 config and tokenizer
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 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: 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 >= 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:
max_train_samples = min(len(train_dataset), data_args.max_train_samples)
train_dataset = train_dataset.select(range(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:
max_eval_samples = min(len(eval_dataset), data_args.max_eval_samples)
eval_dataset = eval_dataset.select(range(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
# TODO: weights should be initialized in pjitted fun, this won't work for REALLY large models
# TODO: when loading from pre-trained model we need to make sure the vocab is divisible by num_partitions
# GPT2's vocab is odd, we need to resize it for fine-tuning
model = FlaxAutoModelForCausalLM.from_pretrained(
model_args.model_name_or_path, seed=training_args.seed, dtype=getattr(jnp, model_args.dtype)
)
# 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,
)
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,
)
def get_initial_state(params):
state = optimizer.init(params)
return tuple(state), params
# Get PartitionSpec for model params
param_spec = set_partitions(unfreeze(model.params))
# Get the PyTree for opt_state, we don't actually initialize the opt_state yet.
params_shapes = jax.tree_map(lambda x: x.shape, model.params)
state_shapes = jax.eval_shape(get_initial_state, params_shapes)
# get PartitionSpec for opt_state, this is very specific to adamw
# TODO: optax returns different state for different optimizers, how can we handle this generically ?
# or maybe we don't since in our examples we just use adamw or adafactor
def get_opt_spec(x):
if isinstance(x, dict):
return param_spec
return None
opt_state_spec, param_spec = jax.tree_map(
get_opt_spec, state_shapes, is_leaf=lambda x: isinstance(x, (dict, optax.EmptyState))
)
# pjit the get_initial_state function to shard params and init
# optimizer state in sharded way
p_get_initial_state = pjit(
get_initial_state,
in_axis_resources=None,
out_axis_resources=(opt_state_spec, param_spec),
)
# hack: move the inital params to CPU to free up device memory
# TODO: allow loading weights on CPU in pre-trained model
model.params = jax.tree_map(lambda x: np.asarray(x), model.params)
# mesh defination
mesh_devices = np.array(jax.devices()).reshape(1, jax.local_device_count())
# actually initialize the opt_state
with mesh(mesh_devices, ("dp", "mp")):
opt_state, params = p_get_initial_state(freeze(model.params))
# cross-entropy with z loss
def loss_fn(logits, labels, z_loss=0):
shift_logits = logits[..., :-1, :]
shift_labels = labels[..., 1:]
shift_labels = onehot(shift_labels, shift_logits.shape[-1])
shift_logits = shift_logits - jax.lax.stop_gradient(shift_logits.max(axis=-1, keepdims=True))
log_z = jnp.log(jnp.sum(jnp.exp(shift_logits), axis=-1, keepdims=True))
log_softmax = shift_logits - log_z
loss = -jnp.sum(shift_labels * log_softmax, axis=-1)
loss += (1e-4 * jnp.square(log_z.squeeze(-1))) * z_loss
return loss.mean()
# Define gradient update step fn
# TODO: try to use TrainState instead of passing params and opt_state individually
def train_step(params, opt_state, dropout_rng, batch, step):
dropout_rng, new_dropout_rng = jax.random.split(dropout_rng)
def compute_loss(params):
labels = batch.pop("labels")
logits = model(**batch, params=params, dropout_rng=dropout_rng, train=True)[0]
loss = loss_fn(logits, labels, z_loss=1.0)
return loss
grad_fn = jax.value_and_grad(compute_loss)
loss, grads = grad_fn(params)
updates, new_opt_state = optimizer.update(grads, opt_state, params)
new_params = optax.apply_updates(params, updates)
metrics = {"loss": loss, "learning_rate": linear_decay_lr_schedule_fn(step)}
return new_params, tuple(new_opt_state), new_dropout_rng, metrics, step + 1
# Define eval fn
def eval_step(input_ids, labels, params):
logits = model(input_ids=input_ids, params=params, train=False)[0]
loss = loss_fn(logits, labels)
# metrics
return {"loss": loss}
p_train_step = pjit(
train_step,
in_axis_resources=(param_spec, opt_state_spec, None, None, None),
out_axis_resources=(param_spec, opt_state_spec, None, None, None),
donate_argnums=(0, 1),
)
p_eval_step = pjit(
eval_step,
in_axis_resources=(None, None, param_spec),
out_axis_resources=None,
)
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=f"Epoch ... (1/{num_epochs})", position=0)
global_step = 0
# we are not doing 2D parallelism (yet!), this just does model parallelism
with mesh(mesh_devices, ("dp", "mp")):
for _ 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_metrics = []
train_loader = data_loader(input_rng, train_dataset, train_batch_size, shuffle=True)
steps_per_epoch = len(train_dataset) // train_batch_size
# train
for _ in tqdm(range(steps_per_epoch), desc="Training...", position=1, leave=False):
batch = next(train_loader)
params, opt_state, dropout_rng, train_metric, global_step = p_train_step(
params,
opt_state,
dropout_rng,
batch,
global_step,
)
train_metrics.append(train_metric)
cur_step = global_step
if cur_step % training_args.logging_steps == 0 and cur_step > 0:
# Save metrics
train_time += time.time() - train_start
if has_tensorboard and jax.process_index() == 0:
write_train_metric(summary_writer, train_metrics, train_time, cur_step)
epochs.write(
f"Step... ({cur_step} | Loss: {train_metric['loss']}, Learning Rate: {train_metric['learning_rate']})"
)
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):
batch = next(eval_loader)
metrics = p_eval_step(batch["input_ids"], batch["labels"], params)
eval_metrics.append(metrics)
# normalize eval metrics
eval_metrics = stack_forest(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")
logger.info(
f"Step... ({cur_step} | Eval loss: {eval_metrics['loss']} | Eval Perplexity: {eval_metrics['perplexity']}"
)
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(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}",
)
