def restore_model_and_put_to_devices(
config: ml_collections.ConfigDict,
workdir: str,
partition_specs: Sequence[PartitionSpec],
model: flax.nn.Module,
optimizer: flax.optim.Optimizer,
train_iter: Iterable[Any],
rngs: Mapping[str, jnp.ndarray],
thread_pool: multiprocessing.pool.ThreadPool,
) -> Tuple[flax.optim.Optimizer, Iterable[Any], jnp.ndarray, Mapping[str, Any]]:
"""Restores from latest available checkpoint and puts model to devices."""
(optimizer, train_iter, rng_state_tf, rngs,
global_state) = train.restore_checkpoints(
workdir=workdir,
step=None,
partition_specs=partition_specs,
optimizer=optimizer,
train_iter=train_iter,
rng_state_tf=tf.random.get_global_generator().state.numpy(),
rng_state_jax=rngs,
global_state={},
thread_pool=thread_pool)
if global_state:
# 1. If a checkpoint is present in the current work dir, continue training.
logging.info("Continuing training from step %d", global_state["step"])
# Shard parameters and optim state and put to the corresponding device.
optimizer = core.tree_shard(optimizer)
elif config.get("model_init_prefix"):
# 2. Alternatively, initialize from the given model_init_prefix checkpoint.
logging.info("Fine-tuning model from %r...", config.model_init_prefix)
if not hasattr(model, "load"):
# Note: Likely due to use of .partial, model may end up being e.g.
# a flax.nn.Base.PatchTransformer instead of experts_nn.PatchTransformer
# This causes explicit checks for class equivalence to fail, and also
# causes static type checking to fail. Checking for .load attribute
# circumvents both these issues.
raise ValueError((f"Loaded model {model} has no load method. Are you sure"
" it is one of 'PatchTransformer' and 'Resformer'?"))
restored_params = model.load(
prefix=config.model_init_prefix,
init_params=optimizer.target,
model_params=config.model,
keep_head=config.get("keep_head", False),
partition_specs=partition_specs)
# Shard restored parameters and replicate original optimizer state.
optimizer = optimizer.replace(
target=core.tree_shard(restored_params),
state=flax.jax_utils.replicate(optimizer.state))
global_state = {"step": 0, "accum_train_time": 0.0}
else:
# 3. Use model initialized from scratch.
logging.info("Initializing training from scratch...")
optimizer = flax.jax_utils.replicate(optimizer)
global_state = {"step": 0, "accum_train_time": 0.0}
# Set TF's global RNG generator and JAX's per-device RNG keys.
train.rng_tf_set_global_generator(rng_state_tf)
rngs_per_device = jax.tree_map(train.rng_jax_fold_host_if_needed_and_shard,
rngs)
return optimizer, train_iter, rngs_per_device, global_state
def init(rng):
image_size = tuple(train_ds.element_spec["image"].shape[1:])
dummy_input = jnp.zeros((local_batch_size,) + image_size, jnp.float32)
params = flax.core.unfreeze(model.init(rng, dummy_input))["params"]
# Set bias in the head to a low value, such that loss is small initially.
params["head"]["bias"] = jnp.full_like(
params["head"]["bias"], config.get("init_head_bias", 0))
return params
def update_fn(opt, lr, images, labels, rng):
"""Update step."""
measurements = {}
if config.get("mixup") and config.mixup.p:
rng, (images, labels), _ = u.mixup(rng, images, labels, **config.mixup)
# Get device-specific loss rng.
rng, rng_model = jax.random.split(rng, 2)
rng_model_local = jax.random.fold_in(rng_model, jax.lax.axis_index("batch"))
def loss_fn(params, images, labels):
logits, _ = model.apply(
{"params": flax.core.freeze(params)}, images,
train=True, rngs={"dropout": rng_model_local})
return getattr(u, config.get("loss", "sigmoid_xent"))(
logits=logits, labels=labels)
# Implementation considerations compared and summarized at
# https://docs.google.com/document/d/1g3kMEvqu1DOawaflKNyUsIoQ4yIVEoyE5ZlIPkIl4Lc/edit?hl=en#
l, g = u.accumulate_gradient(jax.value_and_grad(loss_fn), opt.target,
images, labels,
config.get("grad_accum_steps"))
l, g = jax.lax.pmean((l, g), axis_name="batch")
# Log the gradient norm only if we need to compute it anyways (clipping)
# or if we don't use grad_accum_steps, as they interact badly.
if config.get("grad_accum_steps", 1) == 1 or config.get("grad_clip_norm"):
grads, _ = jax.tree_flatten(g)
l2_g = jnp.sqrt(sum([jnp.vdot(p, p) for p in grads]))
measurements["l2_grads"] = l2_g
# Optionally resize the global gradient to a maximum norm. We found this
# useful in some cases across optimizers, hence it's in the main loop.
if config.get("grad_clip_norm"):
g_factor = jnp.minimum(1.0, config.grad_clip_norm / l2_g)
g = jax.tree_map(lambda p: g_factor * p, g)
opt = opt.apply_gradient(g, learning_rate=lr)
decay_rules = config.get("weight_decay", []) or []
if isinstance(decay_rules, numbers.Number):
decay_rules = [(".*kernel.*", decay_rules)]
sched_m = lr/config.lr.base if config.get("weight_decay_decouple") else lr
def decay_fn(v, wd):
return (1.0 - sched_m * wd) * v
opt = opt.replace(target=u.tree_map_with_regex(
decay_fn, opt.target, decay_rules, name="weight decay"))
params, _ = jax.tree_flatten(opt.target)
measurements["l2_params"] = jnp.sqrt(sum([jnp.vdot(p, p) for p in params]))
return opt, l, rng, measurements
def _get_val_split(dataset, split, pp_eval, data_dir=None):
# We do ceil rounding such that we include the last incomplete batch.
nval_img = input_pipeline.get_num_examples(
dataset, split, data_dir=fillin(data_dir))
val_steps = int(np.ceil(nval_img / batch_size_eval))
logging.info("Running validation for %d steps for %s, %s", val_steps,
dataset, split)
val_it = input_pipeline.get_data(
dataset=dataset,
split=split,
data_dir=fillin(data_dir),
batch_size=local_batch_size_eval,
preprocess_fn=pp_builder.get_preprocess_fn(pp_eval),
cache=config.get("val_cache", "batched"),
repeat_after_batching=True,
prefetch=0, # Save memory since we cache.
drop_remainder=False,
shuffle_files=False)
val_it = u.start_input_pipeline(
val_it, config.get("prefetch_to_device", 1), pad=local_batch_size_eval)
return (val_it, val_steps)
def evaluation_fn(params, images, labels, mask):
# Ignore the entries with all zero labels for evaluation.
mask *= labels.max(axis=1)
logits, _ = model.apply({"params": flax.core.freeze(params)}, images)
losses = getattr(u, config.get("loss", "sigmoid_xent"))(
logits=logits, labels=labels, reduction=False)
loss = jax.lax.psum(losses * mask, axis_name="batch")
top1_idx = jnp.argmax(logits, axis=1)
# Extracts the label at the highest logit index for each image.
top1_correct = jnp.take_along_axis(labels, top1_idx[:, None], axis=1)[:, 0]
ncorrect = jax.lax.psum(top1_correct * mask, axis_name="batch")
n = jax.lax.psum(mask, axis_name="batch")
return ncorrect, loss, n
def main(_):
config = flags.FLAGS.config
workdir = flags.FLAGS.workdir
tf.io.gfile.makedirs(workdir)
partition_specs = []
# Loss to apply.
loss_to_apply = getattr(core, config.get("loss_to_apply", "softmax_xent"))
compute_ece = config.get("compute_ece", False)
is_sigmoid = config.get("loss_to_apply", "softmax_xent") == "sigmoid_xent"
if compute_ece and is_sigmoid:
error_msg = "Inconsistent config: ECE can only be used with 'softmax_xent'."
raise ValueError(error_msg)
ens_size = config.get("model.transformer.ens_size", 1)
# TODO(ghassen): enable sigmoid for ensemble.
if ens_size > 1 and is_sigmoid:
error_msg = "Inconsistent config: Ensemble only works with 'softmax_xent'."
raise ValueError(error_msg)
# The pool is used to perform misc operations such as logging in async way.
pool = multiprocessing.pool.ThreadPool()
# Ideally, this should make code deterministic, but for many reasons we are
# not there yet. For instance, tf.data.map is not determisntic.
rng_generator = tf.random.Generator.from_seed(config.get("seed", 0))
tf.random.set_global_generator(
rng_generator.split(jax.host_count())[jax.host_id()])
logging.info("Number of devices: %s (host_id: %s)", jax.device_count(),
jax.host_id())
logging.info("Config:\n%s", str(config))
if (config.batch_size % jax.device_count() != 0 or
config.batch_size_eval % jax.device_count() != 0):
raise ValueError(f"Batch sizes ({config.batch_size} and "
f"{config.batch_size_eval}) must be divisible by "
f"the number of devices ({jax.device_count()})")
batch_size_per_host = config.batch_size // jax.host_count()
batch_size_per_core = config.batch_size // jax.device_count()
batch_size_per_host_eval = config.batch_size_eval // jax.host_count()
# TODO(basilm): Remove when JFT2.6B is properly submitted.
if config.dataset in jft_latest_pipeline.DATA_INFO:
input_pipeline = jft_latest_pipeline
cache = "loaded"
else:
input_pipeline = default_input_pipeline
cache = "batched"
train_ds = input_pipeline.get_data(
dataset=config.dataset,
data_dir=config.get("dataset_dir"),
split=config.train_split,
batch_size=batch_size_per_host,
preprocess_fn=pp_builder.get_preprocess_fn(config.pp_train),
shuffle_buffer_size=config.shuffle_buffer_size,
cache=False)
steps_per_epoch = input_pipeline.get_num_examples(
config.dataset, config.train_split,
data_dir=config.get("dataset_dir")) / config.batch_size
total_steps = train.get_total_steps_from_config(config, steps_per_epoch)
logging.info("Running for %d steps per epoch (%d steps total)",
steps_per_epoch, total_steps)
opt_def = train.get_optimizer_from_config(config, f"{BIG_VISION_DIR}.optims")
eval_config = copy.deepcopy(config)
if config.get("eval_overrides"):
with eval_config.unlocked():
eval_config.update(config.eval_overrides)
model = getattr(ub.models, config.model_name)
model_train = model(
num_classes=config.num_classes, train=True, **config.model)
model_eval = model(
num_classes=config.num_classes, train=False, **eval_config.model)
image_size = tuple(train_ds.element_spec["image"].shape[1:])
logging.info("Model initialization: Starting.")
opt, rngs = train.model_and_optim_init(
model_train.init, opt_def, (batch_size_per_core * ens_size,) + image_size,
config.get("init_head_bias"), config.get("seed", 0),
config.get("extra_rngs", ["dropout", "gating"]))
logging.info("Model initialization: Done.")
# TODO(jpuigcerver): Support logging parameter count with new sharding.
if config.get("plot_grad_norm_patterns"):
plot_grad_norm_name_fn = experts_utils.make_match_fn_from_prefixes(
config.plot_grad_norm_patterns)
else:
plot_grad_norm_name_fn = None
weight_decay_fn = train.get_weight_decay_function_from_config(config)
batch_loss_fn = train.wrap_module_with_auxiliary_loss_fn(
module=model_train,
loss_fn=loss_to_apply,
auxiliary_loss_weight=config.get("auxiliary_loss_weight", 0.0),
ens_size=ens_size)
if ens_size == 1:
evaluation_fn = functools.partial(
train.evaluation_fn,
apply_fn=model_eval.apply,
loss_fn=loss_to_apply,
correct_fn=train.correct_multilabel,
return_metric_args=compute_ece)
else:
evaluation_fn = functools.partial(
ensemble.evaluation_fn,
apply_fn=model_eval.apply,
return_metric_args=compute_ece,
ens_size=ens_size)
pmap_evaluation_fn = core.pmap_sorted(evaluation_fn, axis_name="batch")
update_fn = functools.partial(
batchensemble_utils.update_fn_be,
weight_decay_fn=weight_decay_fn,
plot_grad_norm_name_fn=plot_grad_norm_name_fn,
plot_grads_nan_inf=config.get("plot_grads_nan_inf", True),
max_grad_norm_global=config.get("clip_grad_norm", None),
frozen_vars_patterns=config.get("frozen_var_patterns", None),
fast_weight_lr_multiplier=config.get("fast_weight_lr_multiplier", None))
pmap_update_fn = core.pmap_sorted(
update_fn, axis_name="batch", donate_argnums=(0, 1),
static_broadcasted_argnums=(5,))
# Restore parameters from checkpoints (if possible) and put to TPU devices.
opt, train_iter, rngs_per_device, global_state = restore_model_and_put_to_devices(
config, workdir, partition_specs, model, opt, iter(train_ds), rngs, pool)
del rngs
first_step = global_state["step"]
accum_train_time = global_state["accum_train_time"]
start_time = time.time()
logging.info("Initial step for training = %d.", first_step)
local_devices = sorted(jax.local_devices(), key=lambda device: device.id)
if config.get("ema", {}):
ema_updater = ema.ExponentialMovingAverage(
target=partitioning.tree_unreplicate_using_partition_specs(
jax.tree_map(np.zeros_like, opt.target),
partition_specs=partition_specs,
local_devices=local_devices),
num_updates=0,
**config.ema)
else:
ema_updater = None
if first_step != 0 and ema_updater is not None:
ema_updater = train.restore_ema_checkpoints(
workdir,
first_step,
partition_specs,
ema_updater,
local_devices=local_devices,
thread_pool=pool)
train_iter = u.start_input_pipeline(train_iter, config.prefetch_to_device)
eval_iters = train.get_dataset_eval_iters_from_config(
config, batch_size_per_host_eval, cache, input_pipeline)
lr_fn = u.create_learning_rate_schedule(
config.batch_size, total_steps, steps_per_epoch, **config.lr)
lr_iter = u.prefetch_scalar(map(lr_fn, range(first_step, total_steps)),
config.get("prefetch_to_device", 1))
writer = metric_writers.create_default_writer(
workdir, just_logging=jax.host_id() > 0, asynchronous_workers=128,
summary_writer=config.get("write_tf_summaries", False))
checkpoint_async_results = []
log_training_first_n_steps = config.get("log_training_first_n_steps", -1)
xm_work_unit.set_notes("First step compilations...")
with metric_writers.ensure_flushes(writer):
if jax.host_id() == 0:
callback_fn = xprof.XmUrlCallbackFn(
description=f"Xprof [{first_step + 171}...{first_step + 190}]",
work_unit=xm_work_unit)
else:
callback_fn = lambda x: x # Do nothing.
xprof_session = xprof.MultiStepXprofSession(
profile_steps=20, # For how many steps to profile after warmup.
warmup_steps=170, # For how many steps to wait before profiling.
stop_callback_fn=callback_fn)
for step, lr_repl in zip(range(first_step + 1, total_steps + 1), lr_iter):
train_batch = next(train_iter)
with xprof_session:
with jax.profiler.StepTraceAnnotation(name="train", step_num=step):
opt, rngs_per_device, loss_value, aux_info = pmap_update_fn(
opt,
rngs_per_device,
lr_repl,
train_batch["image"],
train_batch["labels"],
batch_loss_fn)
if (ema_updater is not None and
step % config.get("ema", {}).get("period", 10) == 0):
ema_updater = ema_updater.update(
partitioning.tree_unreplicate_using_partition_specs(
tree=opt.target,
partition_specs=partition_specs,
local_devices=local_devices))
# Checkpoint saving.
backup_checkpoints_every_n_steps = config.get("backup_checkpoint_steps")
if (step % config.write_checkpoint_every_n_steps == 0 or
(backup_checkpoints_every_n_steps is not None and
step % backup_checkpoints_every_n_steps == 0) or
step == total_steps):
# Before writing new checkpoints, make sure that all the previous
# checkpoint shards have been completely written (hosts are synced).
train.wait_async_results(
checkpoint_async_results,
timeout_secs=config.checkpoint_write_timeout_secs)
train.sync_all_hosts()
# Now host 0 can remove all the checkpoints older than the previous
# checkpointed step. The pool is used to remove files in parallel.
if jax.host_id() == 0:
train.remove_old_checkpoints(
workdir,
keep_steps_from=step - config.write_checkpoint_every_n_steps,
keep_steps_multiple_of=backup_checkpoints_every_n_steps,
thread_pool=pool)
# Save checkpoint for the current step, asynchronously.
# Note: Parameters on TPU are sliced and copied to CPU before scheduling
# the asynchronous copy, to prevent any extra TPU memory usage.
time_since_last_start = float(time.time() - start_time)
checkpoint_async_results = train.save_checkpoints(
workdir=workdir,
step=step,
partition_specs=partition_specs,
optimizer=opt,
# TODO(jpuigcerver): start_input_pipeline() does not return a
# serializable iterator. Also, serialization of a "memory heavy"
# tf.data.Dataset iterator may cause OOM (e.g. big shuffle buffer).
train_iter=None,
rng_state_tf=tf.random.get_global_generator().state.numpy(),
rng_state_jax=rngs_per_device,
global_state={
# Note: "step" is automatically added to this dictionary.
"accum_train_time": accum_train_time + time_since_last_start,
},
thread_pool=pool)
if ema_updater is not None:
checkpoint_async_results.append(train.save_ema_checkpoints(
workdir=workdir,
step=step,
partition_specs=partition_specs,
ema_updater=ema_updater,
local_devices=local_devices,
thread_pool=pool))
# Report training progress
if (jax.host_id() == 0 and config.log_training_every_n_steps > 0 and
(step % config.log_training_every_n_steps == 0 or
step == total_steps or step < log_training_first_n_steps)):
time_elapsed = time.time() - start_time + accum_train_time
img_sec_core = (
config.batch_size * step / time_elapsed / jax.device_count())
writer.write_scalars(step, {"learning_rate": lr_repl[0],
"training_loss": np.mean(loss_value),
"img/sec/core": img_sec_core,
"epoch": step / steps_per_epoch})
if aux_info:
# Per-block info has to be dealt especially.
if "per_block_info" in aux_info:
scalar_metrics_to_aggregate = config.get(
"scalar_metrics_to_aggregate", ())
metrics.write_info_to_metric_writer(
metric_writer=writer,
step=step,
gating_info_dict=jax.tree_map(lambda x: np.mean(x, axis=0),
aux_info["per_block_info"]),
scalar_metrics_to_aggregate=scalar_metrics_to_aggregate,
write_matrices=True)
del aux_info["per_block_info"]
# Plot rest of metrics as scalars.
writer.write_scalars(
step, {key: np.mean(value) for key, value in aux_info.items()})
def progress(start_time, step, num_steps, batch_size):
"""Generates progress note."""
time_elapsed = time.time() - start_time + accum_train_time
steps_per_sec = step / time_elapsed
eta_seconds = (num_steps - step) / (steps_per_sec + 1e-8)
note = ("Steps:{:d}/{:d} [{:.1f}%]\n"
"Images per second:{:.1f},\n"
"ETA:{}, Total time:{}".format(
step, num_steps, 100 * step / num_steps,
steps_per_sec * batch_size,
experts_utils.htime(eta_seconds),
experts_utils.htime((eta_seconds + time_elapsed))))
writer.write_scalars(
step, {"images_per_second": steps_per_sec * batch_size})
return note
progress_note = progress(
start_time,
step,
num_steps=total_steps,
batch_size=config.batch_size)
pool.apply_async(
lambda note=progress_note: xm_work_unit.set_notes(note))
# Run checks to detect if the model partitioning is unhealthy.
# Global health metrics will be written to XM, and in case of problems a
# WARNING or ERROR message will be logged.
train.monitor_partitioning_health(
optimizer=opt,
partition_specs=partition_specs,
metric_writer=writer,
step=step,
first_step=first_step + 1,
every_n_steps=config.get("check_partitioning_health_every_n_steps",
total_steps // 20))
# Evaluate model on validation, test, ...
rngs_per_device = train.run_evaluation_on_multiple_splits(
pmap_evaluation_fn, opt.target, eval_iters, rngs_per_device,
step / steps_per_epoch, step, total_steps,
config.run_evaluation_every_n_steps, writer, compute_ece,
config.get("ece_num_bins", 15), suffix="")
if ema_updater and config.run_evaluation_every_n_steps > 0 and (
step == first_step + 1 or
step % config.run_evaluation_every_n_steps == 0 or
step == total_steps):
logging.info("Evaluation with EMA weights at step %d: started.", step)
# Copy current parameters to CPU. Only one replica of each local
# partition is copied to prevent redundant data transfers (e.g.
# non-expert parameters).
curr_params = partitioning.tree_unreplicate_using_partition_specs(
tree=opt.target,
partition_specs=partition_specs,
local_devices=local_devices)
# Block curr_params until TPU->CPU copy has finished to prevent multiple
# copies of the TPU parameters.
curr_params = core.tree_block_until_ready(curr_params)
# Allow TPU parameters to be freed.
opt = opt.replace(target=None)
# Copy EMA parameters to TPU and run evaluation.
rngs_per_device = train.run_evaluation_on_multiple_splits(
pmap_evaluation_fn,
partitioning.tree_replicate_from_partitioned_tree(
ema_updater.get(),
partition_specs=partition_specs,
local_devices=local_devices),
eval_iters, rngs_per_device, step / steps_per_epoch, step,
total_steps, config.run_evaluation_every_n_steps, writer,
compute_ece, config.get("ece_num_bins", 15), suffix="_ema")
rngs_per_device = core.tree_block_until_ready(rngs_per_device)
# Copy current parameters back to the TPU.
opt = opt.replace(
target=partitioning.tree_replicate_from_partitioned_tree(
curr_params,
partition_specs=partition_specs,
local_devices=local_devices))
logging.info("Evaluation with EMA weights at step %d: finished.", step)
del curr_params
pool.close()
pool.join()
def main(argv):
del argv
config = FLAGS.config
workdir = FLAGS.workdir
logging.info("Workdir: %s", workdir)
save_checkpoint_path = None
if config.get("checkpoint_steps"):
tf.io.gfile.makedirs(workdir)
save_checkpoint_path = os.path.join(workdir, "checkpoint.npz")
# The pool is used to perform misc operations such as logging in async way.
pool = multiprocessing.pool.ThreadPool()
# This seed makes the Jax part of things (like model init) deterministic.
# However, full training still won't be deterministic, for example due to the
# tf.data pipeline not being deterministic even if we would set TF seed.
rng = jax.random.PRNGKey(config.get("seed", 0))
def write_note(note):
if jax.host_id() == 0:
logging.info("NOTE: %s", note)
write_note("Initializing...")
# Verify settings to make sure no checkpoints are accidentally missed.
if config.get("keep_checkpoint_steps"):
assert config.get("checkpoint_steps"), "Specify `checkpoint_steps`."
assert config.keep_checkpoint_steps % config.checkpoint_steps == 0, (
f"`keep_checkpoint_steps` ({config.checkpoint_steps}) should be"
f"divisible by `checkpoint_steps ({config.checkpoint_steps}).`")
batch_size = config.batch_size
batch_size_eval = config.get("batch_size_eval", batch_size)
if (batch_size % jax.device_count() != 0 or
batch_size_eval % jax.device_count() != 0):
raise ValueError(f"Batch sizes ({batch_size} and {batch_size_eval}) must "
f"be divisible by device number ({jax.device_count()})")
local_batch_size = batch_size // jax.host_count()
local_batch_size_eval = batch_size_eval // jax.host_count()
logging.info(
"Global batch size %d on %d hosts results in %d local batch size. "
"With %d dev per host (%d dev total), that's a %d per-device batch size.",
batch_size, jax.host_count(), local_batch_size,
jax.local_device_count(), jax.device_count(),
local_batch_size // jax.local_device_count())
write_note("Initializing train dataset...")
train_ds = input_pipeline.get_data(
dataset=config.dataset,
split=config.train_split,
data_dir=fillin(config.get("dataset_dir")),
batch_size=local_batch_size,
preprocess_fn=pp_builder.get_preprocess_fn(config.pp_train),
shuffle_buffer_size=config.shuffle_buffer_size,
prefetch=config.get("prefetch_to_host", 2),
cache=False)
# Start prefetching already.
train_iter = u.start_input_pipeline(
train_ds, config.get("prefetch_to_device", 1), pad=local_batch_size)
# We always pad to local_batch_size_eval even when less would be enough in
# order to minimize memory fragmentation.
write_note("Initializing val dataset(s)...")
def _get_val_split(dataset, split, pp_eval, data_dir=None):
# We do ceil rounding such that we include the last incomplete batch.
nval_img = input_pipeline.get_num_examples(
dataset, split, data_dir=fillin(data_dir))
val_steps = int(np.ceil(nval_img / batch_size_eval))
logging.info("Running validation for %d steps for %s, %s", val_steps,
dataset, split)
val_it = input_pipeline.get_data(
dataset=dataset,
split=split,
data_dir=fillin(data_dir),
batch_size=local_batch_size_eval,
preprocess_fn=pp_builder.get_preprocess_fn(pp_eval),
cache=config.get("val_cache", "batched"),
repeat_after_batching=True,
prefetch=0, # Save memory since we cache.
drop_remainder=False,
shuffle_files=False)
val_it = u.start_input_pipeline(
val_it, config.get("prefetch_to_device", 1), pad=local_batch_size_eval)
return (val_it, val_steps)
if isinstance(config.val_split, str):
val_ds = {"val": _get_val_split(config.dataset, config.val_split,
config.pp_eval, config.get("dataset_dir"))}
else:
val_ds = {t[0]: _get_val_split(*t[1:]) for t in config.val_split}
ntrain_img = input_pipeline.get_num_examples(
config.dataset, config.train_split,
data_dir=fillin(config.get("dataset_dir")))
steps_per_epoch = ntrain_img / batch_size
if config.get("num_epochs"):
total_steps = int(config.num_epochs * steps_per_epoch)
assert not config.get("total_steps"), "Set either num_epochs or total_steps"
else:
total_steps = config.total_steps
logging.info(
"Running for %d steps, that means %f epochs and %f steps per epoch",
total_steps, total_steps * batch_size / ntrain_img, steps_per_epoch)
mw = u.BigVisionMetricWriter(xm_xp.id, xm_wu.id, steps_per_epoch)
write_note(f"Initializing {config.model_name} model...")
model_mod = importlib.import_module(f"{BASEDIR}.models.{config.model_name}")
model = model_mod.Model(
num_classes=config.num_classes, **config.get("model", {}))
# We want all parameters to be created in host RAM, not on any device, they'll
# be sent there later as needed, otherwise we already encountered two
# situations where we allocate them twice.
@partial(jax.jit, backend="cpu")
def init(rng):
image_size = tuple(train_ds.element_spec["image"].shape[1:])
dummy_input = jnp.zeros((local_batch_size,) + image_size, jnp.float32)
params = flax.core.unfreeze(model.init(rng, dummy_input))["params"]
# Set bias in the head to a low value, such that loss is small initially.
params["head"]["bias"] = jnp.full_like(
params["head"]["bias"], config.get("init_head_bias", 0))
return params
rng, rng_init = jax.random.split(rng)
params_cpu = init(rng_init)
if jax.host_id() == 0:
num_params = sum(p.size for p in jax.tree_flatten(params_cpu)[0])
parameter_overview.log_parameter_overview(params_cpu)
mw.measure("num_params", num_params)
@partial(jax.pmap, axis_name="batch")
def evaluation_fn(params, images, labels, mask):
# Ignore the entries with all zero labels for evaluation.
mask *= labels.max(axis=1)
logits, _ = model.apply({"params": flax.core.freeze(params)}, images)
losses = getattr(u, config.get("loss", "sigmoid_xent"))(
logits=logits, labels=labels, reduction=False)
loss = jax.lax.psum(losses * mask, axis_name="batch")
top1_idx = jnp.argmax(logits, axis=1)
# Extracts the label at the highest logit index for each image.
top1_correct = jnp.take_along_axis(labels, top1_idx[:, None], axis=1)[:, 0]
ncorrect = jax.lax.psum(top1_correct * mask, axis_name="batch")
n = jax.lax.psum(mask, axis_name="batch")
return ncorrect, loss, n
# Setup function for computing representation.
@partial(jax.pmap, axis_name="batch")
def representation_fn(params, images, labels, mask):
_, outputs = model.apply({"params": flax.core.freeze(params)}, images)
representation = outputs[config.fewshot.representation_layer]
representation = jax.lax.all_gather(representation, "batch")
labels = jax.lax.all_gather(labels, "batch")
mask = jax.lax.all_gather(mask, "batch")
return representation, labels, mask
# Load the optimizer either from our folder or from flax.
opt_name = config.get("optim_name", "momentum_hp")
write_note(f"Initializing {opt_name} optimizer...")
try:
opt_mod = importlib.import_module(f"{BASEDIR}.optims.{opt_name}")
opt_def = opt_mod.Optimizer(**config.get("optim", {}))
except ModuleNotFoundError:
opt_def = getattr(flax.optim, opt_name)(**config.get("optim", {}))
# We jit this, such that the arrays that are created are created on the same
# device as the input is, in this case the CPU. Else they'd be on device[0].
opt_cpu = jax.jit(opt_def.create)(params_cpu)
@partial(jax.pmap, axis_name="batch", donate_argnums=(0,))
def update_fn(opt, lr, images, labels, rng):
"""Update step."""
measurements = {}
if config.get("mixup") and config.mixup.p:
rng, (images, labels), _ = u.mixup(rng, images, labels, **config.mixup)
# Get device-specific loss rng.
rng, rng_model = jax.random.split(rng, 2)
rng_model_local = jax.random.fold_in(rng_model, jax.lax.axis_index("batch"))
def loss_fn(params, images, labels):
logits, _ = model.apply(
{"params": flax.core.freeze(params)}, images,
train=True, rngs={"dropout": rng_model_local})
return getattr(u, config.get("loss", "sigmoid_xent"))(
logits=logits, labels=labels)
# Implementation considerations compared and summarized at
# https://docs.google.com/document/d/1g3kMEvqu1DOawaflKNyUsIoQ4yIVEoyE5ZlIPkIl4Lc/edit?hl=en#
l, g = u.accumulate_gradient(jax.value_and_grad(loss_fn), opt.target,
images, labels,
config.get("grad_accum_steps"))
l, g = jax.lax.pmean((l, g), axis_name="batch")
# Log the gradient norm only if we need to compute it anyways (clipping)
# or if we don't use grad_accum_steps, as they interact badly.
if config.get("grad_accum_steps", 1) == 1 or config.get("grad_clip_norm"):
grads, _ = jax.tree_flatten(g)
l2_g = jnp.sqrt(sum([jnp.vdot(p, p) for p in grads]))
measurements["l2_grads"] = l2_g
# Optionally resize the global gradient to a maximum norm. We found this
# useful in some cases across optimizers, hence it's in the main loop.
if config.get("grad_clip_norm"):
g_factor = jnp.minimum(1.0, config.grad_clip_norm / l2_g)
g = jax.tree_map(lambda p: g_factor * p, g)
opt = opt.apply_gradient(g, learning_rate=lr)
decay_rules = config.get("weight_decay", []) or []
if isinstance(decay_rules, numbers.Number):
decay_rules = [(".*kernel.*", decay_rules)]
sched_m = lr/config.lr.base if config.get("weight_decay_decouple") else lr
def decay_fn(v, wd):
return (1.0 - sched_m * wd) * v
opt = opt.replace(target=u.tree_map_with_regex(
decay_fn, opt.target, decay_rules, name="weight decay"))
params, _ = jax.tree_flatten(opt.target)
measurements["l2_params"] = jnp.sqrt(sum([jnp.vdot(p, p) for p in params]))
return opt, l, rng, measurements
# Other things besides optimizer state to be stored.
checkpoint_extra = dict(accum_train_time=0.0)
# Decide how to initialize training. The order is important.
# 1. Always resumes from the existing checkpoint, e.g. resumes a finetune job.
# 2. Resume from a previous checkpoint, e.g. start a cooldown training job.
# 3. Initialize model from something, e,g, start a fine-tuning job.
# 4. Train from scratch.
resume_checkpoint_path = None
if save_checkpoint_path and tf.io.gfile.exists(save_checkpoint_path):
resume_checkpoint_path = save_checkpoint_path
elif config.get("resume"):
resume_checkpoint_path = fillin(config.resume)
if resume_checkpoint_path:
write_note("Resume training from checkpoint...")
checkpoint = {"opt": opt_cpu, "extra": checkpoint_extra}
_, checkpoint_tree = jax.tree_flatten(checkpoint)
loaded = u.load_checkpoint(checkpoint_tree, resume_checkpoint_path)
# bfloat16 type gets lost when data is saved to disk, so we recover it.
checkpoint = jax.tree_map(u.recover_dtype, loaded)
opt_cpu, checkpoint_extra = checkpoint["opt"], checkpoint["extra"]
elif config.get("model_init"):
write_note(f"Initialize model from {config.model_init}...")
loaded = model_mod.load(params_cpu, config.model_init, config.get("model"))
opt_cpu = opt_cpu.replace(target=loaded)
if jax.host_id() == 0:
logging.info("Restored parameter overview:")
parameter_overview.log_parameter_overview(loaded)
write_note("Kicking off misc stuff...")
first_step = int(opt_cpu.state.step) # Might be a DeviceArray type.
chrono = u.Chrono(first_step, total_steps, batch_size,
checkpoint_extra["accum_train_time"])
# Note: switch to ProfileAllHosts() if you need to profile all hosts.
# (Xprof data become much larger and take longer to load for analysis)
profiler = periodic_actions.Profile(
# Create profile after every restart to analyze pre-emption related
# problems and assure we get similar performance in every run.
logdir=workdir, first_profile=first_step + 10)
# Prepare the learning-rate and pre-fetch it to device to avoid delays.
lr_fn = u.create_learning_rate_schedule(
batch_size, total_steps, steps_per_epoch, **config.get("lr", {}))
lr_iter = u.prefetch_scalar(map(lr_fn, range(first_step, total_steps)),
config.get("prefetch_to_device", 1))
write_note(f"Replicating...\n{chrono.note}")
opt_repl = flax_utils.replicate(opt_cpu)
write_note(f"Initializing few-shotters...\n{chrono.note}")
if "fewshot" in config:
fewshotter = fewshot.FewShotEvaluator(
representation_fn, config.fewshot,
config.fewshot.get("batch_size") or batch_size_eval)
rng, rng_loop = jax.random.split(rng, 2)
rngs_loop = flax_utils.replicate(rng_loop)
checkpoint_writer = None
write_note(f"First step compilations...\n{chrono.note}")
# Using a python integer for step here, because opt.state.step is allocated
# on TPU during replication.
for step, train_batch, lr_repl in zip(
range(first_step + 1, total_steps + 1), train_iter, lr_iter):
mw.step_start(step)
with jax.profiler.TraceContext("train_step", step_num=step, _r=1):
opt_repl, loss_value, rngs_loop, extra_measurements = update_fn(
opt_repl,
lr_repl,
train_batch["image"],
train_batch["labels"],
rng=rngs_loop)
if jax.host_id() == 0:
profiler(step)
# Checkpoint saving
if u.itstime(step, config.get("checkpoint_steps"), total_steps, host=0):
chrono.pause()
u.checkpointing_timeout(checkpoint_writer,
config.get("checkpoint_timeout", 1))
checkpoint_extra["accum_train_time"] = chrono.accum_train_time
# We need to transfer the weights over now or else we risk keeping them
# alive while they'll be updated in a future step, creating hard to debug
# memory errors (see b/160593526). Also, takes device 0's params only.
opt_cpu = jax.tree_map(lambda x: np.array(x[0]), opt_repl)
# Check whether we want to keep a copy of the current checkpoint.
copy_step = None
if u.itstime(step, config.get("keep_checkpoint_steps"), total_steps):
copy_step = step
# Checkpoint should be a nested dictionary or FLAX datataclasses from
# `flax.struct`. Both can be present in a checkpoint.
checkpoint = {"opt": opt_cpu, "extra": checkpoint_extra}
checkpoint_writer = pool.apply_async(
u.save_checkpoint, (checkpoint, save_checkpoint_path, copy_step))
chrono.resume()
# Report training progress
if u.itstime(step, config.log_training_steps, total_steps, host=0):
mw.measure("learning_rate", lr_repl[0])
mw.measure("training_loss", loss_value[0])
for name, value in extra_measurements.items():
mw.measure(name, value[0])
chrono.tick(step, mw.measure, write_note)
# Report validation performance
if u.itstime(step, config.log_eval_steps, total_steps):
chrono.pause()
for val_name, (val_iter, val_steps) in val_ds.items():
ncorrect, loss, nseen = 0, 0, 0
for _, batch in zip(range(val_steps), val_iter):
batch_ncorrect, batch_losses, batch_n = evaluation_fn(
opt_repl.target, batch["image"], batch["labels"], batch["mask"])
# All results are a replicated array shaped as follows:
# (local_devices, per_device_batch_size, elem_shape...)
# with each local device's entry being identical as they got psum'd.
# So let's just take the first one to the host as numpy.
ncorrect += np.sum(np.array(batch_ncorrect[0]))
loss += np.sum(np.array(batch_losses[0]))
nseen += np.sum(np.array(batch_n[0]))
mw.measure(f"{val_name}_prec@1", ncorrect / nseen)
mw.measure(f"{val_name}_loss", loss / nseen)
chrono.resume()
if "fewshot" in config:
# Compute few-shot on-the-fly evaluation.
if u.itstime(step, config.fewshot.log_steps, total_steps):
chrono.pause()
write_note(f"Few-shot evaluation...\n{chrono.note}")
r = fewshotter.run_all(opt_repl.target, config.fewshot.datasets)
fewshotter.walk_results(mw.measure, *r)
chrono.resume()
mw.step_end()
write_note(f"Done!\n{chrono.note}")
pool.close()
pool.join()
mw.close()
def loss_fn(params, images, labels):
logits, _ = model.apply(
{"params": flax.core.freeze(params)}, images,
train=True, rngs={"dropout": rng_model_local})
return getattr(u, config.get("loss", "sigmoid_xent"))(
logits=logits, labels=labels)