def load_model(self):
if self.network:
logger.info(
'Attempting to reload model when model is loaded. Returning')
return
with self.lock:
logger.info('Loading Model')
start = timer()
logger.info(f"JAX Devices: {jax.device_count()}")
logger.info(f"JAX Runtime Initialized in {timer(start):.06} secs")
mesh_shape = (jax.device_count() //
self.params['cores_per_replica'],
self.params['cores_per_replica'])
self.devices = np.array(jax.devices()).reshape(mesh_shape)
self.total_batch = self.params[
'per_replica_batch'] * jax.device_count(
) // self.params['cores_per_replica'] * 8
maps.thread_resources.env = maps.ResourceEnv(
maps.Mesh(self.devices, ('dp', 'mp')))
network = CausalTransformer(self.params)
logger.info(f'Loading Checkpoint')
network.state = read_ckpt(network.state, "/app/model/",
self.devices.shape[1])
logger.info(
f"GPTJ Network loaded in {timer(start):.06} secs. Total Batch Size: {self.total_batch}"
)
del network.state["opt_state"]
network.state = network.move_xmap(
network.state, np.zeros(self.params['cores_per_replica']))
self.network = network
def background(self):
logger.info(f'Init Background')
maps.thread_resources.env = maps.ResourceEnv(
maps.Mesh(self.devices, ('dp', 'mp')))
while True:
batch, qids = [], []
while len(batch) <= self.total_batch:
try:
req = self.queue.get(block=False)
logger.info(f'Got Queue Item: {req}')
batch.append(req['item'])
qids.append(req['qidx'])
except Empty:
if len(batch):
break
else:
time.sleep(0.01)
batch_size = len(batch)
logger.info(f'Working on Batch: {batch_size} - {qids}')
while len(batch) < self.total_batch:
batch.append(self.placeholder_item)
start = timer()
results = self.infer_batch(batch)
for res, qid in zip(results, qids):
self.queue_ids[qid].put(res)
logger.info(
f'Completed Current Batch of {batch_size} Items in {timer(start):.2f} secs'
)
def _pjit(inp):
if isinstance(inp, GlobalDeviceArray):
if inp.is_fully_replicated:
return inp.local_data(0).to_py()
global_mesh = inp.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 tiled:
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_pjit_inherits_effects(self):
if jax.default_backend() not in {'gpu', 'tpu'}:
raise unittest.SkipTest("pjit only supports GPU and TPU backends")
def f(x):
effect_p.bind(effect='foo')
effect_p.bind(effect='bar')
return x
f = pjit.pjit(f, in_axis_resources=pjit.PartitionSpec('x'),
out_axis_resources=pjit.PartitionSpec('x'))
with self.assertRaisesRegex(NotImplementedError, 'Effects not supported'):
with maps.Mesh(np.array(jax.devices()), ['x']):
jax.make_jaxpr(f)(jnp.arange(jax.local_device_count()))
def test_unordered_print_with_xmap(self):
def f(x):
debug_print("{}", x, ordered=False)
f = maps.xmap(f, in_axes=['a'], out_axes=None, backend='cpu',
axis_resources={'a': 'dev'})
with maps.Mesh(np.array(jax.devices(backend='cpu')), ['dev']):
with capture_stdout() as output:
f(jnp.arange(40))
jax.effects_barrier()
lines = [f"{i}\n" for i in range(40)]
self._assertLinesEqual(output(), "".join(lines))
def test_pjit_inherits_effects(self):
def f(x):
effect_p.bind(effect='foo')
effect_p.bind(effect='bar')
return x
f = pjit.pjit(f,
in_axis_resources=pjit.PartitionSpec('x'),
out_axis_resources=pjit.PartitionSpec('x'))
with self.assertRaisesRegex(NotImplementedError,
'Effects not supported'):
with maps.Mesh(np.array(jax.devices()), ['x']):
jax.make_jaxpr(f)(jnp.arange(jax.local_device_count()))
"seq": 2048,
"cores_per_replica": 1, # only running on one GPU
"per_replica_batch": 1,
}
per_replica_batch = params["per_replica_batch"]
cores_per_replica = params["cores_per_replica"]
seq = params["seq"]
params["sampler"] = nucleaus_sample
# here we "remove" the optimizer parameters from the model (as we don't need them for inference)
params["optimizer"] = optax.scale(0)
devices = np.array([jax.devices()[0]]).reshape((1, 1))
maps.thread_resources.env = maps.ResourceEnv(maps.Mesh(devices, ('dp', 'mp')))
tokenizer = transformers.GPT2TokenizerFast.from_pretrained('gpt2')
network = CausalTransformer(params)
start = time.time()
# here we load a checkpoint which was written with 8 shards into 1 shard
network.state = read_ckpt(network.state,
"step_383500/",
8,
shards_out=cores_per_replica)
def infer(context, top_p=0.9, temp=1.0, gen_len=512):
def decode_once_spmd_model(
task_p: InstantiableParams,
input_p: Sequence[InstantiableParams],
job_log_dir: Optional[str],
checkpoint_type: CheckpointType,
restore_checkpoint_dir: str,
restore_checkpoint_step: Optional[int],
) -> None:
"""Runs the decoding once on the entire decoder datasets for SPMD model.
Args:
task_p: Params for the task that encapsulates an SPMD model.
input_p: List of input params to be decoded.
job_log_dir: Directory for the job logs.
checkpoint_type: Type of model checkpointing method to use.
restore_checkpoint_dir: The directory from which to restore checkpoint.
restore_checkpoint_step: If set, the checkpoint step to restore. If unset,
try to restore from the latest checkpoint if any.
"""
# TODO(bf-jax): Retrieve the seeds from the model definition instead.
prng_key = jax.random.PRNGKey(1234)
prng_key, init_key = jax.random.split(prng_key)
if restore_checkpoint_dir:
restore_checkpoint_parent_dir = restore_checkpoint_dir
if checkpoint_type == CheckpointType.CHECKPOINT_MULTI_HOST_FLAX:
# TODO(zhouwk): add sanity check on number of subdirs and number of
# processes and fail early if unequal.
restore_checkpoint_dir = os.path.join(restore_checkpoint_dir,
f'{jax.process_index():03d}')
multi_host_checkpointing = bool(checkpoint_type in {
CheckpointType.CHECKPOINT_MULTI_HOST_FLAX, CheckpointType.CHECKPOINT_GDA
})
sample_inputs = input_p[0].Instantiate().get_next()
inputs_shape = tf.nest.map_structure(py_utils.get_global_input_shape_dtype,
sample_inputs)
model_p = task_p.model
# TODO(b/198356509): This is a hack for now as we need to change some
# annotations for mode='decode'. A future cl will move this logic
# to a more generic model_p.update_sharding_params_v1(mode='decode').
model_p.lm = model_p.lm.cls.set_sharding_params_v1(
model_p.lm,
replica_axis=model_p.lm.mesh_axis_names[0],
data_axis=model_p.lm.mesh_axis_names[1],
mdl_axis=model_p.lm.mesh_axis_names[2],
device_ids_mesh=model_p.lm.device_mesh,
mesh_axis_names=model_p.lm.mesh_axis_names,
mode='decode')
mesh_shape = model_p.device_mesh.shape
device_mesh = mesh_utils.create_device_mesh(mesh_shape)
logging.info('device_mesh: %s', device_mesh)
jax_task = task_p.Instantiate()
global_mesh = maps.Mesh(device_mesh, model_p.mesh_axis_names)
with global_mesh:
if restore_checkpoint_dir:
model = jax_task.model
model.instantiate_variable_configs()
# Get the metadata from variables instead of actually instantiating them.
partitioned_specs = jax_task.create_train_state_partition_specs(
model.vars, is_eval=True)
# Instantiate the TrainState directly from the checkpoint.
partitioned_train_state = checkpoints.restore_checkpoint(
None,
restore_checkpoint_dir,
global_mesh=global_mesh,
checkpoint_type=checkpoint_type,
state_specs=partitioned_specs,
step=restore_checkpoint_step)
if multi_host_checkpointing:
py_utils.sync_global_devices(
f'checkpointer:restored:{restore_checkpoint_parent_dir}')
decode_step_fn, inputs_partition_spec = (
trainer_lib.get_partitioned_spmd_model_decode_fn(
jax_task, init_key, partitioned_train_state, partitioned_specs,
inputs_shape))
else:
# When restore is not specified, randomly initiate the train_state.
(partitioned_train_state, inputs_partition_spec, partitioned_specs,
decode_step_fn) = trainer_lib.partition_spmd_model_decode(
task_p, init_key, inputs_shape)
logging.info('partitioned_train_state: %s',
jax.tree_map(lambda x: x.shape, partitioned_train_state))
# We do not fold in jax.process_index in contrast to the pmap version and
# use a single global key instead to rely on pjit to split for different
# replicas.
logging.info('root prng_key: %s', prng_key)
prng_key, decode_key = jax.random.split(prng_key)
logging.info('eval prng_key: %s', decode_key)
spmd_decode_step_fn = functools.partial(decode_step_fn,
partitioned_train_state.mdl_vars,
decode_key,
partitioned_train_state.step)
num_steps = [
-1 if p.reset_for_eval else p.eval_loop_num_batches for p in input_p
]
inputs = [p.Instantiate() for p in input_p]
decodes = [list() for _ in input_p]
process_id = jax.process_index()
for split, num_split_steps in enumerate(num_steps):
logging.info('Start decoding on input %s', input_p[split].name)
step_num = 0
while num_split_steps < 0 or step_num < num_split_steps:
step_num += 1
try:
batch = inputs[split].get_next()
except (tf.errors.OutOfRangeError, StopIteration):
break
if jax.config.jax_parallel_functions_output_gda:
batch = py_utils.create_gda(batch, inputs_shape, global_mesh,
inputs_partition_spec)
_, out = spmd_decode_step_fn(batch)
# Output is fully replicated now, so it's ok to unreplicate it by
# retrieving from device 0 only.
out = py_utils.maybe_unreplicate_gda(out)
global_batch_size = next(iter(out.values())).shape[0]
logging.info('Finished decoding input batch %d with %d examples',
step_num, global_batch_size)
# Manually shard the output per each jax process.
# We require that all fields in the output is batch major.
if global_batch_size % jax.process_count() != 0:
raise ValueError(f'Global batch size {global_batch_size} must divide '
f'jax process count {jax.process_count()}')
for k, v in out.items():
if v.shape[0] != global_batch_size:
raise ValueError('We require that all fields in the decode output '
'to have batch size as the first dim, got shape='
f'{v.shape} with key={k}, expect batch size = '
f'{global_batch_size}')
per_process_batch_size = global_batch_size // jax.process_count()
def shard(x, per_process_batch_size=per_process_batch_size):
return x[(process_id *
per_process_batch_size):((process_id + 1) *
per_process_batch_size)]
out = jax.tree_map(shard, out)
_, processed = jax_task.model.process_decode_out(inputs[split], out)
decodes[split].extend(processed)
logging.info('Finished processing decoded input batch %d', step_num)
basedir = os.path.join(job_log_dir, 'decoder_out')
dirnames = _get_dir_names(input_p)
filename = _get_filename(
py_utils.maybe_unreplicate_gda(partitioned_train_state.step))
for s in dirnames:
dir_path = os.path.join(basedir, s)
if not tf.io.gfile.exists(dir_path):
tf.io.gfile.makedirs(dir_path)
filenames = [os.path.join(basedir, s, filename) for s in dirnames]
for split, output_file in enumerate(filenames):
logging.info('Writing decoder output to %s with %d entries', output_file,
len(decodes[split]))
io_utils.WriteKeyValuePairs(output_file, decodes[split])
def evaluate_spmd_model(
task_p: InstantiableParams,
eval_input_p: Sequence[InstantiableParams],
job_log_dir: Optional[str],
checkpoint_type: CheckpointType,
) -> None:
"""Runs the evaluation loop on the entire test dataset for SPMD model.
Args:
task_p: Params of the task encapsulating an SPMD model.
eval_input_p: List of Params for the eval data pipelines.
job_log_dir: Directory for the job logs.
checkpoint_type: Type of model checkpointing method to use.
"""
logging.info('Using SPMD sharding for model parallelism.')
eval_input_pipelines = [input_p.Instantiate() for input_p in eval_input_p]
# TODO(bf-jax): Retrieve the seeds from the model definition instead.
prng_key = jax.random.PRNGKey(1234)
prng_key, init_key = jax.random.split(prng_key)
checkpoint_dir = os.path.join(job_log_dir, 'checkpoints')
# Note that GDA checkpoint requires all processes to participate in
# checkpointing but it does not require a separate checkpoint_dir per process.
if checkpoint_type == CheckpointType.CHECKPOINT_MULTI_HOST_FLAX:
checkpoint_task_dir = os.path.join(checkpoint_dir,
f'{jax.process_index():03d}')
else:
checkpoint_task_dir = checkpoint_dir
multi_host_checkpointing = bool(checkpoint_type in {
CheckpointType.CHECKPOINT_MULTI_HOST_FLAX, CheckpointType.CHECKPOINT_GDA
})
def get_shape_dtype(x):
y = jax.ShapeDtypeStruct(x.shape, x.dtype)
return y
# Do not ues eval_input_pipelines[0] directly.
sample_model_inputs = eval_input_p[0].Instantiate().get_next()
inputs_shape = tf.nest.map_structure(get_shape_dtype, sample_model_inputs)
model_p = task_p.model
mesh_shape = model_p.device_mesh.shape
device_mesh = mesh_utils.create_device_mesh(mesh_shape)
logging.info('device_mesh: %s', device_mesh)
global_mesh = maps.Mesh(device_mesh, model_p.mesh_axis_names)
with global_mesh:
partitioned_train_state, partitioned_specs, eval_inputs_partition_specs, _, eval_step, _ = (
trainer_lib.partition_spmd_model(task_p, init_key, inputs_shape))
partitioned_train_state = checkpoints.restore_checkpoint(
partitioned_train_state,
checkpoint_task_dir,
global_mesh=global_mesh,
checkpoint_type=checkpoint_type,
state_specs=partitioned_specs)
logging.info('partitioned_train_state: %s',
jax.tree_map(lambda x: x.shape, partitioned_train_state))
if multi_host_checkpointing:
py_utils.sync_global_devices(f'checkpointer:restored:{checkpoint_dir}')
# We do not fold in jax.process_index in contrast to the pmap version and
# use a single global key instead to rely on pjit to split for different
# replicas.
logging.info('root prng_key: %s', prng_key)
prng_key, eval_key = jax.random.split(prng_key)
logging.info('eval prng_key: %s', eval_key)
logging.info('Evaluation loop starting...')
summary_base_dir = os.path.join(job_log_dir, 'summaries')
summary_eval_dirs = [
os.path.join(summary_base_dir, f'eval_{split}')
for split, _ in enumerate(eval_input_p)
]
num_steps = [-1 if p.reset_for_eval else 1 for p in eval_input_p]
last_checkpoint = checkpoints.latest_checkpoint(checkpoint_dir)
with contextlib.ExitStack() as exit_stack:
eval_summary_writers = [
exit_stack.enter_context(summary_utils.get_summary_writer(d))
for d in summary_eval_dirs
]
while True:
step_i = int(jax.device_get(partitioned_train_state.step))
eval_step_fn = functools.partial(eval_step,
partitioned_train_state.mdl_vars,
eval_key, partitioned_train_state.step)
# Run the eval loop.
model_utils.run_eval_loop_over_test_splits(
num_steps,
eval_step_fn,
eval_summary_writers,
step_i,
eval_input_pipelines,
eval_inputs_partition_specs,
inputs_shape,
global_mesh,
reshard_inputs=False)
# If the last check point evaluated matches max train steps, exit.
if last_checkpoint is not None:
last_ckpt_step = checkpoints.get_step_from_checkpoint_asset(
last_checkpoint)
exceeded_ckpt = last_ckpt_step + task_p.train.save_interval_steps
if exceeded_ckpt >= task_p.train.num_train_steps:
break
new_checkpoint = checkpoints.latest_checkpoint(checkpoint_dir)
while new_checkpoint == last_checkpoint:
# Sleep for a minute.
time.sleep(60)
new_checkpoint = checkpoints.latest_checkpoint(checkpoint_dir)
# There must be a new checkpoint here.
logging.info('Found new checkpoint: %s', new_checkpoint)
partitioned_train_state = checkpoints.restore_checkpoint(
partitioned_train_state,
checkpoint_task_dir,
global_mesh=global_mesh,
checkpoint_type=checkpoint_type,
state_specs=partitioned_specs)
if multi_host_checkpointing:
py_utils.sync_global_devices(
f'checkpointer:restored:{checkpoint_dir}')
last_checkpoint = new_checkpoint
def train_and_evaluate_spmd_model(
task_p: InstantiableParams, train_input_p: InstantiableParams,
job_log_dir: Optional[str],
checkpoint_manager: checkpoint_managers.CheckpointManager,
checkpoint_type: CheckpointType, restore_checkpoint_dir: Optional[str],
restore_checkpoint_step: Optional[int],
eval_input_p: Optional[Sequence[InstantiableParams]]) -> None:
"""Runs the training and evaluation loop.
Args:
task_p: Params for task encapsulating the SPMD model.
train_input_p: Params for the train data pipeline.
job_log_dir: Directory for the job logs.
checkpoint_manager: A checkpoint manager controlling how often to save and
delete checkpoints.
checkpoint_type: The type of checkpoint to use.
restore_checkpoint_dir: If set, the directory from which to restore
checkpoint. If unset, use job_log_dir's `checkpoints` subdirectory
instead.
restore_checkpoint_step: If set, the checkpoint step to restore. If unset,
try to restore from the latest checkpoint if any.
eval_input_p: Optional list of params for the eval input pipelines.
"""
logging.info('Using SPMD sharding for model parallelism.')
model_p = task_p.model
if eval_input_p is not None:
eval_input_pipelines = [
input_p.Instantiate() for input_p in eval_input_p
]
# Do not mutate eval_input_pipelines itself. Instantiate a new one
# to get sample input.
sample_eval_model_inputs = eval_input_p[0].Instantiate().get_next()
eval_test_inputs_shape = tf.nest.map_structure(
py_utils.get_global_input_shape_dtype, sample_eval_model_inputs)
eval_test_inputs_pspecs = trainer_lib.get_input_partition_specs(
model_p.mesh_axis_names, eval_test_inputs_shape)
# TODO(bf-jax): Retrieve the seeds from the model definition instead.
prng_key = jax.random.PRNGKey(1234)
prng_key, init_key = jax.random.split(prng_key)
checkpoint_dir = _checkpoint_dir(job_log_dir)
restore_checkpoint_dir = restore_checkpoint_dir or checkpoint_dir
# Note that GDA checkpoint requires all processes to participate in
# checkpointing but it does not require a separate checkpoint_dir per process.
if checkpoint_type == CheckpointType.CHECKPOINT_MULTI_HOST_FLAX:
checkpoint_task_dir = os.path.join(checkpoint_dir,
f'{jax.process_index():03d}')
restore_checkpoint_task_dir = os.path.join(
restore_checkpoint_dir, f'{jax.process_index():03d}')
else:
checkpoint_task_dir = checkpoint_dir
restore_checkpoint_task_dir = restore_checkpoint_dir
multi_host_checkpointing = bool(checkpoint_type in {
CheckpointType.CHECKPOINT_MULTI_HOST_FLAX,
CheckpointType.CHECKPOINT_GDA
})
if jax.process_index() == 0:
tf.io.gfile.makedirs(checkpoint_dir)
if multi_host_checkpointing:
# Block all hosts until directory is ready.
py_utils.sync_global_devices(f'checkpointer:makedirs:{checkpoint_dir}')
logging.info('Retrieving model inputs for shape info.')
train_input_for_shape = train_input_p.Instantiate()
model_inputs_for_shape = train_input_for_shape.get_next()
inputs_shape = tf.nest.map_structure(py_utils.get_global_input_shape_dtype,
model_inputs_for_shape)
mesh_shape = model_p.device_mesh.shape
device_mesh = mesh_utils.create_device_mesh(mesh_shape)
logging.info('device_mesh: %s', device_mesh)
global_mesh = maps.Mesh(device_mesh, model_p.mesh_axis_names)
with global_mesh:
(partitioned_train_state, train_state_pspecs, inputs_pspecs,
train_step, eval_step,
total_num_params) = trainer_lib.partition_spmd_model(
task_p, init_key, inputs_shape)
partitioned_train_state = checkpoints.restore_checkpoint(
partitioned_train_state,
restore_checkpoint_task_dir,
global_mesh=global_mesh,
checkpoint_type=checkpoint_type,
state_specs=train_state_pspecs,
step=restore_checkpoint_step)
logging.info(
'partitioned_train_state shapes '
'(global shape for GDA, host-local shape for non-GDA: %s',
jax.tree_map(lambda x: x.shape, partitioned_train_state))
if multi_host_checkpointing:
py_utils.sync_global_devices(
f'checkpointer:restored:{checkpoint_dir}')
train_p = task_p.train
initial_global_step = int(
jax.device_get(
py_utils.maybe_unreplicate_gda(partitioned_train_state.step)))
logging.info('Model initial global_step=%d', initial_global_step)
_update_latest_model_step(train_input_p, initial_global_step,
train_p.eval_interval_steps)
train_input_pipeline = train_input_p.Instantiate()
# We do not fold in jax.process_index in contrast to the pmap version and
# use a single global key instead to rely on pjit to split for different
# replicas.
logging.info('root prng_key: %s', prng_key)
prng_key, train_key, eval_key = jax.random.split(prng_key, 3)
logging.info('train prng_key: %s', train_key)
logging.info('eval prng_key: %s', eval_key)
logging.info('Training loop starting...')
summary_base_dir = os.path.join(job_log_dir, 'summaries')
summary_train_dir = os.path.join(summary_base_dir, 'train')
summary_eval_dir = os.path.join(summary_base_dir, 'eval_train')
summary_writer = summary_utils.get_summary_writer
if eval_input_p is not None:
summary_eval_test_dirs = [
os.path.join(summary_base_dir, f'eval_test_{split}')
for split, _ in enumerate(eval_input_p)
]
# We either run p.eval_loop_num_batches steps or one epoch (when supported
# by a resettable input) per eval loop during training. When
# p.reset_for_eval is set to True, we run the eval loop until
# tf.errors.OutOfRangeError (or StopIteration) is raised, which can be
# triggered either because input pipeline has reached the end of the input
# sequence, or a pre-determined num_batches has reached.
eval_num_steps = [
-1 if p.reset_for_eval else p.eval_loop_num_batches
for p in eval_input_p
]
else:
summary_eval_test_dirs = []
with contextlib.ExitStack() as exit_stack:
train_summary_writer = exit_stack.enter_context(
summary_writer(summary_train_dir))
eval_summary_writer = exit_stack.enter_context(
summary_writer(summary_eval_dir))
eval_test_summary_writers = [
exit_stack.enter_context(summary_writer(d))
for d in summary_eval_test_dirs
]
# This only prints the view from the first host machine.
summary_utils.write_model_structure(train_summary_writer,
partitioned_train_state,
is_vars_replicated=False)
summary_utils.write_total_num_params(train_summary_writer,
total_num_params)
summary_last_time = time.time()
summary_last_step = None
step_i = int(
jax.device_get(
py_utils.maybe_unreplicate_gda(
partitioned_train_state.step)))
# Start the train loop. Make sure all at the same step.
py_utils.sync_global_devices(
f'Start training loop from step: {step_i}')
while True:
logging.debug('step=`%d`: Beginning', step_i)
if step_i >= train_p.num_train_steps:
logging.info(
'Training loop completed (step (`%d`) greater than '
'num_train_step (`%d`).', step_i,
train_p.num_train_steps)
break
if summary_last_step is None:
summary_last_step = step_i - 1
if checkpoint_manager.should_save(step_i):
logging.info('Saving a ckpt at step: %d', step_i)
if multi_host_checkpointing:
py_utils.sync_global_devices(
f'checkpointer:saving:{checkpoint_dir}:step-{step_i}'
)
if multi_host_checkpointing or jax.process_index() == 0:
checkpoints.save_checkpoint(
partitioned_train_state,
checkpoint_task_dir,
checkpoint_type=checkpoint_type,
state_specs=train_state_pspecs,
unreplicate=False)
checkpoint_manager.save_metadata(global_step_id=step_i)
if multi_host_checkpointing:
py_utils.sync_global_devices(
f'checkpointer:saved:{checkpoint_dir}:step-{step_i}'
)
# Get new model inputs
if step_i <= _N_STEPS_WARMUP_LOGGING:
logging.info('step=`%d`: Retrieving model inputs.', step_i)
logging.debug(' Retrieving inputs.')
model_inputs = train_input_pipeline.get_next()
if jax.config.jax_parallel_functions_output_gda:
if step_i <= _N_STEPS_WARMUP_LOGGING:
start = time.time()
py_utils.assert_same_shape_and_dtype(
inputs_shape,
tf.nest.map_structure(
py_utils.get_global_input_shape_dtype,
model_inputs))
model_inputs = py_utils.create_gda(model_inputs,
inputs_shape,
global_mesh,
inputs_pspecs)
if step_i <= _N_STEPS_WARMUP_LOGGING:
logging.info('GDA train batch input creation time %s',
time.time() - start)
logging.debug(' Retrieved inputs.')
logging.debug(' Performing train_step().')
with jax.profiler.StepTraceAnnotation('train',
step_num=step_i):
(partitioned_train_state, loss, metrics, per_example_out,
summary_tensors) = train_step(partitioned_train_state,
train_key, model_inputs)
logging.debug(' Completed train_step().')
logging.debug(' Writing summaries (attempt).')
if summary_utils.write_summary_every_n_steps(
partitioned_train_state,
train_summary_writer,
step_i,
train_p.summary_interval_steps,
loss,
metrics,
per_example_out,
summary_tensors,
train_p.norm_summary_interval_steps,
summary_last_time,
summary_last_step,
unreplicate_mdl_vars=False,
unreplicate_metrics=False):
summary_last_time = time.time()
summary_last_step = step_i
step_i = int(
py_utils.maybe_unreplicate_gda(
partitioned_train_state.step))
else:
# Increment train step locally to avoid an explicit device sync.
step_i += 1
logging.debug(' Wrote summaries (attempted).')
# Run eval at regular step interval.
if step_i % train_p.eval_interval_steps == 0:
logging.debug(' Starting eval_step().')
logging.debug(' Retrieving eval model_inputs.')
eval_inputs = train_input_pipeline.get_next()
if jax.config.jax_parallel_functions_output_gda:
eval_inputs = py_utils.create_gda(
eval_inputs, inputs_shape, global_mesh,
inputs_pspecs)
logging.debug(' Retrieved eval model_inputs.')
logging.debug(
' Performing eval_step() runs on training split.')
eval_step_fn = functools.partial(
eval_step, partitioned_train_state.mdl_vars, eval_key,
partitioned_train_state.step)
loss, mean_metrics, summary_tensors = model_utils.run_eval_one_step(
eval_inputs, eval_step_fn, reshard_inputs=False)
logging.debug(
' Completed eval_step() runs on training split.')
logging.info('step=`%d`', step_i)
logging.info(' eval loss: %s', loss)
logging.info(' mean_metrics: %s', mean_metrics)
logging.info(' summary_tensors: %s', summary_tensors)
if step_i % train_p.summary_interval_steps == 0:
logging.debug(' Writing eval summaries.')
summary_utils.write_summary_entry(
eval_summary_writer,
step_i,
loss,
mean_metrics,
summary_tensors,
unreplicate_metrics=False)
logging.debug(' Wrote eval summaries.')
# If we have eval test then also evaluate on test.
if eval_input_p is not None:
logging.debug(
' Performing eval_step() runs on test splits.')
model_utils.run_eval_loop_over_test_splits(
eval_num_steps,
eval_step_fn,
eval_test_summary_writers,
step_i,
eval_input_pipelines,
eval_test_inputs_pspecs,
eval_test_inputs_shape,
global_mesh,
reshard_inputs=False)
logging.debug(
' Completed eval_step() runs on test splits.')
logging.debug('step=`%d`: End', step_i - 1)