def LoopBody(i, *input_arrays):
"""Process outfeed data for a single TpuTrainStep.
Args:
i: current loop index.
*input_arrays: One tf.TensorArray per outfeed tensor.
Returns:
i+1 (new index) plus post-write tf.TensorArray handles.
"""
# Outfeed ops execute on each JF node, so they must be located on the
# nodes.
outfeed_devices = []
device_assignment = py_utils.GetTpuDeviceAssignment()
assert device_assignment
for replica in range(device_assignment.num_replicas):
for core in range(device_assignment.num_cores_per_replica):
with tf.device(device_assignment.host_device(
replica, core)):
outfeed_devices.append(
tpu_ops.outfeed_dequeue_tuple(
tensor_types,
tensor_shapes,
device_ordinal=device_assignment.tpu_ordinal(
replica, core)))
offset = i * num_devices
output_arrays = list(input_arrays)
# Each output_array holds a different per-example tensor. We get results
# for each tensor from each TPU for each TpuTrainStep call.
for j in range(len(output_arrays)):
for k in range(len(outfeed_devices)):
output_arrays[j] = output_arrays[j].write(
offset + k, outfeed_devices[k][j])
return tuple([i + 1] + output_arrays)
def create_dequeue_ops(host_id):
"""Create outfeed dequeue ops."""
dequeue_ops = []
tensor_dtypes = []
tensor_shapes = []
for v in self.outfeed_tensors:
dequeue_ops.append([])
tensor_dtypes.append(v.dtype)
tensor_shapes.append(v.shape)
for i in range(FLAGS.tpu_num_shards_per_host):
with tf.device(
low_level_utils.device_for_host(self._get_host(host_id))):
outfeed_tensors = tpu_ops.outfeed_dequeue_tuple(
dtypes=tensor_dtypes, shapes=tensor_shapes, device_ordinal=i)
for j, item in enumerate(outfeed_tensors):
dequeue_ops[j].append(item)
for j in range(len(outfeed_tensors)):
dequeue_ops[j] = tf.concat(dequeue_ops[j], axis=0)
return dequeue_ops
def _OutfeedDequeue(self):
"""Collect outfeed dequeue from all devices."""
num_outfeeds = len(self.metrics_nm.Flatten())
outfeed_ops = [[]] * num_outfeeds
device_assignment = py_utils.GetTpuDeviceAssignment()
assert device_assignment
for replica in range(device_assignment.num_replicas):
num_cores_per_replica = 1 if self.spmd else (
device_assignment.num_cores_per_replica)
for core in range(num_cores_per_replica):
with tf.device(device_assignment.host_device(replica, core)):
outfeeds_per_core = tpu_ops.outfeed_dequeue_tuple(
dtypes=[x.dtype for x in self.metrics_nm.Flatten()],
shapes=[x.shape for x in self.metrics_nm.Flatten()],
device_ordinal=device_assignment.tpu_ordinal(
replica, core))
for idx_outfeed, out_feed in enumerate(outfeeds_per_core):
outfeed_ops[idx_outfeed] = outfeed_ops[idx_outfeed] + [
out_feed
]
return [tf.concat(per_outfeed, 0) for per_outfeed in outfeed_ops]
def create_dequeue_ops(host_id):
"""Create outfeed dequeue ops."""
dequeue_ops = []
tensor_dtypes = []
tensor_shapes = []
for v in self.outfeed_tensors:
tensor_dtypes.append(v.dtype)
tensor_shapes.append(v.shape)
with tf.device(utils.device_for_host(self._get_host(host_id))):
for i in range(self.replicas_per_worker):
if self.use_spatial_partition:
replica_id = self.device_assignment.lookup_replicas(
host_id, 0)[i]
ordinal = self.device_assignment.tpu_ordinal(
replica=replica_id, logical_core=0)
else:
ordinal = i
outfeed = tpu_ops.outfeed_dequeue_tuple(
dtypes=tensor_dtypes,
shapes=tensor_shapes,
device_ordinal=ordinal)
if len(outfeed) == 2:
# 2 outfeed tensors
# is_pad: [batch]
# detections: [batch, 200, 7]
if outfeed[0].shape.ndims == 3:
detections, is_pad = outfeed
else:
is_pad, detections = outfeed
num_non_pad = tf.shape(is_pad)[0] - tf.reduce_sum(
tf.cast(is_pad, tf.int32))
dequeue_ops.append(
tf.slice(detections, [0, 0, 0],
[num_non_pad, -1, -1]))
else:
# no padding, only detections are in the outfeed
dequeue_ops.append(outfeed)
dequeue_ops = tf.concat(dequeue_ops, axis=0)
return dequeue_ops
def _OutfeedDequeue(self):
"""Collect outfeed dequeue from all devices.
Returns:
A list of tensors corresponding to stacked decoded outputs. The decoder
outputs are stacked on the first dimension (usually corresponds to
batch size).
"""
num_decode_tensors = len(self.decode_nm.Flatten())
outfeed_ops = [[]] * num_decode_tensors
device_assignment = py_utils.GetTpuDeviceAssignment()
assert device_assignment
num_cores_per_replica = (1 if self.spmd else
(device_assignment.num_cores_per_replica))
for replica in range(device_assignment.num_replicas):
for core in range(num_cores_per_replica):
with tf.device(device_assignment.host_device(replica, core)):
outfeeds_per_core = tpu_ops.outfeed_dequeue_tuple(
dtypes=[x.dtype for x in self.decode_nm.Flatten()],
shapes=[x.shape for x in self.decode_nm.Flatten()],
device_ordinal=device_assignment.tpu_ordinal(replica, core))
for idx_outfeed, out_feed in enumerate(outfeeds_per_core):
outfeed_ops[idx_outfeed] = outfeed_ops[idx_outfeed] + [out_feed]
return [tf.concat(per_outfeed, axis=0) for per_outfeed in outfeed_ops]
def _run_eval_phase():
"""The real function that runs the evaluation phase."""
# Setup input pipeline.
ds_creator = unet.get_dataset_creator('eval')
mtf_shapes = unet.get_input_mtf_shapes('eval')
model_eval_fn, eval_hooks, output_dtypes_shapes = _get_model_fn(
'eval', mesh_context)
if FLAGS.use_tpu:
assert mesh_context.device_assignment
assert mesh_context.num_cores
simd_input_reader = input_reader.SimdMeshImplInputReader(
mesh_context.mesh_impl, ds_creator, mtf_shapes, is_eval_mode=True)
eval_computation = tpu.replicate(
computation=model_eval_fn,
inputs=[[]] * mesh_context.num_cores,
infeed_queue=simd_input_reader.infeed_queue,
device_assignment=mesh_context.device_assignment)
output_dtypes, output_shapes = output_dtypes_shapes.get()
outfeed_dequeue_ops = []
# Create outfeed_dequeue_ops.
for host_id in range(mesh_context.num_hosts):
# pylint: disable=protected-access
with ops.device(input_reader._host_id_to_tf_device(
host_id, external_worker=True)):
for device_ordinal in range(mesh_context.num_cores_per_host):
outfeed_dequeue_op = tpu_ops.outfeed_dequeue_tuple(
dtypes=output_dtypes,
shapes=output_shapes,
device_ordinal=device_ordinal)
# We don't need output other than from core 0.
if outfeed_dequeue_ops:
outfeed_dequeue_ops.append(
[tf.reduce_mean(x) for x in outfeed_dequeue_op])
else:
outfeed_dequeue_ops.append(outfeed_dequeue_op)
else:
placement_input_reader = input_reader.PlacementMeshImplInputReader(
mesh_context.mesh_impl, ds_creator, mtf_shapes, is_eval_mode=False)
eval_computation = placement_input_reader.gpu_placement(model_eval_fn)
###########################################################
# Evaluation.
master_to_slice_hook, _ = eval_hooks.get()
ckpt_loader_hook = _CkptLoaderHook()
all_hooks = [ckpt_loader_hook, master_to_slice_hook]
if FLAGS.write_summary:
flush_summary = contrib_summary.flush()
with tf.train.MonitoredSession(
session_creator=tf.train.ChiefSessionCreator(
master=FLAGS.master,
config=tf.ConfigProto(allow_soft_placement=True)),
hooks=all_hooks) as sess:
if FLAGS.write_summary:
contrib_summary.initialize(session=sess)
if FLAGS.use_tpu:
simd_input_reader.start_infeed_thread(
sess, FLAGS.num_eval_iterations_per_loop)
else:
placement_input_reader.initialize(sess)
pprocessor = unet.PostProcessor()
for step in range(FLAGS.num_eval_iterations_per_loop):
# Only get results from the 0-th core.
if FLAGS.use_tpu:
sess.run(eval_computation)
results = sess.run(outfeed_dequeue_ops)[0]
else:
results = sess.run(eval_computation)
pprocessor.record(results, FLAGS.pred_output_dir)
if FLAGS.write_summary:
sess.run(flush_summary)
tf.logging.info('eval steps: {}'.format(step))
pprocessor.finish()
def init_graph(self, model_params):
"""Builds moe decode graph.
Args:
model_params: the hyperparams of the specified model.
"""
assert self.graph
self.model_params = model_params
batch_size = model_params.task.batch_size
if (hasattr(model_params.task.builder, 'device_mesh_shape')
and model_params.task.builder.device_mesh_shape):
num_partitions = np.prod(
model_params.task.builder.device_mesh_shape)
else:
num_partitions = model_params.task.builder.num_devices
device_order_mode = (model_params.task.train.tpu_device_order_mode
or tpu_device_assignment.DeviceOrderMode.AUTO)
self._init_tpu(num_partitions, device_order_mode)
assert self.cluster_params # configured by init_tpu
self.cluster = self.cluster_params.Instantiate()
with self.graph.as_default(), self.cluster, tf.device(
self.cluster.GetPlacer()):
_ = py_utils.GetOrCreateGlobalStepVar()
self.heartbeat = tf.constant(np.pi)
device_assignment = py_utils.GetTpuDeviceAssignment()
tf.logging.info('Instantiating model')
model = model_params.Instantiate()
xformer = model.GetTask()
self.task = xformer
self.init_vars_op = tf.global_variables_initializer()
self.saver = tf.train.Saver(sharded=True,
reshape=self._saver_reshape)
infeed = self._config_infeed(num_partitions=num_partitions,
device_assignment=device_assignment,
batch_size=batch_size)
self.outfeed = []
def decode_fn(*infeed_batch): # pylint: disable=missing-docstring
# Length 6 is passed when there is no tgt_mask (e.g. decoding) and
# length 7 is passed when there is a tgt_mask (e.g. fprop).
self.outfeed = self._config_outfeed(xformer, infeed_batch)
with tf.device(tf.tpu.core(0)):
outfeed_op = tpu_ops.outfeed_enqueue_tuple(
tf.nest.flatten(self.outfeed))
return [outfeed_op]
@tpu_function.on_device_training_loop
def decode_loop_fn():
if not self.num_batches:
infinite_repeat(decode_fn, infeed)
else:
training_loop.repeat(self.num_batches,
decode_fn,
infeed_queue=infeed)
self.compile_op, self.decode_loop = tpu_lib.split_compile_and_shard(
decode_loop_fn,
num_shards=1,
device_assignment=device_assignment)
assert self.outfeed
with tf.device(device_assignment.tpu_device(0, 0)):
self.outfeed_op = tpu_ops.outfeed_dequeue_tuple(
dtypes=[x.dtype for x in tf.nest.flatten(self.outfeed)],
shapes=[x.shape for x in tf.nest.flatten(self.outfeed)])
def initialize(self,
train_input_fn,
eval_input_fn,
model_fn,
train_batch_size,
eval_batch_size,
input_partition_dims=None,
init_fn=None,
train_has_labels=True,
eval_has_labels=True,
params=None,
num_partitions=None):
"""Build graphs for the TPU device and the input pipelines."""
num_cores_per_replica = 1
num_cores_per_replica = functools.reduce(
operator.mul, input_partition_dims
) if input_partition_dims else num_partitions if num_partitions else 1
self.device_assignment = device_assignment.device_assignment(
topology=self.device_topology,
computation_shape=_NUM_CORES_TO_COMPUTATION_SHAPE[
num_cores_per_replica],
num_replicas=self.num_replicas)
self.train_batch_size = train_batch_size
self.eval_batch_size = eval_batch_size
self.eval_has_labels = eval_has_labels
self.model_fn = model_fn
if params is None:
params = {}
params[
"dataset_num_shards"] = self.num_replicas // FLAGS.replicas_per_host
per_replica_train_batch_size = train_batch_size // self.num_replicas
per_replica_eval_batch_size = eval_batch_size // self.num_replicas
for i in range(self.num_replicas // FLAGS.replicas_per_host):
params["dataset_index"] = i
params["batch_size"] = per_replica_train_batch_size
self.build_enqueue_ops(train_input_fn, True, input_partition_dims,
params)
if self.eval_steps > 0:
params["batch_size"] = per_replica_eval_batch_size
self.build_enqueue_ops(eval_input_fn, False,
input_partition_dims, params)
def train_step(_):
"""One train step."""
inp = self.infeed_op[True].generate_dequeue_op()
flatten_structure = tf.nest.flatten(self.feature_structure[True])
inp = [
tf.slice(i, [0] * i.shape.ndims, j.shape)
for i, j in zip(inp, flatten_structure)
]
if train_has_labels:
features, labels = tf.nest.pack_sequence_as(
self.feature_structure[True], inp)
else:
features = tf.nest.pack_sequence_as(
self.feature_structure[True], inp)
labels = None
self.maybe_add_embedding_features(features, True)
train_op, _ = model_fn(features, labels, True)
embedding_train_op = self.maybe_get_embedding_train_op()
with tf.device(device_for_tpu_core(self.get_host(0))):
with tf.control_dependencies([train_op, embedding_train_op]):
return tf.constant(0)
@tpu_function.on_device_training_loop
def train_loop():
return training_loop.repeat(self.iterations_per_loop, train_step,
tf.constant(0))
def train_eval_step():
with tf.control_dependencies(train_loop()):
if self.eval_steps > 0:
return self.eval_loop()
else:
return tf.no_op()
@on_device_train_and_eval_loops
def train_eval_loop():
return training_loop.repeat(self.max_train_iterations,
train_eval_step)
with self.graph.as_default():
(self.train_eval_op, ) = tpu.shard(
train_eval_loop,
inputs=[],
num_shards=self.num_replicas,
outputs_from_all_shards=False,
device_assignment=self.device_assignment)
if FLAGS.model_dir:
tf.io.write_graph(self.graph, FLAGS.model_dir, "graph.pbtxt")
output_graph = tf.Graph()
if self.eval_steps > 0:
with output_graph.as_default():
flatten_output = tf.nest.flatten(self.predict_output)
self.dequeue_ops = [[] for _ in flatten_output]
tensor_dtypes = [v.dtype for v in flatten_output]
tensor_shapes = [v.shape for v in flatten_output]
is_padded_index = flatten_output.index(
self.predict_output[_IS_PADDED]
) if _IS_PADDED in self.predict_output else -1
for i in range(self.num_replicas // FLAGS.replicas_per_host):
with tf.device(device_for_host(self.get_host(i))):
host_dequeue_ops = [[] for _ in flatten_output]
for j in range(FLAGS.replicas_per_host):
replica_id = self.device_assignment.lookup_replicas(
i, 0)[j]
ordinal = self.device_assignment.tpu_ordinal(
replica=replica_id, logical_core=0)
dequeue_ops = tpu_ops.outfeed_dequeue_tuple(
dtypes=tensor_dtypes,
shapes=tensor_shapes,
device_ordinal=ordinal)
if is_padded_index >= 0:
num_non_pad = tf.shape(
dequeue_ops[is_padded_index]
)[0] - tf.reduce_sum(
tf.cast(dequeue_ops[is_padded_index],
tf.int32))
dequeue_ops = [
tf.slice(k, [0] * k.shape.ndims,
[num_non_pad] + [-1] *
(k.shape.ndims - 1))
for k in dequeue_ops
]
for k, item in enumerate(dequeue_ops):
host_dequeue_ops[k].append(item)
for k in range(len(self.predict_output)):
self.dequeue_ops[k].append(
tf.concat(host_dequeue_ops[k], axis=0))
self.sess = tf.Session(self.master,
graph=self.graph,
config=self.config)
for is_training in [True, False]:
if is_training or self.eval_steps > 0:
for i in range(self.num_input_graphs):
with self.input_graph[is_training][i].as_default():
self.input_sess[is_training][i] = tf.Session(
self.master,
graph=self.input_graph[is_training][i],
config=self.config)
self.input_sess[is_training][i].run(
self.dataset_initializer[is_training][i])
self.output_sess = tf.Session(self.master,
graph=output_graph,
config=self.config)
with self.graph.as_default():
_ = tf.train.get_or_create_global_step()
if init_fn:
init_fn()
checkpoint_path = tf.train.latest_checkpoint(
FLAGS.model_dir) if FLAGS.model_dir else None
if FLAGS.restore_checkpoint and checkpoint_path:
tf.train.Saver().restore(self.sess, checkpoint_path)
else:
self.sess.run(tf.global_variables_initializer())
self.sess.run(tf.local_variables_initializer())
self.maybe_load_embedding_vars()
self.global_step = self.sess.run(
tf.train.get_global_step(self.graph))
def train_eval_thread_fn(sess, train_eval_op):
sess.run([train_eval_op])
# Start the just in time compilation of the model function
self.train_eval_thread = threading.Thread(target=train_eval_thread_fn,
args=(self.sess,
self.train_eval_op))
self.train_eval_thread.start()
# Sleep for JTC to finish
time.sleep(FLAGS.sleep_after_init)
