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)
def TpuTrain():
loop_result = tpu_training_loop.repeat(
self._train_steps_per_loop,
TpuTrainStep,
inputs=[],
name='train_loop')
return loop_result
def TpuEval():
loop_result = tpu_training_loop.repeat(
self._steps_per_loop,
TpuEvalStep,
inputs=self._eval_metrics.initial_values,
name='eval_loop')
# Final metrics are the avg across self._steps_per_loop steps.
return self._eval_metrics.FinalizeMetrics(loop_result)
def eval_loop(self):
per_replica_eval_batch_size = self.eval_batch_size // self.num_replicas
tf.get_variable_scope().reuse_variables()
predictions = tf.zeros(
[self.eval_steps, per_replica_eval_batch_size, 2])
_, predictions = training_loop.repeat(int(self.eval_steps),
self.eval_step,
[tf.constant(0), predictions])
with tf.control_dependencies(
[tpu_ops.outfeed_enqueue_tuple([predictions])]):
return tf.no_op()
def DecodeLoopFn():
return tpu_training_loop.repeat(self._steps_per_loop,
_DecodeStep,
inputs=[])
def loop():
return training_loop.repeat(5,
training_step,
infeed_queue=infeed)
def _experimental_run_steps_on_iterator(
self, fn, multi_worker_iterator, iterations, initial_loop_values=None):
output_shapes = multi_worker_iterator.output_shapes
shapes = nest.flatten(output_shapes)
if any(not s.is_fully_defined() for s in shapes):
raise ValueError(
"TPU currently requires fully defined shapes. Either use "
"set_shape() on the input tensors or use "
"dataset.batch(..., drop_remainder=True).")
# Wrap `fn` for repeat.
if initial_loop_values is None:
initial_loop_values = {}
initial_loop_values = nest.flatten(initial_loop_values)
ctx = input_lib.MultiStepContext()
def run_fn(inputs):
"""Single step on the TPU device."""
fn_result = fn(ctx, inputs)
flat_last_step_outputs = nest.flatten(ctx.last_step_outputs)
if flat_last_step_outputs:
with ops.control_dependencies([fn_result]):
return [array_ops.identity(f) for f in flat_last_step_outputs]
else:
return fn_result
# We capture the control_flow_context at this point, before we run `fn`
# inside a while_loop and TPU replicate context. This is useful in cases
# where we might need to exit these contexts and get back to the outer
# context to do some things, for e.g. create an op which should be
# evaluated only once at the end of the loop on the host. One such usage
# is in creating metrics' value op.
self._outer_control_flow_context = (
ops.get_default_graph()._get_control_flow_context()) # pylint: disable=protected-access
def rewrite_fn(*args):
"""The rewritten step fn running on TPU."""
del args
per_replica_inputs = multi_worker_iterator.get_next()
replicate_inputs = []
for replica_id in range(self._num_replicas_in_sync):
select_replica = lambda x: values.select_replica(replica_id, x) # pylint: disable=cell-var-from-loop
replicate_inputs.append((nest.map_structure(
select_replica, per_replica_inputs),))
replicate_outputs = tpu.replicate(run_fn, replicate_inputs)
# If run_fn has tensor outputs, tpu.replicate returns a list of list. We
# will flatten it in this case. If run_fn has no tensor outputs,
# tpu.replicate returns a list of no_ops, we will keep the output as it
# is.
if isinstance(replicate_outputs[0], list):
replicate_outputs = nest.flatten(replicate_outputs)
return replicate_outputs
# TODO(sourabhbajaj): The input to while loop should be based on the
# output type of the step_fn
assert isinstance(initial_loop_values, list)
initial_loop_values = initial_loop_values * self._num_replicas_in_sync
# Put the while loop op on TPU host 0.
with ops.device(self._host_device):
if self.steps_per_run == 1:
replicate_outputs = rewrite_fn()
else:
replicate_outputs = training_loop.repeat(iterations, rewrite_fn,
initial_loop_values)
del self._outer_control_flow_context
ctx.run_op = control_flow_ops.group(replicate_outputs)
if isinstance(replicate_outputs, list):
# Filter out any ops from the outputs, typically this would be the case
# when there were no tensor outputs.
last_step_tensor_outputs = [
x for x in replicate_outputs if not isinstance(x, ops.Operation)
]
# Outputs are currently of the structure (flattened)
# [output0_device0, output1_device0, output2_device0,
# output0_device1, output1_device1, output2_device1,
# ...]
# Convert this to the following structure instead: (grouped by output)
# [[output0_device0, output0_device1],
# [output1_device0, output1_device1],
# [output2_device0, output2_device1]]
output_num = len(last_step_tensor_outputs) // self._num_replicas_in_sync
last_step_tensor_outputs = [
last_step_tensor_outputs[i::output_num] for i in range(output_num)
]
else:
# no tensors returned.
last_step_tensor_outputs = []
_set_last_step_outputs(ctx, last_step_tensor_outputs)
return ctx
def train_eval_loop():
return training_loop.repeat(self.num_epochs_tensor,
train_eval_step, [])
def train_eval_loop():
return training_loop.repeat(self.hparams.max_train_epochs,
train_eval_step, [])
def train_loop():
return training_loop.repeat(self.train_steps_tensor,
tpu_train_step, [_INITIAL_LOSS])
def train_eval_loop():
return training_loop.repeat(self.max_train_iterations,
train_eval_step, [0])
def train_loop():
return training_loop.repeat(self.iterations, tpu_train_step,
[_INITIAL_LOSS])
def train_loop():
return training_loop.repeat(self.iterations_per_loop, train_step,
tf.constant(0))
def eval_loop(self):
tf.get_variable_scope().reuse_variables()
return training_loop.repeat(int(self.eval_steps), self.eval_step)
def eval_loop():
return training_loop.repeat(self.eval_steps_tensor, tpu_eval_step,
[])
def _experimental_run_steps_on_iterator(self,
fn,
multi_worker_iterator,
iterations,
initial_loop_values=None):
# Wrap `fn` for repeat.
if initial_loop_values is None:
initial_loop_values = {}
initial_loop_values = nest.flatten(initial_loop_values)
ctx = input_lib.MultiStepContext()
def run_fn(inputs):
"""Single step on the TPU device."""
fn_result = fn(ctx, inputs)
flat_last_step_outputs = nest.flatten(ctx.last_step_outputs)
if flat_last_step_outputs:
with ops.control_dependencies([fn_result]):
return [
array_ops.identity(f) for f in flat_last_step_outputs
]
else:
return fn_result
# We capture the control_flow_context at this point, before we run `fn`
# inside a while_loop and TPU replicate context. This is useful in cases
# where we might need to exit these contexts and get back to the outer
# context to do some things, for e.g. create an op which should be
# evaluated only once at the end of the loop on the host. One such usage
# is in creating metrics' value op.
self._outer_control_flow_context = (
ops.get_default_graph()._get_control_flow_context()) # pylint: disable=protected-access
def rewrite_fn(*args):
"""The rewritten step fn running on TPU."""
del args
per_replica_inputs = multi_worker_iterator.get_next()
replicate_inputs = []
for replica_id in range(self._num_replicas_in_sync):
select_replica = lambda x: values.select_replica(replica_id, x) # pylint: disable=cell-var-from-loop
replicate_inputs.append(
(nest.map_structure(select_replica, per_replica_inputs), ))
replicate_outputs = tpu.replicate(run_fn, replicate_inputs)
# If run_fn has tensor outputs, tpu.replicate returns a list of list. We
# will flatten it in this case. If run_fn has no tensor outputs,
# tpu.replicate returns a list of no_ops, we will keep the output as it
# is.
if isinstance(replicate_outputs[0], list):
replicate_outputs = nest.flatten(replicate_outputs)
return replicate_outputs
# TODO(sourabhbajaj): The input to while loop should be based on the
# output type of the step_fn
assert isinstance(initial_loop_values, list)
initial_loop_values = initial_loop_values * self._num_replicas_in_sync
# Put the while loop op on TPU host 0.
with ops.device(self._host_device):
if self.steps_per_run == 1:
replicate_outputs = rewrite_fn()
else:
replicate_outputs = training_loop.repeat(
iterations, rewrite_fn, initial_loop_values)
del self._outer_control_flow_context
ctx.run_op = control_flow_ops.group(replicate_outputs)
if isinstance(replicate_outputs, list):
# Filter out any ops from the outputs, typically this would be the case
# when there were no tensor outputs.
last_step_tensor_outputs = [
x for x in replicate_outputs
if not isinstance(x, ops.Operation)
]
# Outputs are currently of the structure (flattened)
# [output0_device0, output1_device0, output2_device0,
# output0_device1, output1_device1, output2_device1,
# ...]
# Convert this to the following structure instead: (grouped by output)
# [[output0_device0, output0_device1],
# [output1_device0, output1_device1],
# [output2_device0, output2_device1]]
output_num = len(
last_step_tensor_outputs) // self._num_replicas_in_sync
last_step_tensor_outputs = [
last_step_tensor_outputs[i::output_num]
for i in range(output_num)
]
else:
# no tensors returned.
last_step_tensor_outputs = []
_set_last_step_outputs(ctx, last_step_tensor_outputs)
return ctx
def eval_loop():
if self.eval_steps > 0:
return training_loop.repeat(self.eval_steps, tpu_eval_step, [])
else:
return tf.no_op()