def _evaluate_iterations_per_loop_in_seconds(self, value, expected_value,
expected_unit):
config = tpu_config_lib.RunConfig(tpu_config=tpu_config_lib.TPUConfig(
iterations_per_loop=value))
self.assertEqual(config.tpu_config.iterations_per_loop, value)
d = util_lib.parse_iterations_per_loop(
config.tpu_config.iterations_per_loop)
self.assertEqual(expected_value, d.value)
self.assertEqual(expected_unit, d.unit)
def __new__(
cls,
iterations_per_loop=2,
num_shards=None,
num_cores_per_replica=None,
per_host_input_for_training=True,
tpu_job_name=None,
initial_infeed_sleep_secs=None,
input_partition_dims=None,
eval_training_input_configuration=InputPipelineConfig.PER_HOST_V1,
experimental_host_call_every_n_steps=1):
# Check iterations_per_loop.
util_lib.parse_iterations_per_loop(iterations_per_loop)
# Check num_shards.
if num_shards is not None:
util_lib.check_positive_integer(num_shards, 'TPUConfig num_shards')
if input_partition_dims is not None:
if len(input_partition_dims) != 1 and len(
input_partition_dims) != 2:
raise ValueError(
'input_partition_dims must be a list/tuple with one or two'
' elements.')
if per_host_input_for_training is not InputPipelineConfig.PER_HOST_V2:
raise ValueError(
'input_partition_dims is only supported in PER_HOST_V2 mode.'
)
if num_cores_per_replica is None:
raise ValueError(
'input_partition_dims requires setting num_cores_per_replica.'
)
# Check num_cores_per_replica
if num_cores_per_replica is not None:
if num_cores_per_replica not in ([1, 2, 4, 8, 16, 32, 64, 128]):
raise ValueError(
'num_cores_per_replica must be 1, 2, 4, 8, 16, 32, 64, 128; '
'got {}'.format(str(num_cores_per_replica)))
if eval_training_input_configuration not in [
InputPipelineConfig.PER_HOST_V1, InputPipelineConfig.SLICED
]:
raise ValueError(
'eval_training_input_configuration must be PER_HOST_V1 or SLICED;'
' got {}'.format(str(eval_training_input_configuration)))
# per_host_input_for_training may be True, False, or integer in [1..3].
# Map legacy values (True, False) to numeric values.
if per_host_input_for_training is False:
per_host_input_for_training = InputPipelineConfig.PER_SHARD_V1
elif per_host_input_for_training is True:
per_host_input_for_training = InputPipelineConfig.PER_HOST_V1
# Check initial_infeed_sleep_secs.
if initial_infeed_sleep_secs:
util_lib.check_positive_integer(
initial_infeed_sleep_secs,
'TPUConfig initial_infeed_sleep_secs')
tpu_job_name = tpu_job_name or _get_tpu_job_name_from_tf_config()
return super(TPUConfig, cls).__new__(
cls,
iterations_per_loop=iterations_per_loop,
num_shards=num_shards,
num_cores_per_replica=num_cores_per_replica,
per_host_input_for_training=per_host_input_for_training,
tpu_job_name=tpu_job_name,
initial_infeed_sleep_secs=initial_infeed_sleep_secs,
input_partition_dims=input_partition_dims,
eval_training_input_configuration=eval_training_input_configuration,
experimental_host_call_every_n_steps=
experimental_host_call_every_n_steps)
def _parse_and_validate_iterations_per_loop(self, value, expected_value,
expected_unit):
d = util_lib.parse_iterations_per_loop(value)
self.assertTrue(d)
self.assertEqual(d.value, expected_value)
self.assertEqual(d.unit, expected_unit)
