def testKeepalive(self):
count = Count()
shared_handle = shared.Shared()
other_shared_handle = shared.Shared()
def dummy_acquire_fn():
return None
def acquire_fn():
return Marker(count)
p1 = shared_handle.acquire(acquire_fn)
self.assertEqual(1, count.get_total())
self.assertEqual(1, count.get_active())
del p1
gc.collect()
# Won't be garbage collected, because of the keep-alive
self.assertEqual(1, count.get_active())
# Reacquire.
p2 = shared_handle.acquire(acquire_fn)
self.assertEqual(1, count.get_total()) # No reinitialisation.
self.assertEqual(1, count.get_active())
# Get rid of the keepalive
other_shared_handle.acquire(dummy_acquire_fn)
del p2
gc.collect()
self.assertEqual(0, count.get_active())
def _benchmarkRunMetaGraphDoFnManualActuationCommon(
self, force_tf_compat_v1):
"""Common implementation to benchmark RunMetaGraphDoFn "manually"."""
common_variables = _get_common_variables(self._dataset)
batch_size, batched_records = _get_batched_records(
self._dataset, self._max_num_examples())
fn = tft_beam_impl._RunMetaGraphDoFn( # pylint: disable=protected-access
tf_config=None,
shared_graph_state_handle=shared.Shared(),
passthrough_keys=set(),
exclude_outputs=None,
use_tf_compat_v1=force_tf_compat_v1,
input_tensor_adapter_config=common_variables.tfxio.
TensorAdapterConfig())
fn.setup()
start = time.time()
for batch in batched_records:
_ = list(
fn.process(batch,
saved_model_dir=self._dataset.tft_saved_model_path(
force_tf_compat_v1)))
end = time.time()
delta = end - start
self.report_benchmark(
iters=1,
wall_time=delta,
extras={
"batch_size":
batch_size,
"num_examples":
self._dataset.num_examples(limit=self._max_num_examples())
})
def benchmarkRunMetaGraphDoFnManualActuation(self):
"""Benchmark RunMetaGraphDoFn "manually".
Runs RunMetaGraphDoFn "manually" outside of a Beam pipeline. Records the
wall time taken.
"""
common_variables = _get_common_variables(self._dataset)
batch_size, batched_records = _get_batched_records(self._dataset)
fn = tft_beam_impl._RunMetaGraphDoFn( # pylint: disable=protected-access
input_schema=common_variables.transform_input_dataset_metadata.schema,
tf_config=None,
shared_graph_state_handle=shared.Shared(),
passthrough_keys=set(),
exclude_outputs=None)
start = time.time()
for batch in batched_records:
_ = list(
fn.process(
batch, saved_model_dir=self._dataset.tft_saved_model_path()))
end = time.time()
delta = end - start
self.report_benchmark(
name=benchmark_utils.with_dataset_prefix(
"benchmarkRunMetaGraphDoFnManualActuation", FLAGS.dataset),
iters=1,
wall_time=delta,
extras={
"batch_size": batch_size,
"num_examples": self._dataset.num_examples()
})
def expand(self, inputs):
# We don't deep_copy pcollections used for the first phase, or when
# the user defined `Context` disables it.
if self._phase > 0 and Context.get_use_deep_copy_optimization():
# Obviates unnecessary data materialization when the input data source is
# safe to read more than once.
tf.compat.v1.logging.info('Deep copying inputs for phase: %d',
self._phase)
input_values = deep_copy.deep_copy(self._input_values_pcoll)
else:
input_values = self._input_values_pcoll
if not self._use_tfxio:
input_values |= 'BatchInputs' >> _BatchElements()
return (input_values | 'ApplySavedModel' >> beam.ParDo(
_RunMetaGraphDoFn(
self._tf_config,
use_tfxio=self._use_tfxio,
input_schema=self._input_schema,
input_tensor_adapter_config=self._input_tensor_adapter_config,
shared_graph_state_handle=shared.Shared(),
passthrough_keys=Context.get_passthrough_keys()),
saved_model_dir=beam.pvalue.AsSingleton(inputs[0])))
def testTagCacheEviction(self):
count1 = Count()
count2 = Count()
shared_handle = shared.Shared()
def acquire_fn_1():
return NamedMarker('obj_1', count1)
def acquire_fn_2():
return NamedMarker('obj_2', count2)
# With no tag, shared handle does not know when to evict objects
p1 = shared_handle.acquire(acquire_fn_1)
assert p1.get_name() == 'obj_1'
p1 = shared_handle.acquire(acquire_fn_2)
assert p1.get_name() == 'obj_1'
gc.collect()
self.assertEqual(1, count1.get_active())
self.assertEqual(0, count2.get_active())
# Cache eviction can be forced by specifying different tags
p1 = shared_handle.acquire(acquire_fn_1, tag='1')
assert p1.get_name() == 'obj_1'
p1 = shared_handle.acquire(acquire_fn_2, tag='2')
assert p1.get_name() == 'obj_2'
gc.collect()
self.assertEqual(0, count1.get_active())
self.assertEqual(1, count2.get_active())
def expand(self, dataset_and_transform_fn):
"""Transforms the dataset using the transform_fn.
Args:
dataset_and_transform_fn: A tuple of dataset and preprocessing
function.
Returns:
A dataset transformed according to the transform_fn.
"""
(input_values, input_metadata), (transform_fn, output_metadata) = (
dataset_and_transform_fn)
if self._use_tfxio:
input_schema = None
input_tensor_adapter_config = input_metadata
else:
input_schema = input_metadata.schema
input_tensor_adapter_config = None
# If exclude_outputs is set, update the output metadata.
if self._exclude_outputs is not None:
if isinstance(output_metadata, beam_metadata_io.BeamDatasetMetadata):
new_metadata = _remove_columns_from_metadata(
output_metadata.dataset_metadata, self._exclude_outputs)
new_deferred_metadata = (
output_metadata.deferred_metadata
| 'RemoveColumms' >> beam.Map(_remove_columns_from_metadata,
self._exclude_outputs))
output_metadata = beam_metadata_io.BeamDatasetMetadata(
new_metadata, new_deferred_metadata)
else:
output_metadata = _remove_columns_from_metadata(
output_metadata, self._exclude_outputs)
tf_config = _DEFAULT_TENSORFLOW_CONFIG_BY_BEAM_RUNNER_TYPE.get(
type(self.pipeline.runner))
if not self._use_tfxio:
input_values |= 'Batch' >> _BatchElements()
output_instances = (
input_values
| 'Transform' >> beam.ParDo(
_RunMetaGraphDoFn(
tf_config,
input_schema=input_schema,
input_tensor_adapter_config=input_tensor_adapter_config,
use_tfxio=self._use_tfxio,
shared_graph_state_handle=shared.Shared(),
passthrough_keys=Context.get_passthrough_keys(),
exclude_outputs=self._exclude_outputs),
saved_model_dir=beam.pvalue.AsSingleton(transform_fn))
| 'ConvertAndUnbatch' >> beam.FlatMap(
_convert_and_unbatch_to_instance_dicts,
schema=output_metadata.schema,
passthrough_keys=Context.get_passthrough_keys()))
_clear_shared_state_after_barrier(self.pipeline, output_instances)
return (output_instances, output_metadata)
def __new__(cls,
shared_handle: Optional[shared.Shared] = None,
construct_fn: Optional[Callable[..., Any]] = None):
# TODO(b/140845455): It's likely very brittle to have the shared_handle
# optional since it needs to be tied to the unique shared state it's
# responsible for.
if not shared_handle:
shared_handle = shared.Shared()
return super(ModelLoader, cls).__new__(cls, shared_handle,
construct_fn)
def testConcurrentCallsDeduped(self):
# Test that only one among many calls to acquire will actually run the
# initialisation function.
count = Count()
shared_handle = shared.Shared()
other_shared_handle = shared.Shared()
refs = []
ref_lock = threading.Lock()
def dummy_acquire_fn():
return None
def acquire_fn():
time.sleep(1)
return Marker(count)
def thread_fn():
p = shared_handle.acquire(acquire_fn)
with ref_lock:
refs.append(p)
threads = []
for _ in range(100):
t = threading.Thread(target=thread_fn)
threads.append(t)
t.start()
for t in threads:
t.join()
self.assertEqual(1, count.get_total())
self.assertEqual(1, count.get_active())
other_shared_handle.acquire(dummy_acquire_fn) # Get rid of the keepalive
with ref_lock:
del refs[:]
gc.collect()
self.assertEqual(0, count.get_active())
def _MultiInference(pcoll: beam.pvalue.PCollection, # pylint: disable=invalid-name
inference_spec_type: model_spec_pb2.InferenceSpecType):
"""Performs multi inference PTransform."""
if _using_in_process_inference(inference_spec_type):
return (
pcoll
| 'MultiInference' >> beam.ParDo(
_BatchMultiInferenceDoFn(inference_spec_type, shared.Shared()))
| 'BuildMultiInferenceLog' >> beam.ParDo(_BuildMultiInferenceLogDoFn()))
else:
raise NotImplementedError
def _Regress(pcoll: beam.pvalue.PCollection, # pylint: disable=invalid-name
inference_spec_type: model_spec_pb2.InferenceSpecType):
"""Performs regress PTransform."""
if _using_in_process_inference(inference_spec_type):
return (pcoll
| 'Regress' >> beam.ParDo(
_BatchRegressDoFn(inference_spec_type, shared.Shared()))
| 'BuildPredictionLogForRegressions' >> beam.ParDo(
_BuildPredictionLogForRegressionsDoFn()))
else:
raise NotImplementedError
def testMultiple(self):
count = Count()
shared_handle = shared.Shared()
other_shared_handle = shared.Shared()
def dummy_acquire_fn():
return None
def acquire_fn():
return Marker(count)
p = shared_handle.acquire(acquire_fn)
other_shared_handle.acquire(dummy_acquire_fn) # Get rid of the keepalive
self.assertEqual(1, count.get_total())
self.assertEqual(1, count.get_active())
del p
gc.collect()
self.assertEqual(0, count.get_active())
# Shared value should be garbage collected.
# Acquiring multiple times only results in one initialisation
p1 = shared_handle.acquire(acquire_fn)
# Since shared value was released, expect a reinitialisation.
self.assertEqual(2, count.get_total())
self.assertEqual(1, count.get_active())
p2 = shared_handle.acquire(acquire_fn)
self.assertEqual(2, count.get_total())
self.assertEqual(1, count.get_active())
other_shared_handle.acquire(dummy_acquire_fn) # Get rid of the keepalive
# Check that shared object isn't destroyed if there's still a reference to
# it.
del p2
gc.collect()
self.assertEqual(1, count.get_active())
del p1
gc.collect()
self.assertEqual(0, count.get_active())
def __init__(
self, model_agnostic_config: agnostic_predict.ModelAgnosticConfig
) -> None:
self._model_agnostic_config = model_agnostic_config
# TODO(b/140805724): It's odd that shared_handle is not passed as an
# argument to the constructor. Logically, it seems to have a 1-1
# correspondence with the model_agnostic_config, so it should be passed with
# it.
self._shared_handle = shared.Shared()
self._model_agnostic_wrapper = None
self._model_load_seconds = None
self._model_load_seconds_distribution = beam.metrics.Metrics.distribution(
constants.METRICS_NAMESPACE, 'model_load_seconds')
def testDifferentObjects(self):
sequence = Sequence()
def dummy_acquire_fn():
return None
first_handle = shared.Shared()
second_handle = shared.Shared()
dummy_handle = shared.Shared()
f1 = first_handle.acquire(sequence.make_acquire_fn())
s1 = second_handle.acquire(sequence.make_acquire_fn())
self.assertEqual('sequence1', f1.get_name())
self.assertEqual('sequence2', s1.get_name())
f2 = first_handle.acquire(sequence.make_acquire_fn())
s2 = second_handle.acquire(sequence.make_acquire_fn())
# Check that the repeated acquisitions return the earlier objects
self.assertEqual('sequence1', f2.get_name())
self.assertEqual('sequence2', s2.get_name())
# Release all references and force garbage-collection
del f1
del f2
del s1
del s2
dummy_handle.acquire(dummy_acquire_fn) # Get rid of the keepalive
gc.collect()
# Check that acquiring again after they're released gives new objects
f3 = first_handle.acquire(sequence.make_acquire_fn())
s3 = second_handle.acquire(sequence.make_acquire_fn())
self.assertEqual('sequence3', f3.get_name())
self.assertEqual('sequence4', s3.get_name())
def _Predict(pcoll: beam.pvalue.PCollection, # pylint: disable=invalid-name
inference_spec_type: model_spec_pb2.InferenceSpecType):
"""Performs predict PTransform."""
if _using_in_process_inference(inference_spec_type):
predictions = (
pcoll
| 'Predict' >> beam.ParDo(
_BatchPredictDoFn(inference_spec_type, shared.Shared())))
else:
predictions = (
pcoll
| 'RemotePredict' >> beam.ParDo(
_RemotePredictDoFn(inference_spec_type, pcoll.pipeline.options)))
return (predictions
| 'BuildPredictionLogForPredictions' >> beam.ParDo(
_BuildPredictionLogForPredictionsDoFn()))
def _clear_shared_state_after_barrier(pipeline, input_barrier):
"""Clears any shared state from within a pipeline context.
This will only be cleared once input_barrier becomes available.
Args:
pipeline: A `beam.Pipeline` object.
input_barrier: A `PCollection` which the pipeline should wait for.
Returns:
An empty `PCollection`.
"""
empty_pcoll = input_barrier | 'MakeCheapBarrier' >> beam.FlatMap(
lambda x: None)
return (pipeline
| 'PrepareToClearSharedKeepAlives' >> beam.Create([None])
| 'WaitAndClearSharedKeepAlives' >> beam.Map(
lambda x, empty_side_input: shared.Shared().acquire(lambda: None),
beam.pvalue.AsIter(empty_pcoll)))
def benchmarkRunMetaGraphDoFnManualActuation(self):
"""Benchmark RunMetaGraphDoFn "manually".
Runs RunMetaGraphDoFn "manually" outside of a Beam pipeline. Records the
wall time taken.
"""
common_variables = _get_common_variables(self._dataset)
batch_size, batched_records = _get_batched_records(
self._dataset, self._max_num_examples())
fn = tft_beam_impl._RunMetaGraphDoFn( # pylint: disable=protected-access
tf_config=None,
shared_graph_state_handle=shared.Shared(),
passthrough_keys=set(),
exclude_outputs=None,
# TODO(b/149997088): Add a benchmark with use_tf_compat_v1=False.
use_tf_compat_v1=True,
input_tensor_adapter_config=common_variables.tfxio.
TensorAdapterConfig())
fn.setup()
start = time.time()
for batch in batched_records:
_ = list(
fn.process(
batch,
saved_model_dir=self._dataset.tft_saved_model_path()))
end = time.time()
delta = end - start
self.report_benchmark(
iters=1,
wall_time=delta,
extras={
"batch_size":
batch_size,
"num_examples":
self._dataset.num_examples(limit=self._max_num_examples())
})
def __init__(self,
construct_fn: Callable[[], Any],
tags: Optional[List[Text]] = None):
self.construct_fn = construct_fn
self.tags = tags
self._shared_handle = shared.Shared()
def RunInference( # pylint: disable=invalid-name
examples: beam.pvalue.PCollection,
inference_endpoint: model_spec_pb2.InferenceEndpoint
) -> beam.pvalue.PCollection:
"""Run batch offline inference with a model.
Models need to have the required serving signature as mentioned in
[Tensorflow Serving](https://www.tensorflow.org/tfx/serving/signature_defs)
This function will check model signatures first. Then it will load and run
model inference in batch.
TODO(b/131873699): Add support for the following features:
1. Bytes as Input.
2. PTable input.
3. Models as beam side-input.
4. Remote inference.
Args:
examples: A PCollection containing examples.
inference_endpoint: Model inference endpoint.
Returns:
A PCollection containing prediction logs.
"""
logging.info('RunInference on model: %s', inference_endpoint)
if not inference_endpoint.saved_model_spec:
raise ValueError('SavedModelSpec need to be specified.')
signatures = _get_signatures(
inference_endpoint.saved_model_spec.model_path,
inference_endpoint.saved_model_spec.signature_name,
_get_tags(inference_endpoint))
if not signatures:
raise ValueError('Model does not have valid signature to use')
batched_examples = examples | 'BatchExamples' >> beam.BatchElements()
if len(signatures) == 1:
signature_def = signatures[0].signature_def
if signature_def.method_name == tf.saved_model.CLASSIFY_METHOD_NAME:
return (batched_examples
| 'Classify' >> beam.ParDo(
_BatchClassifyDoFn(inference_endpoint, shared.Shared(),
signatures))
| 'BuildPredictionLogForClassifications' >> beam.ParDo(
_BuildPredictionLogForClassificationsDoFn()))
elif signature_def.method_name == tf.saved_model.REGRESS_METHOD_NAME:
return (batched_examples
| 'Regress' >> beam.ParDo(
_BatchRegressDoFn(inference_endpoint, shared.Shared(),
signatures))
| 'BuildPredictionLogForRegressions' >> beam.ParDo(
_BuildPredictionLogForRegressionsDoFn()))
elif signature_def.method_name == tf.saved_model.PREDICT_METHOD_NAME:
return (batched_examples
| 'Predict' >> beam.ParDo(
_BatchPredictDoFn(inference_endpoint, shared.Shared(),
signatures))
| 'BuildPredictionLogForPredictions' >> beam.ParDo(
_BuildPredictionLogForPredictionsDoFn()))
else:
raise ValueError('Unsupported signature method_name %s' %
signature_def.method_name)
else:
for signature in signatures:
signature_def = signature.signature_def
if (signature_def.method_name !=
tf.saved_model.CLASSIFY_METHOD_NAME
and signature_def.method_name !=
tf.saved_model.REGRESS_METHOD_NAME):
raise ValueError(
'Unsupported signature method_name for multi-head '
'model inference: %s' % signature_def.method_name)
return (batched_examples
| 'MultiInference' >> beam.ParDo(
_BatchMultiInferenceDoFn(inference_endpoint,
shared.Shared(), signatures))
| 'BuildMultiInferenceLog' >> beam.ParDo(
_BuildMultiInferenceLogDoFn()))
