def _perform_combiner_packing_optimization(saved_model_future,
cache_value_nodes):
"""Optimizes the graph by packing possible combine nodes."""
# Inspect the graph to identify all the packable combines.
inspect_combine_visitor = _InspectCombineVisitor()
inspect_combine_traverser = nodes.Traverser(inspect_combine_visitor)
_ = inspect_combine_traverser.visit_value_node(saved_model_future)
if cache_value_nodes:
for value_node in cache_value_nodes.values():
_ = inspect_combine_traverser.visit_value_node(value_node)
packable_combines = inspect_combine_visitor.packable_combines
# Do not pack if we have only a single combine in the group.
packable_combines = {
label: group
for label, group in packable_combines.items() if len(group) > 1
}
# Do another pass over the graph and pack the grouped combines.
pack_combine_visitor = _PackCombineVisitor(packable_combines)
pack_combine_traverser = nodes.Traverser(pack_combine_visitor)
saved_model_future = pack_combine_traverser.visit_value_node(
saved_model_future)
# Replace cache nodes to point to the corresponding new nodes.
if cache_value_nodes:
cache_value_nodes = {
key: pack_combine_traverser.visit_value_node(value_node)
for key, value_node in cache_value_nodes.items()
}
return (saved_model_future, cache_value_nodes)
def get_analysis_dataset_keys(preprocessing_fn, specs, dataset_keys,
input_cache):
"""Computes the dataset keys that are required in order to perform analysis.
Args:
preprocessing_fn: A tf.transform preprocessing_fn.
specs: A dict of feature name to feature specification or tf.TypeSpecs.
dataset_keys: A set of strings which are dataset keys, they uniquely
identify these datasets across analysis runs.
input_cache: A cache dictionary.
Returns:
A pair of:
- A set of dataset keys that are required for analysis.
- A boolean indicating whether or not a flattened version of the entire
dataset is required. See the `flat_data` input to
`AnalyzeDatasetWithCache`.
"""
transform_fn_future, _ = _build_analysis_graph_for_inspection(
preprocessing_fn, specs, dataset_keys, input_cache)
required_dataset_keys_result = set()
inspect_visitor = _InspectVisitor(required_dataset_keys_result)
inspect_traverser = nodes.Traverser(inspect_visitor)
_ = inspect_traverser.visit_value_node(transform_fn_future)
# If None is present this means that a flattened version of the entire dataset
# is required, therefore this will be returning all of the given dataset_keys.
flat_data_required = None in required_dataset_keys_result
if flat_data_required:
required_dataset_keys_result = dataset_keys
return required_dataset_keys_result, flat_data_required
def get_analyze_input_columns(preprocessing_fn,
specs,
force_tf_compat_v1=False):
"""Return columns that are required inputs of `AnalyzeDataset`.
Args:
preprocessing_fn: A tf.transform preprocessing_fn.
specs: A dict of feature name to tf.TypeSpecs. If `force_tf_compat_v1` is
True, this can also be feature specifications.
force_tf_compat_v1: (Optional) If `True`, use Tensorflow in compat.v1 mode.
Defaults to `False`.
Returns:
A list of columns that are required inputs of analyzers.
"""
use_tf_compat_v1 = tf2_utils.use_tf_compat_v1(force_tf_compat_v1)
if not use_tf_compat_v1:
assert all([isinstance(s, tf.TypeSpec) for s in specs.values()]), specs
graph, structured_inputs, _ = (impl_helper.trace_preprocessing_function(
preprocessing_fn, specs, use_tf_compat_v1=use_tf_compat_v1))
tensor_sinks = graph.get_collection(analyzer_nodes.TENSOR_REPLACEMENTS)
visitor = _SourcedTensorsVisitor()
for tensor_sink in tensor_sinks:
nodes.Traverser(visitor).visit_value_node(tensor_sink.future)
analyze_input_tensors = graph_tools.get_dependent_inputs(
graph, structured_inputs, visitor.sourced_tensors)
return list(analyze_input_tensors.keys())
def testTraverserComplexGraphMultipleCalls(self):
a = nodes.apply_operation(_Constant, value='a', label='Constant[a]')
b = nodes.apply_operation(_Constant, value='b', label='Constant[b]')
c = nodes.apply_operation(_Constant, value='c', label='Constant[c]')
b_copy, a_copy = nodes.apply_multi_output_operation(_Swap,
a,
b,
label='Swap')
b_a = nodes.apply_operation(_Concat, b_copy, a_copy, label='Concat[0]')
b_a_c = nodes.apply_operation(_Concat, b_a, c, label='Concat[1]')
mock_visitor = mock.MagicMock()
mock_visitor.visit.side_effect = [('a', ), ('b', ), ('b', 'a'),
('ba', ), ('c', ), ('bac', )]
traverser = nodes.Traverser(mock_visitor)
traverser.visit_value_node(b_a)
traverser.visit_value_node(b_a_c)
mock_visitor.assert_has_calls([
mock.call.visit(_Constant('a', 'Constant[a]'), ()),
mock.call.validate_value('a'),
mock.call.visit(_Constant('b', 'Constant[b]'), ()),
mock.call.validate_value('b'),
mock.call.visit(_Swap('Swap'), ('a', 'b')),
mock.call.validate_value('b'),
mock.call.validate_value('a'),
mock.call.visit(_Concat('Concat[0]'), ('b', 'a')),
mock.call.validate_value('ba'),
mock.call.visit(_Constant('c', 'Constant[c]'), ()),
mock.call.validate_value('c'),
mock.call.visit(_Concat('Concat[1]'), ('ba', 'c')),
mock.call.validate_value('bac'),
])
def get_analysis_dataset_keys(preprocessing_fn, specs, dataset_keys,
input_cache, force_tf_compat_v1):
"""Computes the dataset keys that are required in order to perform analysis.
Args:
preprocessing_fn: A tf.transform preprocessing_fn.
specs: A dict of feature name to tf.TypeSpecs. If `force_tf_compat_v1` is
True, this can also be feature specifications.
dataset_keys: A set of strings which are dataset keys, they uniquely
identify these datasets across analysis runs.
input_cache: A cache dictionary.
force_tf_compat_v1: If `True`, use Tensorflow in compat.v1 mode.
Returns:
A set of dataset keys that are required for analysis.
"""
transform_fn_future, _ = _build_analysis_graph_for_inspection(
preprocessing_fn, specs, dataset_keys, input_cache, force_tf_compat_v1)
result = set()
inspect_visitor = _InspectVisitor(result)
inspect_traverser = nodes.Traverser(inspect_visitor)
_ = inspect_traverser.visit_value_node(transform_fn_future)
# If None is present this means that a flattened version of the entire dataset
# is required, therefore this will be returning all of the given dataset_keys.
if any(k.is_flattened_dataset_key() for k in result):
result = dataset_keys
return result
def testTraverserComplexGraph(self):
a = nodes.apply_operation(_Constant, value='a')
b = nodes.apply_operation(_Constant, value='b')
c = nodes.apply_operation(_Constant, value='c')
b_copy, a_copy = nodes.apply_multi_output_operation(_Swap, a, b)
b_a = nodes.apply_operation(_Concat, b_copy, a_copy)
b_a_c = nodes.apply_operation(_Concat, b_a, c)
mock_visitor = mock.MagicMock()
mock_visitor.visit.side_effect = [('a', ), ('b', ), ('b', 'a'),
('ba', ), ('c', ), ('bac', )]
nodes.Traverser(mock_visitor).visit_value_node(b_a_c)
mock_visitor.assert_has_calls([
mock.call.visit(_Constant('a'), ()),
mock.call.validate_value('a'),
mock.call.visit(_Constant('b'), ()),
mock.call.validate_value('b'),
mock.call.visit(_Swap(), ('a', 'b')),
mock.call.validate_value('b'),
mock.call.validate_value('a'),
mock.call.visit(_Concat(), ('b', 'a')),
mock.call.validate_value('ba'),
mock.call.visit(_Constant('c'), ()),
mock.call.validate_value('c'),
mock.call.visit(_Concat(), ('ba', 'c')),
mock.call.validate_value('bac'),
])
def testTraverserBadNumOutputs(self):
a = nodes.apply_operation(_Constant, value='a', label='Constant[a]')
mock_visitor = mock.MagicMock()
mock_visitor.visit.side_effect = [('a', 'b')]
with self.assertRaisesRegexp(
ValueError, 'has 1 outputs but visitor returned 2 values: '):
nodes.Traverser(mock_visitor).visit_value_node(a)
def testTraverserSimpleGraph(self):
a = nodes.apply_operation(_Constant, value='a', label='Constant[a]')
mock_visitor = mock.MagicMock()
mock_visitor.visit.side_effect = [('a', )]
nodes.Traverser(mock_visitor).visit_value_node(a)
mock_visitor.assert_has_calls([
mock.call.visit(_Constant('a', 'Constant[a]'), ()),
mock.call.validate_value('a'),
])
def testTraverserOutputsNotATuple(self):
a = nodes.apply_operation(_Constant, value='a', label='Constant[a]')
mock_visitor = mock.MagicMock()
mock_visitor.visit.side_effect = ['not a tuple']
with self.assertRaisesRegexp(
ValueError, r'expected visitor to return a tuple, got'):
nodes.Traverser(mock_visitor).visit_value_node(a)
def _perform_cache_optimization(saved_model_future, dataset_keys, cache_dict):
"""Performs cache optimization on the given graph."""
cache_output_nodes = {}
optimize_visitor = _OptimizeVisitor(dataset_keys or {}, cache_dict,
cache_output_nodes)
optimize_traverser = nodes.Traverser(optimize_visitor)
optimized = optimize_traverser.visit_value_node(
saved_model_future).flattened_view
if cache_dict is None:
assert not cache_output_nodes
cache_output_nodes = None
return optimized, cache_output_nodes
def testTraverserCycle(self):
a = nodes.apply_operation(_Constant, value='a', label='Constant[a]')
x_0 = nodes.apply_operation(_Identity, a, label='Identity[0]')
x_1 = nodes.apply_operation(_Identity, x_0, label='Identity[1]')
x_2 = nodes.apply_operation(_Identity, x_1, label='Identity[2]')
x_0.parent_operation._inputs = (x_2, )
mock_visitor = mock.MagicMock()
mock_visitor.visit.return_value = ('x', )
with self.assertRaisesWithLiteralMatch(
AssertionError,
'Cycle detected: [Identity[2], Identity[1], Identity[0], Identity[2]]'
):
nodes.Traverser(mock_visitor).visit_value_node(x_2)
def testTraverserCycle(self):
x = nodes.apply_operation(_Constant, value='x')
x_0 = nodes.apply_operation(_Identity, x, name='x_0')
x_1 = nodes.apply_operation(_Identity, x_0, name='x_1')
x_2 = nodes.apply_operation(_Identity, x_1, name='x_2')
x_0.parent_operation._inputs = (x_2, )
mock_visitor = mock.MagicMock()
mock_visitor.visit.return_value = ('x', )
with self.assertRaisesWithLiteralMatch(
AssertionError,
'Cycle detected: [_Identity(name=\'x_2\'), _Identity(name=\'x_1\'), '
'_Identity(name=\'x_0\'), _Identity(name=\'x_2\')]'):
nodes.Traverser(mock_visitor).visit_value_node(x_2)
def _perform_cache_optimization(saved_model_future, dataset_keys,
tensor_keys_to_paths, cache_dict, num_phases):
"""Performs cache optimization on the given graph."""
cache_output_nodes = {}
optimize_visitor = _OptimizeVisitor(dataset_keys or {}, cache_dict,
tensor_keys_to_paths,
cache_output_nodes, num_phases)
optimize_traverser = nodes.Traverser(optimize_visitor)
optimized = optimize_traverser.visit_value_node(
saved_model_future).flattened_view
if cache_dict is None:
assert not cache_output_nodes
cache_output_nodes = None
return (optimized, cache_output_nodes,
optimize_visitor.get_detached_sideeffect_leafs())
def get_analyze_input_columns(
preprocessing_fn: Callable[[Mapping[str, common_types.TensorType]],
Mapping[str, common_types.TensorType]],
specs: Mapping[str, Union[common_types.FeatureSpecType, tf.TypeSpec]],
force_tf_compat_v1: bool = False) -> List[str]:
"""Return columns that are required inputs of `AnalyzeDataset`.
Args:
preprocessing_fn: A tf.transform preprocessing_fn.
specs: A dict of feature name to tf.TypeSpecs. If `force_tf_compat_v1` is
True, this can also be feature specifications.
force_tf_compat_v1: (Optional) If `True`, use Tensorflow in compat.v1 mode.
Defaults to `False`.
Returns:
A list of columns that are required inputs of analyzers.
"""
use_tf_compat_v1 = tf2_utils.use_tf_compat_v1(force_tf_compat_v1)
if not use_tf_compat_v1:
assert all([isinstance(s, tf.TypeSpec) for s in specs.values()]), specs
graph, structured_inputs, structured_outputs = (
impl_helper.trace_preprocessing_function(
preprocessing_fn, specs, use_tf_compat_v1=use_tf_compat_v1))
tensor_sinks = graph.get_collection(analyzer_nodes.TENSOR_REPLACEMENTS)
visitor = graph_tools.SourcedTensorsVisitor()
for tensor_sink in tensor_sinks:
nodes.Traverser(visitor).visit_value_node(tensor_sink.future)
if use_tf_compat_v1:
control_dependency_ops = []
else:
# If traced in TF2 as a tf.function, inputs that end up in control
# dependencies are required for the function to execute. Return such inputs
# as required inputs of analyzers as well.
_, control_dependency_ops = (
tf2_utils.strip_and_get_tensors_and_control_dependencies(
tf.nest.flatten(structured_outputs, expand_composites=True)))
output_tensors = list(
itertools.chain(visitor.sourced_tensors, control_dependency_ops))
analyze_input_tensors = graph_tools.get_dependent_inputs(
graph, structured_inputs, output_tensors)
return list(analyze_input_tensors.keys())
def get_analysis_dataset_keys(preprocessing_fn, feature_spec, dataset_keys,
input_cache):
"""Computes the dataset keys that are required in order to perform analysis.
Args:
preprocessing_fn: A tf.transform preprocessing_fn.
feature_spec: A dict of feature name to feature specification.
dataset_keys: A set of strings which are dataset keys, they uniquely
identify these datasets across analysis runs.
input_cache: A cache dictionary.
Returns:
A pair of:
- A set of dataset keys that are required for analysis.
- A boolean indicating whether or not a flattened version of the entire
dataset is required. See the `flat_data` input to
`AnalyzeDatasetWithCache`.
"""
with tf.Graph().as_default() as graph:
with tf.compat.v1.name_scope('inputs'):
input_signature = impl_helper.feature_spec_as_batched_placeholders(
feature_spec)
# TODO(b/34288791): This needs to be exactly the same as in impl.py
copied_inputs = impl_helper.copy_tensors(input_signature)
output_signature = preprocessing_fn(copied_inputs)
transform_fn_future, _ = build(
graph,
input_signature,
output_signature,
dataset_keys=dataset_keys,
cache_dict=input_cache)
required_dataset_keys_result = set()
inspect_visitor = _InspectVisitor(required_dataset_keys_result)
inspect_traverser = nodes.Traverser(inspect_visitor)
_ = inspect_traverser.visit_value_node(transform_fn_future)
# If None is present this means that a flattened version of the entire dataset
# is required, therefore this will be returning all of the given dataset_keys.
flat_data_required = None in required_dataset_keys_result
if flat_data_required:
required_dataset_keys_result = dataset_keys
return required_dataset_keys_result, flat_data_required
def get_analyze_input_columns(preprocessing_fn, feature_spec):
"""Return columns that are required inputs of `AnalyzeDataset`.
Args:
preprocessing_fn: A tf.transform preprocessing_fn.
feature_spec: A dict of feature name to feature specification.
Returns:
A list of columns that are required inputs of analyzers.
"""
with tf.compat.v1.Graph().as_default() as graph:
input_signature = impl_helper.feature_spec_as_batched_placeholders(
feature_spec)
_ = preprocessing_fn(input_signature.copy())
tensor_sinks = graph.get_collection(analyzer_nodes.TENSOR_REPLACEMENTS)
visitor = _SourcedTensorsVisitor()
for tensor_sink in tensor_sinks:
nodes.Traverser(visitor).visit_value_node(tensor_sink.future)
analyze_input_tensors = graph_tools.get_dependent_inputs(
graph, input_signature, visitor.sourced_tensors)
return analyze_input_tensors.keys()
def get_analyzers_fingerprint(
graph: tf.Graph, structured_inputs: Mapping[str, common_types.TensorType]
) -> Mapping[str, AnalyzersFingerprint]:
"""Computes fingerprints for all analyzers in `graph`.
Args:
graph: a TF Graph.
structured_inputs: a dict from keys to batches of placeholder graph tensors.
Returns:
A mapping from analyzer name to a set of paths that define its fingerprint.
"""
result = {}
tensor_sinks = graph.get_collection(analyzer_nodes.ALL_REPLACEMENTS)
# The value for the keys in this dictionary are unused and can be arbitrary.
sink_tensors_ready = {
tf_utils.hashable_tensor_or_op(tensor_sink.tensor): False
for tensor_sink in tensor_sinks
}
graph_analyzer = InitializableGraphAnalyzer(
graph, structured_inputs, list(sink_tensors_ready.items()),
describe_path_as_analyzer_cache_hash)
for tensor_sink in tensor_sinks:
# Retrieve tensors that are inputs to the analyzer's value node.
visitor = SourcedTensorsVisitor()
nodes.Traverser(visitor).visit_value_node(tensor_sink.future)
source_keys = _retrieve_source_keys(visitor.sourced_tensors,
structured_inputs)
paths = set()
for tensor in visitor.sourced_tensors:
# Obtain fingerprint for each tensor that is an input to the value node.
path = graph_analyzer.get_unique_path(tensor)
if path is not None:
paths.add(path)
result[str(tensor_sink.tensor.name)] = AnalyzersFingerprint(
source_keys, paths)
return result
def _perform_cache_optimization(saved_model_future, cache_location,
dataset_keys):
optimize_visitor = _OptimizeVisitor(dataset_keys or {}, cache_location)
optimize_traverser = nodes.Traverser(optimize_visitor)
return optimize_traverser.visit_value_node(saved_model_future).flattened_view
def expand(self, dataset):
"""Analyze the dataset.
Args:
dataset: A dataset.
Returns:
A TransformFn containing the deferred transform function.
Raises:
ValueError: If preprocessing_fn has no outputs.
"""
flattened_pcoll, input_values_pcoll_dict, input_metadata = dataset
input_schema = input_metadata.schema
input_values_pcoll_dict = input_values_pcoll_dict or dict()
analyzer_cache.validate_dataset_keys(input_values_pcoll_dict.keys())
with tf.Graph().as_default() as graph:
with tf.name_scope('inputs'):
feature_spec = input_schema.as_feature_spec()
input_signature = impl_helper.feature_spec_as_batched_placeholders(
feature_spec)
# In order to avoid a bug where import_graph_def fails when the
# input_map and return_elements of an imported graph are the same
# (b/34288791), we avoid using the placeholder of an input column as an
# output of a graph. We do this by applying tf.identity to all inputs of
# the preprocessing_fn. Note this applies at the level of raw tensors.
# TODO(b/34288791): Remove this workaround and use a shallow copy of
# inputs instead. A shallow copy is needed in case
# self._preprocessing_fn mutates its input.
copied_inputs = impl_helper.copy_tensors(input_signature)
output_signature = self._preprocessing_fn(copied_inputs)
# At this point we check that the preprocessing_fn has at least one
# output. This is because if we allowed the output of preprocessing_fn to
# be empty, we wouldn't be able to determine how many instances to
# "unbatch" the output into.
if not output_signature:
raise ValueError('The preprocessing function returned an empty dict')
if graph.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES):
raise ValueError(
'The preprocessing function contained trainable variables '
'{}'.format(
graph.get_collection_ref(tf.GraphKeys.TRAINABLE_VARIABLES)))
pipeline = flattened_pcoll.pipeline
serialized_tf_config = common._DEFAULT_TENSORFLOW_CONFIG_BY_RUNNER.get( # pylint: disable=protected-access
pipeline.runner)
extra_args = common.ConstructBeamPipelineVisitor.ExtraArgs(
base_temp_dir=Context.create_base_temp_dir(),
serialized_tf_config=serialized_tf_config,
pipeline=pipeline,
flat_pcollection=flattened_pcoll,
pcollection_dict=input_values_pcoll_dict,
graph=graph,
input_signature=input_signature,
input_schema=input_schema,
cache_location=self._cache_location)
transform_fn_future = analysis_graph_builder.build(
graph, input_signature, output_signature,
input_values_pcoll_dict.keys(), self._cache_location)
transform_fn_pcoll = nodes.Traverser(
common.ConstructBeamPipelineVisitor(extra_args)).visit_value_node(
transform_fn_future)
# Infer metadata. We take the inferred metadata and apply overrides that
# refer to values of tensors in the graph. The override tensors must
# be "constant" in that they don't depend on input data. The tensors can
# depend on analyzer outputs though. This allows us to set metadata that
# depends on analyzer outputs. _augment_metadata will use the analyzer
# outputs stored in `transform_fn` to compute the metadata in a
# deferred manner, once the analyzer outputs are known.
metadata = dataset_metadata.DatasetMetadata(
schema=schema_inference.infer_feature_schema(output_signature, graph))
deferred_metadata = (
transform_fn_pcoll
|
'ComputeDeferredMetadata' >> beam.Map(_infer_metadata_from_saved_model))
full_metadata = beam_metadata_io.BeamDatasetMetadata(
metadata, deferred_metadata)
_clear_shared_state_after_barrier(pipeline, transform_fn_pcoll)
return transform_fn_pcoll, full_metadata
def build(graph,
input_signature,
output_signature,
dataset_keys=None,
cache_dict=None):
"""Returns a list of `Phase`s describing how to execute the pipeline.
The default graph is assumed to contain some `Analyzer`s which must be
executed by doing a full pass over the dataset, and passing the inputs for
that analyzer into some implementation, then taking the results and replacing
the `Analyzer`s outputs with constants in the graph containing these results.
The execution plan is described by a list of `Phase`s. Each phase contains
a list of `Analyzer`s, which are the `Analyzer`s which are ready to run in
that phase, together with a list of ops, which are the table initializers that
are ready to run in that phase.
An `Analyzer` or op is ready to run when all its dependencies in the graph
have been computed. Thus if the graph is constructed by
def preprocessing_fn(input)
x = inputs['x']
scaled_0 = x - tft.min(x)
scaled_0_1 = scaled_0 / tft.max(scaled_0)
Then the first phase will contain the analyzer corresponding to the call to
`min`, because `x` is an input and so is ready to compute in the first phase,
while the second phase will contain the analyzer corresponding to the call to
`max` since `scaled_1` depends on the result of the call to `tft.min` which
is computed in the first phase.
More generally, we define a level for each op and each `Analyzer` by walking
the graph, assigning to each operation the max level of its inputs, to each
`Tensor` the level of its operation, unless it's the output of an `Analyzer`
in which case we assign the level of its `Analyzer` plus one.
Args:
graph: A `tf.Graph`.
input_signature: A dict whose keys are strings and values are `Tensor`s or
`SparseTensor`s.
output_signature: A dict whose keys are strings and values are `Tensor`s or
`SparseTensor`s.
dataset_keys: (Optional) A set of strings which are dataset keys, they
uniquely identify these datasets across analysis runs.
cache_dict: (Optional): A cache dictionary.
Returns:
A pair of:
* list of `Phase`s
* A dictionary of output cache `ValueNode`s.
Raises:
ValueError: if the graph cannot be analyzed.
"""
tensor_sinks = graph.get_collection(analyzer_nodes.TENSOR_REPLACEMENTS)
graph.clear_collection(analyzer_nodes.TENSOR_REPLACEMENTS)
phase = 0
tensor_bindings = []
sink_tensors_ready = {
tf_utils.hashable_tensor_or_op(tensor_sink.tensor): False
for tensor_sink in tensor_sinks
}
translate_visitor = _TranslateVisitor()
translate_traverser = nodes.Traverser(translate_visitor)
analyzers_input_signature = {}
graph_analyzer = None
extracted_input_node = nodes.apply_operation(
beam_nodes.ExtractInputForSavedModel,
dataset_key=analyzer_cache._make_flattened_dataset_key(), # pylint: disable=protected-access
label='ExtractInputForSavedModel[FlattenedDataset]')
while not all(sink_tensors_ready.values()):
infix = 'Phase{}'.format(phase)
# Determine which table init ops are ready to run in this phase
# Determine which keys of pending_tensor_replacements are ready to run
# in this phase, based in whether their dependencies are ready.
graph_analyzer = graph_tools.InitializableGraphAnalyzer(
graph, input_signature, list(sink_tensors_ready.items()),
graph_tools.describe_path_as_analyzer_cache_hash)
ready_traverser = nodes.Traverser(_ReadyVisitor(graph_analyzer))
# Now create and apply a SavedModel with all tensors in tensor_bindings
# bound, which outputs all the tensors in the required tensor tuples.
intermediate_output_signature = collections.OrderedDict()
saved_model_future = nodes.apply_operation(
beam_nodes.CreateSavedModel,
*tensor_bindings,
table_initializers=tuple(graph_analyzer.ready_table_initializers),
output_signature=intermediate_output_signature,
label='CreateSavedModelForAnalyzerInputs[{}]'.format(infix))
extracted_values_dict = nodes.apply_operation(
beam_nodes.ApplySavedModel,
saved_model_future,
extracted_input_node,
phase=phase,
label='ApplySavedModel[{}]'.format(infix))
translate_visitor.phase = phase
translate_visitor.intermediate_output_signature = (
intermediate_output_signature)
translate_visitor.extracted_values_dict = extracted_values_dict
for tensor, value_node, is_asset_filepath in tensor_sinks:
hashable_tensor = tf_utils.hashable_tensor_or_op(tensor)
# Don't compute a binding/sink/replacement that's already been computed
if sink_tensors_ready[hashable_tensor]:
continue
if not ready_traverser.visit_value_node(value_node):
continue
translated_value_node = translate_traverser.visit_value_node(
value_node)
name = _tensor_name(tensor)
tensor_bindings.append(
nodes.apply_operation(
beam_nodes.CreateTensorBinding,
translated_value_node,
tensor_name=str(tensor.name),
dtype_enum=tensor.dtype.as_datatype_enum,
is_asset_filepath=is_asset_filepath,
label=analyzer_nodes.sanitize_label(
'CreateTensorBinding[{}]'.format(name))))
sink_tensors_ready[hashable_tensor] = True
analyzers_input_signature.update(intermediate_output_signature)
phase += 1
# We need to make sure that the representation of this output_signature is
# deterministic.
output_signature = collections.OrderedDict(
sorted(output_signature.items(), key=lambda t: t[0]))
# TODO(KesterTong): check all table initializers are ready, check all output
# tensors are ready.
saved_model_future = nodes.apply_operation(
beam_nodes.CreateSavedModel,
*tensor_bindings,
table_initializers=tuple(
graph.get_collection(tf.compat.v1.GraphKeys.TABLE_INITIALIZERS)),
output_signature=output_signature,
label='CreateSavedModel')
tensor_keys_to_paths = {
tensor_key:
graph_analyzer.get_unique_path(analyzers_input_signature[tensor_key])
for tensor_key in analyzers_input_signature
}
(optimized_saved_model_future, output_cache_value_nodes,
detached_sideeffect_leafs) = _perform_cache_optimization(
saved_model_future, dataset_keys, tensor_keys_to_paths, cache_dict,
phase)
(optimized_saved_model_future, output_cache_value_nodes) = (
combiner_packing_util.perform_combiner_packing_optimization(
optimized_saved_model_future, output_cache_value_nodes, phase))
global _ANALYSIS_GRAPH
_ANALYSIS_GRAPH = optimized_saved_model_future
return (optimized_saved_model_future, output_cache_value_nodes,
detached_sideeffect_leafs)
def perform_combiner_packing_optimization(saved_model_future,
cache_value_nodes, num_phases):
"""Optimizes the graph by packing possible combine nodes."""
# Inspect the graph to identify all the packable combines.
inspect_acc_combine_visitor = _InspectAccumulateCombineVisitor()
inspect_acc_combine_traverser = nodes.Traverser(inspect_acc_combine_visitor)
_ = inspect_acc_combine_traverser.visit_value_node(saved_model_future)
packable_combines = inspect_acc_combine_visitor.packable_combines
# Do not pack if we have only a single combine in the group.
packable_combines = {
label: group for label, group in packable_combines.items()
if len(group) > 1
}
pack_acc_combine_visitor = _PackAccumulateCombineVisitor(packable_combines)
pack_acc_combine_traverser = nodes.Traverser(pack_acc_combine_visitor)
saved_model_future = pack_acc_combine_traverser.visit_value_node(
saved_model_future)
# Replace cache nodes to point to the corresponding new nodes.
cache_value_nodes = _update_cache_value_node_references(
cache_value_nodes, pack_acc_combine_traverser)
# TODO(b/134414978): Consider also packing the merges even when we have
# multiple phases.
if num_phases > 1:
return (saved_model_future, cache_value_nodes)
# Identify the merge combines that can be packed together.
inspect_merge_combine_visitor = _InspectMergeCombineVisitor()
inspect_merge_combine_traverser = nodes.Traverser(
inspect_merge_combine_visitor)
_ = inspect_merge_combine_traverser.visit_value_node(saved_model_future)
# Only pack if we have more than one merge combines.
if len(inspect_merge_combine_visitor.packable_combine_extract_outputs) <= 1:
return (saved_model_future, cache_value_nodes)
# Add flatten and packed merge nodes.
pack_merge_combine_visitor = _PackMergeCombineVisitor(
packable_combine_extract_outputs=
inspect_merge_combine_visitor.packable_combine_extract_outputs)
pack_merge_combine_traverser = nodes.Traverser(pack_merge_combine_visitor)
saved_model_future = pack_merge_combine_traverser.visit_value_node(
saved_model_future)
# Replace cache nodes to point to the corresponding new nodes.
cache_value_nodes = _update_cache_value_node_references(
cache_value_nodes, pack_merge_combine_traverser)
# Remove redundant flatten and packed merge nodes.
remove_redundant_visitor = _RemoveRedundantPackedMergeCombineVisitor(
final_packed_merge_combine_label=
pack_merge_combine_visitor.final_packed_merge_combine_label)
remove_redundant_traverser = nodes.Traverser(remove_redundant_visitor)
saved_model_future = remove_redundant_traverser.visit_value_node(
saved_model_future)
# Replace cache nodes to point to the corresponding new nodes.
cache_value_nodes = _update_cache_value_node_references(
cache_value_nodes, remove_redundant_traverser)
return (saved_model_future, cache_value_nodes)
def build(graph, input_signature, output_signature):
"""Returns a list of `Phase`s describing how to execute the pipeline.
The default graph is assumed to contain some `Analyzer`s which must be
executed by doing a full pass over the dataset, and passing the inputs for
that analyzer into some implementation, then taking the results and replacing
the `Analyzer`s outputs with constants in the graph containing these results.
The execution plan is described by a list of `Phase`s. Each phase contains
a list of `Analyzer`s, which are the `Analyzer`s which are ready to run in
that phase, together with a list of ops, which are the table initializers that
are ready to run in that phase.
An `Analyzer` or op is ready to run when all its dependencies in the graph
have been computed. Thus if the graph is constructed by
def preprocessing_fn(input)
x = inputs['x']
scaled_0 = x - tft.min(x)
scaled_0_1 = scaled_0 / tft.max(scaled_0)
Then the first phase will contain the analyzer corresponding to the call to
`min`, because `x` is an input and so is ready to compute in the first phase,
while the second phase will contain the analyzer corresponding to the call to
`max` since `scaled_1` depends on the result of the call to `tft.min` which
is computed in the first phase.
More generally, we define a level for each op and each `Analyzer` by walking
the graph, assigning to each operation the max level of its inputs, to each
`Tensor` the level of its operation, unless it's the output of an `Analyzer`
in which case we assign the level of its `Analyzer` plus one.
Args:
graph: A `tf.Graph`.
input_signature: A dict whose keys are strings and values are `Tensor`s or
`SparseTensor`s.
output_signature: A dict whose keys are strings and values are `Tensor`s or
`SparseTensor`s.
Returns:
A list of `Phase`s.
Raises:
ValueError: if the graph cannot be analyzed.
"""
tensor_sinks = graph.get_collection(analyzer_nodes.TENSOR_REPLACEMENTS)
graph.clear_collection(analyzer_nodes.TENSOR_REPLACEMENTS)
phase = 0
tensor_bindings = []
sink_tensors_ready = {
tensor_sink.tensor: False
for tensor_sink in tensor_sinks
}
translate_visitor = _TranslateVisitor()
translate_traverser = nodes.Traverser(translate_visitor)
while not all(sink_tensors_ready.values()):
# Determine which table init ops are ready to run in this phase
# Determine which keys of pending_tensor_replacements are ready to run
# in this phase, based in whether their dependencies are ready.
graph_analyzer = graph_tools.InitializableGraphAnalyzer(
graph, input_signature.values(), sink_tensors_ready)
ready_traverser = nodes.Traverser(_ReadyVisitor(graph_analyzer))
# Now create and apply a SavedModel with all tensors in tensor_bindings
# bound, which outputs all the tensors in the required tensor tuples.
intermediate_output_signature = collections.OrderedDict()
saved_model_future = nodes.apply_operation(
beam_nodes.CreateSavedModel,
*tensor_bindings,
table_initializers=tuple(graph_analyzer.ready_table_initializers),
output_signature=intermediate_output_signature,
label='CreateSavedModelForAnalyzerInputs[{}]'.format(phase))
extracted_values_dict = nodes.apply_operation(
beam_nodes.ApplySavedModel,
saved_model_future,
phase=phase,
label='ApplySavedModel[{}]'.format(phase))
translate_visitor.phase = phase
translate_visitor.intermediate_output_signature = (
intermediate_output_signature)
translate_visitor.extracted_values_dict = extracted_values_dict
for tensor, value_node, is_asset_filepath in tensor_sinks:
# Don't compute a binding/sink/replacement that's already been computed
if sink_tensors_ready[tensor]:
continue
if not ready_traverser.visit_value_node(value_node):
continue
translated_value_node = translate_traverser.visit_value_node(
value_node)
name = _tensor_name(tensor)
tensor_bindings.append(
nodes.apply_operation(
beam_nodes.CreateTensorBinding,
translated_value_node,
tensor=str(tensor.name),
is_asset_filepath=is_asset_filepath,
label='CreateTensorBinding[{}]'.format(name)))
sink_tensors_ready[tensor] = True
phase += 1
# We need to make sure that the representation of this output_signature is
# deterministic.
output_signature = collections.OrderedDict(
sorted(output_signature.items(), key=lambda t: t[0]))
return nodes.apply_operation(beam_nodes.CreateSavedModel,
*tensor_bindings,
table_initializers=tuple(
graph.get_collection(
tf.GraphKeys.TABLE_INITIALIZERS)),
output_signature=output_signature,
label='CreateSavedModel')
def expand(self, dataset):
"""Analyze the dataset.
Args:
dataset: A dataset.
Returns:
A TransformFn containing the deferred transform function.
Raises:
ValueError: If preprocessing_fn has no outputs.
"""
(flattened_pcoll, input_values_pcoll_dict, dataset_cache_dict,
input_metadata) = dataset
if self._use_tfxio:
input_schema = None
input_tensor_adapter_config = input_metadata
else:
input_schema = input_metadata.schema
input_tensor_adapter_config = None
input_values_pcoll_dict = input_values_pcoll_dict or dict()
with tf.compat.v1.Graph().as_default() as graph:
with tf.compat.v1.name_scope('inputs'):
if self._use_tfxio:
specs = TensorAdapter(input_tensor_adapter_config).OriginalTypeSpecs()
else:
specs = schema_utils.schema_as_feature_spec(input_schema).feature_spec
input_signature = impl_helper.batched_placeholders_from_specs(specs)
# In order to avoid a bug where import_graph_def fails when the
# input_map and return_elements of an imported graph are the same
# (b/34288791), we avoid using the placeholder of an input column as an
# output of a graph. We do this by applying tf.identity to all inputs of
# the preprocessing_fn. Note this applies at the level of raw tensors.
# TODO(b/34288791): Remove this workaround and use a shallow copy of
# inputs instead. A shallow copy is needed in case
# self._preprocessing_fn mutates its input.
copied_inputs = impl_helper.copy_tensors(input_signature)
output_signature = self._preprocessing_fn(copied_inputs)
# At this point we check that the preprocessing_fn has at least one
# output. This is because if we allowed the output of preprocessing_fn to
# be empty, we wouldn't be able to determine how many instances to
# "unbatch" the output into.
if not output_signature:
raise ValueError('The preprocessing function returned an empty dict')
if graph.get_collection(tf.compat.v1.GraphKeys.TRAINABLE_VARIABLES):
raise ValueError(
'The preprocessing function contained trainable variables '
'{}'.format(
graph.get_collection_ref(
tf.compat.v1.GraphKeys.TRAINABLE_VARIABLES)))
pipeline = self.pipeline or (flattened_pcoll or next(
v for v in input_values_pcoll_dict.values() if v is not None)).pipeline
# Add a stage that inspects graph collections for API use counts and logs
# them as a beam metric.
_ = (pipeline | 'InstrumentAPI' >> _InstrumentAPI(graph))
tf_config = _DEFAULT_TENSORFLOW_CONFIG_BY_BEAM_RUNNER_TYPE.get(
type(pipeline.runner))
extra_args = beam_common.ConstructBeamPipelineVisitor.ExtraArgs(
base_temp_dir=Context.create_base_temp_dir(),
tf_config=tf_config,
pipeline=pipeline,
flat_pcollection=flattened_pcoll,
pcollection_dict=input_values_pcoll_dict,
graph=graph,
input_signature=input_signature,
input_schema=input_schema,
input_tensor_adapter_config=input_tensor_adapter_config,
use_tfxio=self._use_tfxio,
cache_pcoll_dict=dataset_cache_dict)
transform_fn_future, cache_value_nodes = analysis_graph_builder.build(
graph,
input_signature,
output_signature,
input_values_pcoll_dict.keys(),
cache_dict=dataset_cache_dict)
traverser = nodes.Traverser(
beam_common.ConstructBeamPipelineVisitor(extra_args))
transform_fn_pcoll = traverser.visit_value_node(transform_fn_future)
if cache_value_nodes is not None:
output_cache_pcoll_dict = {}
for (dataset_key,
cache_key), value_node in six.iteritems(cache_value_nodes):
if dataset_key not in output_cache_pcoll_dict:
output_cache_pcoll_dict[dataset_key] = {}
output_cache_pcoll_dict[dataset_key][cache_key] = (
traverser.visit_value_node(value_node))
else:
output_cache_pcoll_dict = None
# Infer metadata. We take the inferred metadata and apply overrides that
# refer to values of tensors in the graph. The override tensors must
# be "constant" in that they don't depend on input data. The tensors can
# depend on analyzer outputs though. This allows us to set metadata that
# depends on analyzer outputs. _infer_metadata_from_saved_model will use the
# analyzer outputs stored in `transform_fn` to compute the metadata in a
# deferred manner, once the analyzer outputs are known.
metadata = dataset_metadata.DatasetMetadata(
schema=schema_inference.infer_feature_schema(output_signature, graph))
deferred_metadata = (
transform_fn_pcoll
|
'ComputeDeferredMetadata' >> beam.Map(_infer_metadata_from_saved_model))
full_metadata = beam_metadata_io.BeamDatasetMetadata(
metadata, deferred_metadata)
_clear_shared_state_after_barrier(pipeline, transform_fn_pcoll)
return (transform_fn_pcoll, full_metadata), output_cache_pcoll_dict
