def create_inference_graph(input_graph_def,
outputs,
max_batch_size=1,
max_workspace_size_bytes=2 << 20,
precision_mode=TrtPrecisionMode.FP32,
minimum_segment_size=3,
is_dynamic_op=False,
maximum_cached_engines=1,
cached_engine_batch_sizes=None,
input_saved_model_dir=None,
input_saved_model_tags=None,
output_saved_model_dir=None,
session_config=None):
"""Python wrapper for the TRT transformation.
Args:
input_graph_def: a GraphDef object containing a model to be transformed. If
set to None, the graph will be read from the SavedModel loaded from
input_saved_model_dir.
outputs: list of tensors or node names for the model outputs. Only used when
input_graph_def is not None.
max_batch_size: max size for the input batch.
max_workspace_size_bytes: the maximum GPU temporary memory which the TRT
engine can use at execution time. This corresponds to the 'workspaceSize'
parameter of nvinfer1::IBuilder::setMaxWorkspaceSize().
precision_mode: one of TrtPrecisionMode.supported_precision_modes().
minimum_segment_size: the minimum number of nodes required for a subgraph to
be replaced by TRTEngineOp.
is_dynamic_op: whether to generate dynamic TRT ops which will build the TRT
network and engine at run time.
maximum_cached_engines: max number of cached TRT engines in dynamic TRT ops.
If the number of cached engines is already at max but none of them can
serve the input, the TRTEngineOp will fall back to run the TF function
based on which the TRTEngineOp is created.
cached_engine_batch_sizes: a list of batch sizes used to create cached
engines, only used when is_dynamic_op is True. The length of the list
should be smaller than maximum_cached_engines, and the dynamic TRT op will
use this list to determine the batch sizes of the cached engines, instead
of making the decision on the fly. This is useful when we know the most
common batch size(s) the application is going to generate.
input_saved_model_dir: the directory to load the SavedModel which contains
the input graph to transforms. Used only when input_graph_def is None.
input_saved_model_tags: list of tags to load the SavedModel.
output_saved_model_dir: if not None, construct a SavedModel using the
returned GraphDef and save it to the specified directory. This option only
works when the input graph is loaded from a SavedModel, i.e. when
input_saved_model_dir is specified and input_graph_def is None.
session_config: the ConfigProto used to create a Session. It's also used as
a template to create a TRT-enabled ConfigProto for conversion. If not
specified, a default ConfigProto will be used.
Returns:
A GraphDef transformed from input_graph_def (or the SavedModel graph def
loaded from input_saved_model_dir, if input_graph_def is not present), where
all TRT compatible subgraphs are replaced with TRTEngineOps, and a TF
function is added for each of the subgraphs.
If is_dynamic_op is True, each TRTEngineOp will contain a serialized
subgraph GraphDef, which will be converted to a TRT engine at execution time
and the TRT engine will be cached for future usage. A new TRT engine will be
created each time when none of the cached engines match the input shapes. If
it fails to execute the TRT engine or the number of cached engines reaches
maximum_cached_engines, the op will fall back to call the corresponding TF
function.
If is_dynamic_op is False, each TRTEngineOp will contain a serialized TRT
engine created from the corresponding subgraph. No more engines will be
created on the fly, and the op will fall back to call the corresponding TF
function when it fails to execute the engine.
Raises:
ValueError: if the combination of the parameters is invalid.
RuntimeError: if the TensorRT library version is incompatible.
"""
compiled_version = get_linked_tensorrt_version()
loaded_version = get_loaded_tensorrt_version()
version_mismatch = False
if loaded_version[0] < compiled_version[0]:
tf_logging.error(
"TensorRT version mismatch. Tensorflow was compiled against " +
"TensorRT %s but library loaded from environment is TensorRT %s" %
(".".join([str(x) for x in compiled_version]),
".".join([str(x) for x in loaded_version])) +
". Please make sure that correct version of TensorRT " +
"is available in the system and added to ldconfig or LD_LIBRARY_PATH")
raise RuntimeError("Incompatible TensorRT library version")
for i in zip(loaded_version, compiled_version):
if i[0] != i[1]:
tf_logging.warn("TensorRT mismatch. Compiled against version " +
"%s, but loaded %s. Things may not work" %
(".".join([str(x) for x in compiled_version]),
".".join([str(x) for x in loaded_version])))
version_mismatch = True
break
if not version_mismatch:
tf_logging.info("Running against TensorRT version %s" % ".".join(
[str(x) for x in loaded_version]))
if session_config is None:
session_config = config_pb2.ConfigProto()
if input_saved_model_tags is None:
input_saved_model_tags = [tag_constants.SERVING]
saved_model_loader = None
grappler_meta_graph_def = None
if input_graph_def is None:
# Read from SavedModel and freeze the graph if necessary.
if input_saved_model_dir is None:
raise ValueError("input_graph_def and input_saved_model_dir cannot be "
"both None")
with ops.Graph().as_default():
with session.Session(config=session_config) as sess:
saved_model_loader = loader_impl.SavedModelLoader(input_saved_model_dir)
input_meta_graph_def = saved_model_loader.load(sess,
input_saved_model_tags)
output_node_names = set()
def _gather_names(tensor_info):
"""Get the node names from a TensorInfo."""
return set(
[tensor_info[key].name.split(":")[0] for key in tensor_info])
# Get input and outputs from all SignatureDef.
for key in input_meta_graph_def.signature_def:
signature_def = input_meta_graph_def.signature_def[key]
output_node_names.update(_gather_names(signature_def.inputs))
output_node_names.update(_gather_names(signature_def.outputs))
# Freeze the variables in the SavedModel graph and copy the frozen
# graph over.
frozen_graph_def = graph_util.convert_variables_to_constants(
sess, sess.graph.as_graph_def(add_shapes=True),
list(output_node_names))
grappler_meta_graph_def = meta_graph_pb2.MetaGraphDef()
grappler_meta_graph_def.graph_def.CopyFrom(frozen_graph_def)
# Copy the collections that are not variables.
for key in input_meta_graph_def.collection_def:
# TODO(laigd): currently we use the collection key to filter out
# collections that depend on variable ops, but this may miss some
# other user-defined collections. A better way would be to use
# CollectionDef::NodeList for the filtering.
if key not in [
"variables", "local_variables", "model_variables",
"trainable_variables", "train_op", "table_initializer"
]:
grappler_meta_graph_def.collection_def[key].CopyFrom(
input_meta_graph_def.collection_def[key])
# Copy other information.
grappler_meta_graph_def.meta_info_def.CopyFrom(
input_meta_graph_def.meta_info_def)
for key in input_meta_graph_def.signature_def:
grappler_meta_graph_def.signature_def[key].CopyFrom(
input_meta_graph_def.signature_def[key])
# TODO(laigd): maybe add back AssetFileDef.
else:
if output_saved_model_dir is not None:
raise ValueError("output_saved_model_dir cannot be set when "
"input_graph_def is set")
# Create MetaGraphDef from input graph.
graph = ops.Graph()
with graph.as_default():
importer.import_graph_def(input_graph_def, name="")
grappler_meta_graph_def = saver.export_meta_graph(
graph_def=graph.as_graph_def(add_shapes=True), graph=graph)
if outputs:
output_collection = meta_graph_pb2.CollectionDef()
output_list = output_collection.node_list.value
for i in outputs:
if isinstance(i, ops.Tensor):
output_list.append(_to_bytes(i.name))
else:
output_list.append(_to_bytes(i))
# TODO(laigd): use another key as the outputs are really not train_op.
grappler_meta_graph_def.collection_def["train_op"].CopyFrom(
output_collection)
# Create TRT-enabled ConfigProto.
session_config_with_trt = config_pb2.ConfigProto()
session_config_with_trt.CopyFrom(session_config)
rewriter_config = None
if (session_config_with_trt.HasField("graph_options") and
session_config_with_trt.graph_options.HasField("rewrite_options")):
rewriter_config = session_config_with_trt.graph_options.rewrite_options
rewriter_config_with_trt = get_tensorrt_rewriter_config(
rewriter_config, max_batch_size, max_workspace_size_bytes, precision_mode,
minimum_segment_size, is_dynamic_op, maximum_cached_engines,
cached_engine_batch_sizes)
session_config_with_trt.graph_options.rewrite_options.CopyFrom(
rewriter_config_with_trt)
# Run Grappler.
transformed_graph_def = tf_optimizer.OptimizeGraph(
session_config_with_trt, grappler_meta_graph_def, graph_id=b"tf_graph")
# Optionally write the transformed graphdef as SavedModel.
if output_saved_model_dir is not None:
saved_model_builder = builder.SavedModelBuilder(output_saved_model_dir)
with ops.Graph().as_default():
importer.import_graph_def(transformed_graph_def, name="")
# We don't use TRT here.
with session.Session(config=session_config) as sess:
saved_model_builder.add_meta_graph_and_variables(
sess,
input_saved_model_tags,
signature_def_map=grappler_meta_graph_def.signature_def)
# Ignore other meta graphs from the input SavedModel.
saved_model_builder.save()
return transformed_graph_def
def create_inference_graph(input_graph_def,
outputs,
max_batch_size=1,
max_workspace_size_bytes=2 << 20,
precision_mode="FP32",
minimum_segment_size=3,
is_dynamic_op=False,
maximum_cached_engines=1,
cached_engine_batches=None):
"""Python wrapper for the TRT transformation.
Args:
input_graph_def: GraphDef object containing a model to be transformed.
outputs: list of tensors or node names for the model outputs.
max_batch_size: max size for the input batch
max_workspace_size_bytes: parameter to control memory allocation (in Bytes)
precision_mode: one of 'FP32', 'FP16' and 'INT8'
minimum_segment_size: the minimum number of nodes required for a subgraph to
be replaced by TRTEngineOp.
is_dynamic_op: whether to generate dynamic TRT ops which will build the TRT
network and engine at run time.
maximum_cached_engines: max number of cached TRT engines in dynamic TRT ops.
cached_engine_batches: batch sizes used to pre-create cached engines.
Returns:
New GraphDef with TRTEngineOps placed in graph replacing subgraphs.
Raises:
ValueError: if the provided precision mode is invalid.
RuntimeError: if the returned status message is malformed.
"""
supported_precision_modes = {"FP32": 0, "FP16": 1, "INT8": 2}
if precision_mode.upper() not in supported_precision_modes:
raise ValueError(("precision mode '{}' is not supported."
"It should be one of {}").format(
precision_mode, "{'FP32', 'FP16', 'INT8'}"))
mode = supported_precision_modes[precision_mode.upper()]
compiled_version = get_linked_tensorrt_version()
loaded_version = get_loaded_tensorrt_version()
version_mismatch = False
if loaded_version[0] < compiled_version[0]:
tf_logging.error(
"TensorRT version mismatch. Tensorflow was compiled against " +
"TensorRT %s but library loaded from environment is TensorRT %s" %
(".".join([str(x) for x in compiled_version]),
".".join([str(x) for x in loaded_version])) +
". Please make sure that correct version of TensorRT " +
"is available in the system and added to ldconfig or LD_LIBRARY_PATH")
raise RuntimeError("Incompatible TensorRT library version")
for i in zip(loaded_version, compiled_version):
if i[0] != i[1]:
tf_logging.warn("TensorRT mismatch. Compiled against version " +
"%s, but loaded %s. Things may not work" %
(".".join([str(x) for x in compiled_version]),
".".join([str(x) for x in loaded_version])))
version_mismatch = True
break
if not version_mismatch:
tf_logging.info("Running against TensorRT version %s" % ".".join(
[str(x) for x in loaded_version]))
def py2bytes(inp):
return inp
def py3bytes(inp):
return inp.encode("utf-8", errors="surrogateescape")
def py2string(inp):
return inp
def py3string(inp):
return inp.decode("utf-8")
if _six.PY2:
to_bytes = py2bytes
to_string = py2string
else:
to_bytes = py3bytes
to_string = py3string
# Create MetaGraphDef
graph = ops.Graph()
with graph.as_default():
importer.import_graph_def(input_graph_def, name="")
meta_graph = saver.export_meta_graph(
graph_def=graph.as_graph_def(), graph=graph)
if outputs:
output_collection = meta_graph_pb2.CollectionDef()
output_list = output_collection.node_list.value
for i in outputs:
if isinstance(i, ops.Tensor):
output_list.append(to_bytes(i.name))
else:
output_list.append(to_bytes(i))
meta_graph.collection_def["train_op"].CopyFrom(output_collection)
# Create RewriterConfig.
rewriter_cfg = rewriter_config_pb2.RewriterConfig()
rewriter_cfg.optimizers.extend(["constfold", "layout"])
optimizer = rewriter_cfg.custom_optimizers.add()
optimizer.name = "TensorRTOptimizer"
optimizer.parameter_map["minimum_segment_size"].i = minimum_segment_size
optimizer.parameter_map["max_batch_size"].i = max_batch_size
optimizer.parameter_map["is_dynamic_op"].b = is_dynamic_op
optimizer.parameter_map[
"max_workspace_size_bytes"].i = max_workspace_size_bytes
optimizer.parameter_map["precision_mode"].s = to_bytes(precision_mode)
optimizer.parameter_map["maximum_cached_engines"].i = maximum_cached_engines
if cached_engine_batches:
if not isinstance(cached_engine_batches, list):
raise TypeError("cached_engine_batches should be a list.")
optimizer.parameter_map["cached_engine_batches"].list.i.extend(
cached_engine_batches)
return tf_optimizer.OptimizeGraph(
rewriter_cfg, meta_graph, graph_id=b"tf_graph")
def create_inference_graph(input_graph_def,
outputs,
max_batch_size=1,
max_workspace_size_bytes=2 << 20,
precision_mode=TrtPrecisionMode.FP32,
minimum_segment_size=3,
is_dynamic_op=False,
maximum_cached_engines=1,
cached_engine_batch_sizes=None,
input_saved_model_dir=None,
input_saved_model_tags=None,
output_saved_model_dir=None,
session_config=None):
"""Python wrapper for the TRT transformation.
Args:
input_graph_def: a GraphDef object containing a model to be transformed. If
set to None, the graph will be read from the SavedModel loaded from
input_saved_model_dir.
outputs: list of tensors or node names for the model outputs. Only used when
input_graph_def is not None.
max_batch_size: max size for the input batch.
max_workspace_size_bytes: the maximum GPU temporary memory which the TRT
engine can use at execution time. This corresponds to the 'workspaceSize'
parameter of nvinfer1::IBuilder::setMaxWorkspaceSize().
precision_mode: one of TrtPrecisionMode.supported_precision_modes().
minimum_segment_size: the minimum number of nodes required for a subgraph to
be replaced by TRTEngineOp.
is_dynamic_op: whether to generate dynamic TRT ops which will build the TRT
network and engine at run time.
maximum_cached_engines: max number of cached TRT engines in dynamic TRT ops.
If the number of cached engines is already at max but none of them can
serve the input, the TRTEngineOp will fall back to run the TF function
based on which the TRTEngineOp is created.
cached_engine_batch_sizes: a list of batch sizes used to create cached
engines, only used when is_dynamic_op is True. The length of the list
should be smaller than maximum_cached_engines, and the dynamic TRT op will
use this list to determine the batch sizes of the cached engines, instead
of making the decision on the fly. This is useful when we know the most
common batch size(s) the application is going to generate.
input_saved_model_dir: the directory to load the SavedModel which contains
the input graph to transforms. Used only when input_graph_def is None.
input_saved_model_tags: list of tags to load the SavedModel.
output_saved_model_dir: if not None, construct a SavedModel using the
returned GraphDef and save it to the specified directory. This option only
works when the input graph is loaded from a SavedModel, i.e. when
input_saved_model_dir is specified and input_graph_def is None.
session_config: the ConfigProto used to create a Session. If not specified,
a default ConfigProto will be used.
Returns:
A GraphDef transformed from input_graph_def (or the SavedModel graph def
loaded from input_saved_model_dir, if input_graph_def is not present), where
all TRT compatible subgraphs are replaced with TRTEngineOps, and a TF
function is added for each of the subgraphs.
If is_dynamic_op is True, each TRTEngineOp will contain a serialized
subgraph GraphDef, which will be converted to a TRT engine at execution time
and the TRT engine will be cached for future usage. A new TRT engine will be
created each time when none of the cached engines match the input shapes. If
it fails to execute the TRT engine or the number of cached engines reaches
maximum_cached_engines, the op will fall back to call the corresponding TF
function.
If is_dynamic_op is False, each TRTEngineOp will contain a serialized TRT
engine created from the corresponding subgraph. No more engines will be
created on the fly, and the op will fall back to call the corresponding TF
function when it fails to execute the engine.
Raises:
ValueError: if the combination of the parameters is invalid.
RuntimeError: if the TensorRT library version is incompatible.
"""
compiled_version = get_linked_tensorrt_version()
loaded_version = get_loaded_tensorrt_version()
version_mismatch = False
if loaded_version[0] < compiled_version[0]:
tf_logging.error(
"TensorRT version mismatch. Tensorflow was compiled against " +
"TensorRT %s but library loaded from environment is TensorRT %s" %
(".".join([str(x) for x in compiled_version]),
".".join([str(x) for x in loaded_version])) +
". Please make sure that correct version of TensorRT " +
"is available in the system and added to ldconfig or LD_LIBRARY_PATH"
)
raise RuntimeError("Incompatible TensorRT library version")
for i in zip(loaded_version, compiled_version):
if i[0] != i[1]:
tf_logging.warn("TensorRT mismatch. Compiled against version " +
"%s, but loaded %s. Things may not work" %
(".".join([str(x) for x in compiled_version]),
".".join([str(x) for x in loaded_version])))
version_mismatch = True
break
if not version_mismatch:
tf_logging.info("Running against TensorRT version %s" %
".".join([str(x) for x in loaded_version]))
if session_config is None:
session_config = config_pb2.ConfigProto()
if input_saved_model_tags is None:
input_saved_model_tags = [tag_constants.SERVING]
saved_model_loader = None
grappler_meta_graph_def = None
if input_graph_def is None:
# Read from SavedModel and freeze the graph if necessary.
if input_saved_model_dir is None:
raise ValueError(
"input_graph_def and input_saved_model_dir cannot be "
"both None")
with ops.Graph().as_default():
with session.Session(config=session_config) as sess:
saved_model_loader = loader_impl.SavedModelLoader(
input_saved_model_dir)
input_meta_graph_def = saved_model_loader.load(
sess, input_saved_model_tags)
output_node_names = set()
def _gather_names(tensor_info):
"""Get the node names from a TensorInfo."""
return set([
tensor_info[key].name.split(":")[0]
for key in tensor_info
])
# Get input and outputs from all SignatureDef.
for key in input_meta_graph_def.signature_def:
signature_def = input_meta_graph_def.signature_def[key]
output_node_names.update(
_gather_names(signature_def.inputs))
output_node_names.update(
_gather_names(signature_def.outputs))
# Freeze the variables in the SavedModel graph and copy the frozen
# graph over.
frozen_graph_def = graph_util.convert_variables_to_constants(
sess, sess.graph.as_graph_def(add_shapes=True),
list(output_node_names))
grappler_meta_graph_def = meta_graph_pb2.MetaGraphDef()
grappler_meta_graph_def.graph_def.CopyFrom(frozen_graph_def)
# Copy the collections that are not variables.
for key in input_meta_graph_def.collection_def:
# TODO(laigd): currently we use the collection key to filter out
# collections that depend on variable ops, but this may miss some
# other user-defined collections. A better way would be to use
# CollectionDef::NodeList for the filtering.
if key not in [
"variables", "local_variables", "model_variables",
"trainable_variables", "train_op",
"table_initializer"
]:
grappler_meta_graph_def.collection_def[key].CopyFrom(
input_meta_graph_def.collection_def[key])
# Copy other information.
grappler_meta_graph_def.meta_info_def.CopyFrom(
input_meta_graph_def.meta_info_def)
for key in input_meta_graph_def.signature_def:
grappler_meta_graph_def.signature_def[key].CopyFrom(
input_meta_graph_def.signature_def[key])
# TODO(laigd): maybe add back AssetFileDef.
else:
if output_saved_model_dir is not None:
raise ValueError("output_saved_model_dir cannot be set when "
"input_graph_def is set")
# Create MetaGraphDef from input graph.
graph = ops.Graph()
with graph.as_default():
importer.import_graph_def(input_graph_def, name="")
grappler_meta_graph_def = saver.export_meta_graph(
graph_def=graph.as_graph_def(add_shapes=True), graph=graph)
if outputs:
output_collection = meta_graph_pb2.CollectionDef()
output_list = output_collection.node_list.value
for i in outputs:
if isinstance(i, ops.Tensor):
output_list.append(_to_bytes(i.name))
else:
output_list.append(_to_bytes(i))
# TODO(laigd): use another key as the outputs are really not train_op.
grappler_meta_graph_def.collection_def["train_op"].CopyFrom(
output_collection)
# Create RewriterConfig.
rewriter_cfg = tensorrt_rewriter_config(
max_batch_size, max_workspace_size_bytes, precision_mode,
minimum_segment_size, is_dynamic_op, maximum_cached_engines,
cached_engine_batch_sizes)
# Run Grappler.
transformed_graph_def = tf_optimizer.OptimizeGraph(rewriter_cfg,
grappler_meta_graph_def,
graph_id=b"tf_graph")
# Optionally write the transformed graphdef as SavedModel.
if output_saved_model_dir is not None:
saved_model_builder = builder.SavedModelBuilder(output_saved_model_dir)
with ops.Graph().as_default():
importer.import_graph_def(transformed_graph_def, name="")
with session.Session(config=session_config) as sess:
saved_model_builder.add_meta_graph_and_variables(
sess,
input_saved_model_tags,
signature_def_map=grappler_meta_graph_def.signature_def)
# Ignore other meta graphs from the input SavedModel.
saved_model_builder.save()
return transformed_graph_def
def create_inference_graph(input_graph_def,
outputs,
max_batch_size=1,
max_workspace_size_bytes=2 << 20,
precision_mode="FP32",
minimum_segment_size=3,
is_dynamic_op=False,
maximum_cached_engines=1,
cached_engine_batches=None):
"""Python wrapper for the TRT transformation.
Args:
input_graph_def: GraphDef object containing a model to be transformed.
outputs: list of tensors or node names for the model outputs.
max_batch_size: max size for the input batch
max_workspace_size_bytes: parameter to control memory allocation (in Bytes)
precision_mode: one of 'FP32', 'FP16' and 'INT8'
minimum_segment_size: the minimum number of nodes required for a subgraph to
be replaced by TRTEngineOp.
is_dynamic_op: whether to generate dynamic TRT ops which will build the TRT
network and engine at run time.
maximum_cached_engines: max number of cached TRT engines in dynamic TRT ops.
cached_engine_batches: batch sizes used to pre-create cached engines.
Returns:
New GraphDef with TRTEngineOps placed in graph replacing subgraphs.
Raises:
ValueError: if the provided precision mode is invalid.
RuntimeError: if the returned status message is malformed.
"""
supported_precision_modes = {"FP32": 0, "FP16": 1, "INT8": 2}
if precision_mode.upper() not in supported_precision_modes:
raise ValueError(("precision mode '{}' is not supported."
"It should be one of {}").format(
precision_mode, "{'FP32', 'FP16', 'INT8'}"))
mode = supported_precision_modes[precision_mode.upper()]
compiled_version = get_linked_tensorrt_version()
loaded_version = get_loaded_tensorrt_version()
version_mismatch = False
if loaded_version[0] < compiled_version[0]:
tf_logging.error(
"TensorRT version mismatch. Tensorflow was compiled against " +
"TensorRT %s but library loaded from environment is TensorRT %s" %
(".".join([str(x) for x in compiled_version]),
".".join([str(x) for x in loaded_version])) +
". Please make sure that correct version of TensorRT " +
"is available in the system and added to ldconfig or LD_LIBRARY_PATH")
raise RuntimeError("Incompatible TensorRT library version")
for i in zip(loaded_version, compiled_version):
if i[0] != i[1]:
tf_logging.warn("TensorRT mismatch. Compiled against version " +
"%s, but loaded %s. Things may not work" %
(".".join([str(x) for x in compiled_version]),
".".join([str(x) for x in loaded_version])))
version_mismatch = True
break
if not version_mismatch:
tf_logging.info("Running against TensorRT version %s" % ".".join(
[str(x) for x in loaded_version]))
def py2bytes(inp):
return inp
def py3bytes(inp):
return inp.encode("utf-8", errors="surrogateescape")
def py2string(inp):
return inp
def py3string(inp):
return inp.decode("utf-8")
if _six.PY2:
to_bytes = py2bytes
to_string = py2string
else:
to_bytes = py3bytes
to_string = py3string
# Create MetaGraphDef
graph = ops.Graph()
with graph.as_default():
importer.import_graph_def(input_graph_def, name="")
meta_graph = saver.export_meta_graph(
graph_def=graph.as_graph_def(), graph=graph)
if outputs:
output_collection = meta_graph_pb2.CollectionDef()
output_list = output_collection.node_list.value
for i in outputs:
if isinstance(i, ops.Tensor):
output_list.append(to_bytes(i.name))
else:
output_list.append(to_bytes(i))
meta_graph.collection_def["train_op"].CopyFrom(output_collection)
# Create RewriterConfig.
rewriter_cfg = rewriter_config_pb2.RewriterConfig()
rewriter_cfg.optimizers.extend(["constfold", "layout"])
optimizer = rewriter_cfg.custom_optimizers.add()
optimizer.name = "TensorRTOptimizer"
optimizer.parameter_map["minimum_segment_size"].i = minimum_segment_size
optimizer.parameter_map["max_batch_size"].i = max_batch_size
optimizer.parameter_map["is_dynamic_op"].b = is_dynamic_op
optimizer.parameter_map[
"max_workspace_size_bytes"].i = max_workspace_size_bytes
optimizer.parameter_map["precision_mode"].s = to_bytes(precision_mode)
optimizer.parameter_map["maximum_cached_engines"].i = maximum_cached_engines
if cached_engine_batches:
if not isinstance(cached_engine_batches, list):
raise TypeError("cached_engine_batches should be a list.")
optimizer.parameter_map["cached_engine_batches"].list.i.extend(
cached_engine_batches)
return tf_optimizer.OptimizeGraph(
rewriter_cfg, meta_graph, graph_id=b"tf_graph")
def create_inference_graph(input_graph_def,
outputs,
max_batch_size=1,
max_workspace_size_bytes=2 << 20,
precision_mode="FP32",
minimum_segment_size=3,
is_dynamic_op=False,
maximum_cached_engines=1,
cached_engine_batches=[]):
"""Python wrapper for the TRT transformation.
Args:
input_graph_def: GraphDef object containing a model to be transformed.
outputs: list of tensors or node names for the model outputs.
max_batch_size: max size for the input batch
max_workspace_size_bytes: parameter to control memory allocation (in Bytes)
precision_mode: one of 'FP32', 'FP16' and 'INT8'
minimum_segment_size: the minimum number of nodes required for a subgraph to
be replaced by TRTEngineOp.
is_dynamic_op: whether to generate dynamic TRT ops which will build the TRT
network and engine at run time.
maximum_cached_engines: max number of cached TRT engines in dynamic TRT ops.
cached_engine_batches: batch sizes used to pre-create cached engines.
Returns:
New GraphDef with TRTEngineOps placed in graph replacing subgraphs.
Raises:
ValueError: if the provided precision mode is invalid.
RuntimeError: if the returned status message is malformed.
"""
supported_precision_modes = {"FP32": 0, "FP16": 1, "INT8": 2}
if precision_mode.upper() not in supported_precision_modes:
raise ValueError(
("precision mode '{}' is not supported."
"It should be one of {}").format(precision_mode,
"{'FP32', 'FP16', 'INT8'}"))
mode = supported_precision_modes[precision_mode.upper()]
compiled_version = get_linked_tensorrt_version()
loaded_version = get_loaded_tensorrt_version()
version_mismatch = False
if loaded_version[0] < compiled_version[0]:
tf_logging.error(
"TensorRT version mismatch. Tensorflow was compiled against " +
"TensorRT %s but library loaded from environment is TensorRT %s" %
(".".join([str(x) for x in compiled_version]),
".".join([str(x) for x in loaded_version])) +
". Please make sure that correct version of TensorRT " +
"is available in the system and added to ldconfig or LD_LIBRARY_PATH"
)
raise RuntimeError("Incompatible TensorRT library version")
for i in zip(loaded_version, compiled_version):
if i[0] != i[1]:
tf_logging.warn("TensorRT mismatch. Compiled against version " +
"%s, but loaded %s. Things may not work" %
(".".join([str(x) for x in compiled_version]),
".".join([str(x) for x in loaded_version])))
version_mismatch = True
break
if not version_mismatch:
tf_logging.info("Running against TensorRT version %s" %
".".join([str(x) for x in loaded_version]))
def py2bytes(inp):
return inp
def py3bytes(inp):
return inp.encode("utf-8", errors="surrogateescape")
def py2string(inp):
return inp
def py3string(inp):
return inp.decode("utf-8")
if _six.PY2:
to_bytes = py2bytes
to_string = py2string
else:
to_bytes = py3bytes
to_string = py3string
out_names = []
for i in outputs:
if isinstance(i, ops.Tensor):
out_names.append(to_bytes(i.name))
else:
out_names.append(to_bytes(i))
input_graph_def_str = input_graph_def.SerializeToString()
# TODO(sami): Fix this when we can return status from C++ library
# There is a problem with the TF internal library setup that doesn't
# allow us to return a status object from C++. Thus we return a
# pair or strings where first one is encoded status and the second
# one is the transformed graphs protobuf string.
out = trt_convert(input_graph_def_str, out_names, max_batch_size,
max_workspace_size_bytes, mode, minimum_segment_size,
is_dynamic_op, maximum_cached_engines,
cached_engine_batches)
status = to_string(out[0])
output_graph_def_string = out[1]
del input_graph_def_str # Save some memory
if len(status) < 2:
raise _impl.UnknownError(None, None, status)
if status[:2] != "OK":
msg = status.split(";")
if len(msg) == 1:
raise RuntimeError("Status message is malformed {}".format(status))
# pylint: disable=protected-access
raise _impl._make_specific_exception(None, None, ";".join(msg[1:]),
int(msg[0]))
# pylint: enable=protected-access
output_graph_def = graph_pb2.GraphDef()
output_graph_def.ParseFromString(output_graph_def_string)
del output_graph_def_string # Save some memory
return output_graph_def
def create_inference_graph(input_graph_def,
outputs,
max_batch_size=1,
max_workspace_size_bytes=2 << 20,
precision_mode="FP32",
minimum_segment_size=3,
is_dynamic_op=False,
maximum_cached_engines=1,
cached_engine_batches=[]):
"""Python wrapper for the TRT transformation.
Args:
input_graph_def: GraphDef object containing a model to be transformed.
outputs: list of tensors or node names for the model outputs.
max_batch_size: max size for the input batch
max_workspace_size_bytes: parameter to control memory allocation (in Bytes)
precision_mode: one of 'FP32', 'FP16' and 'INT8'
minimum_segment_size: the minimum number of nodes required for a subgraph to
be replaced by TRTEngineOp.
is_dynamic_op: whether to generate dynamic TRT ops which will build the TRT
network and engine at run time.
maximum_cached_engines: max number of cached TRT engines in dynamic TRT ops.
cached_engine_batches: batch sizes used to pre-create cached engines.
Returns:
New GraphDef with TRTEngineOps placed in graph replacing subgraphs.
Raises:
ValueError: if the provided precision mode is invalid.
RuntimeError: if the returned status message is malformed.
"""
supported_precision_modes = {"FP32": 0, "FP16": 1, "INT8": 2}
if precision_mode.upper() not in supported_precision_modes:
raise ValueError(("precision mode '{}' is not supported."
"It should be one of {}").format(
precision_mode, "{'FP32', 'FP16', 'INT8'}"))
mode = supported_precision_modes[precision_mode.upper()]
compiled_version = get_linked_tensorrt_version()
loaded_version = get_loaded_tensorrt_version()
version_mismatch = False
if loaded_version[0] < compiled_version[0]:
tf_logging.error(
"TensorRT version mismatch. Tensorflow was compiled against " +
"TensorRT %s but library loaded from environment is TensorRT %s" %
(".".join([str(x) for x in compiled_version]),
".".join([str(x) for x in loaded_version])) +
". Please make sure that correct version of TensorRT " +
"is available in the system and added to ldconfig or LD_LIBRARY_PATH"
)
raise RuntimeError("Incompatible TensorRT library version")
for i in zip(loaded_version, compiled_version):
if i[0] != i[1]:
tf_logging.warn("TensorRT mismatch. Compiled against version " +
"%s, but loaded %s. Things may not work" %
(".".join([str(x) for x in compiled_version]),
".".join([str(x) for x in loaded_version])))
version_mismatch = True
break
if not version_mismatch:
tf_logging.info("Running against TensorRT version %s" % ".".join(
[str(x) for x in loaded_version]))
def py2bytes(inp):
return inp
def py3bytes(inp):
return inp.encode("utf-8", errors="surrogateescape")
def py2string(inp):
return inp
def py3string(inp):
return inp.decode("utf-8")
if _six.PY2:
to_bytes = py2bytes
to_string = py2string
else:
to_bytes = py3bytes
to_string = py3string
out_names = []
for i in outputs:
if isinstance(i, ops.Tensor):
out_names.append(to_bytes(i.name))
else:
out_names.append(to_bytes(i))
input_graph_def_str = input_graph_def.SerializeToString()
# TODO(sami): Fix this when we can return status from C++ library
# There is a problem with the TF internal library setup that doesn't
# allow us to return a status object from C++. Thus we return a
# pair or strings where first one is encoded status and the second
# one is the transformed graphs protobuf string.
out = trt_convert(input_graph_def_str, out_names, max_batch_size,
max_workspace_size_bytes, mode, minimum_segment_size,
is_dynamic_op, maximum_cached_engines,
cached_engine_batches)
status = to_string(out[0])
output_graph_def_string = out[1]
del input_graph_def_str # Save some memory
if len(status) < 2:
raise _impl.UnknownError(None, None, status)
if status[:2] != "OK":
msg = status.split(";")
if len(msg) == 1:
raise RuntimeError("Status message is malformed {}".format(status))
# pylint: disable=protected-access
raise _impl._make_specific_exception(None, None, ";".join(msg[1:]),
int(msg[0]))
# pylint: enable=protected-access
output_graph_def = graph_pb2.GraphDef()
output_graph_def.ParseFromString(output_graph_def_string)
del output_graph_def_string # Save some memory
return output_graph_def
