Allow intermediate string tensors

PiperOrigin-RevId: 222298206
This commit is contained in:
A. Unique TensorFlower 2018-11-20 13:34:35 -08:00 committed by TensorFlower Gardener
parent 5c4efd9460
commit 760ef2cc7e
8 changed files with 393 additions and 91 deletions

View File

@ -395,9 +395,10 @@ tf_cc_test(
# :toco is the main public command-line tool exposing the functionality
# of the :toco_tooling library.
tf_cc_binary(
name = "toco",
srcs = ["toco.cc"],
cc_library(
name = "toco_convert",
srcs = ["toco_convert.cc"],
hdrs = ["toco_convert.h"],
visibility = ["//visibility:public"],
deps = [
":model",
@ -416,6 +417,51 @@ tf_cc_binary(
],
)
tf_cc_binary(
name = "toco",
srcs = ["toco.cc"],
visibility = ["//visibility:public"],
deps = [
":model",
":model_cmdline_flags",
":model_flags_proto_cc",
":toco_cmdline_flags",
":toco_convert",
":toco_flags_proto_cc",
":toco_port",
":toco_tooling",
":types_proto_cc",
"@com_google_absl//absl/strings",
"//tensorflow/core:lib",
# We cannot embed the core:ops dependency directly into :toco_tooling as
# it can conflict with downstream deps when toco is used as a library.
"//tensorflow/core:ops",
],
)
tf_cc_test(
name = "toco_convert_test",
srcs = ["toco_convert_test.cc"],
visibility = ["//visibility:public"],
deps = [
":model",
":model_cmdline_flags",
":model_flags_proto_cc",
":toco_cmdline_flags",
":toco_convert",
":toco_flags_proto_cc",
":toco_port",
":toco_tooling",
":types_proto_cc",
"@com_google_googletest//:gtest_main",
"@com_google_absl//absl/strings",
"//tensorflow/core:lib",
# We cannot embed the core:ops dependency directly into :toco_tooling as
# it can conflict with downstream deps when toco is used as a library.
"//tensorflow/core:ops",
],
)
tf_cc_test(
name = "toco_port_test",
srcs = ["toco_port_test.cc"],

View File

@ -48,7 +48,8 @@ using tensorflow::TensorProto;
namespace toco {
namespace {
tensorflow::DataType GetTensorFlowDataType(ArrayDataType data_type) {
tensorflow::DataType GetTensorFlowDataType(ArrayDataType data_type,
const string& error_location) {
switch (data_type) {
case ArrayDataType::kBool:
return tensorflow::DT_BOOL;
@ -66,14 +67,21 @@ tensorflow::DataType GetTensorFlowDataType(ArrayDataType data_type) {
return tensorflow::DT_COMPLEX64;
default:
case ArrayDataType::kNone:
LOG(FATAL) << "Unsupported data type: " << static_cast<int>(data_type);
LOG(FATAL) << "Unsupported data type '" << ArrayDataTypeName(data_type)
<< "' in " << error_location;
return tensorflow::DT_INVALID;
}
}
tensorflow::DataType GetTensorFlowDataTypeForOp(ArrayDataType data_type,
const string& op_name) {
return GetTensorFlowDataType(data_type, "op '" + op_name + "'");
}
tensorflow::DataType GetTensorFlowDataType(const Model& model,
const string& array_name) {
return GetTensorFlowDataType(model.GetArray(array_name).data_type);
return GetTensorFlowDataType(model.GetArray(array_name).data_type,
"array '" + array_name + "'");
}
// TensorFlow sometimes forbids what it calls "legacy scalars",
@ -1285,7 +1293,7 @@ void ConvertRangeOperator(const Model& model, const RangeOperator& src_op,
*range_op->add_input() = src_op.inputs[1];
*range_op->add_input() = src_op.inputs[2];
(*range_op->mutable_attr())["Tidx"].set_type(
GetTensorFlowDataType(src_op.dtype));
GetTensorFlowDataTypeForOp(src_op.dtype, /*op_name=*/src_op.outputs[0]));
}
void ConvertPackOperator(const Model& model, const PackOperator& src_op,
@ -1298,7 +1306,8 @@ void ConvertPackOperator(const Model& model, const PackOperator& src_op,
}
(*pack_op->mutable_attr())["axis"].set_i(src_op.axis);
(*pack_op->mutable_attr())["N"].set_i(src_op.inputs.size());
(*pack_op->mutable_attr())["T"].set_type(GetTensorFlowDataType(src_op.dtype));
(*pack_op->mutable_attr())["T"].set_type(
GetTensorFlowDataTypeForOp(src_op.dtype, src_op.outputs[0]));
}
void ConvertFillOperator(const Model& model, const FillOperator& src_op,
@ -1887,7 +1896,7 @@ void ConvertRandomUniformOperator(const Model& model,
GetTensorFlowDataType(model, src_op.inputs[0]);
(*new_op->mutable_attr())["T"].set_type(shape_type);
(*new_op->mutable_attr())["dtype"].set_type(
GetTensorFlowDataType(src_op.dtype));
GetTensorFlowDataTypeForOp(src_op.dtype, src_op.outputs[0]));
(*new_op->mutable_attr())["seed"].set_i(src_op.seed);
(*new_op->mutable_attr())["seed2"].set_i(src_op.seed2);
}

View File

@ -16,87 +16,9 @@ limitations under the License.
#include <memory>
#include <string>
#include "absl/strings/string_view.h"
#include "tensorflow/lite/toco/model.h"
#include "tensorflow/lite/toco/model_cmdline_flags.h"
#include "tensorflow/lite/toco/model_flags.pb.h"
#include "tensorflow/lite/toco/toco_cmdline_flags.h"
#include "tensorflow/lite/toco/toco_flags.pb.h"
#include "tensorflow/lite/toco/toco_port.h"
#include "tensorflow/lite/toco/toco_tooling.h"
#include "tensorflow/lite/toco/toco_types.h"
#include "tensorflow/core/lib/core/errors.h"
#include "tensorflow/core/platform/logging.h"
namespace toco {
namespace {
// Checks the permissions of the output file to ensure it is writeable.
void CheckOutputFilePermissions(const Arg<string>& output_file) {
QCHECK(output_file.specified()) << "Missing required flag --output_file.\n";
QCHECK(port::file::Writable(output_file.value()).ok())
<< "Specified output_file is not writable: " << output_file.value()
<< ".\n";
}
// Checks the permissions of the frozen model file.
void CheckFrozenModelPermissions(const Arg<string>& input_file) {
QCHECK(input_file.specified()) << "Missing required flag --input_file.\n";
QCHECK(port::file::Exists(input_file.value(), port::file::Defaults()).ok())
<< "Specified input_file does not exist: " << input_file.value() << ".\n";
QCHECK(port::file::Readable(input_file.value(), port::file::Defaults()).ok())
<< "Specified input_file exists, but is not readable: "
<< input_file.value() << ".\n";
}
// Reads the contents of the GraphDef from either the frozen graph file or the
// SavedModel directory. If it reads the SavedModel directory, it updates the
// ModelFlags and TocoFlags accordingly.
void ReadInputData(const ParsedTocoFlags& parsed_toco_flags,
const ParsedModelFlags& parsed_model_flags,
TocoFlags* toco_flags, ModelFlags* model_flags,
string* graph_def_contents) {
port::CheckInitGoogleIsDone("InitGoogle is not done yet.\n");
// Ensure savedmodel_directory is not set.
QCHECK(!parsed_toco_flags.savedmodel_directory.specified())
<< "Use `tensorflow/lite/python/tflite_convert` script with "
<< "SavedModel directories.\n";
// Checks the input file permissions and reads the contents.
CheckFrozenModelPermissions(parsed_toco_flags.input_file);
CHECK(port::file::GetContents(parsed_toco_flags.input_file.value(),
graph_def_contents, port::file::Defaults())
.ok());
}
tensorflow::Status ToolMain(const ParsedTocoFlags& parsed_toco_flags,
const ParsedModelFlags& parsed_model_flags) {
ModelFlags model_flags;
ReadModelFlagsFromCommandLineFlags(parsed_model_flags, &model_flags);
TocoFlags toco_flags;
ReadTocoFlagsFromCommandLineFlags(parsed_toco_flags, &toco_flags);
string graph_def_contents;
ReadInputData(parsed_toco_flags, parsed_model_flags, &toco_flags,
&model_flags, &graph_def_contents);
CheckOutputFilePermissions(parsed_toco_flags.output_file);
std::unique_ptr<Model> model =
Import(toco_flags, model_flags, graph_def_contents);
Transform(toco_flags, model.get());
string output_file_contents;
TF_RETURN_IF_ERROR(Export(toco_flags, *model, toco_flags.allow_custom_ops(),
&output_file_contents));
TF_RETURN_IF_ERROR(
port::file::SetContents(parsed_toco_flags.output_file.value(),
output_file_contents, port::file::Defaults()));
return tensorflow::Status();
}
} // namespace
} // namespace toco
#include "tensorflow/lite/toco/toco_convert.h"
int main(int argc, char** argv) {
toco::string msg;
@ -126,6 +48,6 @@ int main(int argc, char** argv) {
return 1;
}
toco::port::InitGoogle(argv[0], effective_argc, &effective_argv, true);
auto status = toco::ToolMain(parsed_toco_flags, parsed_model_flags);
auto status = toco::Convert(parsed_toco_flags, parsed_model_flags);
return status.ok() ? 0 : -1;
}

View File

@ -0,0 +1,108 @@
/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
==============================================================================*/
#include <cstdio>
#include <memory>
#include <string>
#include "absl/strings/string_view.h"
#include "tensorflow/lite/toco/model.h"
#include "tensorflow/lite/toco/model_cmdline_flags.h"
#include "tensorflow/lite/toco/model_flags.pb.h"
#include "tensorflow/lite/toco/toco_cmdline_flags.h"
#include "tensorflow/lite/toco/toco_flags.pb.h"
#include "tensorflow/lite/toco/toco_port.h"
#include "tensorflow/lite/toco/toco_tooling.h"
#include "tensorflow/lite/toco/toco_types.h"
#include "tensorflow/core/lib/core/errors.h"
#include "tensorflow/core/platform/logging.h"
namespace toco {
namespace {
// Checks the permissions of the output file to ensure it is writeable.
void CheckOutputFilePermissions(const Arg<string>& output_file) {
QCHECK(output_file.specified()) << "Missing required flag --output_file.\n";
QCHECK(port::file::Writable(output_file.value()).ok())
<< "Specified output_file is not writable: " << output_file.value()
<< ".\n";
}
// Checks the permissions of the frozen model file.
void CheckFrozenModelPermissions(const Arg<string>& input_file) {
QCHECK(input_file.specified()) << "Missing required flag --input_file.\n";
QCHECK(port::file::Exists(input_file.value(), port::file::Defaults()).ok())
<< "Specified input_file does not exist: " << input_file.value() << ".\n";
QCHECK(port::file::Readable(input_file.value(), port::file::Defaults()).ok())
<< "Specified input_file exists, but is not readable: "
<< input_file.value() << ".\n";
}
// Reads the contents of the GraphDef from either the frozen graph file or the
// SavedModel directory. If it reads the SavedModel directory, it updates the
// ModelFlags and TocoFlags accordingly.
void ReadInputData(const ParsedTocoFlags& parsed_toco_flags,
const ParsedModelFlags& parsed_model_flags,
TocoFlags* toco_flags, ModelFlags* model_flags,
string* graph_def_contents) {
port::CheckInitGoogleIsDone("InitGoogle is not done yet.\n");
// Ensure savedmodel_directory is not set.
QCHECK(!parsed_toco_flags.savedmodel_directory.specified())
<< "Use `tensorflow/lite/python/tflite_convert` script with "
<< "SavedModel directories.\n";
// Checks the input file permissions and reads the contents.
CheckFrozenModelPermissions(parsed_toco_flags.input_file);
CHECK(port::file::GetContents(parsed_toco_flags.input_file.value(),
graph_def_contents, port::file::Defaults())
.ok());
}
} // namespace
tensorflow::Status Convert(const string& graph_def_contents,
const TocoFlags& toco_flags,
const ModelFlags& model_flags,
string* output_file_contents) {
std::unique_ptr<Model> model =
Import(toco_flags, model_flags, graph_def_contents);
Transform(toco_flags, model.get());
return Export(toco_flags, *model, toco_flags.allow_custom_ops(),
output_file_contents);
}
tensorflow::Status Convert(const ParsedTocoFlags& parsed_toco_flags,
const ParsedModelFlags& parsed_model_flags) {
ModelFlags model_flags;
ReadModelFlagsFromCommandLineFlags(parsed_model_flags, &model_flags);
TocoFlags toco_flags;
ReadTocoFlagsFromCommandLineFlags(parsed_toco_flags, &toco_flags);
string graph_def_contents;
ReadInputData(parsed_toco_flags, parsed_model_flags, &toco_flags,
&model_flags, &graph_def_contents);
CheckOutputFilePermissions(parsed_toco_flags.output_file);
string output_file_contents;
TF_RETURN_IF_ERROR(Convert(graph_def_contents, toco_flags, model_flags,
&output_file_contents));
TF_RETURN_IF_ERROR(
port::file::SetContents(parsed_toco_flags.output_file.value(),
output_file_contents, port::file::Defaults()));
return tensorflow::Status();
}
} // namespace toco

View File

@ -0,0 +1,34 @@
/* Copyright 2018 The TensorFlow Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
==============================================================================*/
#ifndef TENSORFLOW_LITE_TOCO_TOCO_CONVERT_H_
#define TENSORFLOW_LITE_TOCO_TOCO_CONVERT_H_
#include "tensorflow/core/lib/core/status.h"
#include "tensorflow/lite/toco/args.h"
#include "tensorflow/lite/toco/model_flags.pb.h"
#include "tensorflow/lite/toco/toco_flags.pb.h"
namespace toco {
tensorflow::Status Convert(const string& graph_def_contents,
const TocoFlags& toco_flags,
const ModelFlags& model_flags,
string* output_file_contents);
tensorflow::Status Convert(const ParsedTocoFlags& parsed_toco_flags,
const ParsedModelFlags& parsed_model_flags);
} // namespace toco
#endif // TENSORFLOW_LITE_TOCO_TOCO_CONVERT_H_

View File

@ -0,0 +1,173 @@
/* Copyright 2018 The TensorFlow Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
==============================================================================*/
#include "tensorflow/lite/toco/toco_convert.h"
#include <gmock/gmock.h>
#include <gtest/gtest.h>
namespace toco {
namespace {
TEST(TocoTest, MissingInputFile) {
ParsedTocoFlags toco_flags;
ParsedModelFlags model_flags;
EXPECT_DEATH(Convert(toco_flags, model_flags).ok(),
"Missing required flag --input_file");
}
TEST(TocoTest, BadInputFormat) {
TocoFlags toco_flags;
ModelFlags model_flags;
string input;
string output;
EXPECT_DEATH(Convert(input, toco_flags, model_flags, &output).ok(),
"Unhandled input_format='FILE_FORMAT_UNKNOWN'");
}
TEST(TocoTest, MissingOuputArrays) {
TocoFlags toco_flags;
ModelFlags model_flags;
toco_flags.set_input_format(TENSORFLOW_GRAPHDEF);
string input;
string output;
EXPECT_DEATH(Convert(input, toco_flags, model_flags, &output).ok(),
"This model does not define output arrays, so a --output_arrays "
"flag must be given on the command-line");
}
TEST(TocoTest, BadOutputArray) {
TocoFlags toco_flags;
ModelFlags model_flags;
toco_flags.set_input_format(TENSORFLOW_GRAPHDEF);
model_flags.add_output_arrays("output1");
string input;
string output;
EXPECT_DEATH(Convert(input, toco_flags, model_flags, &output).ok(),
"Specified output array .output1. is not produced by any op "
"in this graph. Is it a typo. To silence this message, pass "
"this flag: allow_nonexistent_arrays");
}
TEST(TocoTest, BadOutputFormat) {
TocoFlags toco_flags;
ModelFlags model_flags;
toco_flags.set_input_format(TENSORFLOW_GRAPHDEF);
model_flags.add_output_arrays("output1");
string input = R"GraphDef(
node {
name: "output1"
input: "input1"
input: "input2"
op: "Sub"
attr { key: "T" value { type: DT_FLOAT } }
}
)GraphDef";
string output;
EXPECT_DEATH(Convert(input, toco_flags, model_flags, &output).ok(),
"Unhandled output_format='FILE_FORMAT_UNKNOWN'");
}
TEST(TocoTest, SimpleFloatModel) {
TocoFlags toco_flags;
ModelFlags model_flags;
toco_flags.set_input_format(TENSORFLOW_GRAPHDEF);
toco_flags.set_output_format(TENSORFLOW_GRAPHDEF);
// Inputs are automatically selected (but that might not be a good idea).
model_flags.add_output_arrays("output1");
string input = R"GraphDef(
node {
name: "input1"
op: "Placeholder"
attr { key: "dtype" value { type: DT_INT64 } }
}
node {
name: "input2"
op: "Placeholder"
attr { key: "dtype" value { type: DT_INT64 } }
}
node {
name: "output1"
input: "input1"
input: "input2"
op: "Sub"
attr { key: "T" value { type: DT_FLOAT } }
}
)GraphDef";
string output;
EXPECT_TRUE(Convert(input, toco_flags, model_flags, &output).ok());
EXPECT_TRUE(!output.empty());
}
TEST(TocoTest, TransientStringTensors) {
TocoFlags toco_flags;
ModelFlags model_flags;
toco_flags.set_input_format(TENSORFLOW_GRAPHDEF);
// We need to do a couple of things to trigger the transient array
// initialization code: output format must support memory planning, and the
// input array must have a shape.
toco_flags.set_output_format(TFLITE);
model_flags.add_output_arrays("output1");
string input = R"GraphDef(
node {
name: "input1"
op: "Placeholder"
attr { key: "dtype" value { type: DT_STRING } }
attr { key: "shape" value { shape { dim { size:1 }}}}
}
node {
name: "indices1"
op: "Placeholder"
attr { key: "dtype" value { type: DT_INT64 } }
}
node {
name: "intermediate1"
op: "Gather"
input: "input1"
input: "indices1"
attr { key: "Tparams" value { type: DT_STRING } }
attr { key: "Tindices" value { type: DT_INT64 } }
}
node {
name: "output1"
op: "Gather"
input: "intermediate1"
input: "indices2"
attr { key: "Tparams" value { type: DT_STRING } }
attr { key: "Tindices" value { type: DT_INT64 } }
}
)GraphDef";
string output;
EXPECT_TRUE(Convert(input, toco_flags, model_flags, &output).ok());
EXPECT_TRUE(!output.empty());
}
} // namespace
} // namespace toco

View File

@ -210,7 +210,8 @@ std::unique_ptr<Model> Import(const TocoFlags& toco_flags,
CheckInvariants(*model);
break;
default:
LOG(FATAL) << "Unhandled input_format";
LOG(FATAL) << "Unhandled input_format='"
<< FileFormat_Name(toco_flags.input_format()) << "'";
}
LogDump(kLogLevelModelChanged, "AT IMPORT", *model);
@ -424,7 +425,8 @@ tensorflow::Status Export(const TocoFlags& toco_flags, const Model& model,
DumpGraphviz(model, output_file_contents);
break;
default:
LOG(FATAL) << "Unhandled output_format";
LOG(FATAL) << "Unhandled output_format='"
<< FileFormat_Name(toco_flags.output_format()) << "'";
}
return tensorflow::Status();
}

View File

@ -1770,6 +1770,14 @@ bool IsAllocatableTransientArray(const Model& model, const string& array_name) {
if (!array->has_shape()) {
return false;
}
// The size of string tensors is rarely known ahead of time, so all transient
// tensors of this type will need to be dynamically allocated.
if (array->final_data_type == ArrayDataType::kString ||
array->data_type == ArrayDataType::kString) {
return false;
}
return true;
}