ba167d161e
This takes a ConcreteFunction, collects a FunctionDef for the function and an associated FunctionDefLibrary, and imports the FunctionDef and FunctionDefLibrary via `ConvertFunctionToMlir`. Control rets/target nodes of the entry function are also now supported in `ConvertFunctionToMlir`. PiperOrigin-RevId: 331195841 Change-Id: Ib3a7264e90ca303ab7a850bf18c8d5e330063a4f
80 lines
3.5 KiB
C++
80 lines
3.5 KiB
C++
/* Copyright 2020 The TensorFlow Authors. All Rights Reserved.
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Licensed under the Apache License, Version 2.0 (the "License");
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you may not use this file except in compliance with the License.
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You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software
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distributed under the License is distributed on an "AS IS" BASIS,
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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See the License for the specific language governing permissions and
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limitations under the License.
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==============================================================================*/
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#include "pybind11/pybind11.h"
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#include "tensorflow/c/tf_status.h"
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#include "tensorflow/compiler/mlir/python/mlir.h"
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#include "tensorflow/python/lib/core/pybind11_lib.h"
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#include "tensorflow/python/lib/core/pybind11_status.h"
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#include "tensorflow/python/lib/core/safe_ptr.h"
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PYBIND11_MODULE(_pywrap_mlir, m) {
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m.def("ImportGraphDef",
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[](const std::string &graphdef, const std::string &pass_pipeline) {
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tensorflow::Safe_TF_StatusPtr status =
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tensorflow::make_safe(TF_NewStatus());
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std::string output =
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tensorflow::ImportGraphDef(graphdef, pass_pipeline, status.get());
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tensorflow::MaybeRaiseRegisteredFromTFStatus(status.get());
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return output;
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});
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m.def("ImportFunction", [](const std::string &functiondef,
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const std::string &functiondef_library,
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const std::string &pass_pipeline) {
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tensorflow::Safe_TF_StatusPtr status =
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tensorflow::make_safe(TF_NewStatus());
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std::string output = tensorflow::ImportFunction(
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functiondef, functiondef_library, pass_pipeline, status.get());
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tensorflow::MaybeRaiseRegisteredFromTFStatus(status.get());
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return output;
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});
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m.def("ExperimentalConvertSavedModelToMlir",
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[](const std::string &saved_model_path,
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const std::string &exported_names, bool show_debug_info) {
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tensorflow::Safe_TF_StatusPtr status =
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tensorflow::make_safe(TF_NewStatus());
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std::string output = tensorflow::ExperimentalConvertSavedModelToMlir(
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saved_model_path, exported_names, show_debug_info, status.get());
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tensorflow::MaybeRaiseRegisteredFromTFStatus(status.get());
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return output;
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});
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m.def("ExperimentalConvertSavedModelV1ToMlir",
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[](const std::string &saved_model_path, const std::string &tags,
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bool lift_variables, bool upgrade_legacy, bool show_debug_info) {
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tensorflow::Safe_TF_StatusPtr status =
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tensorflow::make_safe(TF_NewStatus());
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std::string output =
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tensorflow::ExperimentalConvertSavedModelV1ToMlir(
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saved_model_path, tags, lift_variables, upgrade_legacy,
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show_debug_info, status.get());
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tensorflow::MaybeRaiseRegisteredFromTFStatus(status.get());
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return output;
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});
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m.def("ExperimentalRunPassPipeline",
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[](const std::string &mlir_txt, const std::string &pass_pipeline,
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bool show_debug_info) {
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tensorflow::Safe_TF_StatusPtr status =
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tensorflow::make_safe(TF_NewStatus());
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std::string output = tensorflow::ExperimentalRunPassPipeline(
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mlir_txt, pass_pipeline, show_debug_info, status.get());
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tensorflow::MaybeRaiseRegisteredFromTFStatus(status.get());
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return output;
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});
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};
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