wehub-resource-sync
129 lines
5.8 KiB
C++
129 lines
5.8 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 <string>
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#include "pybind11/pybind11.h" // from @pybind11
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#include "pybind11/pytypes.h" // from @pybind11
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#include "pybind11/stl.h" // from @pybind11
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#include "tensorflow/c/eager/c_api.h"
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#include "tensorflow/c/safe_ptr.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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PYBIND11_MODULE(_pywrap_mlir, m, pybind11::mod_gil_not_used()) {
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m.def("ImportGraphDef",
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[](const std::string &graphdef, 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::ImportGraphDef(
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graphdef, 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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m.def("ImportFunction",
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[](const py::handle &context, const std::string &functiondef,
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const std::string &pass_pipeline, 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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auto* ctxt = static_cast<TFE_Context*>(
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PyCapsule_GetPointer(context.ptr(), "TFE_Context"));
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if (!ctxt) throw py::error_already_set();
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std::string output = tensorflow::ImportFunction(
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functiondef, pass_pipeline, show_debug_info, ctxt, 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("ImportGraphDef",
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[](const std::string &graphdef, const std::string &pass_pipeline,
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bool show_debug_info, const std::string &input_names,
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const std::string &input_data_types,
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const std::string &input_data_shapes,
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const std::string &output_names) {
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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::ImportGraphDef(
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graphdef, pass_pipeline, show_debug_info, input_names,
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input_data_types, input_data_shapes, output_names, 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("ExperimentalConvertSavedModelV1ToMlirLite",
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[](const std::string &saved_model_path,
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const std::string &exported_names_str, const std::string &tags,
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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::ExperimentalConvertSavedModelV1ToMlirLite(
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saved_model_path, exported_names_str, tags, 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("ExperimentalConvertSavedModelV1ToMlir",
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[](const std::string &saved_model_path,
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const std::string &exported_names_str, const std::string &tags,
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bool lift_variables, bool include_variables_in_initializers,
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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, exported_names_str, tags, lift_variables,
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include_variables_in_initializers, 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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m.def("ExperimentalWriteBytecode", [](const std::string &filename,
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const std::string &mlir_txt) {
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tensorflow::Safe_TF_StatusPtr status =
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tensorflow::make_safe(TF_NewStatus());
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tensorflow::ExperimentalWriteBytecode(filename, mlir_txt, status.get());
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tensorflow::MaybeRaiseRegisteredFromTFStatus(status.get());
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});
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};
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