cf3c40795d
Load a .pbptx, convert to MLIR, and (optionally) optimize the module before returning it as a string. This is an early experimental API, intended for example to play with some some colab examples during development. PiperOrigin-RevId: 268073235
75 lines
2.2 KiB
OpenEdge ABL
75 lines
2.2 KiB
OpenEdge ABL
/* Copyright 2019 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 "tensorflow/python/platform/base.i"
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%{
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#include "tensorflow/compiler/mlir/tensorflow/translate/import_model.h"
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#include "tensorflow/compiler/mlir/tensorflow/utils/error_util.h"
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#include "tensorflow/compiler/mlir/tensorflow/utils/import_utils.h"
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namespace tensorflow {
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namespace swig {
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// Simple wrapper to support tf.mlir.experimental.convert_graph_def.
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// Load a .pbptx, convert to MLIR, and (optionally) optimize the module before
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// returning it as a string.
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// This is an early experimental API, ideally we should return a wrapper object
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// around a Python binding to the MLIR module.
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string ImportGraphDef(const string &proto, TF_Status* status) {
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GraphDef graphdef;
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auto s = tensorflow::LoadProtoFromBuffer(proto, &graphdef);
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if (!s.ok()) {
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Set_TF_Status_from_Status(status, s);
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return "// error";
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}
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GraphDebugInfo debug_info;
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NodeSpecs specs;
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mlir::MLIRContext context;
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auto module = ConvertGraphdefToMlir(graphdef, debug_info, specs, &context);
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if (!module.ok()) {
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Set_TF_Status_from_Status(status, module.status());
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return "// error";
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}
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return MlirModuleToString(*module.ConsumeValueOrDie());
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}
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} // namespace swig
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} // namespace tensorflow
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%}
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%ignoreall
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%unignore tensorflow;
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%unignore tensorflow::swig;
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%unignore tensorflow::swig::ImportGraphDef;
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// Wrap this function
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namespace tensorflow {
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namespace swig {
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static string ImportGraphDef(const string &graphdef, TF_Status* status);
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} // namespace swig
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} // namespace tensorflow
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%insert("python") %{
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def import_graphdef(graphdef):
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return str(ImportGraphDef(str(graphdef).encode('utf-8')));
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%}
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%unignoreall
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