wehub-resource-sync
115 lines
5.1 KiB
C++
115 lines
5.1 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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// Functions for getting information about kernels registered in the binary.
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// Migrated from previous SWIG file (mlir.i) authored by aminim@.
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#ifndef TENSORFLOW_COMPILER_MLIR_PYTHON_MLIR_H_
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#define TENSORFLOW_COMPILER_MLIR_PYTHON_MLIR_H_
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#include <string>
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#include <vector>
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#include "absl/strings/string_view.h"
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#include "tensorflow/c/eager/c_api.h"
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#include "tensorflow/c/tf_status.h"
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namespace tensorflow {
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// Simple wrapper to support tf.mlir.experimental.convert_graph_def.
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// Load a GraphDef (binary or textual proto format), convert to MLIR, and
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// (optionally) optimize the module before 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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std::string ImportGraphDef(const std::string &proto,
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const std::string &pass_pipeline,
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bool show_debug_info, TF_Status *status);
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// Simple wrapper to support tf.mlir.experimental.convert_function.
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// Load FunctionDef (binary or textual proto format), convert to MLIR, and
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// (optionally) optimize the module before 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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std::string ImportFunction(const std::string &functiondef_proto,
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const std::string &pass_pipeline,
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bool show_debug_info, TFE_Context *context,
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TF_Status *status);
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// This wrapper passes the graph_def taking names of input nodes, the shapes and
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// types of its inputs and the output nodes as parameters to MLIR.
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std::string ImportGraphDef(const std::string &proto,
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const std::string &pass_pipeline,
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bool show_debug_info, absl::string_view(input_names),
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absl::string_view(input_data_types),
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absl::string_view(input_data_shapes),
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absl::string_view(output_names), TF_Status *status);
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// Load a SavedModel and return a textual MLIR string corresponding to it.
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//
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// Args:
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// saved_model_path: File path from which to load the SavedModel.
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// exported_names_str: Comma-separated list of names to export.
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// Empty means "export all".
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//
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// Returns:
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// A string of textual MLIR representing the raw imported SavedModel.
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std::string ExperimentalConvertSavedModelToMlir(
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const std::string &saved_model_path, const std::string &exported_names_str,
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bool show_debug_info, TF_Status *status);
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// Load a SavedModel V1 and return a textual MLIR string corresponding to it
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// without any MLIR graph transformation.
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//
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// Args:
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// saved_model_path: File path from which to load the SavedModel.
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// tags: Tags to identify MetaGraphDef that need to be loaded.
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// upgrade_legacy: Boolean flag that indicates whether to upgrade legacy
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// graphs
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//
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// Returns:
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// A string of textual MLIR representing the raw imported SavedModel.
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std::string ExperimentalConvertSavedModelV1ToMlirLite(
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const std::string &saved_model_path, const std::string &exported_names_str,
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const std::string &tags, bool upgrade_legacy, bool show_debug_info,
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TF_Status *status);
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// Load a SavedModel V1 and return a textual MLIR string corresponding to it.
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//
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// Args:
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// saved_model_path: File path from which to load the SavedModel.
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// tags: Tags to identify MetaGraphDef that need to be loaded.
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// lift_variables: Boolean flag that indicates whether to hoist variables
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// after loading the SavedModel.
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//
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// Returns:
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// A string of textual MLIR representing the raw imported SavedModel.
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std::string ExperimentalConvertSavedModelV1ToMlir(
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const std::string &saved_model_path, const std::string &exported_names_str,
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const std::string &tags, bool lift_variables,
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bool include_variables_in_initializers, bool upgrade_legacy,
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bool show_debug_info, TF_Status *status);
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std::string ExperimentalRunPassPipeline(const std::string &mlir_txt,
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const std::string &pass_pipeline,
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bool show_debug_info,
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TF_Status *status);
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// Writes the input textual MLIR as bytecode to output file.
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void ExperimentalWriteBytecode(const std::string &filename,
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const std::string &mlir_txt, TF_Status *status);
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} // namespace tensorflow
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#endif // TENSORFLOW_COMPILER_MLIR_PYTHON_MLIR_H_
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