| # Copyright 2019 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. |
| # ============================================================================= |
| """mlir is an experimental library that provides support APIs for MLIR.""" |
| |
| from __future__ import absolute_import |
| from __future__ import division |
| from __future__ import print_function |
| |
| from tensorflow.python import pywrap_mlir |
| from tensorflow.python.util.tf_export import tf_export |
| |
| |
| @tf_export('mlir.experimental.convert_graph_def') |
| def convert_graph_def(graph_def, pass_pipeline='tf-standard-pipeline'): |
| """Import a GraphDef and convert it to a textual MLIR module. |
| |
| Args: |
| graph_def: An object of type graph_pb2.GraphDef or a textual proto |
| representation of a valid GraphDef. |
| pass_pipeline: A textual description of an MLIR Pass Pipeline to run on the |
| module, see MLIR documentation for the |
| [textual pass pipeline syntax](https://github.com/tensorflow/mlir/blob/master/g3doc/WritingAPass.md#textual-pass-pipeline-specification). |
| |
| Returns: |
| A textual representation of the MLIR module corresponding to the graphdef. |
| Raises a RuntimeError on error. |
| |
| """ |
| return pywrap_mlir.import_graphdef(graph_def, pass_pipeline) |