Create a list of arguments for reordered functions automatically.
PiperOrigin-RevId: 224229831
This commit is contained in:
Anna R
TensorFlower Gardener
parent
5abe466090
commit
c5d064bd4b
@ -52,13 +52,20 @@ py_library(
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)
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py_library(
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name = "tf_upgrade_v2_lib",
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srcs = [
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"renames_v2.py",
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"tf_upgrade_v2.py",
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],
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name = "reorders_v2",
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srcs = ["reorders_v2.py"],
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srcs_version = "PY2AND3",
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deps = [":ast_edits"],
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)
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py_library(
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name = "tf_upgrade_v2_lib",
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srcs = ["tf_upgrade_v2.py"],
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srcs_version = "PY2AND3",
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deps = [
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":ast_edits",
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":renames_v2",
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":reorders_v2",
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],
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)
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py_binary(
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98
tensorflow/tools/compatibility/reorders_v2.py
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98
tensorflow/tools/compatibility/reorders_v2.py
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@ -0,0 +1,98 @@
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# Copyright 2018 The TensorFlow Authors. All Rights Reserved.
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#
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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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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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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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# pylint: disable=line-too-long
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"""List of renames to apply when converting from TF 1.0 to TF 2.0.
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THIS FILE IS AUTOGENERATED: To update, please run:
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bazel build tensorflow/tools/compatibility/update:generate_v2_reorders_map
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bazel-bin/tensorflow/tools/compatibility/update/generate_v2_reorders_map
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This file should be updated whenever a function is added to
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self.reordered_function_names in tf_upgrade_v2.py.
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"""
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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reorders = {
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'tf.argmax': ['input', 'axis', 'name', 'dimension', 'output_type'],
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'tf.argmin': ['input', 'axis', 'name', 'dimension', 'output_type'],
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'tf.batch_to_space': ['input', 'crops', 'block_size', 'name'],
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'tf.boolean_mask': ['tensor', 'mask', 'name', 'axis'],
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'tf.confusion_matrix': ['labels', 'predictions', 'num_classes', 'dtype', 'name', 'weights'],
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'tf.convert_to_tensor': ['value', 'dtype', 'name', 'preferred_dtype'],
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'tf.decode_csv': ['records', 'record_defaults', 'field_delim', 'use_quote_delim', 'name', 'na_value', 'select_cols'],
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'tf.feature_column.categorical_column_with_vocabulary_file': ['key', 'vocabulary_file', 'vocabulary_size', 'num_oov_buckets', 'default_value', 'dtype'],
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'tf.io.decode_csv': ['records', 'record_defaults', 'field_delim', 'use_quote_delim', 'name', 'na_value', 'select_cols'],
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'tf.io.parse_example': ['serialized', 'features', 'name', 'example_names'],
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'tf.io.parse_single_example': ['serialized', 'features', 'name', 'example_names'],
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'tf.io.serialize_many_sparse': ['sp_input', 'name', 'out_type'],
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'tf.io.serialize_sparse': ['sp_input', 'name', 'out_type'],
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'tf.math.argmax': ['input', 'axis', 'name', 'dimension', 'output_type'],
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'tf.math.argmin': ['input', 'axis', 'name', 'dimension', 'output_type'],
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'tf.math.confusion_matrix': ['labels', 'predictions', 'num_classes', 'dtype', 'name', 'weights'],
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'tf.math.reduce_all': ['input_tensor', 'axis', 'keepdims', 'name', 'reduction_indices', 'keep_dims'],
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'tf.math.reduce_any': ['input_tensor', 'axis', 'keepdims', 'name', 'reduction_indices', 'keep_dims'],
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'tf.math.reduce_logsumexp': ['input_tensor', 'axis', 'keepdims', 'name', 'reduction_indices', 'keep_dims'],
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'tf.math.reduce_max': ['input_tensor', 'axis', 'keepdims', 'name', 'reduction_indices', 'keep_dims'],
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'tf.math.reduce_mean': ['input_tensor', 'axis', 'keepdims', 'name', 'reduction_indices', 'keep_dims'],
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'tf.math.reduce_min': ['input_tensor', 'axis', 'keepdims', 'name', 'reduction_indices', 'keep_dims'],
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'tf.math.reduce_prod': ['input_tensor', 'axis', 'keepdims', 'name', 'reduction_indices', 'keep_dims'],
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'tf.math.reduce_sum': ['input_tensor', 'axis', 'keepdims', 'name', 'reduction_indices', 'keep_dims'],
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'tf.multinomial': ['logits', 'num_samples', 'seed', 'name', 'output_dtype'],
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'tf.nn.convolution': ['input', 'filter', 'padding', 'strides', 'dilation_rate', 'name', 'data_format'],
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'tf.nn.crelu': ['features', 'name', 'axis'],
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'tf.nn.depthwise_conv2d': ['input', 'filter', 'strides', 'padding', 'rate', 'name', 'data_format'],
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'tf.nn.moments': ['x', 'axes', 'shift', 'name', 'keep_dims'],
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'tf.nn.pool': ['input', 'window_shape', 'pooling_type', 'padding', 'dilation_rate', 'strides', 'name', 'data_format'],
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'tf.nn.separable_conv2d': ['input', 'depthwise_filter', 'pointwise_filter', 'strides', 'padding', 'rate', 'name', 'data_format'],
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'tf.nn.weighted_moments': ['x', 'axes', 'frequency_weights', 'name', 'keep_dims'],
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'tf.pad': ['tensor', 'paddings', 'mode', 'name', 'constant_values'],
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'tf.parse_example': ['serialized', 'features', 'name', 'example_names'],
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'tf.parse_single_example': ['serialized', 'features', 'name', 'example_names'],
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'tf.quantize_v2': ['input', 'min_range', 'max_range', 'T', 'mode', 'name', 'round_mode'],
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'tf.random.multinomial': ['logits', 'num_samples', 'seed', 'name', 'output_dtype'],
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'tf.random.poisson': ['lam', 'shape', 'dtype', 'seed', 'name'],
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'tf.random_poisson': ['lam', 'shape', 'dtype', 'seed', 'name'],
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'tf.reduce_all': ['input_tensor', 'axis', 'keepdims', 'name', 'reduction_indices', 'keep_dims'],
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'tf.reduce_any': ['input_tensor', 'axis', 'keepdims', 'name', 'reduction_indices', 'keep_dims'],
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'tf.reduce_join': ['inputs', 'axis', 'keep_dims', 'separator', 'name', 'reduction_indices'],
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'tf.reduce_logsumexp': ['input_tensor', 'axis', 'keepdims', 'name', 'reduction_indices', 'keep_dims'],
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'tf.reduce_max': ['input_tensor', 'axis', 'keepdims', 'name', 'reduction_indices', 'keep_dims'],
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'tf.reduce_mean': ['input_tensor', 'axis', 'keepdims', 'name', 'reduction_indices', 'keep_dims'],
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'tf.reduce_min': ['input_tensor', 'axis', 'keepdims', 'name', 'reduction_indices', 'keep_dims'],
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'tf.reduce_prod': ['input_tensor', 'axis', 'keepdims', 'name', 'reduction_indices', 'keep_dims'],
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'tf.reduce_sum': ['input_tensor', 'axis', 'keepdims', 'name', 'reduction_indices', 'keep_dims'],
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'tf.serialize_many_sparse': ['sp_input', 'name', 'out_type'],
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'tf.serialize_sparse': ['sp_input', 'name', 'out_type'],
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'tf.shape': ['input', 'name', 'out_type'],
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'tf.size': ['input', 'name', 'out_type'],
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'tf.sparse.add': ['a', 'b', 'threshold', 'thresh'],
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'tf.sparse.concat': ['axis', 'sp_inputs', 'name', 'expand_nonconcat_dim', 'concat_dim'],
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'tf.sparse.segment_mean': ['data', 'indices', 'segment_ids', 'name', 'num_segments'],
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'tf.sparse.segment_sqrt_n': ['data', 'indices', 'segment_ids', 'name', 'num_segments'],
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'tf.sparse.segment_sum': ['data', 'indices', 'segment_ids', 'name', 'num_segments'],
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'tf.sparse_add': ['a', 'b', 'threshold', 'thresh'],
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'tf.sparse_concat': ['axis', 'sp_inputs', 'name', 'expand_nonconcat_dim', 'concat_dim'],
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'tf.sparse_matmul': ['a', 'b', 'transpose_a', 'transpose_b', 'a_is_sparse', 'b_is_sparse', 'name'],
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'tf.sparse_segment_mean': ['data', 'indices', 'segment_ids', 'name', 'num_segments'],
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'tf.sparse_segment_sqrt_n': ['data', 'indices', 'segment_ids', 'name', 'num_segments'],
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'tf.sparse_segment_sum': ['data', 'indices', 'segment_ids', 'name', 'num_segments'],
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'tf.strings.length': ['input', 'name', 'unit'],
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'tf.strings.reduce_join': ['inputs', 'axis', 'keep_dims', 'separator', 'name', 'reduction_indices'],
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'tf.strings.substr': ['input', 'pos', 'len', 'name', 'unit'],
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'tf.transpose': ['a', 'perm', 'name', 'conjugate'],
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'tf.tuple': ['tensors', 'name', 'control_inputs'],
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'tf.while_loop': ['cond', 'body', 'loop_vars', 'shape_invariants', 'parallel_iterations', 'back_prop', 'swap_memory', 'name', 'maximum_iterations', 'return_same_structure']
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}
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@ -20,6 +20,7 @@ from __future__ import print_function
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from tensorflow.tools.compatibility import ast_edits
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from tensorflow.tools.compatibility import renames_v2
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from tensorflow.tools.compatibility import reorders_v2
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class TFAPIChangeSpec(ast_edits.APIChangeSpec):
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@ -483,187 +484,80 @@ class TFAPIChangeSpec(ast_edits.APIChangeSpec):
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# Variables that should be changed to functions.
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self.change_to_function = {}
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# pylint: disable=line-too-long
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# This list should just contain names of functions that had
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# their arguments reordered. After adding a function name to the list
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# run the following to update reorders_v2.py:
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# bazel build tensorflow/tools/compatibility/update:generate_v2_reorders_map
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# bazel-bin/tensorflow/tools/compatibility/update/generate_v2_reorders_map
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# pylint: enable=line-too-long
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self.reordered_function_names = {
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"tf.io.serialize_sparse",
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"tf.io.serialize_many_sparse",
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"tf.argmax",
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"tf.argmin",
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"tf.batch_to_space",
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"tf.boolean_mask",
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"tf.convert_to_tensor",
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"tf.nn.moments",
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"tf.nn.convolution",
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"tf.nn.crelu",
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"tf.nn.weighted_moments",
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"tf.nn.pool",
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"tf.nn.separable_conv2d",
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"tf.nn.depthwise_conv2d",
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"tf.multinomial",
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"tf.random.multinomial",
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"tf.pad",
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"tf.quantize_v2",
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"tf.feature_column.categorical_column_with_vocabulary_file",
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"tf.shape",
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"tf.size",
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"tf.random.poisson",
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"tf.sparse.add",
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"tf.sparse_add",
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"tf.sparse.concat",
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"tf.sparse_concat",
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"tf.sparse.segment_mean",
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"tf.sparse.segment_sqrt_n",
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"tf.sparse.segment_sum",
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"tf.sparse_matmul",
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"tf.io.decode_csv",
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"tf.strings.substr",
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"tf.strings.reduce_join",
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"tf.strings.length",
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"tf.transpose",
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"tf.tuple",
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"tf.parse_example",
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"tf.parse_single_example",
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"tf.io.parse_example",
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"tf.io.parse_single_example",
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"tf.while_loop",
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"tf.reduce_all",
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"tf.math.reduce_all",
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"tf.reduce_any",
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"tf.math.reduce_any",
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"tf.reduce_min",
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"tf.math.reduce_min",
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"tf.reduce_max",
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"tf.math.reduce_max",
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"tf.reduce_sum",
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"tf.math.reduce_sum",
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"tf.reduce_mean",
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"tf.math.reduce_mean",
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"tf.reduce_prod",
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"tf.math.reduce_prod",
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"tf.reduce_logsumexp",
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"tf.math.reduce_logsumexp",
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"tf.reduce_join",
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"tf.confusion_matrix",
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"tf.math.confusion_matrix",
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}
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# Functions that were reordered should be changed to the new keyword args
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# for safety, if positional arguments are used. If you have reversed the
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# positional arguments yourself, this could do the wrong thing.
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# IMPORTANT: order here should correspond to OLD argument order.
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# We just prepend "arg_name=" to all arguments in function calls.
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self.function_reorders = {
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"tf.io.serialize_sparse": ["sp_input", "name", "out_type"],
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"tf.io.serialize_many_sparse": ["sp_input", "name", "out_type"],
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"tf.argmax": ["input", "axis", "name", "axis", "output_type"],
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"tf.argmin": ["input", "axis", "name", "axis", "output_type"],
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"tf.batch_to_space": ["input", "crops", "block_size", "name"],
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"tf.boolean_mask": ["tensor", "mask", "name", "axis"],
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"tf.convert_to_tensor": ["value", "dtype", "name", "preferred_dtype"],
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"tf.nn.moments": ["x", "axes", "shift", "keepdims", "name"],
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"tf.nn.convolution": [
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"input", "filter", "padding", "strides", "dilation_rate", "name",
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"data_format"
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],
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"tf.nn.crelu": ["features", "name", "axis"],
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"tf.nn.weighted_moments": [
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"x", "axes", "frequency_weights", "name", "keep_dims"
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],
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"tf.nn.pool": [
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"input", "window_shape", "pooling_type", "padding", "dilation_rate",
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"strides", "name", "data_format"
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],
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"tf.nn.separable_conv2d": [
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"input", "depthwise_filter", "pointwise_filter", "strides",
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"padding", "rate", "name", "data_format"
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],
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"tf.nn.depthwise_conv2d": [
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"input", "filter", "strides", "padding", "rate", "name",
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"data_format"
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],
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"tf.multinomial": [
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"logits", "num_samples", "seed", "name", "output_dtype"
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],
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"tf.random.multinomial": [
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"logits", "num_samples", "seed", "name", "output_dtype"
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],
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"tf.pad": ["tensor", "paddings", "mode", "name", "constant_values"],
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"tf.quantize_v2": [
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"input", "min_range", "max_range", "T", "mode", "name", "round_mode"
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],
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"tf.feature_column.categorical_column_with_vocabulary_file": [
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"key", "vocabulary_file", "vocabulary_size", "num_oov_buckets",
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"default_value", "dtype"
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],
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"tf.shape": ["input", "name", "out_type"],
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"tf.size": ["input", "name", "out_type"],
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"tf.random.poisson": ["lam", "shape", "dtype", "seed", "name"],
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"tf.sparse.add": ["a", "b", "thresh"],
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"tf.sparse_add": ["a", "b", "thresh"],
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"tf.sparse.concat": [
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"axis", "sp_inputs", "name", "expand_nonconcat_dim", "concat_dim"
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],
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"tf.sparse_concat": [
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"axis", "sp_inputs", "name", "expand_nonconcat_dim", "concat_dim"
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],
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"tf.sparse.segment_mean": [
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"data", "indices", "segment_ids", "name", "num_segments"
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],
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"tf.sparse.segment_sqrt_n": [
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"data", "indices", "segment_ids", "name", "num_segments"
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],
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"tf.sparse.segment_sum": [
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"data", "indices", "segment_ids", "name", "num_segments"
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],
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"tf.sparse_matmul": [
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"a", "b", "transpose_a", "transpose_b", "a_is_sparse",
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"b_is_sparse", "name"
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],
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"tf.io.decode_csv": [
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"records",
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"record_defaults",
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"field_delim",
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"use_quote_delim",
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"name",
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"na_value",
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"select_cols",
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],
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"tf.strings.substr": ["input", "pos", "len", "name", "unit"],
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"tf.strings.reduce_join": [
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"input", "axis", "keep_dims", "separator", "name",
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"reduction_indices"
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],
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"tf.strings.length": ["input", "name", "unit"],
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"tf.transpose": ["a", "perm", "name", "conjugate"],
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"tf.tuple": ["tensors", "name", "control_inputs"],
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"tf.parse_example": [
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"serialized", "features", "name", "example_names"
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],
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"tf.parse_single_example": [
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"serialized", "features", "name", "example_names"
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],
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"tf.io.parse_example": [
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"serialized", "features", "name", "example_names"
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],
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"tf.io.parse_single_example": [
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"serialized", "features", "name", "example_names"
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],
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"tf.while_loop": [
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"cond", "body", "loop_vars", "shape_invariants",
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"parallel_iterations", "back_prop", "swap_memory", "name",
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"maximum_iterations", "return_same_structure"
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],
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"tf.reduce_all": [
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"input_tensor", "axis", "keepdims", "name", "reduction_indices",
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"keep_dims"
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],
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"tf.math.reduce_all": [
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"input_tensor", "axis", "keepdims", "name", "reduction_indices",
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"keep_dims"
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],
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"tf.reduce_any": [
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"input_tensor", "axis", "keepdims", "name", "reduction_indices",
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"keep_dims"
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],
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"tf.math.reduce_any": [
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"input_tensor", "axis", "keepdims", "name", "reduction_indices",
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"keep_dims"
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],
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"tf.reduce_min": [
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"input_tensor", "axis", "keepdims", "name", "reduction_indices",
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"keep_dims"
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],
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"tf.math.reduce_min": [
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"input_tensor", "axis", "keepdims", "name", "reduction_indices",
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"keep_dims"
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],
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"tf.reduce_max": [
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"input_tensor", "axis", "keepdims", "name", "reduction_indices",
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"keep_dims"
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],
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"tf.math.reduce_max": [
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"input_tensor", "axis", "keepdims", "name", "reduction_indices",
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"keep_dims"
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],
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"tf.reduce_sum": [
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"input_tensor", "axis", "keepdims", "name", "reduction_indices",
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"keep_dims"
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],
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"tf.math.reduce_sum": [
|
||||
"input_tensor", "axis", "keepdims", "name", "reduction_indices",
|
||||
"keep_dims"
|
||||
],
|
||||
"tf.reduce_mean": [
|
||||
"input_tensor", "axis", "keepdims", "name", "reduction_indices",
|
||||
"keep_dims"
|
||||
],
|
||||
"tf.math.reduce_mean": [
|
||||
"input_tensor", "axis", "keepdims", "name", "reduction_indices",
|
||||
"keep_dims"
|
||||
],
|
||||
"tf.reduce_prod": [
|
||||
"input_tensor", "axis", "keepdims", "name", "reduction_indices",
|
||||
"keep_dims"
|
||||
],
|
||||
"tf.math.reduce_prod": [
|
||||
"input_tensor", "axis", "keepdims", "name", "reduction_indices",
|
||||
"keep_dims"
|
||||
],
|
||||
"tf.reduce_logsumexp": [
|
||||
"input_tensor", "axis", "keepdims", "name", "reduction_indices",
|
||||
"keep_dims"
|
||||
],
|
||||
"tf.math.reduce_logsumexp": [
|
||||
"input_tensor", "axis", "keepdims", "name", "reduction_indices",
|
||||
"keep_dims"
|
||||
],
|
||||
"tf.reduce_join": [
|
||||
"input", "axis", "keep_dims", "separator", "name",
|
||||
"reduction_indices"
|
||||
],
|
||||
"tf.confusion_matrix": [
|
||||
"labels", "predictions", "num_classes", "dtype", "name", "weights"
|
||||
],
|
||||
"tf.math.confusion_matrix": [
|
||||
"labels", "predictions", "num_classes", "dtype", "name", "weights"
|
||||
]
|
||||
}
|
||||
self.function_reorders = reorders_v2.reorders
|
||||
|
||||
# Specially handled functions.
|
||||
self.function_handle = {
|
||||
|
||||
@ -63,6 +63,15 @@ def get_v2_names(symbol):
|
||||
return list(names_v2)
|
||||
|
||||
|
||||
def get_symbol_for_name(root, name):
|
||||
name_parts = name.split(".")
|
||||
symbol = root
|
||||
# Iterate starting with second item since 1st item is "tf.".
|
||||
for part in name_parts[1:]:
|
||||
symbol = getattr(symbol, part)
|
||||
return symbol
|
||||
|
||||
|
||||
def get_func_and_args_from_str(call_str):
|
||||
"""Parse call string to get function and argument names.
|
||||
|
||||
@ -246,6 +255,41 @@ class TestUpgrade(test_util.TensorFlowTestCase):
|
||||
visitor.private_map["tf.compat"] = ["v1", "v2"]
|
||||
traverse.traverse(tf.compat.v1, visitor)
|
||||
|
||||
def testReorderFileNeedsUpdate(self):
|
||||
reordered_function_names = (
|
||||
tf_upgrade_v2.TFAPIChangeSpec().reordered_function_names)
|
||||
function_reorders = (
|
||||
tf_upgrade_v2.TFAPIChangeSpec().function_reorders)
|
||||
|
||||
added_names_message = """Some function names in
|
||||
self.reordered_function_names are not in reorders_v2.py.
|
||||
Please run the following commands to update reorders_v2.py:
|
||||
bazel build tensorflow/tools/compatibility/update:generate_v2_reorders_map
|
||||
bazel-bin/tensorflow/tools/compatibility/update/generate_v2_reorders_map
|
||||
"""
|
||||
removed_names_message = """%s in self.reorders_v2 does not match
|
||||
any name in self.reordered_function_names.
|
||||
Please run the following commands to update reorders_v2.py:
|
||||
bazel build tensorflow/tools/compatibility/update:generate_v2_reorders_map
|
||||
bazel-bin/tensorflow/tools/compatibility/update/generate_v2_reorders_map
|
||||
"""
|
||||
self.assertTrue(
|
||||
reordered_function_names.issubset(function_reorders),
|
||||
added_names_message)
|
||||
# function_reorders should contain reordered_function_names
|
||||
# and their TensorFlow V1 aliases.
|
||||
for name in function_reorders:
|
||||
# get other names for this function
|
||||
attr = get_symbol_for_name(tf.compat.v1, name)
|
||||
_, attr = tf_decorator.unwrap(attr)
|
||||
v1_names = get_v1_names(attr)
|
||||
self.assertTrue(v1_names)
|
||||
v1_names = ["tf.%s" % n for n in v1_names]
|
||||
# check if any other name is in
|
||||
self.assertTrue(
|
||||
any(n in reordered_function_names for n in v1_names),
|
||||
removed_names_message % name)
|
||||
|
||||
def testRenameConstant(self):
|
||||
text = "tf.MONOLITHIC_BUILD\n"
|
||||
_, unused_report, unused_errors, new_text = self._upgrade(text)
|
||||
|
||||
@ -15,3 +15,17 @@ py_binary(
|
||||
"//tensorflow/tools/compatibility:tf_upgrade_v2_lib",
|
||||
],
|
||||
)
|
||||
|
||||
py_binary(
|
||||
name = "generate_v2_reorders_map",
|
||||
srcs = ["generate_v2_reorders_map.py"],
|
||||
srcs_version = "PY2AND3",
|
||||
deps = [
|
||||
"//tensorflow:tensorflow_py",
|
||||
"//tensorflow/python:lib",
|
||||
"//tensorflow/python:no_contrib",
|
||||
"//tensorflow/tools/common:public_api",
|
||||
"//tensorflow/tools/common:traverse",
|
||||
"//tensorflow/tools/compatibility:tf_upgrade_v2_lib",
|
||||
],
|
||||
)
|
||||
|
||||
@ -0,0 +1,166 @@
|
||||
# Copyright 2018 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.
|
||||
# ==============================================================================
|
||||
# pylint: disable=line-too-long
|
||||
"""Script for updating tensorflow/tools/compatibility/reorders_v2.py.
|
||||
|
||||
To update reorders_v2.py, run:
|
||||
bazel build tensorflow/tools/compatibility/update:generate_v2_reorders_map
|
||||
bazel-bin/tensorflow/tools/compatibility/update/generate_v2_reorders_map
|
||||
"""
|
||||
# pylint: enable=line-too-long
|
||||
import tensorflow as tf
|
||||
|
||||
# This import is needed so that TensorFlow python modules are in sys.modules.
|
||||
from tensorflow import python as tf_python # pylint: disable=unused-import
|
||||
from tensorflow.python.lib.io import file_io
|
||||
from tensorflow.python.platform import app
|
||||
from tensorflow.python.util import tf_decorator
|
||||
from tensorflow.python.util import tf_export
|
||||
from tensorflow.python.util import tf_inspect
|
||||
from tensorflow.tools.common import public_api
|
||||
from tensorflow.tools.common import traverse
|
||||
from tensorflow.tools.compatibility import tf_upgrade_v2
|
||||
|
||||
|
||||
_OUTPUT_FILE_PATH = 'third_party/tensorflow/tools/compatibility/reorders_v2.py'
|
||||
_FILE_HEADER = """# Copyright 2018 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.
|
||||
# ==============================================================================
|
||||
# pylint: disable=line-too-long
|
||||
\"\"\"List of renames to apply when converting from TF 1.0 to TF 2.0.
|
||||
|
||||
THIS FILE IS AUTOGENERATED: To update, please run:
|
||||
bazel build tensorflow/tools/compatibility/update:generate_v2_reorders_map
|
||||
bazel-bin/tensorflow/tools/compatibility/update/generate_v2_reorders_map
|
||||
This file should be updated whenever a function is added to
|
||||
self.reordered_function_names in tf_upgrade_v2.py.
|
||||
\"\"\"
|
||||
from __future__ import absolute_import
|
||||
from __future__ import division
|
||||
from __future__ import print_function
|
||||
|
||||
"""
|
||||
|
||||
_TENSORFLOW_API_ATTR_V1 = (
|
||||
tf_export.API_ATTRS_V1[tf_export.TENSORFLOW_API_NAME].names)
|
||||
_TENSORFLOW_API_ATTR = tf_export.API_ATTRS[tf_export.TENSORFLOW_API_NAME].names
|
||||
_TENSORFLOW_CONSTANTS_ATTR_V1 = (
|
||||
tf_export.API_ATTRS_V1[tf_export.TENSORFLOW_API_NAME].constants)
|
||||
_TENSORFLOW_CONSTANTS_ATTR = (
|
||||
tf_export.API_ATTRS[tf_export.TENSORFLOW_API_NAME].constants)
|
||||
|
||||
_ESTIMATOR_API_ATTR_V1 = (
|
||||
tf_export.API_ATTRS_V1[tf_export.ESTIMATOR_API_NAME].names)
|
||||
_ESTIMATOR_API_ATTR = tf_export.API_ATTRS[tf_export.ESTIMATOR_API_NAME].names
|
||||
_ESTIMATOR_CONSTANTS_ATTR_V1 = (
|
||||
tf_export.API_ATTRS_V1[tf_export.ESTIMATOR_API_NAME].constants)
|
||||
_ESTIMATOR_CONSTANTS_ATTR = (
|
||||
tf_export.API_ATTRS[tf_export.ESTIMATOR_API_NAME].constants)
|
||||
|
||||
|
||||
def get_v1_names(symbol):
|
||||
names_v1 = []
|
||||
if hasattr(symbol, _TENSORFLOW_API_ATTR_V1):
|
||||
names_v1.extend(getattr(symbol, _TENSORFLOW_API_ATTR_V1))
|
||||
if hasattr(symbol, _ESTIMATOR_API_ATTR_V1):
|
||||
names_v1.extend(getattr(symbol, _ESTIMATOR_API_ATTR_V1))
|
||||
return names_v1
|
||||
|
||||
|
||||
def get_v2_names(symbol):
|
||||
names_v2 = []
|
||||
if hasattr(symbol, _TENSORFLOW_API_ATTR):
|
||||
names_v2.extend(getattr(symbol, _TENSORFLOW_API_ATTR))
|
||||
if hasattr(symbol, _ESTIMATOR_API_ATTR):
|
||||
names_v2.extend(getattr(symbol, _ESTIMATOR_API_ATTR))
|
||||
return list(names_v2)
|
||||
|
||||
|
||||
def collect_function_arg_names(function_names):
|
||||
"""Determines argument names for reordered function signatures.
|
||||
|
||||
Args:
|
||||
function_names: Functions to collect arguments for.
|
||||
|
||||
Returns:
|
||||
Dictionary mapping function name to its arguments.
|
||||
"""
|
||||
# Map from reordered function name to its arguments.
|
||||
function_to_args = {}
|
||||
|
||||
def visit(unused_path, unused_parent, children):
|
||||
"""Visitor that collects arguments for reordered functions."""
|
||||
for child in children:
|
||||
_, attr = tf_decorator.unwrap(child[1])
|
||||
api_names_v1 = get_v1_names(attr)
|
||||
api_names_v1 = ['tf.%s' % name for name in api_names_v1]
|
||||
matches_function_names = any(
|
||||
name in function_names for name in api_names_v1)
|
||||
if matches_function_names:
|
||||
arg_list = tf_inspect.getargspec(attr)[0]
|
||||
for name in api_names_v1:
|
||||
function_to_args[name] = arg_list
|
||||
|
||||
visitor = public_api.PublicAPIVisitor(visit)
|
||||
visitor.do_not_descend_map['tf'].append('contrib')
|
||||
visitor.do_not_descend_map['tf.compat'] = ['v1', 'v2']
|
||||
traverse.traverse(tf, visitor)
|
||||
|
||||
return function_to_args
|
||||
|
||||
|
||||
def get_reorder_line(name, arg_list):
|
||||
return ' \'%s\': %s' % (name, str(arg_list))
|
||||
|
||||
|
||||
def update_reorders_v2(output_file_path):
|
||||
"""Writes a Python dictionary mapping function name to argument order.
|
||||
|
||||
Args:
|
||||
output_file_path: File path to write output to. Any existing contents
|
||||
would be replaced.
|
||||
"""
|
||||
reordered_function_names = (
|
||||
tf_upgrade_v2.TFAPIChangeSpec().reordered_function_names)
|
||||
|
||||
all_reorders = collect_function_arg_names(reordered_function_names)
|
||||
|
||||
# List of reorder lines to write to output file in the form:
|
||||
# 'tf.function_name': ['arg1', 'arg2', ...]
|
||||
rename_lines = [
|
||||
get_reorder_line(name, arg_names)
|
||||
for name, arg_names in all_reorders.items()]
|
||||
renames_file_text = '%sreorders = {\n%s\n}\n' % (
|
||||
_FILE_HEADER, ',\n'.join(sorted(rename_lines)))
|
||||
file_io.write_string_to_file(output_file_path, renames_file_text)
|
||||
|
||||
|
||||
def main(unused_argv):
|
||||
update_reorders_v2(_OUTPUT_FILE_PATH)
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
app.run(main=main)
|
||||
Loading…
Reference in New Issue
Block a user