83 lines
3.1 KiB
Python
83 lines
3.1 KiB
Python
# Copyright 2020 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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"""Generate some SavedModels for use by AOT compilation tests."""
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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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import os
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from absl import flags
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from tensorflow.python.eager import def_function
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from tensorflow.python.framework import dtypes
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from tensorflow.python.framework import tensor_spec
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from tensorflow.python.ops import array_ops
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from tensorflow.python.ops import math_ops
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from tensorflow.python.platform import app
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from tensorflow.python.saved_model import save
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from tensorflow.python.training.tracking import tracking
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flags.DEFINE_string('out_dir', None,
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'Directory to output saved models to.')
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FLAGS = flags.FLAGS
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def create_large_matmul_savedmodel(out_dir):
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"""Create a SavedModel that performs a large matmul."""
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root = tracking.AutoTrackable()
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root.f = def_function.function(
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lambda x, y: math_ops.matmul(x, y), # pylint: disable=unnecessary-lambda
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input_signature=[tensor_spec.TensorSpec([3000, 5000], dtypes.float32),
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tensor_spec.TensorSpec([5000, 4000], dtypes.float32),])
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root.f(x=array_ops.zeros((3000, 5000)),
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y=array_ops.zeros((5000, 4000)))
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save_dir = os.path.join(out_dir, 'x_matmul_y_large')
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save.save(root, save_dir, root.f)
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# This simple SavedModel lacks any variables, but we need to create a
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# variables.index file to make bazel genrule happy.
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with open(os.path.join(save_dir, 'variables', 'variables.index'), 'w'):
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pass
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def create_small_matmul_savedmodel(out_dir):
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"""Create a SavedModel that performs a small matmul."""
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root = tracking.AutoTrackable()
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root.f = def_function.function(
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lambda x, y: math_ops.matmul(x, y), # pylint: disable=unnecessary-lambda
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input_signature=[tensor_spec.TensorSpec([3, 5], dtypes.float32),
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tensor_spec.TensorSpec([5, 4], dtypes.float32),])
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root.f(x=array_ops.zeros((3, 5)),
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y=array_ops.zeros((5, 4)))
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save_dir = os.path.join(out_dir, 'x_matmul_y_small')
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save.save(root, save_dir, root.f)
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# This simple SavedModel lacks any variables, but we need to create a
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# variables.index file to make bazel genrule happy.
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with open(os.path.join(save_dir, 'variables', 'variables.index'), 'w'):
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pass
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def main(unused_args):
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create_small_matmul_savedmodel(FLAGS.out_dir)
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create_large_matmul_savedmodel(FLAGS.out_dir)
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if __name__ == '__main__':
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flags.mark_flag_as_required('out_dir')
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app.run(main)
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