wehub-resource-sync
113 lines
3.9 KiB
Python
113 lines
3.9 KiB
Python
# Copyright (c) 2021 PaddlePaddle 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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import json
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import os
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import tempfile
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import unittest
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import numpy as np
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from op_test import OpTest, is_custom_device
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import paddle
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def transpose_layout(x, src_layout, dst_layout):
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return x.transpose([0, 2, 3, 1])
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class TestTransferLayoutFP16Op(OpTest):
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def setUp(self):
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paddle.enable_static()
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self.op_type = 'transfer_layout'
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self.dtype = np.float16
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x = np.random.random(size=[2, 5, 10, 10])
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self.inputs = {'X': x.astype(self.dtype)}
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self.outputs = {'Out': x.transpose([0, 2, 3, 1])}
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self.attrs = {'src_layout': 0, 'dst_layout': 1}
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self.python_api = transpose_layout
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def test_check_output(self):
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self.check_output()
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class LayoutAutoTune(unittest.TestCase):
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def test_config(self):
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paddle.base.core.enable_layout_autotune()
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if self.use_autotune():
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self.assertEqual(paddle.base.core.use_layout_autotune(), True)
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paddle.base.core.disable_layout_autotune()
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self.assertEqual(paddle.base.core.use_layout_autotune(), False)
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self.use_autotune()
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def setUp(self):
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paddle.disable_static()
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self.use_autotune()
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def use_autotune(self):
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if paddle.is_compiled_with_cuda() or is_custom_device():
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paddle.incubate.autotune.set_config(
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config={"layout": {"enable": True}}
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)
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return paddle.base.core.use_layout_autotune()
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else:
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config = {"layout": {"enable": False}}
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tfile = tempfile.NamedTemporaryFile(mode="w+", delete=False)
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json.dump(config, tfile)
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tfile.close()
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paddle.incubate.autotune.set_config(tfile.name)
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os.remove(tfile.name)
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return paddle.base.core.use_layout_autotune()
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def test_flatten_op_transposer(self):
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conv = paddle.nn.Conv2D(3, 8, (3, 3))
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flatten = paddle.nn.Flatten(start_axis=1, stop_axis=2)
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data = paddle.rand([1, 3, 16, 14])
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with paddle.amp.auto_cast(level="O2"):
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conv_out = conv(data)
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# conv_out.shape = [1, 14, 12, 8] with NHWC
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# layout tuner will transpose conv_out to
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# [1, 8, 14, 12] with NCHW before the following flatten op
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# because it flatten the C and H dimensions.
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out = flatten(conv_out)
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self.assertEqual(conv_out.shape, [1, 8, 14, 12])
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self.assertEqual(out.shape, [1, 112, 12])
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def test_argmax_op_transposer_keep_dims(self):
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conv = paddle.nn.Conv2D(3, 8, (3, 3))
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data = paddle.rand([1, 3, 16, 14])
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with paddle.amp.auto_cast(level="O2"):
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conv_out = conv(data)
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# conv_out.shape = [1, 14, 12, 8] with NHWC
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out = paddle.argmax(conv_out, axis=1, keepdim=True)
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self.assertEqual(conv_out.shape, [1, 8, 14, 12])
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self.assertEqual(out.shape, [1, 1, 14, 12])
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def test_concat_op_transposer(self):
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in1 = paddle.rand([1, 8, 14, 12])
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conv = paddle.nn.Conv2D(3, 8, (3, 3))
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data = paddle.rand([1, 3, 16, 14])
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with paddle.amp.auto_cast(level="O2"):
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conv_out = conv(data)
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# conv_out.shape = [1, 14, 12, 8] with NHWC
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out = paddle.concat(x=[conv_out, in1], axis=0)
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self.assertEqual(conv_out.shape, [1, 8, 14, 12])
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self.assertEqual(out.shape, [2, 8, 14, 12])
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if __name__ == '__main__':
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unittest.main()
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