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- # Copyright (c) 2022 PaddlePaddle 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.
-
- import json
- import os
- import tempfile
- import unittest
- import warnings
-
- import paddle
- import paddle.nn.functional as F
-
-
- class SimpleNet(paddle.nn.Layer):
- def __init__(self, data_format="NCHW", class_num=2):
- super().__init__()
- self.conv = paddle.nn.Conv2D(3, 8, (3, 3))
- self.bn = paddle.nn.BatchNorm(num_channels=8)
- self.relu = paddle.nn.ReLU()
- self.pool = paddle.nn.AvgPool2D(kernel_size=2, stride=2)
- self.flatten = paddle.nn.Flatten()
- self.fc = paddle.nn.Linear(392, class_num)
-
- def forward(self, image):
- conv_out = self.conv(image)
- bn_out = self.bn(conv_out)
- out = self.relu(bn_out)
- out = self.pool(out)
- out = self.flatten(out)
- out = self.fc(out)
- return conv_out, out
-
-
- class LayoutAutoTune(unittest.TestCase):
- def test_config(self):
- paddle.base.core.enable_layout_autotune()
- if self.use_autotune():
- self.assertEqual(paddle.base.core.use_layout_autotune(), True)
- paddle.base.core.disable_layout_autotune()
- self.assertEqual(paddle.base.core.use_layout_autotune(), False)
- self.use_autotune()
-
- def setUp(self):
- self.use_autotune()
-
- def use_autotune(self):
- if paddle.is_compiled_with_cuda():
- paddle.incubate.autotune.set_config(
- config={"layout": {"enable": True}}
- )
- return paddle.base.core.use_layout_autotune()
- else:
- config = {"layout": {"enable": False}}
- tfile = tempfile.NamedTemporaryFile(mode="w+", delete=False)
- json.dump(config, tfile)
- tfile.close()
- paddle.incubate.autotune.set_config(tfile.name)
- os.remove(tfile.name)
- return paddle.base.core.use_layout_autotune()
-
- def train(self, data_format):
- model = SimpleNet(data_format="NCHW", class_num=2)
- data = paddle.rand([1, 3, 16, 16])
- if data_format == "NHWC":
- data = paddle.rand([1, 16, 16, 3])
- label_data = paddle.randint(0, 1, shape=[1, 1], dtype="int64")
- optimizer = paddle.optimizer.SGD(
- learning_rate=0.0001, parameters=model.parameters()
- )
- scaler = paddle.amp.GradScaler()
- for i in range(2):
- with paddle.amp.auto_cast(level="O2"):
- conv_out, predict = model(data)
- loss = F.cross_entropy(predict, label=label_data)
- loss = loss.mean()
-
- scaled = scaler.scale(loss)
- scaled.backward()
- scaler.minimize(optimizer, scaled)
- return conv_out, predict
-
- def test_enable_autotune(self):
- conv_out, predict = self.train(data_format="NCHW")
- self.assertEqual(conv_out.shape, [1, 8, 14, 14])
- self.assertEqual(predict.shape, [1, 2])
-
- def test_transpose_op_transposer(self):
- conv = paddle.nn.Conv2D(3, 8, (3, 3))
- data = paddle.rand([1, 3, 16, 14])
- label_data = paddle.randint(0, 1, shape=[1, 1], dtype="int64")
- optimizer = paddle.optimizer.SGD(
- learning_rate=0.0001, parameters=conv.parameters()
- )
- scaler = paddle.amp.GradScaler()
- with paddle.amp.auto_cast(level="O2"):
- conv_out = conv(data)
- # conv_out.shape = [1, 14, 12, 8] with NHWC
- # layout tuner will transpose conv_out to
- # [1, 8, 14, 12] with NCHW before the following transpose op.
- out = paddle.transpose(conv_out, perm=[0, 3, 1, 2])
- loss = out.mean()
- scaled = scaler.scale(loss)
- scaled.backward()
- scaler.minimize(optimizer, scaled)
-
- self.assertEqual(conv_out.shape, [1, 8, 14, 12])
- self.assertEqual(out.shape, [1, 12, 8, 14])
-
- def test_flatten_op_transposer(self):
- conv = paddle.nn.Conv2D(3, 8, (3, 3))
- flatten = paddle.nn.Flatten(start_axis=1, stop_axis=2)
- data = paddle.rand([1, 3, 16, 14])
- with paddle.amp.auto_cast(level="O2"):
- conv_out = conv(data)
- # conv_out.shape = [1, 14, 12, 8] with NHWC
- # layout tuner will transpose conv_out to
- # [1, 8, 14, 12] with NCHW before the following flatten op
- # because it flatten the C and H dimensions.
- out = flatten(conv_out)
-
- self.assertEqual(conv_out.shape, [1, 8, 14, 12])
- self.assertEqual(out.shape, [1, 112, 12])
-
- def test_argmax_op_transposer_keep_dims(self):
- conv = paddle.nn.Conv2D(3, 8, (3, 3))
- data = paddle.rand([1, 3, 16, 14])
- with paddle.amp.auto_cast(level="O2"):
- conv_out = conv(data)
- # conv_out.shape = [1, 14, 12, 8] with NHWC
- out = paddle.argmax(conv_out, axis=1, keepdim=True)
- self.assertEqual(conv_out.shape, [1, 8, 14, 12])
- self.assertEqual(out.shape, [1, 1, 14, 12])
-
- def test_concat_op_transposer(self):
- in1 = paddle.rand([1, 8, 14, 12])
- conv = paddle.nn.Conv2D(3, 8, (3, 3))
- data = paddle.rand([1, 3, 16, 14])
- with paddle.amp.auto_cast(level="O2"):
- conv_out = conv(data)
- # conv_out.shape = [1, 14, 12, 8] with NHWC
- out = paddle.concat(x=[conv_out, in1], axis=0)
-
- self.assertEqual(conv_out.shape, [1, 8, 14, 12])
- self.assertEqual(out.shape, [2, 8, 14, 12])
-
- def test_concat_op_no_transposer(self):
- conv = paddle.nn.Conv2D(3, 8, (3, 3))
- data1 = paddle.rand([1, 3, 16, 14])
- data2 = paddle.rand([1, 3, 16, 14])
- with paddle.amp.auto_cast(level="O2"):
- conv_out1 = conv(data1)
- conv_out2 = conv(data2)
- # conv_out.shape = [1, 14, 12, 8] with NHWC
- out = paddle.concat(x=[conv_out1, conv_out2], axis=0)
-
- self.assertEqual(conv_out1.shape, [1, 8, 14, 12])
- self.assertEqual(out.shape, [2, 8, 14, 12])
-
- def test_padding_transpose(self):
- conv = paddle.nn.Conv2D(3, 8, (3, 3))
- data = paddle.rand([1, 3, 16, 14])
- mode = "constant"
- pad = [1, 0, 1, 2]
- padding = paddle.nn.Pad2D(padding=pad, mode=mode, data_format='NCHW')
- with paddle.amp.auto_cast(level="O2", dtype="bfloat16"):
- conv_out = conv(data)
- # conv_out.shape = [1, 14, 12, 8] with NHWC
- out = padding(conv_out)
- # from NHWC to NCHW
-
- self.assertEqual(conv_out.shape, [1, 8, 14, 12])
- self.assertEqual(out.shape, [1, 8, 17, 13])
-
-
- class TestAutoTuneAPI(unittest.TestCase):
- def test_set_config_warnings(self):
- with warnings.catch_warnings(record=True) as w:
- config = {"layout": {"enable": 1}}
- # On linux, we can open the file again to read the content
- # without closing the file, but on windows system, there is
- # no permission to open it again without closing it.
- tfile = tempfile.NamedTemporaryFile(mode="w+", delete=False)
- json.dump(config, tfile)
- tfile.close()
- paddle.incubate.autotune.set_config(tfile.name)
- os.remove(tfile.name)
- self.assertTrue(len(w) == 1)
-
-
- if __name__ == '__main__':
- unittest.main()
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