wehub-resource-sync
204 lines
7.7 KiB
Python
204 lines
7.7 KiB
Python
# Copyright (c) 2022 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 warnings
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from op_test import is_custom_device
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import paddle
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import paddle.nn.functional as F
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class SimpleNet(paddle.nn.Layer):
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def __init__(self, data_format="NCHW", class_num=2):
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super().__init__()
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self.conv = paddle.nn.Conv2D(3, 8, (3, 3))
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self.bn = paddle.nn.BatchNorm(num_channels=8)
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self.relu = paddle.nn.ReLU()
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self.pool = paddle.nn.AvgPool2D(kernel_size=2, stride=2)
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self.flatten = paddle.nn.Flatten()
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self.fc = paddle.nn.Linear(392, class_num)
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def forward(self, image):
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conv_out = self.conv(image)
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bn_out = self.bn(conv_out)
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out = self.relu(bn_out)
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out = self.pool(out)
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out = self.flatten(out)
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out = self.fc(out)
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return conv_out, out
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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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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 train(self, data_format):
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model = SimpleNet(data_format="NCHW", class_num=2)
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data = paddle.rand([1, 3, 16, 16])
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if data_format == "NHWC":
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data = paddle.rand([1, 16, 16, 3])
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label_data = paddle.randint(0, 1, shape=[1, 1], dtype="int64")
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optimizer = paddle.optimizer.SGD(
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learning_rate=0.0001, parameters=model.parameters()
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)
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scaler = paddle.amp.GradScaler()
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for i in range(2):
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with paddle.amp.auto_cast(level="O2"):
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conv_out, predict = model(data)
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loss = F.cross_entropy(predict, label=label_data)
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loss = loss.mean()
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scaled = scaler.scale(loss)
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scaled.backward()
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scaler.minimize(optimizer, scaled)
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return conv_out, predict
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def test_enable_autotune(self):
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conv_out, predict = self.train(data_format="NCHW")
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self.assertEqual(conv_out.shape, [1, 8, 14, 14])
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self.assertEqual(predict.shape, [1, 2])
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def test_transpose_op_transposer(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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label_data = paddle.randint(0, 1, shape=[1, 1], dtype="int64")
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optimizer = paddle.optimizer.SGD(
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learning_rate=0.0001, parameters=conv.parameters()
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)
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scaler = paddle.amp.GradScaler()
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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 transpose op.
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out = paddle.transpose(conv_out, perm=[0, 3, 1, 2])
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loss = out.mean()
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scaled = scaler.scale(loss)
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scaled.backward()
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scaler.minimize(optimizer, scaled)
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self.assertEqual(conv_out.shape, [1, 8, 14, 12])
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self.assertEqual(out.shape, [1, 12, 8, 14])
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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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def test_concat_op_no_transposer(self):
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conv = paddle.nn.Conv2D(3, 8, (3, 3))
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data1 = paddle.rand([1, 3, 16, 14])
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data2 = paddle.rand([1, 3, 16, 14])
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with paddle.amp.auto_cast(level="O2"):
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conv_out1 = conv(data1)
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conv_out2 = conv(data2)
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# conv_out.shape = [1, 14, 12, 8] with NHWC
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out = paddle.concat(x=[conv_out1, conv_out2], axis=0)
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self.assertEqual(conv_out1.shape, [1, 8, 14, 12])
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self.assertEqual(out.shape, [2, 8, 14, 12])
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def test_padding_transpose(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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mode = "constant"
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pad = [1, 0, 1, 2]
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padding = paddle.nn.Pad2D(padding=pad, mode=mode, data_format='NCHW')
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with paddle.amp.auto_cast(level="O2", dtype="bfloat16"):
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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 = padding(conv_out)
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# from NHWC to NCHW
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self.assertEqual(conv_out.shape, [1, 8, 14, 12])
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self.assertEqual(out.shape, [1, 8, 17, 13])
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class TestAutoTuneAPI(unittest.TestCase):
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def test_set_config_warnings(self):
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with warnings.catch_warnings(record=True) as w:
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config = {"layout": {"enable": 1}}
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# On linux, we can open the file again to read the content
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# without closing the file, but on windows system, there is
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# no permission to open it again without closing it.
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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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self.assertTrue(len(w) == 1)
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if __name__ == '__main__':
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unittest.main()
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