wehub-resource-sync
116 lines
3.3 KiB
Python
116 lines
3.3 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 unittest
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import numpy as np
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import paddle
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import paddle.nn.functional as F
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from paddle import nn
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from paddle.base import core, framework
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from paddle.nn import BatchNorm
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np.random.seed(2023)
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class PrimeNet(paddle.nn.Layer):
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def __init__(self):
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super().__init__()
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self.conv = nn.Conv2D(2, 4, (3, 3), bias_attr=False)
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self.bn = BatchNorm(4, act="relu")
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def forward(self, x):
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y = self.conv(x)
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out = self.bn(y)
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res = F.max_pool2d(out, kernel_size=2, stride=2, padding=0)
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return res
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class TestPrimAMPO1(unittest.TestCase):
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"""
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Test PrimeNet with @to_static + prim v.s Dygraph in AMPO1.
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"""
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def setUp(self):
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paddle.seed(2022)
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self.x = paddle.randn([4, 2, 6, 6], dtype="float32")
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self.x.stop_gradient = False
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self.atol = 1e-3
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self.rtol = 1e-3
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if paddle.is_compiled_with_xpu():
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self.atol = 5e-3
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self.rtol = 5e-3
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def train(self, use_prim):
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core._set_prim_all_enabled(use_prim)
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paddle.seed(2022)
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net = PrimeNet()
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sgd = paddle.optimizer.SGD(
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learning_rate=0.1, parameters=net.parameters()
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)
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if use_prim:
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net = paddle.jit.to_static(
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net, build_strategy=False, full_graph=True
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)
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with paddle.amp.auto_cast(level='O1'):
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out = net(self.x)
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loss = paddle.mean(out)
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loss.backward()
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sgd.step()
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sgd.clear_grad()
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return loss
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def test_amp_01(self):
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if not isinstance(framework._current_expected_place(), core.CPUPlace):
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expected = self.train(False)
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actual = self.train(True)
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np.testing.assert_allclose(
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expected,
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actual,
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rtol=self.rtol,
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atol=self.atol,
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)
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def test_amp_O1_infer(self):
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if not isinstance(framework._current_expected_place(), core.CPUPlace):
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net = PrimeNet()
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core._set_prim_all_enabled(False)
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net.eval()
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static_net = paddle.jit.to_static(
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net, build_strategy=False, full_graph=True
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)
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res = static_net(self.x)
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# set prim all enabled
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core._set_prim_all_enabled(True)
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net.eval()
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static_net = paddle.jit.to_static(
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net, build_strategy=False, full_graph=True
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)
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with paddle.amp.auto_cast(level='O1'):
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res_amp = static_net(self.x)
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np.testing.assert_allclose(
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res,
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res_amp,
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rtol=self.rtol,
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atol=self.atol,
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)
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if __name__ == '__main__':
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unittest.main()
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