wehub-resource-sync
122 lines
3.5 KiB
Python
122 lines
3.5 KiB
Python
# Copyright (c) 2024 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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from dygraph_to_static_utils import (
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Dy2StTestBase,
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test_ast_only,
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)
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import paddle
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paddle.core._set_prim_all_enabled(True)
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class HighOrderNet(paddle.nn.Layer):
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def __init__(self):
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super().__init__()
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self.bilinear = paddle.nn.Bilinear(
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in1_features=5, in2_features=4, out_features=1000
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)
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def forward(self, x, y):
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y = self.bilinear(x, y)
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z = paddle.pow(y, 2)
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x_grad = paddle.grad(z, x, create_graph=True)[0]
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x_grad_grad = paddle.grad(x_grad, x, create_graph=True)[0]
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return x_grad_grad.mean()
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class TestBackwardHasNoGradError(Dy2StTestBase):
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@test_ast_only
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def _test_backward_has_no_grad_error(self):
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net = HighOrderNet()
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static_net = paddle.jit.to_static(net, full_graph=True)
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x = layer1 = paddle.rand((5, 5)).astype('float32')
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x.stop_gradient = False
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y = layer1 = paddle.rand((5, 4)).astype('float32')
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y.stop_gradient = False
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with self.assertRaisesRegex(
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ValueError,
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"op 'pd_op.bilinear_grad' has no grad op, consider enable prim to decompose it.",
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):
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x_grad_grad = static_net(x, y)
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x_grad_grad.backward()
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class HighOrderControlFlowNet(paddle.nn.Layer):
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def __init__(self):
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super().__init__()
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self.eps = 1e-5
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def forward(self, x):
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if x.numel() > 0:
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variance, mean = (
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paddle.var(x, axis=-1, unbiased=False, keepdim=True),
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paddle.mean(x, axis=-1, keepdim=True),
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)
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y = (x - mean) / paddle.sqrt(variance + self.eps)
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else:
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y = x
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x_grad = paddle.grad(y, x, create_graph=True)[0]
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return x_grad.mean()
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class HighOrderCompareNet(HighOrderControlFlowNet):
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def __init__(self):
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super().__init__()
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self.eps = 1e-5
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def forward(self, x):
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variance, mean = (
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paddle.var(x, axis=-1, unbiased=False, keepdim=True),
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paddle.mean(x, axis=-1, keepdim=True),
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)
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y = (x - mean) / paddle.sqrt(variance + self.eps)
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x_grad = paddle.grad(y, x, create_graph=True)[0]
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return x_grad.mean()
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class TestBackwardControlFlow(Dy2StTestBase):
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@test_ast_only
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def test_control_flow_hign_order_backward(self):
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conf_net = HighOrderControlFlowNet()
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net = HighOrderCompareNet()
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x = paddle.rand((5, 5)).astype('float32')
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x.stop_gradient = False
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static_net = paddle.jit.to_static(net, full_graph=True)
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x_grad_grad = static_net(x)
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conf_static_net = paddle.jit.to_static(conf_net, full_graph=True)
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x_grad_grad_conf = conf_static_net(x)
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np.testing.assert_allclose(
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x_grad_grad.numpy(),
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x_grad_grad_conf.numpy(),
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rtol=1e-06,
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atol=1e-06,
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)
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if __name__ == "__main__":
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unittest.main()
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