wehub-resource-sync
92 lines
2.9 KiB
Python
92 lines
2.9 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 os
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import unittest
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import numpy as np
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from utils import (
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check_output_allclose,
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extra_cc_args,
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extra_nvcc_args,
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paddle_includes,
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)
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import paddle
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from paddle.utils.cpp_extension import get_build_directory, load
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from paddle.utils.cpp_extension.extension_utils import run_cmd
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# Because Windows don't use docker, the shared lib already exists in the
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# cache dir, it will not be compiled again unless the shared lib is removed.
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file = f'{get_build_directory()}\\custom_tanh\\custom_tanh.pyd'
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if os.name == 'nt' and os.path.isfile(file):
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cmd = f'del {file}'
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run_cmd(cmd, True)
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custom_ops = load(
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name='custom_tanh_jit',
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sources=['custom_tanh_op.cc'],
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extra_include_paths=paddle_includes, # add for Coverage CI
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extra_cxx_cflags=extra_cc_args, # test for cc flags
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extra_cuda_cflags=extra_nvcc_args, # test for nvcc flags
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verbose=True,
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)
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def custom_tanh_double_grad_dynamic(func, device, dtype, np_x):
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paddle.set_device(device)
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t = paddle.to_tensor(np_x, dtype=dtype, stop_gradient=False)
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t.retain_grads()
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out = func(t)
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out.stop_gradient = False
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out.retain_grads()
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dx = paddle.grad(
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outputs=[out], inputs=[t], create_graph=True, retain_graph=True
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)
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dx[0].retain_grads()
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dx[0].backward()
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assert out.grad is not None
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assert dx[0].grad is not None
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return dx[0].numpy(), dx[0].grad.numpy(), out.grad.numpy()
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class TestCustomTanhDoubleGradJit(unittest.TestCase):
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def setUp(self):
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paddle.set_device('cpu')
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self.dtypes = ['float32', 'float64']
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self.devices = ['cpu']
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def test_double_grad_dynamic(self):
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for device in self.devices:
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for dtype in self.dtypes:
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x = np.random.uniform(-1, 1, [4, 8]).astype(dtype)
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out, dx_grad, dout = custom_tanh_double_grad_dynamic(
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custom_ops.custom_tanh, device, dtype, x
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)
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pd_out, pd_dx_grad, pd_dout = custom_tanh_double_grad_dynamic(
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paddle.tanh, device, dtype, x
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)
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check_output_allclose(out, pd_out, "out", rtol=1e-05)
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check_output_allclose(dx_grad, pd_dx_grad, "out", rtol=1e-05)
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check_output_allclose(dout, pd_dout, "dout", rtol=1e-05)
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if __name__ == "__main__":
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unittest.main()
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