wehub-resource-sync
150 lines
4.6 KiB
Python
150 lines
4.6 KiB
Python
# Copyright (c) 2021 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 check_output, extra_cc_args, extra_nvcc_args, paddle_includes
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import paddle
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from paddle import static
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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_conj\\custom_conj.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_conj_jit',
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sources=['custom_conj_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 is_complex(dtype):
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return dtype == paddle.complex64 or dtype == paddle.complex128
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def to_complex(dtype):
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if dtype == "float32":
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return np.complex64
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elif dtype == "float64":
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return np.complex128
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else:
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return dtype
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def conj_dynamic(func, dtype, np_input):
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paddle.set_device("cpu")
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x = paddle.to_tensor(np_input)
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out = func(x)
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out.stop_gradient = False
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sum_out = paddle.sum(out)
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if is_complex(sum_out.dtype):
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sum_out.real().backward()
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else:
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sum_out.backward()
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if x.grad is None:
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return out.numpy(), x.grad
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else:
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return out.numpy(), x.grad.numpy()
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def conj_static(func, shape, dtype, np_input):
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paddle.enable_static()
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paddle.set_device("cpu")
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with (
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static.scope_guard(static.Scope()),
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static.program_guard(static.Program()),
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):
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x = static.data(name="x", shape=shape, dtype=dtype)
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x.stop_gradient = False
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out = func(x)
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sum_out = paddle.sum(out)
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static.append_backward(sum_out)
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exe = static.Executor()
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exe.run(static.default_startup_program())
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if paddle.framework.in_pir_mode():
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ops = static.default_main_program().global_block().ops
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fetch_list = [out, ops[-1].result(0)]
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else:
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fetch_list = [out.name, x.name + "@GRAD"]
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out_v, x_grad_v = exe.run(
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static.default_main_program(),
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feed={"x": np_input},
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fetch_list=fetch_list,
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)
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paddle.disable_static()
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return out_v, x_grad_v
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class TestCustomConjJit(unittest.TestCase):
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def setUp(self):
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self.dtypes = ['float32', 'float64']
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self.shape = [2, 20, 2, 3]
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def test_dynamic(self):
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for dtype in self.dtypes:
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np_input = np.random.random(self.shape).astype(dtype)
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out, x_grad = conj_dynamic(custom_ops.custom_conj, dtype, np_input)
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pd_out, pd_x_grad = conj_dynamic(paddle.conj, dtype, np_input)
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check_output(out, pd_out, "out")
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check_output(x_grad, pd_x_grad, "x's grad")
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def test_static(self):
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for dtype in self.dtypes:
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np_input = np.random.random(self.shape).astype(dtype)
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out, x_grad = conj_static(
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custom_ops.custom_conj, self.shape, dtype, np_input
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)
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pd_out, pd_x_grad = conj_static(
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paddle.conj, self.shape, dtype, np_input
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)
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check_output(out, pd_out, "out")
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check_output(x_grad, pd_x_grad, "x's grad")
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# complex only used in dynamic mode now
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def test_complex_dynamic(self):
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for dtype in self.dtypes:
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np_input = np.random.random(self.shape).astype(
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dtype
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) + 1j * np.random.random(self.shape).astype(dtype)
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out, x_grad = conj_dynamic(
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custom_ops.custom_conj, to_complex(dtype), np_input
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)
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pd_out, pd_x_grad = conj_dynamic(
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paddle.conj, to_complex(dtype), np_input
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)
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check_output(out, pd_out, "out")
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check_output(x_grad, pd_x_grad, "x's grad")
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if __name__ == "__main__":
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unittest.main()
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