[CustomOp] Support complex dtype in custom op (#31657)
* support custom complex op * fix detail error * add inference support * fix setup windows failedtest_benchmark_ci
Chen Weihang
4 years ago
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GitHub
parent
fe241fd02f
commit
87852616aa
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// 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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// WIdata_tHOUdata_t WARRANdata_tIES OR CONDIdata_tIONS OF ANY KIND, either
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// 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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#include <iostream>
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#include <vector>
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#include "paddle/extension.h"
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#define CHECK_INPUT(x) \
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PD_CHECK(x.place() == paddle::PlaceType::kCPU, #x " must be a CPU Tensor.")
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template <typename data_t>
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using EnableComplex = typename std::enable_if<
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std::is_same<data_t, paddle::complex64>::value ||
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std::is_same<data_t, paddle::complex128>::value>::type;
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template <typename data_t>
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using DisableComplex = typename std::enable_if<
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!std::is_same<data_t, paddle::complex64>::value &&
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!std::is_same<data_t, paddle::complex128>::value>::type;
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template <typename data_t, typename Enable = void>
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struct ConjFunctor;
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template <typename data_t>
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struct ConjFunctor<data_t, EnableComplex<data_t>> {
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ConjFunctor(const data_t* input, int64_t numel, data_t* output)
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: input_(input), numel_(numel), output_(output) {}
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void operator()(size_t idx) const {
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output_[idx] = data_t(input_[idx].real, -input_[idx].imag);
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}
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const data_t* input_;
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int64_t numel_;
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data_t* output_;
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};
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template <typename data_t>
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struct ConjFunctor<data_t, DisableComplex<data_t>> {
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ConjFunctor(const data_t* input, int64_t numel, data_t* output)
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: input_(input), numel_(numel), output_(output) {}
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void operator()(size_t idx) const { output_[idx] = input_[idx]; }
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const data_t* input_;
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int64_t numel_;
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data_t* output_;
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};
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template <typename data_t>
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void ConjCPUKernel(const data_t* x_data, int64_t numel, data_t* out_data) {
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ConjFunctor<data_t> conj(x_data, numel, out_data);
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for (int64_t i = 0; i < numel; ++i) {
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conj(i);
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}
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}
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std::vector<paddle::Tensor> ConjFunction(const paddle::Tensor& x) {
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CHECK_INPUT(x);
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paddle::Tensor out(x.place());
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out.reshape(x.shape());
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PD_DISPATCH_FLOATING_AND_COMPLEX_TYPES(
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x.type(), "ConjCPUKernel", ([&] {
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ConjCPUKernel<data_t>(
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x.data<data_t>(), x.size(), out.mutable_data<data_t>());
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}));
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return {out};
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}
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PD_BUILD_OP(custom_conj)
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.Inputs({"X"})
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.Outputs({"Out"})
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.SetKernelFn(PD_KERNEL(ConjFunction));
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PD_BUILD_GRAD_OP(custom_conj)
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.Inputs({paddle::Grad("Out")})
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.Outputs({paddle::Grad("X")})
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.SetKernelFn(PD_KERNEL(ConjFunction));
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@ -0,0 +1,136 @@
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# 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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import paddle
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import paddle.static as static
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from paddle.utils.cpp_extension import load, get_build_directory
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from paddle.utils.cpp_extension.extension_utils import run_cmd
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from utils import paddle_includes, extra_cc_args, extra_nvcc_args
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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 = '{}\\custom_relu_module_jit\\custom_relu_module_jit.pyd'.format(
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get_build_directory())
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if os.name == 'nt' and os.path.isfile(file):
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cmd = 'del {}'.format(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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def is_complex(dtype):
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return dtype == paddle.fluid.core.VarDesc.VarType.COMPLEX64 or \
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dtype == paddle.fluid.core.VarDesc.VarType.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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return out.numpy(), x.grad
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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 static.scope_guard(static.Scope()):
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with static.program_guard(static.Program()):
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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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out_v, x_grad_v = exe.run(static.default_main_program(),
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feed={"x": np_input},
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fetch_list=[out.name, x.name + "@GRAD"])
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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 check_output(self, out, pd_out, name):
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self.assertTrue(
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np.array_equal(out, pd_out),
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"custom op {}: {},\n paddle api {}: {}".format(name, out, name,
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pd_out))
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def run_dynamic(self, dtype, np_input):
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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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self.check_output(out, pd_out, "out")
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self.check_output(x_grad, pd_x_grad, "x's grad")
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def run_static(self, dtype, np_input):
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out, x_grad = conj_static(custom_ops.custom_conj, self.shape, dtype,
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np_input)
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pd_out, pd_x_grad = conj_static(paddle.conj, self.shape, dtype,
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np_input)
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self.check_output(out, pd_out, "out")
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self.check_output(x_grad, pd_x_grad, "x's grad")
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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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self.run_dynamic(dtype, np_input)
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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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self.run_static(dtype, np_input)
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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) + 1j * np.random.random(self.shape).astype(dtype)
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self.run_dynamic(to_complex(dtype), np_input)
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if __name__ == "__main__":
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unittest.main()
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