wehub-resource-sync
158 lines
6.4 KiB
C++
158 lines
6.4 KiB
C++
/* Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
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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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http://www.apache.org/licenses/LICENSE-2.0
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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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#include "paddle/phi/kernels/bitwise_kernel.h"
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#include "paddle/phi/backends/cpu/cpu_context.h"
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#include "paddle/phi/core/kernel_registry.h"
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#include "paddle/phi/kernels/funcs/bitwise_functors.h"
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#include "paddle/phi/kernels/funcs/elementwise_base.h"
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#include "paddle/phi/common/transform.h"
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namespace phi {
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#define DEFINE_BITWISE_KERNEL(op_type) \
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template <typename T, typename Context> \
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void Bitwise##op_type##Kernel(const Context& dev_ctx, \
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const DenseTensor& x, \
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const DenseTensor& y, \
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DenseTensor* out) { \
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funcs::Bitwise##op_type##Functor<T> func; \
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funcs::ElementwiseCompute<funcs::Bitwise##op_type##Functor<T>, T>( \
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dev_ctx, x, y, func, out); \
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}
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DEFINE_BITWISE_KERNEL(And)
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DEFINE_BITWISE_KERNEL(Or)
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DEFINE_BITWISE_KERNEL(Xor)
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#undef DEFINE_BITWISE_KERNEL
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#define DEFINE_BITWISE_KERNEL_WITH_INVERSE(op_type) \
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template <typename T, typename Context> \
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void Bitwise##op_type##Kernel(const Context& dev_ctx, \
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const DenseTensor& x, \
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const DenseTensor& y, \
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bool is_arithmetic, \
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DenseTensor* out) { \
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auto x_dims = x.dims(); \
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auto y_dims = y.dims(); \
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if (x_dims.size() >= y_dims.size()) { \
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if (is_arithmetic) { \
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funcs::Bitwise##op_type##ArithmeticFunctor<T> func; \
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funcs::ElementwiseCompute< \
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funcs::Bitwise##op_type##ArithmeticFunctor<T>, \
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T>(dev_ctx, x, y, func, out); \
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} else { \
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funcs::Bitwise##op_type##LogicFunctor<T> func; \
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funcs::ElementwiseCompute<funcs::Bitwise##op_type##LogicFunctor<T>, \
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T>(dev_ctx, x, y, func, out); \
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} \
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} else { \
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if (is_arithmetic) { \
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funcs::InverseBitwise##op_type##ArithmeticFunctor<T> inv_func; \
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funcs::ElementwiseCompute< \
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funcs::InverseBitwise##op_type##ArithmeticFunctor<T>, \
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T>(dev_ctx, x, y, inv_func, out); \
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} else { \
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funcs::InverseBitwise##op_type##LogicFunctor<T> inv_func; \
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funcs::ElementwiseCompute< \
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funcs::InverseBitwise##op_type##LogicFunctor<T>, \
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T>(dev_ctx, x, y, inv_func, out); \
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} \
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} \
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}
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DEFINE_BITWISE_KERNEL_WITH_INVERSE(LeftShift)
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DEFINE_BITWISE_KERNEL_WITH_INVERSE(RightShift)
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#undef DEFINE_BITWISE_KERNEL_WITH_INVERSE
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template <typename T, typename Context>
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void BitwiseNotKernel(const Context& dev_ctx,
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const DenseTensor& x,
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DenseTensor* out) {
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const T* x_data = x.data<T>();
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T* out_data = dev_ctx.template Alloc<T>(out);
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size_t numel = x.numel();
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funcs::BitwiseNotFunctor<T> func;
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Transform<Context> trans;
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trans(dev_ctx, x_data, x_data + numel, out_data, func);
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}
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} // namespace phi
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PD_REGISTER_KERNEL(bitwise_and,
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CPU,
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ALL_LAYOUT,
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phi::BitwiseAndKernel,
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bool,
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uint8_t,
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int8_t,
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int16_t,
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int,
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int64_t) {}
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PD_REGISTER_KERNEL(bitwise_or,
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CPU,
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ALL_LAYOUT,
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phi::BitwiseOrKernel,
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bool,
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uint8_t,
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int8_t,
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int16_t,
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int,
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int64_t) {}
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PD_REGISTER_KERNEL(bitwise_xor,
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CPU,
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ALL_LAYOUT,
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phi::BitwiseXorKernel,
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bool,
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uint8_t,
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int8_t,
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int16_t,
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int,
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int64_t) {}
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PD_REGISTER_KERNEL(bitwise_not,
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CPU,
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ALL_LAYOUT,
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phi::BitwiseNotKernel,
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bool,
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uint8_t,
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int8_t,
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int16_t,
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int,
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int64_t) {}
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PD_REGISTER_KERNEL(bitwise_left_shift,
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CPU,
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ALL_LAYOUT,
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phi::BitwiseLeftShiftKernel,
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uint8_t,
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int8_t,
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int16_t,
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int,
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int64_t) {}
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PD_REGISTER_KERNEL(bitwise_right_shift,
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CPU,
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ALL_LAYOUT,
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phi::BitwiseRightShiftKernel,
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uint8_t,
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int8_t,
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int16_t,
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int,
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int64_t) {}
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