chore: import upstream snapshot with attribution
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wehub-resource-sync
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// Copyright (c) 2025 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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#if defined(PADDLE_WITH_CUDA) || defined(PADDLE_WITH_HIP)
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#include "paddle/phi/kernels/logical_kernel.h"
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#include "paddle/common/flags.h"
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#include "paddle/phi/backends/gpu/gpu_context.h"
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#include "paddle/phi/core/kernel_registry.h"
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#include "paddle/phi/kernels/bitwise_kernel.h"
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#include "paddle/phi/kernels/funcs/logical_functor.h"
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#include "paddle/phi/kernels/stride/elementwise_stride_base.cu.h"
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#if defined(__NVCC__) || defined(__HIPCC__) || defined(__xpu__)
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#include "paddle/phi/kernels/funcs/dims_simplifier.h"
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#endif
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COMMON_DECLARE_bool(use_stride_kernel);
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COMMON_DECLARE_bool(use_stride_compute_kernel);
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COMMON_DECLARE_bool(force_stride_compute_contig_out);
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namespace phi {
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template <typename T, typename Context, typename Functor>
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void LaunchLogicalNotStrideKernel(const Context &dev_ctx,
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const DenseTensor &x,
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Functor func,
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DenseTensor *out) {
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std::vector<const DenseTensor *> inputs = {&x};
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std::vector<DenseTensor *> outputs = {out};
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dev_ctx.template Alloc<bool>(out);
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UnaryStrideElementwiseKernel<bool, Context>(dev_ctx, inputs, &outputs, func);
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}
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template <typename T, typename Context, typename Functor>
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void LogicalKernelStrideImpl(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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dev_ctx.template Alloc<bool>(out);
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Functor binary_func;
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std::vector<const DenseTensor *> inputs = {&x, &y};
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std::vector<DenseTensor *> outputs = {out};
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BinaryStrideBroadcastKernel<bool, Context>(
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dev_ctx, inputs, &outputs, binary_func, -1);
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}
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template <typename T, typename Context, typename Functor>
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void InplaceLogicalKernelStrideImpl(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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auto x_origin = x;
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dev_ctx.template Alloc<bool>(out);
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out->set_type(phi::DataType::BOOL);
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Functor binary_func;
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std::vector<const DenseTensor *> inputs = {&x, &y};
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std::vector<DenseTensor *> outputs = {out};
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BinaryStrideBroadcastKernel<bool, Context>(
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dev_ctx, inputs, &outputs, binary_func, -1);
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}
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#define DEFINE_CUDA_BINARY_LOGICAL_STRIDE_OP(name) \
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template <typename T, typename Context> \
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void Logical##name##StrideKernel(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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if (!FLAGS_use_stride_kernel) { \
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PADDLE_THROW(common::errors::Fatal( \
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"FLAGS_use_stride_kernel is closed. Strided kernel " \
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"be called, something wrong has happened!")); \
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} \
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DenseTensor x_; \
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DenseTensor y_; \
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bool zero_size = false; \
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if (x.numel() == 0 || y.numel() == 0) { \
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zero_size = true; \
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} \
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if (!FLAGS_use_stride_compute_kernel || zero_size) { \
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if (!x.meta().is_contiguous()) { \
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x_ = Tensor2Contiguous<Context>(dev_ctx, x); \
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} else { \
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x_ = x; \
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} \
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if (!y.meta().is_contiguous()) { \
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y_ = Tensor2Contiguous<Context>(dev_ctx, y); \
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} else { \
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y_ = y; \
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} \
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} else { \
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x_ = x; \
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y_ = y; \
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} \
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if (x_.meta().is_contiguous() && y_.meta().is_contiguous()) { \
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auto meta = out->meta(); \
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meta.strides = meta.calc_strides(out->dims()); \
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out->set_meta(meta); \
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phi::Logical##name##Kernel<T, Context>(dev_ctx, x_, y_, out); \
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return; \
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} \
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if (!FLAGS_use_stride_compute_kernel) { \
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PADDLE_THROW( \
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common::errors::Fatal("FLAGS_use_stride_compute_kernel is closed. " \
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"Kernel using DenseTensorIterator " \
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"be called, something wrong has happened!")); \
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} \
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if (FLAGS_force_stride_compute_contig_out) { \
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auto meta = out->meta(); \
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meta.strides = meta.calc_strides(out->dims()); \
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out->set_meta(meta); \
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} \
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if (out->IsSharedWith(x_)) { \
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InplaceLogicalKernelStrideImpl<T, \
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Context, \
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funcs::Logical##name##Functor<T>>( \
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dev_ctx, x_, y_, out); \
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} else { \
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LogicalKernelStrideImpl<T, Context, funcs::Logical##name##Functor<T>>( \
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dev_ctx, x_, y_, out); \
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} \
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}
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DEFINE_CUDA_BINARY_LOGICAL_STRIDE_OP(And)
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DEFINE_CUDA_BINARY_LOGICAL_STRIDE_OP(Or)
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DEFINE_CUDA_BINARY_LOGICAL_STRIDE_OP(Xor)
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#undef DEFINE_CUDA_BINARY_LOGICAL_STRIDE_OP
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template <typename T, typename Context>
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void LogicalNotStrideKernel(const Context &dev_ctx,
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const DenseTensor &x,
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DenseTensor *out) {
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if (!FLAGS_use_stride_kernel) {
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PADDLE_THROW(common::errors::Fatal(
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"FLAGS_use_stride_kernel is closed. Strided kernel "
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"be called, something wrong has happened!"));
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}
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DenseTensor x_;
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bool zero_size = false;
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if (x.numel() == 0) {
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zero_size = true;
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}
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if (!FLAGS_use_stride_compute_kernel || zero_size) {
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if (!x.meta().is_contiguous()) {
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x_ = Tensor2Contiguous<Context>(dev_ctx, x);
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} else {
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x_ = x;
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}
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} else {
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x_ = x;
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}
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if (x_.meta().is_contiguous()) {
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auto meta = out->meta();
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meta.strides = meta.calc_strides(out->dims());
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out->set_meta(meta);
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phi::LogicalNotKernel<T, Context>(dev_ctx, x_, out);
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return;
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}
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if (!FLAGS_use_stride_compute_kernel) {
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PADDLE_THROW(
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common::errors::Fatal("FLAGS_use_stride_compute_kernel is closed. "
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"Kernel using DenseTensorIterator "
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"be called, something wrong has happened!"));
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}
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if (FLAGS_force_stride_compute_contig_out) {
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auto meta = out->meta();
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meta.strides = meta.calc_strides(out->dims());
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out->set_meta(meta);
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}
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if (!out->IsSharedWith(x_)) {
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LaunchLogicalNotStrideKernel<T, Context>(
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dev_ctx, x_, funcs::LogicalNotFunctor<T>(), out);
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} else {
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auto x_origin = x_;
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out->set_type(phi::DataType::BOOL);
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LaunchLogicalNotStrideKernel<T, Context>(
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dev_ctx, x_origin, funcs::LogicalNotFunctor<T>(), out);
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}
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}
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} // namespace phi
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#define REGISTER_LOGICAL_CUDA_STRIDE_KERNEL(logical_and, func_type) \
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PD_REGISTER_KERNEL(logical_and, \
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GPU, \
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STRIDED, \
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phi::Logical##func_type##StrideKernel, \
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float, \
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phi::float16, \
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phi::bfloat16, \
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double, \
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bool, \
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int64_t, \
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int, \
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int8_t, \
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phi::complex64, \
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phi::complex128, \
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int16_t) { \
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kernel->OutputAt(0).SetDataType(phi::DataType::BOOL); \
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}
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REGISTER_LOGICAL_CUDA_STRIDE_KERNEL(logical_and, And)
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REGISTER_LOGICAL_CUDA_STRIDE_KERNEL(logical_or, Or)
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REGISTER_LOGICAL_CUDA_STRIDE_KERNEL(logical_xor, Xor)
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REGISTER_LOGICAL_CUDA_STRIDE_KERNEL(logical_not, Not)
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#undef REGISTER_LOGICAL_CUDA_STRIDE_KERNEL
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#endif
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