wehub-resource-sync
274 lines
8.4 KiB
Plaintext
274 lines
8.4 KiB
Plaintext
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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/common/flags.h"
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#include "paddle/phi/core/kernel_registry.h"
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#include "paddle/phi/kernels/prod_kernel.h"
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#include "paddle/phi/kernels/reduce_all_kernel.h"
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#include "paddle/phi/kernels/reduce_amax_kernel.h"
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#include "paddle/phi/kernels/reduce_amin_kernel.h"
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#include "paddle/phi/kernels/reduce_any_kernel.h"
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#include "paddle/phi/kernels/reduce_max_kernel.h"
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#include "paddle/phi/kernels/reduce_mean_kernel.h"
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#include "paddle/phi/kernels/reduce_min_kernel.h"
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#include "paddle/phi/kernels/reduce_nansum_kernel.h"
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#include "paddle/phi/kernels/reduce_sum_kernel.h"
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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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inline void PrepareStridedOut_reduce(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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"should not be called!"));
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}
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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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template <typename T, typename Context>
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void AMaxStrideKernel(const Context& dev_ctx,
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const DenseTensor& x,
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const std::vector<int64_t>& dims,
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bool keep_dim,
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DenseTensor* out) {
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PrepareStridedOut_reduce(out);
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phi::AMaxKernel<T, Context>(dev_ctx, x, dims, keep_dim, out);
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}
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template <typename T, typename Context>
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void AMinStrideKernel(const Context& dev_ctx,
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const DenseTensor& x,
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const std::vector<int64_t>& dims,
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bool keep_dim,
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DenseTensor* out) {
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PrepareStridedOut_reduce(out);
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phi::AMinKernel<T, Context>(dev_ctx, x, dims, keep_dim, out);
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}
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template <typename T, typename Context>
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void MaxStrideKernel(const Context& dev_ctx,
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const DenseTensor& x,
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const IntArray& dims,
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bool keep_dim,
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DenseTensor* out) {
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PrepareStridedOut_reduce(out);
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phi::MaxKernel<T, Context>(dev_ctx, x, dims, keep_dim, out);
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}
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template <typename T, typename Context>
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void MinStrideKernel(const Context& dev_ctx,
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const DenseTensor& x,
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const IntArray& dims,
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bool keep_dim,
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DenseTensor* out) {
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PrepareStridedOut_reduce(out);
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phi::MinKernel<T, Context>(dev_ctx, x, dims, keep_dim, out);
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}
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template <typename T, typename Context>
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void ProdStrideKernel(const Context& dev_ctx,
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const DenseTensor& x,
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const IntArray& dims,
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bool keep_dim,
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bool reduce_all,
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DenseTensor* out) {
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PrepareStridedOut_reduce(out);
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phi::ProdKernel<T, Context>(dev_ctx, x, dims, keep_dim, reduce_all, out);
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}
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template <typename T, typename Context>
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void AllStrideKernel(const Context& dev_ctx,
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const DenseTensor& x,
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const std::vector<int64_t>& dims,
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bool keep_dim,
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DenseTensor* out) {
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PrepareStridedOut_reduce(out);
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phi::AllKernel<T, Context>(dev_ctx, x, dims, keep_dim, out);
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}
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template <typename T, typename Context>
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void AnyStrideKernel(const Context& dev_ctx,
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const DenseTensor& x,
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const std::vector<int64_t>& dims,
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bool keep_dim,
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DenseTensor* out) {
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PrepareStridedOut_reduce(out);
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phi::AnyKernel<T, Context>(dev_ctx, x, dims, keep_dim, out);
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}
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template <typename T, typename Context>
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void SumStrideKernel(const Context& dev_ctx,
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const DenseTensor& x,
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const IntArray& dims,
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DataType out_dtype,
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bool keep_dim,
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DenseTensor* out) {
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PrepareStridedOut_reduce(out);
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phi::SumKernel<T, Context>(dev_ctx, x, dims, out_dtype, keep_dim, out);
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}
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template <typename T, typename Context>
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void NansumStrideKernel(const Context& dev_ctx,
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const DenseTensor& x,
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const IntArray& dims,
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DataType out_dtype,
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bool keep_dim,
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DenseTensor* out) {
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PrepareStridedOut_reduce(out);
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phi::NansumKernel<T, Context>(dev_ctx, x, dims, out_dtype, keep_dim, out);
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}
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template <typename T, typename Context>
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void MeanStrideKernel(const Context& dev_ctx,
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const DenseTensor& x,
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const IntArray& dims,
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bool keep_dim,
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DenseTensor* out) {
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PrepareStridedOut_reduce(out);
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phi::MeanKernel<T, Context>(dev_ctx, x, dims, keep_dim, out);
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}
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} // namespace phi
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using float16 = phi::float16;
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using bfloat16 = phi::bfloat16;
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using complex64 = phi::complex64;
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using complex128 = phi::complex128;
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PD_REGISTER_KERNEL(
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amax, GPU, STRIDED, phi::AMaxStrideKernel, float, double, int, int64_t) {}
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PD_REGISTER_KERNEL(
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amin, GPU, STRIDED, phi::AMinStrideKernel, float, double, int, int64_t) {}
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PD_REGISTER_KERNEL(
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max, GPU, STRIDED, phi::MaxStrideKernel, float, double, int, int64_t) {}
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PD_REGISTER_KERNEL(
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min, GPU, STRIDED, phi::MinStrideKernel, float, double, int, int64_t) {}
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PD_REGISTER_KERNEL(prod,
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GPU,
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STRIDED,
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phi::ProdStrideKernel,
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float,
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double,
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int,
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int64_t,
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phi::float16,
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phi::bfloat16,
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phi::complex64,
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phi::complex128) {}
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PD_REGISTER_KERNEL(any,
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GPU,
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STRIDED,
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phi::AnyStrideKernel,
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float,
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double,
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int,
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int64_t,
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bool,
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complex64,
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complex128) {
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kernel->OutputAt(0).SetDataType(phi::DataType::BOOL);
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}
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PD_REGISTER_KERNEL(all,
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GPU,
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STRIDED,
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phi::AllStrideKernel,
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float,
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double,
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int,
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int64_t,
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bool,
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complex64,
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complex128) {
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kernel->OutputAt(0).SetDataType(phi::DataType::BOOL);
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}
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PD_REGISTER_KERNEL(sum,
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GPU,
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STRIDED,
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phi::SumStrideKernel,
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bool,
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float,
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double,
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phi::float16,
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phi::bfloat16,
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int16_t,
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int,
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int64_t,
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uint8_t,
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int8_t,
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phi::complex64,
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phi::complex128) {
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kernel->OutputAt(0).SetDataType(phi::DataType::UNDEFINED);
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}
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PD_REGISTER_KERNEL(nansum,
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GPU,
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STRIDED,
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phi::NansumStrideKernel,
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bool,
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float,
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double,
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phi::float16,
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phi::bfloat16,
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int8_t,
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uint8_t,
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int16_t,
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int,
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int64_t,
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phi::complex64,
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phi::complex128) {
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kernel->OutputAt(0).SetDataType(phi::DataType::UNDEFINED);
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}
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PD_REGISTER_KERNEL(mean,
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GPU,
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STRIDED,
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phi::MeanStrideKernel,
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float,
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double,
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bool,
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int,
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int64_t,
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phi::float16,
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phi::bfloat16,
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phi::float8_e4m3fn,
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phi::complex64,
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phi::complex128) {}
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#endif
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