wehub-resource-sync
130 lines
4.6 KiB
Plaintext
130 lines
4.6 KiB
Plaintext
// 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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#include "paddle/phi/kernels/eig_kernel.h"
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#include "paddle/phi/backends/context_pool.h"
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#include "paddle/phi/common/place.h"
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#include "paddle/phi/core/kernel_registry.h"
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#include "paddle/phi/kernels/cpu/eig.h"
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namespace phi {
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template <typename T, typename Context>
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void EigKernel(const Context& dev_ctx,
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const DenseTensor& x,
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DenseTensor* out_w,
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DenseTensor* out_v) {
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dev_ctx.template Alloc<dtype::Complex<T>>(out_w);
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dev_ctx.template Alloc<dtype::Complex<T>>(out_v);
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if (x.numel() == 0) {
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return;
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}
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auto cpu_place = CPUPlace();
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DeviceContextPool& pool = DeviceContextPool::Instance();
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auto* cpu_ctx = static_cast<CPUContext*>(pool.Get(cpu_place));
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// prepare cpu Tensor here, since magma requires output on cpu
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DenseTensor out_w_cpu, out_v_cpu;
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out_w_cpu.Resize(out_w->dims());
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(*cpu_ctx).template Alloc<dtype::Complex<T>>(&out_w_cpu);
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out_v_cpu.Resize(x.dims());
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(*cpu_ctx).template Alloc<dtype::Complex<T>>(&out_v_cpu);
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if (!IsComplexType(x.dtype())) {
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// output still be complex though input is real
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int batch_count = BatchCount(x);
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int order = static_cast<int>(x.dims()[x.dims().size() - 1]);
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DenseTensor real_w_cpu, real_v_cpu;
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std::vector<int64_t> real_w_dim = vectorize<int64_t>(out_w->dims());
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real_w_dim.back() *= 2;
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real_w_cpu.Resize(real_w_dim);
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(*cpu_ctx).template Alloc<phi::dtype::Real<T>>(&real_w_cpu);
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real_v_cpu.Resize(x.dims());
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(*cpu_ctx).template Alloc<dtype::Real<T>>(&real_v_cpu);
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ApplyEigKernelMagma<dtype::Real<T>, Context>(
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dev_ctx, x, &real_w_cpu, &real_v_cpu);
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// 1. extract real part & imag part from real_w_cpu
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DenseTensor real_part_cpu = funcs::Slice<dtype::Real<T>>(
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(*cpu_ctx), real_w_cpu, {-1}, {0}, {order});
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DenseTensor imag_part_cpu = funcs::Slice<dtype::Real<T>>(
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(*cpu_ctx), real_w_cpu, {-1}, {order}, {order * 2});
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// 2. construct complex values
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auto* real_part_data = real_part_cpu.data<dtype::Real<T>>();
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auto* imag_part_data = imag_part_cpu.data<dtype::Real<T>>();
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int64_t out_w_numel = static_cast<int64_t>(out_w->numel());
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funcs::ForRange<CPUContext> for_range((*cpu_ctx), out_w_numel);
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funcs::RealImagToComplexFunctor<dtype::Complex<T>> functor(
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real_part_data,
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imag_part_data,
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out_w_cpu.data<dtype::Complex<T>>(),
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out_w_numel);
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for_range(functor);
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// 3. construct complex vectors
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DenseTensor real_v_trans_cpu =
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TransposeLast2Dim<dtype::Real<T>, CPUContext>((*cpu_ctx), real_v_cpu);
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DenseTensor out_v_trans_cpu;
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out_v_trans_cpu.Resize(x.dims());
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(*cpu_ctx).template Alloc<dtype::Complex<T>>(&out_v_trans_cpu);
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ConstructComplexVectors<dtype::Real<T>, dtype::Complex<T>, CPUContext>(
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&out_v_trans_cpu,
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out_w_cpu,
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real_v_trans_cpu,
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(*cpu_ctx),
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batch_count,
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order);
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TransposeTwoAxis<dtype::Complex<T>, CPUContext>(out_v_trans_cpu,
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&out_v_cpu,
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x.dims().size() - 1,
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x.dims().size() - 2,
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(*cpu_ctx));
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} else {
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ApplyEigKernelMagma<T, Context>(dev_ctx, x, &out_w_cpu, &out_v_cpu);
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}
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// copy result from cpu to gpu tensor
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Copy(dev_ctx, out_w_cpu, GPUPlace(), false, out_w);
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Copy(dev_ctx, out_v_cpu, GPUPlace(), false, out_v);
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}
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} // namespace phi
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#ifdef PADDLE_WITH_MAGMA
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PD_REGISTER_KERNEL(eig,
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GPU,
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ALL_LAYOUT,
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phi::EigKernel,
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float,
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double,
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phi::complex64,
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phi::complex128) {
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if (kernel_key.dtype() == phi::DataType::FLOAT32 ||
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kernel_key.dtype() == phi::DataType::FLOAT64) {
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kernel->OutputAt(0).SetDataType(phi::dtype::ToComplex(kernel_key.dtype()));
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kernel->OutputAt(1).SetDataType(phi::dtype::ToComplex(kernel_key.dtype()));
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}
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}
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#endif
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