wehub-resource-sync
103 lines
3.8 KiB
C++
103 lines
3.8 KiB
C++
// 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/lu_solve_kernel.h"
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#include "paddle/phi/backends/cpu/cpu_context.h"
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#include "paddle/phi/core/enforce.h"
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#include "paddle/phi/core/kernel_registry.h"
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#include "paddle/phi/kernels/funcs/lapack/lapack_function.h"
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#include "paddle/phi/kernels/impl/lu_kernel_impl.h"
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namespace phi {
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template <typename T, typename Context>
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void LuSolveKernel(const Context& dev_ctx,
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const DenseTensor& b,
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const DenseTensor& lu,
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const DenseTensor& pivots,
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const std::string& trans,
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DenseTensor* out) {
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// Get lu matrix dimensions
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auto lu_dims = lu.dims();
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// Get x matrix dimensions
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auto x_dims = b.dims();
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// Allocate output tensor
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dev_ctx.template Alloc<T>(out);
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// Copy RHS data to output (will be overwritten with solution)
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*out = Transpose2DTo6D<Context, T>(dev_ctx, b);
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DenseTensor tem_lu = Transpose2DTo6D<Context, T>(dev_ctx, lu);
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// Prepare LAPACK parameters
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char trans_char = (trans == "N") ? 'N' : ((trans == "T") ? 'T' : 'C');
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auto n_last_dim = lu_dims[lu_dims.size() - 1];
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PADDLE_ENFORCE_LE_INT_MAX(
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n_last_dim,
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"TODO(large-tensor): LAPACK input n does not support int64 overflow.");
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int n_int = static_cast<int>(n_last_dim);
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auto nrhs_last_dim = x_dims[x_dims.size() - 1];
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PADDLE_ENFORCE_LE_INT_MAX(nrhs_last_dim,
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"TODO(large-tensor): LAPACK nrhs does not "
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"support int64 overflow.");
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int nrhs_int = static_cast<int>(nrhs_last_dim);
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int lda = std::max(1, n_int); // Leading dimension of A (LU matrix)
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int ldb = std::max(1, n_int); // Leading dimension of B (RHS/solution matrix)
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int info = 0;
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auto outdims = out->dims();
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auto outrank = outdims.size();
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auto batchsize_64 = product(slice_ddim(outdims, 0, outrank - 2));
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PADDLE_ENFORCE_LE_INT_MAX(
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batchsize_64,
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"TODO(large-tensor): LAPACK batch size does not support int64 overflow.");
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int batchsize = static_cast<int>(batchsize_64);
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auto out_data = out->data<T>();
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auto lu_data = tem_lu.data<T>();
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auto pivots_data =
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reinterpret_cast<int*>(const_cast<int*>(pivots.data<int>()));
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for (int i = 0; i < batchsize; i++) {
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auto* out_data_item = &out_data[i * lda * nrhs_int];
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auto* lu_data_item = &lu_data[i * ldb * n_int];
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auto* pivots_data_item = &pivots_data[i * n_int];
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funcs::lapackLuSolve<T>(trans_char,
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n_int,
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nrhs_int,
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lu_data_item,
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lda,
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pivots_data_item,
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out_data_item,
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ldb,
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&info);
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PADDLE_ENFORCE_EQ(
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info,
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0,
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common::errors::PreconditionNotMet(
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"LU solve failed with error code %d. Check if matrix is singular.",
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info));
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}
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*out = Transpose2DTo6D<Context, T>(dev_ctx, *out);
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}
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} // namespace phi
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PD_REGISTER_KERNEL(lu_solve,
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CPU,
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ALL_LAYOUT,
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phi::LuSolveKernel,
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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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