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// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
#include "paddle/phi/backends/gpu/gpu_context.h"
#include "paddle/phi/core/ddim.h"
#include "paddle/phi/core/kernel_registry.h"
#include "paddle/phi/kernels/empty_kernel.h"
#include "paddle/phi/kernels/expand_kernel.h"
#include "paddle/phi/kernels/funcs/blas/blas.h"
#include "paddle/phi/kernels/funcs/common_shape.h"
#include "paddle/phi/kernels/triangular_solve_kernel.h"
// See Note [ Why still include the fluid headers? ]
#include "paddle/fluid/memory/allocation/allocator.h"
#include "paddle/fluid/memory/memory.h"
namespace phi {
template <typename T, typename Context>
void TriangularSolveKernel(const Context& dev_ctx,
const DenseTensor& x,
const DenseTensor& y,
bool upper,
bool transpose,
bool unitriangular,
DenseTensor* out) {
// get broadcast dim
std::vector<int64_t> x_bst_dims_vec;
std::vector<int64_t> y_bst_dims_vec;
std::tie(x_bst_dims_vec, y_bst_dims_vec) =
funcs::MatrixGetBroadcastDims(x, y);
int x_bst_ndim = x_bst_dims_vec.size();
int y_bst_ndim = y_bst_dims_vec.size();
// Tensor broadcast to 'out' and temp 'x_bst'
IntArray x_bst_dims(x_bst_dims_vec);
DenseTensor x_bst = phi::Empty<T, Context>(dev_ctx, x_bst_dims);
const T* x_bst_data = x_bst.data<T>();
ExpandKernel<T, Context>(dev_ctx, x, x_bst_dims, &x_bst);
out->Resize(phi::make_ddim(y_bst_dims_vec));
T* out_data = dev_ctx.template Alloc<T>(out);
IntArray y_bst_dims(y_bst_dims_vec);
ExpandKernel<T, Context>(dev_ctx, y, y_bst_dims, out);
// calculate use cublas library
CBLAS_UPLO uplo = upper ? CblasUpper : CblasLower;
CBLAS_TRANSPOSE transA = transpose ? CblasTrans : CblasNoTrans;
CBLAS_DIAG diag = unitriangular ? CblasUnit : CblasNonUnit;
int M = static_cast<int>(y_bst_dims_vec[y_bst_ndim - 2]);
int N = static_cast<int>(y_bst_dims_vec[y_bst_ndim - 1]);
auto lda = std::max(1, M);
auto ldb = std::max(1, N);
int batch_size = 1;
for (int i = 0; i < x_bst_ndim - 2; i++) {
batch_size *= x_bst_dims_vec[i];
}
auto blas = phi::funcs::GetBlas<GPUContext, T>(dev_ctx);
if (batch_size <= 8 && M >= 64) {
for (auto i = 0; i < batch_size; i++) {
blas.TRSM(CblasLeft,
uplo,
transA,
diag,
M,
N,
T(1),
x_bst_data + i * M * M,
lda,
out_data + i * N * M,
ldb);
}
} else {
std::vector<const T*> cpu_ptrs(batch_size * 2);
for (int i = 0; i < batch_size; ++i) {
cpu_ptrs[i] = x_bst_data + i * M * M;
cpu_ptrs[i + batch_size] = out_data + i * M * N;
}
// Copy the addresses of A and tmp_b from host to device.
paddle::memory::allocation::AllocationPtr tmp_gpu_ptrs_data =
paddle::memory::Alloc(dev_ctx, cpu_ptrs.size() * sizeof(T*));
paddle::memory::Copy(dev_ctx.GetPlace(),
tmp_gpu_ptrs_data->ptr(),
paddle::platform::CPUPlace(),
static_cast<void*>(cpu_ptrs.data()),
cpu_ptrs.size() * sizeof(T*),
dev_ctx.stream());
const T** gpu_a_ptrs =
reinterpret_cast<const T**>(tmp_gpu_ptrs_data->ptr());
T** gpu_b_ptrs =
reinterpret_cast<T**>(tmp_gpu_ptrs_data->ptr()) + batch_size;
blas.BatchedTRSM(CblasLeft,
uplo,
transA,
diag,
M,
N,
static_cast<T>(1.0),
gpu_a_ptrs,
lda,
gpu_b_ptrs,
ldb,
batch_size);
}
}
} // namespace phi
PD_REGISTER_KERNEL(triangular_solve,
GPU,
ALL_LAYOUT,
phi::TriangularSolveKernel,
float,
double) {}