#include "src/cuda/cutlass/singleton.h"
#include "src/cuda/handle.h"
#include "src/cuda/matrix_mul/algos.h"
#include "src/cuda/utils.h"
#if CUDA_VERSION >= 9020
using namespace megdnn;
using namespace cuda;
const void* MatrixMulForwardImpl::AlgoFloat32SIMT::get_available_op(
const SizeArgs& args) const {
using namespace cutlass::library;
auto&& param = args.opr->param();
auto layoutA =
param.transposeA ? LayoutTypeID::kColumnMajor : LayoutTypeID::kRowMajor;
auto layoutB =
param.transposeB ? LayoutTypeID::kColumnMajor : LayoutTypeID::kRowMajor;
int alignment = min_alignment_requirement();
GemmKey key{
NumericTypeID::kF32,
layoutA,
NumericTypeID::kF32,
layoutB,
NumericTypeID::kF32,
LayoutTypeID::kRowMajor,
NumericTypeID::kF32,
m_algo_param.threadblock_m,
m_algo_param.threadblock_n,
m_algo_param.threadblock_k,
m_algo_param.warp_m,
m_algo_param.warp_n,
m_algo_param.warp_k,
1,
1,
1,
2,
alignment,
alignment,
SplitKMode::kNone};
return (void*)Singleton::get().operation_table.find_op(key);
}
bool MatrixMulForwardImpl::AlgoFloat32SIMT::is_available(const SizeArgs& args) const {
bool available = args.opr->param().format == param::MatrixMul::Format::DEFAULT &&
args.layout_a.dtype == dtype::Float32() &&
args.layout_b.dtype == dtype::Float32() &&
args.layout_c.dtype == dtype::Float32();
int n = args.layout_c.shape[1];
auto&& device_prop = cuda::current_device_prop();
int y_grid_limit = device_prop.maxGridSize[1];
available &=
((n + m_algo_param.threadblock_n - 1) / m_algo_param.threadblock_n <=
y_grid_limit);
available &= (get_available_op(args) != nullptr);
return available;
}
size_t MatrixMulForwardImpl::AlgoFloat32SIMT::get_workspace_in_bytes(
const SizeArgs& args) const {
return 0_z;
}
void MatrixMulForwardImpl::AlgoFloat32SIMT::do_exec(const ExecArgs& args) const {
int64_t lda = args.tensor_a.layout.stride[0], ldb = args.tensor_b.layout.stride[0],
ldc = args.tensor_c.layout.stride[0];
auto&& param = args.opr->param();
int m = args.tensor_c.layout.shape[0], n = args.tensor_c.layout.shape[1],
k = args.tensor_a.layout.shape[param.transposeA ? 0 : 1];
cutlass::gemm::GemmCoord problem_size{m, n, k};
auto&& stream = cuda_stream(args.opr->handle());
int* workspace = reinterpret_cast<int*>(args.workspace.raw_ptr);
float alpha = 1.f, beta = 0.f;
using namespace cutlass::library;
const Operation* op = (const Operation*)get_available_op(args);
GemmArguments gemm_args{
problem_size,
args.tensor_a.raw_ptr(),
args.tensor_b.raw_ptr(),
args.tensor_c.raw_ptr(),
args.tensor_c.raw_ptr(),
lda,
ldb,
ldc,
ldc,
1,
&alpha,
&beta};
cutlass_check(op->run(&gemm_args, workspace, stream));
}
#endif