leavelet/cutlass
1
0
Fork 0
Code Issues Pull Requests Actions 26 Packages Projects Releases Wiki Activity

CUTLASS 2.3 initial commit (#134)

Browse Source 
CUTLASS 2.3 adds GEMMs targeting Sparse Tensor Cores on the NVIDIA Ampere Architecture, fast SGEMM, and small matrix classes, bug fixes, and performance enhancements.
This commit is contained in:
Andrew Kerr
2020-09-23 14:00:58 -07:00
committed by GitHub
parent 4dac7490e6
commit c53f3339bb
209 changed files with 46922 additions and 1677 deletions
Download Patch File Download Diff File Expand all files Collapse all files
tools/profiler/src/cublas_helpers.cpp
+71 -28
View File
@@ -51,6 +51,18 @@ Status get_cutlass_status(cublasStatus_t cublas) {
return Status::kErrorInternal;
}
/// Converts a cuBLASS status to cutlass::profiler::Disposition
Disposition get_cutlass_disposition(cublasStatus_t cublas_status) {
if (cublas_status == CUBLAS_STATUS_INVALID_VALUE) {
return Disposition::kInvalidProblem;
}
else if (cublas_status == CUBLAS_STATUS_NOT_SUPPORTED) {
return Disposition::kNotSupported;
}
return Disposition::kFailed;
}
/// Maps a CUTLASS tensor layout to a cuBLAS transpose operation
bool get_cublas_transpose_operation(
cublasOperation_t &operation,
@@ -156,7 +168,6 @@ bool get_cublas_datatype(cublasDataType_t &data_type, library::NumericTypeID ele
/// Gets the cublas algorithm given threadblock tile dimensions and math opcode class
cublasGemmAlgo_t get_cublas_gemm_algo(int cta_m, int cta_n, int cta_k, library::OpcodeClassID opcode_class) {
// TODO
return (opcode_class == library::OpcodeClassID::kSimt ?
CUBLAS_GEMM_DEFAULT : CUBLAS_GEMM_DEFAULT_TENSOR_OP);
}
@@ -252,37 +263,69 @@ cublasGemmExDispatcher::cublasGemmExDispatcher(
/// Executes GEMM using these arguments
cublasStatus_t cublasGemmExDispatcher::operator()(cublasHandle_t handle) {
return cublasGemmEx(
handle,
trans_A,
trans_B,
configuration.problem_size.m(),
configuration.problem_size.n(),
configuration.problem_size.k(),
arguments.alpha,
arguments.A,
data_type_A,
int(configuration.lda),
arguments.B,
data_type_B,
int(configuration.ldb),
arguments.beta,
arguments.D,
data_type_C,
int(configuration.ldc),
#if (__CUDA_VER_MAJOR__ >= 11)
compute_type,
#else
compute_data_type,
#endif
algo
);
if (configuration.mode == library::GemmUniversalMode::kBatched) {
return cublasGemmStridedBatchedEx(
handle,
trans_A,
trans_B,
configuration.problem_size.m(),
configuration.problem_size.n(),
configuration.problem_size.k(),
arguments.alpha,
arguments.A,
data_type_A,
int(configuration.lda),
arguments.batch_stride_A,
arguments.B,
data_type_B,
int(configuration.ldb),
arguments.batch_stride_B,
arguments.beta,
arguments.D,
data_type_C,
int(configuration.ldc),
arguments.batch_stride_C,
configuration.batch_count,
#if (__CUDA_VER_MAJOR__ >= 11)
compute_type,
#else
compute_data_type,
#endif
algo
);
}
else {
return cublasGemmEx(
handle,
trans_A,
trans_B,
configuration.problem_size.m(),
configuration.problem_size.n(),
configuration.problem_size.k(),
arguments.alpha,
arguments.A,
data_type_A,
int(configuration.lda),
arguments.B,
data_type_B,
int(configuration.ldb),
arguments.beta,
arguments.D,
data_type_C,
int(configuration.ldc),
#if (__CUDA_VER_MAJOR__ >= 11)
compute_type,
#else
compute_data_type,
#endif
algo
);
}
}
/////////////////////////////////////////////////////////////////////////////////////////////////
} // namespace detail
/////////////////////////////////////////////////////////////////////////////////////////////////
} // namespace profiler
} // namespace cutlass