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CUTLASS 2.6.1 - functional and performance enhancements to strided DGRAD, fixes, and tuning

Browse Source 
* cutlass 2.6 update

* remove debug prints

* cutlass 2.6.1 (minor update)

* Updated CHANGELOG.

* Minor edit to readme to indicate patch version.

* Minor edit to readme.

Co-authored-by:  Haicheng Wu <haichengw@nvidia.com>, Andrew Kerr <akerr@nvidia.com>
This commit is contained in:
Manish Gupta 2021-09-03 10:26:15 -07:00 committed by GitHub
parent a01feb93d9
commit 6c2f8f2fb8
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GPG Key ID: 4AEE18F83AFDEB23
55 changed files with 317 additions and 315 deletions
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9
CHANGELOG.md
View File

@ -2,6 +2,12 @@
# CUTLASS 2.x
## [2.6.1](https://github.com/NVIDIA/cutlass/releases/tag/v2.6.1) (2021-09-03)
* Arbitrary padding and striding for CUTLASS Strided DGRAD Convolution operator (Analytic Iterators)
* Tuning for GEMMs fused with partial reductions
* Corrections and bug fixes reported by the CUTLASS community
* Thank you for filing these issues!
## [2.6.0](https://github.com/NVIDIA/cutlass/releases/tag/v2.6.0) (2021-07-22)
* Optimal performance when compiled with the [CUDA 11.4 Toolkit](https://developer.nvidia.com/cuda-toolkit)
* Adopt the new L2 prefetch feature in [cp.async](/include/cutlass/arch/memory.h) and [global load](/include/cutlass/arch/memory_sm80.h)
@ -23,7 +29,8 @@
* Many improvements to the epilogue.
* Provide an [option](/include/cutlass/epilogue/threadblock/epilogue.h) to not fully unroll the epilogue to reduce the code size and improve the performance when using complicated elementwise operations
* Performance improvement for FP16 tensor core kernels
* Bug fixes
* Bug fixes
* Enhanced Clang support and the combination of Clang 13 and CUDA 11.4 can build and run kernels from Pascal and Ampere.
* Updated minimum CUDA Toolkit requirement to 10.2
* [CUDA 11.4 Toolkit](https://developer.nvidia.com/cuda-toolkit) recommended
* Corrections and bug fixes reported by the CUTLASS community

8
CMakeLists.txt
View File

@ -168,6 +168,11 @@ if (${CUTLASS_NVCC_VERBOSE})
list(APPEND CUTLASS_CUDA_NVCC_FLAGS -v)
endif()
#
# CUTLASS NAMESPACE
#
set(CUTLASS_NAMESPACE "cutlass" CACHE STRING "Top level namespace of CUTLASS")
set(CUTLASS_NVCC_EMBED_CUBIN ON CACHE BOOL "Embed compiled CUDA kernel binaries into executables.")
set(CUTLASS_NVCC_EMBED_PTX ON CACHE BOOL "Embed compiled PTX into executables.")
set(CUTLASS_NVCC_KEEP OFF CACHE BOOL "Keep intermediate files generated by NVCC.")
@ -383,6 +388,8 @@ function(cutlass_apply_standard_compile_options TARGET)
set(_FLAGS_DEBUG ${__CUTLASS_CUDA_FLAGS_DEBUG} ${__CUTLASS_CUDA_NVCC_FLAGS_DEBUG})
endif()
target_link_libraries(${TARGET} PRIVATE CUTLASS)
target_compile_options(
${TARGET}
PRIVATE
@ -425,6 +432,7 @@ set(CUTLASS_TOOLS_UTIL_INCLUDE_DIR ${CMAKE_CURRENT_SOURCE_DIR}/tools/util/includ
include_directories(${CUTLASS_INCLUDE_DIR})
target_compile_features(CUTLASS INTERFACE cxx_std_11)
target_compile_definitions(CUTLASS INTERFACE CUTLASS_NAMESPACE=${CUTLASS_NAMESPACE})
if (NOT DEFINED CUTLASS_REVISION)

7
README.md
View File

@ -2,7 +2,7 @@
# CUTLASS 2.6
_CUTLASS 2.6 - July 2021_
_CUTLASS 2.6.1 - September 2021_
CUTLASS is a collection of CUDA C++ template abstractions for implementing
high-performance matrix-multiplication (GEMM) at all levels and scales within CUDA.
@ -34,6 +34,8 @@ See the [Quick Start Guide](/media/docs/quickstart.md) to get started quickly.
See the [functionality listing](/media/docs/functionality.md) for the list of operations
supported at each level of the execution model hierarchy.
See the [CHANGELOG](CHANGELOG.md) for descriptions of recent updates.
# What's New in CUTLASS 2.6
CUTLASS 2.6 is a minor update to CUTLASS adding:
- Fused [broadcast](test/unit/gemm/device/gemm_with_broadcast_f16n_f16n_f16n_tensorop_f32_sm75.cu) and [reductions](/test/unit/gemm/device/gemm_with_reduction_f16n_f16n_f16n_tensorop_f32_sm75.cu) in the epilogues of GEMM and Convolution
@ -41,11 +43,12 @@ CUTLASS 2.6 is a minor update to CUTLASS adding:
- [New strided Dgrad](test/unit/conv/device/conv2d_strided_dgrad_implicit_gemm_f16nhwc_f16nhwc_f32nhwc_tensor_op_f32_sm80.cu) implementation offers up to 4x performance improvements over previous strided Dgrad
- 64-bit strides for large tensor allocations
- [General affine layouts](/examples/18_ampere_fp64_tensorop_affine2_gemm/ampere_fp64_tensorop_affine2_gemm.cu) fp64 tensor core and simt GEMM
- [Batched GEMV](/test/unit/gemm/device/gemv.cu) preview implementation
- Enhanced functionality, boosted performance, and bug fixes in the epilogue.
- Optimal performance when compiled with the [CUDA 11.4 Toolkit](https://developer.nvidia.com/cuda-toolkit)
- Adopt new L2 prefetch feature in [ptx instruction](https://docs.nvidia.com/cuda/parallel-thread-execution/index.html#ptx-isa-version-7-4).
- Enhanced Clang support and the combination of Clang 13 and CUDA 11.4 can build and run kernels from Pascal and Ampere.
- Numerous updates from the community (thanks!)
- See the [CHANGELOG](CHANGELOG.md) for more details
# What's New in CUTLASS 2.5
CUTLASS 2.5 is a minor update to CUTLASS adding:

8
examples/13_two_tensor_op_fusion/device/b2b_gemm.h
View File

@ -390,14 +390,6 @@ public:
if (result != cudaSuccess) {
return Status::kErrorInternal;
}
result = cudaFuncSetAttribute(
Kernel<B2bGemmKernel>,
cudaFuncAttributePreferredSharedMemoryCarveout, 100);
if (result != cudaSuccess) {
return Status::kErrorInternal;
}
}
cutlass::Kernel<B2bGemmKernel><<<grid, block, smem_size, stream>>>(params_);

8
examples/13_two_tensor_op_fusion/device/b2b_implicit_gemm_convolution.h
View File

@ -197,14 +197,6 @@ public:
if (result != cudaSuccess) {
return Status::kErrorInternal;
}
result = cudaFuncSetAttribute(
cutlass::Kernel<B2bImplicitGemmKernel>,
cudaFuncAttributePreferredSharedMemoryCarveout, 100);
if (result != cudaSuccess) {
return Status::kErrorInternal;
}
}
return Status::kSuccess;

30
examples/13_two_tensor_op_fusion/threadblock/b2b_mma_multistage.h
View File

@ -395,10 +395,8 @@ public:
for (int stage = 0; stage < Base::kStages - 1;
++stage, --gemm_k_iterations_0) {
if (gemm_k_iterations_0 == 0) {
iterator_A0.clear_mask();
iterator_B0.clear_mask();
}
iterator_A0.clear_mask(gemm_k_iterations_0 == 0);
iterator_B0.clear_mask(gemm_k_iterations_0 == 0);
iterator_A0.set_iteration_index(0);
this->smem_iterator_A0_.set_iteration_index(0);
@ -490,10 +488,8 @@ public:
++this->warp_tile_iterator_A0_;
++this->warp_tile_iterator_B0_;
if (gemm_k_iterations_0 == 0) {
iterator_A0.clear_mask();
iterator_B0.clear_mask();
}
iterator_A0.clear_mask(gemm_k_iterations_0 == 0);
iterator_B0.clear_mask(gemm_k_iterations_0 == 0);
int smem_write_stage_idx = Base::kStages - 1;
int smem_read_stage_idx = 0;
@ -601,10 +597,8 @@ public:
}
--gemm_k_iterations_0;
if (gemm_k_iterations_0 == 0) {
iterator_A0.clear_mask();
iterator_B0.clear_mask();
}
iterator_A0.clear_mask(gemm_k_iterations_0 == 0);
iterator_B0.clear_mask(gemm_k_iterations_0 == 0);
}
// Do any conversions feeding the first stage at the end of the loop so
@ -634,9 +628,7 @@ public:
for (int stage = 0; stage < Base::kStages - 1;
++stage, --gemm_k_iterations_1) {
if (gemm_k_iterations_1 == 0) {
iterator_B1.clear_mask();
}
iterator_B1.clear_mask(gemm_k_iterations_1 == 0);
iterator_B1.set_iteration_index(0);
this->smem_iterator_B1_.set_iteration_index(0);
@ -694,9 +686,7 @@ public:
++warp_tile_iterator_A1_;
++this->warp_tile_iterator_B1_;
if (gemm_k_iterations_1 == 0) {
iterator_B1.clear_mask();
}
iterator_B1.clear_mask(gemm_k_iterations_1 == 0);
smem_write_stage_idx = Base::kStages - 1;
smem_read_stage_idx = 0;
@ -793,9 +783,7 @@ public:
++smem_read_stage_idx;
}
if (gemm_k_iterations_1 == 1) {
iterator_B1.clear_mask();
}
iterator_B1.clear_mask(gemm_k_iterations_1 == 1);
}
// Do any conversions feeding the first stage at the end of the loop so

22
include/cutlass/conv/conv2d_problem_size.h
View File

@ -47,6 +47,7 @@
#include "cutlass/gemm/gemm.h"
#include "cutlass/matrix_coord.h"
#include "cutlass/conv/convolution.h"
#include "cutlass/functional.h"
namespace cutlass {
namespace conv {
@ -485,6 +486,27 @@ int strided_dgrad_tile_m_per_filter(
return tile_m_per_filter;
}
// Computes starting Dx coord (h, w) for given starting filter postion
CUTLASS_HOST_DEVICE
void strided_dgrad_starting_coords(
Conv2dProblemSize const &problem_size,
FastDivmod const &stride_h_divmod, FastDivmod const &stride_w_divmod,
int r, int s,
int &start_h, int &start_w) {
// function locals for remainder by fast divmod
int pad_h_rem_, pad_w_rem_;
// start_h = std::abs(problem_size.stride_h - ((problem_size.pad_h % problem_size.stride_h) - r)) % problem_size.stride_h;
stride_h_divmod.divmod(pad_h_rem_, problem_size.pad_h);
int r_ = std::abs(problem_size.stride_h - (pad_h_rem_ - r));
stride_h_divmod.divmod(start_h, r_);
//start_w = std::abs(problem_size.stride_w - ((problem_size.pad_w % problem_size.stride_w) - s)) % problem_size.stride_w;
stride_w_divmod.divmod(pad_w_rem_, problem_size.pad_w);
int s_ = std::abs(problem_size.stride_w - (pad_w_rem_ - s));
stride_w_divmod.divmod(start_w, s_);
}
} // namespace conv
} // namespace cutlass

8
include/cutlass/conv/device/implicit_gemm_convolution.h
View File

@ -217,14 +217,6 @@ public:
if (result != cudaSuccess) {
return Status::kErrorInternal;
}
result = cudaFuncSetAttribute(
cutlass::Kernel<ImplicitGemmKernel>,
cudaFuncAttributePreferredSharedMemoryCarveout, 100);
if (result != cudaSuccess) {
return Status::kErrorInternal;
}
}
return Status::kSuccess;

11
include/cutlass/conv/kernel/implicit_gemm_convolution_strided_dgrad.h
View File

@ -199,7 +199,8 @@ struct ImplicitGemmConvolutionStridedDgrad {
struct Params {
ConvProblemSize problem_size;
cutlass::gemm::GemmCoord grid_tiled_shape;
FastDivmod filter_s_divmod;
FastDivmod stride_h_divmod;
FastDivmod stride_w_divmod;
int gemm_k_iterations;
typename Mma::IteratorA::Params iterator_A;
typename Mma::IteratorA::Element const *ptr_A;
@ -227,7 +228,8 @@ struct ImplicitGemmConvolutionStridedDgrad {
int *semaphore = nullptr
):
problem_size(args.problem_size),
filter_s_divmod(args.problem_size.stride_w),
stride_h_divmod(args.problem_size.stride_h),
stride_w_divmod(args.problem_size.stride_w),
iterator_A(Mma::IteratorA::getParams(args.problem_size, args.ref_A.layout())),
ptr_A(args.ref_A.data()),
iterator_B(args.problem_size, args.ref_B.layout()),
@ -297,7 +299,7 @@ struct ImplicitGemmConvolutionStridedDgrad {
// int start_s = filter_tile_m % (params.problem_size.stride_w);
int start_r, start_s;
params.filter_s_divmod(start_r, start_s, filter_tile_m);
params.stride_w_divmod(start_r, start_s, filter_tile_m);
typename Mma::FragmentC accumulators;
@ -320,6 +322,7 @@ struct ImplicitGemmConvolutionStridedDgrad {
params.problem_size,
params.ptr_A,
thread_idx,
params.stride_h_divmod, params.stride_w_divmod,
start_r, start_s,
MatrixCoord(
threadblock_tile_idx.m() * Mma::Shape::kM,
@ -386,6 +389,7 @@ struct ImplicitGemmConvolutionStridedDgrad {
params.ptr_D,
ConvOutputIteratorParameter::extent(params.problem_size),
thread_idx,
params.stride_h_divmod, params.stride_w_divmod,
start_r, start_s,
threadblock_offset
);
@ -396,6 +400,7 @@ struct ImplicitGemmConvolutionStridedDgrad {
params.ptr_C,
ConvOutputIteratorParameter::extent(params.problem_size),
thread_idx,
params.stride_h_divmod, params.stride_w_divmod,
start_r, start_s,
threadblock_offset
);

11
include/cutlass/conv/threadblock/conv2d_dgrad_output_gradient_tile_access_iterator_analytic.h
View File

@ -130,7 +130,6 @@ private:
int offset_p_[ThreadMap::Iterations::kStrided];
int offset_q_[ThreadMap::Iterations::kStrided];
public:
CUTLASS_HOST_DEVICE
@ -139,6 +138,7 @@ public:
Conv2dProblemSize const &problem_size,
Element const *ptr,
int thread_idx,
FastDivmod const &stride_h_divmod, FastDivmod const &stride_w_divmod,
int start_r, int start_s,
MatrixCoord const &threadblock_offset = MatrixCoord() // threadblock offset - units are whole CTA tiles
):
@ -164,9 +164,12 @@ public:
}
// Starting h, w positions for filter position in gemm_k=0
int start_h = std::abs((problem_size_.pad_h - filter_r) % problem_size_.stride_h);
int start_w = std::abs((problem_size_.pad_w - filter_s) % problem_size_.stride_w);
int start_h, start_w;
strided_dgrad_starting_coords(
problem_size_,
stride_h_divmod, stride_w_divmod,
filter_r, filter_s,
start_h, start_w);
// Effective P and Q for filter position required for remapping NHW rows
int P = (problem_size_.H - start_h + problem_size_.stride_h - 1) / problem_size_.stride_h;

29
include/cutlass/conv/threadblock/conv2d_tile_iterator.h
View File

@ -200,7 +200,27 @@ private:
public:
/// Constructor
/// Constructor (output gradient (Dy) OperandA ctor)
CUTLASS_HOST_DEVICE
TileIteratorStridedDgrad(
Params const &params,
ConvProblemSize const &problem_size,
Element const *ptr,
int thread_idx,
FastDivmod const &stride_h_divmod, FastDivmod const &stride_w_divmod,
int start_r, int start_s,
MatrixCoord const &threadblock_offset = MatrixCoord()
):
tile_access_iterator_(
params,
problem_size,
ptr,
thread_idx,
stride_h_divmod, stride_w_divmod,
start_r, start_s,
threadblock_offset) { }
/// Constructor (filter (w) OperandB ctor)
CUTLASS_HOST_DEVICE
TileIteratorStridedDgrad(
Params const &params,
@ -210,7 +230,12 @@ public:
int start_r, int start_s,
MatrixCoord const &threadblock_offset = MatrixCoord()
):
tile_access_iterator_(params, problem_size, ptr, thread_idx, start_r, start_s, threadblock_offset) { }
tile_access_iterator_(params,
problem_size,
ptr,
thread_idx,
start_r, start_s,
threadblock_offset) { }
CUTLASS_HOST_DEVICE
static Params getParams(ConvProblemSize const &problem_size, Layout const &layout) {

6
include/cutlass/cutlass.h
View File

@ -31,6 +31,12 @@
////////////////////////////////////////////////////////////////////////////////////////////////////
#ifdef CUTLASS_NAMESPACE
#define cutlass CUTLASS_NAMESPACE
#endif
////////////////////////////////////////////////////////////////////////////////////////////////////
#define CUTLASS_UNUSED(expr) do { (void)(expr); } while (0)
#if defined(_MSC_VER)

2
include/cutlass/epilogue/thread/linear_combination.h
View File

@ -174,12 +174,12 @@ public:
// Convert to destination numeric type
NumericArrayConverter<ElementOutput, ElementCompute, kCount, Round> destination_converter;
ComputeFragment converted_source = source_converter(source);
ComputeFragment converted_accumulator = accumulator_converter(accumulator);
if (Scale == ScaleType::Nothing)
return destination_converter(converted_accumulator);
ComputeFragment converted_source = source_converter(source);
// Perform binary operations
ComputeFragment intermediate;

5
include/cutlass/epilogue/threadblock/default_epilogue_tensor_op.h
View File

@ -309,9 +309,12 @@ struct DefaultEpilogueTensorOp {
kElementsPerAccess
>::Type;
static bool const UseCUDAStore = platform::is_same<ElementOutput, double>::value;
using OutputTileIterator = cutlass::epilogue::threadblock::PredicatedTileIterator<
OutputTileThreadMap,
ElementOutput
ElementOutput,
UseCUDAStore
>;
using AccumulatorFragmentIterator = typename std::conditional<is_complex<ElementOutput>::value,

18
include/cutlass/epilogue/threadblock/predicated_tile_iterator.h
View File

@ -62,7 +62,8 @@ namespace threadblock {
///
template <
typename ThreadMap_, ///< Thread map (conept: OutputTileThreadMap)
typename Element_ ///< Element data type
typename Element_, ///< Element data type
bool UseCUDAStore = false
>
class PredicatedTileIterator {
public:
@ -341,10 +342,17 @@ public:
bool guard = row_guard && mask_.predicates[column];
cutlass::arch::global_store<AccessType, sizeof(AccessType)>(
frag_ptr[frag_row_idx * ThreadMap::Iterations::kColumn + column],
(void *)&memory_pointer[column * ThreadMap::Delta::kColumn / kElementsPerAccess],
guard);
if (UseCUDAStore) {
if (guard) {
memory_pointer[column * ThreadMap::Delta::kColumn / kElementsPerAccess] =
frag_ptr[frag_row_idx * ThreadMap::Iterations::kColumn + column];
}
} else {
cutlass::arch::global_store<AccessType, sizeof(AccessType)>(
frag_ptr[frag_row_idx * ThreadMap::Iterations::kColumn + column],
(void *)&memory_pointer[column * ThreadMap::Delta::kColumn / kElementsPerAccess],
guard);
}
}
if (row + 1 < ThreadMap::Iterations::kRow) {

10
include/cutlass/epilogue/threadblock/predicated_tile_iterator_strided_dgrad.h
View File

@ -222,6 +222,7 @@ public:
Element *pointer,
TensorCoord extent,
int thread_idx,
FastDivmod const &stride_h_divmod, FastDivmod const &stride_w_divmod,
int start_r, int start_s,
TensorCoord threadblock_offset = TensorCoord()
):
@ -238,9 +239,12 @@ public:
s = (params_.problem_size.S - 1 - s);
}
// check if start_h_ and start_w_ are always positive
start_h_ = std::abs((params_.problem_size.pad_h - r) % params_.problem_size.stride_h);
start_w_ = std::abs((params_.problem_size.pad_w - s) % params_.problem_size.stride_w);
// compute starting coordinates in Dx start_h_ and start_w_
strided_dgrad_starting_coords(
params_.problem_size,
stride_h_divmod, stride_w_divmod,
r, s,
start_h_, start_w_);
p_ = (params_.problem_size.H - start_h_ + params_.problem_size.stride_h - 1) / params_.problem_size.stride_h;
q_ = (params_.problem_size.W - start_w_ + params_.problem_size.stride_w - 1) / params_.problem_size.stride_w;

15
include/cutlass/epilogue/warp/tile_iterator_tensor_op_mixed.h
View File

@ -256,20 +256,7 @@ public:
int offset = n * Detail::kLanesInQuad + pointer_offset / Policy::kElementsPerAccess;
#if 0
// Using inline PTX to avoid generic memory
AccessType *smem_ptr = pointers_[ptr_idx];
smem_ptr[offset] = frag_ptr[n];
#else
uint32_t smem_addr = arch::cutlass_get_smem_pointer(ptr);
uint32_t const *data = reinterpret_cast<uint32_t const *>(frag_ptr + n);
uint32_t offset_in_bytes = offset * sizeof(AccessType);
asm volatile(
"{ .reg .u32 smem_ptr; add.u32 smem_ptr, %0, %1; st.shared.v2.u32 [smem_ptr], {%2, %3}; }\n"
: : "r"(smem_addr), "r"(offset_in_bytes), "r"(data[0]), "r"(data[1])
);
#endif
ptr[offset] = frag_ptr[n];
}
}

8
include/cutlass/gemm/device/gemm.h
View File

@ -455,14 +455,6 @@ public:
if (result != cudaSuccess) {
return Status::kErrorInternal;
}
result = cudaFuncSetAttribute(
Kernel<GemmKernel>,
cudaFuncAttributePreferredSharedMemoryCarveout, 100);
if (result != cudaSuccess) {
return Status::kErrorInternal;
}
}
cutlass::Kernel<GemmKernel><<<grid, block, smem_size, stream>>>(params_);

8
include/cutlass/gemm/device/gemm_array.h
View File

@ -445,14 +445,6 @@ public:
if (result != cudaSuccess) {
return Status::kErrorInternal;
}
result = cudaFuncSetAttribute(
Kernel<GemmKernel>,
cudaFuncAttributePreferredSharedMemoryCarveout, 100);
if (result != cudaSuccess) {
return Status::kErrorInternal;
}
}
cutlass::Kernel<GemmKernel><<<grid, block, smem_size, stream>>>(params_);

8
include/cutlass/gemm/device/gemm_batched.h
View File

@ -423,14 +423,6 @@ public:
if (result != cudaSuccess) {
return Status::kErrorInternal;
}
result = cudaFuncSetAttribute(
Kernel<GemmKernel>,
cudaFuncAttributePreferredSharedMemoryCarveout, 100);
if (result != cudaSuccess) {
return Status::kErrorInternal;
}
}
cutlass::Kernel<GemmKernel><<<grid, block, smem_size, stream>>>(params_);

8
include/cutlass/gemm/device/gemm_complex.h
View File

@ -437,14 +437,6 @@ public:
if (result != cudaSuccess) {
return Status::kErrorInternal;
}
result = cudaFuncSetAttribute(
Kernel<GemmKernel>,
cudaFuncAttributePreferredSharedMemoryCarveout, 100);
if (result != cudaSuccess) {
return Status::kErrorInternal;
}
}
cutlass::Kernel<GemmKernel><<<grid, block, smem_size, stream>>>(params_);

8
include/cutlass/gemm/device/gemm_sparse.h
View File

@ -438,14 +438,6 @@ public:
if (result != cudaSuccess) {
return Status::kErrorInternal;
}
result = cudaFuncSetAttribute(
Kernel<GemmKernel>,
cudaFuncAttributePreferredSharedMemoryCarveout, 100);
if (result != cudaSuccess) {
return Status::kErrorInternal;
}
}
return Status::kSuccess;

8
include/cutlass/gemm/device/gemm_splitk_parallel.h
View File

@ -352,14 +352,6 @@ public:
if (result != cudaSuccess) {
return Status::kErrorInternal;
}
result = cudaFuncSetAttribute(
Kernel<GemmKernel>,
cudaFuncAttributePreferredSharedMemoryCarveout, 100);
if (result != cudaSuccess) {
return Status::kErrorInternal;
}
}
Kernel<GemmKernel><<<grid, block, smem_size, stream>>>(gemm_params_);

8
include/cutlass/gemm/device/gemm_universal_base.h
View File

@ -325,14 +325,6 @@ public:
if (result != cudaSuccess) {
return Status::kErrorInternal;
}
result = cudaFuncSetAttribute(
Kernel<GemmKernel>,
cudaFuncAttributePreferredSharedMemoryCarveout, 100);
if (result != cudaSuccess) {
return Status::kErrorInternal;
}
}
return Status::kSuccess;

6
include/cutlass/gemm/kernel/default_gemm_with_k_reduction.h
View File

@ -103,8 +103,8 @@ template <
int Stages,
/// Operation performed by GEMM
typename Operator,
/// Use zfill or predicate for SM80 out-of-bound cp.async
bool UseZfill = false,
/// Use zfill or predicate for out-of-bound cp.async
SharedMemoryClearOption SharedMemoryClear = SharedMemoryClearOption::kNone,
///
typename Enable = void>
struct DefaultGemmWithKReduction {
@ -116,7 +116,7 @@ struct DefaultGemmWithKReduction {
ElementA, LayoutA, kAlignmentA, ElementB, LayoutB, kAlignmentB,
ElementAccumulator, layout::RowMajor, arch::OpClassTensorOp, kReduceKForA, arch::Sm80,
ThreadblockShape, WarpShape, InstructionShape, Stages,
Operator, false, UseZfill>::ThreadblockMma;
Operator, false, SharedMemoryClear>::ThreadblockMma;
static const int kPartitionsK = ThreadblockShape::kK / WarpShape::kK;

1
include/cutlass/gemm/kernel/gemm.h
View File

@ -34,6 +34,7 @@
#include "cutlass/gemm/gemm.h"
#include "cutlass/matrix_coord.h"
#include "cutlass/semaphore.h"
#include "cutlass/arch/arch.h"
/////////////////////////////////////////////////////////////////////////////////////////////////

1
include/cutlass/gemm/threadblock/default_mma.h
View File

@ -130,7 +130,6 @@ struct DefaultMma<ElementA, LayoutA, kAlignmentA, ElementB, LayoutB,
arch::OpClassSimt, ArchTag, ThreadblockShape, WarpShape,
InstructionShape, 2, Operator, false, SharedMemoryClearOption::kNone> {
static_assert(platform::is_same<LayoutC, layout::RowMajor>::value
|| platform::is_same<LayoutC, layout::AffineRankN<2>>::value,
"simt epilogue must be row major");

4
include/cutlass/gemm/threadblock/default_mma_core_with_reduction.h
View File

@ -141,8 +141,8 @@ struct DefaultMmaWithReductionCore {
using SmemLayoutB = typename Base::SmemLayoutB;
using WarpCount = typename Base::WarpCount;
static cutlass::arch::CacheOperation::Kind const kCacheOpA = cutlass::arch::CacheOperation::Always;
static cutlass::arch::CacheOperation::Kind const kCacheOpB = cutlass::arch::CacheOperation::Always;
static cutlass::arch::CacheOperation::Kind const kCacheOpA = CacheOpA;
static cutlass::arch::CacheOperation::Kind const kCacheOpB = CacheOpB;
// Define the warp-level tensor op
using MmaTensorOp = typename cutlass::gemm::warp::DefaultMmaWithReductionTensorOp<

5
include/cutlass/gemm/threadblock/default_mma_with_reduction.h
View File

@ -82,9 +82,10 @@ template <
/// when output layout is interleaved.
bool AccumulatorsInRowMajor = false,
/// Use zfill or predicate for SM80 out-of-bound cp.async
bool UseZfill = false
SharedMemoryClearOption SharedMemoryClear = SharedMemoryClearOption::kNone
>
struct DefaultMmaWithReduction {
static cutlass::arch::CacheOperation::Kind const CacheOpA =
((sizeof_bits<ElementA>::value * kAlignmentA) == 128)
? cutlass::arch::CacheOperation::Global
@ -122,7 +123,7 @@ struct DefaultMmaWithReduction {
typename MmaCore::Shape, IteratorA, typename MmaCore::SmemIteratorA,
MmaCore::kCacheOpA, IteratorB, typename MmaCore::SmemIteratorB,
MmaCore::kCacheOpB, ElementAccumulator, layout::RowMajor,
typename MmaCore::MmaPolicy, Stages, UseZfill>;
typename MmaCore::MmaPolicy, Stages, SharedMemoryClear>;
};
////////////////////////////////////////////////////////////////////////////////

18
include/cutlass/gemm/threadblock/mma_multistage.h
View File

@ -303,10 +303,8 @@ public:
for (int stage = 0; stage < Base::kStages - 1;
++stage, --gemm_k_iterations) {
if (gemm_k_iterations == 0) {
iterator_A.clear_mask();
iterator_B.clear_mask();
}
iterator_A.clear_mask(gemm_k_iterations == 0);
iterator_B.clear_mask(gemm_k_iterations == 0);
iterator_A.set_iteration_index(0);
this->smem_iterator_A_.set_iteration_index(0);
@ -447,10 +445,8 @@ public:
++this->warp_tile_iterator_A_;
++this->warp_tile_iterator_B_;
if (gemm_k_iterations == 0) {
iterator_A.clear_mask();
iterator_B.clear_mask();
}
iterator_A.clear_mask(gemm_k_iterations == 0);
iterator_B.clear_mask(gemm_k_iterations == 0);
int smem_write_stage_idx = Base::kStages - 1;
int smem_read_stage_idx = 0;
@ -558,10 +554,8 @@ public:
}
--gemm_k_iterations;
if (gemm_k_iterations == 0) {
iterator_A.clear_mask();
iterator_B.clear_mask();
}
iterator_A.clear_mask(gemm_k_iterations == 0);
iterator_B.clear_mask(gemm_k_iterations == 0);
}
// Do any conversions feeding the first stage at the end of the loop so

12
include/cutlass/gemm/threadblock/mma_pipelined.h
View File

@ -231,10 +231,8 @@ public:
int smem_write_stage_idx = 1;
// Avoid reading out of bounds
if (gemm_k_iterations <= 1) {
iterator_A.clear_mask();
iterator_B.clear_mask();
}
iterator_A.clear_mask(gemm_k_iterations <= 1);
iterator_B.clear_mask(gemm_k_iterations <= 1);
// Issue loads during the first warp-level matrix multiply-add *AFTER* issuing
// shared memory loads (which have the tighest latency requirement).
@ -302,10 +300,8 @@ public:
++iterator_B;
// Avoid reading out of bounds if this was the last loop iteration
if (gemm_k_iterations <= 2) {
iterator_A.clear_mask();
iterator_B.clear_mask();
}
iterator_A.clear_mask(gemm_k_iterations <= 2);
iterator_B.clear_mask(gemm_k_iterations <= 2);
}
warp_mma(accum, warp_frag_A[warp_mma_k % 2],

30
include/cutlass/gemm/threadblock/mma_planar_complex_multistage.h
View File

@ -370,12 +370,10 @@ public:
for (int stage = 0; stage < Base::kStages - 1;
++stage, --gemm_k_iterations) {
if (gemm_k_iterations == 0) {
iterator_A_real.clear_mask();
iterator_A_imag.clear_mask();
iterator_B_real.clear_mask();
iterator_B_imag.clear_mask();
}
iterator_A_real.clear_mask(gemm_k_iterations == 0);
iterator_A_imag.clear_mask(gemm_k_iterations == 0);
iterator_B_real.clear_mask(gemm_k_iterations == 0);
iterator_B_imag.clear_mask(gemm_k_iterations == 0);
iterator_A_real.set_iteration_index(0);
iterator_A_imag.set_iteration_index(0);
@ -501,12 +499,10 @@ public:
++this->warp_tile_iterator_A_;
++this->warp_tile_iterator_B_;
if (gemm_k_iterations == 0) {
iterator_A_real.clear_mask();
iterator_A_imag.clear_mask();
iterator_B_real.clear_mask();
iterator_B_imag.clear_mask();
}
iterator_A_real.clear_mask(gemm_k_iterations == 0);
iterator_A_imag.clear_mask(gemm_k_iterations == 0);
iterator_B_real.clear_mask(gemm_k_iterations == 0);
iterator_B_imag.clear_mask(gemm_k_iterations == 0);
// Start issuing the first group of the next stage outside of the mainloop
copy_tiles_and_advance(iterator_A_real, iterator_A_imag, iterator_B_real, iterator_B_imag);
@ -611,12 +607,10 @@ public:
}
--gemm_k_iterations;
if (gemm_k_iterations == 0) {
iterator_A_real.clear_mask();
iterator_A_imag.clear_mask();
iterator_B_real.clear_mask();
iterator_B_imag.clear_mask();
}
iterator_A_real.clear_mask(gemm_k_iterations == 0);
iterator_A_imag.clear_mask(gemm_k_iterations == 0);
iterator_B_real.clear_mask(gemm_k_iterations == 0);
iterator_B_imag.clear_mask(gemm_k_iterations == 0);
}
warp_mma_planar_complex(

22
include/cutlass/gemm/threadblock/mma_planar_complex_pipelined.h
View File

@ -308,13 +308,11 @@ public:
int smem_write_stage_idx = 1;
// Avoid reading out of bounds
if (gemm_k_iterations <= 1) {
iterator_A_real.clear_mask();
iterator_A_imag.clear_mask();
iterator_B_real.clear_mask();
iterator_B_imag.clear_mask();
}
iterator_A_real.clear_mask(gemm_k_iterations <= 1);
iterator_A_imag.clear_mask(gemm_k_iterations <= 1);
iterator_B_real.clear_mask(gemm_k_iterations <= 1);
iterator_B_imag.clear_mask(gemm_k_iterations <= 1);
// Issue loads during the first warp-level matrix multiply-add *AFTER* issuing
// shared memory loads (which have the tighest latency requirement).
@ -392,12 +390,10 @@ public:
++iterator_B_imag;
// Avoid reading out of bounds if this was the last loop iteration
if (gemm_k_iterations <= 2) {
iterator_A_real.clear_mask();
iterator_A_imag.clear_mask();
iterator_B_real.clear_mask();
iterator_B_imag.clear_mask();
}
iterator_A_real.clear_mask(gemm_k_iterations <= 2);
iterator_A_imag.clear_mask(gemm_k_iterations <= 2);
iterator_B_real.clear_mask(gemm_k_iterations <= 2);
iterator_B_imag.clear_mask(gemm_k_iterations <= 2);
}
warp_mma_planar_complex(

12
include/cutlass/gemm/threadblock/mma_singlestage.h
View File

@ -196,10 +196,8 @@ public:
Operator warp_mma;
// Avoid reading out of bounds
if (gemm_k_iterations <= 1) {
iterator_A.clear_mask();
iterator_B.clear_mask();
}
iterator_A.clear_mask(gemm_k_iterations <= 1);
iterator_B.clear_mask(gemm_k_iterations <= 1);
//
// Mainloop
@ -247,10 +245,8 @@ public:
++iterator_B;
// Avoid reading out of bounds if this was the last loop iteration
if (gemm_k_iterations <= 2) {
iterator_A.clear_mask();
iterator_B.clear_mask();
}
iterator_A.clear_mask(gemm_k_iterations <= 2);
iterator_B.clear_mask(gemm_k_iterations <= 2);
}
}

24
include/cutlass/gemm/threadblock/mma_sparse_multistage.h
View File

@ -379,11 +379,9 @@ public:
for (int stage = 0; stage < Base::kStages - 1;
++stage, --gemm_k_iterations) {
if (gemm_k_iterations == 0) {
iterator_A.clear_mask();
iterator_B.clear_mask();
iterator_E.clear_mask();
}
iterator_A.clear_mask(gemm_k_iterations == 0);
iterator_B.clear_mask(gemm_k_iterations == 0);
iterator_E.clear_mask(gemm_k_iterations == 0);
iterator_A.set_iteration_index(0);
this->smem_iterator_A_.set_iteration_index(0);
@ -500,11 +498,9 @@ public:
++this->warp_tile_iterator_B_;
++this->warp_tile_iterator_E_;
if (gemm_k_iterations == 0) {
iterator_A.clear_mask();
iterator_B.clear_mask();
iterator_E.clear_mask();
}
iterator_A.clear_mask(gemm_k_iterations == 0);
iterator_B.clear_mask(gemm_k_iterations == 0);
iterator_E.clear_mask(gemm_k_iterations == 0);
int smem_write_stage_idx = Base::kStages - 1;
int smem_read_stage_idx = 0;
@ -637,11 +633,9 @@ public:
}
--gemm_k_iterations;
if (gemm_k_iterations == 0) {
iterator_A.clear_mask();
iterator_B.clear_mask();
iterator_E.clear_mask();
}
iterator_A.clear_mask(gemm_k_iterations == 0);
iterator_B.clear_mask(gemm_k_iterations == 0);
iterator_E.clear_mask(gemm_k_iterations == 0);
}
// Do any conversions feeding the first stage at the end of the loop so

28
include/cutlass/gemm/threadblock/mma_with_reduction_multistage.h
View File

@ -78,7 +78,7 @@ template <
/// Number of stages,
int Stages,
/// Use zfill or predicate for out-of-bound cp.async
bool UseZfill = false,
SharedMemoryClearOption SharedMemoryClear = SharedMemoryClearOption::kNone,
/// Used for partial specialization
typename Enable = bool>
class MmaWithReductionMultistage :
@ -234,7 +234,7 @@ public:
for (int v = 0; v < IteratorA::kAccessesPerVector; ++v) {
auto gmem_ptr = iterator_A.get();
if (UseZfill) {
if (SharedMemoryClear == SharedMemoryClearOption::kZfill) {
cutlass::arch::cp_async_zfill<kSrcBytes, kCacheOpA>(
dst_ptr + v, gmem_ptr, iterator_A.valid());
} else {
@ -269,7 +269,7 @@ public:
for (int v = 0; v < IteratorB::kAccessesPerVector; ++v) {
auto gmem_ptr = iterator_B.get();
if (UseZfill) {
if (SharedMemoryClear == SharedMemoryClearOption::kZfill) {
cutlass::arch::cp_async_zfill<kSrcBytes, kCacheOpB>(
dst_ptr + v, gmem_ptr, iterator_B.valid());
} else {
@ -302,16 +302,14 @@ public:
//
// Prologue
//
// Issue several complete stages
CUTLASS_PRAGMA_UNROLL
for (int stage = 0; stage < Base::kStages - 1;
++stage, --gemm_k_iterations) {
if (gemm_k_iterations == 0) {
iterator_A.clear_mask();
iterator_B.clear_mask();
}
iterator_A.clear_mask(gemm_k_iterations == 0);
iterator_B.clear_mask(gemm_k_iterations == 0);
iterator_A.set_iteration_index(0);
this->smem_iterator_A_.set_iteration_index(0);
@ -403,10 +401,8 @@ public:
++this->warp_tile_iterator_A_;
++this->warp_tile_iterator_B_;
if (gemm_k_iterations == 0) {
iterator_A.clear_mask();
iterator_B.clear_mask();
}
iterator_A.clear_mask(gemm_k_iterations == 0);
iterator_B.clear_mask(gemm_k_iterations == 0);
int smem_write_stage_idx = Base::kStages - 1;
int smem_read_stage_idx = 0;
@ -515,10 +511,8 @@ public:
}
--gemm_k_iterations;
if (gemm_k_iterations == 0) {
iterator_A.clear_mask();
iterator_B.clear_mask();
}
iterator_A.clear_mask(gemm_k_iterations == 0);
iterator_B.clear_mask(gemm_k_iterations == 0);
}
// Do any conversions feeding the first stage at the end of the loop so
@ -532,7 +526,7 @@ public:
}
if (UseZfill) {
if (SharedMemoryClear == SharedMemoryClearOption::kZfill) {
// commit and drain all pending and predicated LDGSTS pnz from the GEMM mainloop
cutlass::arch::cp_async_fence();
cutlass::arch::cp_async_wait<0>();

17
include/cutlass/gemm/warp/mma_tensor_op_fragment_iterator.h
View File

@ -49,7 +49,6 @@ class MmaTensorOpFragmentIterator;
// Partial specialization for col-major accumulator tile
// And Element type is the same as Accumulator Element type
template <
/// Shape of warp tile to load (concept: MatrixShape)
@ -58,13 +57,15 @@ template <
typename AccumulatorShape_,
/// KBlocks columns to compute residual
int KBlocksColumn_,
/// Accumulator Element type
typename ElementAccumulator_,
/// Element type
typename Element_,
/// Shape of one matrix product operation (concept: MatrixShape)
typename InstructionShape_,
/// Output operation on fragment
typename OutputOp_>
class MmaTensorOpFragmentIterator<Shape_, AccumulatorShape_, KBlocksColumn_, Element_, Element_,
class MmaTensorOpFragmentIterator<Shape_, AccumulatorShape_, KBlocksColumn_, ElementAccumulator_, Element_,
cutlass::layout::ColumnMajor,
InstructionShape_, OutputOp_> {
public:
@ -78,6 +79,9 @@ class MmaTensorOpFragmentIterator<Shape_, AccumulatorShape_, KBlocksColumn_, Ele
/// KBlocks columns to compute residual
static int const kKBlockColumn = KBlocksColumn_;
/// Accumulator Element type
using ElementAccumulator = ElementAccumulator_;
/// Element type
using Element = Element_;
@ -143,13 +147,14 @@ public:
using Fragment = Array<Element, Shape::kCount / kThreads>;
/// Accumulator Fragment object
using AccumulatorFragment = Array<Element, AccumulatorShape::kCount / kThreads>;
using AccumulatorFragment = Array<ElementAccumulator, AccumulatorShape::kCount / kThreads>;
private:
/// Internal access type
using AccessType = Array<Element, kElementsPerAccess>;
using AccessType = Array<ElementAccumulator, kElementsPerAccess>;
using FragmentAccessType = Array<Element, kElementsPerAccess>;
private:
//
@ -203,10 +208,10 @@ public:
if (output_op.is_source_needed()) //beta must be zero
assert(0);
AccessType src_fragment;
FragmentAccessType src_fragment;
src_fragment.clear();
AccessType *frag_ptr = reinterpret_cast<AccessType *>(&frag);
FragmentAccessType *frag_ptr = reinterpret_cast<FragmentAccessType *>(&frag);
int index = index_ * MmaIterations::kCount;

6
include/cutlass/matrix.h
View File

@ -14030,15 +14030,15 @@ struct Matrix<Element_, 4, 4> {
/// Returns a perspective projection matrix typical of OpenGL applications
CUTLASS_HOST_DEVICE
static Matrix perspective(Element near, Element far, Element fovH, Element fovV) {
static Matrix perspective(Element near_plane, Element far_plane, Element fovH, Element fovV) {
Element aspect = fovH / fovV;
Element f = Element(cos(fovV)) / Element(fovH);
Element Q = near - far;
Element Q = near_plane - far_plane;
return Matrix(
f / aspect, 0, 0, 0,
0, f, 0, 0,
0, 0, (near + far) / Q, Element(2) * far * near / Q,
0, 0, (near_plane + far_plane) / Q, Element(2) * far_plane * near_plane / Q,
0, 0, -1, 0
);
}

22
include/cutlass/transform/threadblock/predicated_tile_access_iterator.h
View File

@ -245,10 +245,10 @@ class PredicatedTileAccessIteratorPredicates {
/// Clears the predicate set efficiently
CUTLASS_HOST_DEVICE
void clear_mask() {
void clear_mask(bool enable = true) {
CUTLASS_PRAGMA_UNROLL
for (int i = 0; i < kPredicateWordCount; ++i) {
predicates_[i] = 0u;
predicates_[i] = enable ? 0u : predicates_[i];
}
}
@ -551,8 +551,8 @@ class PredicatedTileAccessIterator<Shape_, Element_, layout::PitchLinear,
/// Clears the predicate set efficiently
CUTLASS_HOST_DEVICE
void clear_mask() {
the_predicates.clear_mask();
void clear_mask(bool enable = true) {
the_predicates.clear_mask(enable);
}
/// Clears the predicate set efficiently
@ -741,7 +741,7 @@ class PredicatedTileAccessIterator<Shape_, Element_, layout::ColumnMajor,
/// Clears the predicate set efficiently
CUTLASS_HOST_DEVICE
void clear_mask() { iterator_.clear_mask(); }
void clear_mask(bool enable = true) { iterator_.clear_mask(enable); }
/// Clears the predicate set efficiently
CUTLASS_HOST_DEVICE
@ -922,7 +922,7 @@ class PredicatedTileAccessIterator<Shape_, Element_, layout::RowMajor,
/// Clears the predicate set efficiently
CUTLASS_HOST_DEVICE
void clear_mask() { iterator_.clear_mask(); }
void clear_mask(bool enable = true) { iterator_.clear_mask(enable); }
/// Clears the predicate set efficiently
CUTLASS_HOST_DEVICE
@ -1224,7 +1224,7 @@ class PredicatedTileAccessIterator<Shape_, Element_, layout::AffineRankN<2>,
/// Clears the predicate set efficiently
CUTLASS_HOST_DEVICE
void clear_mask() { the_predicates.clear_mask(); }
void clear_mask(bool enable = true) { the_predicates.clear_mask(enable); }
/// Clears the predicate set efficiently
CUTLASS_HOST_DEVICE
@ -1401,7 +1401,7 @@ class PredicatedTileAccessIterator<Shape_, Element_, layout::AffineRank2ColumnMa
/// Clears the predicate set efficiently
CUTLASS_HOST_DEVICE
void clear_mask() { iterator_.clear_mask(); }
void clear_mask(bool enable = true) { iterator_.clear_mask(enable); }
/// Clears the predicate set efficiently
CUTLASS_HOST_DEVICE
@ -1578,7 +1578,7 @@ class PredicatedTileAccessIterator<Shape_, Element_, layout::AffineRank2RowMajor
/// Clears the predicate set efficiently
CUTLASS_HOST_DEVICE
void clear_mask() { iterator_.clear_mask(); }
void clear_mask(bool enable = true) { iterator_.clear_mask(enable); }
/// Clears the predicate set efficiently
CUTLASS_HOST_DEVICE
@ -1764,7 +1764,7 @@ class PredicatedTileAccessIterator<Shape_, Element_,
/// Clears the predicate set efficiently
CUTLASS_HOST_DEVICE
void clear_mask() { iterator_.clear_mask(); }
void clear_mask(bool enable = true) { iterator_.clear_mask(enable); }
/// Clears the predicate set efficiently
CUTLASS_HOST_DEVICE
@ -1948,7 +1948,7 @@ class PredicatedTileAccessIterator<Shape_, Element_,
/// Clears the predicate set efficiently
CUTLASS_HOST_DEVICE
void clear_mask() { iterator_.clear_mask(); }
void clear_mask(bool enable = true) { iterator_.clear_mask(enable); }
/// Clears the predicate set efficiently
CUTLASS_HOST_DEVICE

8
include/cutlass/transform/threadblock/predicated_tile_access_iterator_2dthreadtile.h
View File

@ -403,10 +403,10 @@ class PredicatedTileAccessIterator2dThreadTile<Shape_, Element_, layout::PitchLi
/// Clears the predicate set efficiently
CUTLASS_HOST_DEVICE
void clear_mask() {
void clear_mask(bool enable = true) {
CUTLASS_PRAGMA_UNROLL
for (int i = 0; i < kPredicateWordCount; ++i) {
predicates_[i] = 0u;
predicates_[i] = enable ? 0u : predicates_[i];
}
}
@ -617,7 +617,7 @@ class PredicatedTileAccessIterator2dThreadTile<Shape_, Element_, layout::ColumnM
/// Clears the predicate set efficiently
CUTLASS_HOST_DEVICE
void clear_mask() { iterator_.clear_mask(); }
void clear_mask(bool enable = true) { iterator_.clear_mask(enable); }
/// Clears the predicate set efficiently
CUTLASS_HOST_DEVICE
@ -796,7 +796,7 @@ class PredicatedTileAccessIterator2dThreadTile<Shape_, Element_, layout::RowMajo
/// Clears the predicate set efficiently
CUTLASS_HOST_DEVICE
void clear_mask() { iterator_.clear_mask(); }
void clear_mask(bool enable = true) { iterator_.clear_mask(enable); }
/// Clears the predicate set efficiently
CUTLASS_HOST_DEVICE

24
include/cutlass/transform/threadblock/predicated_tile_iterator.h
View File

@ -288,7 +288,7 @@ class PredicatedTileIterator<Shape_, Element_, layout::PitchLinear, AdvanceRank,
/// Clears the predicate set efficiently
CUTLASS_HOST_DEVICE
void clear_mask() { address_iterator_.clear_mask(); }
void clear_mask(bool enable = true) { address_iterator_.clear_mask(enable); }
/// Clears the predicate set efficiently
CUTLASS_HOST_DEVICE
@ -530,8 +530,8 @@ public:
/// Clears the predicate set efficiently
CUTLASS_HOST_DEVICE
void clear_mask() {
iterator_.clear_mask();
void clear_mask(bool enable = true) {
iterator_.clear_mask(enable);
}
/// Clears the predicate set efficiently
@ -738,8 +738,8 @@ public:
/// Clears the predicate set efficiently
CUTLASS_HOST_DEVICE
void clear_mask() {
iterator_.clear_mask();
void clear_mask(bool enable = true) {
iterator_.clear_mask(enable);
}
/// Clears the predicate set efficiently
@ -946,7 +946,7 @@ class PredicatedTileIterator<Shape_, Element_, layout::AffineRankN<2>, AdvanceRa
/// Clears the predicate set efficiently
CUTLASS_HOST_DEVICE
void clear_mask() { address_iterator_.clear_mask(); }
void clear_mask(bool enable = true) { address_iterator_.clear_mask(enable); }
/// Clears the predicate set efficiently
CUTLASS_HOST_DEVICE
@ -1184,8 +1184,8 @@ public:
/// Clears the predicate set efficiently
CUTLASS_HOST_DEVICE
void clear_mask() {
iterator_.clear_mask();
void clear_mask(bool enable = true) {
iterator_.clear_mask(enable);
}
/// Clears the predicate set efficiently
@ -1388,8 +1388,8 @@ public:
/// Clears the predicate set efficiently
CUTLASS_HOST_DEVICE
void clear_mask() {
iterator_.clear_mask();
void clear_mask(bool enable = true) {
iterator_.clear_mask(enable);
}
/// Clears the predicate set efficiently
@ -1600,7 +1600,7 @@ class PredicatedTileIterator<Shape_, Element_,
/// Clears the predicate set efficiently
CUTLASS_HOST_DEVICE
void clear_mask() { iterator_.clear_mask(); }
void clear_mask(bool enable = true) { iterator_.clear_mask(enable); }
/// Clears the predicate set efficiently
CUTLASS_HOST_DEVICE
@ -1785,7 +1785,7 @@ class PredicatedTileIterator<Shape_, Element_,
/// Clears the predicate set efficiently
CUTLASS_HOST_DEVICE
void clear_mask() { iterator_.clear_mask(); }
void clear_mask(bool enable = true) { iterator_.clear_mask(enable); }
/// Clears the predicate set efficiently
CUTLASS_HOST_DEVICE

10
include/cutlass/transform/threadblock/predicated_tile_iterator_2dthreadtile.h
View File

@ -293,7 +293,7 @@ class PredicatedTileIterator2dThreadTile<Shape_, Element_, layout::PitchLinear,
/// Clears the predicate set efficiently
CUTLASS_HOST_DEVICE
void clear_mask() { address_iterator_.clear_mask(); }
void clear_mask(bool enable = true) { address_iterator_.clear_mask(enable); }
/// Clears the predicate set efficiently
CUTLASS_HOST_DEVICE
@ -525,8 +525,8 @@ public:
/// Clears the predicate set efficiently
CUTLASS_HOST_DEVICE
void clear_mask() {
iterator_.clear_mask();
void clear_mask(bool enable = true) {
iterator_.clear_mask(enable);
}
/// Clears the predicate set efficiently
@ -721,8 +721,8 @@ public:
/// Clears the predicate set efficiently
CUTLASS_HOST_DEVICE
void clear_mask() {
iterator_.clear_mask();
void clear_mask(bool enable = true) {
iterator_.clear_mask(enable);
}
/// Clears the predicate set efficiently

1
media/docs/profiler.md
View File

@ -103,7 +103,6 @@ Profiling:
--profiling-enabled=<bool> If true, profiling is actually conducted.
Verification:
--verification-enabled=<bool> Whether to perform verification checks.

9
media/docs/quickstart.md
View File

@ -206,9 +206,12 @@ $ cmake .. -DCUTLASS_NVCC_ARCHS="50;53" # compiles for NVIDIA Maxwell G
## Clang
For experimental purposes, CUTLASS may be compiled with
[clang 8.0](https://github.com/llvm/llvm-project/releases/download/llvmorg-8.0.1/clang+llvm-8.0.1-amd64-unknown-freebsd11.tar.xz) using the
For experimental purposes, CUTLASS has been verified to compile with the following versions of Clang and CUDA.
* [clang 8.0](https://github.com/llvm/llvm-project/releases/download/llvmorg-8.0.1/clang+llvm-8.0.1-amd64-unknown-freebsd11.tar.xz) using the
[CUDA 10.0 Toolkit](https://developer.nvidia.com/cuda-10.0-download-archive).
* [clang release/13.x](https://github.com/llvm/llvm-project/tree/release/13.x) using [CUDA 11.4](https://developer.nvidia.com/cuda-toolkit-archive)
At this time, compiling with clang enables the CUTLASS SIMT GEMM kernels (sgemm, dgemm, hgemm, igemm)
but does not enable TensorCores.
@ -216,6 +219,8 @@ but does not enable TensorCores.
$ mkdir build && cd build
$ cmake -DCUDA_COMPILER=clang -DCMAKE_CXX_COMPILER=clang++ ..
# Add -DCMAKE_CXX_FLAGS=-D__NV_NO_HOST_COMPILER_CHECK=1 -DCMAKE_CUDA_FLAGS=-D__NV_NO_HOST_COMPILER_CHECK=1 if compiler
# checks fail during CMake configuration.
$ make test_unit -j
```

4
test/unit/common/cutlass_unit_test.h
View File

@ -26,9 +26,9 @@
#pragma once
#pragma warning (disable : 4068 ) /* disable unknown pragma warnings for vistual studio */
#pragma diag_suppress boolean_controlling_expr_is_constant
#pragma nv_diag_suppress boolean_controlling_expr_is_constant
#include <gtest/gtest.h>
#pragma diag_warning boolean_controlling_expr_is_constant
#pragma nv_diag_warning boolean_controlling_expr_is_constant
#pragma warning( disable : 4503)
/////////////////////////////////////////////////////////////////////////////////////////////////

43
test/unit/conv/device/conv2d_problems.h
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@ -281,6 +281,22 @@ struct TestbedConv2dProblemSizes {
{1, 1} // dilation (dilation_h, dilation_w)
));
conv2d_default_sizes.push_back(cutlass::conv::Conv2dProblemSize(
{1, 8, 8, 8}, // input size (NHWC)
{8, 3, 3, 8}, // filter size (KRSC)
{1, 1, 1, 1}, // padding (pad_h, _, pad_w, _)
{3, 3}, // stride (stride_h, stride_w)
{1, 1} // dilation (dilation_h, dilation_w)
));
conv2d_default_sizes.push_back(cutlass::conv::Conv2dProblemSize(
{1, 16, 16, 8}, // input size (NHWC)
{8, 3, 3, 8}, // filter size (KRSC)
{3, 3, 3, 3}, // padding (pad_h, _, pad_w, _)
{3, 3}, // stride (stride_h, stride_w)
{1, 1} // dilation (dilation_h, dilation_w)
));
////////////////////////////////////////////////////////////////////////////////////
// Medium input size (1x16x16x128), filter size (1x1, 2x2, 3x3, 5x5), stride (1, 1)
////////////////////////////////////////////////////////////////////////////////////
@ -389,6 +405,25 @@ struct TestbedConv2dProblemSizes {
{1, 1} // dilation (dilation_h, dilation_w)
));
////////////////////////////////////////////////////////////////////////////////////
// Medium input size padding > stride, asymmetric filter, padding and striding
////////////////////////////////////////////////////////////////////////////////////
conv2d_default_sizes.push_back(cutlass::conv::Conv2dProblemSize(
{1, 27, 31, 256}, // input size (NHWC)
{512, 3, 3, 256}, // filter size (KRSC)
{5, 5, 7, 7}, // padding (pad_h, _, pad_w, _)
{3, 4}, // stride (stride_h, stride_w)
{1, 1} // dilation (dilation_h, dilation_w)
));
conv2d_default_sizes.push_back(cutlass::conv::Conv2dProblemSize(
{1, 27, 35, 256}, // input size (NHWC)
{512, 7, 5, 256}, // filter size (KRSC)
{11, 11, 7, 7}, // padding (pad_h, _, pad_w, _)
{3, 5}, // stride (stride_h, stride_w)
{1, 1} // dilation (dilation_h, dilation_w)
));
////////////////////////////////////////////////////////////////////////////////////
// Medium input size *mixed* stride (1, 2) and (2, 1),
// filter (3, 3), default padding
@ -419,6 +454,14 @@ struct TestbedConv2dProblemSizes {
{2, 2}, // stride (stride_h, stride_w)
{1, 1} // dilation (dilation_h, dilation_w)
));
conv2d_default_sizes.push_back(cutlass::conv::Conv2dProblemSize(
{1, 32, 32, 16}, // input size (NHWC)
{32, 3, 3, 16}, // filter size (KRSC)
{1, 1, 1, 1}, // padding (pad_h, _, pad_w, _)
{6, 2}, // stride (stride_h, stride_w)
{1, 1} // dilation (dilation_h, dilation_w)
));
conv2d_default_sizes.push_back(cutlass::conv::Conv2dProblemSize(
{32, 32, 32, 32}, // input size (NHWC)

20
test/unit/conv/device/conv2d_strided_dgrad_implicit_gemm_f16nhwc_f16nhwc_f32nhwc_tensor_op_f32_sm80.cu
View File

@ -78,23 +78,15 @@ TEST(SM80_Device_Conv2d_Strided_Dgrad_Analytic_ImplicitGemm_f16nhwc_f16nhwc_f32n
test::conv::device::Conv2dProblemVector problem_size_list;
#if 0 // run specific problem size in the unit test first
problem_size_list.push_back(cutlass::conv::Conv2dProblemSize(
{1, 56, 56, 8}, // input size (NHWC)
{8, 1, 1, 8}, // filter size (KRSC)
{0, 0, 0, 0}, // padding (pad_h, _, pad_w, _)
{2, 2}, // stride (stride_h, stride_w)
{1, 1} // dilation (dilation_h, dilation_w)
{1, 4, 4, 8}, // input size (NHWC)
{8, 3, 3, 8}, // filter size (KRSC)
{0, 0, 0, 0}, // padding (pad_h, _, pad_w, _)
{3, 3}, // stride (stride_h, stride_w)
{1, 1} // dilation (dilation_h, dilation_w)
));
problem_size_list.push_back(cutlass::conv::Conv2dProblemSize(
{1, 55, 55, 8}, // input size (NHWC)
{8, 1, 1, 8}, // filter size (KRSC)
{0, 0, 0, 0}, // padding (pad_h, _, pad_w, _)
{2, 2}, // stride (stride_h, stride_w)
{1, 1} // dilation (dilation_h, dilation_w)
));
#endif
/// Run all unit test sizes with device-level Conv2d instance

1
test/unit/nvrtc/cutlass/nvrtc/environment.h
View File

@ -25,6 +25,7 @@
#pragma once
#include <stddef.h>
#include "cutlass/cutlass.h"
namespace cutlass {
namespace nvrtc {

9
tools/library/scripts/generator.py
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@ -1312,6 +1312,7 @@ def GenerateSM80_TensorOp_16816(manifest, args):
TileDescription([256, 64, 32], 4, [4, 1, 1], math_inst, min_cc, max_cc),
TileDescription([ 64, 256, 32], 4, [1, 4, 1], math_inst, min_cc, max_cc),
TileDescription([128, 128, 32], 3, [2, 2, 1], math_inst, min_cc, max_cc),
TileDescription([128, 128, 32], 4, [2, 2, 1], math_inst, min_cc, max_cc),
TileDescription([128, 128, 32], 5, [2, 2, 1], math_inst, min_cc, max_cc),
TileDescription([128, 64, 32], 6, [2, 2, 1], math_inst, min_cc, max_cc),
TileDescription([ 64, 128, 32], 6, [2, 2, 1], math_inst, min_cc, max_cc),
@ -1392,7 +1393,7 @@ def GenerateSM80_SparseTensorOp_16832(manifest, args):
max_cc = 1024
max_cc_smem_limited = 80
alignment_constraints = [8, 4, 2]
alignment_constraints = [8]
for math_inst in math_instructions:
tile_descriptions = [
@ -1967,6 +1968,8 @@ def GenerateSM80_TensorOp_1688(manifest, args):
TileDescription([256, 64, 16], 4, [4, 1, 1], math_inst, min_cc, max_cc),
TileDescription([ 64, 256, 16], 4, [1, 4, 1], math_inst, min_cc, max_cc),
TileDescription([128, 128, 16], 5, [2, 2, 1], math_inst, min_cc, max_cc),
TileDescription([128, 128, 16], 4, [2, 2, 1], math_inst, min_cc, max_cc),
TileDescription([128, 128, 16], 3, [2, 2, 1], math_inst, min_cc, max_cc),
TileDescription([128, 64, 16], 6, [2, 2, 1], math_inst, min_cc, max_cc),
TileDescription([ 64, 128, 16], 6, [2, 2, 1], math_inst, min_cc, max_cc),
TileDescription([ 64, 64, 16], 10, [2, 2, 1], math_inst, min_cc, max_cc),
@ -2051,6 +2054,8 @@ def GenerateSM80_TensorOp_1688_fast_math(manifest, args):
TileDescription([256, 64, 16], 4, [4, 1, 1], math_inst, min_cc, max_cc),
TileDescription([ 64, 256, 16], 4, [1, 4, 1], math_inst, min_cc, max_cc),
TileDescription([128, 128, 16], 5, [2, 2, 1], math_inst, min_cc, max_cc),
TileDescription([128, 128, 16], 4, [2, 2, 1], math_inst, min_cc, max_cc),
TileDescription([128, 128, 16], 3, [2, 2, 1], math_inst, min_cc, max_cc),
TileDescription([128, 64, 16], 6, [2, 2, 1], math_inst, min_cc, max_cc),
TileDescription([ 64, 128, 16], 6, [2, 2, 1], math_inst, min_cc, max_cc),
TileDescription([ 64, 64, 16], 10, [2, 2, 1], math_inst, min_cc, max_cc),
@ -2100,7 +2105,7 @@ def GenerateSM80_SparseTensorOp_16816_fast_math(manifest, args):
max_cc = 1024
max_cc_smem_limited = 80
alignment_constraints = [4, 2, 1]
alignment_constraints = [4]
for math_inst in math_instructions:
tile_descriptions = [

3
tools/library/scripts/manifest.py
View File

@ -20,7 +20,6 @@ class EmitOperationKindLibrary:
self.generated_path = generated_path
self.kind = kind
self.args = args
self.emitters = {
OperationKind.Gemm: EmitGemmConfigurationLibrary
, OperationKind.Conv2d: EmitConv2dConfigurationLibrary
@ -347,7 +346,7 @@ class Manifest:
with interface_emitters[target](generated_path, self.operation_count, self.args) as iface_emitter:
for operation_kind, configurations in self.operations.items():
iface_emitter.emit(OperationKindNames[operation_kind])
iface_emitter.emit(OperationKindNames[operation_kind])
source_files += iface_emitter.source_files

20
tools/library/src/reference/gemm_reference_operation.h
View File

@ -186,12 +186,6 @@ public:
GemmUniversalConfiguration const &config = *static_cast<GemmUniversalConfiguration const *>(host_workspace);
GemmUniversalArguments const &args = *static_cast<GemmUniversalArguments const *>(arguments);
ElementCompute alpha;
ElementCompute beta;
alpha = *static_cast<ElementCompute const *>(args.alpha);
beta = *static_cast<ElementCompute const *>(args.beta);
TensorRefA ref_A{static_cast<ElementA *>(const_cast<void *>(args.A)), LayoutA(int(config.lda))};
TensorRefB ref_B{static_cast<ElementB *>(const_cast<void *>(args.B)), LayoutB(int(config.ldb))};
TensorRefC ref_C{static_cast<ElementC *>(const_cast<void *>(args.C)), LayoutC(int(config.ldc))};
@ -212,16 +206,16 @@ public:
InnerProductOp
>(
config.problem_size,
alpha,
*static_cast<ElementCompute const *>(args.alpha),
ref_A,
kTransformA,
ref_B,
kTransformB,
beta,
*static_cast<ElementCompute const *>(args.beta),
ref_C,
ref_D,
ElementAccumulator(),
config.batch_count,
((config.mode == library::GemmUniversalMode::kBatched) ? config.batch_count : 1),
args.batch_stride_A,
args.batch_stride_B,
args.batch_stride_C,
@ -245,16 +239,16 @@ public:
InnerProductOp
>(
config.problem_size,
alpha,
*static_cast<ElementCompute const *>(args.alpha),
ref_A,
kTransformA,
ref_B,
kTransformB,
beta,
*static_cast<ElementCompute const *>(args.beta),
ref_C,
ref_D,
ElementAccumulator(),
config.batch_count,
((config.mode == library::GemmUniversalMode::kBatched) ? config.batch_count : 1),
args.batch_stride_A,
args.batch_stride_B,
args.batch_stride_C,
@ -263,7 +257,7 @@ public:
return Status::kSuccess;
}
return Status::kErrorNotSupported;
}
};

2
tools/profiler/src/gemm_operation_profiler.cu
View File

@ -791,7 +791,7 @@ bool GemmOperationProfiler::verify_with_reference_(
handle.set_provider(provider);
Status status = handle.gemm_universal(
library::GemmUniversalMode::kGemm,
problem_.mode,
gemm_workspace_.configuration.problem_size.m(),
gemm_workspace_.configuration.problem_size.n(),
gemm_workspace_.configuration.problem_size.k(),

1
tools/profiler/src/gpu_timer.h
View File

@ -29,6 +29,7 @@
#pragma once
#include <cuda_runtime.h>
#include "cutlass/cutlass.h"
namespace cutlass {
namespace profiler {

4
tools/profiler/src/options.cu
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@ -425,7 +425,9 @@ void Options::Profiling::print_usage(std::ostream &out) const {
<< " Number of ms to sleep between profiling periods (ms).\n\n"
<< " --profiling-enabled=<bool> "
<< " If true, profiling is actually conducted.\n\n";
<< " If true, profiling is actually conducted.\n\n"
;
}
void Options::Profiling::print_options(std::ostream &out, int indent) const {

2
tools/util/include/cutlass/util/command_line.h
View File

@ -32,6 +32,8 @@
#include <cuda_runtime.h>
#include "cutlass/cutlass.h"
namespace cutlass {
/******************************************************************************