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cutlass/test/unit/gemm/warp/gemm_sm75.cu

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CUTLASS 2.0 (#62) CUTLASS 2.0 Substantially refactored for - Better performance, particularly for native Turing Tensor Cores - Robust and durable templates spanning the design space - Encapsulated functionality embodying modern C++11 programming techniques - Optimized containers and data types for efficient, generic, portable device code Updates to: - Quick start guide - Documentation - Utilities - CUTLASS Profiler Native Turing Tensor Cores - Efficient GEMM kernels targeting Turing Tensor Cores - Mixed-precision floating point, 8-bit integer, 4-bit integer, and binarized operands Coverage of existing CUTLASS functionality: - GEMM kernels targeting CUDA and Tensor Cores in NVIDIA GPUs - Volta Tensor Cores through native mma.sync and through WMMA API - Optimizations such as parallel reductions, threadblock rasterization, and intra-threadblock reductions - Batched GEMM operations - Complex-valued GEMMs Note: this commit and all that follow require a host compiler supporting C++11 or greater.
2019-11-20 08:55:34 +08:00
/***************************************************************************************************
CUTLASS 2.5
2021-02-26 22:58:26 +08:00
* Copyright (c) 2017-2021, NVIDIA CORPORATION. All rights reserved.
CUTLASS 2.0 (#62) CUTLASS 2.0 Substantially refactored for - Better performance, particularly for native Turing Tensor Cores - Robust and durable templates spanning the design space - Encapsulated functionality embodying modern C++11 programming techniques - Optimized containers and data types for efficient, generic, portable device code Updates to: - Quick start guide - Documentation - Utilities - CUTLASS Profiler Native Turing Tensor Cores - Efficient GEMM kernels targeting Turing Tensor Cores - Mixed-precision floating point, 8-bit integer, 4-bit integer, and binarized operands Coverage of existing CUTLASS functionality: - GEMM kernels targeting CUDA and Tensor Cores in NVIDIA GPUs - Volta Tensor Cores through native mma.sync and through WMMA API - Optimizations such as parallel reductions, threadblock rasterization, and intra-threadblock reductions - Batched GEMM operations - Complex-valued GEMMs Note: this commit and all that follow require a host compiler supporting C++11 or greater.
2019-11-20 08:55:34 +08:00
*
* Redistribution and use in source and binary forms, with or without modification, are permitted
* provided that the following conditions are met:
* * Redistributions of source code must retain the above copyright notice, this list of
* conditions and the following disclaimer.
* * Redistributions in binary form must reproduce the above copyright notice, this list of
* conditions and the following disclaimer in the documentation and/or other materials
* provided with the distribution.
* * Neither the name of the NVIDIA CORPORATION nor the names of its contributors may be used
* to endorse or promote products derived from this software without specific prior written
* permission.
*
* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR
* IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND
* FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE
* FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
* BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS;
* OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT,
* STRICT LIABILITY, OR TOR (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
* OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
*
**************************************************************************************************/
/*! \file
\brief Unit tests for thread-level GEMM
*/
#include "../../common/cutlass_unit_test.h"
#include "cutlass/aligned_buffer.h"
#include "cutlass/half.h"
#include "cutlass/gemm/warp/default_mma_tensor_op.h"
#include "cutlass/core_io.h"
#include "cutlass/util/host_tensor.h"
#include "cutlass/util/tensor_view_io.h"
#include "cutlass/util/reference/host/tensor_fill.h"
#include "cutlass/util/reference/host/tensor_compare.h"
#include "cutlass/util/reference/host/gemm.h"
#include "testbed.h"
#if defined(CUTLASS_ARCH_MMA_SM75_SUPPORTED)
////////////////////////////////////////////////////////////////////////////////
TEST(SM75_warp_gemm_tensor_op_congruous_f16, 128x128x8_32x128x8_16x8x8) {
using Shape = cutlass::gemm::GemmShape<32, 128, 8>;
using InstructionShape = cutlass::gemm::GemmShape<16, 8, 8>;
using Element = cutlass::half_t;
using ElementC = float;
using LayoutA = cutlass::layout::ColumnMajorTensorOpMultiplicandCongruous<
cutlass::sizeof_bits<Element>::value, 64>;
using LayoutB = cutlass::layout::RowMajorTensorOpMultiplicandCongruous<
cutlass::sizeof_bits<Element>::value, 64>;
using MmaTensorOp = typename cutlass::gemm::warp::DefaultMmaTensorOp<
Shape, InstructionShape, Element, LayoutA, Element, LayoutB, ElementC,
cutlass::layout::RowMajor>::Type;
test::gemm::warp::Testbed<MmaTensorOp,
cutlass::gemm::GemmShape<128, 128, 8> >()
.run();
}
////////////////////////////////////////////////////////////////////////////////
TEST(SM75_warp_gemm_tensor_op_congruous_f16, 128x128x32_64x64x32_16x8x8) {
using Shape = cutlass::gemm::GemmShape<64, 64, 32>;
using InstructionShape = cutlass::gemm::GemmShape<16, 8, 8>;
using Element = cutlass::half_t;
using ElementC = float;
using LayoutA = cutlass::layout::ColumnMajorTensorOpMultiplicandCongruous<
cutlass::sizeof_bits<Element>::value, 64>;
using LayoutB = cutlass::layout::RowMajorTensorOpMultiplicandCongruous<
cutlass::sizeof_bits<Element>::value, 64>;
using MmaTensorOp = typename cutlass::gemm::warp::DefaultMmaTensorOp<
Shape, InstructionShape, Element, LayoutA, Element, LayoutB, ElementC,
cutlass::layout::RowMajor>::Type;
test::gemm::warp::Testbed<MmaTensorOp,
cutlass::gemm::GemmShape<128, 128, 32> >()
.run();
}
////////////////////////////////////////////////////////////////////////////////
TEST(SM75_warp_gemm_tensor_op_congruous_f16, 128x128x32_32x32x32_16x8x8) {
using Shape = cutlass::gemm::GemmShape<32, 32, 32>;
using InstructionShape = cutlass::gemm::GemmShape<16, 8, 8>;
using Element = cutlass::half_t;
using ElementC = float;
using LayoutA = cutlass::layout::ColumnMajorTensorOpMultiplicandCongruous<
cutlass::sizeof_bits<Element>::value, 64>;
using LayoutB = cutlass::layout::RowMajorTensorOpMultiplicandCongruous<
cutlass::sizeof_bits<Element>::value, 64>;
using MmaTensorOp = typename cutlass::gemm::warp::DefaultMmaTensorOp<
Shape, InstructionShape, Element, LayoutA, Element, LayoutB, ElementC,
cutlass::layout::RowMajor>::Type;
test::gemm::warp::Testbed<MmaTensorOp,
cutlass::gemm::GemmShape<128, 128, 32> >()
.run();
}
CUTLASS 2.2 (#96) Adds support for NVIDIA Ampere Architecture features. CUDA 11 Toolkit recommended.
2020-06-09 07:17:35 +08:00
////////////////////////////////////////////////////////////////////////////////
CUTLASS 2.0 (#62) CUTLASS 2.0 Substantially refactored for - Better performance, particularly for native Turing Tensor Cores - Robust and durable templates spanning the design space - Encapsulated functionality embodying modern C++11 programming techniques - Optimized containers and data types for efficient, generic, portable device code Updates to: - Quick start guide - Documentation - Utilities - CUTLASS Profiler Native Turing Tensor Cores - Efficient GEMM kernels targeting Turing Tensor Cores - Mixed-precision floating point, 8-bit integer, 4-bit integer, and binarized operands Coverage of existing CUTLASS functionality: - GEMM kernels targeting CUDA and Tensor Cores in NVIDIA GPUs - Volta Tensor Cores through native mma.sync and through WMMA API - Optimizations such as parallel reductions, threadblock rasterization, and intra-threadblock reductions - Batched GEMM operations - Complex-valued GEMMs Note: this commit and all that follow require a host compiler supporting C++11 or greater.
2019-11-20 08:55:34 +08:00
TEST(SM75_warp_gemm_tensor_op_crosswise_f16, 128x128x32_64x64x32_16x8x8) {
using Shape = cutlass::gemm::GemmShape<64, 64, 32>;
using InstructionShape = cutlass::gemm::GemmShape<16, 8, 8>;
using Element = cutlass::half_t;
using ElementC = float;
using LayoutA = cutlass::layout::RowMajorTensorOpMultiplicandCrosswise<
cutlass::sizeof_bits<Element>::value, 32>;
using LayoutB = cutlass::layout::ColumnMajorTensorOpMultiplicandCrosswise<
cutlass::sizeof_bits<Element>::value, 32>;
using MmaTensorOp = typename cutlass::gemm::warp::DefaultMmaTensorOp<
Shape, InstructionShape, Element, LayoutA, Element, LayoutB, ElementC,
cutlass::layout::RowMajor>::Type;
test::gemm::warp::Testbed<MmaTensorOp,
cutlass::gemm::GemmShape<128, 128, 32> >()
.run();
}
////////////////////////////////////////////////////////////////////////////////
TEST(SM75_warp_gemm_tensor_op_crosswise_f16, 128x128x32_64x32x32_16x8x8) {
using Shape = cutlass::gemm::GemmShape<64, 32, 32>;
using InstructionShape = cutlass::gemm::GemmShape<16, 8, 8>;
using Element = cutlass::half_t;
using ElementC = float;
using LayoutA = cutlass::layout::RowMajorTensorOpMultiplicandCrosswise<
cutlass::sizeof_bits<Element>::value, 32>;
using LayoutB = cutlass::layout::ColumnMajorTensorOpMultiplicandCrosswise<
cutlass::sizeof_bits<Element>::value, 32>;
using MmaTensorOp = typename cutlass::gemm::warp::DefaultMmaTensorOp<
Shape, InstructionShape, Element, LayoutA, Element, LayoutB, ElementC,
cutlass::layout::RowMajor>::Type;
test::gemm::warp::Testbed<MmaTensorOp,
cutlass::gemm::GemmShape<128, 128, 32> >()
.run();
}
////////////////////////////////////////////////////////////////////////////////
TEST(SM75_warp_gemm_tensor_op_crosswise_f16, 128x128x32_32x32x32_16x8x8) {
using Shape = cutlass::gemm::GemmShape<32, 32, 32>;
using InstructionShape = cutlass::gemm::GemmShape<16, 8, 8>;
using Element = cutlass::half_t;
using ElementC = float;
using LayoutA = cutlass::layout::RowMajorTensorOpMultiplicandCrosswise<
cutlass::sizeof_bits<Element>::value, 32>;
using LayoutB = cutlass::layout::ColumnMajorTensorOpMultiplicandCrosswise<
cutlass::sizeof_bits<Element>::value, 32>;
using MmaTensorOp = typename cutlass::gemm::warp::DefaultMmaTensorOp<
Shape, InstructionShape, Element, LayoutA, Element, LayoutB, ElementC,
cutlass::layout::RowMajor>::Type;
test::gemm::warp::Testbed<MmaTensorOp,
cutlass::gemm::GemmShape<128, 128, 32> >()
.run();
}
////////////////////////////////////////////////////////////////////////////////
TEST(SM75_warp_gemm_tensor_op_crosswise_f16, 128x128x32_32x16x32_16x8x8) {
using Shape = cutlass::gemm::GemmShape<32, 16, 32>;
using InstructionShape = cutlass::gemm::GemmShape<16, 8, 8>;
using Element = cutlass::half_t;
using ElementC = float;
using LayoutA = cutlass::layout::RowMajorTensorOpMultiplicandCrosswise<
cutlass::sizeof_bits<Element>::value, 32>;
using LayoutB = cutlass::layout::ColumnMajorTensorOpMultiplicandCrosswise<
cutlass::sizeof_bits<Element>::value, 32>;
using MmaTensorOp = typename cutlass::gemm::warp::DefaultMmaTensorOp<
Shape, InstructionShape, Element, LayoutA, Element, LayoutB, ElementC,
cutlass::layout::RowMajor>::Type;
test::gemm::warp::Testbed<MmaTensorOp,
cutlass::gemm::GemmShape<128, 128, 32> >()
.run();
}
////////////////////////////////////////////////////////////////////////////////
TEST(SM75_warp_gemm_tensor_op_crosswise_f16, 128x128x32_16x16x32_16x8x8) {
using Shape = cutlass::gemm::GemmShape<16, 16, 32>;
using InstructionShape = cutlass::gemm::GemmShape<16, 8, 8>;
using Element = cutlass::half_t;
using ElementC = float;
using LayoutA = cutlass::layout::RowMajorTensorOpMultiplicandCrosswise<
cutlass::sizeof_bits<Element>::value, 32>;
using LayoutB = cutlass::layout::ColumnMajorTensorOpMultiplicandCrosswise<
cutlass::sizeof_bits<Element>::value, 32>;
using MmaTensorOp = typename cutlass::gemm::warp::DefaultMmaTensorOp<
Shape, InstructionShape, Element, LayoutA, Element, LayoutB, ElementC,
cutlass::layout::RowMajor>::Type;
test::gemm::warp::Testbed<MmaTensorOp,
cutlass::gemm::GemmShape<128, 128, 32> >()
.run();
}
////////////////////////////////////////////////////////////////////////////////
TEST(SM75_warp_gemm_tensor_op_crosswise_f16, 128x128x64_64x64x64_16x8x8) {
using Shape = cutlass::gemm::GemmShape<64, 64, 64>;
using InstructionShape = cutlass::gemm::GemmShape<16, 8, 8>;
using Element = cutlass::half_t;
using ElementC = float;
using LayoutA = cutlass::layout::RowMajorTensorOpMultiplicandCrosswise<
cutlass::sizeof_bits<Element>::value, 64>;
using LayoutB = cutlass::layout::ColumnMajorTensorOpMultiplicandCrosswise<
cutlass::sizeof_bits<Element>::value, 64>;
using MmaTensorOp = typename cutlass::gemm::warp::DefaultMmaTensorOp<
Shape, InstructionShape, Element, LayoutA, Element, LayoutB, ElementC,
cutlass::layout::RowMajor>::Type;
test::gemm::warp::Testbed<MmaTensorOp,
cutlass::gemm::GemmShape<128, 128, 64> >()
.run();
}
////////////////////////////////////////////////////////////////////////////////
TEST(SM75_warp_gemm_tensor_op_crosswise_f16, 128x128x64_64x32x64_16x8x8) {
using Shape = cutlass::gemm::GemmShape<64, 32, 64>;
using InstructionShape = cutlass::gemm::GemmShape<16, 8, 8>;
using Element = cutlass::half_t;
using ElementC = float;
using LayoutA = cutlass::layout::RowMajorTensorOpMultiplicandCrosswise<
cutlass::sizeof_bits<Element>::value, 64>;
using LayoutB = cutlass::layout::ColumnMajorTensorOpMultiplicandCrosswise<
cutlass::sizeof_bits<Element>::value, 64>;
using MmaTensorOp = typename cutlass::gemm::warp::DefaultMmaTensorOp<
Shape, InstructionShape, Element, LayoutA, Element, LayoutB, ElementC,
cutlass::layout::RowMajor>::Type;
test::gemm::warp::Testbed<MmaTensorOp,
cutlass::gemm::GemmShape<128, 128, 64> >()
.run();
}
////////////////////////////////////////////////////////////////////////////////
TEST(SM75_warp_gemm_tensor_op_crosswise_f16, 128x128x64_32x32x64_16x8x8) {
using Shape = cutlass::gemm::GemmShape<32, 32, 64>;
using InstructionShape = cutlass::gemm::GemmShape<16, 8, 8>;
using Element = cutlass::half_t;
using ElementC = float;
using LayoutA = cutlass::layout::RowMajorTensorOpMultiplicandCrosswise<
cutlass::sizeof_bits<Element>::value, 64>;
using LayoutB = cutlass::layout::ColumnMajorTensorOpMultiplicandCrosswise<
cutlass::sizeof_bits<Element>::value, 64>;
using MmaTensorOp = typename cutlass::gemm::warp::DefaultMmaTensorOp<
Shape, InstructionShape, Element, LayoutA, Element, LayoutB, ElementC,
cutlass::layout::RowMajor>::Type;
test::gemm::warp::Testbed<MmaTensorOp,
cutlass::gemm::GemmShape<128, 128, 64> >()
.run();
}
////////////////////////////////////////////////////////////////////////////////
TEST(SM75_warp_gemm_tensor_op_crosswise_f16, 128x128x64_32x16x64_16x8x8) {
using Shape = cutlass::gemm::GemmShape<32, 16, 64>;
using InstructionShape = cutlass::gemm::GemmShape<16, 8, 8>;
using Element = cutlass::half_t;
using ElementC = float;
using LayoutA = cutlass::layout::RowMajorTensorOpMultiplicandCrosswise<
cutlass::sizeof_bits<Element>::value, 64>;
using LayoutB = cutlass::layout::ColumnMajorTensorOpMultiplicandCrosswise<
cutlass::sizeof_bits<Element>::value, 64>;
using MmaTensorOp = typename cutlass::gemm::warp::DefaultMmaTensorOp<
Shape, InstructionShape, Element, LayoutA, Element, LayoutB, ElementC,
cutlass::layout::RowMajor>::Type;
test::gemm::warp::Testbed<MmaTensorOp,
cutlass::gemm::GemmShape<128, 128, 64> >()
.run();
}
////////////////////////////////////////////////////////////////////////////////
TEST(SM75_warp_gemm_tensor_op_crosswise_f16, 128x128x64_16x16x64_16x8x8) {
using Shape = cutlass::gemm::GemmShape<16, 16, 64>;
using InstructionShape = cutlass::gemm::GemmShape<16, 8, 8>;
using Element = cutlass::half_t;
using ElementC = float;
using LayoutA = cutlass::layout::RowMajorTensorOpMultiplicandCrosswise<
cutlass::sizeof_bits<Element>::value, 64>;
using LayoutB = cutlass::layout::ColumnMajorTensorOpMultiplicandCrosswise<
cutlass::sizeof_bits<Element>::value, 64>;
using MmaTensorOp = typename cutlass::gemm::warp::DefaultMmaTensorOp<
Shape, InstructionShape, Element, LayoutA, Element, LayoutB, ElementC,
cutlass::layout::RowMajor>::Type;
test::gemm::warp::Testbed<MmaTensorOp,
cutlass::gemm::GemmShape<128, 128, 64> >()
.run();
}
CUTLASS 2.2 (#96) Adds support for NVIDIA Ampere Architecture features. CUDA 11 Toolkit recommended.
2020-06-09 07:17:35 +08:00
////////////////////////////////////////////////////////////////////////////////
CUTLASS 2.0 (#62) CUTLASS 2.0 Substantially refactored for - Better performance, particularly for native Turing Tensor Cores - Robust and durable templates spanning the design space - Encapsulated functionality embodying modern C++11 programming techniques - Optimized containers and data types for efficient, generic, portable device code Updates to: - Quick start guide - Documentation - Utilities - CUTLASS Profiler Native Turing Tensor Cores - Efficient GEMM kernels targeting Turing Tensor Cores - Mixed-precision floating point, 8-bit integer, 4-bit integer, and binarized operands Coverage of existing CUTLASS functionality: - GEMM kernels targeting CUDA and Tensor Cores in NVIDIA GPUs - Volta Tensor Cores through native mma.sync and through WMMA API - Optimizations such as parallel reductions, threadblock rasterization, and intra-threadblock reductions - Batched GEMM operations - Complex-valued GEMMs Note: this commit and all that follow require a host compiler supporting C++11 or greater.
2019-11-20 08:55:34 +08:00
TEST(SM75_warp_gemm_tensor_op_crosswise_i8, 128x128x64_64x64x64_8x8x16) {
using Shape = cutlass::gemm::GemmShape<64, 64, 64>;
using InstructionShape = cutlass::gemm::GemmShape<8, 8, 16>;
using Element = int8_t;
using ElementC = int;
using LayoutA = cutlass::layout::RowMajorTensorOpMultiplicandCrosswise<
cutlass::sizeof_bits<Element>::value, 64>;
using LayoutB = cutlass::layout::ColumnMajorTensorOpMultiplicandCrosswise<
cutlass::sizeof_bits<Element>::value, 64>;
using MmaTensorOp = typename cutlass::gemm::warp::DefaultMmaTensorOp<
Shape, InstructionShape, Element, LayoutA, Element, LayoutB, ElementC,
cutlass::layout::RowMajor, cutlass::arch::OpMultiplyAddSaturate>::Type;
test::gemm::warp::Testbed<MmaTensorOp,
cutlass::gemm::GemmShape<128, 128, 64> >()
.run();
}
////////////////////////////////////////////////////////////////////////////////
TEST(SM75_warp_gemm_tensor_op_crosswise_i8, 128x128x64_64x32x64_8x8x16) {
using Shape = cutlass::gemm::GemmShape<64, 32, 64>;
using InstructionShape = cutlass::gemm::GemmShape<8, 8, 16>;
using Element = int8_t;
using ElementC = int;
using LayoutA = cutlass::layout::RowMajorTensorOpMultiplicandCrosswise<
cutlass::sizeof_bits<Element>::value, 64>;
using LayoutB = cutlass::layout::ColumnMajorTensorOpMultiplicandCrosswise<
cutlass::sizeof_bits<Element>::value, 64>;
using MmaTensorOp = typename cutlass::gemm::warp::DefaultMmaTensorOp<
Shape, InstructionShape, Element, LayoutA, Element, LayoutB, ElementC,
cutlass::layout::RowMajor, cutlass::arch::OpMultiplyAddSaturate>::Type;
test::gemm::warp::Testbed<MmaTensorOp,
cutlass::gemm::GemmShape<128, 128, 64> >()
.run();
}
////////////////////////////////////////////////////////////////////////////////
TEST(SM75_warp_gemm_tensor_op_crosswise_i8, 128x128x64_32x32x64_8x8x16) {
using Shape = cutlass::gemm::GemmShape<32, 32, 64>;
using InstructionShape = cutlass::gemm::GemmShape<8, 8, 16>;
using Element = int8_t;
using ElementC = int;
using LayoutA = cutlass::layout::RowMajorTensorOpMultiplicandCrosswise<
cutlass::sizeof_bits<Element>::value, 64>;
using LayoutB = cutlass::layout::ColumnMajorTensorOpMultiplicandCrosswise<
cutlass::sizeof_bits<Element>::value, 64>;
using MmaTensorOp = typename cutlass::gemm::warp::DefaultMmaTensorOp<
Shape, InstructionShape, Element, LayoutA, Element, LayoutB, ElementC,
cutlass::layout::RowMajor, cutlass::arch::OpMultiplyAddSaturate>::Type;
test::gemm::warp::Testbed<MmaTensorOp,
cutlass::gemm::GemmShape<128, 128, 64> >()
.run();
}
////////////////////////////////////////////////////////////////////////////////
TEST(SM75_warp_gemm_tensor_op_crosswise_i8, 128x128x64_32x16x64_8x8x16) {
using Shape = cutlass::gemm::GemmShape<32, 16, 64>;
using InstructionShape = cutlass::gemm::GemmShape<8, 8, 16>;
using Element = int8_t;
using ElementC = int;
using LayoutA = cutlass::layout::RowMajorTensorOpMultiplicandCrosswise<
cutlass::sizeof_bits<Element>::value, 64>;
using LayoutB = cutlass::layout::ColumnMajorTensorOpMultiplicandCrosswise<
cutlass::sizeof_bits<Element>::value, 64>;
using MmaTensorOp = typename cutlass::gemm::warp::DefaultMmaTensorOp<
Shape, InstructionShape, Element, LayoutA, Element, LayoutB, ElementC,
cutlass::layout::RowMajor, cutlass::arch::OpMultiplyAddSaturate>::Type;
test::gemm::warp::Testbed<MmaTensorOp,
cutlass::gemm::GemmShape<128, 128, 64> >()
.run();
}
////////////////////////////////////////////////////////////////////////////////
TEST(SM75_warp_gemm_tensor_op_crosswise_i8, 128x128x64_16x16x64_8x8x16) {
using Shape = cutlass::gemm::GemmShape<16, 16, 64>;
using InstructionShape = cutlass::gemm::GemmShape<8, 8, 16>;
using Element = int8_t;
using ElementC = int;
using LayoutA = cutlass::layout::RowMajorTensorOpMultiplicandCrosswise<
cutlass::sizeof_bits<Element>::value, 64>;
using LayoutB = cutlass::layout::ColumnMajorTensorOpMultiplicandCrosswise<
cutlass::sizeof_bits<Element>::value, 64>;
using MmaTensorOp = typename cutlass::gemm::warp::DefaultMmaTensorOp<
Shape, InstructionShape, Element, LayoutA, Element, LayoutB, ElementC,
cutlass::layout::RowMajor, cutlass::arch::OpMultiplyAddSaturate>::Type;
test::gemm::warp::Testbed<MmaTensorOp,
cutlass::gemm::GemmShape<128, 128, 64> >()
.run();
}
////////////////////////////////////////////////////////////////////////////////
TEST(SM75_warp_gemm_tensor_op_interleaved_i8, 128x128x64_64x64x64_8x8x16) {
using Shape = cutlass::gemm::GemmShape<64, 64, 64>;
using InstructionShape = cutlass::gemm::GemmShape<8, 8, 16>;
using Element = int8_t;
using ElementC = int;
using LayoutA = cutlass::layout::RowMajorTensorOpMultiplicandCrosswise<
cutlass::sizeof_bits<Element>::value, 32>;
using LayoutB = cutlass::layout::ColumnMajorTensorOpMultiplicandCrosswise<
cutlass::sizeof_bits<Element>::value, 32>;
using MmaTensorOp = typename cutlass::gemm::warp::DefaultMmaTensorOp<
Shape, InstructionShape, Element, LayoutA, Element, LayoutB, ElementC,
cutlass::layout::RowMajor, cutlass::arch::OpMultiplyAddSaturate>::Type;
test::gemm::warp::Testbed<MmaTensorOp,
cutlass::gemm::GemmShape<128, 128, 64> >()
.run();
}
////////////////////////////////////////////////////////////////////////////////
TEST(SM75_warp_gemm_tensor_op_interleaved_i8, 128x128x64_64x32x64_8x8x16) {
using Shape = cutlass::gemm::GemmShape<64, 32, 64>;
using InstructionShape = cutlass::gemm::GemmShape<8, 8, 16>;
using Element = int8_t;
using ElementC = int;
using LayoutA = cutlass::layout::RowMajorTensorOpMultiplicandCrosswise<
cutlass::sizeof_bits<Element>::value, 32>;
using LayoutB = cutlass::layout::ColumnMajorTensorOpMultiplicandCrosswise<
cutlass::sizeof_bits<Element>::value, 32>;
using MmaTensorOp = typename cutlass::gemm::warp::DefaultMmaTensorOp<
Shape, InstructionShape, Element, LayoutA, Element, LayoutB, ElementC,
cutlass::layout::RowMajor, cutlass::arch::OpMultiplyAddSaturate>::Type;
test::gemm::warp::Testbed<MmaTensorOp,
cutlass::gemm::GemmShape<128, 128, 64> >()
.run();
}
////////////////////////////////////////////////////////////////////////////////
TEST(SM75_warp_gemm_tensor_op_interleaved_i8, 128x128x64_32x32x64_8x8x16) {
using Shape = cutlass::gemm::GemmShape<32, 32, 64>;
using InstructionShape = cutlass::gemm::GemmShape<8, 8, 16>;
using Element = int8_t;
using ElementC = int;
using LayoutA = cutlass::layout::RowMajorTensorOpMultiplicandCrosswise<
cutlass::sizeof_bits<Element>::value, 32>;
using LayoutB = cutlass::layout::ColumnMajorTensorOpMultiplicandCrosswise<
cutlass::sizeof_bits<Element>::value, 32>;
using MmaTensorOp = typename cutlass::gemm::warp::DefaultMmaTensorOp<
Shape, InstructionShape, Element, LayoutA, Element, LayoutB, ElementC,
cutlass::layout::RowMajor, cutlass::arch::OpMultiplyAddSaturate>::Type;
test::gemm::warp::Testbed<MmaTensorOp,
cutlass::gemm::GemmShape<128, 128, 64> >()
.run();
}
////////////////////////////////////////////////////////////////////////////////
TEST(SM75_warp_gemm_tensor_op_interleaved_i8, 128x128x64_32x16x64_8x8x16) {
using Shape = cutlass::gemm::GemmShape<32, 16, 64>;
using InstructionShape = cutlass::gemm::GemmShape<8, 8, 16>;
using Element = int8_t;
using ElementC = int;
using LayoutA = cutlass::layout::RowMajorTensorOpMultiplicandCrosswise<
cutlass::sizeof_bits<Element>::value, 32>;
using LayoutB = cutlass::layout::ColumnMajorTensorOpMultiplicandCrosswise<
cutlass::sizeof_bits<Element>::value, 32>;
using MmaTensorOp = typename cutlass::gemm::warp::DefaultMmaTensorOp<
Shape, InstructionShape, Element, LayoutA, Element, LayoutB, ElementC,
cutlass::layout::RowMajor, cutlass::arch::OpMultiplyAddSaturate>::Type;
test::gemm::warp::Testbed<MmaTensorOp,
cutlass::gemm::GemmShape<128, 128, 64> >()
.run();
}
////////////////////////////////////////////////////////////////////////////////
TEST(SM75_warp_gemm_tensor_op_interleaved_i8, 128x128x64_16x16x64_8x8x16) {
using Shape = cutlass::gemm::GemmShape<16, 16, 64>;
using InstructionShape = cutlass::gemm::GemmShape<8, 8, 16>;
using Element = int8_t;
using ElementC = int;
using LayoutA = cutlass::layout::RowMajorTensorOpMultiplicandCrosswise<
cutlass::sizeof_bits<Element>::value, 32>;
using LayoutB = cutlass::layout::ColumnMajorTensorOpMultiplicandCrosswise<
cutlass::sizeof_bits<Element>::value, 32>;
using MmaTensorOp = typename cutlass::gemm::warp::DefaultMmaTensorOp<
Shape, InstructionShape, Element, LayoutA, Element, LayoutB, ElementC,
cutlass::layout::RowMajor, cutlass::arch::OpMultiplyAddSaturate>::Type;
test::gemm::warp::Testbed<MmaTensorOp,
cutlass::gemm::GemmShape<128, 128, 64> >()
.run();
}
////////////////////////////////////////////////////////////////////////////////
TEST(SM75_warp_gemm_tensor_op_crosswise_i4, 128x128x128_64x64x128_8x8x32) {
using Shape = cutlass::gemm::GemmShape<64, 64, 128>;
using InstructionShape = cutlass::gemm::GemmShape<8, 8, 32>;
using Element = cutlass::int4b_t;
using ElementC = int;
using LayoutA = cutlass::layout::RowMajorTensorOpMultiplicandCrosswise<
cutlass::sizeof_bits<Element>::value, 128>;
using LayoutB = cutlass::layout::ColumnMajorTensorOpMultiplicandCrosswise<
cutlass::sizeof_bits<Element>::value, 128>;
using MmaTensorOp = typename cutlass::gemm::warp::DefaultMmaTensorOp<
Shape, InstructionShape, Element, LayoutA, Element, LayoutB, ElementC,
cutlass::layout::RowMajor, cutlass::arch::OpMultiplyAddSaturate>::Type;
test::gemm::warp::Testbed<MmaTensorOp,
cutlass::gemm::GemmShape<128, 128, 128> >()
.run();
}
////////////////////////////////////////////////////////////////////////////////
TEST(SM75_warp_gemm_tensor_op_crosswise_i4, 128x128x128_64x32x128_8x8x32) {
using Shape = cutlass::gemm::GemmShape<64, 32, 128>;
using InstructionShape = cutlass::gemm::GemmShape<8, 8, 32>;
using Element = cutlass::int4b_t;
using ElementC = int;
using LayoutA = cutlass::layout::RowMajorTensorOpMultiplicandCrosswise<
cutlass::sizeof_bits<Element>::value, 128>;
using LayoutB = cutlass::layout::ColumnMajorTensorOpMultiplicandCrosswise<
cutlass::sizeof_bits<Element>::value, 128>;
using MmaTensorOp = typename cutlass::gemm::warp::DefaultMmaTensorOp<
Shape, InstructionShape, Element, LayoutA, Element, LayoutB, ElementC,
cutlass::layout::RowMajor, cutlass::arch::OpMultiplyAddSaturate>::Type;
test::gemm::warp::Testbed<MmaTensorOp,
cutlass::gemm::GemmShape<128, 128, 128> >()
.run();
}
////////////////////////////////////////////////////////////////////////////////
TEST(SM75_warp_gemm_tensor_op_crosswise_i4, 128x128x128_32x32x128_8x8x32) {
using Shape = cutlass::gemm::GemmShape<32, 32, 128>;
using InstructionShape = cutlass::gemm::GemmShape<8, 8, 32>;
using Element = cutlass::int4b_t;
using ElementC = int;
using LayoutA = cutlass::layout::RowMajorTensorOpMultiplicandCrosswise<
cutlass::sizeof_bits<Element>::value, 128>;
using LayoutB = cutlass::layout::ColumnMajorTensorOpMultiplicandCrosswise<
cutlass::sizeof_bits<Element>::value, 128>;
using MmaTensorOp = typename cutlass::gemm::warp::DefaultMmaTensorOp<
Shape, InstructionShape, Element, LayoutA, Element, LayoutB, ElementC,
cutlass::layout::RowMajor, cutlass::arch::OpMultiplyAddSaturate>::Type;
test::gemm::warp::Testbed<MmaTensorOp,
cutlass::gemm::GemmShape<128, 128, 128> >()
.run();
}
////////////////////////////////////////////////////////////////////////////////
TEST(SM75_warp_gemm_tensor_op_crosswise_i4, 128x128x128_32x16x128_8x8x32) {
using Shape = cutlass::gemm::GemmShape<32, 16, 128>;
using InstructionShape = cutlass::gemm::GemmShape<8, 8, 32>;
using Element = cutlass::int4b_t;
using ElementC = int;
using LayoutA = cutlass::layout::RowMajorTensorOpMultiplicandCrosswise<
cutlass::sizeof_bits<Element>::value, 128>;
using LayoutB = cutlass::layout::ColumnMajorTensorOpMultiplicandCrosswise<
cutlass::sizeof_bits<Element>::value, 128>;
using MmaTensorOp = typename cutlass::gemm::warp::DefaultMmaTensorOp<
Shape, InstructionShape, Element, LayoutA, Element, LayoutB, ElementC,
cutlass::layout::RowMajor, cutlass::arch::OpMultiplyAddSaturate>::Type;
test::gemm::warp::Testbed<MmaTensorOp,
cutlass::gemm::GemmShape<128, 128, 128> >()
.run();
}
////////////////////////////////////////////////////////////////////////////////
TEST(SM75_warp_gemm_tensor_op_crosswise_i4, 128x128x128_16x16x128_8x8x32) {
using Shape = cutlass::gemm::GemmShape<16, 16, 128>;
using InstructionShape = cutlass::gemm::GemmShape<8, 8, 32>;
using Element = cutlass::int4b_t;
using ElementC = int;
using LayoutA = cutlass::layout::RowMajorTensorOpMultiplicandCrosswise<
cutlass::sizeof_bits<Element>::value, 128>;
using LayoutB = cutlass::layout::ColumnMajorTensorOpMultiplicandCrosswise<
cutlass::sizeof_bits<Element>::value, 128>;
using MmaTensorOp = typename cutlass::gemm::warp::DefaultMmaTensorOp<
Shape, InstructionShape, Element, LayoutA, Element, LayoutB, ElementC,
cutlass::layout::RowMajor, cutlass::arch::OpMultiplyAddSaturate>::Type;
test::gemm::warp::Testbed<MmaTensorOp,
cutlass::gemm::GemmShape<128, 128, 128> >()
.run();
}
////////////////////////////////////////////////////////////////////////////////
TEST(SM75_warp_gemm_tensor_op_interleaved_i4, 128x128x128_64x64x128_8x8x32) {
using Shape = cutlass::gemm::GemmShape<64, 64, 128>;
using InstructionShape = cutlass::gemm::GemmShape<8, 8, 32>;
using Element = cutlass::int4b_t;
using ElementC = int;
using LayoutA = cutlass::layout::RowMajorTensorOpMultiplicandCrosswise<
cutlass::sizeof_bits<Element>::value, 64>;
using LayoutB = cutlass::layout::ColumnMajorTensorOpMultiplicandCrosswise<
cutlass::sizeof_bits<Element>::value, 64>;
using MmaTensorOp = typename cutlass::gemm::warp::DefaultMmaTensorOp<
Shape, InstructionShape, Element, LayoutA, Element, LayoutB, ElementC,
cutlass::layout::RowMajor, cutlass::arch::OpMultiplyAddSaturate>::Type;
test::gemm::warp::Testbed<MmaTensorOp,
cutlass::gemm::GemmShape<128, 128, 128> >()
.run();
}
////////////////////////////////////////////////////////////////////////////////
TEST(SM75_warp_gemm_tensor_op_interleaved_i4, 128x128x128_64x32x128_8x8x32) {
using Shape = cutlass::gemm::GemmShape<64, 32, 128>;
using InstructionShape = cutlass::gemm::GemmShape<8, 8, 32>;
using Element = cutlass::int4b_t;
using ElementC = int;
using LayoutA = cutlass::layout::RowMajorTensorOpMultiplicandCrosswise<
cutlass::sizeof_bits<Element>::value, 64>;
using LayoutB = cutlass::layout::ColumnMajorTensorOpMultiplicandCrosswise<
cutlass::sizeof_bits<Element>::value, 64>;
using MmaTensorOp = typename cutlass::gemm::warp::DefaultMmaTensorOp<
Shape, InstructionShape, Element, LayoutA, Element, LayoutB, ElementC,
cutlass::layout::RowMajor, cutlass::arch::OpMultiplyAddSaturate>::Type;
test::gemm::warp::Testbed<MmaTensorOp,
cutlass::gemm::GemmShape<128, 128, 128> >()
.run();
}
////////////////////////////////////////////////////////////////////////////////
TEST(SM75_warp_gemm_tensor_op_interleaved_i4, 128x128x128_32x32x128_8x8x32) {
using Shape = cutlass::gemm::GemmShape<32, 32, 128>;
using InstructionShape = cutlass::gemm::GemmShape<8, 8, 32>;
using Element = cutlass::int4b_t;
using ElementC = int;
using LayoutA = cutlass::layout::RowMajorTensorOpMultiplicandCrosswise<
cutlass::sizeof_bits<Element>::value, 64>;
using LayoutB = cutlass::layout::ColumnMajorTensorOpMultiplicandCrosswise<
cutlass::sizeof_bits<Element>::value, 64>;
using MmaTensorOp = typename cutlass::gemm::warp::DefaultMmaTensorOp<
Shape, InstructionShape, Element, LayoutA, Element, LayoutB, ElementC,
cutlass::layout::RowMajor, cutlass::arch::OpMultiplyAddSaturate>::Type;
test::gemm::warp::Testbed<MmaTensorOp,
cutlass::gemm::GemmShape<128, 128, 128> >()
.run();
}
////////////////////////////////////////////////////////////////////////////////
TEST(SM75_warp_gemm_tensor_op_interleaved_i4, 128x128x128_32x16x128_8x8x32) {
using Shape = cutlass::gemm::GemmShape<32, 16, 128>;
using InstructionShape = cutlass::gemm::GemmShape<8, 8, 32>;
using Element = cutlass::int4b_t;
using ElementC = int;
using LayoutA = cutlass::layout::RowMajorTensorOpMultiplicandCrosswise<
cutlass::sizeof_bits<Element>::value, 64>;
using LayoutB = cutlass::layout::ColumnMajorTensorOpMultiplicandCrosswise<
cutlass::sizeof_bits<Element>::value, 64>;
using MmaTensorOp = typename cutlass::gemm::warp::DefaultMmaTensorOp<
Shape, InstructionShape, Element, LayoutA, Element, LayoutB, ElementC,
cutlass::layout::RowMajor, cutlass::arch::OpMultiplyAddSaturate>::Type;
test::gemm::warp::Testbed<MmaTensorOp,
cutlass::gemm::GemmShape<128, 128, 128> >()
.run();
}
////////////////////////////////////////////////////////////////////////////////
TEST(SM75_warp_gemm_tensor_op_interleaved_i4, 128x128x128_16x16x128_8x8x32) {
using Shape = cutlass::gemm::GemmShape<16, 16, 128>;
using InstructionShape = cutlass::gemm::GemmShape<8, 8, 32>;
using Element = cutlass::int4b_t;
using ElementC = int;
using LayoutA = cutlass::layout::RowMajorTensorOpMultiplicandCrosswise<
cutlass::sizeof_bits<Element>::value, 64>;
using LayoutB = cutlass::layout::ColumnMajorTensorOpMultiplicandCrosswise<
cutlass::sizeof_bits<Element>::value, 64>;
using MmaTensorOp = typename cutlass::gemm::warp::DefaultMmaTensorOp<
Shape, InstructionShape, Element, LayoutA, Element, LayoutB, ElementC,
cutlass::layout::RowMajor, cutlass::arch::OpMultiplyAddSaturate>::Type;
test::gemm::warp::Testbed<MmaTensorOp,
cutlass::gemm::GemmShape<128, 128, 128> >()
.run();
}
////////////////////////////////////////////////////////////////////////////////
TEST(SM75_warp_gemm_tensor_op_crosswise_b1, 128x128x512_64x64x512_8x8x128) {
using Shape = cutlass::gemm::GemmShape<64, 64, 512>;
using InstructionShape = cutlass::gemm::GemmShape<8, 8, 128>;
using Element = cutlass::uint1b_t;
using ElementC = int;
using LayoutA = cutlass::layout::RowMajorTensorOpMultiplicandCrosswise<
cutlass::sizeof_bits<Element>::value, 512>;
using LayoutB = cutlass::layout::ColumnMajorTensorOpMultiplicandCrosswise<
cutlass::sizeof_bits<Element>::value, 512>;
using MmaTensorOp = typename cutlass::gemm::warp::DefaultMmaTensorOp<
Shape, InstructionShape, Element, LayoutA, Element, LayoutB, ElementC,
cutlass::layout::RowMajor, cutlass::arch::OpXorPopc>::Type;
test::gemm::warp::Testbed<MmaTensorOp,
cutlass::gemm::GemmShape<128, 128, 512>,
cutlass::arch::OpXorPopc>()
.run();
}
////////////////////////////////////////////////////////////////////////////////
TEST(SM75_warp_gemm_tensor_op_crosswise_b1, 128x128x512_64x32x512_8x8x128) {
using Shape = cutlass::gemm::GemmShape<64, 32, 512>;
using InstructionShape = cutlass::gemm::GemmShape<8, 8, 128>;
using Element = cutlass::uint1b_t;
using ElementC = int;
using LayoutA = cutlass::layout::RowMajorTensorOpMultiplicandCrosswise<
cutlass::sizeof_bits<Element>::value, 512>;
using LayoutB = cutlass::layout::ColumnMajorTensorOpMultiplicandCrosswise<
cutlass::sizeof_bits<Element>::value, 512>;
using MmaTensorOp = typename cutlass::gemm::warp::DefaultMmaTensorOp<
Shape, InstructionShape, Element, LayoutA, Element, LayoutB, ElementC,
cutlass::layout::RowMajor, cutlass::arch::OpXorPopc>::Type;
test::gemm::warp::Testbed<MmaTensorOp,
cutlass::gemm::GemmShape<128, 128, 512>,
cutlass::arch::OpXorPopc>()
.run();
}
////////////////////////////////////////////////////////////////////////////////
TEST(SM75_warp_gemm_tensor_op_crosswise_b1, 128x128x512_32x32x512_8x8x128) {
using Shape = cutlass::gemm::GemmShape<32, 32, 512>;
using InstructionShape = cutlass::gemm::GemmShape<8, 8, 128>;
using Element = cutlass::uint1b_t;
using ElementC = int;
using LayoutA = cutlass::layout::RowMajorTensorOpMultiplicandCrosswise<
cutlass::sizeof_bits<Element>::value, 512>;
using LayoutB = cutlass::layout::ColumnMajorTensorOpMultiplicandCrosswise<
cutlass::sizeof_bits<Element>::value, 512>;
using MmaTensorOp = typename cutlass::gemm::warp::DefaultMmaTensorOp<
Shape, InstructionShape, Element, LayoutA, Element, LayoutB, ElementC,
cutlass::layout::RowMajor, cutlass::arch::OpXorPopc>::Type;
test::gemm::warp::Testbed<MmaTensorOp,
cutlass::gemm::GemmShape<128, 128, 512>,
cutlass::arch::OpXorPopc>()
.run();
}
////////////////////////////////////////////////////////////////////////////////
TEST(SM75_warp_gemm_tensor_op_crosswise_b1, 128x128x512_32x16x512_8x8x128) {
using Shape = cutlass::gemm::GemmShape<32, 16, 512>;
using InstructionShape = cutlass::gemm::GemmShape<8, 8, 128>;
using Element = cutlass::uint1b_t;
using ElementC = int;
using LayoutA = cutlass::layout::RowMajorTensorOpMultiplicandCrosswise<
cutlass::sizeof_bits<Element>::value, 512>;
using LayoutB = cutlass::layout::ColumnMajorTensorOpMultiplicandCrosswise<
cutlass::sizeof_bits<Element>::value, 512>;
using MmaTensorOp = typename cutlass::gemm::warp::DefaultMmaTensorOp<
Shape, InstructionShape, Element, LayoutA, Element, LayoutB, ElementC,
cutlass::layout::RowMajor, cutlass::arch::OpXorPopc>::Type;
test::gemm::warp::Testbed<MmaTensorOp,
cutlass::gemm::GemmShape<128, 128, 512>,
cutlass::arch::OpXorPopc>()
.run();
}
////////////////////////////////////////////////////////////////////////////////
TEST(SM75_warp_gemm_tensor_op_crosswise_b1, 128x128x512_16x16x512_8x8x128) {
using Shape = cutlass::gemm::GemmShape<16, 16, 512>;
using InstructionShape = cutlass::gemm::GemmShape<8, 8, 128>;
using Element = cutlass::uint1b_t;
using ElementC = int;
using LayoutA = cutlass::layout::RowMajorTensorOpMultiplicandCrosswise<
cutlass::sizeof_bits<Element>::value, 512>;
using LayoutB = cutlass::layout::ColumnMajorTensorOpMultiplicandCrosswise<
cutlass::sizeof_bits<Element>::value, 512>;
using MmaTensorOp = typename cutlass::gemm::warp::DefaultMmaTensorOp<
Shape, InstructionShape, Element, LayoutA, Element, LayoutB, ElementC,
cutlass::layout::RowMajor, cutlass::arch::OpXorPopc>::Type;
test::gemm::warp::Testbed<MmaTensorOp,
cutlass::gemm::GemmShape<128, 128, 512>,
cutlass::arch::OpXorPopc>()
.run();
}
////////////////////////////////////////////////////////////////////////////////
#endif
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