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epilogue_tensor_op.cu
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/***************************************************************************************************
* Copyright (c) 2017 - 2022 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
* SPDX-License-Identifier: BSD-3-Clause
*
* Redistribution and use in source and binary forms, with or without
* modification, are permitted provided that the following conditions are met:
*
* 1. Redistributions of source code must retain the above copyright notice, this
* list of conditions and the following disclaimer.
*
* 2. 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.
*
* 3. Neither the name of the copyright holder 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 THE COPYRIGHT HOLDER OR CONTRIBUTORS 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 TORT (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 <fstream>
#include "../../common/cutlass_unit_test.h"
#include "cutlass/aligned_buffer.h"
#include "cutlass/half.h"
#include "cutlass/epilogue/thread/linear_combination.h"
#include "cutlass/epilogue/thread/linear_combination_clamp.h"
#include "cutlass/gemm/warp/default_mma_tensor_op.h"
#include "cutlass/epilogue/threadblock/default_epilogue_tensor_op.h"
#include "cutlass/util/host_tensor.h"
#include "cutlass/util/tensor_view_io.h"
#include "cutlass/util/reference/host/tensor_fill.h"
#include "testbed.h"
/////////////////////////////////////////////////////////////////////////////////////////////////
TEST(SM75_Epilogue_threadblock_epilogue, s4_tensor_op_64x64_64x64x32) {
//
// Define the warp-level matrix multiply
//
using ElementOutput = cutlass::int4b_t;
using ElementAccumulator = int;
using ElementCompute = float;
int const kElementsPerAccess = 32 / cutlass::sizeof_bits<ElementOutput>::value;
int const kPartitionsK = 1;
using Shape = cutlass::gemm::GemmShape<64, 64, 32>;
using WarpShape = cutlass::gemm::GemmShape<64, 64, 32>;
using InstructionShape = cutlass::gemm::GemmShape<8, 8, 32>;
using Element = ElementOutput;
using LayoutA = cutlass::layout::RowMajorTensorOpMultiplicandCrosswise<
cutlass::sizeof_bits<Element>::value, 64>;
using LayoutB = cutlass::layout::ColumnMajorTensorOpMultiplicandCrosswise<
cutlass::sizeof_bits<Element>::value, 64>;
using WarpMmaTensorOp = typename cutlass::gemm::warp::DefaultMmaTensorOp<
WarpShape, InstructionShape, Element, LayoutA, Element, LayoutB, ElementAccumulator,
cutlass::layout::RowMajor, cutlass::arch::OpMultiplyAddSaturate>::Type;
//
// Output operator
//
using OutputOp = cutlass::epilogue::thread::LinearCombination<
ElementOutput,
kElementsPerAccess,
ElementAccumulator,
ElementCompute
>;
//
// Define the epilogue
//
using Epilogue = typename cutlass::epilogue::threadblock::DefaultEpilogueTensorOp<
Shape,
WarpMmaTensorOp,
kPartitionsK,
OutputOp,
kElementsPerAccess
>::Epilogue;
//
// Instantiate epilogue
//
EpilogueTestbed<Epilogue> testbed;
bool passed = testbed.run_all();
EXPECT_TRUE(passed);
}
TEST(SM75_Epilogue_threadblock_epilogue, s4_tensor_op_64x64_32x32x32) {
//
// Define the warp-level matrix multiply
//
using ElementOutput = cutlass::int4b_t;
using ElementAccumulator = int;
using ElementCompute = float;
int const kElementsPerAccess = 32 / cutlass::sizeof_bits<ElementOutput>::value;
int const kPartitionsK = 1;
using Shape = cutlass::gemm::GemmShape<64, 64, 32>;
using WarpShape = cutlass::gemm::GemmShape<32, 32, 32>;
using InstructionShape = cutlass::gemm::GemmShape<8, 8, 32>;
using Element = ElementOutput;
using LayoutA = cutlass::layout::RowMajorTensorOpMultiplicandCrosswise<
cutlass::sizeof_bits<Element>::value, 64>;
using LayoutB = cutlass::layout::ColumnMajorTensorOpMultiplicandCrosswise<
cutlass::sizeof_bits<Element>::value, 64>;
using WarpMmaTensorOp = typename cutlass::gemm::warp::DefaultMmaTensorOp<
WarpShape, InstructionShape, Element, LayoutA, Element, LayoutB, ElementAccumulator,
cutlass::layout::RowMajor, cutlass::arch::OpMultiplyAddSaturate>::Type;
//
// Output operator
//
using OutputOp = cutlass::epilogue::thread::LinearCombination<
ElementOutput,
kElementsPerAccess,
ElementAccumulator,
ElementCompute
>;
//
// Define the epilogue
//
using Epilogue = typename cutlass::epilogue::threadblock::DefaultEpilogueTensorOp<
Shape,
WarpMmaTensorOp,
kPartitionsK,
OutputOp,
kElementsPerAccess
>::Epilogue;
//
// Instantiate epilogue
//
EpilogueTestbed<Epilogue> testbed;
bool passed = testbed.run_all();
EXPECT_TRUE(passed);
}
TEST(SM75_Epilogue_threadblock_epilogue, s4_tensor_op_128x128_64x64x32) {
//
// Define the warp-level matrix multiply
//
using ElementOutput = cutlass::int4b_t;
using ElementAccumulator = int;
using ElementCompute = float;
int const kElementsPerAccess = 64 / cutlass::sizeof_bits<ElementOutput>::value;
int const kPartitionsK = 1;
using Shape = cutlass::gemm::GemmShape<128, 128, 32>;
using WarpShape = cutlass::gemm::GemmShape<64, 64, 32>;
using InstructionShape = cutlass::gemm::GemmShape<8, 8, 32>;
using Element = ElementOutput;
using LayoutA = cutlass::layout::RowMajorTensorOpMultiplicandCrosswise<
cutlass::sizeof_bits<Element>::value, 64>;
using LayoutB = cutlass::layout::ColumnMajorTensorOpMultiplicandCrosswise<
cutlass::sizeof_bits<Element>::value, 64>;
using WarpMmaTensorOp = typename cutlass::gemm::warp::DefaultMmaTensorOp<
WarpShape, InstructionShape, Element, LayoutA, Element, LayoutB, ElementAccumulator,
cutlass::layout::RowMajor, cutlass::arch::OpMultiplyAddSaturate>::Type;
//
// Output operator
//
using OutputOp = cutlass::epilogue::thread::LinearCombination<
ElementOutput,
kElementsPerAccess,
ElementAccumulator,
ElementCompute
>;
//
// Define the epilogue
//
using Epilogue = typename cutlass::epilogue::threadblock::DefaultEpilogueTensorOp<
Shape,
WarpMmaTensorOp,
kPartitionsK,
OutputOp,
kElementsPerAccess
>::Epilogue;
//
// Instantiate epilogue
//
EpilogueTestbed<Epilogue> testbed;
bool passed = testbed.run_all();
EXPECT_TRUE(passed);
}
TEST(SM75_Epilogue_threadblock_epilogue, s4_tensor_op_128x64_64x32x32) {
//
// Define the warp-level matrix multiply
//
using ElementOutput = cutlass::int4b_t;
using ElementAccumulator = int;
using ElementCompute = float;
int const kElementsPerAccess = 32 / cutlass::sizeof_bits<ElementOutput>::value;
int const kPartitionsK = 1;
using Shape = cutlass::gemm::GemmShape<128, 64, 32>;
using WarpShape = cutlass::gemm::GemmShape<64, 32, 32>;
using InstructionShape = cutlass::gemm::GemmShape<8, 8, 32>;
using Element = ElementOutput;
using LayoutA = cutlass::layout::RowMajorTensorOpMultiplicandCrosswise<
cutlass::sizeof_bits<Element>::value, 64>;
using LayoutB = cutlass::layout::ColumnMajorTensorOpMultiplicandCrosswise<
cutlass::sizeof_bits<Element>::value, 64>;
using WarpMmaTensorOp = typename cutlass::gemm::warp::DefaultMmaTensorOp<
WarpShape, InstructionShape, Element, LayoutA, Element, LayoutB, ElementAccumulator,
cutlass::layout::RowMajor, cutlass::arch::OpMultiplyAddSaturate>::Type;
//
// Output operator
//
using OutputOp = cutlass::epilogue::thread::LinearCombination<
ElementOutput,
kElementsPerAccess,
ElementAccumulator,
ElementCompute
>;
//
// Define the epilogue
//
using Epilogue = typename cutlass::epilogue::threadblock::DefaultEpilogueTensorOp<
Shape,
WarpMmaTensorOp,
kPartitionsK,
OutputOp,
kElementsPerAccess
>::Epilogue;
//
// Instantiate epilogue
//
EpilogueTestbed<Epilogue> testbed;
bool passed = testbed.run_all();
EXPECT_TRUE(passed);
}
TEST(SM75_Epilogue_threadblock_epilogue, s4_tensor_op_64x128_32x64x32) {
//
// Define the warp-level matrix multiply
//
using ElementOutput = cutlass::int4b_t;
using ElementAccumulator = int;
using ElementCompute = float;
int const kElementsPerAccess = 64 / cutlass::sizeof_bits<ElementOutput>::value;
int const kPartitionsK = 1;
using Shape = cutlass::gemm::GemmShape<64, 128, 32>;
using WarpShape = cutlass::gemm::GemmShape<32, 64, 32>;
using InstructionShape = cutlass::gemm::GemmShape<8, 8, 32>;
using Element = ElementOutput;
using LayoutA = cutlass::layout::RowMajorTensorOpMultiplicandCrosswise<
cutlass::sizeof_bits<Element>::value, 64>;
using LayoutB = cutlass::layout::ColumnMajorTensorOpMultiplicandCrosswise<
cutlass::sizeof_bits<Element>::value, 64>;
using WarpMmaTensorOp = typename cutlass::gemm::warp::DefaultMmaTensorOp<
WarpShape, InstructionShape, Element, LayoutA, Element, LayoutB, ElementAccumulator,
cutlass::layout::RowMajor, cutlass::arch::OpMultiplyAddSaturate>::Type;
//
// Output operator
//
using OutputOp = cutlass::epilogue::thread::LinearCombination<
ElementOutput,
kElementsPerAccess,
ElementAccumulator,
ElementCompute
>;
//
// Define the epilogue
//
using Epilogue = typename cutlass::epilogue::threadblock::DefaultEpilogueTensorOp<
Shape,
WarpMmaTensorOp,
kPartitionsK,
OutputOp,
kElementsPerAccess
>::Epilogue;
//
// Instantiate epilogue
//
EpilogueTestbed<Epilogue> testbed;
bool passed = testbed.run_all();
EXPECT_TRUE(passed);
}
TEST(SM75_Epilogue_threadblock_epilogue, s4_tensor_op_32x128_32x64x32) {
//
// Define the warp-level matrix multiply
//
using ElementOutput = cutlass::int4b_t;
using ElementAccumulator = int;
using ElementCompute = float;
int const kElementsPerAccess = 64 / cutlass::sizeof_bits<ElementOutput>::value;
int const kPartitionsK = 1;
using Shape = cutlass::gemm::GemmShape<32, 128, 32>;
using WarpShape = cutlass::gemm::GemmShape<32, 64, 32>;
using InstructionShape = cutlass::gemm::GemmShape<8, 8, 32>;
using Element = ElementOutput;
using LayoutA = cutlass::layout::RowMajorTensorOpMultiplicandCrosswise<
cutlass::sizeof_bits<Element>::value, 64>;
using LayoutB = cutlass::layout::ColumnMajorTensorOpMultiplicandCrosswise<
cutlass::sizeof_bits<Element>::value, 64>;
using WarpMmaTensorOp = typename cutlass::gemm::warp::DefaultMmaTensorOp<
WarpShape, InstructionShape, Element, LayoutA, Element, LayoutB, ElementAccumulator,
cutlass::layout::RowMajor, cutlass::arch::OpMultiplyAddSaturate>::Type;
//
// Output operator
//
using OutputOp = cutlass::epilogue::thread::LinearCombination<
ElementOutput,
kElementsPerAccess,
ElementAccumulator,
ElementCompute
>;
//
// Define the epilogue
//
using Epilogue = typename cutlass::epilogue::threadblock::DefaultEpilogueTensorOp<
Shape,
WarpMmaTensorOp,
kPartitionsK,
OutputOp,
kElementsPerAccess
>::Epilogue;
//
// Instantiate epilogue
//
EpilogueTestbed<Epilogue> testbed;
bool passed = testbed.run_all();
EXPECT_TRUE(passed);
}
TEST(SM75_Epilogue_threadblock_epilogue, s4_tensor_op_128x32_64x32x32) {
//
// Define the warp-level matrix multiply
//
using ElementOutput = cutlass::int4b_t;
using ElementAccumulator = int;
using ElementCompute = float;
int const kElementsPerAccess = 32 / cutlass::sizeof_bits<ElementOutput>::value;
int const kPartitionsK = 1;
using Shape = cutlass::gemm::GemmShape<128, 32, 32>;
using WarpShape = cutlass::gemm::GemmShape<64, 32, 32>;
using InstructionShape = cutlass::gemm::GemmShape<8, 8, 32>;
using Element = ElementOutput;
using LayoutA = cutlass::layout::RowMajorTensorOpMultiplicandCrosswise<
cutlass::sizeof_bits<Element>::value, 64>;
using LayoutB = cutlass::layout::ColumnMajorTensorOpMultiplicandCrosswise<
cutlass::sizeof_bits<Element>::value, 64>;
using WarpMmaTensorOp = typename cutlass::gemm::warp::DefaultMmaTensorOp<
WarpShape, InstructionShape, Element, LayoutA, Element, LayoutB, ElementAccumulator,
cutlass::layout::RowMajor, cutlass::arch::OpMultiplyAddSaturate>::Type;
//
// Output operator
//
using OutputOp = cutlass::epilogue::thread::LinearCombination<
ElementOutput,
kElementsPerAccess,
ElementAccumulator,
ElementCompute
>;
//
// Define the epilogue
//
using Epilogue = typename cutlass::epilogue::threadblock::DefaultEpilogueTensorOp<
Shape,
WarpMmaTensorOp,
kPartitionsK,
OutputOp,
kElementsPerAccess
>::Epilogue;
//
// Instantiate epilogue
//
EpilogueTestbed<Epilogue> testbed;
bool passed = testbed.run_all();
EXPECT_TRUE(passed);
}
TEST(SM75_Epilogue_threadblock_epilogue, s4_tensor_op_256x128_64x64x32) {
//
// Define the warp-level matrix multiply
//
using ElementOutput = cutlass::int4b_t;
using ElementAccumulator = int;
using ElementCompute = float;
int const kElementsPerAccess = 64 / cutlass::sizeof_bits<ElementOutput>::value;
int const kPartitionsK = 1;
using Shape = cutlass::gemm::GemmShape<256, 128, 32>;
using WarpShape = cutlass::gemm::GemmShape<64, 64, 32>;
using InstructionShape = cutlass::gemm::GemmShape<8, 8, 32>;
using Element = ElementOutput;
using LayoutA = cutlass::layout::RowMajorTensorOpMultiplicandCrosswise<
cutlass::sizeof_bits<Element>::value, 64>;
using LayoutB = cutlass::layout::ColumnMajorTensorOpMultiplicandCrosswise<
cutlass::sizeof_bits<Element>::value, 64>;
using WarpMmaTensorOp = typename cutlass::gemm::warp::DefaultMmaTensorOp<
WarpShape, InstructionShape, Element, LayoutA, Element, LayoutB, ElementAccumulator,
cutlass::layout::RowMajor, cutlass::arch::OpMultiplyAddSaturate>::Type;
//
// Output operator
//
using OutputOp = cutlass::epilogue::thread::LinearCombination<
ElementOutput,
kElementsPerAccess,
ElementAccumulator,
ElementCompute
>;
//
// Define the epilogue
//
using Epilogue = typename cutlass::epilogue::threadblock::DefaultEpilogueTensorOp<
Shape,
WarpMmaTensorOp,
kPartitionsK,
OutputOp,
kElementsPerAccess
>::Epilogue;
//
// Instantiate epilogue
//
EpilogueTestbed<Epilogue> testbed;
bool passed = testbed.run_all();
EXPECT_TRUE(passed);
}
TEST(SM75_Epilogue_threadblock_epilogue, s4_tensor_op_128x256_64x64x32) {
//
// Define the warp-level matrix multiply
//
using ElementOutput = cutlass::int4b_t;
using ElementAccumulator = int;
using ElementCompute = float;
int const kElementsPerAccess = 32 / cutlass::sizeof_bits<ElementOutput>::value;
int const kPartitionsK = 1;
using Shape = cutlass::gemm::GemmShape<128, 256, 32>;
using WarpShape = cutlass::gemm::GemmShape<64, 64, 32>;
using InstructionShape = cutlass::gemm::GemmShape<8, 8, 32>;
using Element = ElementOutput;
using LayoutA = cutlass::layout::RowMajorTensorOpMultiplicandCrosswise<
cutlass::sizeof_bits<Element>::value, 64>;
using LayoutB = cutlass::layout::ColumnMajorTensorOpMultiplicandCrosswise<
cutlass::sizeof_bits<Element>::value, 64>;
using WarpMmaTensorOp = typename cutlass::gemm::warp::DefaultMmaTensorOp<
WarpShape, InstructionShape, Element, LayoutA, Element, LayoutB, ElementAccumulator,
cutlass::layout::RowMajor, cutlass::arch::OpMultiplyAddSaturate>::Type;
//
// Output operator
//
using OutputOp = cutlass::epilogue::thread::LinearCombination<
ElementOutput,
kElementsPerAccess,
ElementAccumulator,
ElementCompute
>;
//
// Define the epilogue
//
using Epilogue = typename cutlass::epilogue::threadblock::DefaultEpilogueTensorOp<
Shape,
WarpMmaTensorOp,
kPartitionsK,
OutputOp,
kElementsPerAccess
>::Epilogue;
//
// Instantiate epilogue
//
EpilogueTestbed<Epilogue> testbed;
bool passed = testbed.run_all();
EXPECT_TRUE(passed);
}
/////////////////////////////////////////////////////////////////////////////////////////////////
TEST(SM75_Epilogue_threadblock_epilogue, s8_tensor_op_64x64_64x64x16) {
//
// Define the warp-level matrix multiply
//
using ElementOutput = int8_t;
using ElementAccumulator = int;
using ElementCompute = float;
int const kElementsPerAccess = 128 / cutlass::sizeof_bits<ElementOutput>::value;
int const kPartitionsK = 1;
using Shape = cutlass::gemm::GemmShape<64, 64, 16>;
using WarpShape = cutlass::gemm::GemmShape<64, 64, 16>;
using InstructionShape = cutlass::gemm::GemmShape<8, 8, 16>;
using Element = ElementOutput;
using LayoutA = cutlass::layout::RowMajorTensorOpMultiplicandCrosswise<
cutlass::sizeof_bits<Element>::value, 64>;
using LayoutB = cutlass::layout::ColumnMajorTensorOpMultiplicandCrosswise<
cutlass::sizeof_bits<Element>::value, 64>;
using WarpMmaTensorOp = typename cutlass::gemm::warp::DefaultMmaTensorOp<
WarpShape, InstructionShape, Element, LayoutA, Element, LayoutB, ElementAccumulator,
cutlass::layout::RowMajor, cutlass::arch::OpMultiplyAddSaturate>::Type;
//
// Output operator
//
using OutputOp = cutlass::epilogue::thread::LinearCombination<
ElementOutput,
kElementsPerAccess,
ElementAccumulator,
ElementCompute
>;
//
// Define the epilogue
//
using Epilogue = typename cutlass::epilogue::threadblock::DefaultEpilogueTensorOp<
Shape,
WarpMmaTensorOp,
kPartitionsK,
OutputOp,
kElementsPerAccess
>::Epilogue;
//
// Instantiate epilogue
//
EpilogueTestbed<Epilogue> testbed;
bool passed = testbed.run_all();
EXPECT_TRUE(passed);
}
TEST(SM75_Epilogue_threadblock_epilogue, s8_tensor_op_64x64_32x3216) {
//
// Define the warp-level matrix multiply
//
using ElementOutput = int8_t;
using ElementAccumulator = int;
using ElementCompute = float;
int const kElementsPerAccess = 64 / cutlass::sizeof_bits<ElementOutput>::value;
int const kPartitionsK = 1;
using Shape = cutlass::gemm::GemmShape<64, 64, 16>;
using WarpShape = cutlass::gemm::GemmShape<32, 32, 16>;
using InstructionShape = cutlass::gemm::GemmShape<8, 8, 16>;
using Element = ElementOutput;
using LayoutA = cutlass::layout::RowMajorTensorOpMultiplicandCrosswise<
cutlass::sizeof_bits<Element>::value, 64>;
using LayoutB = cutlass::layout::ColumnMajorTensorOpMultiplicandCrosswise<
cutlass::sizeof_bits<Element>::value, 64>;
using WarpMmaTensorOp = typename cutlass::gemm::warp::DefaultMmaTensorOp<
WarpShape, InstructionShape, Element, LayoutA, Element, LayoutB, ElementAccumulator,
cutlass::layout::RowMajor, cutlass::arch::OpMultiplyAddSaturate>::Type;
//
// Output operator
//
using OutputOp = cutlass::epilogue::thread::LinearCombination<
ElementOutput,
kElementsPerAccess,
ElementAccumulator,
ElementCompute
>;
//
// Define the epilogue
//
using Epilogue = typename cutlass::epilogue::threadblock::DefaultEpilogueTensorOp<
Shape,
WarpMmaTensorOp,
kPartitionsK,
OutputOp,
kElementsPerAccess
>::Epilogue;
//
// Instantiate epilogue
//
EpilogueTestbed<Epilogue> testbed;
bool passed = testbed.run_all();
EXPECT_TRUE(passed);
}
TEST(SM75_Epilogue_threadblock_epilogue, s8_tensor_op_128x128_64x64x16) {
//
// Define the warp-level matrix multiply
//
using ElementOutput = int8_t;
using ElementAccumulator = int;
using ElementCompute = float;
int const kElementsPerAccess = 128 / cutlass::sizeof_bits<ElementOutput>::value;
int const kPartitionsK = 1;
using Shape = cutlass::gemm::GemmShape<128, 128, 16>;
using WarpShape = cutlass::gemm::GemmShape<64, 64, 16>;
using InstructionShape = cutlass::gemm::GemmShape<8, 8, 16>;
using Element = ElementOutput;
using LayoutA = cutlass::layout::RowMajorTensorOpMultiplicandCrosswise<
cutlass::sizeof_bits<Element>::value, 64>;
using LayoutB = cutlass::layout::ColumnMajorTensorOpMultiplicandCrosswise<
cutlass::sizeof_bits<Element>::value, 64>;
using WarpMmaTensorOp = typename cutlass::gemm::warp::DefaultMmaTensorOp<
WarpShape, InstructionShape, Element, LayoutA, Element, LayoutB, ElementAccumulator,
cutlass::layout::RowMajor, cutlass::arch::OpMultiplyAddSaturate>::Type;
//
// Output operator
//
using OutputOp = cutlass::epilogue::thread::LinearCombination<
ElementOutput,
kElementsPerAccess,
ElementAccumulator,
ElementCompute
>;
//
// Define the epilogue
//
using Epilogue = typename cutlass::epilogue::threadblock::DefaultEpilogueTensorOp<
Shape,
WarpMmaTensorOp,
kPartitionsK,
OutputOp,
kElementsPerAccess
>::Epilogue;
//
// Instantiate epilogue
//
EpilogueTestbed<Epilogue> testbed;
bool passed = testbed.run_all();
EXPECT_TRUE(passed);
}
TEST(SM75_Epilogue_threadblock_epilogue, s8_tensor_op_64x128_64x64x16) {
//
// Define the warp-level matrix multiply
//
using ElementOutput = int8_t;
using ElementAccumulator = int;
using ElementCompute = float;
int const kElementsPerAccess = 128 / cutlass::sizeof_bits<ElementOutput>::value;
int const kPartitionsK = 1;
using Shape = cutlass::gemm::GemmShape<128, 128, 16>;
using WarpShape = cutlass::gemm::GemmShape<64, 64, 16>;
using InstructionShape = cutlass::gemm::GemmShape<8, 8, 16>;
using Element = ElementOutput;
using LayoutA = cutlass::layout::RowMajorTensorOpMultiplicandCrosswise<
cutlass::sizeof_bits<Element>::value, 64>;
using LayoutB = cutlass::layout::ColumnMajorTensorOpMultiplicandCrosswise<
cutlass::sizeof_bits<Element>::value, 64>;
using WarpMmaTensorOp = typename cutlass::gemm::warp::DefaultMmaTensorOp<
WarpShape, InstructionShape, Element, LayoutA, Element, LayoutB, ElementAccumulator,
cutlass::layout::RowMajor, cutlass::arch::OpMultiplyAddSaturate>::Type;
//
// Output operator
//
using OutputOp = cutlass::epilogue::thread::LinearCombination<
ElementOutput,
kElementsPerAccess,
ElementAccumulator,
ElementCompute
>;
//
// Define the epilogue
//
using Epilogue = typename cutlass::epilogue::threadblock::DefaultEpilogueTensorOp<
Shape,
WarpMmaTensorOp,
kPartitionsK,
OutputOp,
kElementsPerAccess
>::Epilogue;
//
// Instantiate epilogue
//
EpilogueTestbed<Epilogue> testbed;
bool passed = testbed.run_all();
EXPECT_TRUE(passed);
}
TEST(SM75_Epilogue_threadblock_epilogue, s8_tensor_op_128x64_64x32x16) {
//
// Define the warp-level matrix multiply
//
using ElementOutput = int8_t;
using ElementAccumulator = int;
using ElementCompute = float;
int const kElementsPerAccess = 64 / cutlass::sizeof_bits<ElementOutput>::value;
int const kPartitionsK = 1;
using Shape = cutlass::gemm::GemmShape<128, 64, 16>;
using WarpShape = cutlass::gemm::GemmShape<64, 32, 16>;
using InstructionShape = cutlass::gemm::GemmShape<8, 8, 16>;
using Element = ElementOutput;
using LayoutA = cutlass::layout::RowMajorTensorOpMultiplicandCrosswise<
cutlass::sizeof_bits<Element>::value, 64>;
using LayoutB = cutlass::layout::ColumnMajorTensorOpMultiplicandCrosswise<
cutlass::sizeof_bits<Element>::value, 64>;
using WarpMmaTensorOp = typename cutlass::gemm::warp::DefaultMmaTensorOp<
WarpShape, InstructionShape, Element, LayoutA, Element, LayoutB, ElementAccumulator,
cutlass::layout::RowMajor, cutlass::arch::OpMultiplyAddSaturate>::Type;
//
// Output operator
//
using OutputOp = cutlass::epilogue::thread::LinearCombination<
ElementOutput,
kElementsPerAccess,
ElementAccumulator,
ElementCompute
>;
//
// Define the epilogue
//
using Epilogue = typename cutlass::epilogue::threadblock::DefaultEpilogueTensorOp<
Shape,
WarpMmaTensorOp,
kPartitionsK,
OutputOp,
kElementsPerAccess
>::Epilogue;
//
// Instantiate epilogue
//
EpilogueTestbed<Epilogue> testbed;
bool passed = testbed.run_all();
EXPECT_TRUE(passed);
}
TEST(SM75_Epilogue_threadblock_epilogue, s8_tensor_op_64x128_32x64x16) {
//
// Define the warp-level matrix multiply
//
using ElementOutput = int8_t;
using ElementAccumulator = int;
using ElementCompute = float;
int const kElementsPerAccess = 128 / cutlass::sizeof_bits<ElementOutput>::value;
int const kPartitionsK = 1;
using Shape = cutlass::gemm::GemmShape<64, 128, 16>;
using WarpShape = cutlass::gemm::GemmShape<32, 64, 16>;
using InstructionShape = cutlass::gemm::GemmShape<8, 8, 16>;
using Element = ElementOutput;
using LayoutA = cutlass::layout::RowMajorTensorOpMultiplicandCrosswise<
cutlass::sizeof_bits<Element>::value, 64>;
using LayoutB = cutlass::layout::ColumnMajorTensorOpMultiplicandCrosswise<
cutlass::sizeof_bits<Element>::value, 64>;
using WarpMmaTensorOp = typename cutlass::gemm::warp::DefaultMmaTensorOp<
WarpShape, InstructionShape, Element, LayoutA, Element, LayoutB, ElementAccumulator,
cutlass::layout::RowMajor, cutlass::arch::OpMultiplyAddSaturate>::Type;
//
// Output operator
//
using OutputOp = cutlass::epilogue::thread::LinearCombination<
ElementOutput,
kElementsPerAccess,
ElementAccumulator,
ElementCompute
>;
//
// Define the epilogue
//
using Epilogue = typename cutlass::epilogue::threadblock::DefaultEpilogueTensorOp<
Shape,
WarpMmaTensorOp,
kPartitionsK,
OutputOp,
kElementsPerAccess
>::Epilogue;
//
// Instantiate epilogue
//
EpilogueTestbed<Epilogue> testbed;
bool passed = testbed.run_all();
EXPECT_TRUE(passed);
}
TEST(SM75_Epilogue_threadblock_epilogue, s8_tensor_op_32x128_32x64x16) {
//
// Define the warp-level matrix multiply
//
using ElementOutput = int8_t;
using ElementAccumulator = int;
using ElementCompute = float;
int const kElementsPerAccess = 128 / cutlass::sizeof_bits<ElementOutput>::value;
int const kPartitionsK = 1;
using Shape = cutlass::gemm::GemmShape<32, 128, 16>;
using WarpShape = cutlass::gemm::GemmShape<32, 64, 16>;
using InstructionShape = cutlass::gemm::GemmShape<8, 8, 16>;
using Element = ElementOutput;
using LayoutA = cutlass::layout::RowMajorTensorOpMultiplicandCrosswise<
cutlass::sizeof_bits<Element>::value, 64>;
using LayoutB = cutlass::layout::ColumnMajorTensorOpMultiplicandCrosswise<
cutlass::sizeof_bits<Element>::value, 64>;
using WarpMmaTensorOp = typename cutlass::gemm::warp::DefaultMmaTensorOp<
WarpShape, InstructionShape, Element, LayoutA, Element, LayoutB, ElementAccumulator,
cutlass::layout::RowMajor, cutlass::arch::OpMultiplyAddSaturate>::Type;
//
// Output operator
//
using OutputOp = cutlass::epilogue::thread::LinearCombination<
ElementOutput,
kElementsPerAccess,
ElementAccumulator,
ElementCompute
>;
//
// Define the epilogue
//
using Epilogue = typename cutlass::epilogue::threadblock::DefaultEpilogueTensorOp<
Shape,
WarpMmaTensorOp,
kPartitionsK,
OutputOp,
kElementsPerAccess
>::Epilogue;
//
// Instantiate epilogue
//
EpilogueTestbed<Epilogue> testbed;
bool passed = testbed.run_all();
EXPECT_TRUE(passed);