3077 lines
83 KiB
Plaintext
3077 lines
83 KiB
Plaintext
/***************************************************************************************************
|
|
* Copyright (c) 2017 - 2024 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 "../../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);
|
|
}
|
|
|
|
TEST(SM75_Epilogue_threadblock_epilogue, s8_tensor_op_128x32_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, 32, 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, tensor_op_64x64_64x64x8) {
|
|
|
|
//
|
|
// Define the warp-level matrix multiply
|
|
//
|
|
|
|
using ElementOutput = float;
|
|
using ElementAccumulator = float;
|
|
using ElementCompute = float;
|
|
int const kElementsPerAccess = 128 / cutlass::sizeof_bits<ElementOutput>::value;
|
|
int const kPartitionsK = 1;
|
|
|
|
using Shape = cutlass::gemm::GemmShape<64, 64, 8>;
|
|
using WarpShape = cutlass::gemm::GemmShape<64, 64, 8>;
|
|
using InstructionShape = cutlass::gemm::GemmShape<16, 8, 8>;
|
|
using Element = cutlass::half_t;
|
|
using ElementC = ElementAccumulator;
|
|
using LayoutA = cutlass::layout::ColumnMajorTensorOpMultiplicandCongruous<
|
|
cutlass::sizeof_bits<Element>::value, 64>;
|
|
using LayoutB = cutlass::layout::RowMajorTensorOpMultiplicandCongruous<
|
|
cutlass::sizeof_bits<Element>::value, 64>;
|
|
using LayoutC = cutlass::layout::RowMajor;
|
|
|
|
using WarpMmaTensorOp = typename cutlass::gemm::warp::DefaultMmaTensorOp<
|
|
WarpShape, InstructionShape, Element, LayoutA, Element, LayoutB, ElementC,
|
|
LayoutC>::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, tensor_op_128x128_64x64x8) {
|
|
|
|
//
|
|
// Define the warp-level matrix multiply
|
|
//
|
|
|
|
using ElementOutput = float;
|
|
using ElementAccumulator = float;
|
|
using ElementCompute = float;
|
|
int const kElementsPerAccess = 128 / cutlass::sizeof_bits<ElementOutput>::value;
|
|
int const kPartitionsK = 1;
|
|
|
|
using Shape = cutlass::gemm::GemmShape<128, 128, 8>;
|
|
using WarpShape = cutlass::gemm::GemmShape<64, 64, 8>;
|
|
using InstructionShape = cutlass::gemm::GemmShape<16, 8, 8>;
|
|
using Element = cutlass::half_t;
|
|
using ElementC = ElementAccumulator;
|
|
using LayoutA = cutlass::layout::ColumnMajorTensorOpMultiplicandCongruous<
|
|
cutlass::sizeof_bits<Element>::value, 64>;
|
|
using LayoutB = cutlass::layout::RowMajorTensorOpMultiplicandCongruous<
|
|
cutlass::sizeof_bits<Element>::value, 64>;
|
|
using LayoutC = cutlass::layout::RowMajor;
|
|
|
|
using WarpMmaTensorOp = typename cutlass::gemm::warp::DefaultMmaTensorOp<
|
|
WarpShape, InstructionShape, Element, LayoutA, Element, LayoutB, ElementC,
|
|
LayoutC>::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, tensor_op_128x256_64x64x8) {
|
|
|
|
//
|
|
// Define the warp-level matrix multiply
|
|
//
|
|
|
|
using ElementOutput = float;
|
|
using ElementAccumulator = float;
|
|
using ElementCompute = float;
|
|
int const kElementsPerAccess = 128 / cutlass::sizeof_bits<ElementOutput>::value;
|
|
int const kPartitionsK = 1;
|
|
|
|
using Shape = cutlass::gemm::GemmShape<128, 256, 8>;
|
|
using WarpShape = cutlass::gemm::GemmShape<64, 64, 8>;
|
|
using InstructionShape = cutlass::gemm::GemmShape<16, 8, 8>;
|
|
using Element = cutlass::half_t;
|
|
using ElementC = ElementAccumulator;
|
|
using LayoutA = cutlass::layout::ColumnMajorTensorOpMultiplicandCongruous<
|
|
cutlass::sizeof_bits<Element>::value, 64>;
|
|
using LayoutB = cutlass::layout::RowMajorTensorOpMultiplicandCongruous<
|
|
cutlass::sizeof_bits<Element>::value, 64>;
|
|
using LayoutC = cutlass::layout::RowMajor;
|
|
|
|
using WarpMmaTensorOp = typename cutlass::gemm::warp::DefaultMmaTensorOp<
|
|
WarpShape, InstructionShape, Element, LayoutA, Element, LayoutB, ElementC,
|
|
LayoutC>::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, tensor_op_256x128_64x64x8) {
|
|
|
|
//
|
|
// Define the warp-level matrix multiply
|
|
//
|
|
|
|
using ElementOutput = float;
|
|
using ElementAccumulator = float;
|
|
using ElementCompute = float;
|
|
int const kElementsPerAccess = 128 / cutlass::sizeof_bits<ElementOutput>::value;
|
|
int const kPartitionsK = 1;
|
|
|
|
using Shape = cutlass::gemm::GemmShape<256, 128, 8>;
|
|
using WarpShape = cutlass::gemm::GemmShape<64, 64, 8>;
|
|
using InstructionShape = cutlass::gemm::GemmShape<16, 8, 8>;
|
|
using Element = cutlass::half_t;
|
|
using ElementC = ElementAccumulator;
|
|
using LayoutA = cutlass::layout::ColumnMajorTensorOpMultiplicandCongruous<
|
|
cutlass::sizeof_bits<Element>::value, 64>;
|
|
using LayoutB = cutlass::layout::RowMajorTensorOpMultiplicandCongruous<
|
|
cutlass::sizeof_bits<Element>::value, 64>;
|
|
using LayoutC = cutlass::layout::RowMajor;
|
|
|
|
using WarpMmaTensorOp = typename cutlass::gemm::warp::DefaultMmaTensorOp<
|
|
WarpShape, InstructionShape, Element, LayoutA, Element, LayoutB, ElementC,
|
|
LayoutC>::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, tensor_op_32x32_32x32x8) {
|
|
|
|
//
|
|
// Define the warp-level matrix multiply
|
|
//
|
|
|
|
using ElementOutput = float;
|
|
using ElementAccumulator = float;
|
|
using ElementCompute = float;
|
|
int const kElementsPerAccess = 128 / cutlass::sizeof_bits<ElementOutput>::value;
|
|
int const kPartitionsK = 1;
|
|
|
|
using Shape = cutlass::gemm::GemmShape<32, 32, 8>;
|
|
using WarpShape = cutlass::gemm::GemmShape<32, 32, 8>;
|
|
using InstructionShape = cutlass::gemm::GemmShape<16, 8, 8>;
|
|
using Element = cutlass::half_t;
|
|
using ElementC = ElementAccumulator;
|
|
using LayoutA = cutlass::layout::ColumnMajorTensorOpMultiplicandCongruous<
|
|
cutlass::sizeof_bits<Element>::value, 64>;
|
|
using LayoutB = cutlass::layout::RowMajorTensorOpMultiplicandCongruous<
|
|
cutlass::sizeof_bits<Element>::value, 64>;
|
|
using LayoutC = cutlass::layout::RowMajor;
|
|
|
|
using WarpMmaTensorOp = typename cutlass::gemm::warp::DefaultMmaTensorOp<
|
|
WarpShape, InstructionShape, Element, LayoutA, Element, LayoutB, ElementC,
|
|
LayoutC>::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, tensor_op_64x64_32x32x8) {
|
|
|
|
//
|
|
// Define the warp-level matrix multiply
|
|
//
|
|
|
|
using ElementOutput = float;
|
|
using ElementAccumulator = float;
|
|
using ElementCompute = float;
|
|
int const kElementsPerAccess = 128 / cutlass::sizeof_bits<ElementOutput>::value;
|
|
int const kPartitionsK = 1;
|
|
|
|
using Shape = cutlass::gemm::GemmShape<64, 64, 8>;
|
|
using WarpShape = cutlass::gemm::GemmShape<32, 32, 8>;
|
|
using InstructionShape = cutlass::gemm::GemmShape<16, 8, 8>;
|
|
using Element = cutlass::half_t;
|
|
using ElementC = ElementAccumulator;
|
|
using LayoutA = cutlass::layout::ColumnMajorTensorOpMultiplicandCongruous<
|
|
cutlass::sizeof_bits<Element>::value, 64>;
|
|
using LayoutB = cutlass::layout::RowMajorTensorOpMultiplicandCongruous<
|
|
cutlass::sizeof_bits<Element>::value, 64>;
|
|
using LayoutC = cutlass::layout::RowMajor;
|
|
|
|
using WarpMmaTensorOp = typename cutlass::gemm::warp::DefaultMmaTensorOp<
|
|
WarpShape, InstructionShape, Element, LayoutA, Element, LayoutB, ElementC,
|
|
LayoutC>::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, tensor_op_64x128_32x64x8) {
|
|
|
|
//
|
|
// Define the warp-level matrix multiply
|
|
//
|
|
|
|
using ElementOutput = float;
|
|
using ElementAccumulator = float;
|
|
using ElementCompute = float;
|
|
int const kElementsPerAccess = 128 / cutlass::sizeof_bits<ElementOutput>::value;
|
|
int const kPartitionsK = 1;
|
|
|
|
using Shape = cutlass::gemm::GemmShape<64, 128, 8>;
|
|
using WarpShape = cutlass::gemm::GemmShape<32, 64, 8>;
|
|
using InstructionShape = cutlass::gemm::GemmShape<16, 8, 8>;
|
|
using Element = cutlass::half_t;
|
|
using ElementC = ElementAccumulator;
|
|
using LayoutA = cutlass::layout::ColumnMajorTensorOpMultiplicandCongruous<
|
|
cutlass::sizeof_bits<Element>::value, 64>;
|
|
using LayoutB = cutlass::layout::RowMajorTensorOpMultiplicandCongruous<
|
|
cutlass::sizeof_bits<Element>::value, 64>;
|
|
using LayoutC = cutlass::layout::RowMajor;
|
|
|
|
using WarpMmaTensorOp = typename cutlass::gemm::warp::DefaultMmaTensorOp<
|
|
WarpShape, InstructionShape, Element, LayoutA, Element, LayoutB, ElementC,
|
|
LayoutC>::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, tensor_op_128x64_64x32x8) {
|
|
|
|
//
|
|
// Define the warp-level matrix multiply
|
|
//
|
|
|
|
using ElementOutput = float;
|
|
using ElementAccumulator = float;
|
|
using ElementCompute = float;
|
|
int const kElementsPerAccess = 128 / cutlass::sizeof_bits<ElementOutput>::value;
|
|
int const kPartitionsK = 1;
|
|
|
|
using Shape = cutlass::gemm::GemmShape<128, 64, 8>;
|
|
using WarpShape = cutlass::gemm::GemmShape<64, 32, 8>;
|
|
using InstructionShape = cutlass::gemm::GemmShape<16, 8, 8>;
|
|
using Element = cutlass::half_t;
|
|
using ElementC = ElementAccumulator;
|
|
using LayoutA = cutlass::layout::ColumnMajorTensorOpMultiplicandCongruous<
|
|
cutlass::sizeof_bits<Element>::value, 64>;
|
|
using LayoutB = cutlass::layout::RowMajorTensorOpMultiplicandCongruous<
|
|
cutlass::sizeof_bits<Element>::value, 64>;
|
|
using LayoutC = cutlass::layout::RowMajor;
|
|
|
|
using WarpMmaTensorOp = typename cutlass::gemm::warp::DefaultMmaTensorOp<
|
|
WarpShape, InstructionShape, Element, LayoutA, Element, LayoutB, ElementC,
|
|
LayoutC>::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);
|
|
}
|
|
|
|
/////////////////////////////////////////////////////////////////////////////////////////////////
|
|
//
|
|
// Mixed precision tests
|
|
//
|
|
/////////////////////////////////////////////////////////////////////////////////////////////////
|
|
|
|
TEST(SM75_Epilogue_threadblock_epilogue, mixed_f16_f32_tensor_op_64x64_64x64x8) {
|
|
|
|
//
|
|
// Define the warp-level matrix multiply
|
|
//
|
|
|
|
using ElementOutput = cutlass::half_t;
|
|
using ElementAccumulator = float;
|
|
using ElementCompute = float;
|
|
int const kElementsPerAccess = 128 / cutlass::sizeof_bits<ElementOutput>::value;
|
|
int const kPartitionsK = 1;
|
|
|
|
using Shape = cutlass::gemm::GemmShape<64, 64, 8>;
|
|
using WarpShape = cutlass::gemm::GemmShape<64, 64, 8>;
|
|
using InstructionShape = cutlass::gemm::GemmShape<16, 8, 8>;
|
|
using Element = cutlass::half_t;
|
|
using ElementC = ElementAccumulator;
|
|
using LayoutA = cutlass::layout::ColumnMajorTensorOpMultiplicandCongruous<
|
|
cutlass::sizeof_bits<Element>::value, 64>;
|
|
using LayoutB = cutlass::layout::RowMajorTensorOpMultiplicandCongruous<
|
|
cutlass::sizeof_bits<Element>::value, 64>;
|
|
using LayoutC = cutlass::layout::RowMajor;
|
|
|
|
using WarpMmaTensorOp = typename cutlass::gemm::warp::DefaultMmaTensorOp<
|
|
WarpShape, InstructionShape, Element, LayoutA, Element, LayoutB, ElementC,
|
|
LayoutC>::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, mixed_f16_f32_tensor_op_128x128_64x64x8) {
|
|
|
|
//
|
|
// Define the warp-level matrix multiply
|
|
//
|
|
|
|
using ElementOutput = cutlass::half_t;
|
|
using ElementAccumulator = float;
|
|
using ElementCompute = float;
|
|
int const kElementsPerAccess = 128 / cutlass::sizeof_bits<ElementOutput>::value;
|
|
int const kPartitionsK = 1;
|
|
|
|
using Shape = cutlass::gemm::GemmShape<128, 128, 8>;
|
|
using WarpShape = cutlass::gemm::GemmShape<64, 64, 8>;
|
|
using InstructionShape = cutlass::gemm::GemmShape<16, 8, 8>;
|
|
using Element = cutlass::half_t;
|
|
using ElementC = ElementAccumulator;
|
|
using LayoutA = cutlass::layout::ColumnMajorTensorOpMultiplicandCongruous<
|
|
cutlass::sizeof_bits<Element>::value, 64>;
|
|
using LayoutB = cutlass::layout::RowMajorTensorOpMultiplicandCongruous<
|
|
cutlass::sizeof_bits<Element>::value, 64>;
|
|
using LayoutC = cutlass::layout::RowMajor;
|
|
|
|
using WarpMmaTensorOp = typename cutlass::gemm::warp::DefaultMmaTensorOp<
|
|
WarpShape, InstructionShape, Element, LayoutA, Element, LayoutB, ElementC,
|
|
LayoutC>::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, mixed_f16_f32_tensor_op_128x256_64x64x8) {
|
|
|
|
//
|
|
// Define the warp-level matrix multiply
|
|
//
|
|
|
|
using ElementOutput = cutlass::half_t;
|
|
using ElementAccumulator = float;
|
|
using ElementCompute = float;
|
|
int const kElementsPerAccess = 128 / cutlass::sizeof_bits<ElementOutput>::value;
|
|
int const kPartitionsK = 1;
|
|
|
|
using Shape = cutlass::gemm::GemmShape<128, 256, 8>;
|
|
using WarpShape = cutlass::gemm::GemmShape<64, 64, 8>;
|
|
using InstructionShape = cutlass::gemm::GemmShape<16, 8, 8>;
|
|
using Element = cutlass::half_t;
|
|
using ElementC = ElementAccumulator;
|
|
using LayoutA = cutlass::layout::ColumnMajorTensorOpMultiplicandCongruous<
|
|
cutlass::sizeof_bits<Element>::value, 64>;
|
|
using LayoutB = cutlass::layout::RowMajorTensorOpMultiplicandCongruous<
|
|
cutlass::sizeof_bits<Element>::value, 64>;
|
|
using LayoutC = cutlass::layout::RowMajor;
|
|
|
|
using WarpMmaTensorOp = typename cutlass::gemm::warp::DefaultMmaTensorOp<
|
|
WarpShape, InstructionShape, Element, LayoutA, Element, LayoutB, ElementC,
|
|
LayoutC>::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, mixed_f16_f32_tensor_op_256x128_64x64x8) {
|
|
|
|
//
|
|
// Define the warp-level matrix multiply
|
|
//
|
|
|
|
using ElementOutput = cutlass::half_t;
|
|
using ElementAccumulator = float;
|
|
using ElementCompute = float;
|
|
int const kElementsPerAccess = 128 / cutlass::sizeof_bits<ElementOutput>::value;
|
|
int const kPartitionsK = 1;
|
|
|
|
using Shape = cutlass::gemm::GemmShape<256, 128, 8>;
|
|
using WarpShape = cutlass::gemm::GemmShape<64, 64, 8>;
|
|
using InstructionShape = cutlass::gemm::GemmShape<16, 8, 8>;
|
|
using Element = cutlass::half_t;
|
|
using ElementC = ElementAccumulator;
|
|
using LayoutA = cutlass::layout::ColumnMajorTensorOpMultiplicandCongruous<
|
|
cutlass::sizeof_bits<Element>::value, 64>;
|
|
using LayoutB = cutlass::layout::RowMajorTensorOpMultiplicandCongruous<
|
|
cutlass::sizeof_bits<Element>::value, 64>;
|
|
using LayoutC = cutlass::layout::RowMajor;
|
|
|
|
using WarpMmaTensorOp = typename cutlass::gemm::warp::DefaultMmaTensorOp<
|
|
WarpShape, InstructionShape, Element, LayoutA, Element, LayoutB, ElementC,
|
|
LayoutC>::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, mixed_f16_f32_tensor_op_32x32_32x32x8) {
|
|
|
|
//
|
|
// Define the warp-level matrix multiply
|
|
//
|
|
|
|
using ElementOutput = cutlass::half_t;
|
|
using ElementAccumulator = float;
|
|
using ElementCompute = float;
|
|
int const kElementsPerAccess = 128 / cutlass::sizeof_bits<ElementOutput>::value;
|
|
int const kPartitionsK = 1;
|
|
|
|
using Shape = cutlass::gemm::GemmShape<32, 32, 8>;
|
|
using WarpShape = cutlass::gemm::GemmShape<32, 32, 8>;
|
|
using InstructionShape = cutlass::gemm::GemmShape<16, 8, 8>;
|
|
using Element = cutlass::half_t;
|
|
using ElementC = ElementAccumulator;
|
|
using LayoutA = cutlass::layout::ColumnMajorTensorOpMultiplicandCongruous<
|
|
cutlass::sizeof_bits<Element>::value, 64>;
|
|
using LayoutB = cutlass::layout::RowMajorTensorOpMultiplicandCongruous<
|
|
cutlass::sizeof_bits<Element>::value, 64>;
|
|
using LayoutC = cutlass::layout::RowMajor;
|
|
|
|
using WarpMmaTensorOp = typename cutlass::gemm::warp::DefaultMmaTensorOp<
|
|
WarpShape, InstructionShape, Element, LayoutA, Element, LayoutB, ElementC,
|
|
LayoutC>::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, mixed_f16_f32_tensor_op_64x64_32x32x8) {
|
|
|
|
//
|
|
// Define the warp-level matrix multiply
|
|
//
|
|
|
|
using ElementOutput = cutlass::half_t;
|
|
using ElementAccumulator = float;
|
|
using ElementCompute = float;
|
|
int const kElementsPerAccess = 128 / cutlass::sizeof_bits<ElementOutput>::value;
|
|
int const kPartitionsK = 1;
|
|
|
|
using Shape = cutlass::gemm::GemmShape<64, 64, 8>;
|
|
using WarpShape = cutlass::gemm::GemmShape<32, 32, 8>;
|
|
using InstructionShape = cutlass::gemm::GemmShape<16, 8, 8>;
|
|
using Element = cutlass::half_t;
|
|
using ElementC = ElementAccumulator;
|
|
using LayoutA = cutlass::layout::ColumnMajorTensorOpMultiplicandCongruous<
|
|
cutlass::sizeof_bits<Element>::value, 64>;
|
|
using LayoutB = cutlass::layout::RowMajorTensorOpMultiplicandCongruous<
|
|
cutlass::sizeof_bits<Element>::value, 64>;
|
|
using LayoutC = cutlass::layout::RowMajor;
|
|
|
|
using WarpMmaTensorOp = typename cutlass::gemm::warp::DefaultMmaTensorOp<
|
|
WarpShape, InstructionShape, Element, LayoutA, Element, LayoutB, ElementC,
|
|
LayoutC>::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, mixed_f16_f32_tensor_op_64x128_32x64x8) {
|
|
|
|
//
|
|
// Define the warp-level matrix multiply
|
|
//
|
|
|
|
using ElementOutput = cutlass::half_t;
|
|
using ElementAccumulator = float;
|
|
using ElementCompute = float;
|
|
int const kElementsPerAccess = 128 / cutlass::sizeof_bits<ElementOutput>::value;
|
|
int const kPartitionsK = 1;
|
|
|
|
using Shape = cutlass::gemm::GemmShape<64, 128, 8>;
|
|
using WarpShape = cutlass::gemm::GemmShape<32, 64, 8>;
|
|
using InstructionShape = cutlass::gemm::GemmShape<16, 8, 8>;
|
|
using Element = cutlass::half_t;
|
|
using ElementC = ElementAccumulator;
|
|
using LayoutA = cutlass::layout::ColumnMajorTensorOpMultiplicandCongruous<
|
|
cutlass::sizeof_bits<Element>::value, 64>;
|
|
using LayoutB = cutlass::layout::RowMajorTensorOpMultiplicandCongruous<
|
|
cutlass::sizeof_bits<Element>::value, 64>;
|
|
using LayoutC = cutlass::layout::RowMajor;
|
|
|
|
using WarpMmaTensorOp = typename cutlass::gemm::warp::DefaultMmaTensorOp<
|
|
WarpShape, InstructionShape, Element, LayoutA, Element, LayoutB, ElementC,
|
|
LayoutC>::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, mixed_f16_f32_tensor_op_128x64_64x32x8) {
|
|
|
|
//
|
|
// Define the warp-level matrix multiply
|
|
//
|
|
|
|
using ElementOutput = cutlass::half_t;
|
|
using ElementAccumulator = float;
|
|
using ElementCompute = float;
|
|
int const kElementsPerAccess = 128 / cutlass::sizeof_bits<ElementOutput>::value;
|
|
int const kPartitionsK = 1;
|
|
|
|
using Shape = cutlass::gemm::GemmShape<128, 64, 8>;
|
|
using WarpShape = cutlass::gemm::GemmShape<64, 32, 8>;
|
|
using InstructionShape = cutlass::gemm::GemmShape<16, 8, 8>;
|
|
using Element = cutlass::half_t;
|
|
using ElementC = ElementAccumulator;
|
|
using LayoutA = cutlass::layout::ColumnMajorTensorOpMultiplicandCongruous<
|
|
cutlass::sizeof_bits<Element>::value, 64>;
|
|
using LayoutB = cutlass::layout::RowMajorTensorOpMultiplicandCongruous<
|
|
cutlass::sizeof_bits<Element>::value, 64>;
|
|
using LayoutC = cutlass::layout::RowMajor;
|
|
|
|
using WarpMmaTensorOp = typename cutlass::gemm::warp::DefaultMmaTensorOp<
|
|
WarpShape, InstructionShape, Element, LayoutA, Element, LayoutB, ElementC,
|
|
LayoutC>::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);
|
|
}
|
|
|
|
/////////////////////////////////////////////////////////////////////////////////////////////////
|
|
//
|
|
// F16 acumulation
|
|
//
|
|
/////////////////////////////////////////////////////////////////////////////////////////////////
|
|
|
|
|
|
TEST(SM75_Epilogue_threadblock_epilogue, f16_tensor_op_64x64_64x64x8) {
|
|
|
|
//
|
|
// Define the warp-level matrix multiply
|
|
//
|
|
|
|
using ElementOutput = cutlass::half_t;
|
|
using ElementAccumulator = cutlass::half_t;
|
|
using ElementCompute = cutlass::half_t;
|
|
int const kElementsPerAccess = 128 / cutlass::sizeof_bits<ElementOutput>::value;
|
|
int const kPartitionsK = 1;
|
|
|
|
using Shape = cutlass::gemm::GemmShape<64, 64, 8>;
|
|
using WarpShape = cutlass::gemm::GemmShape<64, 64, 8>;
|
|
using InstructionShape = cutlass::gemm::GemmShape<16, 8, 8>;
|
|
using Element = cutlass::half_t;
|
|
using ElementC = ElementAccumulator;
|
|
using LayoutA = cutlass::layout::ColumnMajorTensorOpMultiplicandCongruous<
|
|
cutlass::sizeof_bits<Element>::value, 64>;
|
|
using LayoutB = cutlass::layout::RowMajorTensorOpMultiplicandCongruous<
|
|
cutlass::sizeof_bits<Element>::value, 64>;
|
|
using LayoutC = cutlass::layout::RowMajor;
|
|
|
|
using WarpMmaTensorOp = typename cutlass::gemm::warp::DefaultMmaTensorOp<
|
|
WarpShape, InstructionShape, Element, LayoutA, Element, LayoutB, ElementC,
|
|
LayoutC>::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, f16_tensor_op_128x128_64x64x8) {
|
|
|
|
//
|
|
// Define the warp-level matrix multiply
|
|
//
|
|
|
|
using ElementOutput = cutlass::half_t;
|
|
using ElementAccumulator = cutlass::half_t;
|
|
using ElementCompute = cutlass::half_t;
|
|
int const kElementsPerAccess = 128 / cutlass::sizeof_bits<ElementOutput>::value;
|
|
int const kPartitionsK = 1;
|
|
|
|
using Shape = cutlass::gemm::GemmShape<128, 128, 8>;
|
|
using WarpShape = cutlass::gemm::GemmShape<64, 64, 8>;
|
|
using InstructionShape = cutlass::gemm::GemmShape<16, 8, 8>;
|
|
using Element = cutlass::half_t;
|
|
using ElementC = ElementAccumulator;
|
|
using LayoutA = cutlass::layout::ColumnMajorTensorOpMultiplicandCongruous<
|
|
cutlass::sizeof_bits<Element>::value, 64>;
|
|
using LayoutB = cutlass::layout::RowMajorTensorOpMultiplicandCongruous<
|
|
cutlass::sizeof_bits<Element>::value, 64>;
|
|
using LayoutC = cutlass::layout::RowMajor;
|
|
|
|
using WarpMmaTensorOp = typename cutlass::gemm::warp::DefaultMmaTensorOp<
|
|
WarpShape, InstructionShape, Element, LayoutA, Element, LayoutB, ElementC,
|
|
LayoutC>::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, f16_tensor_op_128x256_64x64x8) {
|
|
|
|
//
|
|
// Define the warp-level matrix multiply
|
|
//
|
|
|
|
using ElementOutput = cutlass::half_t;
|
|
using ElementAccumulator = cutlass::half_t;
|
|
using ElementCompute = cutlass::half_t;
|
|
int const kElementsPerAccess = 128 / cutlass::sizeof_bits<ElementOutput>::value;
|
|
int const kPartitionsK = 1;
|
|
|
|
using Shape = cutlass::gemm::GemmShape<128, 256, 8>;
|
|
using WarpShape = cutlass::gemm::GemmShape<64, 64, 8>;
|
|
using InstructionShape = cutlass::gemm::GemmShape<16, 8, 8>;
|
|
using Element = cutlass::half_t;
|
|
using ElementC = ElementAccumulator;
|
|
using LayoutA = cutlass::layout::ColumnMajorTensorOpMultiplicandCongruous<
|
|
cutlass::sizeof_bits<Element>::value, 64>;
|
|
using LayoutB = cutlass::layout::RowMajorTensorOpMultiplicandCongruous<
|
|
cutlass::sizeof_bits<Element>::value, 64>;
|
|
using LayoutC = cutlass::layout::RowMajor;
|
|
|
|
using WarpMmaTensorOp = typename cutlass::gemm::warp::DefaultMmaTensorOp<
|
|
WarpShape, InstructionShape, Element, LayoutA, Element, LayoutB, ElementC,
|
|
LayoutC>::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, f16_tensor_op_256x128_64x64x8) {
|
|
|
|
//
|
|
// Define the warp-level matrix multiply
|
|
//
|
|
|
|
using ElementOutput = cutlass::half_t;
|
|
using ElementAccumulator = cutlass::half_t;
|
|
using ElementCompute = cutlass::half_t;
|
|
int const kElementsPerAccess = 128 / cutlass::sizeof_bits<ElementOutput>::value;
|
|
int const kPartitionsK = 1;
|
|
|
|
using Shape = cutlass::gemm::GemmShape<256, 128, 8>;
|
|
using WarpShape = cutlass::gemm::GemmShape<64, 64, 8>;
|
|
using InstructionShape = cutlass::gemm::GemmShape<16, 8, 8>;
|
|
using Element = cutlass::half_t;
|
|
using ElementC = ElementAccumulator;
|
|
using LayoutA = cutlass::layout::ColumnMajorTensorOpMultiplicandCongruous<
|
|
cutlass::sizeof_bits<Element>::value, 64>;
|
|
using LayoutB = cutlass::layout::RowMajorTensorOpMultiplicandCongruous<
|
|
cutlass::sizeof_bits<Element>::value, 64>;
|
|
using LayoutC = cutlass::layout::RowMajor;
|
|
|
|
using WarpMmaTensorOp = typename cutlass::gemm::warp::DefaultMmaTensorOp<
|
|
WarpShape, InstructionShape, Element, LayoutA, Element, LayoutB, ElementC,
|
|
LayoutC>::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, f16_tensor_op_32x32_32x32x8) {
|
|
|
|
//
|
|
// Define the warp-level matrix multiply
|
|
//
|
|
|
|
using ElementOutput = cutlass::half_t;
|
|
using ElementAccumulator = cutlass::half_t;
|
|
using ElementCompute = cutlass::half_t;
|
|
int const kElementsPerAccess = 128 / cutlass::sizeof_bits<ElementOutput>::value;
|
|
int const kPartitionsK = 1;
|
|
|
|
using Shape = cutlass::gemm::GemmShape<32, 32, 8>;
|
|
using WarpShape = cutlass::gemm::GemmShape<32, 32, 8>;
|
|
using InstructionShape = cutlass::gemm::GemmShape<16, 8, 8>;
|
|
using Element = cutlass::half_t;
|
|
using ElementC = ElementAccumulator;
|
|
using LayoutA = cutlass::layout::ColumnMajorTensorOpMultiplicandCongruous<
|
|
cutlass::sizeof_bits<Element>::value, 64>;
|
|
using LayoutB = cutlass::layout::RowMajorTensorOpMultiplicandCongruous<
|
|
cutlass::sizeof_bits<Element>::value, 64>;
|
|
using LayoutC = cutlass::layout::RowMajor;
|
|
|
|
using WarpMmaTensorOp = typename cutlass::gemm::warp::DefaultMmaTensorOp<
|
|
WarpShape, InstructionShape, Element, LayoutA, Element, LayoutB, ElementC,
|
|
LayoutC>::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, f16_tensor_op_64x64_32x32x8) {
|
|
|
|
//
|
|
// Define the warp-level matrix multiply
|
|
//
|
|
|
|
using ElementOutput = cutlass::half_t;
|
|
using ElementAccumulator = cutlass::half_t;
|
|
using ElementCompute = cutlass::half_t;
|
|
int const kElementsPerAccess = 128 / cutlass::sizeof_bits<ElementOutput>::value;
|
|
int const kPartitionsK = 1;
|
|
|
|
using Shape = cutlass::gemm::GemmShape<64, 64, 8>;
|
|
using WarpShape = cutlass::gemm::GemmShape<32, 32, 8>;
|
|
using InstructionShape = cutlass::gemm::GemmShape<16, 8, 8>;
|
|
using Element = cutlass::half_t;
|
|
using ElementC = ElementAccumulator;
|
|
using LayoutA = cutlass::layout::ColumnMajorTensorOpMultiplicandCongruous<
|
|
cutlass::sizeof_bits<Element>::value, 64>;
|
|
using LayoutB = cutlass::layout::RowMajorTensorOpMultiplicandCongruous<
|
|
cutlass::sizeof_bits<Element>::value, 64>;
|
|
using LayoutC = cutlass::layout::RowMajor;
|
|
|
|
using WarpMmaTensorOp = typename cutlass::gemm::warp::DefaultMmaTensorOp<
|
|
WarpShape, InstructionShape, Element, LayoutA, Element, LayoutB, ElementC,
|
|
LayoutC>::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, f16_tensor_op_64x128_32x64x8) {
|
|
|
|
//
|
|
// Define the warp-level matrix multiply
|
|
//
|
|
|
|
using ElementOutput = cutlass::half_t;
|
|
using ElementAccumulator = cutlass::half_t;
|
|
using ElementCompute = cutlass::half_t;
|
|
int const kElementsPerAccess = 128 / cutlass::sizeof_bits<ElementOutput>::value;
|
|
int const kPartitionsK = 1;
|
|
|
|
using Shape = cutlass::gemm::GemmShape<64, 128, 8>;
|
|
using WarpShape = cutlass::gemm::GemmShape<32, 64, 8>;
|
|
using InstructionShape = cutlass::gemm::GemmShape<16, 8, 8>;
|
|
using Element = cutlass::half_t;
|
|
using ElementC = ElementAccumulator;
|
|
using LayoutA = cutlass::layout::ColumnMajorTensorOpMultiplicandCongruous<
|
|
cutlass::sizeof_bits<Element>::value, 64>;
|
|
using LayoutB = cutlass::layout::RowMajorTensorOpMultiplicandCongruous<
|
|
cutlass::sizeof_bits<Element>::value, 64>;
|
|
using LayoutC = cutlass::layout::RowMajor;
|
|
|
|
using WarpMmaTensorOp = typename cutlass::gemm::warp::DefaultMmaTensorOp<
|
|
WarpShape, InstructionShape, Element, LayoutA, Element, LayoutB, ElementC,
|
|
LayoutC>::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, f16_tensor_op_128x64_64x32x8) {
|
|
|
|
//
|
|
// Define the warp-level matrix multiply
|
|
//
|
|
|
|
using ElementOutput = cutlass::half_t;
|
|
using ElementAccumulator = cutlass::half_t;
|
|
using ElementCompute = cutlass::half_t;
|
|
int const kElementsPerAccess = 128 / cutlass::sizeof_bits<ElementOutput>::value;
|
|
int const kPartitionsK = 1;
|
|
|
|
using Shape = cutlass::gemm::GemmShape<128, 64, 8>;
|
|
using WarpShape = cutlass::gemm::GemmShape<64, 32, 8>;
|
|
using InstructionShape = cutlass::gemm::GemmShape<16, 8, 8>;
|
|
using Element = cutlass::half_t;
|
|
using ElementC = ElementAccumulator;
|
|
using LayoutA = cutlass::layout::ColumnMajorTensorOpMultiplicandCongruous<
|
|
cutlass::sizeof_bits<Element>::value, 64>;
|
|
using LayoutB = cutlass::layout::RowMajorTensorOpMultiplicandCongruous<
|
|
cutlass::sizeof_bits<Element>::value, 64>;
|
|
using LayoutC = cutlass::layout::RowMajor;
|
|
|
|
using WarpMmaTensorOp = typename cutlass::gemm::warp::DefaultMmaTensorOp<
|
|
WarpShape, InstructionShape, Element, LayoutA, Element, LayoutB, ElementC,
|
|
LayoutC>::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(SM80_Epilogue_threadblock_epilogue, f64_tensor_op_64x64_32x32x4) {
|
|
|
|
//
|
|
// Define the warp-level matrix multiply
|
|
//
|
|
|
|
using ElementOutput = double;
|
|
using ElementAccumulator = double;
|
|
using ElementCompute = double;
|
|
int const kElementsPerAccess = 1;
|
|
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, 4>;
|
|
using Element = double;
|
|
using ElementC = ElementAccumulator;
|
|
using LayoutA = cutlass::layout::ColumnMajorTensorOpMultiplicandCongruous64b;
|
|
using LayoutB = cutlass::layout::RowMajorTensorOpMultiplicandCongruous64b;
|
|
using LayoutC = cutlass::layout::RowMajor;
|
|
|
|
using WarpMmaTensorOp = typename cutlass::gemm::warp::DefaultMmaTensorOp<
|
|
WarpShape, InstructionShape, Element, LayoutA, Element, LayoutB, ElementC,
|
|
LayoutC>::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(SM80_Epilogue_threadblock_epilogue, f64_tensor_op_128x64_64x32x4) {
|
|
|
|
//
|
|
// Define the warp-level matrix multiply
|
|
//
|
|
|
|
using ElementOutput = double;
|
|
using ElementAccumulator = double;
|
|
using ElementCompute = double;
|
|
int const kElementsPerAccess = 1;
|
|
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, 4>;
|
|
using Element = double;
|
|
using ElementC = ElementAccumulator;
|
|
using LayoutA = cutlass::layout::ColumnMajorTensorOpMultiplicandCongruous64b;
|
|
using LayoutB = cutlass::layout::RowMajorTensorOpMultiplicandCongruous64b;
|
|
using LayoutC = cutlass::layout::RowMajor;
|
|
|
|
using WarpMmaTensorOp = typename cutlass::gemm::warp::DefaultMmaTensorOp<
|
|
WarpShape, InstructionShape, Element, LayoutA, Element, LayoutB, ElementC,
|
|
LayoutC>::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(SM80_Epilogue_threadblock_epilogue, f64_tensor_op_64x128_32x64x4) {
|
|
|
|
//
|
|
// Define the warp-level matrix multiply
|
|
//
|
|
|
|
using ElementOutput = double;
|
|
using ElementAccumulator = double;
|
|
using ElementCompute = double;
|
|
int const kElementsPerAccess = 1;
|
|
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, 4>;
|
|
using Element = double;
|
|
using ElementC = ElementAccumulator;
|
|
using LayoutA = cutlass::layout::ColumnMajorTensorOpMultiplicandCongruous64b;
|
|
using LayoutB = cutlass::layout::RowMajorTensorOpMultiplicandCongruous64b;
|
|
using LayoutC = cutlass::layout::RowMajor;
|
|
|
|
using WarpMmaTensorOp = typename cutlass::gemm::warp::DefaultMmaTensorOp<
|
|
WarpShape, InstructionShape, Element, LayoutA, Element, LayoutB, ElementC,
|
|
LayoutC>::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(SM80_Epilogue_threadblock_epilogue, f64_tensor_op_128x128_32x64x4) {
|
|
|
|
//
|
|
// Define the warp-level matrix multiply
|
|
//
|
|
|
|
using ElementOutput = double;
|
|
using ElementAccumulator = double;
|
|
using ElementCompute = double;
|
|
int const kElementsPerAccess = 1;
|
|
int const kPartitionsK = 1;
|
|
|
|
using Shape = cutlass::gemm::GemmShape<128, 128, 16>;
|
|
using WarpShape = cutlass::gemm::GemmShape<32, 64, 16>;
|
|
using InstructionShape = cutlass::gemm::GemmShape<8, 8, 4>;
|
|
using Element = double;
|
|
using ElementC = ElementAccumulator;
|
|
using LayoutA = cutlass::layout::ColumnMajorTensorOpMultiplicandCongruous64b;
|
|
using LayoutB = cutlass::layout::RowMajorTensorOpMultiplicandCongruous64b;
|
|
using LayoutC = cutlass::layout::RowMajor;
|
|
|
|
using WarpMmaTensorOp = typename cutlass::gemm::warp::DefaultMmaTensorOp<
|
|
WarpShape, InstructionShape, Element, LayoutA, Element, LayoutB, ElementC,
|
|
LayoutC>::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, vec1_mixed_f16_f32_tensor_op_128x128_64x64x8) {
|
|
|
|
//
|
|
// Define the warp-level matrix multiply
|
|
//
|
|
|
|
using ElementOutput = cutlass::half_t;
|
|
using ElementAccumulator = float;
|
|
using ElementCompute = float;
|
|
int const kElementsPerAccess = 1;
|
|
int const kPartitionsK = 1;
|
|
|
|
using Shape = cutlass::gemm::GemmShape<128, 128, 8>;
|
|
using WarpShape = cutlass::gemm::GemmShape<64, 64, 8>;
|
|
using InstructionShape = cutlass::gemm::GemmShape<16, 8, 8>;
|
|
using Element = cutlass::half_t;
|
|
using ElementC = ElementAccumulator;
|
|
using LayoutA = cutlass::layout::ColumnMajorTensorOpMultiplicandCongruous<
|
|
cutlass::sizeof_bits<Element>::value, 64>;
|
|
using LayoutB = cutlass::layout::RowMajorTensorOpMultiplicandCongruous<
|
|
cutlass::sizeof_bits<Element>::value, 64>;
|
|
using LayoutC = cutlass::layout::RowMajor;
|
|
|
|
using WarpMmaTensorOp = typename cutlass::gemm::warp::DefaultMmaTensorOp<
|
|
WarpShape, InstructionShape, Element, LayoutA, Element, LayoutB, ElementC,
|
|
LayoutC>::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, vec1_mixed_f16_f32_tensor_op_128x256_64x64x8) {
|
|
|
|
//
|
|
// Define the warp-level matrix multiply
|
|
//
|
|
|
|
using ElementOutput = cutlass::half_t;
|
|
using ElementAccumulator = float;
|
|
using ElementCompute = float;
|
|
int const kElementsPerAccess = 1;
|
|
int const kPartitionsK = 1;
|
|
|
|
using Shape = cutlass::gemm::GemmShape<128, 256, 8>;
|
|
using WarpShape = cutlass::gemm::GemmShape<64, 64, 8>;
|
|
using InstructionShape = cutlass::gemm::GemmShape<16, 8, 8>;
|
|
using Element = cutlass::half_t;
|
|
using ElementC = ElementAccumulator;
|
|
using LayoutA = cutlass::layout::ColumnMajorTensorOpMultiplicandCongruous<
|
|
cutlass::sizeof_bits<Element>::value, 64>;
|
|
using LayoutB = cutlass::layout::RowMajorTensorOpMultiplicandCongruous<
|
|
cutlass::sizeof_bits<Element>::value, 64>;
|
|
using LayoutC = cutlass::layout::RowMajor;
|
|
|
|
using WarpMmaTensorOp = typename cutlass::gemm::warp::DefaultMmaTensorOp<
|
|
WarpShape, InstructionShape, Element, LayoutA, Element, LayoutB, ElementC,
|
|
LayoutC>::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, vec1_tensor_op_128x128_64x64x8) {
|
|
|
|
//
|
|
// Define the warp-level matrix multiply
|
|
//
|
|
|
|
using ElementOutput = float;
|
|
using ElementAccumulator = float;
|
|
using ElementCompute = float;
|
|
int const kElementsPerAccess = 1;
|
|
int const kPartitionsK = 1;
|
|
|
|
using Shape = cutlass::gemm::GemmShape<128, 128, 8>;
|
|
using WarpShape = cutlass::gemm::GemmShape<64, 64, 8>;
|
|
using InstructionShape = cutlass::gemm::GemmShape<16, 8, 8>;
|
|
using Element = cutlass::half_t;
|
|
using ElementC = ElementAccumulator;
|
|
using LayoutA = cutlass::layout::ColumnMajorTensorOpMultiplicandCongruous<
|
|
cutlass::sizeof_bits<Element>::value, 64>;
|
|
using LayoutB = cutlass::layout::RowMajorTensorOpMultiplicandCongruous<
|
|
cutlass::sizeof_bits<Element>::value, 64>;
|
|
using LayoutC = cutlass::layout::RowMajor;
|
|
|
|
using WarpMmaTensorOp = typename cutlass::gemm::warp::DefaultMmaTensorOp<
|
|
WarpShape, InstructionShape, Element, LayoutA, Element, LayoutB, ElementC,
|
|
LayoutC>::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, vec1_tensor_op_128x256_64x64x8) {
|
|
|
|
//
|
|
// Define the warp-level matrix multiply
|
|
//
|
|
|
|
using ElementOutput = float;
|
|
using ElementAccumulator = float;
|
|
using ElementCompute = float;
|
|
int const kElementsPerAccess = 1;
|
|
int const kPartitionsK = 1;
|
|
|
|
using Shape = cutlass::gemm::GemmShape<128, 256, 8>;
|
|
using WarpShape = cutlass::gemm::GemmShape<64, 64, 8>;
|
|
using InstructionShape = cutlass::gemm::GemmShape<16, 8, 8>;
|
|
using Element = cutlass::half_t;
|
|
using ElementC = ElementAccumulator;
|
|
using LayoutA = cutlass::layout::ColumnMajorTensorOpMultiplicandCongruous<
|
|
cutlass::sizeof_bits<Element>::value, 64>;
|
|
using LayoutB = cutlass::layout::RowMajorTensorOpMultiplicandCongruous<
|
|
cutlass::sizeof_bits<Element>::value, 64>;
|
|
using LayoutC = cutlass::layout::RowMajor;
|
|
|
|
using WarpMmaTensorOp = typename cutlass::gemm::warp::DefaultMmaTensorOp<
|
|
WarpShape, InstructionShape, Element, LayoutA, Element, LayoutB, ElementC,
|
|
LayoutC>::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);
|
|
}
|
|
|
|
/////////////////////////////////////////////////////////////////////////////////////////////////
|