CUTLASS 2.0 (#62)
CUTLASS 2.0 Substantially refactored for - Better performance, particularly for native Turing Tensor Cores - Robust and durable templates spanning the design space - Encapsulated functionality embodying modern C++11 programming techniques - Optimized containers and data types for efficient, generic, portable device code Updates to: - Quick start guide - Documentation - Utilities - CUTLASS Profiler Native Turing Tensor Cores - Efficient GEMM kernels targeting Turing Tensor Cores - Mixed-precision floating point, 8-bit integer, 4-bit integer, and binarized operands Coverage of existing CUTLASS functionality: - GEMM kernels targeting CUDA and Tensor Cores in NVIDIA GPUs - Volta Tensor Cores through native mma.sync and through WMMA API - Optimizations such as parallel reductions, threadblock rasterization, and intra-threadblock reductions - Batched GEMM operations - Complex-valued GEMMs Note: this commit and all that follow require a host compiler supporting C++11 or greater.
This commit is contained in:
Andrew Kerr
GitHub
29
test/unit/nvrtc/thread/CMakeLists.txt
Normal file
29
test/unit/nvrtc/thread/CMakeLists.txt
Normal file
@ -0,0 +1,29 @@
|
||||
# Copyright (c) 2017-2019, NVIDIA CORPORATION. All rights reserved.
|
||||
#
|
||||
# Redistribution and use in source and binary forms, with or without modification, are permitted
|
||||
# provided that the following conditions are met:
|
||||
# * Redistributions of source code must retain the above copyright notice, this list of
|
||||
# conditions and the following disclaimer.
|
||||
# * Redistributions in binary form must reproduce the above copyright notice, this list of
|
||||
# conditions and the following disclaimer in the documentation and/or other materials
|
||||
# provided with the distribution.
|
||||
# * Neither the name of the NVIDIA CORPORATION nor the names of its contributors may be used
|
||||
# to endorse or promote products derived from this software without specific prior written
|
||||
# permission.
|
||||
#
|
||||
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR
|
||||
# IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND
|
||||
# FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE
|
||||
# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
|
||||
# BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS;
|
||||
# OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT,
|
||||
# STRICT LIABILITY, OR TOR (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
|
||||
# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
|
||||
|
||||
cutlass_test_unit_add_executable(
|
||||
cutlass_test_unit_nvrtc_thread
|
||||
gemm_nvrtc.cu
|
||||
testbed.h
|
||||
)
|
||||
|
||||
target_link_libraries(cutlass_test_unit_nvrtc_thread PRIVATE cutlass_nvrtc)
|
||||
197
test/unit/nvrtc/thread/gemm_nvrtc.cu
Normal file
197
test/unit/nvrtc/thread/gemm_nvrtc.cu
Normal file
@ -0,0 +1,197 @@
|
||||
/***************************************************************************************************
|
||||
* Copyright (c) 2017-2019, NVIDIA CORPORATION. All rights reserved.
|
||||
*
|
||||
* Redistribution and use in source and binary forms, with or without modification, are permitted
|
||||
* provided that the following conditions are met:
|
||||
* * Redistributions of source code must retain the above copyright notice, this list of
|
||||
* conditions and the following disclaimer.
|
||||
* * Redistributions in binary form must reproduce the above copyright notice, this list of
|
||||
* conditions and the following disclaimer in the documentation and/or other materials
|
||||
* provided with the distribution.
|
||||
* * Neither the name of the NVIDIA CORPORATION nor the names of its contributors may be used
|
||||
* to endorse or promote products derived from this software without specific prior written
|
||||
* permission.
|
||||
*
|
||||
* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR
|
||||
* IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND
|
||||
* FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE
|
||||
* FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
|
||||
* BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS;
|
||||
* OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT,
|
||||
* STRICT LIABILITY, OR TOR (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
|
||||
* OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
|
||||
*
|
||||
**************************************************************************************************/
|
||||
/*! \file
|
||||
\brief Unit tests for thread-level GEMM
|
||||
*/
|
||||
|
||||
#include "../../common/cutlass_unit_test.h"
|
||||
|
||||
#include "cutlass/gemm/thread/mma.h"
|
||||
|
||||
#include "testbed.h"
|
||||
|
||||
#if 0
|
||||
int main() {
|
||||
nvrtc::thread::Testbed<
|
||||
cutlass::gemm::GemmShape<3, 4, 2>,
|
||||
float,
|
||||
cutlass::layout::ColumnMajor,
|
||||
float,
|
||||
cutlass::layout::RowMajor,
|
||||
float,
|
||||
cutlass::layout::ColumnMajor
|
||||
>().run("cutlass::gemm::thread::Mma<cutlass::gemm::GemmShape<3, 4, 2>, float, cutlass::layout::ColumnMajor, float, cutlass::layout::RowMajor, float, cutlass::layout::ColumnMajor >");
|
||||
return 0;
|
||||
}
|
||||
#endif
|
||||
|
||||
TEST(SM50_Sgemm_thread_nvrtc, DISABLED_col_row_3x4x2) {
|
||||
|
||||
test::nvrtc::thread::Testbed<
|
||||
cutlass::gemm::GemmShape<3, 4, 2>,
|
||||
float,
|
||||
cutlass::layout::ColumnMajor,
|
||||
float,
|
||||
cutlass::layout::RowMajor,
|
||||
float,
|
||||
cutlass::layout::ColumnMajor
|
||||
>().run("cutlass::gemm::thread::Mma<cutlass::gemm::GemmShape<3, 4, 2>, float, cutlass::layout::ColumnMajor, float, cutlass::layout::RowMajor, float, cutlass::layout::ColumnMajor >");
|
||||
}
|
||||
|
||||
/////////////////////////////////////////////////////////////////////////////////////////////////
|
||||
#if 0
|
||||
TEST(SM50_Sgemm_thread, col_row_3x4x2) {
|
||||
|
||||
test::gemm::thread::Testbed<
|
||||
cutlass::gemm::GemmShape<3, 4, 2>,
|
||||
float,
|
||||
cutlass::layout::ColumnMajor,
|
||||
float,
|
||||
cutlass::layout::RowMajor,
|
||||
float,
|
||||
cutlass::layout::ColumnMajor
|
||||
>().run();
|
||||
}
|
||||
|
||||
TEST(SM50_Sgemm_thread, col_row_4x4x2) {
|
||||
|
||||
test::gemm::thread::Testbed<
|
||||
cutlass::gemm::GemmShape<4, 4, 2>,
|
||||
float,
|
||||
cutlass::layout::ColumnMajor,
|
||||
float,
|
||||
cutlass::layout::RowMajor,
|
||||
float,
|
||||
cutlass::layout::ColumnMajor
|
||||
>().run();
|
||||
}
|
||||
|
||||
TEST(SM50_Sgemm_thread, row_col_4x4x2) {
|
||||
|
||||
test::gemm::thread::Testbed<
|
||||
cutlass::gemm::GemmShape<4, 4, 2>,
|
||||
float,
|
||||
cutlass::layout::RowMajor,
|
||||
float,
|
||||
cutlass::layout::ColumnMajor,
|
||||
float,
|
||||
cutlass::layout::ColumnMajor
|
||||
>().run();
|
||||
}
|
||||
|
||||
TEST(SM50_Sgemm_thread, col_row_4x5x3) {
|
||||
|
||||
test::gemm::thread::Testbed<
|
||||
cutlass::gemm::GemmShape<4, 5, 3>,
|
||||
float,
|
||||
cutlass::layout::ColumnMajor,
|
||||
float,
|
||||
cutlass::layout::RowMajor,
|
||||
float,
|
||||
cutlass::layout::ColumnMajor
|
||||
>().run();
|
||||
}
|
||||
|
||||
TEST(SM50_Sgemm_thread, col_row) {
|
||||
|
||||
test::gemm::thread::Testbed<
|
||||
cutlass::gemm::GemmShape<8, 8, 1>,
|
||||
float,
|
||||
cutlass::layout::ColumnMajor,
|
||||
float,
|
||||
cutlass::layout::RowMajor,
|
||||
float,
|
||||
cutlass::layout::ColumnMajor
|
||||
>().run();
|
||||
}
|
||||
|
||||
TEST(SM50_Sgemm_thread, row_col) {
|
||||
|
||||
test::gemm::thread::Testbed<
|
||||
cutlass::gemm::GemmShape<8, 8, 1>,
|
||||
float,
|
||||
cutlass::layout::RowMajor,
|
||||
float,
|
||||
cutlass::layout::ColumnMajor,
|
||||
float,
|
||||
cutlass::layout::ColumnMajor
|
||||
>().run();
|
||||
}
|
||||
|
||||
TEST(SM50_Sgemm_thread, col_col) {
|
||||
|
||||
test::gemm::thread::Testbed<
|
||||
cutlass::gemm::GemmShape<8, 8, 1>,
|
||||
float,
|
||||
cutlass::layout::ColumnMajor,
|
||||
float,
|
||||
cutlass::layout::ColumnMajor,
|
||||
float,
|
||||
cutlass::layout::ColumnMajor
|
||||
>().run();
|
||||
}
|
||||
|
||||
TEST(SM50_Sgemm_thread, row_row) {
|
||||
|
||||
test::gemm::thread::Testbed<
|
||||
cutlass::gemm::GemmShape<8, 8, 1>,
|
||||
float,
|
||||
cutlass::layout::RowMajor,
|
||||
float,
|
||||
cutlass::layout::RowMajor,
|
||||
float,
|
||||
cutlass::layout::ColumnMajor
|
||||
>().run();
|
||||
}
|
||||
|
||||
/////////////////////////////////////////////////////////////////////////////////////////////////
|
||||
|
||||
TEST(SM50_Dgemm_thread, col_row) {
|
||||
|
||||
test::gemm::thread::Testbed<
|
||||
cutlass::gemm::GemmShape<8, 8, 1>,
|
||||
double,
|
||||
cutlass::layout::ColumnMajor,
|
||||
double,
|
||||
cutlass::layout::RowMajor,
|
||||
double,
|
||||
cutlass::layout::ColumnMajor
|
||||
>().run();
|
||||
}
|
||||
|
||||
TEST(SM50_Dgemm_thread, row_col) {
|
||||
|
||||
test::gemm::thread::Testbed<
|
||||
cutlass::gemm::GemmShape<8, 8, 1>,
|
||||
double,
|
||||
cutlass::layout::RowMajor,
|
||||
double,
|
||||
cutlass::layout::ColumnMajor,
|
||||
double,
|
||||
cutlass::layout::ColumnMajor
|
||||
>().run();
|
||||
}
|
||||
#endif
|
||||
/////////////////////////////////////////////////////////////////////////////////////////////////
|
||||
317
test/unit/nvrtc/thread/testbed.h
Normal file
317
test/unit/nvrtc/thread/testbed.h
Normal file
@ -0,0 +1,317 @@
|
||||
/***************************************************************************************************
|
||||
* Copyright (c) 2017-2019, NVIDIA CORPORATION. All rights reserved.
|
||||
*
|
||||
* Redistribution and use in source and binary forms, with or without modification, are permitted
|
||||
* provided that the following conditions are met:
|
||||
* * Redistributions of source code must retain the above copyright notice, this list of
|
||||
* conditions and the following disclaimer.
|
||||
* * Redistributions in binary form must reproduce the above copyright notice, this list of
|
||||
* conditions and the following disclaimer in the documentation and/or other materials
|
||||
* provided with the distribution.
|
||||
* * Neither the name of the NVIDIA CORPORATION nor the names of its contributors may be used
|
||||
* to endorse or promote products derived from this software without specific prior written
|
||||
* permission.
|
||||
*
|
||||
* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR
|
||||
* IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND
|
||||
* FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE
|
||||
* FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
|
||||
* BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS;
|
||||
* OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT,
|
||||
* STRICT LIABILITY, OR TOR (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
|
||||
* OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
|
||||
*
|
||||
**************************************************************************************************/
|
||||
/*! \file
|
||||
\brief Unit tests for thread-level GEMM
|
||||
*/
|
||||
|
||||
#pragma once
|
||||
|
||||
#include <iostream>
|
||||
|
||||
#include "cutlass/gemm/thread/mma.h"
|
||||
#include "../kernel/thread/testbed_kernel.h"
|
||||
|
||||
#include "cutlass/util/host_tensor.h"
|
||||
#include "cutlass/util/tensor_view_io.h"
|
||||
|
||||
#include "cutlass/util/reference/host/tensor_copy.h"
|
||||
#include "cutlass/util/reference/host/tensor_fill.h"
|
||||
#include "cutlass/util/reference/host/tensor_compare.h"
|
||||
#include "cutlass/util/reference/host/gemm.h"
|
||||
|
||||
#include <cuda.h>
|
||||
#include <nvrtc.h>
|
||||
#include "../cutlass/nvrtc/environment.h"
|
||||
#include <assert.h>
|
||||
|
||||
/////////////////////////////////////////////////////////////////////////////////////////////////
|
||||
|
||||
namespace test {
|
||||
namespace nvrtc {
|
||||
namespace thread {
|
||||
|
||||
/// Structure to compute the matrix product
|
||||
template <
|
||||
/// Size of the Gemm problem - concept: gemm::GemmShape<>
|
||||
typename Shape,
|
||||
/// Data type of A elements
|
||||
typename ElementA,
|
||||
/// Layout of A matrix (concept: MatrixLayout)
|
||||
typename LayoutA,
|
||||
/// Data type of B elements
|
||||
typename ElementB,
|
||||
/// Layout of B matrix (concept: MatrixLayout)
|
||||
typename LayoutB,
|
||||
/// Element type of C matrix
|
||||
typename ElementC,
|
||||
/// Layout of C matrix (concept: MatrixLayout)
|
||||
typename LayoutC
|
||||
>
|
||||
struct Testbed {
|
||||
|
||||
/// Thread-level matrix multiply-accumulate operator
|
||||
using Mma = cutlass::gemm::thread::Mma<
|
||||
Shape,
|
||||
ElementA,
|
||||
LayoutA,
|
||||
ElementB,
|
||||
LayoutB,
|
||||
ElementC,
|
||||
LayoutC
|
||||
>;
|
||||
|
||||
//
|
||||
// Data members
|
||||
//
|
||||
|
||||
cutlass::HostTensor<ElementA, LayoutA> tensor_A;
|
||||
cutlass::HostTensor<ElementB, LayoutB> tensor_B;
|
||||
cutlass::HostTensor<ElementC, LayoutC> tensor_C;
|
||||
cutlass::HostTensor<ElementC, LayoutC> tensor_D_computed;
|
||||
cutlass::HostTensor<ElementC, LayoutC> tensor_D_reference;
|
||||
|
||||
//
|
||||
// Methods
|
||||
//
|
||||
|
||||
/// Allocates workspace in device memory
|
||||
Testbed() {
|
||||
|
||||
tensor_A.reset(cutlass::make_Coord(Shape::kM, Shape::kK));
|
||||
tensor_B.reset(cutlass::make_Coord(Shape::kK, Shape::kN));
|
||||
tensor_C.reset(cutlass::make_Coord(Shape::kM, Shape::kN));
|
||||
tensor_D_computed.reset(cutlass::make_Coord(Shape::kM, Shape::kN));
|
||||
tensor_D_reference.reset(cutlass::make_Coord(Shape::kM, Shape::kN), false);
|
||||
}
|
||||
|
||||
static inline bool check_nvrtc_error(nvrtcResult error) {
|
||||
if (error != NVRTC_SUCCESS) {
|
||||
std::cerr << "failed to compile ";
|
||||
return false;
|
||||
}
|
||||
return true;
|
||||
}
|
||||
|
||||
/// Runs the test
|
||||
bool run(std::string const &gemm_traits) {
|
||||
|
||||
//
|
||||
// initialize device memory
|
||||
//
|
||||
|
||||
cutlass::reference::host::BlockFillSequential(
|
||||
tensor_A.host_data(),
|
||||
tensor_A.capacity()
|
||||
);
|
||||
|
||||
cutlass::reference::host::BlockFillSequential(
|
||||
tensor_B.host_data(),
|
||||
tensor_B.capacity(),
|
||||
ElementB(1),
|
||||
ElementB(2)
|
||||
);
|
||||
|
||||
cutlass::reference::host::TensorFill(
|
||||
tensor_C.host_view(),
|
||||
ElementC(0)
|
||||
);
|
||||
|
||||
cutlass::reference::host::TensorFill(
|
||||
tensor_D_computed.host_view(),
|
||||
ElementC(0)
|
||||
);
|
||||
|
||||
cutlass::reference::host::TensorFill(
|
||||
tensor_D_reference.host_view(),
|
||||
ElementC(0)
|
||||
);
|
||||
|
||||
tensor_A.sync_device();
|
||||
tensor_B.sync_device();
|
||||
tensor_C.sync_device();
|
||||
tensor_D_computed.sync_device();
|
||||
|
||||
#if 0
|
||||
// launch kernel
|
||||
cutlass::gemm::kernel::testbed_kernel<Mma><<< dim3(1, 1), dim3(1, 1, 1) >>>(
|
||||
tensor_D_computed.device_data(),
|
||||
tensor_A.device_data(),
|
||||
tensor_B.device_data(),
|
||||
tensor_C.device_data());
|
||||
|
||||
#else
|
||||
// Instantiate gemm_kernel
|
||||
nvrtcResult result_nvrtc;
|
||||
nvrtcProgram program;
|
||||
static char const *src =
|
||||
"#include \"cutlass/gemm/thread/mma.h\"\n"
|
||||
"#include \"cutlass/gemm/gemm.h\"\n"
|
||||
"#include \"cutlass/layout/matrix.h\"\n"
|
||||
"#include \"unit/nvrtc/kernel/thread/testbed_kernel.h\"\n"
|
||||
;
|
||||
|
||||
std::string type_name;
|
||||
#if 0
|
||||
// TODO Ideally we'd use nvrtcGetTypeName to determine the type, but it cannot resolve enum symbol names
|
||||
// As altername solution we might want to implement to_string<GemmTraits>() to get the traits string.
|
||||
nvrtcGetTypeName<typename GemmTraits_>(&type_name);
|
||||
#else
|
||||
type_name = gemm_traits;
|
||||
#endif
|
||||
|
||||
result_nvrtc = nvrtcCreateProgram(&program,
|
||||
src,
|
||||
NULL,
|
||||
(int)cutlass::nvrtc::kCutlassHeaderCount,
|
||||
cutlass::nvrtc::kCutlassHeaders,
|
||||
cutlass::nvrtc::kCutlassHeaderNames);
|
||||
check_nvrtc_error(result_nvrtc);
|
||||
|
||||
std::string gemm_kernel_instantiation =
|
||||
"test::nvrtc::kernel::thread::testbed_kernel< " + type_name + " >";
|
||||
nvrtcAddNameExpression(program, gemm_kernel_instantiation.c_str());
|
||||
|
||||
const char *opts[] = {"--gpu-architecture=compute_75",
|
||||
"--std=c++11",
|
||||
"--include-path=/usr/local/cuda-10.1/include"};
|
||||
|
||||
result_nvrtc = nvrtcCompileProgram(program, 3, opts);
|
||||
if (result_nvrtc != NVRTC_SUCCESS) {
|
||||
size_t logSize;
|
||||
nvrtcGetProgramLogSize(program, &logSize);
|
||||
std::vector<char> log(logSize);
|
||||
nvrtcGetProgramLog(program, log.data());
|
||||
std::cout << "Compile log:" << std::endl << log.data() << std::endl;
|
||||
}
|
||||
if (!check_nvrtc_error(result_nvrtc)) {
|
||||
assert(0);
|
||||
}
|
||||
|
||||
// The lowered name is the name of the template instantiation in the generated PTX code.
|
||||
char const *gemm_kernel_lowered_name;
|
||||
nvrtcGetLoweredName(program, gemm_kernel_instantiation.c_str(), &gemm_kernel_lowered_name);
|
||||
if (!check_nvrtc_error(result_nvrtc)) {
|
||||
assert(0);
|
||||
}
|
||||
|
||||
// Query the size of the genereated PTX so that we can allocate storage and retrieve it afterwards
|
||||
size_t ptx_size;
|
||||
result_nvrtc = nvrtcGetPTXSize(program, &ptx_size);
|
||||
if (!check_nvrtc_error(result_nvrtc)) {
|
||||
assert(0);
|
||||
}
|
||||
|
||||
std::vector<char> ptx(ptx_size);
|
||||
result_nvrtc = nvrtcGetPTX(program, ptx.data());
|
||||
if (!check_nvrtc_error(result_nvrtc)) {
|
||||
assert(0);
|
||||
}
|
||||
|
||||
// we do not need the nvrtc program anymore
|
||||
//nvrtcDestroyProgram(&program);
|
||||
|
||||
CUmodule module;
|
||||
CUresult result_cuda;
|
||||
result_cuda = cuModuleLoadDataEx(&module, ptx.data(), 0, 0, 0);
|
||||
if (result_cuda != CUDA_SUCCESS) {
|
||||
assert(0);
|
||||
}
|
||||
|
||||
CUfunction kernel;
|
||||
result_cuda = cuModuleGetFunction(&kernel, module, gemm_kernel_lowered_name);
|
||||
if (result_cuda != CUDA_SUCCESS) {
|
||||
assert(0);
|
||||
}
|
||||
|
||||
void* d_a = (void*)tensor_A.device_data();
|
||||
void* d_b = (void*)tensor_B.device_data();
|
||||
void* d_c = (void*)tensor_C.device_data();
|
||||
void* d_d = (void*)tensor_D_computed.device_data();
|
||||
void* args[] = { &d_d, &d_a, &d_b, &d_c };
|
||||
|
||||
// CUfunction f, unsigned int gridDimX, unsigned int gridDimY, unsigned int gridDimZ, unsigned int blockDimX, unsigned int blockDimY, unsigned int blockDimZ, unsigned int sharedMemBytes, CUstream hStream, void** kernelParams, void** extra
|
||||
result_cuda = cuLaunchKernel(kernel, 1, 1, 1, 1, 1, 1, 0, 0 /*cudaStreamDefault*/, args, 0);
|
||||
if (result_cuda != CUDA_SUCCESS) {
|
||||
assert(0);
|
||||
} else {
|
||||
}
|
||||
#endif
|
||||
|
||||
// verify no errors
|
||||
cudaError_t result = cudaDeviceSynchronize();
|
||||
|
||||
if (result != cudaSuccess) {
|
||||
std::cout << "CUDA ERROR: " << cudaGetErrorString(result);
|
||||
return false;
|
||||
}
|
||||
|
||||
tensor_D_computed.sync_host();
|
||||
|
||||
//
|
||||
// Reference implementation
|
||||
//
|
||||
|
||||
//tensor_D_reference.fill(tensor_C.host_view());
|
||||
|
||||
cutlass::reference::host::Gemm<ElementA, LayoutA, ElementB, LayoutB,
|
||||
ElementC, LayoutC, ElementC, ElementC> reference_gemm;
|
||||
|
||||
reference_gemm(
|
||||
{Shape::kM, Shape::kN, Shape::kK},
|
||||
ElementC(1),
|
||||
tensor_A.host_ref(),
|
||||
tensor_B.host_ref(),
|
||||
ElementC(0),
|
||||
tensor_D_reference.host_ref()
|
||||
);
|
||||
|
||||
//
|
||||
// Verify equivalence
|
||||
//
|
||||
|
||||
// compare
|
||||
bool passed = cutlass::reference::host::TensorEquals(
|
||||
tensor_D_computed.host_view(),
|
||||
tensor_D_reference.host_view()
|
||||
);
|
||||
|
||||
if(!passed) std::cout
|
||||
<< "A:\n" << tensor_A.host_view() << "\n\n"
|
||||
<< "B:\n" << tensor_B.host_view() << "\n\n"
|
||||
<< "C:\n" << tensor_C.host_view() << "\n\n"
|
||||
<< "Reference:\n" << tensor_D_reference.host_view() << "\n\n"
|
||||
<< "Computed:\n" << tensor_D_computed.host_view() << std::endl;
|
||||
|
||||
std::cout << "passed " << passed << std::endl;
|
||||
|
||||
return passed;
|
||||
}
|
||||
};
|
||||
|
||||
/////////////////////////////////////////////////////////////////////////////////////////////////
|
||||
|
||||
} // namespace thread
|
||||
} // namespace nvrtc
|
||||
} // namespace test
|
||||
Reference in New Issue
Block a user