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dual_gemm_run.h
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dual_gemm_run.h
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/***************************************************************************************************
* Copyright (c) 2017 - 2022 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
* SPDX-License-Identifier: BSD-3-Clause
*
* Redistribution and use in source and binary forms, with or without
* modification, are permitted provided that the following conditions are met:
*
* 1. Redistributions of source code must retain the above copyright notice, this
* list of conditions and the following disclaimer.
*
* 2. Redistributions in binary form must reproduce the above copyright notice,
* this list of conditions and the following disclaimer in the documentation
* and/or other materials provided with the distribution.
*
* 3. Neither the name of the copyright holder nor the names of its
* contributors may be used to endorse or promote products derived from
* this software without specific prior written permission.
*
* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
* AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
* IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
* DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
* FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
* DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
* SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
* CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
* OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
* OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
*
**************************************************************************************************/
#pragma once
#include <iostream>
#include <fstream>
#include <sstream>
#include "cutlass/util/host_tensor.h"
#include "cutlass/util/tensor_view_io.h"
#include "cutlass/util/distribution.h"
#include "cutlass/util/reference/host/tensor_fill.h"
#include "cutlass/util/reference/host/tensor_copy.h"
#include "cutlass/util/reference/host/tensor_compare.h"
#include "cutlass/util/reference/host/tensor_norm.h"
#include "cutlass/util/reference/device/gemm.h"
#include "cutlass/util/reference/device/tensor_relu.h"
#include "helper.h"
#define CHECK_GT(val1, val2) \
if((val1) <= (val2)) \
std::cerr << __FILE__ << " " << __LINE__ << ": CHECK_GT failed\n";
#define CHECK_TRUE(val) \
if(!(val)) \
std::cerr << __FILE__ << " " << __LINE__ << ": CHECK_TRUE failed\n";
template <
typename OutputOp,
typename Element,
typename Layout>
struct TensorEpilogueForEachFunc {
/// View type
using TensorView = cutlass::TensorView<Element, Layout>;
/// Coordinate in tensor's index space
using TensorCoord = typename TensorView::TensorCoord;
/// Parameters structure
struct Params {
//
// Data members
//
TensorView view_x0;
TensorView view_x1;
TensorView view_y;
OutputOp output_op;
//
// Methods
//
Params(
TensorView view_x0_ = TensorView(),
TensorView view_x1_ = TensorView(),
TensorView view_y_ = TensorView(),
OutputOp output_op_ = OutputOp(typename OutputOp::Params{})
):
view_x0(view_x0_), view_x1(view_x1_), view_y(view_y_), output_op(output_op_) {
}
};
Params params;
CUTLASS_DEVICE
TensorEpilogueForEachFunc(Params const ¶ms): params(params) {
}
CUTLASS_DEVICE
void operator()(TensorCoord const &coord) {
Element const & x0 = params.view_x0.at(coord);
Element const & x1 = params.view_x1.at(coord);
Element& y = params.view_y.at(coord);
y = params.output_op(x0, x1);
}
};
template <
typename OutputOp,
typename Element,
typename Layout>
void TensorEpilogueForEach(
cutlass::TensorView<Element, Layout> x0,
cutlass::TensorView<Element, Layout> x1,
cutlass::TensorView<Element, Layout> y) {
using Func = TensorEpilogueForEachFunc<OutputOp, Element, Layout>;
using Params = typename Func::Params;
cutlass::reference::device::TensorForEach<Func, Layout::kRank, Params>(
y.extent(),
Params(x0, x1, y)
);
}
////////////////////////////////////////////////////////////////////////////////
template <typename Gemm0_, typename Gemm1_>
struct NonFusedDualGemmRun
{
using Gemm0 = Gemm0_;
using Gemm1 = Gemm1_;
using ElementAccumulator = typename Gemm0::ElementAccumulator;
using ElementCompute = typename Gemm0::GemmKernel::Epilogue::OutputOp::ElementCompute;
/// Initialization
cutlass::Distribution::Kind init_A;
cutlass::Distribution::Kind init_B;
cutlass::Distribution::Kind init_C;
cutlass::Distribution::Kind init_Bias;
uint64_t seed;
//
// Methods
//
NonFusedDualGemmRun(
cutlass::Distribution::Kind init_A_ = cutlass::Distribution::Uniform,
cutlass::Distribution::Kind init_B_ = cutlass::Distribution::Uniform,
cutlass::Distribution::Kind init_C_ = cutlass::Distribution::Uniform,
cutlass::Distribution::Kind init_Bias_ = cutlass::Distribution::Uniform,
uint64_t seed_ = 2080
):
init_A(init_A_), init_B(init_B_), init_C(init_C_), init_Bias(init_Bias_), seed(seed_) { }
/// Helper to initialize a tensor view
template <typename Element, typename Layout>
bool initialize_tensor(
cutlass::TensorView<Element, Layout> view,
cutlass::Distribution::Kind dist_kind,
uint64_t seed) {
if (dist_kind == cutlass::Distribution::Uniform) {
cutlass::reference::host::TensorFillRandomUniform(
view, seed, 2, -2, 0);
}
else if (dist_kind == cutlass::Distribution::Identity) {
cutlass::reference::host::TensorFillIdentity(view);
}
else if (dist_kind == cutlass::Distribution::Gaussian) {
cutlass::reference::host::TensorFillRandomGaussian(view, seed, 0, 0.5);
}
else if (dist_kind == cutlass::Distribution::Sequential) {
cutlass::reference::host::BlockFillSequential(
view.data(), view.capacity());
}
else if (dist_kind == cutlass::Distribution::AllZeros) {
cutlass::reference::host::TensorFill(view, Element(0));
}
else if (dist_kind == cutlass::Distribution::AllOnes) {
cutlass::reference::host::TensorFill(view, Element(1));
}
else {
std::cerr << "Not implemented\n";
return false;
}
return true;
}
/// Executes one test
bool run(
cutlass::gemm::GemmCoord problem_size,
ElementCompute alpha0 = ElementCompute(1),
ElementCompute beta0 = ElementCompute(0),
ElementCompute alpha1 = ElementCompute(1),
ElementCompute beta1 = ElementCompute(0),
bool relu = false,
int warm_ups = 1,
int runs = 100) {
//
// Allocate the GEMM workspace
//
cutlass::HostTensor<
typename Gemm0::ElementA,
typename Gemm0::LayoutA> tensor_A0(problem_size.mk());
cutlass::HostTensor<
typename Gemm0::ElementB,
typename Gemm0::LayoutB> tensor_B0(problem_size.kn());
cutlass::HostTensor<
typename Gemm0::ElementC,
typename Gemm0::LayoutC> tensor_C0(problem_size.mn());
cutlass::HostTensor<
typename Gemm1::ElementC,
typename Gemm0::LayoutC> tensor_Bias0({1, problem_size.n()});
cutlass::HostTensor<
typename Gemm0::ElementC,
typename Gemm0::LayoutC> tensor_D0(problem_size.mn());
cutlass::HostTensor<
typename Gemm0::ElementC,
typename Gemm0::LayoutC> reference_D0(problem_size.mn());
cutlass::HostTensor<
typename Gemm1::ElementB,
typename Gemm1::LayoutB> tensor_B1(problem_size.kn());
cutlass::HostTensor<
typename Gemm1::ElementC,
typename Gemm1::LayoutC> tensor_C1(problem_size.mn());
cutlass::HostTensor<
typename Gemm1::ElementC,
typename Gemm1::LayoutC> tensor_Bias1({1, problem_size.n()});
cutlass::HostTensor<
typename Gemm1::ElementC,
typename Gemm1::LayoutC> tensor_D1(problem_size.mn());
cutlass::HostTensor<
typename Gemm1::ElementC,
typename Gemm1::LayoutC> reference_D1(problem_size.mn());
CHECK_TRUE(initialize_tensor(tensor_A0.host_view(), init_A, seed + 2019));
CHECK_TRUE(initialize_tensor(tensor_B0.host_view(), init_B, seed + 2018));
CHECK_TRUE(initialize_tensor(tensor_C0.host_view(), init_C, seed + 2017));
CHECK_TRUE(initialize_tensor(tensor_Bias0.host_view(), init_Bias, seed + 2014));
CHECK_TRUE(initialize_tensor(tensor_B1.host_view(), init_B, seed + 2016));
CHECK_TRUE(initialize_tensor(tensor_C1.host_view(), init_C, seed + 2015));
CHECK_TRUE(initialize_tensor(tensor_Bias1.host_view(), init_Bias, seed + 2013));
cutlass::reference::host::TensorFill(
tensor_D0.host_view());
cutlass::reference::host::TensorFill(
tensor_D1.host_view());
cutlass::reference::host::TensorFill(
reference_D0.host_view());
cutlass::reference::host::TensorFill(
reference_D1.host_view());
tensor_A0.sync_device();
tensor_B0.sync_device();
tensor_C0.sync_device();
tensor_Bias0.sync_device();
tensor_D0.sync_device();
reference_D0.sync_device();
tensor_B1.sync_device();
tensor_C1.sync_device();
tensor_Bias1.sync_device();
tensor_D1.sync_device();
reference_D1.sync_device();
//
// Initialize the GEMM operator
//
int split_k_slices = Gemm0::kSplitKSerial ? 2 : 1;
typename Gemm0::Arguments arguments_0{
problem_size,
tensor_A0.device_ref(),
tensor_B0.device_ref(),
{tensor_Bias0.device_data(), typename Gemm0::LayoutC::Stride(0)},
tensor_D0.device_ref(),
{alpha0, beta0},
split_k_slices
};
split_k_slices = Gemm1::kSplitKSerial ? 2 : 1;
typename Gemm1::Arguments arguments_1{
problem_size,
tensor_A0.device_ref(),
tensor_B1.device_ref(),
{tensor_Bias1.device_data(), typename Gemm1::LayoutC::Stride(0)},
tensor_D1.device_ref(),
{alpha1, beta1},
split_k_slices
};
Gemm0 gemm_op_0;
Gemm1 gemm_op_1;
// Allocate workspace memory
cutlass::device_memory::allocation<uint8_t> workspace0(gemm_op_0.get_workspace_size(arguments_0));
cutlass::device_memory::allocation<uint8_t> workspace1(gemm_op_1.get_workspace_size(arguments_1));
cutlass::Status status = gemm_op_0.initialize(arguments_0, workspace0.get());
CUTLASS_CHECK(status);
status = gemm_op_1.initialize(arguments_1, workspace1.get());
CUTLASS_CHECK(status);
for(int i = 0; i < warm_ups; i++) {
status = gemm_op_0();
CUTLASS_CHECK(status);
status = gemm_op_1();
CUTLASS_CHECK(status);
}
#ifdef IS_PROFILING
return true;
#endif
//
// Run the GEMM
//
cudaEvent_t start, stop1, stop2;
cudaEventCreate(&start);
cudaEventCreate(&stop1);
cudaEventCreate(&stop2);
cudaEventRecord(start);
for(int i = 0; i < runs; i++) {
status = gemm_op_0();
CUTLASS_CHECK(status);
}
cudaEventRecord(stop1);
for(int i = 0; i < runs; i++) {
status = gemm_op_1();
CUTLASS_CHECK(status);
}
cudaEventRecord(stop2);
cudaDeviceSynchronize();
float gemm0Time, gemm1Time, totalTime;
cudaEventElapsedTime(&gemm0Time, start, stop1);
cudaEventElapsedTime(&gemm1Time, stop1, stop2);
cudaEventElapsedTime(&totalTime, start, stop2);
std::cout << "gemm 0 time " << gemm0Time / (float)runs << " ms\n";
std::cout << "gemm 1 time " << gemm1Time / (float)runs << " ms\n";
std::cout << "Non-fusion GEMM only time " << totalTime / (float)runs << " ms\n";
tensor_D0.sync_host();
tensor_D1.sync_host();
//
// Verify
//
cutlass::reference::device::Gemm<
typename Gemm0::ElementA, typename Gemm0::LayoutA,
typename Gemm0::ElementB, typename Gemm0::LayoutB,
typename Gemm0::ElementC, typename Gemm0::LayoutC, ElementCompute,
ElementAccumulator, typename Gemm0::Operator>
reference_gemm_0;
cutlass::reference::device::Gemm<
typename Gemm1::ElementA, typename Gemm1::LayoutA,
typename Gemm1::ElementB, typename Gemm1::LayoutB,
typename Gemm1::ElementC, typename Gemm1::LayoutC, ElementCompute,
ElementAccumulator, typename Gemm1::Operator>
reference_gemm_1;
reference_gemm_0(
problem_size,
alpha0,
tensor_A0.device_ref(),
tensor_B0.device_ref(),
beta0,
{tensor_Bias0.device_data(), typename Gemm0::LayoutC::Stride(0)},
reference_D0.device_ref()
);
if(relu) {
cutlass::reference::device::TensorReLu(reference_D0.device_view());
}
reference_gemm_1(
problem_size,
alpha1,
tensor_A0.device_ref(),
tensor_B1.device_ref(),
beta1,
{tensor_Bias1.device_data(), typename Gemm1::LayoutC::Stride(0)},
reference_D1.device_ref()
);
if(relu) {
cutlass::reference::device::TensorReLu(reference_D1.device_view());
}
// Wait for kernels to finish
cudaDeviceSynchronize();
reference_D0.sync_host();
reference_D1.sync_host();
CHECK_GT(cutlass::reference::host::TensorNorm(tensor_D0.host_view()), 0);
CHECK_GT(cutlass::reference::host::TensorNorm(reference_D0.host_view()), 0);
CHECK_GT(cutlass::reference::host::TensorNorm(tensor_D1.host_view()), 0);
CHECK_GT(cutlass::reference::host::TensorNorm(reference_D1.host_view()), 0);
bool passed0 = cutlass::reference::host::TensorEquals(
reference_D1.host_view(),
tensor_D1.host_view());
CHECK_TRUE(passed0);
bool passed1 = cutlass::reference::host::TensorEquals(
reference_D1.host_view(),
tensor_D1.host_view());
CHECK_TRUE(passed1);
if (!passed0 || !passed1) {
std::stringstream fname;
fname << "error_DualGemm_device_nonfused.txt";
std::cerr << "Dumping results in " << fname.str() << "\n";
std::ofstream file(fname.str());
file
<< "A0 =\n" << tensor_A0.host_view()
<< "\nB0 =\n" << tensor_B0.host_view()
<< "\nC0 =\n" << tensor_C0.host_view()
<< "\nBias0:\n" << tensor_Bias0.host_view() << "\n"
<< "\nD0 =\n" << tensor_D0.host_view()
<< "\nB1 =\n" << tensor_B1.host_view()
<< "\nC1 =\n" << tensor_C1.host_view()
<< "\nBias1:\n" << tensor_Bias1.host_view() << "\n"
<< "\n\nReference =\n" << reference_D1.host_view()
<< "\nComputed =\n" << tensor_D1.host_view();
}
return passed0 && passed1;
}
};
template <typename DualGemm_>
struct DualFusedGemmRun
{
using DualGemm = DualGemm_;
using ElementAccumulator = typename DualGemm::ElementAccumulator;
using ElementCompute = typename DualGemm::DualGemmKernel::Epilogue0::OutputOp::ElementCompute;
using EpilogueOutputOp2 = typename DualGemm::EpilogueOutputOp2;
/// Initialization
cutlass::Distribution::Kind init_A;
cutlass::Distribution::Kind init_B;
cutlass::Distribution::Kind init_C;
cutlass::Distribution::Kind init_Scale;
cutlass::Distribution::Kind init_Bias;
uint64_t seed;
//
// Methods
//
DualFusedGemmRun(
cutlass::Distribution::Kind init_A_ = cutlass::Distribution::Uniform,
cutlass::Distribution::Kind init_B_ = cutlass::Distribution::Uniform,
cutlass::Distribution::Kind init_C_ = cutlass::Distribution::Uniform,
cutlass::Distribution::Kind init_Scale_ = cutlass::Distribution::Uniform,
cutlass::Distribution::Kind init_Bias_ = cutlass::Distribution::Uniform,
uint64_t seed_ = 2080
):
init_A(init_A_), init_B(init_B_), init_C(init_C_),
init_Scale(init_Scale_), init_Bias(init_Bias_), seed(seed_) { }
/// Helper to initialize a tensor view
template <typename Element, typename Layout>
bool initialize_tensor(
cutlass::TensorView<Element, Layout> view,
cutlass::Distribution::Kind dist_kind,
uint64_t seed) {
if (dist_kind == cutlass::Distribution::Uniform) {
cutlass::reference::host::TensorFillRandomUniform(
view, seed, 2, -2, 0);
}
else if (dist_kind == cutlass::Distribution::Identity) {
cutlass::reference::host::TensorFillIdentity(view);
}
else if (dist_kind == cutlass::Distribution::Gaussian) {
cutlass::reference::host::TensorFillRandomGaussian(view, seed, 0, 0.5);
}
else if (dist_kind == cutlass::Distribution::Sequential) {
cutlass::reference::host::BlockFillSequential(
view.data(), view.capacity());
}
else if (dist_kind == cutlass::Distribution::AllZeros) {
cutlass::reference::host::TensorFill(view, Element(0));
}
else if (dist_kind == cutlass::Distribution::AllOnes) {
cutlass::reference::host::TensorFill(view, Element(1));
}
else {
std::cerr << "Not implemented\n";
return false;
}
return true;
}
/// Executes one test
bool run(
cutlass::gemm::GemmCoord problem_size,
ElementCompute alpha0 = ElementCompute(1),
ElementCompute beta0 = ElementCompute(1),
ElementCompute alpha1 = ElementCompute(1),
ElementCompute beta1 = ElementCompute(1),
bool relu = false,
int warm_ups = 1,
int runs = 100) {
//
// Allocate the GEMM workspace
//
cutlass::HostTensor<
typename DualGemm::ElementA,
typename DualGemm::LayoutA> tensor_A0(problem_size.mk());
cutlass::HostTensor<
typename DualGemm::ElementB,
typename DualGemm::LayoutB> tensor_B0(problem_size.kn());
cutlass::HostTensor<
typename DualGemm::ElementC,
typename DualGemm::LayoutC> tensor_C0(problem_size.mn());
cutlass::HostTensor<
typename DualGemm::ElementC,
typename DualGemm::LayoutScaleBias> tensor_Bias0({1, problem_size.n()});
cutlass::HostTensor<
typename DualGemm::ElementC,
typename DualGemm::LayoutC> tensor_D0(problem_size.mn());
cutlass::HostTensor<
typename DualGemm::ElementC,
typename DualGemm::LayoutC> reference_D0(problem_size.mn());
cutlass::HostTensor<
typename DualGemm::ElementB,
typename DualGemm::LayoutB> tensor_B1(problem_size.kn());
cutlass::HostTensor<
typename DualGemm::ElementC,
typename DualGemm::LayoutC> tensor_C1(problem_size.mn());
cutlass::HostTensor<
typename DualGemm::ElementC,
typename DualGemm::LayoutScaleBias> tensor_Bias1({1, problem_size.n()});
cutlass::HostTensor<
typename DualGemm::ElementC,
typename DualGemm::LayoutC> tensor_D1(problem_size.mn());
cutlass::HostTensor<
typename DualGemm::ElementC,
typename DualGemm::LayoutC> tensor_D2(problem_size.mn());
cutlass::HostTensor<
typename DualGemm::ElementC,
typename DualGemm::LayoutC> reference_D1(problem_size.mn());
cutlass::HostTensor<
typename DualGemm::ElementC,
typename DualGemm::LayoutC> reference_D2(problem_size.mn());
CHECK_TRUE(initialize_tensor(tensor_A0.host_view(), init_A, seed + 2019));
CHECK_TRUE(initialize_tensor(tensor_B0.host_view(), init_B, seed + 2118));
CHECK_TRUE(initialize_tensor(tensor_C0.host_view(), init_C, seed + 2017));
CHECK_TRUE(initialize_tensor(tensor_Bias0.host_view(), init_Bias, seed + 2011));
CHECK_TRUE(initialize_tensor(tensor_B1.host_view(), init_B, seed + 2113));
CHECK_TRUE(initialize_tensor(tensor_C1.host_view(), init_C, seed + 2015));
CHECK_TRUE(initialize_tensor(tensor_Bias1.host_view(), init_Bias, seed + 2012));
cutlass::reference::host::TensorFill(
tensor_D0.host_view());
cutlass::reference::host::TensorFill(
tensor_D1.host_view());
cutlass::reference::host::TensorFill(
tensor_D2.host_view());
cutlass::reference::host::TensorFill(
reference_D0.host_view());
cutlass::reference::host::TensorFill(
reference_D1.host_view());
cutlass::reference::host::TensorFill(
reference_D2.host_view());
tensor_A0.sync_device();
tensor_B0.sync_device();
tensor_C0.sync_device();
tensor_Bias0.sync_device();
tensor_B1.sync_device();
tensor_C1.sync_device();
tensor_Bias1.sync_device();
tensor_D0.sync_device();
tensor_D1.sync_device();
tensor_D2.sync_device();
reference_D0.sync_device();
reference_D1.sync_device();
reference_D2.sync_device();
//
// Initialize the GEMM operator
//
int split_k_slices = DualGemm::kSplitKSerial ? 2 : 1;
typename cutlass::TensorRef<typename DualGemm::ElementC, typename DualGemm::LayoutC> nullptr_ref{};
decltype(nullptr_ref) ref_B0, ref_B1;
if (beta0 != ElementCompute(0)) {
ref_B0 = {tensor_Bias0.device_data(), typename DualGemm::LayoutC::Stride(0)};
}
if (beta1 != ElementCompute(0)) {
ref_B1 = {tensor_Bias1.device_data(), typename DualGemm::LayoutC::Stride(0)};
}
typename DualGemm::Arguments arguments{
problem_size,
tensor_A0.device_ref(),
tensor_B0.device_ref(),
ref_B0,
DualGemm::kStoreD0 ? tensor_D0.device_ref() : nullptr_ref,
tensor_B1.device_ref(),
ref_B1,
DualGemm::kStoreD1 ? tensor_D1.device_ref() : nullptr_ref,
tensor_D2.device_ref(),
{alpha0, beta0},
{alpha1, beta1},
{},
split_k_slices
};
DualGemm b2b_gemm_op;
cutlass::device_memory::allocation<uint8_t> workspace(b2b_gemm_op.get_workspace_size(arguments));
cutlass::Status status = b2b_gemm_op.can_implement(arguments);
CUTLASS_CHECK(status);
status = b2b_gemm_op.initialize(arguments, workspace.get());
CUTLASS_CHECK(status);
for(int i = 0; i < warm_ups; i++) {
status = b2b_gemm_op();
CUTLASS_CHECK(status);
}
#ifdef IS_PROFILING
return true;
#endif
//
// Run the GEMM
//
cudaEvent_t start, stop;
cudaEventCreate(&start);
cudaEventCreate(&stop);
cudaEventRecord(start);
for(int i = 0; i < runs; i++) {
status = b2b_gemm_op();
CUTLASS_CHECK(status);
}
cudaEventRecord(stop);
cudaDeviceSynchronize();
float gemmTime;
cudaEventElapsedTime(&gemmTime, start, stop);
std::cout << "Fusion time " << gemmTime / (float)runs << " ms\n";
tensor_D0.sync_host();
tensor_D1.sync_host();
tensor_D2.sync_host();
//
// Verify
//
cutlass::reference::device::Gemm<
typename DualGemm::ElementA, typename DualGemm::LayoutA,
typename DualGemm::ElementB, typename DualGemm::LayoutB,
typename DualGemm::ElementC, typename DualGemm::LayoutC,
ElementAccumulator, ElementAccumulator>
reference_gemm_0;
cutlass::reference::device::Gemm<
typename DualGemm::ElementA, typename DualGemm::LayoutA,
typename DualGemm::ElementB, typename DualGemm::LayoutB,
typename DualGemm::ElementC, typename DualGemm::LayoutC, ElementCompute,
ElementAccumulator, typename DualGemm::Operator>
reference_gemm_1;
reference_gemm_0(
problem_size,
alpha0,
tensor_A0.device_ref(),
tensor_B0.device_ref(),
beta0,
{tensor_Bias0.device_data(), typename DualGemm::LayoutC::Stride(0)},
reference_D0.device_ref()
);
if(relu) {
cutlass::reference::device::TensorReLu(reference_D0.device_view());
}
reference_gemm_1(
problem_size,
alpha1,
tensor_A0.device_ref(),
tensor_B1.device_ref(),
beta1,
{tensor_Bias1.device_data(), typename DualGemm::LayoutC::Stride(0)},
reference_D1.device_ref()
);
if(relu) {
cutlass::reference::device::TensorReLu(reference_D1.device_view());
}
TensorEpilogueForEach<EpilogueOutputOp2>(reference_D0.device_view(), reference_D1.device_view(), reference_D2.device_view());
cudaDeviceSynchronize();
reference_D0.sync_host();
reference_D1.sync_host();
reference_D2.sync_host();
CHECK_GT(cutlass::reference::host::TensorNorm(reference_D0.host_view()), 0);
CHECK_GT(cutlass::reference::host::TensorNorm(reference_D1.host_view()), 0);
CHECK_GT(cutlass::reference::host::TensorNorm(tensor_D2.host_view()), 0);
CHECK_GT(cutlass::reference::host::TensorNorm(reference_D2.host_view()), 0);
bool passed_out0 = true;
if (DualGemm::kStoreD0) {
CHECK_GT(cutlass::reference::host::TensorNorm(tensor_D0.host_view()), 0);
passed_out0 = cutlass::reference::host::TensorEquals(
reference_D0.host_view(),
tensor_D0.host_view());
}
CHECK_TRUE(passed_out0);
bool passed_out1 = true;
if (DualGemm::kStoreD1) {
CHECK_GT(cutlass::reference::host::TensorNorm(tensor_D1.host_view()), 0);
passed_out1 = cutlass::reference::host::TensorEquals(
reference_D1.host_view(),
tensor_D1.host_view());
}
CHECK_TRUE(passed_out1);
bool passed_out2 = cutlass::reference::host::TensorEquals(
reference_D2.host_view(),
tensor_D2.host_view());
CHECK_TRUE(passed_out2);
bool passed = passed_out0 && passed_out1 && passed_out2;
if (!passed)
{
std::stringstream fname;
fname << "error_DualGemm_device_fused.txt";
std::cerr << "Dumping results in " << fname.str() << "\n";
std::ofstream file(fname.str());
file
<< "A0 =\n" << tensor_A0.host_view()
<< "\nB0 =\n" << tensor_B0.host_view()
<< "\nC0 =\n" << tensor_C0.host_view()
<< "\nBias0:\n" << tensor_Bias0.host_view() << "\n"
<< "\nB1 =\n" << tensor_B1.host_view()
<< "\nC1 =\n" << tensor_C1.host_view()
<< "\nBias1:\n" << tensor_Bias1.host_view() << "\n"
<< "\n\nReference0 =\n" << reference_D0.host_view()
<< "\nComputed0 =\n" << tensor_D0.host_view()
<< "\n\nReference1 =\n" << reference_D1.host_view()
<< "\nComputed1 =\n" << tensor_D1.host_view()
<< "\n\nReference2 =\n" << reference_D2.host_view()
<< "\nComputed2 =\n" << tensor_D2.host_view();
}
//std::cout << "A0 " << tensor_A0.host_view() << std::endl;
// std::cout << "reference_D0 " << reference_D0.host_view() << std::endl;
// std::cout << "reference_D1 " << reference_D1.host_view() << std::endl;
// std::cout << "reference_D2 " << reference_D2.host_view() << std::endl;
//std::cout << "reference_D0 " << reference_D0.host_view() << std::endl;
return passed;
}
};
////////////////////////////////////////////////////////////////////////////////