548 lines
17 KiB
C++
548 lines
17 KiB
C++
/***************************************************************************************************
|
|
* 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 Tests for device-wide GEMM interface
|
|
*/
|
|
|
|
#pragma once
|
|
|
|
#include <iostream>
|
|
#include <fstream>
|
|
#include <sstream>
|
|
|
|
#include "../../common/cutlass_unit_test.h"
|
|
|
|
#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/host/gemm.h"
|
|
#include "cutlass/util/reference/host/gemm_complex.h"
|
|
|
|
#include "testbed_utils.h"
|
|
|
|
namespace test {
|
|
namespace gemm {
|
|
namespace device {
|
|
|
|
/////////////////////////////////////////////////////////////////////////////////////////////////
|
|
|
|
template <typename Gemm, bool Relu = false>
|
|
struct TestbedUniversal {
|
|
|
|
using ElementA = typename Gemm::ElementA;
|
|
using ElementB = typename Gemm::ElementB;
|
|
using ElementC = typename Gemm::ElementC;
|
|
using ElementAccumulator = typename Gemm::ElementAccumulator;
|
|
using ElementCompute = typename Gemm::GemmKernel::Epilogue::OutputOp::ElementCompute;
|
|
|
|
/// Initialization
|
|
cutlass::Distribution::Kind init_A;
|
|
cutlass::Distribution::Kind init_B;
|
|
cutlass::Distribution::Kind init_C;
|
|
uint64_t seed;
|
|
|
|
cutlass::HostTensor<typename Gemm::ElementA, typename Gemm::LayoutA> tensor_A;
|
|
cutlass::HostTensor<typename Gemm::ElementB, typename Gemm::LayoutB> tensor_B;
|
|
cutlass::HostTensor<typename Gemm::ElementC, typename Gemm::LayoutC> tensor_C;
|
|
cutlass::HostTensor<typename Gemm::ElementC, typename Gemm::LayoutC> tensor_D;
|
|
cutlass::HostTensor<typename Gemm::ElementC, typename Gemm::LayoutC> reference_D;
|
|
|
|
//
|
|
// Methods
|
|
//
|
|
|
|
TestbedUniversal(
|
|
cutlass::Distribution::Kind init_A_ = cutlass::Distribution::Uniform,
|
|
cutlass::Distribution::Kind init_B_ = cutlass::Distribution::Uniform,
|
|
cutlass::Distribution::Kind init_C_ = cutlass::Distribution::Uniform,
|
|
uint64_t seed_ = 2080
|
|
):
|
|
init_A(init_A_), init_B(init_B_), init_C(init_C_), 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) {
|
|
|
|
double scope_max, scope_min;
|
|
int bits_input = cutlass::sizeof_bits<Element>::value;
|
|
int bits_output = cutlass::sizeof_bits<typename Gemm::ElementC>::value;
|
|
bool is_unsigned_int = std::numeric_limits<Element>::is_integer && !std::numeric_limits<Element>::is_signed;
|
|
|
|
if (bits_input == 1) {
|
|
scope_max = 2;
|
|
scope_min = 0;
|
|
} else if (bits_input <= 8) {
|
|
scope_max = is_unsigned_int ? 2 : 1;
|
|
scope_min = is_unsigned_int ? 0 : -1;
|
|
} else if (bits_output == 16) {
|
|
scope_max = is_unsigned_int ? 10 : 5;
|
|
scope_min = is_unsigned_int ? 0 : -5;
|
|
} else {
|
|
scope_max = 8;
|
|
scope_min = -8;
|
|
}
|
|
|
|
cutlass::reference::host::TensorFillRandomUniform(
|
|
view, seed, scope_max, scope_min, 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 {
|
|
EXPECT_TRUE(false) << "Not implemented";
|
|
return false;
|
|
}
|
|
|
|
return true;
|
|
}
|
|
|
|
/// Initializes data structures
|
|
void initialize(cutlass::gemm::GemmCoord problem_size) {
|
|
//
|
|
// Allocate the GEMM workspace
|
|
//
|
|
|
|
tensor_A.resize(problem_size.mk());
|
|
tensor_B.resize(problem_size.kn());
|
|
tensor_C.resize(problem_size.mn());
|
|
tensor_D.resize(problem_size.mn());
|
|
reference_D.resize(problem_size.mn(), false);
|
|
|
|
EXPECT_TRUE(initialize_tensor(tensor_A.host_view(), init_A, seed + 2019));
|
|
EXPECT_TRUE(initialize_tensor(tensor_B.host_view(), init_B, seed + 2018));
|
|
EXPECT_TRUE(initialize_tensor(tensor_C.host_view(), init_C, seed + 2017));
|
|
|
|
// It is possible to randomly initialize to all zeros, so override this with non-zeros
|
|
// in the upper left corner of each operand.
|
|
cutlass::Coord<2> origin(0);
|
|
tensor_A.host_view().at(origin) = typename Gemm::ElementA(1);
|
|
tensor_B.host_view().at(origin) = typename Gemm::ElementB(1);
|
|
tensor_C.host_view().at(origin) = typename Gemm::ElementC(1);
|
|
|
|
cutlass::reference::host::TensorCopy(reference_D.host_view(), tensor_C.host_view());
|
|
|
|
tensor_A.sync_device();
|
|
tensor_B.sync_device();
|
|
tensor_C.sync_device();
|
|
tensor_D.sync_device();
|
|
}
|
|
|
|
/// Compares computed reference with device reference and outputs to a file if incorrect
|
|
bool compare_reference(
|
|
cutlass::gemm::GemmCoord problem_size,
|
|
ElementCompute alpha,
|
|
ElementCompute beta) {
|
|
|
|
tensor_D.sync_host();
|
|
|
|
EXPECT_GT(cutlass::reference::host::TensorNorm(tensor_A.host_view()), 0);
|
|
EXPECT_GT(cutlass::reference::host::TensorNorm(tensor_B.host_view()), 0);
|
|
EXPECT_GT(cutlass::reference::host::TensorNorm(tensor_C.host_view()), 0);
|
|
|
|
EXPECT_GT(cutlass::reference::host::TensorNorm(tensor_D.host_view()), 0);
|
|
EXPECT_GT(cutlass::reference::host::TensorNorm(reference_D.host_view()), 0);
|
|
|
|
bool passed = cutlass::reference::host::TensorEquals(reference_D.host_view(), tensor_D.host_view());
|
|
|
|
EXPECT_TRUE(passed) << " mismatched reference";
|
|
|
|
if (!passed) {
|
|
|
|
/*
|
|
std::stringstream fname;
|
|
|
|
fname << "error_Gemm_device_"
|
|
<< problem_size.m() << "x"
|
|
<< problem_size.n() << "x"
|
|
<< problem_size.k() << "_"
|
|
<< Gemm::ThreadblockShape::kM << "x"
|
|
<< Gemm::ThreadblockShape::kN << "x"
|
|
<< Gemm::ThreadblockShape::kK << "_"
|
|
<< Gemm::WarpShape::kM << "x"
|
|
<< Gemm::WarpShape::kN << "x"
|
|
<< Gemm::WarpShape::kK << ".txt";
|
|
|
|
std::ofstream file(fname.str());
|
|
*/
|
|
|
|
std::ofstream file("testbed_universal_errors.txt");
|
|
|
|
file
|
|
<< "problem: " << problem_size
|
|
<< ", alpha: " << alpha << ", beta: " << beta << "\n\n";
|
|
|
|
file
|
|
<< "A =\n" << tensor_A.host_view()
|
|
<< "\nB =\n" << tensor_B.host_view()
|
|
<< "\nC =\n" << tensor_C.host_view()
|
|
<< "\n\nReference =\n" << reference_D.host_view()
|
|
<< "\nComputed =\n" << tensor_D.host_view();
|
|
}
|
|
|
|
return passed;
|
|
}
|
|
|
|
/// Verifies the result is a GEMM
|
|
bool verify(
|
|
cutlass::gemm::GemmCoord problem_size,
|
|
ElementCompute alpha,
|
|
ElementCompute beta) {
|
|
|
|
//
|
|
// Verify
|
|
//
|
|
|
|
cutlass::reference::host::GemmComplex<
|
|
typename Gemm::ElementA, typename Gemm::LayoutA,
|
|
typename Gemm::ElementB, typename Gemm::LayoutB,
|
|
typename Gemm::ElementC, typename Gemm::LayoutC,
|
|
ElementCompute, ElementAccumulator
|
|
>(
|
|
problem_size,
|
|
alpha,
|
|
tensor_A.host_ref(),
|
|
Gemm::kTransformA,
|
|
tensor_B.host_ref(),
|
|
Gemm::kTransformB,
|
|
beta,
|
|
tensor_C.host_ref(),
|
|
reference_D.host_ref(),
|
|
ElementAccumulator(0)
|
|
);
|
|
|
|
if (Relu) {
|
|
for (int i = 0; i < problem_size.m(); ++i) {
|
|
for (int j = 0; j < problem_size.n(); ++j) {
|
|
reference_D.at(cutlass::MatrixCoord(i, j)) =
|
|
((ElementCompute)reference_D.at(cutlass::MatrixCoord(i, j)) < (ElementCompute)0)
|
|
? (typename Gemm::ElementC)0
|
|
: reference_D.at(cutlass::MatrixCoord(i, j));
|
|
}
|
|
}
|
|
}
|
|
|
|
return compare_reference(problem_size, alpha, beta);
|
|
}
|
|
|
|
/// Returns true if the CUDA device is sufficient to execute the kernel.
|
|
bool sufficient() const {
|
|
//
|
|
// Determine SMEM requirements and waive if not satisfied
|
|
//
|
|
|
|
size_t smem_size = sizeof(typename Gemm::GemmKernel::SharedStorage);
|
|
|
|
cudaDeviceProp properties;
|
|
int device_idx;
|
|
cudaError_t result = cudaGetDevice(&device_idx);
|
|
|
|
if (result != cudaSuccess) {
|
|
throw std::runtime_error("cudaGetDevice() API call failed.");
|
|
}
|
|
|
|
result = cudaGetDeviceProperties(&properties, device_idx);
|
|
|
|
if (result != cudaSuccess) {
|
|
throw std::runtime_error("cudaGetDeviceProperties() failed");
|
|
}
|
|
|
|
if (properties.sharedMemPerBlockOptin < smem_size) {
|
|
return false;
|
|
}
|
|
|
|
return true;
|
|
}
|
|
|
|
/// Executes one test
|
|
bool run(
|
|
cutlass::gemm::GemmUniversalMode mode,
|
|
cutlass::gemm::GemmCoord problem_size,
|
|
int batch_count = 1,
|
|
ElementCompute alpha = ElementCompute(1),
|
|
ElementCompute beta = ElementCompute(0))
|
|
{
|
|
/*
|
|
std::cout << "\n-----------------------\n";
|
|
std::cout << "mode: " << (int) mode << "\n";
|
|
std::cout << "problem size: " << problem_size << "\n";
|
|
std::cout << "batch_count: " << batch_count << "\n";
|
|
std::cout << "alpha: " << alpha << "\n";
|
|
std::cout << "beta: " << beta << "\n";
|
|
std::cout << "-----------------------\n\n";
|
|
*/
|
|
|
|
// Waive test if insufficient CUDA device
|
|
if (!sufficient()) {
|
|
if (CUTLASS_TEST_UNIT_ENABLE_WARNINGS) {
|
|
std::cerr << "Test waived due to insufficient CUDA device." << std::endl;
|
|
}
|
|
return true;
|
|
}
|
|
|
|
this->initialize(problem_size);
|
|
|
|
//
|
|
// Initialize the GEMM operator
|
|
//
|
|
|
|
typename Gemm::Arguments arguments{
|
|
mode,
|
|
problem_size,
|
|
batch_count,
|
|
{alpha, beta},
|
|
tensor_A.device_data(),
|
|
tensor_B.device_data(),
|
|
tensor_C.device_data(),
|
|
tensor_D.device_data(),
|
|
problem_size.m() * problem_size.k(),
|
|
problem_size.n() * problem_size.k(),
|
|
problem_size.m() * problem_size.n(),
|
|
problem_size.m() * problem_size.n(),
|
|
tensor_A.layout().stride(0),
|
|
tensor_B.layout().stride(0),
|
|
tensor_C.layout().stride(0),
|
|
tensor_D.layout().stride(0)
|
|
};
|
|
|
|
Gemm gemm_op;
|
|
|
|
size_t workspace_size = Gemm::get_workspace_size(arguments);
|
|
|
|
cutlass::device_memory::allocation<uint8_t> workspace(workspace_size);
|
|
|
|
cutlass::Status status = gemm_op.initialize(arguments, workspace.get());
|
|
|
|
EXPECT_TRUE(status == cutlass::Status::kSuccess) << to_string(status);
|
|
|
|
//
|
|
// Run the GEMM
|
|
//
|
|
|
|
status = gemm_op();
|
|
|
|
EXPECT_TRUE(status == cutlass::Status::kSuccess) << to_string(status);
|
|
|
|
//
|
|
// Verify
|
|
//
|
|
|
|
bool passed = this->verify(problem_size, alpha, beta);
|
|
|
|
if (!passed) {
|
|
std::cout << "Failed with batch_count/split_k_slices = " << batch_count << std::endl;
|
|
}
|
|
|
|
return passed;
|
|
}
|
|
};
|
|
|
|
/////////////////////////////////////////////////////////////////////////////////////////////////
|
|
template <typename Gemm, bool Relu = false>
|
|
bool TestGemmUniversal(
|
|
cutlass::gemm::GemmCoord const & problem_size,
|
|
cutlass::gemm::GemmUniversalMode mode,
|
|
int batch_count,
|
|
double alpha = 1.0,
|
|
double beta = 2.0) {
|
|
|
|
bool passed = true;
|
|
|
|
TestbedUniversal<Gemm, Relu> testbed;
|
|
|
|
using ElementCompute = typename Gemm::EpilogueOutputOp::ElementCompute;
|
|
|
|
passed = testbed.run(
|
|
mode,
|
|
problem_size,
|
|
batch_count,
|
|
cutlass::from_real<ElementCompute>(alpha),
|
|
cutlass::from_real<ElementCompute>(beta)
|
|
);
|
|
|
|
return passed;
|
|
}
|
|
|
|
template <typename Gemm, bool Relu = false>
|
|
bool TestAllGemmUniversal() {
|
|
bool passed = true;
|
|
|
|
|
|
int const kMinimumOperandElementSize =
|
|
std::min(
|
|
int(cutlass::sizeof_bits<typename Gemm::ElementA>::value),
|
|
int(cutlass::sizeof_bits<typename Gemm::ElementB>::value));
|
|
|
|
int const kAlignment = cutlass::platform::is_same<
|
|
typename Gemm::OperatorClass,
|
|
cutlass::arch::OpClassSimt>::value ? 1 : 128 / kMinimumOperandElementSize;
|
|
|
|
// int8_t gemm alignment constraints
|
|
int const kAlignmentM = cutlass::platform::is_same<typename Gemm::OperatorClass, cutlass::arch::OpClassSimt>::value &&
|
|
cutlass::platform::is_same<typename Gemm::ElementA, int8_t>::value &&
|
|
cutlass::platform::is_same<typename Gemm::LayoutA, cutlass::layout::ColumnMajor>::value ? 4 : kAlignment;
|
|
|
|
int const kAlignmentN = cutlass::platform::is_same<typename Gemm::OperatorClass, cutlass::arch::OpClassSimt>::value &&
|
|
cutlass::platform::is_same<typename Gemm::ElementB, int8_t>::value &&
|
|
cutlass::platform::is_same<typename Gemm::LayoutB, cutlass::layout::RowMajor>::value ? 4 : kAlignment;
|
|
|
|
int const kAlignmentK = cutlass::platform::is_same<typename Gemm::OperatorClass, cutlass::arch::OpClassSimt>::value &&
|
|
cutlass::platform::is_same<typename Gemm::ElementA, int8_t>::value &&
|
|
cutlass::platform::is_same<typename Gemm::ElementB, int8_t>::value &&
|
|
(cutlass::platform::is_same<typename Gemm::LayoutA, cutlass::layout::RowMajor>::value ||
|
|
cutlass::platform::is_same<typename Gemm::LayoutB, cutlass::layout::ColumnMajor>::value) ? 4 : kAlignment;
|
|
|
|
|
|
|
|
cutlass::gemm::GemmUniversalMode modes[] = {
|
|
cutlass::gemm::GemmUniversalMode::kGemm,
|
|
};
|
|
|
|
int problem_size_m[] = {
|
|
kAlignmentM, 512 - 3*kAlignmentM
|
|
};
|
|
|
|
int problem_size_n[] = {
|
|
kAlignmentN, 512 - 2*kAlignmentN
|
|
};
|
|
|
|
int problem_size_k[] = {
|
|
kAlignmentK,
|
|
Gemm::ThreadblockShape::kK * Gemm::kStages - kAlignmentK,
|
|
Gemm::ThreadblockShape::kK * Gemm::kStages * 3 - kAlignmentK
|
|
};
|
|
|
|
int batch_counts[] = { // may be interpretted as batch count or split-K slices
|
|
1, 2, 3, 5, 7
|
|
};
|
|
|
|
double problem_alpha[] = {
|
|
1
|
|
};
|
|
|
|
double problem_beta[] = {
|
|
2.0
|
|
};
|
|
|
|
|
|
using ElementCompute = typename Gemm::EpilogueOutputOp::ElementCompute;
|
|
|
|
for (cutlass::gemm::GemmUniversalMode mode : modes) {
|
|
for (int m : problem_size_m) {
|
|
for (int n : problem_size_n) {
|
|
for (int k : problem_size_k) {
|
|
for (int batch_count : batch_counts) {
|
|
|
|
for (auto alpha : problem_alpha) {
|
|
for (auto beta : problem_beta) {
|
|
|
|
if (mode == cutlass::gemm::GemmUniversalMode::kGemm ||
|
|
mode == cutlass::gemm::GemmUniversalMode::kGemmSplitKParallel) {
|
|
|
|
// skip very small K problems
|
|
if (k / batch_count < 2 * Gemm::ThreadblockShape::kK) {
|
|
continue;
|
|
}
|
|
}
|
|
|
|
cutlass::gemm::GemmCoord problem_size(m, n, k);
|
|
|
|
TestbedUniversal<Gemm, Relu> testbed;
|
|
|
|
passed = testbed.run(
|
|
mode,
|
|
problem_size,
|
|
batch_count,
|
|
cutlass::from_real<ElementCompute>(alpha),
|
|
cutlass::from_real<ElementCompute>(beta)
|
|
);
|
|
|
|
if (!passed) {
|
|
return false;
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
/*
|
|
// large problem with high coverage
|
|
for (int split_k_slices = 1; split_k_slices <= 3; ++split_k_slices) {
|
|
TestbedUniversal<Gemm> testbed;
|
|
|
|
cutlass::gemm::GemmCoord problem_size(72, 56, 8192);
|
|
|
|
passed = testbed.run(
|
|
cutlass::gemm::GemmUniversalMode::kGemm,
|
|
problem_size,
|
|
split_k_slices,
|
|
cutlass::from_real<ElementCompute>(1.0),
|
|
cutlass::from_real<ElementCompute>(2.0)
|
|
);
|
|
|
|
if (!passed) {
|
|
break;
|
|
}
|
|
}
|
|
*/
|
|
|
|
return passed;
|
|
}
|
|
|
|
/////////////////////////////////////////////////////////////////////////////////////////////////
|
|
|
|
} // namespace device
|
|
} // namespace gemm
|
|
} // namespace test
|
|
|
|
/////////////////////////////////////////////////////////////////////////////////////////////////
|