squall/cutlass
1
0
Fork 0
Code Issues Pull Requests Actions 1 Packages Projects Releases Wiki Activity
5e1a0a5adb
cutlass/test/unit/gemm/device/gemm_testbed_3x.hpp
ANIKET SHIVAM 90d3b0fb18
CUTLASS 3.2.1 (#1113) 
* Updates for 3.2.1 release.

* Minor fix in gemm op profiler for raster order.

* Add scheduler mapping for raster order in the kernels.
2023-09-26 17:24:26 -04:00

1436 lines
51 KiB
C++
Raw Blame History

/***************************************************************************************************
* Copyright (c) 2017 - 2023 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/packed_stride.hpp"
#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/gett.hpp"
#include "testbed_utils.h"
#include "cutlass/kernel_hardware_info.hpp"
#include "cutlass/layout/matrix.h"
#include "cutlass/matrix_coord.h"
#include "cutlass/gemm/gemm.h"
#include "cutlass/epilogue/fusion/operations.hpp"
#include "cute/int_tuple.hpp"
namespace test {
namespace gemm {
namespace device {
/////////////////////////////////////////////////////////////////////////////////////////////////
namespace detail{
// Helper classes that take default data type when
// the Gemm::EpilogueOutputOp does not have ElementCompute
// and ElementScalar.
// (e.g. when Sm90TreeVisitor is used as FusionCallbacks)
template <typename Gemm, typename Default, typename = void>
struct ElementComputeType {
using Type = Default;
};
template <typename Gemm, typename Default>
struct ElementComputeType<Gemm, Default, std::void_t<typename Gemm::EpilogueOutputOp::ElementCompute>> {
using Type = typename Gemm::EpilogueOutputOp::ElementCompute;
};
template <typename Gemm, typename Default, typename = void>
struct ElementScalarType {
using Type = Default;
};
template <typename Gemm, typename Default>
struct ElementScalarType<Gemm, Default, std::void_t<typename Gemm::EpilogueOutputOp::ElementScalar>> {
using Type = typename Gemm::EpilogueOutputOp::ElementScalar;
};
// The number of splits to test.
//
// This class makes it harder to confuse the order of arguments
// of the various run(...) functions in this file. The constructor
// is explicit, so one can't just type 42 (or false, which the
// compiler unhelpfully turns into 0); one has to type Splits(42).
// Splits() picks the default number of splits, 1.
//
// The conversion-to-int operator (operator int()) MUST be explicit!
// Conversion to int MUST require static_cast<int>.
// Otherwise, that defeats a key purpose of this class,
// which is to catch common errors of confusing the order
// of function arguments.
class Splits {
public:
Splits() = default;
template<class IntegralNotBool,
__CUTE_REQUIRES((std::is_integral_v<IntegralNotBool> &&
!std::is_same_v<IntegralNotBool, bool>)) >
explicit Splits(IntegralNotBool splits) : splits_(splits) {}
explicit operator int() const { return splits_; }
private:
int splits_ = 1;
};
// The number of iterations to test.
//
// This class, like Splits above makes it harder to confuse
// the order of arguments of the various run(...) functions in this file.
// Iterations() picks the default number of iterations, 20.
class Iterations {
public:
Iterations() = default;
template<class IntegralNotBool,
__CUTE_REQUIRES((std::is_integral_v<IntegralNotBool> &&
!std::is_same_v<IntegralNotBool, bool>)) >
explicit Iterations(IntegralNotBool iterations) : iterations_(iterations) {}
explicit operator int() const { return iterations_; }
private:
int iterations_ = 20;
};
template <
typename Gemm,
template <class T> class ActivationFunctor_ = cutlass::epilogue::thread::Identity
>
struct TestbedImpl {
// Kernel data types
using ElementA = typename Gemm::GemmKernel::ElementA;
using StrideA = typename Gemm::GemmKernel::StrideA;
using ElementB = typename Gemm::GemmKernel::ElementB;
using StrideB = typename Gemm::GemmKernel::StrideB;
using ElementC = std::conditional_t<std::is_void_v<typename Gemm::GemmKernel::ElementC>,
typename Gemm::GemmKernel::ElementD,typename Gemm::GemmKernel::ElementC>;
using StrideC = typename Gemm::GemmKernel::StrideC;
using ElementD = typename Gemm::GemmKernel::ElementD;
using StrideD = typename Gemm::GemmKernel::StrideD;
using ElementAccumulator = typename Gemm::GemmKernel::ElementAccumulator;
using ProblemShapeType = typename Gemm::GemmKernel::ProblemShape;
using EpilogueOutputOp = typename Gemm::EpilogueOutputOp;
/// For custom EVTs
using ElementCompute = typename ElementComputeType<Gemm, ElementAccumulator>::Type;
using ElementScalar = typename ElementScalarType<Gemm, ElementCompute>::Type;
using ActivationFunctor = ActivationFunctor_<ElementCompute>;
static_assert(rank(StrideC{}) == 3, "StrideCD must be rank-3: [M, N, L]");
static_assert(rank(StrideD{}) == 3, "StrideCD must be rank-3: [M, N, L]");
static constexpr uint32_t mma_promotion_interval = 4;
// Looks at Cute Stride to check Row / Column Major
template<typename Stride>
static constexpr bool is_row_or_col_major(){
int stride_0 = int(cute::size<0>(Stride{}));
int stride_1 = int(cute::size<1>(Stride{}));
int depth = cute::depth(Stride{});
return ((stride_0 == 1) || (stride_1 == 1)) && (depth == 1);
}
// Note: this limitation comes from testbed / not the library
static_assert(is_row_or_col_major<StrideA>(),
"ERROR : A Layout is neither Row / Column Major)");
static_assert(is_row_or_col_major<StrideB>(),
"ERROR : B Layout is neither Row / Column Major)");
static_assert(is_row_or_col_major<StrideC>(),
"ERROR : C Layout is neither Row / Column Major)");
static_assert(is_row_or_col_major<StrideD>(),
"ERROR : D Layout is neither Row / Column Major)");
// Deduce Cutlass Layouts (RowMajor & ColumnMajor)
using LayoutTagA = cutlass::detail::StrideToLayoutTagA_t<StrideA>;
using LayoutTagB = cutlass::detail::StrideToLayoutTagB_t<StrideB>;
using LayoutTagC = cutlass::detail::StrideToLayoutTagA_t<StrideC>;
using LayoutTagD = cutlass::detail::StrideToLayoutTagA_t<StrideD>;
/// Initialization
StrideA stride_a;
StrideB stride_b;
StrideC stride_c;
StrideD stride_d;
typename LayoutTagA::Stride stride_factor_A;
typename LayoutTagB::Stride stride_factor_B;
typename LayoutTagC::Stride stride_factor_C;
typename LayoutTagD::Stride stride_factor_D;
cutlass::Distribution::Kind init_A;
cutlass::Distribution::Kind init_B;
cutlass::Distribution::Kind init_C;
uint64_t seed;
static constexpr uint64_t kDefaultSeed = 4096;
cutlass::HostTensor<ElementA, LayoutTagA> tensor_A;
cutlass::HostTensor<ElementB, LayoutTagB> tensor_B;
cutlass::HostTensor<ElementC, LayoutTagC> tensor_C;
cutlass::HostTensor<ElementD, LayoutTagD> tensor_D;
cutlass::HostTensor<ElementD, LayoutTagD> reference_D;
uint32_t sm_count;
// Used to force multi-wave tests for persistent kernel schedules
constexpr static int MaxSmCount = 16;
//
// Methods
//
TestbedImpl(
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_ = kDefaultSeed
):
stride_factor_A(typename LayoutTagA::Stride()),
stride_factor_B(typename LayoutTagB::Stride()),
stride_factor_C(typename LayoutTagC::Stride()),
stride_factor_D(typename LayoutTagD::Stride()),
init_A(init_A_), init_B(init_B_), init_C(init_C_), seed(seed_) { }
TestbedImpl(
typename LayoutTagA::Stride stride_factor_A_,
typename LayoutTagB::Stride stride_factor_B_,
typename LayoutTagC::Stride stride_factor_C_,
typename LayoutTagD::Stride stride_factor_D_,
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_ = kDefaultSeed
):
stride_factor_A(stride_factor_A_),
stride_factor_B(stride_factor_B_),
stride_factor_C(stride_factor_C_),
stride_factor_D(stride_factor_D_),
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<ElementD>::value;
if (bits_input == 1) {
scope_max = 2;
scope_min = 0;
}
else if (bits_input <= 8) {
scope_max = 2;
scope_min = -2;
}
else if (bits_output == 16) {
scope_max = 5;
scope_min = -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 if (dist_kind == cutlass::Distribution::AllOnes) {
cutlass::reference::host::TensorFill(view, Element(1));
}
else {
EXPECT_TRUE(false) << "Not implemented";
return false;
}
return true;
}
/// Initializes data structures
void initialize(ProblemShapeType problem_size) {
//
// Allocate the GEMM workspace
//
auto problem_shape_MNKL = cute::append<4>(problem_size, 1);
auto M = cute::size<0>(problem_shape_MNKL);
auto N = cute::size<1>(problem_shape_MNKL);
auto K = cute::size<2>(problem_shape_MNKL);
auto L = cute::size<3>(problem_shape_MNKL);
stride_a = cutlass::make_cute_packed_stride(StrideA{}, cute::make_shape(M, K, L));
stride_b = cutlass::make_cute_packed_stride(StrideB{}, cute::make_shape(N, K, L));
stride_c = cutlass::make_cute_packed_stride(StrideC{}, cute::make_shape(M, N, L));
stride_d = cutlass::make_cute_packed_stride(StrideD{}, cute::make_shape(M, N, L));
// 2.x host tensor does not natively contain a batch stride or coord, so we spoof if by folding it into the outer mode
auto a_coord = cutlass::make_Coord(M * L, K);
auto c_coord = cutlass::make_Coord(M * L, N);
// Cutlass has Row/Col major refers to MxK times KxN matrix product,
// so the HostTensorB should be treated as KxN in "coord"'s view
auto b_coord = cutlass::make_Coord(K, N * L);
tensor_A.resize(a_coord, cutlass::layout::Affine2Layout_Factory<LayoutTagA>::layout_factory(a_coord, stride_factor_A));
tensor_B.resize(b_coord, cutlass::layout::Affine2Layout_Factory<LayoutTagB>::layout_factory(b_coord, stride_factor_B));
tensor_C.resize(c_coord, cutlass::layout::Affine2Layout_Factory<LayoutTagC>::layout_factory(c_coord, stride_factor_C));
tensor_D.resize(c_coord, cutlass::layout::Affine2Layout_Factory<LayoutTagD>::layout_factory(c_coord, stride_factor_D));
reference_D.resize(c_coord, cutlass::layout::Affine2Layout_Factory<LayoutTagD>::layout_factory(c_coord, stride_factor_D), false);
EXPECT_TRUE(initialize_tensor(tensor_A.host_view(), init_A, seed + 2022));
EXPECT_TRUE(initialize_tensor(tensor_B.host_view(), init_B, seed + 2021));
EXPECT_TRUE(initialize_tensor(tensor_C.host_view(), init_C, seed + 2020));
// It is possible to randomly initialize to all zeros, so override this with non-zeros
// in the upper left corner of each operand.
tensor_A.host_view().at({0, 0}) = ElementA(1);
tensor_B.host_view().at({0, 0}) = ElementB(1);
tensor_C.host_view().at({0, 0}) = 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(
cute::Shape<int,int,int,int> problem_shape_MNKL,
ElementScalar alpha,
ElementScalar beta)
{
auto [M, N, K, L] = problem_shape_MNKL;
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);
if (tensor_D.size() > 1) {
EXPECT_GT(cutlass::reference::host::TensorNorm(tensor_D.host_view()), 0);
}
if (reference_D.size() > 1) {
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);
if (!passed) {
std::stringstream fname;
fname << "error_Gemm_device_"
<< M << "x" << N << "x" << K << "x" << L << "_"
<< cute::get<0>(typename Gemm::GemmKernel::TileShape{}) << "_"
<< cute::get<1>(typename Gemm::GemmKernel::TileShape{}) << "_"
<< cute::get<2>(typename Gemm::GemmKernel::TileShape{}) << ".txt";
std::ofstream file(fname.str());
file
<< "problem: " << ' ' << M << "x" << N << "x" << K << ", Batch count = " << L
<< ", alpha: " << float(alpha) << ", beta: " << float(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()
<< "\n\nComputed =\n" << tensor_D.host_view();
}
return passed;
}
/// Verifies the result is a GEMM
bool verify(
ProblemShapeType problem_size,
ElementScalar alpha,
ElementScalar beta)
{
auto problem_shape_MNKL = cute::append<4>(problem_size, 1);
auto M = cute::size<0>(problem_shape_MNKL);
auto N = cute::size<1>(problem_shape_MNKL);
auto K = cute::size<2>(problem_shape_MNKL);
auto L = cute::size<3>(problem_shape_MNKL);
auto A = cute::make_tensor(tensor_A.host_data(),
cute::make_layout(cute::make_shape(M, K, L), stride_a));
auto B = cute::make_tensor(tensor_B.host_data(),
cute::make_layout(cute::make_shape(N, K, L), stride_b));
auto C = cute::make_tensor(tensor_C.host_data(),
cute::make_layout(cute::make_shape(M, N, L), stride_c));
auto D = cute::make_tensor(reference_D.host_data(),
cute::make_layout(cute::make_shape(M, N, L), stride_d));
auto Bias = cute::make_tensor(static_cast<ElementCompute*>(nullptr),
cute::make_layout(cute::make_shape(M, cute::_1{})));
auto Aux = cute::make_tensor(static_cast<ElementD*>(nullptr),
cute::make_layout(cute::make_shape(M, N, L), stride_d));
auto Valpha = cute::make_tensor(static_cast<ElementCompute*>(nullptr),
cute::make_layout(cute::make_shape(M, cute::_1{})));
auto Vbeta = cute::make_tensor(static_cast<ElementCompute*>(nullptr),
cute::make_layout(cute::make_shape(M, cute::_1{})));
cutlass::reference::host::GettMainloopParams<ElementAccumulator, decltype(A), decltype(B)> mainloop_params{A, B};
cutlass::reference::host::GettEpilogueParams<
ElementScalar,
ElementScalar,
ElementAccumulator,
ElementCompute,
decltype(C),
decltype(D),
decltype(Bias),
decltype(Aux),
decltype(Valpha),
decltype(Vbeta),
ActivationFunctor
>
epilogue_params{
alpha, beta,
C, D, Bias, Aux
, Valpha, Vbeta
};
cutlass::reference::host::Gemm3x(mainloop_params, epilogue_params);
return compare_reference(problem_shape_MNKL, alpha, beta);
}
/// Determine if the CUDA device is sufficient to run the kernel
bool sufficient() {
//
// Determine SMEM requirements and waive if not satisfied
//
int smem_size = Gemm::GemmKernel::SharedStorageSize;
int device_idx;
cudaError_t result = cudaGetDevice(&device_idx);
if (result != cudaSuccess) {
throw std::runtime_error("cudaGetDevice() API call failed.");
}
cudaDeviceProp properties;
result = cudaGetDeviceProperties(&properties, device_idx);
this->sm_count = properties.multiProcessorCount;
if (result != cudaSuccess) {
throw std::runtime_error("cudaGetDeviceProperties() failed");
}
if (properties.sharedMemPerBlockOptin < smem_size) {
return false;
}
return true;
}
bool profile(
ProblemShapeType problem_size,
int iterations,
Gemm& gemm_op,
typename Gemm::Arguments& arguments,
cutlass::device_memory::allocation<uint8_t>& workspace) {
int M = cute::size<0>(problem_size);
int N = cute::size<1>(problem_size);
int K = cute::size<2>(problem_size);
int L = 1;
if constexpr(cute::rank(ProblemShapeType{}) == 4) {
L = cute::size<3>(problem_size);
}
cutlass::Status status;
//
// Run the GEMM
//
cudaError_t result;
for (int iter = 0; iter < iterations; ++iter) {
status = gemm_op(arguments, workspace.get());
if (status != cutlass::Status::kSuccess) {
EXPECT_TRUE(status == cutlass::Status::kSuccess) << to_string(status);
return false;
}
}
result = cudaDeviceSynchronize();
if (result != cudaSuccess) {
EXPECT_EQ(result, cudaSuccess) << "Error at Kernel Sync.";
return false;
}
return true;
}
/// Executes one test
bool run(
ProblemShapeType problem_size,
ElementScalar alpha = ElementScalar(1),
ElementScalar beta = ElementScalar(0),
bool profiling = false,
detail::Iterations iterations = Iterations{},
detail::Splits splits = Splits{})
{
// Fail test if insufficient CUDA device
if (!sufficient()) {
std::cout << "Test failed due to insufficient CUDA device." << std::endl;
return false;
}
this->initialize(problem_size);
//
// Initialize the GEMM operator
//
cutlass::KernelHardwareInfo hw_info;
hw_info.device_id = 0;
if (not profiling) {
this->sm_count = min(MaxSmCount, cutlass::KernelHardwareInfo::query_device_multiprocessor_count(hw_info.device_id));
hw_info.sm_count = this->sm_count;
}
else {
this->sm_count = cutlass::KernelHardwareInfo::query_device_multiprocessor_count(hw_info.device_id);
hw_info.sm_count = this->sm_count;
}
typename Gemm::GemmKernel::TileScheduler::Arguments scheduler_args;
if constexpr (std::is_same_v<typename Gemm::GemmKernel::TileSchedulerTag, cutlass::gemm::StreamKScheduler>) {
scheduler_args = { static_cast<int>(splits) };
}
// DefaultEpilogue
auto arguments = typename Gemm::Arguments {
cutlass::gemm::GemmUniversalMode::kGemm,
problem_size,
{
tensor_A.device_data(), stride_a,
tensor_B.device_data(), stride_b
},
{
{alpha, beta},
tensor_C.device_data(), stride_c, tensor_D.device_data(), stride_d
},
hw_info,
scheduler_args
};
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.can_implement(arguments);
if (status != cutlass::Status::kSuccess) {
cudaError_t error = cudaGetLastError();
std::cerr << "This test is not supported: " << cudaGetErrorString(error) << "\n";
return true;
}
//
// Run the GEMM
//
if (profiling) {
return profile(problem_size, static_cast<int>(iterations), gemm_op, arguments, workspace);
}
else {
cudaError_t result;
status = gemm_op.initialize(arguments, workspace.get());
status = gemm_op.run();
result = cudaDeviceSynchronize();
if (result != cudaSuccess) {
EXPECT_EQ(result, cudaSuccess) << "Error at Kernel Sync.";
return false;
}
EXPECT_TRUE(status == cutlass::Status::kSuccess) << to_string(status);
//
// Verify
//
bool passed = this->verify(problem_size, alpha, beta);
if (!passed) {
std::cout << "Error : Failed : with alpha: " << float(alpha) << ", beta: " << float(beta)
<< "\n";
}
return passed;
}
}
};
} // namespace detail
/////////////////////////////////////////////////////////////////////////////////////////////////
/////////////////////////////////////////////////////////////////////////////////////////////////
template <
typename Gemm,
template <class T> class ActivationFunctor
>
struct Testbed3x {
using TestBedImpl = typename detail::TestbedImpl<Gemm, ActivationFunctor>;
using Kernel = typename Gemm::GemmKernel;
using Epilogue = typename Gemm::GemmKernel::CollectiveEpilogue;
using ElementAccumulator = typename TestBedImpl::ElementAccumulator;
using ElementCompute = typename TestBedImpl::ElementCompute;
using ElementScalar = typename TestBedImpl::ElementScalar;
using LayoutTagA = typename TestBedImpl::LayoutTagA;
using LayoutTagB = typename TestBedImpl::LayoutTagB;
using LayoutTagC = typename TestBedImpl::LayoutTagC;
using LayoutTagD = typename TestBedImpl::LayoutTagD;
// Detail Implementation
TestBedImpl impl_;
//
// Methods
//
Testbed3x(
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_ = TestBedImpl::kDefaultSeed)
: impl_(init_A_, init_B_, init_C_, seed_) {}
Testbed3x(
typename LayoutTagA::Stride stride_factor_A_,
typename LayoutTagB::Stride stride_factor_B_,
typename LayoutTagC::Stride stride_factor_C_,
typename LayoutTagD::Stride stride_factor_D_,
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_ = TestBedImpl::kDefaultSeed)
: impl_(stride_factor_A_,
stride_factor_B_,
stride_factor_C_,
stride_factor_D_,
init_A_,
init_B_,
init_C_,
seed_) {}
/// Executes one test
bool run(
typename TestBedImpl::ProblemShapeType problem_size,
ElementScalar alpha = ElementScalar(1),
ElementScalar beta = ElementScalar(0),
detail::Splits splits = detail::Splits{},
bool profiling = false,
detail::Iterations iterations = detail::Iterations{})
{
return impl_.run(
problem_size, alpha, beta, profiling, iterations, splits
);
}
};
/////////////////////////////////////////////////////////////////////////////////////////////////
// Testbed for GEMMs with fused epilogues using the fusion::FusionOperation API
// Does not support testing of custom EVTs
template <typename Gemm>
struct Testbed3xFusionOperation {
using TestBedImpl = typename detail::TestbedImpl<Gemm>;
using Kernel = typename Gemm::GemmKernel;
using Epilogue = typename Gemm::GemmKernel::CollectiveEpilogue;
using LayoutTagA = typename TestBedImpl::LayoutTagA;
using LayoutTagB = typename TestBedImpl::LayoutTagB;
using LayoutTagC = typename TestBedImpl::LayoutTagC;
using LayoutTagD = typename TestBedImpl::LayoutTagD;
using LayoutTagScalar = cutlass::layout::PackedVectorLayout; // scalars are size-1 vectors
using LayoutTagVector = cutlass::layout::PackedVectorLayout;
using ElementA = typename Kernel::ElementA;
using StrideA = typename Kernel::StrideA;
using ElementB = typename Kernel::ElementB;
using StrideB = typename Kernel::StrideB;
using ElementC = typename Kernel::ElementC;
using StrideC = typename Kernel::StrideC;
using ElementD = typename Kernel::ElementD;
using StrideD = typename Kernel::StrideD;
using ProblemShapeType = typename Kernel::ProblemShape;
using ElementAccumulator = typename Kernel::ElementAccumulator;
//
// FusionOperation derived types/queries
//
using FusionOp = typename Gemm::EpilogueOutputOp;
static_assert(cute::is_base_of_v<cutlass::epilogue::fusion::FusionOperation, FusionOp>);
// fusion types are potentially void if the fusion is not supported
// helper so we don't try to construct HostTensor with void type
template <typename T, typename U = uint8_t>
using non_void_t = cute::conditional_t<cute::is_void_v<T>, U, T>;
using ElementCompute = typename FusionOp::ElementCompute;
using ElementScalar = typename FusionOp::ElementScalar;
using ElementBias = non_void_t<typename FusionOp::ElementBias>;
using ElementAux = non_void_t<typename FusionOp::ElementAux>;
using ElementAmax = non_void_t<typename FusionOp::ElementAmax>;
using LayoutTagAux = non_void_t<typename FusionOp::GmemLayoutTagAux, LayoutTagD>;
using ActivationFunctor = non_void_t<typename FusionOp::ActivationFn,
cutlass::epilogue::thread::Identity<ElementCompute>>;
static constexpr bool IsBiasEnabled = FusionOp::IsPerRowBiasSupported;
static constexpr bool IsPerRowScaleEnabled = FusionOp::IsPerRowScaleSupported;
static constexpr bool IsScaleFactorEnabled = FusionOp::IsScaleFactorSupported;
static constexpr bool IsAuxEnabled = FusionOp::IsAuxOutSupported;
static constexpr bool IsAbsMaxEnabled = FusionOp::IsAbsMaxSupported;
// Legacy support for deprecated bias-elementwise collective, will be removed next release
using EpiloguePolicy = typename Epilogue::DispatchPolicy;
static constexpr bool IsLegacy =
cute::is_same_v<
EpiloguePolicy,
cutlass::epilogue::Sm90TmaWarpSpecializedBiasElementwise<
EpiloguePolicy::StagesC, EpiloguePolicy::StagesD, EpiloguePolicy::FragmentSize>
>;
// Inputs
cutlass::HostTensor<ElementScalar, LayoutTagScalar> alpha;
cutlass::HostTensor<ElementScalar, LayoutTagScalar> beta;
cutlass::HostTensor<ElementScalar, LayoutTagScalar> scale_A;
cutlass::HostTensor<ElementScalar, LayoutTagScalar> scale_B;
cutlass::HostTensor<ElementScalar, LayoutTagScalar> scale_C;
cutlass::HostTensor<ElementScalar, LayoutTagScalar> scale_D;
cutlass::HostTensor<ElementScalar, LayoutTagScalar> scale_Aux;
cutlass::HostTensor<ElementBias , LayoutTagVector> bias;
// Outputs
cutlass::HostTensor<ElementAmax, LayoutTagScalar> abs_max_Aux;
cutlass::HostTensor<ElementAmax, LayoutTagScalar> abs_max_D;
cutlass::HostTensor<ElementAux , LayoutTagAux > tensor_Aux;
cutlass::gemm::TagToStrideC_t< LayoutTagAux > stride_Aux;
// References
cutlass::HostTensor<ElementAux , LayoutTagAux > reference_Aux;
cutlass::HostTensor<ElementAmax, LayoutTagScalar> reference_abs_max_Aux;
cutlass::HostTensor<ElementAmax, LayoutTagScalar> reference_abs_max_D;
// Detail Implementation
TestBedImpl impl_;
// Whether to use relative equality checks
bool check_relative_equality = false;
// Are scalars copied to device memory before kernel launch
bool use_device_scalars = false;
// If per-row scale is enabled and this is true, beta is passed as a host scalar instead of device vector
bool disable_vector_beta = false;
// Random distribution with which to initialize the A/B/C/D/Aux scaling factors
cutlass::Distribution::Kind init_scale = cutlass::Distribution::Uniform;
// Random distribution with which to initialize the bias vector
cutlass::Distribution::Kind init_bias = cutlass::Distribution::Uniform;
// Factors used for calculating relative equality. These default
// values are borrowed from those used by default in the CUTLASS
// profiler for performing relative equality checks.
float epsilon = 0.05f;
float nonzero_floor = 1.0f / 256.0f;
//
// Methods
//
Testbed3xFusionOperation(
bool check_relative_equality_ = false,
bool use_device_scalars_ = false,
bool disable_vector_beta_ = false,
cutlass::Distribution::Kind init_scale_ = cutlass::Distribution::Uniform,
cutlass::Distribution::Kind init_bias_ = cutlass::Distribution::Uniform,
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_ = TestBedImpl::kDefaultSeed
) : impl_(init_A_, init_B_, init_C_, seed_),
check_relative_equality(check_relative_equality_),
use_device_scalars(use_device_scalars_),
init_scale(init_scale_), init_bias(init_bias_) { }
Testbed3xFusionOperation(
typename LayoutTagA::Stride stride_factor_A_,
typename LayoutTagB::Stride stride_factor_B_,
typename LayoutTagC::Stride stride_factor_C_,
typename LayoutTagD::Stride stride_factor_D_,
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_ = TestBedImpl::kDefaultSeed
) : impl_(stride_factor_A_,
stride_factor_B_,
stride_factor_C_,
stride_factor_D_,
init_A_,
init_B_,
init_C_,
seed_) { }
/// Initializes data structures
void initialize(ProblemShapeType problem_size, ElementScalar alpha_=1.f, ElementScalar beta_=0.f) {
auto problem_shape_MNKL = cute::append<4>(problem_size, 1);
auto [M, N, K, L] = problem_shape_MNKL;
auto scalar_coord = cutlass::make_Coord(1);
auto col_vector_coord = cutlass::make_Coord(M);
// Allocate the GEMM workspace for A/B/C/D tensor
impl_.initialize(problem_size);
if constexpr (IsPerRowScaleEnabled) {
alpha.resize(col_vector_coord);
EXPECT_TRUE(impl_.initialize_tensor(alpha.host_view(), init_scale, impl_.seed + 2023));
if (disable_vector_beta) {
beta.resize(scalar_coord, false);
cutlass::reference::host::TensorFill(beta.host_view(), beta_);
}
else {
beta.resize(col_vector_coord);
EXPECT_TRUE(impl_.initialize_tensor(beta.host_view(), init_scale, impl_.seed + 2024));
}
}
else {
alpha.resize(scalar_coord, use_device_scalars);
beta.resize(scalar_coord, use_device_scalars);
cutlass::reference::host::TensorFill(alpha.host_view(), alpha_);
cutlass::reference::host::TensorFill(beta.host_view(), beta_);
}
alpha.sync_device();
beta.sync_device();
if constexpr (IsScaleFactorEnabled) {
scale_A.resize(scalar_coord, use_device_scalars);
scale_B.resize(scalar_coord, use_device_scalars);
scale_C.resize(scalar_coord, use_device_scalars);
scale_D.resize(scalar_coord, use_device_scalars);
EXPECT_TRUE(impl_.initialize_tensor(scale_A.host_view(), init_scale, impl_.seed + 2023));
EXPECT_TRUE(impl_.initialize_tensor(scale_B.host_view(), init_scale, impl_.seed + 2024));
EXPECT_TRUE(impl_.initialize_tensor(scale_C.host_view(), init_scale, impl_.seed + 2025));
EXPECT_TRUE(impl_.initialize_tensor(scale_D.host_view(), init_scale, impl_.seed + 2026));
scale_A.sync_device();
scale_B.sync_device();
scale_C.sync_device();
scale_D.sync_device();
}
if constexpr (IsBiasEnabled) {
bias.resize(col_vector_coord);
EXPECT_TRUE(impl_.initialize_tensor(bias.host_view(), init_bias, impl_.seed + 2023));
bias.sync_device();
}
if constexpr (IsAbsMaxEnabled) {
abs_max_D.resize(scalar_coord);
abs_max_D.sync_device();
reference_abs_max_D.resize(scalar_coord);
}
if constexpr (IsAuxEnabled) {
auto aux_coord = cutlass::make_Coord(M * L, N);
auto aux_layout = cutlass::layout::Affine2Layout_Factory<LayoutTagD>::layout_factory(aux_coord, typename LayoutTagAux::Stride{});
tensor_Aux.resize(aux_coord, aux_layout);
reference_Aux.resize(aux_coord, aux_layout, false);
tensor_Aux.sync_device();
stride_Aux = cutlass::make_cute_packed_stride(cutlass::gemm::TagToStrideC_t<LayoutTagAux>{}, cute::make_shape(M, N, L));
if constexpr (IsScaleFactorEnabled) {
scale_Aux.resize(scalar_coord, use_device_scalars);
EXPECT_TRUE(impl_.initialize_tensor(scale_Aux.host_view(), init_scale, impl_.seed + 2027));
scale_Aux.sync_device();
}
if constexpr (IsAbsMaxEnabled) {
abs_max_Aux.resize(scalar_coord);
abs_max_Aux.sync_device();
reference_abs_max_Aux.resize(scalar_coord);
}
}
}
template <
class Element,
class Layout
>
bool equality_check(
cutlass::TensorView<Element, Layout> const& lhs,
cutlass::TensorView<Element, Layout> const& rhs) const {
if (check_relative_equality) {
return cutlass::reference::host::TensorRelativelyEquals(
lhs, rhs, Element(epsilon), Element(nonzero_floor));
}
else {
return cutlass::reference::host::TensorEquals(lhs, rhs);
}
}
/// Compares computed reference with device reference and outputs to a file if incorrect
bool compare_reference(cute::Shape<int,int,int,int> problem_shape_MNKL) {
auto [M, N, K, L] = problem_shape_MNKL;
auto coord_0 = cutlass::make_Coord(0);
EXPECT_GT(cutlass::reference::host::TensorNorm(impl_.tensor_A.host_view()), 0);
EXPECT_GT(cutlass::reference::host::TensorNorm(impl_.tensor_B.host_view()), 0);
EXPECT_GT(cutlass::reference::host::TensorNorm(impl_.tensor_C.host_view()), 0);
impl_.tensor_D.sync_host();
if (impl_.tensor_D.size() > 1) {
EXPECT_GT(cutlass::reference::host::TensorNorm(impl_.tensor_D.host_view()), 0);
EXPECT_GT(cutlass::reference::host::TensorNorm(impl_.reference_D.host_view()), 0);
}
bool passed = equality_check(impl_.reference_D.host_view(), impl_.tensor_D.host_view());
if constexpr (IsAbsMaxEnabled) {
abs_max_D.sync_host();
passed &= equality_check(reference_abs_max_D.host_view(), abs_max_D.host_view());
}
if constexpr (IsAuxEnabled) {
tensor_Aux.sync_host();
EXPECT_GT(cutlass::reference::host::TensorNorm(tensor_Aux.host_view()), 0);
EXPECT_GT(cutlass::reference::host::TensorNorm(reference_Aux.host_view()), 0);
passed &= equality_check(reference_Aux.host_view(), tensor_Aux.host_view());
if constexpr (IsAbsMaxEnabled) {
abs_max_Aux.sync_host();
passed &= equality_check(reference_abs_max_Aux.host_view(), abs_max_Aux.host_view());
}
}
EXPECT_TRUE(passed);
if (!passed) {
std::stringstream fname;
fname << "error_Gemm_device_"
<< M << "x" << N << "x" << K << "x" << L << "_"
<< cute::get<0>(typename Gemm::GemmKernel::TileShape{}) << "_"
<< cute::get<1>(typename Gemm::GemmKernel::TileShape{}) << "_"
<< cute::get<2>(typename Gemm::GemmKernel::TileShape{}) << ".txt";
std::ofstream file(fname.str());
file
<< "problem: " << ' ' << M << "x" << N << "x" << K << ", Batch count = " << L;
if constexpr (IsScaleFactorEnabled) {
file
<< ", scale_a: " << scale_A.at(coord_0)
<< ", scale_b: " << scale_B.at(coord_0)
<< ", scale_c: " << scale_C.at(coord_0);
}
if constexpr (IsPerRowScaleEnabled) {
file << "\n\nvalpha = \n" << alpha.host_view();
file << "\n\nvbeta = \n" << beta.host_view();
} else {
file
<< ", alpha: " << alpha.at(coord_0) << ", beta: " << beta.at(coord_0);
}
file << "\n\n";
if constexpr (IsAbsMaxEnabled) {
file << "scale_d: " << float(scale_D.at(coord_0));
file << "\nReference abs_max_D :";
file << " " << float(reference_abs_max_D.at(coord_0));
file << "\nComputed abs_max_D :";
file << " " << float(abs_max_D.at(coord_0));
file << "\n\n";
if constexpr (IsAuxEnabled) {
file << "scale_aux: " << float(scale_Aux.at(coord_0));
file << "\nReference abs_max_Aux :";
file << " " << float(reference_abs_max_Aux.at(coord_0));
file << "\nComputed abs_max_Aux :";
file << " " << float(abs_max_Aux.at(coord_0));
file << "\n\n";
}
}
file
<< "A =\n" << impl_.tensor_A.host_view()
<< "\nB =\n" << impl_.tensor_B.host_view()
<< "\nC =\n" << impl_.tensor_C.host_view();
if constexpr (IsBiasEnabled) {
file << "\n\nBias = \n" << bias.host_view();
}
if constexpr (IsAuxEnabled) {
file
<< "\n\nReference Aux =\n" << reference_Aux.host_view()
<< "\n\nComputed Aux =\n" << tensor_Aux.host_view();
}
file
<< "\n\nReference D =\n" << impl_.reference_D.host_view()
<< "\n\nComputed D =\n" << impl_.tensor_D.host_view();
}
return passed;
}
/// Verifies the result against a reference implementation
bool verify(ProblemShapeType problem_size)
{
auto problem_shape_MNKL = cute::append<4>(problem_size, 1);
auto M = cute::get<0>(problem_shape_MNKL);
auto N = cute::get<1>(problem_shape_MNKL);
auto K = cute::get<2>(problem_shape_MNKL);
auto L = cute::get<3>(problem_shape_MNKL);
auto coord_0 = cutlass::make_Coord(0);
auto A = cute::make_tensor(impl_.tensor_A.host_data(),
cute::make_layout(cute::make_shape(M, K, L), impl_.stride_a));
auto B = cute::make_tensor(impl_.tensor_B.host_data(),
cute::make_layout(cute::make_shape(N, K, L), impl_.stride_b));
auto C = cute::make_tensor(impl_.tensor_C.host_data(),
cute::make_layout(cute::make_shape(M, N, L), impl_.stride_c));
auto D = cute::make_tensor(impl_.reference_D.host_data(),
cute::make_layout(cute::make_shape(M, N, L), impl_.stride_d));
auto Bias = cute::make_tensor(bias.host_data(),
cute::make_layout(cute::make_shape(M, cute::_1{})));
auto Aux = cute::make_tensor(reference_Aux.host_data(),
cute::make_layout(cute::make_shape(M, N, L), stride_Aux));
auto Valpha = cute::make_tensor(alpha.host_data(),
cute::make_layout(cute::make_shape(M, cute::_1{})));
auto Vbeta = cute::make_tensor(beta.host_data(),
cute::make_layout(cute::make_shape(M, cute::_1{})));
cutlass::reference::host::GettMainloopParams<ElementAccumulator, decltype(A), decltype(B)> mainloop_params{A, B};
cutlass::reference::host::GettEpilogueParams<
ElementScalar,
ElementScalar,
ElementAccumulator,
ElementCompute,
decltype(C),
decltype(D),
decltype(Bias),
decltype(Aux),
decltype(Valpha),
decltype(Vbeta),
ActivationFunctor>
epilogue_params{};
epilogue_params.C = C;
epilogue_params.D = D;
epilogue_params.alpha = alpha.at(coord_0);
epilogue_params.beta = beta.at(coord_0);
if constexpr (IsScaleFactorEnabled) {
epilogue_params.scale_a = scale_A.at(coord_0);
epilogue_params.scale_b = scale_B.at(coord_0);
epilogue_params.scale_c = scale_C.at(coord_0);
epilogue_params.scale_d = scale_D.at(coord_0);
}
if constexpr (IsBiasEnabled) {
epilogue_params.Bias = Bias;
}
if constexpr (IsAbsMaxEnabled) {
epilogue_params.abs_max_D = reference_abs_max_D.host_data();
}
if constexpr (IsAuxEnabled) {
epilogue_params.Aux = Aux;
if constexpr (IsScaleFactorEnabled) {
epilogue_params.scale_aux = scale_Aux.at(coord_0);
}
if constexpr (IsAbsMaxEnabled) {
epilogue_params.abs_max_Aux = reference_abs_max_Aux.host_data();
}
}
if constexpr (IsPerRowScaleEnabled) {
epilogue_params.Valpha = Valpha;
if (not disable_vector_beta) {
epilogue_params.Vbeta = Vbeta;
}
}
cutlass::reference::host::Gemm3x(mainloop_params, epilogue_params);
return compare_reference(problem_shape_MNKL);
}
/// Executes one test
bool run(
ProblemShapeType problem_size,
ElementScalar alpha_ = ElementScalar(1),
ElementScalar beta_ = ElementScalar(0),
detail::Splits splits = detail::Splits{},
bool profiling = false,
detail::Iterations iterations = detail::Iterations{})
{
// Fail test if insufficient CUDA device
if (!impl_.sufficient()) {
std::cout << "Test failed due to insufficient CUDA device." << std::endl;
return false;
}
//
// Initialize the GEMM operator
//
typename Gemm::Arguments arguments;
cutlass::KernelHardwareInfo hw_info;
hw_info.device_id = 0;
if (not profiling) {
impl_.sm_count = min(impl_.MaxSmCount, cutlass::KernelHardwareInfo::query_device_multiprocessor_count(hw_info.device_id));
hw_info.sm_count = impl_.sm_count;
}
else {
impl_.sm_count = cutlass::KernelHardwareInfo::query_device_multiprocessor_count(hw_info.device_id);
hw_info.sm_count = impl_.sm_count;
}
/// Initializes data structures
/// A/B/C/D Tensor
initialize(problem_size, alpha_, beta_);
typename Gemm::GemmKernel::TileScheduler::Arguments scheduler_args;
if constexpr (std::is_same_v<typename Gemm::GemmKernel::TileSchedulerTag, cutlass::gemm::StreamKScheduler>) {
scheduler_args = { static_cast<int>(splits) };
}
arguments = typename Gemm::Arguments{
cutlass::gemm::GemmUniversalMode::kGemm,
problem_size,
{
impl_.tensor_A.device_data(), impl_.stride_a,
impl_.tensor_B.device_data(), impl_.stride_b
},
{ // Epilogue arguments
{}, // thread
impl_.tensor_C.device_data(),
impl_.stride_c,
impl_.tensor_D.device_data(),
impl_.stride_d
}, // Epilogue arguments end
hw_info,
scheduler_args
};
auto coord_0 = cutlass::make_Coord(0);
if constexpr (IsLegacy) {
arguments.epilogue.thread = {
alpha.at(coord_0),
beta.at(coord_0),
alpha.device_data(),
beta.device_data()
};
arguments.epilogue.ptr_Bias = bias.device_data();
arguments.epilogue.ptr_T = tensor_Aux.device_data();
}
else {
auto &fusion_args = arguments.epilogue.thread;
fusion_args.alpha = alpha.at(coord_0);
fusion_args.beta = beta.at(coord_0);
fusion_args.alpha_ptr = alpha.device_data();
fusion_args.beta_ptr = beta.device_data(); // if disable_vector_beta is true this is nullptr
if constexpr (IsScaleFactorEnabled) {
fusion_args.scale_a = scale_A.at(coord_0);
fusion_args.scale_b = scale_B.at(coord_0);
fusion_args.scale_c = scale_C.at(coord_0);
fusion_args.scale_d = scale_D.at(coord_0);
fusion_args.scale_a_ptr = scale_A.device_data();
fusion_args.scale_b_ptr = scale_B.device_data();
fusion_args.scale_c_ptr = scale_C.device_data();
fusion_args.scale_d_ptr = scale_D.device_data();
}
if constexpr (IsBiasEnabled) {
fusion_args.bias_ptr = bias.device_data();
}
// example of how to set kernel activation arguments
if constexpr (cute::is_same_v<ActivationFunctor, cutlass::epilogue::thread::ScaledGELU_taylor<ElementCompute>>) {
// see ActivationFunctor::Arguments in activation.h for definition
// if Arguments doesn't exist then fusion_args.activation is empty
fusion_args.activation.scale = ElementCompute(1);
}
if constexpr (IsAbsMaxEnabled) {
fusion_args.amax_D_ptr = abs_max_D.device_data();
}
if constexpr (IsAuxEnabled) {
fusion_args.aux_ptr = tensor_Aux.device_data();
fusion_args.dAux = stride_Aux;
if constexpr (IsScaleFactorEnabled) {
fusion_args.scale_aux = scale_Aux.at(coord_0);
fusion_args.scale_aux_ptr = scale_Aux.device_data();
}
if constexpr (IsAbsMaxEnabled) {
fusion_args.amax_aux_ptr = abs_max_Aux.device_data();
}
}
}
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.can_implement(arguments);
if (status != cutlass::Status::kSuccess) {
cudaError_t error = cudaGetLastError();
std::cerr << "This test is not supported: " << cudaGetErrorString(error) << "\n";
return true;
}
//
// Run the GEMM
//
if (profiling) {
return impl_.profile(problem_size, static_cast<int>(iterations), gemm_op, arguments, workspace);
}
else {
cudaError_t result;
status = gemm_op.initialize(arguments, workspace.get());
status = gemm_op.run();
result = cudaDeviceSynchronize();
if (result != cudaSuccess) {
EXPECT_EQ(result, cudaSuccess) << "Error at Kernel Sync.";
return false;
}
EXPECT_TRUE(status == cutlass::Status::kSuccess) << to_string(status);
//
// Verify
//
bool passed = this->verify(problem_size);
if (!passed) {
std::cout << "Error : Failed : with alpha: " << float(alpha_) << ", beta: " << float(beta_)
<< "\n";
}
return passed;
}
}
};
/////////////////////////////////////////////////////////////////////////////////////////////////
template <
typename Gemm,
typename Testbed = Testbed3x<Gemm, cutlass::epilogue::thread::Identity>
>
bool TestAll(double alpha = 1.0, double beta = 0.0, Testbed testbed = {}) {
using ElementScalar = typename Gemm::EpilogueOutputOp::ElementScalar;
using ProblemShapeType = typename Gemm::GemmKernel::ProblemShape;
int max_alignment = std::max(Gemm::kAlignmentA, Gemm::kAlignmentB);
std::vector<int> problem_size_m = {max_alignment, 512 - 3 * max_alignment};
std::vector<int> problem_size_n = {max_alignment, 512 - 2 * max_alignment};
if constexpr (std::is_same_v<typename Gemm::GemmKernel::DispatchPolicy::Schedule,
cutlass::gemm::KernelTmaWarpSpecializedPingpong>) {
problem_size_m.push_back(768);
problem_size_n.push_back(768);
}
constexpr int Stages = Gemm::GemmKernel::DispatchPolicy::Stages;
constexpr int TileShapeK = cute::size<2>(typename Gemm::GemmKernel::TileShape{});
std::vector<int> problem_size_k = {max_alignment, TileShapeK * (Stages + 1) - max_alignment};
std::vector<int> problem_splits = {1};
if constexpr (std::is_same_v<typename Gemm::GemmKernel::TileSchedulerTag, cutlass::gemm::StreamKScheduler>) {
problem_splits.push_back(2);
problem_splits.push_back(3);
// As many splits as there are maximum k tiles
problem_splits.push_back(Stages + 1);
}
bool passed = true;
for (int m : problem_size_m) {
for (int n : problem_size_n) {
for (int k : problem_size_k) {
for (int splits : problem_splits) {
ProblemShapeType problem_size;
if constexpr (cute::rank(ProblemShapeType{}) == 4) {
problem_size = ProblemShapeType{m, n, k, /* l */ 1};
}
else {
problem_size = ProblemShapeType{m, n, k};
}
passed = testbed.run(
problem_size,
cutlass::from_real<ElementScalar>(alpha),
cutlass::from_real<ElementScalar>(beta),
detail::Splits(splits)
);
if (!passed) {
return false;
}
}
}
}
}
// if we do support batched GEMM, just run one test on it to save on test time
if constexpr (cute::rank(ProblemShapeType{}) == 4) {
auto problem_size = ProblemShapeType{256 + max_alignment, 256 + max_alignment, 160 + max_alignment, /* l */ 3};
passed = testbed.run(
problem_size,
cutlass::from_real<ElementScalar>(alpha),
cutlass::from_real<ElementScalar>(beta)
);
if (!passed) {
return false;
}
}
return passed;
}
/////////////////////////////////////////////////////////////////////////////////////////////////
template <typename Gemm>
bool TestAllBiasElementwise(double alpha = 1.0, double beta = 0.0, bool check_relative_equality=false) {
Testbed3xFusionOperation<Gemm> testbed(check_relative_equality);
return TestAll<Gemm>(alpha, beta, testbed);
}
/////////////////////////////////////////////////////////////////////////////////////////////////
template <typename Gemm>
bool TestGemmPerf3x(int iterations = 20) {
using ProblemShapeType = typename Gemm::GemmKernel::ProblemShape;
using ElementAccumulator = typename Gemm::GemmKernel::ElementAccumulator;
using ElementScalar = ElementAccumulator;
bool passed = true;
std::vector<int> problem_size_m = { 4608 };
std::vector<int> problem_size_n = { 4608 };
std::vector<int> problem_size_k = { 8192 };
Testbed3x<Gemm, cutlass::epilogue::thread::Identity> testbed;
for (int m : problem_size_m) {
for (int n : problem_size_n) {
for (int k : problem_size_k) {
ProblemShapeType problem_size;
if constexpr (cute::rank(ProblemShapeType{}) == 4) {
problem_size = ProblemShapeType{m, n, k, /* l */ 1};
}
else {
problem_size = ProblemShapeType{m, n, k};
}
passed = testbed.run(
problem_size,
cutlass::from_real<ElementScalar>(1),
cutlass::from_real<ElementScalar>(0),
true,
detail::Iterations(iterations)
);
if (!passed) {
return false;
}
}
}
}
// if we do support batched GEMM, just run it once
if constexpr (cute::rank(ProblemShapeType{}) == 4) {
auto problem_size = ProblemShapeType{problem_size_m[0], problem_size_n[0], problem_size_k[0], /* l */ 4};
passed = testbed.run(
problem_size,
cutlass::from_real<ElementScalar>(1),
cutlass::from_real<ElementScalar>(0),
true,
detail::Iterations(iterations)
);
if (!passed) {
return false;
}
}
return passed;
}
} // namespace device
} // namespace gemm
} // namespace test
/////////////////////////////////////////////////////////////////////////////////////////////////
Reference in New Issue View Git Blame Copy Permalink