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Add SpMM tests #875

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Feb 8, 2025
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Add SpMM tests #875

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21 changes: 12 additions & 9 deletions include/matx/transforms/convert/dense2sparse_cusparse.h
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Original file line number Diff line number Diff line change
Expand Up @@ -258,19 +258,22 @@ void dense2sparse_impl(OutputTensorType &o, const InputTensorType &a,
using TA = typename InputTensorType::value_type;
using TO = typename OutputTensorType::value_type;

static constexpr int RANKA = InputTensorType::Rank();
static constexpr int RANKO = OutputTensorType::Rank();

// Restrictions.
static_assert(OutputTensorType::Rank() == InputTensorType::Rank(),
"tensors must have same rank");
static_assert(RANKA == RANKO, "tensors must have same rank");
static_assert(std::is_same_v<TA, TO>,
"tensors must have the same data type");
static_assert(std::is_same_v<TA, int8_t> ||
std::is_same_v<TA, matx::matxFp16> ||
std::is_same_v<TA, matx::matxBf16> ||
std::is_same_v<TA, float> ||
std::is_same_v<TA, double> ||
std::is_same_v<TA, cuda::std::complex<float>> ||
std::is_same_v<TA, cuda::std::complex<double>>,
static_assert(std::is_same_v<TO, int8_t> ||
std::is_same_v<TO, matx::matxFp16> ||
std::is_same_v<TO, matx::matxBf16> ||
std::is_same_v<TO, float> ||
std::is_same_v<TO, double> ||
std::is_same_v<TO, cuda::std::complex<float>> ||
std::is_same_v<TO, cuda::std::complex<double>>,
"unsupported data type");
MATX_ASSERT(a.Stride(RANKA - 1) == 1, matxInvalidParameter);

// Get parameters required by these tensors (for caching).
auto params =
Expand Down
21 changes: 12 additions & 9 deletions include/matx/transforms/convert/sparse2dense_cusparse.h
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Expand Up @@ -218,19 +218,22 @@ void sparse2dense_impl(OutputTensorType &o, const InputTensorType &a,
using TA = typename InputTensorType::value_type;
using TO = typename OutputTensorType::value_type;

static constexpr int RANKA = InputTensorType::Rank();
static constexpr int RANKO = OutputTensorType::Rank();

// Restrictions.
static_assert(OutputTensorType::Rank() == InputTensorType::Rank(),
"tensors must have same rank");
static_assert(RANKA == RANKO, "tensors must have same rank");
static_assert(std::is_same_v<TA, TO>,
"tensors must have the same data type");
static_assert(std::is_same_v<TA, int8_t> ||
std::is_same_v<TA, matx::matxFp16> ||
std::is_same_v<TA, matx::matxBf16> ||
std::is_same_v<TA, float> ||
std::is_same_v<TA, double> ||
std::is_same_v<TA, cuda::std::complex<float>> ||
std::is_same_v<TA, cuda::std::complex<double>>,
static_assert(std::is_same_v<TO, int8_t> ||
std::is_same_v<TO, matx::matxFp16> ||
std::is_same_v<TO, matx::matxBf16> ||
std::is_same_v<TO, float> ||
std::is_same_v<TO, double> ||
std::is_same_v<TO, cuda::std::complex<float>> ||
std::is_same_v<TO, cuda::std::complex<double>>,
"unsupported data type");
MATX_ASSERT(o.Stride(RANKO - 1) == 1, matxInvalidParameter);

// Get parameters required by these tensors (for caching).
auto params =
Expand Down
24 changes: 21 additions & 3 deletions include/matx/transforms/matmul/matmul_cusparse.h
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Expand Up @@ -92,6 +92,20 @@ class MatMulCUSPARSEHandle_t {
MATX_NVTX_START("", matx::MATX_NVTX_LOG_INTERNAL)
params_ = GetGemmParams(c, a, b, stream, alpha, beta);

// Properly typed alpha, beta.
if constexpr (std::is_same_v<TC, cuda::std::complex<float>> ||
std::is_same_v<TC, cuda::std::complex<double>>) {
salpha_ = {alpha, 0};
sbeta_ = {beta, 0};
}
else if constexpr (std::is_same_v<TC, float> ||
std::is_same_v<TC, double>) {
salpha_ = alpha;;
sbeta_ = beta;
} else {
MATX_THROW(matxNotSupported, "SpMM currently only supports uniform FP");
}

[[maybe_unused]] cusparseStatus_t ret = cusparseCreate(&handle_);
MATX_ASSERT(ret == CUSPARSE_STATUS_SUCCESS, matxMatMulError);

Expand Down Expand Up @@ -137,7 +151,7 @@ class MatMulCUSPARSEHandle_t {
const cusparseSpMMAlg_t algo = CUSPARSE_SPMM_ALG_DEFAULT;
const cudaDataType comptp = dtc; // TODO: support separate comp type?!
ret = cusparseSpMM_bufferSize(handle_, params_.opA, params_.opB,
&params_.alpha, matA_, matB_, &params_.beta,
&salpha_, matA_, matB_, &sbeta_,
matC_, comptp, algo, &workspaceSize_);
MATX_ASSERT(ret == CUSPARSE_STATUS_SUCCESS, matxMatMulError);
if (workspaceSize_) {
Expand Down Expand Up @@ -189,8 +203,8 @@ class MatMulCUSPARSEHandle_t {
const cusparseSpMMAlg_t algo = CUSPARSE_SPMM_ALG_DEFAULT;
const cudaDataType comptp = MatXTypeToCudaType<TC>(); // TODO: see above
[[maybe_unused]] cusparseStatus_t ret =
cusparseSpMM(handle_, params_.opA, params_.opB, &params_.alpha, matA_,
matB_, &params_.beta, matC_, comptp, algo, workspace_);
cusparseSpMM(handle_, params_.opA, params_.opB, &salpha_, matA_,
matB_, &sbeta_, matC_, comptp, algo, workspace_);
MATX_ASSERT(ret == CUSPARSE_STATUS_SUCCESS, matxMatMulError);
}

Expand All @@ -202,6 +216,8 @@ class MatMulCUSPARSEHandle_t {
size_t workspaceSize_ = 0;
void *workspace_ = nullptr;
detail::MatMulCUSPARSEParams_t params_;
TC salpha_;
TC sbeta_;
};

/**
Expand Down Expand Up @@ -272,6 +288,8 @@ void sparse_matmul_impl(TensorTypeC &c, const TensorTypeA &a, const TensorTypeB
a.Size(RANKA - 1) == b.Size(RANKB - 2) &&
c.Size(RANKC - 1) == b.Size(RANKB - 1) &&
c.Size(RANKC - 2) == a.Size(RANKA - 2), matxInvalidSize);
MATX_ASSERT(b.Stride(RANKB - 1) == 1 &&
c.Stride(RANKC - 1) == 1, matxInvalidParameter);

// Get parameters required by these tensors (for caching).
auto params =
Expand Down
4 changes: 3 additions & 1 deletion include/matx/transforms/solve/solve_cudss.h
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Expand Up @@ -101,8 +101,8 @@ class SolveCUDSSHandle_t {
MATX_ASSERT(ret == CUDSS_STATUS_SUCCESS, matxSolverError);

// Create cuDSS handle for dense matrices B and C.
static_assert(is_tensor_view_v<TensorTypeA>);
static_assert(is_tensor_view_v<TensorTypeB>);
static_assert(is_tensor_view_v<TensorTypeC>);
cudaDataType dtb = MatXTypeToCudaType<TB>();
cudaDataType dtc = MatXTypeToCudaType<TC>();
cudssLayout_t layout = CUDSS_LAYOUT_COL_MAJOR; // no ROW-MAJOR in cuDSS yet
Expand Down Expand Up @@ -250,6 +250,8 @@ void sparse_solve_impl_trans(TensorTypeC &c, const TensorTypeA &a,
a.Size(RANKA - 1) == b.Size(RANKB - 1) &&
a.Size(RANKA - 2) == b.Size(RANKB - 1) &&
b.Size(RANKB - 2) == c.Size(RANKC - 2), matxInvalidSize);
MATX_ASSERT(b.Stride(RANKB - 1) == 1 &&
c.Stride(RANKC - 1) == 1, matxInvalidParameter);
static_assert(std::is_same_v<typename TensorTypeA::pos_type, int32_t> &&
std::is_same_v<typename TensorTypeA::crd_type, int32_t>, "unsupported index type");

Expand Down
227 changes: 227 additions & 0 deletions test/00_sparse/Matmul.cu
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@@ -0,0 +1,227 @@
////////////////////////////////////////////////////////////////////////////////
// BSD 3-Clause License
//
// Copyright (c) 2025, 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:
//
// 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.
/////////////////////////////////////////////////////////////////////////////////

#include "assert.h"
#include "matx.h"
#include "test_types.h"
#include "utilities.h"
#include "gtest/gtest.h"

using namespace matx;

//
// Helper method to construct:
//
// | 1, 2, 0, 0, 0, 0, 0, 0 |
// | 0, 0, 0, 0, 0, 0, 0, 3 |
// | 0, 0, 0, 0, 0, 0, 4, 0 |
// | 0, 0, 5, 6, 0, 7, 0, 0 |
//
template <typename T> static auto makeA() {
const index_t m = 4;
const index_t n = 8;
tensor_t<T, 2> A = make_tensor<T>({m, n});
for (index_t i = 0; i < m; i++) {
for (index_t j = 0; j < n; j++) {
A(i, j) = static_cast<T>(0);
}
}
A(0, 0) = static_cast<T>(1);
A(0, 1) = static_cast<T>(2);
A(1, 7) = static_cast<T>(3);
A(2, 6) = static_cast<T>(4);
A(3, 2) = static_cast<T>(5);
A(3, 3) = static_cast<T>(6);
A(3, 5) = static_cast<T>(7);
return A;
}

template <typename T> static auto makeB() {
const index_t m = 8;
const index_t n = 2;
tensor_t<T, 2> B = make_tensor<T>({m, n});
B(0, 0) = static_cast<T>(1); B(0, 1) = static_cast<T>(2);
B(1, 0) = static_cast<T>(3); B(1, 1) = static_cast<T>(4);
B(2, 0) = static_cast<T>(5); B(2, 1) = static_cast<T>(6);
B(3, 0) = static_cast<T>(7); B(3, 1) = static_cast<T>(8);
B(4, 0) = static_cast<T>(9); B(4, 1) = static_cast<T>(10);
B(5, 0) = static_cast<T>(11); B(5, 1) = static_cast<T>(12);
B(6, 0) = static_cast<T>(13); B(6, 1) = static_cast<T>(14);
B(7, 0) = static_cast<T>(15); B(7, 1) = static_cast<T>(16);
return B;
}

template <typename T> static auto makeE() {
const index_t m = 4;
const index_t n = 2;
tensor_t<T, 2> E = make_tensor<T>({m, n});
E(0, 0) = static_cast<T>(7); E(0, 1) = static_cast<T>(10);
E(1, 0) = static_cast<T>(45); E(1, 1) = static_cast<T>(48);
E(2, 0) = static_cast<T>(52); E(2, 1) = static_cast<T>(56);
E(3, 0) = static_cast<T>(144); E(3, 1) = static_cast<T>(162);
return E;
}

template <typename T> class MatmulSparseTest : public ::testing::Test {
protected:
float thresh = 0.001f;
};

template <typename T> class MatmulSparseTestsAll : public MatmulSparseTest<T> { };

TYPED_TEST_SUITE(MatmulSparseTestsAll, MatXFloatNonHalfTypesCUDAExec);

TYPED_TEST(MatmulSparseTestsAll, MatmulCOO) {
MATX_ENTER_HANDLER();
using TestType = cuda::std::tuple_element_t<0, TypeParam>;
using ExecType = cuda::std::tuple_element_t<1, TypeParam>;

ExecType exec{};

auto A = makeA<TestType>();
auto B = makeB<TestType>();
auto E = makeE<TestType>();
const auto m = A.Size(0);
const auto k = A.Size(1);
const auto n = B.Size(1);

// Convert dense A to sparse S.
auto S = experimental::make_zero_tensor_coo<TestType, index_t>({m, k});
(S = dense2sparse(A)).run(exec);
ASSERT_EQ(S.Nse(), 7);
ASSERT_EQ(S.posSize(1), 0);

// Matmul.
auto O = make_tensor<TestType>({m, n});
(O = matmul(S, B)).run(exec);

// Verify result.
exec.sync();
for (index_t i = 0; i < m; i++) {
for (index_t j = 0; j < n; j++) {
if constexpr (is_complex_v<TestType>) {
ASSERT_NEAR(O(i, j).real(), E(i, j).real(), this->thresh);
ASSERT_NEAR(O(i, j).imag(), E(i,j ).imag(), this->thresh);
}
else {
ASSERT_NEAR(O(i, j), E(i, j), this->thresh);
}

}
}

MATX_EXIT_HANDLER();
}

TYPED_TEST(MatmulSparseTestsAll, MatmulCSR) {
MATX_ENTER_HANDLER();
using TestType = cuda::std::tuple_element_t<0, TypeParam>;
using ExecType = cuda::std::tuple_element_t<1, TypeParam>;

ExecType exec{};

auto A = makeA<TestType>();
auto B = makeB<TestType>();
auto E = makeE<TestType>();
const auto m = A.Size(0);
const auto k = A.Size(1);
const auto n = B.Size(1);

// Convert dense A to sparse S.
auto S = experimental::make_zero_tensor_csr<TestType, index_t, index_t>({m, k});
(S = dense2sparse(A)).run(exec);
ASSERT_EQ(S.Nse(), 7);
ASSERT_EQ(S.posSize(1), m + 1);

// Matmul.
auto O = make_tensor<TestType>({m, n});
(O = matmul(S, B)).run(exec);

// Verify result.
exec.sync();
for (index_t i = 0; i < m; i++) {
for (index_t j = 0; j < n; j++) {
if constexpr (is_complex_v<TestType>) {
ASSERT_NEAR(O(i, j).real(), E(i, j).real(), this->thresh);
ASSERT_NEAR(O(i, j).imag(), E(i,j ).imag(), this->thresh);
}
else {
ASSERT_NEAR(O(i, j), E(i, j), this->thresh);
}

}
}

MATX_EXIT_HANDLER();
}

TYPED_TEST(MatmulSparseTestsAll, MatmulCSC) {
MATX_ENTER_HANDLER();
using TestType = cuda::std::tuple_element_t<0, TypeParam>;
using ExecType = cuda::std::tuple_element_t<1, TypeParam>;

ExecType exec{};

auto A = makeA<TestType>();
auto B = makeB<TestType>();
auto E = makeE<TestType>();
const auto m = A.Size(0);
const auto k = A.Size(1);
const auto n = B.Size(1);

// Convert dense A to sparse S.
auto S = experimental::make_zero_tensor_csc<TestType, index_t, index_t>({m, k});
(S = dense2sparse(A)).run(exec);
ASSERT_EQ(S.Nse(), 7);
ASSERT_EQ(S.posSize(1), k + 1);

// Matmul.
auto O = make_tensor<TestType>({m, n});
(O = matmul(S, B)).run(exec);

// Verify result.
exec.sync();
for (index_t i = 0; i < m; i++) {
for (index_t j = 0; j < n; j++) {
if constexpr (is_complex_v<TestType>) {
ASSERT_NEAR(O(i, j).real(), E(i, j).real(), this->thresh);
ASSERT_NEAR(O(i, j).imag(), E(i,j ).imag(), this->thresh);
}
else {
ASSERT_NEAR(O(i, j), E(i, j), this->thresh);
}

}
}

MATX_EXIT_HANDLER();
}
1 change: 1 addition & 0 deletions test/CMakeLists.txt
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Expand Up @@ -39,6 +39,7 @@ set (test_sources
01_radar/dct.cu
00_sparse/Basic.cu
00_sparse/Convert.cu
00_sparse/Matmul.cu
00_sparse/Solve.cu
main.cu
)
Expand Down