cu-block-matrix-test.cc
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// cudamatrix/cu-block-matrix-test.cc
// Copyright 2013 Johns Hopkins University (author: Daniel Povey)
// See ../../COPYING for clarification regarding multiple authors
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// THIS CODE IS PROVIDED *AS IS* BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
// KIND, EITHER EXPRESS OR IMPLIED, INCLUDING WITHOUT LIMITATION ANY IMPLIED
// WARRANTIES OR CONDITIONS OF TITLE, FITNESS FOR A PARTICULAR PURPOSE,
// MERCHANTABLITY OR NON-INFRINGEMENT.
// See the Apache 2 License for the specific language governing permissions and
// limitations under the License.
#include <iostream>
#include <vector>
#include <cstdlib>
#include "base/kaldi-common.h"
#include "util/common-utils.h"
#include "cudamatrix/cu-matrix-lib.h"
using namespace kaldi;
namespace kaldi {
template<typename Real>
static bool ApproxEqual(const CuBlockMatrix<Real> &A,
const CuBlockMatrix<Real> &B,
float tol = 0.001) {
CuMatrix<Real> Acopy(A), Bcopy(B);
return Acopy.ApproxEqual(Bcopy, tol);
}
template<class Real>
static void UnitTestCuBlockMatrixIO() {
for (int32 i = 0; i < 10; i++) {
int32 num_blocks = Rand() % 5;
std::vector<CuMatrix<Real> > data(num_blocks);
for (int32 b = 0; b < num_blocks; b++) {
int32 dimM = 100 + Rand() % 255, dimN = 10 + Rand() % 20;
if (b % 2 == 0) std::swap(dimM, dimN);
data[b].Resize(dimM, dimN);
data[b].SetRandn();
}
CuBlockMatrix<Real> B(data);
std::ostringstream os;
bool binary = (i % 4 < 2);
B.Write(os, binary);
CuBlockMatrix<Real> B2;
std::istringstream is(os.str());
B2.Read(is, binary);
CuMatrix<Real> mat(B), mat2(B2);
AssertEqual(mat, mat2);
if (!data.empty())
KALDI_ASSERT(mat.Sum() != 0.0);
}
}
template<class Real>
static void UnitTestCuBlockMatrixAddMatBlock() {
for (int32 i = 0; i < 20; i++) {
int32 num_blocks = Rand() % 5;
std::vector<CuMatrix<Real> > data(num_blocks);
for (int32 b = 0; b < num_blocks; b++) {
int32 dimM = 100 + Rand() % 255, dimN = 10 + Rand() % 20;
// early failures will have small dim for easier eyeballing.
if (b % 2 == 0) std::swap(dimM, dimN);
data[b].Resize(dimM, dimN);
data[b].SetRandn();
}
CuBlockMatrix<Real> B(data);
int32 B_num_rows = B.NumRows(), B_num_cols = B.NumCols();
// will do X += A B
MatrixTransposeType transB = (i % 2 == 1 ? kTrans : kNoTrans),
transA = (i % 3 == 1 ? kTrans : kNoTrans);
if (transB == kTrans) std::swap(B_num_rows, B_num_cols);
int32 X_num_rows = 100 + Rand() % 255, X_num_cols = B_num_cols,
A_num_rows = X_num_rows, A_num_cols = B_num_rows;
if (data.size() == 0) { X_num_rows = 0; A_num_rows = 0; }
if (transA == kTrans) std::swap(A_num_rows, A_num_cols);
Real alpha = 2.0, beta = -1.0;
CuMatrix<Real> X(X_num_rows, X_num_cols);
X.SetRandn();
CuMatrix<Real> A(A_num_rows, A_num_cols);
A.SetRandn();
CuMatrix<Real> Xcopy(X), Bcopy(B), Xorig(X), Aorig(A);
Xcopy.AddMatMat(alpha, A, transA, Bcopy, transB, beta);
X.AddMatBlock(alpha, A, transA, B, transB, beta);
AssertEqual(X, Xcopy);
}
}
template<class Real>
static void UnitTestCuBlockMatrixAddMatMat() {
for (int32 i = 0; i < 20; i++) {
int32 num_blocks = Rand() % 5 + 1;
std::vector<CuMatrix<Real> > data(num_blocks);
for (int32 b = 0; b < num_blocks; b++) {
int32 dimM = 100 + Rand() % 255, dimN = 10 + Rand() % 20;
if (i == 0) { dimM = 1; dimN = 1; }
// early failures will have small dim for easier eyeballing.
if (b % 2 == 0) std::swap(dimM, dimN);
data[b].Resize(dimM, dimN);
KALDI_LOG << "dimM " << dimM << ", dimN " << dimN << ", stride " << data[b].Stride();
data[b].SetRandn();
}
CuBlockMatrix<Real> B(data);
int32 B_num_rows = B.NumRows(), B_num_cols = B.NumCols();
// will do B += C D
int32 C_num_rows = B_num_rows, C_num_cols = 100 + Rand() % 255;
if (C_num_rows == 0) C_num_cols = 0;
int32 D_num_rows = C_num_cols, D_num_cols = B_num_cols;
MatrixTransposeType transC = (i % 2 == 1 ? kTrans : kNoTrans),
transD = (i % 3 == 1 ? kTrans : kNoTrans);
if (transC == kTrans) std::swap(C_num_rows, C_num_cols);
if (transD == kTrans) std::swap(D_num_rows, D_num_cols);
CuMatrix<Real> C(C_num_rows, C_num_cols), D(D_num_rows, D_num_cols);
C.SetRandn();
D.SetRandn();
CuMatrix<Real> Bmat(B);
Real alpha = 2.0, beta = -1.0;
CuBlockMatrix<Real> Bcopy(B);
B.AddMatMat(alpha, C, transC, D, transD, beta);
Bmat.AddMatMat(alpha, C, transC, D, transD, beta);
// Now check that the block-structured part of Bmat is the
// same as B.
Bcopy.CopyFromMat(Bmat); // copy block-structured part from Bmat to Bcopy.
if (!ApproxEqual(B, Bcopy)) {
KALDI_WARN << "CuBlockMatrixTest failure, please report to maintainers: Bcopy = "
<< Bcopy << ", B = " << B << ", C = " << C << ", D = " << D
<< ", Bmat = " << B << " transD = " << transD << ", transC = "
<< transC;
KALDI_ERR << "Please give this log to the maintainers.";
}
KALDI_ASSERT(Bmat.Sum() != 0 || B_num_rows == 0);
}
}
template<typename Real> void CuBlockMatrixUnitTest() {
UnitTestCuBlockMatrixIO<Real>();
UnitTestCuBlockMatrixAddMatBlock<Real>();
UnitTestCuBlockMatrixAddMatMat<Real>();
}
} // namespace kaldi
int main() {
SetVerboseLevel(1);
int32 loop = 0;
#if HAVE_CUDA == 1
for (; loop < 2; loop++) {
CuDevice::Instantiate().SetDebugStrideMode(true);
if (loop == 0)
CuDevice::Instantiate().SelectGpuId("no"); // -1 means no GPU
else
CuDevice::Instantiate().SelectGpuId("yes"); // -2 .. automatic selection
#endif
kaldi::CuBlockMatrixUnitTest<float>();
#if HAVE_CUDA == 1
if (CuDevice::Instantiate().DoublePrecisionSupported()) {
kaldi::CuBlockMatrixUnitTest<double>();
} else {
KALDI_WARN << "Double precision not supported";
}
#else
kaldi::CuBlockMatrixUnitTest<double>();
#endif
if (loop == 0)
KALDI_LOG << "Tests without GPU use succeeded.";
else
KALDI_LOG << "Tests with GPU use (if available) succeeded.";
#if HAVE_CUDA == 1
}
CuDevice::Instantiate().PrintProfile();
#endif
return 0;
}