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cu-vector.cc
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cu-vector.cc
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// cudamatrix/cu-vector.cc
// Copyright 2012-2013 Karel Vesely
// 2012-2014 Johns Hopkins University (author: Daniel Povey)
// 2017 Daniel Galvez
// 2016-2018 Shiyin Kang
// 2019 Yiwen Shao
// 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.
#if HAVE_CUDA == 1
#include <cuda_runtime_api.h>
#include <cublas_v2.h>
#endif
#include "base/timer.h"
#include "cudamatrix/cu-common.h"
#include "cudamatrix/cu-vector.h"
#include "cudamatrix/cu-device.h"
#include "cudamatrix/cu-kernels.h"
#include "cudamatrix/cu-math.h"
#include "cudamatrix/cu-vector.h"
#include "cudamatrix/cu-matrix.h"
#include "cudamatrix/cu-rand.h"
#include "cudamatrix/cu-tp-matrix.h"
#include "cudamatrix/cu-sp-matrix.h"
#include "cudamatrix/cu-sparse-matrix.h"
#include "cudamatrix/cublas-wrappers.h"
namespace kaldi {
template<typename Real>
Real VecVec(const CuVectorBase<Real> &a,
const CuVectorBase<Real> &b) {
//MatrixIndexT a_dim = a.Dim();
KALDI_ASSERT(a.Dim() == b.Dim());
Real result = 0;
#if HAVE_CUDA == 1
if (CuDevice::Instantiate().Enabled()) {
CuTimer tim;
CUBLAS_SAFE_CALL(cublas_dot(GetCublasHandle(), a.Dim(), a.Data(), 1, b.Data(),
1, &result));
CuDevice::Instantiate().AccuProfile(__func__, tim);
} else
#endif
{
result = VecVec(a.Vec(), b.Vec());
}
return result;
}
// instantiate the template above
template float VecVec(const CuVectorBase<float> &a, const CuVectorBase<float> &b);
template double VecVec(const CuVectorBase<double> &a, const CuVectorBase<double> &b);
// The version of VecVec that can do type conversion. For now we give this a
// stupid implementation that converts one of the vectors. If it ever becomes
// an efficiency bottleneck, we can revisit this.
template<typename Real, typename OtherReal>
Real VecVec(const CuVectorBase<Real> &A, const CuVectorBase<OtherReal> &B) {
CuVector<Real> B2(B);
return VecVec(A, B2); // This will call the single-parameter template.
}
// instantiate the template above
template float VecVec(const CuVectorBase<float> &A, const CuVectorBase<double> &B);
template double VecVec(const CuVectorBase<double> &A, const CuVectorBase<float> &B);
template<typename Real>
Real VecMatVec(const CuVectorBase<Real> &v1, const CuMatrixBase<Real> &M,
const CuVectorBase<Real> &v2) {
KALDI_ASSERT(v1.Dim() == M.NumRows() && M.NumCols() == v2.Dim());
if (v1.Dim() > v2.Dim()) { // do v2*M first
CuVector<Real> v2M(v1.Dim());
v2M.AddMatVec(1.0, M, kNoTrans, v2, 0.0);
return VecVec(v2M, v1);
} else { // do v1*M first
CuVector<Real> v1M(v2.Dim());
v1M.AddMatVec(1.0, M, kTrans, v1, 0.0);
return VecVec(v1M, v2);
}
}
// instantiate the template above
template float VecMatVec(const CuVectorBase<float> &v1, const CuMatrixBase<float> &M,
const CuVectorBase<float> &v2);
template double VecMatVec(const CuVectorBase<double> &v1, const CuMatrixBase<double> &M,
const CuVectorBase<double> &v2);
template<typename Real>
void CuVectorBase<Real>::CopyColFromMat(const CuMatrixBase<Real> &mat, MatrixIndexT col) {
KALDI_ASSERT(col < mat.NumCols());
KALDI_ASSERT(dim_ == mat.NumRows());
#if HAVE_CUDA == 1
if (CuDevice::Instantiate().Enabled()) {
CuTimer tim;
cublas_copy(GetCublasHandle(),
this->dim_, mat.Data() + col, mat.Stride(), this->data_, 1);
CU_SAFE_CALL(cudaGetLastError());
CuDevice::Instantiate().AccuProfile("CuVectorBase::CopyColFromMat", tim);
} else
#endif
{
Vec().CopyColFromMat(mat.Mat(),col);
}
}
template<>
template<>
void CuVectorBase<double>::CopyColFromMat(const CuMatrixBase<float> &mat, MatrixIndexT col) {
KALDI_ASSERT(col < mat.NumCols());
KALDI_ASSERT(dim_ == mat.NumRows());
#if HAVE_CUDA == 1
if (CuDevice::Instantiate().Enabled()) {
CuTimer tim;
int dimBlock(CU1DBLOCK);
int dimGrid(n_blocks(dim_,CU1DBLOCK));
cuda_copy_col_from_mat_df(dimGrid, dimBlock, data_, col, mat.Data(), mat.Dim(), dim_);
CU_SAFE_CALL(cudaGetLastError());
CuDevice::Instantiate().AccuProfile("CuVectorBase::CopyColFromMat", tim);
} else
#endif
{
Vec().CopyColFromMat(mat.Mat(), col);
}
}
template<>
template<>
void CuVectorBase<float>::CopyColFromMat(const CuMatrixBase<double> &mat, MatrixIndexT col) {
KALDI_ASSERT(col < mat.NumCols());
KALDI_ASSERT(dim_ == mat.NumRows());
#if HAVE_CUDA == 1
if (CuDevice::Instantiate().Enabled()) {
CuTimer tim;
int dimBlock(CU1DBLOCK);
int dimGrid(n_blocks(dim_,CU1DBLOCK));
cuda_copy_col_from_mat_fd(dimGrid, dimBlock, data_, col, mat.Data(), mat.Dim(), dim_);
CU_SAFE_CALL(cudaGetLastError());
CuDevice::Instantiate().AccuProfile("CuVectorBase::CopyColFromMat", tim);
} else
#endif
{
Vec().CopyColFromMat(mat.Mat(), col);
}
}
template<typename Real>
void CuVectorBase<Real>::CopyRowsFromMat(const CuMatrixBase<Real> &mat) {
KALDI_ASSERT(dim_ == mat.NumCols() * mat.NumRows());
#if HAVE_CUDA == 1
if (CuDevice::Instantiate().Enabled()) {
if (dim_ == 0) return;
CuTimer tim;
if (mat.Stride() == mat.NumCols() && mat.NumRows() != 0) {
CU_SAFE_CALL(
cudaMemcpyAsync(data_, mat.Data(), sizeof(Real)*dim_,
cudaMemcpyDeviceToDevice, cudaStreamPerThread));
} else {
Real* vec_data = data_;
for (MatrixIndexT r = 0; r < mat.NumRows(); r++) {
CU_SAFE_CALL(cudaMemcpyAsync(vec_data, mat.RowData(r),
sizeof(Real) * mat.NumCols(),
cudaMemcpyDeviceToDevice,
cudaStreamPerThread));
vec_data += mat.NumCols();
}
}
CuDevice::Instantiate().AccuProfile("CuVectorBase::CopyRowsFromMat", tim);
} else
#endif
{
Vec().CopyRowsFromMat(mat.Mat());
}
}
template<typename Real>
Real CuVectorBase<Real>::Norm(Real p) {
#if HAVE_CUDA == 1
if (CuDevice::Instantiate().Enabled()) {
CuTimer tim;
Real ans;
KALDI_ASSERT(p == 1.0 || p == 2.0);
if (dim_ == 0) return 0.0;
if (p == 1.0) {
cublas_asum(GetCublasHandle(), dim_, data_, 1, &ans);
} else {
cublas_nrm2(GetCublasHandle(), dim_, data_, 1, &ans);
}
CuDevice::Instantiate().AccuProfile(__func__, tim);
if (ans != ans) {
KALDI_ERR << "NaN in norm " << *this;
}
return ans;
} else
#endif
{
return Vec().Norm(p);
}
}
template<typename Real>
void CuVectorBase<Real>::CopyRowsFromMat(const MatrixBase<Real> &mat) {
KALDI_ASSERT(dim_ == mat.NumCols() * mat.NumRows());
#if HAVE_CUDA == 1
if (CuDevice::Instantiate().Enabled()) {
if (dim_ == 0) return;
CuTimer tim;
if (mat.Stride() == mat.NumCols()) {
CU_SAFE_CALL(cudaMemcpyAsync(data_, mat.Data(), sizeof(Real)*dim_,
cudaMemcpyHostToDevice, cudaStreamPerThread));
} else {
Real* vec_data = data_;
for (MatrixIndexT r = 0; r < mat.NumRows(); r++) {
CU_SAFE_CALL(cudaMemcpyAsync(vec_data, mat.RowData(r),
sizeof(Real) * mat.NumCols(),
cudaMemcpyHostToDevice, cudaStreamPerThread));
vec_data += mat.NumCols();
}
}
CU_SAFE_CALL(cudaStreamSynchronize(cudaStreamPerThread));
CuDevice::Instantiate().AccuProfile(__func__, tim);
} else
#endif
{
Vec().CopyRowsFromMat(mat);
}
}
template<typename Real>
void MatrixBase<Real>::CopyRowsFromVec(const CuVectorBase<Real> &v) {
KALDI_ASSERT(v.Dim() == NumCols() * NumRows());
#if HAVE_CUDA == 1
if (CuDevice::Instantiate().Enabled()) {
if (num_rows_ == 0) return;
CuTimer tim;
if (Stride() == NumCols()) {
CU_SAFE_CALL(cudaMemcpyAsync(data_, v.Data(),
sizeof(Real)*v.Dim(),
cudaMemcpyDeviceToHost,
cudaStreamPerThread));
} else {
const Real* vec_data = v.Data();
for (MatrixIndexT r = 0; r < NumRows(); r++) {
CU_SAFE_CALL(cudaMemcpyAsync(RowData(r), vec_data,
sizeof(Real) * NumCols(),
cudaMemcpyDeviceToHost,
cudaStreamPerThread));
vec_data += NumCols();
}
}
CU_SAFE_CALL(cudaStreamSynchronize(cudaStreamPerThread));
CuDevice::Instantiate().AccuProfile(__func__, tim);
} else
#endif
{
CopyRowsFromVec(v.Vec());
}
}
// instantiate the template above.
template void MatrixBase<float>::CopyRowsFromVec(const CuVectorBase<float> &v);
template void MatrixBase<double>::CopyRowsFromVec(const CuVectorBase<double> &v);
template<typename Real>
void CuVectorBase<Real>::SetRandn() {
if (dim_ == 0) return;
CuRand<Real> tmp;
tmp.RandGaussian(this);
}
template<typename Real>
void CuVectorBase<Real>::SetRandUniform() {
if (dim_ == 0) return;
CuRand<Real> tmp;
tmp.RandUniform(this);
}
template<typename Real>
Real CuVectorBase<Real>::Sum() const {
if (dim_ == 0)
return 0.0;
#if HAVE_CUDA == 1
if (CuDevice::Instantiate().Enabled()) {
Real result;
CuTimer tim;
// Small vectors are copied to RAM and reduced on CPU.
// The length is chosen by cu-vector-speed-test
if (dim_ < 4096) {
Vector<Real> ans_cpu(*this);
result = ans_cpu.Sum();
} else {
// Use no more than 256 blocks (still too many?)
int dimBlock = CU1DBLOCK;
int dimGrid = n_blocks(dim_, dimBlock);
if (dimGrid > 256) {
dimGrid = 256;
}
CuVector<Real> ans(dimGrid, kUndefined);
cuda_vec_sum(dimGrid, dimBlock, data_, ans.Data(), dim_, 1);
CU_SAFE_CALL(cudaGetLastError());
Vector<Real> ans_cpu(ans);
result = ans_cpu.Sum();
}
CuDevice::Instantiate().AccuProfile(__func__, tim);
return result;
} else
#endif
{
return Vec().Sum();
}
}
template<typename Real>
void CuVectorBase<Real>::ApplySoftMax() {
#if HAVE_CUDA == 1
if (CuDevice::Instantiate().Enabled()) {
if (dim_ == 0) return;
CuTimer tim;
size_t dimBlock = CU1DBLOCK;
size_t dimGrid = 1; // dimGrid value represent the number of rows
::MatrixDim dim = { 1, this->dim_, this->dim_};
cuda_softmax_reduce(dimGrid, dimBlock, data_, data_, dim, this->dim_);//actually dim is not stride...
CU_SAFE_CALL(cudaGetLastError());
CuDevice::Instantiate().AccuProfile(__func__, tim);
} else
#endif
{
Vec().ApplySoftMax();
}
}
template<typename Real>
void CuVectorBase<Real>::Floor(const CuVectorBase<Real> &src, Real floor_val, MatrixIndexT *floored_count) {
#if HAVE_CUDA == 1
if (CuDevice::Instantiate().Enabled()) {
int dimBlock(CU1DBLOCK);
int dimGrid(n_blocks(dim_,CU1DBLOCK));
if (floored_count == nullptr) {
if (dim_ == 0) return;
CuTimer tim;
// We are calling a function meant for matrices, by viewing the
// vector as a matrix with a single row.
::MatrixDim dim = {1, Dim(), 1};
cuda_floor(dimGrid, dimBlock, this->data_, src.Data(), floor_val, dim, 1);
CuDevice::Instantiate().AccuProfile("CuVectorBase::FloorNoCount", tim);
} else {
if (dim_ == 0) { *floored_count = 0; return; }
CuTimer tim;
CuVector<float> count_vec(dim_, kUndefined);
cuda_vec_apply_floor(dimGrid, dimBlock, data_, floor_val, count_vec.Data(), dim_);
CU_SAFE_CALL(cudaGetLastError());
*floored_count = count_vec.Sum();
CuDevice::Instantiate().AccuProfile("CuVectorBase::Floor", tim);
}
} else
#endif
{
Vec().Floor(src.Vec(), floor_val, floored_count);
}
}
template<typename Real>
void CuVectorBase<Real>::Ceiling(const CuVectorBase<Real> &src, Real ceiling_val,
MatrixIndexT *ceiled_count) {
#if HAVE_CUDA == 1
if (CuDevice::Instantiate().Enabled()) {
int dimBlock(CU1DBLOCK);
int dimGrid(n_blocks(dim_,CU1DBLOCK));
if (ceiled_count == nullptr) {
if (dim_ == 0) return;
CuTimer tim;
// We are calling a function meant for matrices, by viewing the
// vector as a matrix with a single row.
::MatrixDim dim = {1, Dim(), 1};
cuda_ceiling(dimGrid, dimBlock, this->data_, src.Data(), ceiling_val, dim, 1);
CuDevice::Instantiate().AccuProfile("CuVectorBase::CeilingNoCount", tim);
} else {
if (dim_ == 0) { *ceiled_count = 0; return; }
CuTimer tim;
CuVector<float> count_vec(dim_, kUndefined);
cuda_vec_apply_ceiling(dimGrid, dimBlock, data_, ceiling_val, count_vec.Data(), dim_);
CU_SAFE_CALL(cudaGetLastError());
*ceiled_count = count_vec.Sum();
CuDevice::Instantiate().AccuProfile("CuVectorBase::Ceiling", tim);
}
} else
#endif
{
Vec().Ceiling(src.Vec(), ceiling_val, ceiled_count);
}
}
template<typename Real>
void CuVectorBase<Real>::Pow(const CuVectorBase<Real> &src, Real power) {
#if HAVE_CUDA == 1
if (CuDevice::Instantiate().Enabled()) {
if (dim_ == 0) return;
CuTimer tim;
// for this particular kernel, x is #rows, y is #cols. so
// fake matrix with 1 row, Dim() cols.
dim3 dimBlock(CU1DBLOCK, 1);
dim3 dimGrid(n_blocks(Dim(), CU1DBLOCK), 1);
::MatrixDim fake_matrix_dim = { 1, Dim(), 1 };
// num_cols is Dim(), num_rows is 1, stride is 1 (it's a don't-care).
cuda_pow(dimGrid, dimBlock, this->data_, src.Data(), power, fake_matrix_dim, 1);
CU_SAFE_CALL(cudaGetLastError());
CuDevice::Instantiate().AccuProfile("CuVectorBase::ApplyPow", tim);
} else
#endif
{
Vec().Pow(src.Vec(), power);
}
}
template<typename Real>
void CuVectorBase<Real>::ApplyExp() {
#if HAVE_CUDA == 1
if (CuDevice::Instantiate().Enabled()) {
if (dim_ == 0) return;
CuTimer tim;
int dimBlock(CU1DBLOCK);
int dimGrid(n_blocks(dim_,CU1DBLOCK));
cuda_vec_apply_exp(dimGrid, dimBlock, data_, dim_);
CU_SAFE_CALL(cudaGetLastError());
CuDevice::Instantiate().AccuProfile("CuVectorBase::ApplyExp", tim);
} else
#endif
{
Vec().ApplyExp();
}
}
template<typename Real>
void CuVectorBase<Real>::ApplyLog() {
#if HAVE_CUDA == 1
if (CuDevice::Instantiate().Enabled()) {
if (dim_ == 0) return;
CuTimer tim;
int dimBlock(CU1DBLOCK);
int dimGrid(n_blocks(dim_,CU1DBLOCK));
CuVector<Real> flag(1);
cuda_vec_apply_log(dimGrid, dimBlock, data_, flag.Data(), dim_);
CU_SAFE_CALL(cudaGetLastError());
if (flag(0) > 0)
KALDI_ERR << "Trying to take log of a negative number.";
CuDevice::Instantiate().AccuProfile("CuVectorBase::ApplyLog", tim);
} else
#endif
{
Vec().ApplyLog();
}
}
template<typename Real>
void CuVectorBase<Real>::ApplyLogSoftMax() {
#if HAVE_CUDA == 1
if (CuDevice::Instantiate().Enabled()) {
if (dim_ == 0) return;
CuTimer tim;
size_t dimBlock = CU1DBLOCK;
size_t dimGrid = 1; // dimGrid value represent the number of rows
::MatrixDim dim = { 1, this->dim_, this->dim_};
cuda_log_softmax_reduce(dimGrid, dimBlock, data_, data_, dim, this->dim_);
CU_SAFE_CALL(cudaGetLastError());
CuDevice::Instantiate().AccuProfile(__func__, tim);
} else
#endif
{
Vec().ApplyLogSoftMax();
}
}
template<typename Real>
void CuVectorBase<Real>::AddMatVec(const Real alpha,
const CuMatrixBase<Real> &M,
MatrixTransposeType trans,
const CuVectorBase<Real> &v,
const Real beta) {
KALDI_ASSERT((trans == kNoTrans && M.NumCols() == v.dim_ && M.NumRows() == dim_) ||
(trans == kTrans && M.NumRows() == v.dim_ && M.NumCols() == dim_));
KALDI_ASSERT(&v != this);
#if HAVE_CUDA == 1
if (CuDevice::Instantiate().Enabled()) {
if (dim_ == 0) return;
CuTimer tim;
// Everything is backwards in CuBlas. We need to reverse rows, columns,
// transpose-ness.
CUBLAS_SAFE_CALL(cublas_gemv(GetCublasHandle(),
(trans==kTrans? CUBLAS_OP_N:CUBLAS_OP_T),
M.NumCols(), M.NumRows(), alpha, M.Data(),
M.Stride(), v.Data(), 1, beta, data_, 1));
CuDevice::Instantiate().AccuProfile(__func__, tim);
} else
#endif
{
Vec().AddMatVec(alpha,M.Mat(),trans,v.Vec(),beta);
}
}
template<typename Real>
void CuVectorBase<Real>::AddSpVec(const Real alpha,
const CuSpMatrix<Real> &M,
const CuVectorBase<Real> &v,
const Real beta) {
KALDI_ASSERT(M.NumCols() == v.dim_ && M.NumRows() == dim_);
KALDI_ASSERT(&v != this);
#if HAVE_CUDA == 1
if (CuDevice::Instantiate().Enabled()) {
if (dim_ == 0) return;
CuTimer tim;
// Note: in our opinion the CuSpMatrix represents a lower-triangular matrix, but
// in CUBLAS, for some stupid reason, everything is reversed.
CUBLAS_SAFE_CALL(cublas_spmv(GetCublasHandle(), CUBLAS_FILL_MODE_UPPER, Dim(),
alpha, M.Data(), v.Data(), 1, beta, data_, 1));
CuDevice::Instantiate().AccuProfile(__func__, tim);
} else
#endif
{
Vec().AddSpVec(alpha,M.Mat(),v.Vec(),beta);
}
}
template<typename Real>
void CuVectorBase<Real>::AddVecVec(Real alpha, const CuVectorBase<Real> &v,
const CuVectorBase<Real> &r, Real beta) {
KALDI_ASSERT((dim_ == v.dim_ && dim_ == r.dim_));
KALDI_ASSERT(this != &v && this != &r);
#if HAVE_CUDA == 1
if (CuDevice::Instantiate().Enabled()) {
if (dim_ == 0) return;
CuTimer tim;
int dimBlock(CU1DBLOCK);
int dimGrid(n_blocks(dim_,CU1DBLOCK));
cuda_add_vec_vec(dimGrid, dimBlock, alpha, data_, v.Data(), r.Data(), beta, dim_);
CU_SAFE_CALL(cudaGetLastError());
CuDevice::Instantiate().AccuProfile("CuVectorBase::AddVecVec", tim);
} else
#endif
{
Vec().AddVecVec(alpha, v.Vec(), r.Vec(), beta);
}
}
template<typename Real>
bool CuVectorBase<Real>::ApproxEqual(const CuVectorBase<Real> &other, float tol) const {
if (dim_ != other.dim_) KALDI_ERR << "ApproxEqual: size mismatch "
<< dim_ << " vs. " << other.dim_;
KALDI_ASSERT(tol >= 0.0);
CuVector<Real> tmp(*this);
tmp.AddVec(-1.0, other);
BaseFloat tmp_norm = sqrt(VecVec(tmp, tmp)), this_norm = sqrt(VecVec(*this, *this));
return tmp_norm <= static_cast<Real>(tol) * this_norm;
}
template<typename Real>
void CuVectorBase<Real>::AddDiagMat2(Real alpha, const CuMatrixBase<Real> &M,
MatrixTransposeType trans, Real beta) {
#if HAVE_CUDA == 1
if (CuDevice::Instantiate().Enabled()) {
if (dim_ == 0) return;
MatrixTransposeType other_trans = (trans == kTrans ? kNoTrans : kTrans);
KALDI_ASSERT(dim_ == (trans == kNoTrans ? M.NumRows() : M.NumCols()));
this->AddDiagMatMat(alpha, M, trans, M, other_trans, beta);
} else
#endif
{
Vec().AddDiagMat2(alpha, M.Mat(), trans, beta);
}
}
template<typename Real>
void CuVectorBase<Real>::AddDiagMatMat(Real alpha, const CuMatrixBase<Real> &M,
MatrixTransposeType transM,
const CuMatrixBase<Real> &N,
MatrixTransposeType transN, Real beta) {
#if HAVE_CUDA == 1
if (CuDevice::Instantiate().Enabled()) {
CuTimer tim;
if (transM != transN) {
KALDI_ASSERT(M.NumCols() == N.NumCols());
KALDI_ASSERT(M.NumRows() == N.NumRows());
if (transM == kNoTrans) {
// Case 1: diag(M*N') == sum(M.*N, 2)
// 1D grid and 1D block. One block per row of N.
// 1D grid expands along the column of N.
int dimBlock(CU1DBLOCK);
int dimGrid(M.NumRows());
cuda_add_diag_mat_mat_MNT(dimGrid, dimBlock, alpha, M.Data(), M.Dim(),
N.Data(), N.Stride(), beta, data_);
} else {
// Case 2: diag(M'*N) == sum(M.*N, 1)
// 16x16 or 8x32 2D block for coalesced memory access.
// Grid shape is designed as follows,
// 1. for small matrices, use 1D grid with only 1 row of 16x16 block,
// to avoid multiple kernel launch;
// 2. for large enough matrices (no matter thin or fat),
// use 1- or 2-D grid so that the grid contains
// at least and not much larger than 'kOptNumBlocks' blocks
// to fully utilize the GPU;
const int32 warpSize = 32;
const int32 kOptNumBlocks = 512;
const int32 tile_dim =
(N.NumRows() < 4096 && N.NumCols() < kOptNumBlocks * warpSize) ?
16 : 32;
dim3 dimBlock(tile_dim, CU1DBLOCK / tile_dim);
dim3 dimGrid(n_blocks(N.NumCols(), dimBlock.x),
n_blocks(N.NumRows(), dimBlock.y));
dimGrid.y = std::min(dimGrid.y, (kOptNumBlocks - 1) / dimGrid.x + 1);
dimGrid.y = tile_dim == 16 ? 1 : dimGrid.y;
if (dimGrid.y > 1) {
CuMatrix<Real> buf(dimGrid.y, N.NumCols());
cuda_add_diag_mat_mat_MTN(dimGrid, dimBlock, Real(1), M.Data(),
M.Stride(), N.Data(), N.Dim(), Real(0),
buf.Data(), buf.Stride());
this->AddRowSumMat(alpha, buf, beta);
} else {
cuda_add_diag_mat_mat_MTN(dimGrid, dimBlock, alpha, M.Data(),
M.Stride(), N.Data(), N.Dim(), beta, data_,
dim_);
}
}
} else {
KALDI_ASSERT(M.NumCols() == N.NumRows());
KALDI_ASSERT(N.NumCols() == M.NumRows());
if (transM == kNoTrans) {
// Case 3: diag(M*N) == sum(M'.*N, 1)
// 16x16 or 8x32 2D block for matrix transpose and coalesced memory access.
// One block per 'tile_dim' columns of N.
// 1D grid expands along the row of N.
int tile_dim =
sizeof(Real) == sizeof(float) && N.NumCols() >= 2048 ? 32 : 16;
dim3 dimBlock(tile_dim, CU1DBLOCK / tile_dim);
dim3 dimGrid(n_blocks(N.NumCols(), tile_dim));
cuda_add_diag_mat_mat_MN(dimGrid, dimBlock, alpha, M.Data(), M.Stride(),
N.Data(), N.Dim(), beta, data_);
} else {
// Case 4: diag(M'*N') == sum(N'.*M, 1)
// Same kernel and config as case 3 except M and N are swapped.
int tile_dim =
sizeof(Real) == sizeof(float) && N.NumCols() >= 2048 ? 32 : 16;
dim3 dimBlock(tile_dim, CU1DBLOCK / tile_dim);
dim3 dimGrid(n_blocks(M.NumCols(), tile_dim));
cuda_add_diag_mat_mat_MN(dimGrid, dimBlock, alpha, N.Data(), N.Stride(),
M.Data(), M.Dim(), beta, data_);
}
}
CU_SAFE_CALL(cudaGetLastError());
CuDevice::Instantiate().AccuProfile(__func__, tim);
} else
#endif
{
Vec().AddDiagMatMat(alpha, M.Mat(), transM, N.Mat(), transN, beta);
}
}
template<typename Real>
void CuVectorBase<Real>::AddTpVec(const Real alpha, const CuTpMatrix<Real> &M,
const MatrixTransposeType trans,
const CuVectorBase<Real> &v,
const Real beta) {
KALDI_ASSERT(dim_ == v.dim_ && dim_ == M.NumRows());
#if HAVE_CUDA == 1
if (CuDevice::Instantiate().Enabled()) {
if (dim_ == 0) return;
CuTimer tim;
if (beta == 0.0) {
if (&v != this) CopyFromVec(v);
MulTp(M, trans);
if (alpha != 1.0) Scale(alpha);
} else {
CuVector<Real> tmp(v);
tmp.MulTp(M, trans);
if (beta != 1.0) Scale(beta); // *this <-- beta * *this
AddVec(alpha, tmp, 1.0); // *this += alpha * M * v
}
CuDevice::Instantiate().AccuProfile(__func__, tim);
} else
#endif
{
Vec().AddTpVec(alpha, M.Mat(), trans, v.Vec(), beta);
}
}
template<typename Real>
void CuVectorBase<Real>::MulTp(const CuTpMatrix<Real> &M, const MatrixTransposeType trans) {
KALDI_ASSERT(M.NumRows() == dim_);
#if HAVE_CUDA == 1
if (CuDevice::Instantiate().Enabled()) {
if (dim_ == 0) return;
CuTimer tim;
cublas_tpmv(GetCublasHandle(), (trans==kTrans? CUBLAS_OP_N:CUBLAS_OP_T),
M.NumRows(), M.Data(), data_, 1);
CuDevice::Instantiate().AccuProfile("CuVectorBase::MulTp", tim);
} else
#endif
{
Vec().MulTp(M.Mat(), trans);
}
}
template<typename Real>
Real CuVectorBase<Real>::Min() const {
Real result = 0.0;
#if HAVE_CUDA == 1
if (CuDevice::Instantiate().Enabled()) {
if (dim_ == 0) { // min of an empty set is infinity.
return std::numeric_limits<Real>::infinity();
}
CuTimer tim;
// Small vectors are copied to RAM and reduced on CPU.
// The length is chosen by cu-vector-speed-test
if (dim_ < 4096) {
Vector<Real> ans_cpu(*this);
result = ans_cpu.Min();
} else {
// Use no more than 256 blocks (still too many?)
int dimBlock = CU1DBLOCK;
int dimGrid = n_blocks(dim_, dimBlock);
if (dimGrid > 256) {
dimGrid = 256;
}
CuVector<Real> ans(dimGrid, kUndefined);
cuda_vec_min(dimGrid, dimBlock, data_, ans.Data(), dim_, 1);
CU_SAFE_CALL(cudaGetLastError());
Vector<Real> ans_cpu(ans);
result = ans_cpu.Min();
}
CuDevice::Instantiate().AccuProfile(__func__, tim);
} else
#endif
{
result = (this->Vec()).Min();
}
return result;
}
template<typename Real>
Real CuVectorBase<Real>::Max() const {
Real result = 0.0;
#if HAVE_CUDA == 1
if (CuDevice::Instantiate().Enabled()) {
if (dim_ == 0) { // max of an empty set is -infinity.
return -std::numeric_limits<Real>::infinity();
}
CuTimer tim;
// Small vectors are copied to RAM and reduced on CPU.
// The length is chosen by cu-vector-speed-test
if (dim_ < 4096) {
Vector<Real> ans_cpu(*this);
result = ans_cpu.Max();
} else {
// Use no more than 256 blocks (still too many?)
int dimBlock = CU1DBLOCK;
int dimGrid = n_blocks(dim_, dimBlock);
if (dimGrid > 256) {
dimGrid = 256;
}
CuVector<Real> ans(dimGrid, kUndefined);
cuda_vec_max(dimGrid, dimBlock, data_, ans.Data(), dim_, 1);
CU_SAFE_CALL(cudaGetLastError());
Vector<Real> ans_cpu(ans);
result = ans_cpu.Max();
}
CuDevice::Instantiate().AccuProfile(__func__, tim);
} else
#endif
{
result = (this->Vec()).Max();
}
return result;
}
template<typename Real>
void CuVectorBase<Real>::ReplaceValue(Real orig, Real changed) {
#if HAVE_CUDA == 1
if (CuDevice::Instantiate().Enabled()) {
if (dim_ == 0) return;
CuTimer tim;
int dimBlock(CU1DBLOCK);
int dimGrid(n_blocks(dim_, CU1DBLOCK));
cuda_replace_value(dimGrid, dimBlock, data_, dim_, orig, changed);
CU_SAFE_CALL(cudaGetLastError());
CuDevice::Instantiate().AccuProfile(__func__, tim);
} else
#endif
{
Vec().ReplaceValue(orig, changed);
}
}
template<typename Real>
void CuVectorBase<Real>::MulElements(const CuVectorBase<Real> &v) {
KALDI_ASSERT(dim_ == v.dim_);
#if HAVE_CUDA == 1
if (CuDevice::Instantiate().Enabled()) {
if (dim_ == 0) return;
CuTimer tim;
int dimBlock(CU1DBLOCK);
int dimGrid(n_blocks(dim_, CU1DBLOCK));
cuda_vec_mul_elements(dimGrid, dimBlock, data_, v.Data(), dim_);
CU_SAFE_CALL(cudaGetLastError());
CuDevice::Instantiate().AccuProfile("CuVectorBase::MulElements", tim);
} else
#endif
{
Vec().MulElements(v.Vec());
}
}
template<typename Real>
void CuVectorBase<Real>::DivElements(const CuVectorBase<Real> &v) {
// this just creates a matrix and calls the matrix version.
KALDI_ASSERT(dim_ == v.dim_);
CuSubMatrix<Real> this_mat(this->Data(), 1, dim_, dim_),
v_mat(v.Data(), 1, dim_, dim_);
this_mat.DivElements(v_mat);
}
template<>
template<>
void CuVectorBase<double>::CopyFromVec(const CuVectorBase<float> &src) {
KALDI_ASSERT(src.Dim() == dim_);
#if HAVE_CUDA == 1
if (CuDevice::Instantiate().Enabled()) {
if (dim_ == 0) return;
CuTimer tim;
CUBLAS_SAFE_CALL(cublas_copy(GetCublasHandle(), dim_, src.Data(), 1, data_, 1));
CuDevice::Instantiate().AccuProfile(__func__, tim);
} else
#endif
{
Vec().CopyFromVec(src.Vec());
}
}
template<>
template<>
void CuVectorBase<float>::CopyFromVec(const CuVectorBase<double> &src) {
KALDI_ASSERT(src.Dim() == dim_);
#if HAVE_CUDA == 1
if (CuDevice::Instantiate().Enabled()) {
if (dim_ == 0) return;
CuTimer tim;
CUBLAS_SAFE_CALL(cublas_copy(GetCublasHandle(), dim_, src.Data(), 1, data_, 1));
CuDevice::Instantiate().AccuProfile(__func__, tim);
} else
#endif
{
Vec().CopyFromVec(src.Vec());
}
}
template<typename Real>
template<typename OtherReal>
void CuVectorBase<Real>::CopyFromVec(const VectorBase<OtherReal> &src) {
#if HAVE_CUDA == 1
if (CuDevice::Instantiate().Enabled()) {
if (sizeof(Real) != sizeof(OtherReal)) {
CuVector<OtherReal> temp(dim_, kUndefined);
temp.CopyFromVec(src);
this->CopyFromVec(temp);
} else {
KALDI_ASSERT(src.Dim() == dim_);
if (dim_ == 0) return;
CuTimer tim;
CU_SAFE_CALL(cudaMemcpyAsync(data_, src.Data(), src.Dim()*sizeof(Real),
cudaMemcpyHostToDevice, cudaStreamPerThread));
CU_SAFE_CALL(cudaStreamSynchronize(cudaStreamPerThread));
CuDevice::Instantiate().AccuProfile("CuVector::CopyFromVecH2D", tim);
}
} else
#endif
{
Vec().CopyFromVec(src);
}
}
// Instantiate the template above.
template
void CuVectorBase<float>::CopyFromVec(const VectorBase<float> &src);
template
void CuVectorBase<double>::CopyFromVec(const VectorBase<float> &src);
template
void CuVectorBase<float>::CopyFromVec(const VectorBase<double> &src);
template
void CuVectorBase<double>::CopyFromVec(const VectorBase<double> &src);
template<typename Real>
template<typename OtherReal>
void CuVectorBase<Real>::CopyToVec(VectorBase<OtherReal> *dst) const {
KALDI_ASSERT(dim_ == dst->Dim());
#if HAVE_CUDA == 1
if (CuDevice::Instantiate().Enabled()) {
if (sizeof(Real) != sizeof(OtherReal)) {
CuVector<OtherReal> temp(*this);
temp.CopyToVec(dst);
} else {
if (dim_ == 0) return;
CuTimer tim;
CU_SAFE_CALL(cudaMemcpyAsync(dst->Data(), this->data_,
sizeof(Real) * dim_, cudaMemcpyDeviceToHost,
cudaStreamPerThread));
CU_SAFE_CALL(cudaStreamSynchronize(cudaStreamPerThread));
CuDevice::Instantiate().AccuProfile(__func__, tim);
}
} else
#endif
{
dst->CopyFromVec(this->Vec());
}
}
template<typename Real>
void CuVector<Real>::Read(std::istream &is, bool binary) {
Vector<Real> temp;
temp.Read(is, binary);
Destroy();
Swap(&temp);
}
template<typename Real>
void CuVector<Real>::Write(std::ostream &os, bool binary) const {
Vector<BaseFloat> temp(this->dim_, kUndefined);
this->CopyToVec(&temp);
temp.Write(os, binary);
}
template<typename Real>
CuVector<Real>::CuVector(const CuVectorBase<Real> &v) {
this->Resize(v.Dim());
this->CopyFromVec(v);
}
template<typename Real>
CuVector<Real>::CuVector(const VectorBase<Real> &v) {
this->Resize(v.dim_);
this->CopyFromVec(v);
}
template<typename Real>
void CuVector<Real>::Resize(MatrixIndexT dim, MatrixResizeType t) {
KALDI_ASSERT(t == kSetZero || t == kUndefined); // Others not implemented
// yet.
if (this->dim_ == dim) {
this->SetZero();
return;
}
if (this->dim_ != 0)