private static void Run_M_V_float(TSCudaContext context, Tensor result, Tensor mat, Tensor vec)
{
// Require lhs to be row-major. This means we must tell BLAS to transpose it (BLAS expects column-major matrices)
if (mat.Strides[1] != 1)
{
throw new ArgumentException("lhs must be contiguous in the last dimension");
}
using (var blas = context.BlasForTensor(mat))
{
var yPtr = CudaHelpers.GetBufferStart(result);
var aPtr = CudaHelpers.GetBufferStart(mat);
var xPtr = CudaHelpers.GetBufferStart(vec);
Operation trans = Operation.Transpose;
int m = (int)mat.Sizes[1];
int n = (int)mat.Sizes[0];
int incx = (int)vec.Strides[0];
int lda = (int)mat.Strides[0];
int incy = (int)result.Strides[0];
float alpha = 1;
float beta = 0;
CudaBlasNativeMethods.cublasSgemv_v2(blas.Value.CublasHandle, trans, m, n, ref alpha, aPtr, lda, xPtr, incx, ref beta, yPtr, incy);
}
}
public IMatrix TransposeThisAndMultiply(IMatrix matrix)
{
Debug.Assert(IsValid && matrix.IsValid);
var other = (GpuMatrix)matrix;
Debug.Assert(_rows == other._rows);
var ret = _cuda.Allocate(_columns * other.ColumnCount);
int rowsA = _rows, columnsA = _columns, columnsB = other.ColumnCount, rowsB = other.RowCount;
float alpha = 1.0f, beta = 0.0f;
CudaBlasNativeMethods.cublasSgemm_v2(_cuda.Blas.CublasHandle,
Operation.Transpose,
Operation.NonTranspose,
columnsA,
columnsB,
rowsB,
ref alpha,
_data.DevicePointer,
rowsA,
other._data.DevicePointer,
rowsB,
ref beta,
ret.DevicePointer,
columnsA
);
return(new GpuMatrix(_cuda, _columns, other.ColumnCount, ret));
}
public IVector Row(int index)
{
Debug.Assert(IsValid);
var ret = _cuda.Allocate(_columns);
int offset = index * sizeof(float);
CudaBlasNativeMethods.cublasScopy_v2(_cuda.Blas.CublasHandle, _columns, _data.DevicePointer + offset, _rows, ret.DevicePointer, 1);
return(new GpuVector(_cuda, ret));
}
public IVector GetRowSegment(int rowIndex, int columnIndex, int length)
{
Debug.Assert(IsValid);
int offset = (rowIndex + (columnIndex * _rows)) * sizeof(float);
var ret = _cuda.Allocate(length);
CudaBlasNativeMethods.cublasScopy_v2(_cuda.Blas.CublasHandle, length, _data.DevicePointer + offset, _rows, ret.DevicePointer, 1);
return(new GpuVector(_cuda, ret));
}
public IVector Row(int index)
{
Debug.Assert(IsValid);
var ret = _cuda.Allocate(ColumnCount);
int offset = index * CudaProvider.FLOAT_SIZE;
CudaBlasNativeMethods.cublasScopy_v2(_cuda.Blas.CublasHandle, ColumnCount,
Memory.DevicePointer + offset, RowCount, ret.DevicePointer, 1);
return(new GpuVector(_cuda, ret, true));
}
public IVector GetRowSegment(int rowIndex, int columnIndex, int length)
{
Debug.Assert(IsValid);
int offset = (rowIndex + (columnIndex * RowCount)) * CudaProvider.FLOAT_SIZE;
var ret = _cuda.Allocate(length);
CudaBlasNativeMethods.cublasScopy_v2(_cuda.Blas.CublasHandle, length,
Memory.DevicePointer + offset, RowCount, ret.DevicePointer, 1);
return(new GpuVector(_cuda, ret, true));
}
public IMatrix Transpose()
{
Debug.Assert(IsValid);
var ret = _cuda.Allocate(RowCount * ColumnCount);
float alpha = 1.0f, beta = 0.0f;
CudaBlasNativeMethods.cublasSgeam(_cuda.Blas.CublasHandle, Operation.Transpose,
Operation.NonTranspose, ColumnCount, RowCount, ref alpha, Memory.DevicePointer, RowCount,
ref beta, new CUdeviceptr(0), ColumnCount, ret.DevicePointer, ColumnCount);
return(new GpuMatrix(_cuda, ColumnCount, RowCount, ret, true));
}
public I3DTensor TransposeThisAndMultiply(I4DTensor tensor)
{
var other = (Gpu4DTensor)tensor;
#if DEBUG
Debug.Assert(tensor.Count == Depth && IsValid && other.IsValid);
#endif
var ptr = Memory.DevicePointer;
var ptr2 = other.Memory.DevicePointer;
int rowsA = _rows, columnsA = _columns, columnsB = other.Depth,
rowsB = other.RowCount * other.ColumnCount, blockSize2 = columnsB * rowsB;
float alpha = 1.0f, beta = 0.0f;
var output = new Gpu3DTensor(_cuda, _columns, columnsB, _depth,
_cuda.Allocate(_columns * columnsB * _depth), true);
var status = CudaBlasNativeMethods.cublasSgemmStridedBatched(_cuda.Blas.CublasHandle,
Operation.Transpose, Operation.NonTranspose, columnsA, columnsB, rowsB, ref alpha, ptr,
rowsA, _blockSize, ptr2, rowsB, blockSize2, ref beta, output.Memory.DevicePointer, columnsA,
_columns * columnsB, _depth);
if (status != CublasStatus.Success)
{
throw new CudaBlasException(status);
}
return(output);
//var output = Enumerable.Range(0, _depth).Select(i => new GpuMatrix(_cuda, _columns, columnsB, _cuda.Allocate(_columns * columnsB), true)).ToList();
//using (var aPtrs = new PtrToDeviceMemoryList(Enumerable.Range(0, _depth).Select(i => ptr + i * _blockSize * CudaProvider.FLOAT_SIZE).ToArray()))
//using (var bPtrs = new PtrToDeviceMemoryList(Enumerable.Range(0, _depth).Select(i => ptr2 + i * blockSize2 * CudaProvider.FLOAT_SIZE).ToArray()))
//using (var cPtrs = new PtrToDeviceMemoryList(output.Select(m => m.Memory.DevicePointer).ToArray())) {
// var status = CudaBlasNativeMethods.cublasSgemmBatched(_cuda.Blas.CublasHandle,
// Operation.Transpose,
// Operation.NonTranspose,
// columnsA,
// columnsB,
// rowsB,
// ref alpha,
// aPtrs.DevicePointer,
// rowsA,
// bPtrs.DevicePointer,
// rowsB,
// ref beta,
// cPtrs.DevicePointer,
// columnsA,
// _depth
// );
// if (status != CublasStatus.Success)
// throw new CudaBlasException(status);
//}
//return _cuda.Create3DTensor(output);
}
public IMatrix TransposeAndMultiply(IMatrix matrix)
{
Debug.Assert(IsValid && matrix.IsValid);
var other = (GpuMatrix)matrix;
Debug.Assert(ColumnCount == other.ColumnCount);
var ret = _cuda.Allocate(RowCount * other.RowCount);
int rowsA = RowCount, columnsArowsB = ColumnCount, rowsB = other.RowCount;
float alpha = 1.0f, beta = 0.0f;
CudaBlasNativeMethods.cublasSgemm_v2(_cuda.Blas.CublasHandle, Operation.NonTranspose,
Operation.Transpose, rowsA, rowsB, columnsArowsB, ref alpha, Memory.DevicePointer, rowsA,
other.Memory.DevicePointer, rowsB, ref beta, ret.DevicePointer, rowsA);
return(new GpuMatrix(_cuda, RowCount, other.RowCount, ret, true));
}
public IMatrix GetNewMatrixFromRows(IReadOnlyList <int> rowIndices)
{
Debug.Assert(IsValid);
int offset = 0;
var ret = _cuda.Allocate(ColumnCount * rowIndices.Count);
foreach (var item in rowIndices)
{
CudaBlasNativeMethods.cublasScopy_v2(_cuda.Blas.CublasHandle, n: ColumnCount,
x: Memory.DevicePointer + (item * CudaProvider.FLOAT_SIZE), incx: RowCount,
y: ret.DevicePointer + (offset * CudaProvider.FLOAT_SIZE), incy: rowIndices.Count);
offset += 1;
}
return(new GpuMatrix(_cuda, rowIndices.Count, ColumnCount, ret, true));
}
public I3DTensor Multiply(IMatrix matrix)
{
var other = (GpuMatrix)matrix;
var ptr = Memory.DevicePointer;
int rowsA = _rows, columnsArowsB = _columns, columnsB = matrix.ColumnCount;
float alpha = 1.0f, beta = 0.0f;
var output = new Gpu3DTensor(_cuda, _rows, columnsB, _depth,
_cuda.Allocate(_rows * columnsB * _depth), true);
var status = CudaBlasNativeMethods.cublasSgemmStridedBatched(_cuda.Blas.CublasHandle,
Operation.NonTranspose, Operation.NonTranspose, rowsA, columnsB, columnsArowsB, ref alpha,
ptr, rowsA, _blockSize, other.Memory.DevicePointer, columnsArowsB, 0, ref beta,
output.Memory.DevicePointer, rowsA, _rows * columnsB, _depth);
if (status != CublasStatus.Success)
{
throw new CudaBlasException(status);
}
return(output);
//var output = Enumerable.Range(0, _depth).Select(i => new GpuMatrix(_cuda, _rows, columnsB, _cuda.Allocate(_rows * columnsB), true)).ToList();
//using (var aPtrs = new PtrToDeviceMemoryList(Enumerable.Range(0, _depth).Select(i => ptr + i * _blockSize * CudaProvider.FLOAT_SIZE).ToArray()))
//using (var bPtrs = new PtrToDeviceMemoryList(Enumerable.Range(0, _depth).Select(i => other.Memory.DevicePointer).ToArray()))
//using (var cPtrs = new PtrToDeviceMemoryList(output.Select(m => m.Memory.DevicePointer).ToArray())) {
// var status = CudaBlasNativeMethods.cublasSgemmBatched(_cuda.Blas.CublasHandle,
// Operation.NonTranspose,
// Operation.NonTranspose,
// rowsA,
// columnsB,
// columnsArowsB,
// ref alpha,
// aPtrs.DevicePointer,
// rowsA,
// bPtrs.DevicePointer,
// columnsArowsB,
// ref beta,
// cPtrs.DevicePointer,
// rowsA,
// _depth
// );
// if (status != CublasStatus.Success)
// throw new CudaBlasException(status);
//}
//return _cuda.Create3DTensor(output);
}
public IMatrix GetNewMatrixFromRows(IReadOnlyList <int> rowIndices)
{
Debug.Assert(IsValid);
int offset = 0;
var ret = _cuda.Allocate(_columns * rowIndices.Count);
foreach (var item in rowIndices)
{
CudaBlasNativeMethods.cublasScopy_v2(_cuda.Blas.CublasHandle,
n: _columns,
x: _data.DevicePointer + (item * sizeof(float)),
incx: _rows,
y: ret.DevicePointer + (offset * sizeof(float)),
incy: rowIndices.Count
);
offset += 1;
}
return(new GpuMatrix(_cuda, rowIndices.Count, _columns, ret));
}
/// <summary>
/// Runs the dot float.
/// </summary>
/// <param name="context">The context.</param>
/// <param name="result">The result.</param>
/// <param name="lhs">The LHS.</param>
/// <param name="rhs">The RHS.</param>
/// <exception cref="CudaBlasException"></exception>
private static void Run_Dot_float(TSCudaContext context, NDArray result, NDArray lhs, NDArray rhs)
{
using (var blas = context.BlasForTensor(lhs))
{
//var resultPtr = CudaNativeHelpers.GetBufferStart(result);
var lhsPtr = CudaHelpers.GetBufferStart(lhs);
var rhsPtr = CudaHelpers.GetBufferStart(rhs);
int n = (int)lhs.Shape[0];
int incx = (int)lhs.Strides[0];
int incy = (int)rhs.Strides[0];
float resultVal = 0;
var _status = CudaBlasNativeMethods.cublasSdot_v2(blas.Value.CublasHandle, n, lhsPtr, incx, rhsPtr, incy, ref resultVal);
if (_status != CublasStatus.Success)
{
throw new CudaBlasException(_status);
}
result.Storage.SetElementAsFloat(result.StorageOffset, resultVal);
}
}
public IMatrix Transpose()
{
Debug.Assert(IsValid);
var ret = _cuda.Allocate(_rows * _columns);
float alpha = 1.0f, beta = 0.0f;
CudaBlasNativeMethods.cublasSgeam(_cuda.Blas.CublasHandle,
Operation.Transpose,
Operation.NonTranspose,
_columns,
_rows,
ref alpha,
_data.DevicePointer,
_rows,
ref beta,
new ManagedCuda.BasicTypes.CUdeviceptr(0),
_columns,
ret.DevicePointer,
_columns
);
return(new GpuMatrix(_cuda, _columns, _rows, ret));
}
private static void Run_Dot_double(TSCudaContext context, Tensor result, Tensor lhs, Tensor rhs)
{
using var blas = context.BlasForTensor(lhs);
//var resultPtr = CudaNativeHelpers.GetBufferStart(result);
var lhsPtr = CudaHelpers.GetBufferStart(lhs);
var rhsPtr = CudaHelpers.GetBufferStart(rhs);
var n = (int)lhs.Sizes[0];
var incx = (int)lhs.Strides[0];
var incy = (int)rhs.Strides[0];
// TODO add SetElementAsDouble to prevent need to round to float here
double resultVal = 0;
var _status = CudaBlasNativeMethods.cublasDdot_v2(blas.Value.CublasHandle, n, lhsPtr, incx, rhsPtr, incy, ref resultVal);
if (_status != CublasStatus.Success)
{
throw new CudaBlasException(_status);
}
result.Storage.SetElementAsFloat(result.StorageOffset, (float)resultVal);
}
private static void GemmOp(TSCudaContext context, BlasOp transA, BlasOp transB, float alpha, Tensor a, Tensor b, float beta, Tensor c)
{
if (a.Strides[0] != 1)
{
throw new ArgumentException($"a must be contiguous in the first dimension (column major / fortran order). ({a.Strides[0]},{a.Strides[1]}) ({b.Strides[0]},{b.Strides[1]}) ({c.Strides[0]},{c.Strides[1]})");
}
if (b.Strides[0] != 1)
{
throw new ArgumentException("b must be contiguous in the first dimension (column major / fortran order)");
}
if (c.Strides[0] != 1)
{
throw new ArgumentException("c must be contiguous in the first dimension (column major / fortran order)");
}
using (var blas = context.BlasForTensor(c))
{
bool nta = transA == BlasOp.NonTranspose;
bool ntb = transB == BlasOp.NonTranspose;
Operation transa = GetCudaBlasOp(transA);
Operation transb = GetCudaBlasOp(transB);
int m = (int)a.Sizes[nta ? 0 : 1];
int k = (int)b.Sizes[ntb ? 0 : 1];
int n = (int)b.Sizes[ntb ? 1 : 0];
int lda = (int)a.Strides[1];
int ldb = (int)b.Strides[1];
int ldc = (int)c.Strides[1];
if (c.ElementType == DType.Float32)
{
var aPtrSingle = CudaHelpers.GetBufferStart(a);
var bPtrSingle = CudaHelpers.GetBufferStart(b);
var cPtrSingle = CudaHelpers.GetBufferStart(c);
var _statusF32 = CudaBlasNativeMethods.cublasSgemm_v2(blas.Value.CublasHandle,
transa, transb, m, n, k, ref alpha, aPtrSingle, lda, bPtrSingle, ldb, ref beta, cPtrSingle, ldc);
if (_statusF32 != CublasStatus.Success)
{
throw new CudaBlasException(_statusF32);
}
}
else if (c.ElementType == DType.Float64)
{
var aPtrDouble = CudaHelpers.GetBufferStart(a);
var bPtrDouble = CudaHelpers.GetBufferStart(b);
var cPtrDouble = CudaHelpers.GetBufferStart(c);
var alphaDouble = (double)alpha;
var betaDouble = (double)beta;
var _statusF64 = CudaBlasNativeMethods.cublasDgemm_v2(blas.Value.CublasHandle,
transa, transb, m, n, k, ref alphaDouble, aPtrDouble, lda, bPtrDouble, ldb, ref betaDouble, cPtrDouble, ldc);
if (_statusF64 != CublasStatus.Success)
{
throw new CudaBlasException(_statusF64);
}
}
else
{
throw new NotSupportedException("CUDA GEMM with element type " + c.ElementType + " not supported");
}
}
}
private static void GemmOpBatch(TSCudaContext context, BlasOp transA, BlasOp transB, float alpha, Tensor a, Tensor b, float beta, Tensor c)
{
if (a.Strides[1] != 1)
{
throw new ArgumentException($"a must be contiguous in the first dimension (column major / fortran order). ({a.Strides[0]},{a.Strides[1]}) ({b.Strides[0]},{b.Strides[1]}) ({c.Strides[0]},{c.Strides[1]})");
}
if (b.Strides[1] != 1)
{
throw new ArgumentException("b must be contiguous in the first dimension (column major / fortran order)");
}
if (c.Strides[1] != 1)
{
throw new ArgumentException($"c must be contiguous in the first dimension (column major / fortran order) ({a.Strides[0]}, {a.Strides[1]}, {a.Strides[2]}) ({b.Strides[0]}, {b.Strides[1]}, {b.Strides[2]}) ({c.Strides[0]}, {c.Strides[1]}, {c.Strides[2]})");
}
using (Util.PooledObject <CudaBlas> blas = context.BlasForTensor(c))
{
bool nta = transA == BlasOp.NonTranspose;
bool ntb = transB == BlasOp.NonTranspose;
Operation transa = GetCudaBlasOp(transA);
Operation transb = GetCudaBlasOp(transB);
int m = (int)a.Sizes[nta ? 1 : 2];
int k = (int)b.Sizes[ntb ? 1 : 2];
int n = (int)b.Sizes[ntb ? 2 : 1];
int lda = (int)a.Strides[2];
int ldb = (int)b.Strides[2];
int ldc = (int)c.Strides[2];
int stra = (int)a.Strides[0];
int strb = (int)b.Strides[0];
int strc = (int)c.Strides[0];
int batchSize = (int)c.Sizes[0];
//// Set the math mode to allow cuBLAS to use Tensor Cores:
//cublasStat = cublasSetMathMode(handle, CUBLAS_TENSOR_OP_MATH);
CublasStatus status = CudaBlasNativeMethods.cublasSetMathMode(blas.Value.CublasHandle, ManagedCuda.CudaBlas.Math.TensorOpMath);
if (status != CublasStatus.Success)
{
throw new CudaBlasException($"Failed to set math mode to tensor ops.");
}
if (c.ElementType == DType.Float32)
{
CUdeviceptr aPtrSingle = CudaHelpers.GetBufferStart(a);
CUdeviceptr bPtrSingle = CudaHelpers.GetBufferStart(b);
CUdeviceptr cPtrSingle = CudaHelpers.GetBufferStart(c);
CublasStatus _statusF32 = CudaBlasNativeMethods.cublasSgemmStridedBatched(blas.Value.CublasHandle,
transa, transb, m, n, k, ref alpha, aPtrSingle, lda, stra, bPtrSingle, ldb, strb, ref beta, cPtrSingle, ldc, strc, batchSize);
if (_statusF32 != CublasStatus.Success)
{
throw new CudaBlasException(_statusF32);
}
}
else if (c.ElementType == DType.Float64)
{
CUdeviceptr aPtrDouble = CudaHelpers.GetBufferStart(a);
CUdeviceptr bPtrDouble = CudaHelpers.GetBufferStart(b);
CUdeviceptr cPtrDouble = CudaHelpers.GetBufferStart(c);
double alphaDouble = alpha;
double betaDouble = beta;
CublasStatus _statusF64 = CudaBlasNativeMethods.cublasDgemmStridedBatched(blas.Value.CublasHandle,
transa, transb, m, n, k, ref alphaDouble, aPtrDouble, lda, stra, bPtrDouble, ldb, strb, ref betaDouble, cPtrDouble, ldc, strc, batchSize);
if (_statusF64 != CublasStatus.Success)
{
throw new CudaBlasException(_statusF64);
}
}
else
{
throw new NotSupportedException("CUDA GEMM with element type " + c.ElementType + " not supported");
}
}
}
