public PooledObject <CudaBlas> BlasForTensor(Tensor tensor)
{
return(BlasForDevice(CudaHelpers.GetDeviceId(tensor)));
}
public CudaContext CudaContextForTensor(Tensor tensor)
{
return(CudaContextForDevice(CudaHelpers.GetDeviceId(tensor)));
}
//TODO this is not actually used at the moment. It probably should be.
// excludeDim may be -1 to not exclude any dimension
/// <summary>
/// Collapses the dims.
/// </summary>
/// <param name="tensor">The tensor.</param>
/// <param name="excludeDim">The exclude dim.</param>
/// <param name="info">The information.</param>
/// <returns>System.Int32.</returns>
/// <exception cref="ArgumentException">excludeDim must equal -1 if all dims are of size 1 - excludeDim</exception>
public static int CollapseDims(Tensor tensor, int excludeDim, out TensorInfo info)
{
info.buffer = CudaHelpers.GetBufferStart(tensor);
var firstNonOneDim = GetInnermostNon1Dim(tensor.Shape, excludeDim);
// If all dims are size 1 (ie. tensor contains 1 element)
if (firstNonOneDim == -1)
{
if (excludeDim != -1)
{
throw new ArgumentException("excludeDim must equal -1 if all dims are of size 1", "excludeDim");
}
info.sizes = new long[] { 1 };
info.strides = new long[] { 1 };
return(0);
}
// Count the number of successive dimensions that can be collapsed, from
// innermost to outermost.
int numCollapsed = 0;
// Skip the leading size 1 dims
numCollapsed += tensor.DimensionCount - 1 - firstNonOneDim;
// We perform one pass through to determine how many dimensions we
// can collapse, before calculating the actual size of the collapsed
// dimensions.
// size/strideInner are the size/strides of the previous inner
// non-collapsible dim we encounter.
var sizeInner = tensor.Shape[firstNonOneDim];
var strideInner = tensor.Strides[firstNonOneDim];
for (int i = firstNonOneDim - 1; i >= 0; --i)
{
var sizeOuter = tensor.Shape[i];
var strideOuter = tensor.Strides[i];
// Don't collapse this dimension if we want to exclude it from
// collapsing.
// Since this code is attempting to collapse a subsequent
// dimension (i) with the preceding dimension (i + 1), we can only
// perform collapsing if the preceding dimension can be collapsed
// (i.e., not excludeDim)
if ((excludeDim != i) && (excludeDim != i + 1))
{
// The next outermost dimension can be skipped if size 1
if (sizeOuter == 1)
{
++numCollapsed;
continue;
}
// If the next outermost dimension is contiguous with the
// previous non-collapsed one, collapse it
if (strideOuter == strideInner * sizeInner)
{
++numCollapsed;
// This is the run of collapsed dimensions' size
sizeInner = sizeInner * sizeOuter;
continue;
}
}
// Otherwise, this new outer dimension at `i` cannot be collapsed
// because it is excluded from collapsing, or it is not contiguous
// with the previous inner dimension.
sizeInner = sizeOuter;
strideInner = strideOuter;
}
// This will be our new size/stride and dimension.
var newSizes = new long[TSCudaContext.MaxDims];
var newStrides = new long[TSCudaContext.MaxDims];
int newDims = tensor.DimensionCount - numCollapsed;
// We return the index of the excluded dimension that is excluded
// from being collapsed here.
int returnDim = -1;
// We perform a second pass through the dimensions to actually
// calculate the size of the collapsed dimensions.
int collapsedIndex = tensor.DimensionCount - numCollapsed - 1;
newSizes[collapsedIndex] = tensor.Shape[firstNonOneDim];
newStrides[collapsedIndex] = tensor.Strides[firstNonOneDim];
if (firstNonOneDim == excludeDim)
{
returnDim = collapsedIndex;
}
for (int i = firstNonOneDim - 1; i >= 0; --i)
{
var sizeOuter = tensor.Shape[i];
var strideOuter = tensor.Strides[i];
if ((excludeDim != i) && (excludeDim != i + 1))
{
if (sizeOuter == 1)
{
// skip
continue;
}
if (strideOuter == newSizes[collapsedIndex] * newStrides[collapsedIndex])
{
// collapse
newSizes[collapsedIndex] *= sizeOuter;
continue;
}
}
// Otherwise, strides don't match, or dim `i` is excluded from
// collapsing.
--collapsedIndex;
//assert(collapsedIndex >= 0);
//assert(collapsedIndex < newDims);
newSizes[collapsedIndex] = sizeOuter;
newStrides[collapsedIndex] = strideOuter;
if (excludeDim == i)
{
returnDim = collapsedIndex;
}
}
info.sizes = newSizes.Take(newDims).ToArray();
info.strides = newStrides.Take(newDims).ToArray();
return(returnDim);
}
public Tensor Addmm(Tensor result, float beta, Tensor src, float alpha, Tensor m1, Tensor m2)
{
try
{
TSCudaContext context = CudaHelpers.TSContextForTensor(src);
if (src.ElementType != m1.ElementType || src.ElementType != m2.ElementType || (result != null && result.ElementType != src.ElementType))
{
throw new InvalidOperationException("All tensors must have the same element type");
}
if (result != null && !(result.Storage is CudaStorage))
{
throw new ArgumentException("result must be a CUDA tensor", "result");
}
if (!(m1.Storage is CudaStorage))
{
throw new ArgumentException("m1 must be a CUDA tensor", "m1");
}
if (!(m2.Storage is CudaStorage))
{
throw new ArgumentException("m2 must be a CUDA tensor", "m2");
}
if (src.DimensionCount != 2)
{
throw new ArgumentException("src must be a matrix", "src");
}
if (m1.DimensionCount != 2)
{
throw new ArgumentException("m1 must be a matrix", "m1");
}
if (m2.DimensionCount != 2)
{
throw new ArgumentException("m2 must be a matrix", "m2");
}
if (src.Sizes[0] != m1.Sizes[0] || src.Sizes[1] != m2.Sizes[1] || m1.Sizes[1] != m2.Sizes[0])
{
throw new InvalidOperationException($"Size mismatch, srcSize0 = {src.Sizes[0]}, m1Size0 = {m1.Sizes[0]}, srcSize1 = {src.Sizes[1]}, m2Size1 = {m2.Sizes[1]}, m1Size1 = '{m1.Sizes[1]}', m2Size0 = '{m2.Sizes[0]}'");
}
Tensor writeTarget = TensorResultBuilder.GetWriteTarget(result, src, true, src.Sizes);
if (writeTarget != src)
{
Ops.Copy(writeTarget, src);
}
CudaMatrixMulMM.Gemm(context, alpha, m1, m2, beta, writeTarget);
return(writeTarget);
}
catch (Exception err)
{
Logger.WriteLine($"Exception in Addmm: '{err.Message}'");
Logger.WriteLine($"Call stack: '{err.StackTrace}'");
throw err;
}
}
private static CudaDeviceVariable <float> GetDeviceVar(Tensor tensor)
{
var ptr = CudaHelpers.GetBufferStart(tensor);
return(new CudaDeviceVariable <float>(ptr, false, 0));// set size to 0 because we never end up using it
}
/// <summary>
/// DNNs for tensor.
/// </summary>
/// <param name="tensor">The tensor.</param>
/// <returns>PooledObject<ManagedCuda.CudaDNN.CudaDNNContext>.</returns>
public PooledObject <ManagedCuda.CudaDNN.CudaDNNContext> DNNForTensor(NDArray tensor)
{
var deviceId = CudaHelpers.GetDeviceId(tensor);
return(devices[deviceId].DnnHandles.Get());
}
