private void BuildSelfTriMask(TSCudaContext context, Tensor result, Tensor originalLengths, int paddedSeqLen, float value, float maskedValue)
{
CudaContext cudaContext = context.CudaContextForTensor(originalLengths);
cudaContext.SetCurrent();
int ndim = result.DimensionCount;
long storageSize = TensorDimensionHelpers.GetStorageSize(result.Sizes, result.Strides);
long cols = result.Sizes[ndim - 1];
if (storageSize % cols != 0)
{
throw new Exception($"Invalid tensor storage size = '{storageSize}', and cols = '{cols}'");
}
long rows = storageSize / cols;
dim3 threads = new dim3((uint)Math.Min(512, rows));
dim3 grid = new dim3((uint)Math.Min(1024, ApplyUtils.CeilDiv(rows, threads.y)));
CUdeviceptr resultPtr = CudaHelpers.GetBufferStart(result);
CUdeviceptr originalLengthsPtr = CudaHelpers.GetBufferStart(originalLengths);
Invoke(context, cudaContext, "BuildSelfTriMask", grid, threads, threads.x * sizeof(float), CUstream.NullStream, resultPtr, originalLengthsPtr, rows, cols, paddedSeqLen, value, maskedValue);
}
public static unsafe Tensor Deserialize(TensorProto proto)
{
if (!_dataTypeToDType.Keys.Contains(proto.Type))
{
throw new Exception($"Tensors don't support '{proto.Type.ToString()}' data type");
}
var allocator = new CpuAllocator();
var dtype = _dataTypeToDType[proto.Type];
var storage = (CpuStorage)allocator.Allocate(dtype, proto.Data.Length / dtype.Size());
var bytes = proto.Data.ToByteArray();
fixed(byte *p = bytes)
{
IntPtr ptr = (IntPtr)p;
storage.CopyToStorage(0, ptr, bytes.Length);
}
var sizes = proto.Shape.Select(i => (long)i).ToArray();
var strides = TensorDimensionHelpers.GetContiguousStride(sizes);
return(new Tensor(sizes, strides, storage, 0));
}
private void AddLayerNormGrad(TSCudaContext context, Tensor out1Grad, Tensor out2Grad, Tensor alphaGrad, Tensor betaGrad, Tensor inGrad, Tensor y, Tensor x1, Tensor x2, Tensor alpha, Tensor beta, float eps = 1e-9f)
{
var cudaContext = context.CudaContextForTensor(inGrad);
cudaContext.SetCurrent();
var ndim = inGrad.DimensionCount;
var storageSize = TensorDimensionHelpers.GetStorageSize(inGrad.Sizes, inGrad.Strides);
var cols = inGrad.Sizes[ndim - 1];
if (storageSize % cols != 0)
{
throw new Exception($"Invalid tensor storage size = '{storageSize}', and cols = '{cols}'");
}
var rows = storageSize / cols;
var threads = new dim3((uint)Math.Min(512, rows));
var grid = new dim3((uint)Math.Min(1024, ApplyUtils.CeilDiv(rows, threads.y)));
var out1GradPtr = CudaHelpers.GetBufferStart(out1Grad);
var out2GradPtr = CudaHelpers.GetBufferStart(out2Grad);
var alphaGradPtr = CudaHelpers.GetBufferStart(alphaGrad);
var betaGradPtr = CudaHelpers.GetBufferStart(betaGrad);
var inGradPtr = CudaHelpers.GetBufferStart(inGrad);
var yPtr = CudaHelpers.GetBufferStart(y);
var x1Ptr = CudaHelpers.GetBufferStart(x1);
var x2Ptr = CudaHelpers.GetBufferStart(x2);
var alphaPtr = CudaHelpers.GetBufferStart(alpha);
var betaPtr = CudaHelpers.GetBufferStart(beta);
this.Invoke(context, cudaContext, "gAddLayerNormalizationGrad", grid, threads, threads.x * sizeof(float) * 4, CUstream.NullStream, out1GradPtr, out2GradPtr, alphaGradPtr, betaGradPtr, inGradPtr, yPtr, x1Ptr, x2Ptr, alphaPtr, betaPtr, rows, cols, eps);
}
private void LayerNorm(TSCudaContext context, Tensor result, Tensor src, Tensor alpha, Tensor beta, float eps = 1e-9f)
{
var cudaContext = context.CudaContextForTensor(src);
cudaContext.SetCurrent();
var ndim = src.DimensionCount;
var storageSize = TensorDimensionHelpers.GetStorageSize(src.Sizes, src.Strides);
var cols = src.Sizes[ndim - 1];
if (storageSize % cols != 0)
{
throw new Exception($"Invalid tensor storage size = '{storageSize}', and cols = '{cols}'");
}
var rows = storageSize / cols;
var threads = new dim3((uint)Math.Min(512, rows));
var grid = new dim3((uint)Math.Min(1024, ApplyUtils.CeilDiv(rows, threads.y)));
var resultPtr = CudaHelpers.GetBufferStart(result);
var srcPtr = CudaHelpers.GetBufferStart(src);
var alphaPtr = CudaHelpers.GetBufferStart(alpha);
var betaPtr = CudaHelpers.GetBufferStart(beta);
this.Invoke(context, cudaContext, "gLNormalization", grid, threads, threads.x * sizeof(float), CUstream.NullStream, resultPtr, srcPtr, alphaPtr, betaPtr, rows, cols, eps);
}
private void RMSProp(TSCudaContext context, Tensor weight, Tensor gradient, Tensor cache, int batchSize, float step_size, float clipval, float regc, float decay_rate, float eps)
{
var cudaContext = context.CudaContextForTensor(weight);
cudaContext.SetCurrent();
var ndim = weight.DimensionCount;
var storageSize = TensorDimensionHelpers.GetStorageSize(weight.Sizes, weight.Strides);
var cols = weight.Sizes[ndim - 1];
if (storageSize % cols != 0)
{
throw new Exception($"Invalid tensor storage size = '{storageSize}', and cols = '{cols}'");
}
var rows = storageSize / cols;
var threads = new dim3((uint)Math.Min(512, rows));
var grid = new dim3((uint)Math.Min(1024, ApplyUtils.CeilDiv(rows, threads.y)));
var weightPtr = CudaHelpers.GetBufferStart(weight);
var gradientPtr = CudaHelpers.GetBufferStart(gradient);
var cachePtr = CudaHelpers.GetBufferStart(cache);
this.Invoke(context, cudaContext, "RMSProp", grid, threads, 0, CUstream.NullStream, weightPtr, gradientPtr, cachePtr, rows, cols, batchSize, step_size, clipval, regc, decay_rate, eps);
}
private void SoftmaxGrad(TSCudaContext context, Tensor grad, Tensor adj, Tensor val, bool addGrad = true)
{
var cudaContext = context.CudaContextForTensor(grad);
cudaContext.SetCurrent();
var ndim = grad.DimensionCount;
var storageSize = TensorDimensionHelpers.GetStorageSize(grad.Sizes, grad.Strides);
var cols = grad.Sizes[ndim - 1];
if (storageSize % cols != 0)
{
throw new Exception($"Invalid tensor storage size = '{storageSize}', and cols = '{cols}'");
}
var rows = storageSize / cols;
var iAddGrad = addGrad ? 1 : 0;
var threads = new dim3((uint)Math.Min(512, rows));
var grid = new dim3((uint)Math.Min(1024, ApplyUtils.CeilDiv(rows, threads.y)));
var gradPtr = CudaHelpers.GetBufferStart(grad);
var adjPtr = CudaHelpers.GetBufferStart(adj);
var valPtr = CudaHelpers.GetBufferStart(val);
this.Invoke(context, cudaContext, "gSoftmaxGrad", grid, threads, threads.x * sizeof(float), CUstream.NullStream, gradPtr, adjPtr, valPtr, rows, cols, iAddGrad);
}
private void LayerNormGrad(TSCudaContext context, Tensor outGrad, Tensor alphaGrad, Tensor betaGrad, Tensor inGrad, Tensor y, Tensor x, Tensor alpha, Tensor beta, float eps = 1e-9f)
{
CudaContext cudaContext = context.CudaContextForTensor(inGrad);
cudaContext.SetCurrent();
int ndim = inGrad.DimensionCount;
long storageSize = TensorDimensionHelpers.GetStorageSize(inGrad.Sizes, inGrad.Strides);
long cols = inGrad.Sizes[ndim - 1];
if (storageSize % cols != 0)
{
throw new Exception($"Invalid tensor storage size = '{storageSize}', and cols = '{cols}'");
}
long rows = storageSize / cols;
dim3 threads = new dim3((uint)Math.Min(512, rows));
dim3 grid = new dim3((uint)Math.Min(1024, ApplyUtils.CeilDiv(rows, threads.y)));
CUdeviceptr outGradPtr = CudaHelpers.GetBufferStart(outGrad);
CUdeviceptr alphaGradPtr = CudaHelpers.GetBufferStart(alphaGrad);
CUdeviceptr betaGradPtr = CudaHelpers.GetBufferStart(betaGrad);
CUdeviceptr inGradPtr = CudaHelpers.GetBufferStart(inGrad);
CUdeviceptr yPtr = CudaHelpers.GetBufferStart(y);
CUdeviceptr xPtr = CudaHelpers.GetBufferStart(x);
CUdeviceptr alphaPtr = CudaHelpers.GetBufferStart(alpha);
CUdeviceptr betaPtr = CudaHelpers.GetBufferStart(beta);
Invoke(context, cudaContext, "gLayerNormalizationGrad", grid, threads, threads.x * sizeof(float) * 4, CUstream.NullStream, outGradPtr, alphaGradPtr, betaGradPtr, inGradPtr, yPtr, xPtr, alphaPtr, betaPtr, rows, cols, eps);
}
private void AddLayerNorm(TSCudaContext context, Tensor result, Tensor src1, Tensor src2, Tensor alpha, Tensor beta, float eps = 1e-9f)
{
CudaContext cudaContext = context.CudaContextForTensor(src1);
cudaContext.SetCurrent();
int ndim = src1.DimensionCount;
long storageSize = TensorDimensionHelpers.GetStorageSize(src1.Sizes, src1.Strides);
long cols = src1.Sizes[ndim - 1];
if (storageSize % cols != 0)
{
throw new Exception($"Invalid tensor storage size = '{storageSize}', and cols = '{cols}'");
}
long rows = storageSize / cols;
dim3 threads = new dim3((uint)Math.Min(512, rows));
dim3 grid = new dim3((uint)Math.Min(1024, ApplyUtils.CeilDiv(rows, threads.y)));
CUdeviceptr resultPtr = CudaHelpers.GetBufferStart(result);
CUdeviceptr src1Ptr = CudaHelpers.GetBufferStart(src1);
CUdeviceptr src2Ptr = CudaHelpers.GetBufferStart(src2);
CUdeviceptr alphaPtr = CudaHelpers.GetBufferStart(alpha);
CUdeviceptr betaPtr = CudaHelpers.GetBufferStart(beta);
Invoke(context, cudaContext, "gAddLNormalization", grid, threads, threads.x * sizeof(float), CUstream.NullStream, resultPtr, src1Ptr, src2Ptr, alphaPtr, betaPtr, rows, cols, eps);
}
private void Softmax(TSCudaContext context, Tensor result, Tensor src)
{
CudaContext cudaContext = context.CudaContextForTensor(src);
cudaContext.SetCurrent();
int ndim = src.DimensionCount;
long storageSize = TensorDimensionHelpers.GetStorageSize(src.Sizes, src.Strides);
long cols = src.Sizes[ndim - 1];
if (storageSize % cols != 0)
{
throw new Exception($"Invalid tensor storage size = '{storageSize}', and cols = '{cols}'");
}
long rows = storageSize / cols;
dim3 threads = new dim3((uint)Math.Min(512, rows));
dim3 grid = new dim3((uint)Math.Min(1024, ApplyUtils.CeilDiv(rows, threads.y)));
CUdeviceptr resultPtr = CudaHelpers.GetBufferStart(result);
CUdeviceptr srcPtr = CudaHelpers.GetBufferStart(src);
Invoke(context, cudaContext, "gSoftmax", grid, threads, threads.x * sizeof(float), CUstream.NullStream, resultPtr, srcPtr, rows, cols);
}
private void Adam(TSCudaContext context, Tensor weight, Tensor gradient, Tensor v, Tensor m, int batchSize, float step_size, float clipval, float regc, float decay_rate_v, float decay_rate_m, int iter, float eps)
{
CudaContext cudaContext = context.CudaContextForTensor(weight);
cudaContext.SetCurrent();
int ndim = weight.DimensionCount;
long storageSize = TensorDimensionHelpers.GetStorageSize(weight.Sizes, weight.Strides);
long cols = weight.Sizes[ndim - 1];
if (storageSize % cols != 0)
{
throw new Exception($"Invalid tensor storage size = '{storageSize}', and cols = '{cols}'");
}
long rows = storageSize / cols;
dim3 threads = new dim3((uint)Math.Min(512, rows));
dim3 grid = new dim3((uint)Math.Min(1024, ApplyUtils.CeilDiv(rows, threads.y)));
CUdeviceptr weightPtr = CudaHelpers.GetBufferStart(weight);
CUdeviceptr gradientPtr = CudaHelpers.GetBufferStart(gradient);
CUdeviceptr vPtr = CudaHelpers.GetBufferStart(v);
CUdeviceptr mPtr = CudaHelpers.GetBufferStart(m);
Invoke(context, cudaContext, "Adam", grid, threads, 0, CUstream.NullStream, weightPtr, gradientPtr, vPtr, mPtr, rows, cols, batchSize, step_size, clipval, regc, decay_rate_v, decay_rate_m, iter, eps);
}
private void IndexSelectGrad(TSCudaContext context, Tensor grad, Tensor adj, Tensor indice)
{
CudaContext cudaContext = context.CudaContextForTensor(adj);
cudaContext.SetCurrent();
int ndim = adj.DimensionCount;
long storageSize = TensorDimensionHelpers.GetStorageSize(adj.Sizes, adj.Strides);
long cols = adj.Sizes[ndim - 1];
if (storageSize % cols != 0)
{
throw new Exception($"Invalid tensor storage size = '{storageSize}', and cols = '{cols}'");
}
long rows = storageSize / cols;
dim3 threads = new dim3((uint)Math.Min(512, rows));
dim3 grid = new dim3((uint)Math.Min(1024, ApplyUtils.CeilDiv(rows, threads.y)));
CUdeviceptr gradPtr = CudaHelpers.GetBufferStart(grad);
CUdeviceptr adjPtr = CudaHelpers.GetBufferStart(adj);
CUdeviceptr indicePtr = CudaHelpers.GetBufferStart(indice);
Invoke(context, cudaContext, "IndexSelectGrad", grid, threads, threads.x * sizeof(float), CUstream.NullStream, gradPtr, adjPtr, indicePtr, rows, cols);
}
private void SoftmaxMask(TSCudaContext context, Tensor result, Tensor src, Tensor mask)
{
var cudaContext = context.CudaContextForTensor(src);
cudaContext.SetCurrent();
var ndim = src.DimensionCount;
var storageSize = TensorDimensionHelpers.GetStorageSize(src.Sizes, src.Strides);
var cols = src.Sizes[ndim - 1];
if (storageSize % cols != 0)
{
throw new Exception($"Invalid tensor storage size = '{storageSize}', and cols = '{cols}'");
}
var rows = storageSize / cols;
var maskNdim = mask.DimensionCount;
var maskStorageSize = TensorDimensionHelpers.GetStorageSize(mask.Sizes, mask.Strides);
var maskCols = mask.Sizes[maskNdim - 1];
if (maskStorageSize % maskCols != 0)
{
throw new Exception($"Invalid mask tensor storage size = '{maskStorageSize}', and cols = '{maskCols}'");
}
var maskRows = maskStorageSize / maskCols;
if (rows % maskRows != 0)
{
throw new Exception($"Invalid tensor rows = '{rows}' and mask tensor rows = '{maskRows}'");
}
if (cols != maskCols)
{
throw new Exception($"Tensor cols = '{cols}', mask tensor cols = '{maskCols}'. They should be equal.");
}
var threads = new dim3((uint)Math.Min(512, rows));
var grid = new dim3((uint)Math.Min(1024, ApplyUtils.CeilDiv(rows, threads.y)));
var resultPtr = CudaHelpers.GetBufferStart(result);
var srcPtr = CudaHelpers.GetBufferStart(src);
var maskPtr = CudaHelpers.GetBufferStart(mask);
this.Invoke(context, cudaContext, "gSoftmaxMask", grid, threads, threads.x * sizeof(float), CUstream.NullStream, resultPtr, srcPtr, maskPtr, rows, cols, maskRows);
}
public Tensor BuildSrcTgtMask(Tensor result, Tensor srcOriginalLengths, Tensor tgtOriginalLengths, int srcPaddedSeqLen, int tgtPaddedSeqLen, float value, float maskedValue)
{
int ndim = result.DimensionCount;
long storageSize = TensorDimensionHelpers.GetStorageSize(result.Sizes, result.Strides);
long cols = result.Sizes[ndim - 1];
if (storageSize % cols != 0)
{
throw new Exception($"Invalid tensor storage size = '{storageSize}', and cols = '{cols}'");
}
long rows = storageSize / cols;
TensorApplyCPU.BuildSrcTgtMask(result, srcOriginalLengths, tgtOriginalLengths, (int)rows, (int)cols, tgtPaddedSeqLen, value, maskedValue);
return(result);
}
public Tensor BuildTriMask(Tensor result, float value, float maskedValue)
{
int ndim = result.DimensionCount;
long storageSize = TensorDimensionHelpers.GetStorageSize(result.Sizes, result.Strides);
long cols = result.Sizes[ndim - 1];
if (storageSize % cols != 0)
{
throw new Exception($"Invalid tensor storage size = '{storageSize}', and cols = '{cols}'");
}
long rows = storageSize / cols;
TensorApplyCPU.BuildTriMask(result, (int)rows, (int)cols, value, maskedValue);
return(result);
}
public Tensor SoftmaxGrad(Tensor grad_, Tensor adj_, Tensor val_, bool addGrad = true)
{
var ndim = adj_.DimensionCount;
var storageSize = TensorDimensionHelpers.GetStorageSize(adj_.Sizes, adj_.Strides);
var cols = adj_.Sizes[ndim - 1];
if (storageSize % cols != 0)
{
throw new Exception($"Invalid tensor storage size = '{storageSize}', and cols = '{cols}'");
}
var rows = storageSize / cols;
var writeTarget = TensorResultBuilder.GetWriteTarget(grad_, adj_, false, adj_.Sizes);
NativeWrapper.InvokeTypeMatch(this.softmaxGrad_func, writeTarget, adj_, val_, (int)rows, (int)cols, addGrad);
return(writeTarget);
}
public Tensor Softmax(Tensor result, Tensor src)
{
int ndim = src.DimensionCount;
long storageSize = TensorDimensionHelpers.GetStorageSize(src.Sizes, src.Strides);
long cols = src.Sizes[ndim - 1];
if (storageSize % cols != 0)
{
throw new Exception($"Invalid tensor storage size = '{storageSize}', and cols = '{cols}'");
}
long rows = storageSize / cols;
Tensor writeTarget = TensorResultBuilder.GetWriteTarget(result, src, true, src.Sizes);
TensorApplyCPU.Softmax(writeTarget, src, (int)rows, (int)cols);
return(writeTarget);
}
public Tensor IndexSelect(Tensor result, Tensor src, Tensor indice)
{
int ndim = result.DimensionCount;
long storageSize = TensorDimensionHelpers.GetStorageSize(result.Sizes, result.Strides);
long cols = result.Sizes[ndim - 1];
if (storageSize % cols != 0)
{
throw new Exception($"Invalid tensor storage size = '{storageSize}', and cols = '{cols}'");
}
long rows = storageSize / cols;
Tensor writeTarget = TensorResultBuilder.GetWriteTarget(result, src, false, new long[] { indice.Sizes[0], src.Sizes[1] });
TensorApplyCPU.IndexSelect(writeTarget, src, indice, (int)rows, (int)cols);
return(writeTarget);
}
public Tensor Softmax(Tensor result, Tensor src)
{
var ndim = src.DimensionCount;
var storageSize = TensorDimensionHelpers.GetStorageSize(src.Sizes, src.Strides);
var cols = src.Sizes[ndim - 1];
if (storageSize % cols != 0)
{
throw new Exception($"Invalid tensor storage size = '{storageSize}', and cols = '{cols}'");
}
var rows = storageSize / cols;
var writeTarget = TensorResultBuilder.GetWriteTarget(result, src, false, src.Sizes);
NativeWrapper.InvokeTypeMatch(this.softmax_func, writeTarget, src, (int)rows, (int)cols);
return(writeTarget);
}
public Tensor SoftmaxGrad(Tensor grad_, Tensor adj_, Tensor val_, bool addGrad = true)
{
int ndim = adj_.DimensionCount;
long storageSize = TensorDimensionHelpers.GetStorageSize(adj_.Sizes, adj_.Strides);
long cols = adj_.Sizes[ndim - 1];
if (storageSize % cols != 0)
{
throw new Exception($"Invalid tensor storage size = '{storageSize}', and cols = '{cols}'");
}
long rows = storageSize / cols;
Tensor writeTarget = TensorResultBuilder.GetWriteTarget(grad_, adj_, true, adj_.Sizes);
TensorApplyCPU.SoftmaxGrad(writeTarget, adj_, val_, (int)rows, (int)cols, addGrad);
return(writeTarget);
}
public Tensor IndexSelectGrad(Tensor grad, Tensor adj, Tensor indice)
{
if (grad == null)
{
throw new ArgumentNullException($"Tensor grad should not be null.");
}
int ndim = adj.DimensionCount;
long storageSize = TensorDimensionHelpers.GetStorageSize(adj.Sizes, adj.Strides);
long cols = adj.Sizes[ndim - 1];
if (storageSize % cols != 0)
{
throw new Exception($"Invalid tensor storage size = '{storageSize}', and cols = '{cols}'");
}
long rows = storageSize / cols;
TensorApplyCPU.IndexSelectGrad(grad, adj, indice, (int)rows, (int)cols);
return(grad);
}
public Tensor SoftmaxMask(Tensor result, Tensor src, Tensor mask)
{
var ndim = src.DimensionCount;
var storageSize = TensorDimensionHelpers.GetStorageSize(src.Sizes, src.Strides);
var cols = src.Sizes[ndim - 1];
if (storageSize % cols != 0)
{
throw new Exception($"Invalid tensor storage size = '{storageSize}', and cols = '{cols}'");
}
var rows = storageSize / cols;
var maskNdim = mask.DimensionCount;
var maskStorageSize = TensorDimensionHelpers.GetStorageSize(mask.Sizes, mask.Strides);
var maskCols = mask.Sizes[maskNdim - 1];
if (maskStorageSize % maskCols != 0)
{
throw new Exception($"Invalid mask tensor storage size = '{maskStorageSize}', and cols = '{maskCols}'");
}
var maskRows = maskStorageSize / maskCols;
if (rows % maskRows != 0)
{
throw new Exception($"Invalid tensor rows = '{rows}' and mask tensor rows = '{maskRows}'");
}
if (cols != maskCols)
{
throw new Exception($"Tensor cols = '{cols}', mask tensor cols = '{maskCols}'. They should be equal.");
}
var writeTarget = TensorResultBuilder.GetWriteTarget(result, src, false, src.Sizes);
NativeWrapper.InvokeTypeMatch(this.softmaxmask_func, writeTarget, src, mask, (int)rows, (int)cols, (int)maskRows);
return(writeTarget);
}
public TensorShape(DType elementType, long[] sizes)
: this(elementType, sizes, TensorDimensionHelpers.GetContiguousStride(sizes))
{
}
