public static void ConvolutionBackwardFilter(DNNConvolutionBwdFilterAlgo algo, Cpu.ConvolutionDesc2d cd, CudaStorage workspace, Tensor x, Tensor dy, Tensor dw) { using (var dnn = CudaHelpers.TSContextForTensor(x).DNNForTensor(x)) { var convDesc = GetConvDescriptor(cd, x.ElementType); using (var workspacePtr = new CudaDeviceVariable <byte>(workspace.DevicePtrAtElement(0), false, workspace.ByteLength)) using (var xPtr = GetDeviceVar(x)) using (var dyPtr = GetDeviceVar(dy)) using (var dwPtr = GetDeviceVar(dw)) using (var xDesc = GetDescriptor(x)) using (var dyDesc = GetDescriptor(dy)) using (var dwDesc = GetFilterDescriptor(dw)) { dnn.Value.ConvolutionBackwardFilter(1, xDesc, xPtr, dyDesc, dyPtr, convDesc, (cudnnConvolutionBwdFilterAlgo)algo, workspacePtr, 0, dwDesc, dwPtr); } } }
public static void PoolingBackward(DNNPoolingDesc desc, Tensor x, Tensor y, Tensor dx, Tensor dy) { using (var dnn = CudaHelpers.TSContextForTensor(x).DNNForTensor(x)) { var poolingDesc = new PoolingDescriptor(); poolingDesc.SetPoolingNdDescriptor((cudnnPoolingMode)desc.Mode, cudnnNanPropagation.PropagateNan, desc.WindowDims.Length, desc.WindowDims, desc.Padding, desc.Strides); using (var xPtr = GetDeviceVar(x)) using (var yPtr = GetDeviceVar(y)) using (var dxPtr = GetDeviceVar(dx)) using (var dyPtr = GetDeviceVar(dy)) using (var xDesc = GetDescriptor(x)) using (var yDesc = GetDescriptor(y)) using (var dxDesc = GetDescriptor(dx)) using (var dyDesc = GetDescriptor(dy)) { // Note: ManagedCUDA argument names may be slightly misleading (src refers to 'y' here, and dest to 'x') dnn.Value.PoolingBackward(poolingDesc, 1, yDesc, yPtr, dyDesc, dyPtr, xDesc, xPtr, 0, dxDesc, dxPtr); } } }
public static void ActivationBackward(Tensor x, Tensor y, Tensor dx, Tensor dy, DNNActivation activationType, double clippedReluCeiling) { using (var dnn = CudaHelpers.TSContextForTensor(x).DNNForTensor(x)) { var activationDesc = new ActivationDescriptor(); activationDesc.SetActivationDescriptor((cudnnActivationMode)activationType, cudnnNanPropagation.PropagateNan, clippedReluCeiling); using (var xPtr = GetDeviceVar(x)) using (var yPtr = GetDeviceVar(y)) using (var dxPtr = GetDeviceVar(dx)) using (var dyPtr = GetDeviceVar(dy)) using (var xDesc = GetDescriptor(x)) using (var yDesc = GetDescriptor(y)) using (var dxDesc = GetDescriptor(dx)) using (var dyDesc = GetDescriptor(dy)) { dnn.Value.ActivationBackward(activationDesc, 1, xDesc, xPtr, dxDesc, dxPtr, yDesc, yPtr, 0, dyDesc, dyPtr); } } }
/// <summary> /// Convs the forward. /// </summary> /// <param name="algo">The algo.</param> /// <param name="cd">The cd.</param> /// <param name="workspace">The workspace.</param> /// <param name="x">The x.</param> /// <param name="w">The w.</param> /// <param name="y">The y.</param> public static void ConvForward(DNNConvolutionFwdAlgo algo, Cpu.ConvolutionDesc2d cd, CudaStorage workspace, NDArray x, NDArray w, NDArray y) { using (var dnn = CudaHelpers.TSContextForTensor(x).DNNForTensor(x)) { var convDesc = GetConvDescriptor(cd, x.ElementType); using (var workspacePtr = new CudaDeviceVariable <byte>(workspace.DevicePtrAtElement(0), false, workspace.ByteLength)) using (var xPtr = GetDeviceVar(x)) using (var wPtr = GetDeviceVar(w)) using (var yPtr = GetDeviceVar(y)) using (var xDesc = GetDescriptor(x)) using (var wDesc = GetFilterDescriptor(w)) using (var yDesc = GetDescriptor(y)) { dnn.Value.ConvolutionForward(1, xDesc, xPtr, wDesc, wPtr, convDesc, (cudnnConvolutionFwdAlgo)algo, workspacePtr, 0, yDesc, yPtr); } } }
public Tensor AddmmBatch(Tensor result, float beta, Tensor src, float alpha, Tensor m1, Tensor m2) { 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 != 3) { throw new ArgumentException("src must be a matrix", "src"); } if (m1.DimensionCount != 3) { throw new ArgumentException("m1 must be a matrix", "m1"); } if (m2.DimensionCount != 3) { throw new ArgumentException("m2 must be a matrix", "m2"); } if (src.Sizes[1] != m1.Sizes[1] || src.Sizes[2] != m2.Sizes[2] || m1.Sizes[2] != m2.Sizes[1]) { 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.GemmBatch(context, alpha, m1, m2, beta, writeTarget); return(writeTarget); }
public NDArray Addmm(NDArray result, float beta, NDArray src, float alpha, NDArray m1, NDArray m2) { var 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.Shape[0] != m1.Shape[0] || src.Shape[1] != m2.Shape[1] || m1.Shape[1] != m2.Shape[0]) { throw new InvalidOperationException("Size mismatch"); } var writeTarget = TensorResultBuilder.GetWriteTarget(result, src, true, src.Shape); if (writeTarget != src) { Ops.Copy(writeTarget, src); } CudaMatrixMulMM.Gemm(context, alpha, m1, m2, beta, writeTarget); return(writeTarget); }
public static void ConvolutionBackwardBias(Cpu.ConvolutionDesc2d cd, Tensor dy, Tensor db) { using (var dnn = CudaHelpers.TSContextForTensor(dy).DNNForTensor(dy)) { using (var dyPtr = GetDeviceVar(dy)) using (var dbPtr = GetDeviceVar(db)) using (var dyDesc = GetDescriptor(dy)) using (var dbDesc = GetDescriptor(db)) { dnn.Value.ConvolutionBackwardBias(1, dyDesc, dyPtr, 0, dbDesc, dbPtr); } } }
public static void AddTensor(Tensor src, Tensor result) { using (var dnn = CudaHelpers.TSContextForTensor(src).DNNForTensor(src)) { using (var srcPtr = GetDeviceVar(src)) using (var resultPtr = GetDeviceVar(result)) using (var srcDesc = GetDescriptor(src)) using (var resultDesc = GetDescriptor(result)) { dnn.Value.AddTensor(1, srcDesc, srcPtr, 1, resultDesc, resultPtr); } } }
public static void SoftmaxForward(DNNSoftmaxAlgorithm algorithm, DNNSoftmaxMode mode, Tensor x, Tensor y) { using (var dnn = CudaHelpers.TSContextForTensor(x).DNNForTensor(x)) { using (var xPtr = GetDeviceVar(x)) using (var yPtr = GetDeviceVar(y)) using (var xDesc = GetDescriptor(x)) using (var yDesc = GetDescriptor(y)) { dnn.Value.SoftmaxForward((cudnnSoftmaxAlgorithm)algorithm, (cudnnSoftmaxMode)mode, 1, xDesc, xPtr, 0, yDesc, yPtr); } } }
public static void SoftmaxBackward(DNNSoftmaxAlgorithm algorithm, DNNSoftmaxMode mode, Tensor y, Tensor dx, Tensor dy) { using (var dnn = CudaHelpers.TSContextForTensor(y).DNNForTensor(y)) { using (var yPtr = GetDeviceVar(y)) using (var dxPtr = GetDeviceVar(dx)) using (var dyPtr = GetDeviceVar(dy)) using (var yDesc = GetDescriptor(y)) using (var dxDesc = GetDescriptor(dx)) using (var dyDesc = GetDescriptor(dy)) { dnn.Value.SoftmaxBackward((cudnnSoftmaxAlgorithm)algorithm, (cudnnSoftmaxMode)mode, 1, yDesc, yPtr, dyDesc, dyPtr, 0, dxDesc, dxPtr); } } }
public static void PoolingForward(DNNPoolingDesc desc, Tensor x, Tensor y) { using (var dnn = CudaHelpers.TSContextForTensor(x).DNNForTensor(x)) { var poolingDesc = new PoolingDescriptor(); poolingDesc.SetPoolingNdDescriptor((cudnnPoolingMode)desc.Mode, cudnnNanPropagation.PropagateNan, desc.WindowDims.Length, desc.WindowDims, desc.Padding, desc.Strides); using (var xPtr = GetDeviceVar(x)) using (var yPtr = GetDeviceVar(y)) using (var xDesc = GetDescriptor(x)) using (var yDesc = GetDescriptor(y)) { dnn.Value.PoolingForward(poolingDesc, 1, xDesc, xPtr, 0, yDesc, yPtr); } } }
public Tensor Dot(Tensor result, Tensor lhs, Tensor rhs) { var context = CudaHelpers.TSContextForTensor(lhs); if (lhs.DimensionCount == 1 && rhs.DimensionCount == 1) { return(CudaMatrixMulDot.Dot(context, result, lhs, rhs)); } else if (lhs.DimensionCount == 2 && rhs.DimensionCount == 1) { return(CudaMatrixMulMV.Mul_M_V(context, result, lhs, rhs)); } else if (lhs.DimensionCount == 2 && rhs.DimensionCount == 2) { return(CudaMatrixMulMM.Mul_M_M(context, result, lhs, rhs)); } else { throw new NotSupportedException(string.Format("Multiplication of {0}D with {1}D tensor is not supported")); } }
public NDArray Dot(NDArray result, NDArray lhs, NDArray rhs) { var context = CudaHelpers.TSContextForTensor(lhs); if (lhs.DimensionCount == 1 && rhs.DimensionCount == 1) { return(CudaMatrixMulDot.Dot(context, result, lhs, rhs)); } else if (lhs.DimensionCount == 2 && (rhs.DimensionCount == 1 || rhs.PossibleVector)) { return(CudaMatrixMulMV.Mul_M_V(context, result, lhs, rhs.Ravel()).Reshape(lhs.Shape[0], 1)); } else if (lhs.DimensionCount == 2 && rhs.DimensionCount == 2) { return(CudaMatrixMulMM.Mul_M_M(context, result, lhs, rhs)); } else { throw new NotSupportedException(string.Format("Multiplication of {0}D with {1}D tensor is not supported")); } }
/// <summary> /// Convolutions the backward data. /// </summary> /// <param name="algo">The algo.</param> /// <param name="cd">The cd.</param> /// <param name="workspace">The workspace.</param> /// <param name="w">The w.</param> /// <param name="dy">The dy.</param> /// <param name="dx">The dx.</param> public static void ConvolutionBackwardData(DNNConvolutionBwdDataAlgo algo, Cpu.ConvolutionDesc2d cd, CudaStorage workspace, NDArray w, NDArray dy, NDArray dx) { using (var dnn = CudaHelpers.TSContextForTensor(w).DNNForTensor(w)) { var convDesc = GetConvDescriptor(cd, w.ElementType); using (var workspacePtr = new CudaDeviceVariable <byte>(workspace.DevicePtrAtElement(0), false, workspace.ByteLength)) using (var wPtr = GetDeviceVar(w)) using (var dxPtr = GetDeviceVar(dx)) using (var dyPtr = GetDeviceVar(dy)) using (var wDesc = GetFilterDescriptor(w)) using (var dxDesc = GetDescriptor(dx)) using (var dyDesc = GetDescriptor(dy)) { dnn.Value.ConvolutionBackwardData(1, wDesc, wPtr, dyDesc, dyPtr, convDesc, (cudnnConvolutionBwdDataAlgo)algo, workspacePtr, 0f, dxDesc, dxPtr); } } }
public static 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", nameof(result)); } if (!(m1.Storage is CudaStorage)) { throw new ArgumentException("m1 must be a CUDA tensor", nameof(m1)); } if (!(m2.Storage is CudaStorage)) { throw new ArgumentException("m2 must be a CUDA tensor", nameof(m2)); } if (src.DimensionCount != 2) { throw new ArgumentException("src must be a matrix", nameof(src)); } if (m1.DimensionCount != 2) { throw new ArgumentException("m1 must be a matrix", nameof(m1)); } if (m2.DimensionCount != 2) { throw new ArgumentException("m2 must be a matrix", nameof(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, false, 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; } }