public FloatTensor createZerosTensorLike(FloatTensor tensor)
{
FloatTensor new_tensor = tensor.Copy();
new_tensor.Zero_();
return(new_tensor);
}
public FloatTensor createOnesTensorLike(FloatTensor tensor)
{
FloatTensor new_tensor = tensor.Copy();
new_tensor.Zero_();
new_tensor.Add((float)1, true);
return(new_tensor);
}
public void Copy()
{
float[] array = { 1, 2, 3, 4, 5 };
int[] shape = { 5 };
var tensor = new FloatTensor(array, shape);
var copy = tensor.Copy();
Assert.AreEqual(copy.Shape, tensor.Shape);
Assert.AreEqual(copy.Data, tensor.Data);
Assert.AreNotEqual(copy.Id, tensor.Id);
}
// TODO: Softmax will run on GPU, when below OPS have a GPU implementation!
// TODO: Improve the implementation!!!
public static FloatTensor Softmax(FloatTensor input, int dim = -1)
{
if (!input.IsContiguous())
{
throw new NotImplementedException(
"Softmax Gradient does not support non-contiguous tensors at the moment!");
}
//TODO: GPU support
var gpu = false;
if (input.DataOnGpu)
{
input.Cpu();
gpu = true;
}
var _dim = (dim == -1) ? input.Shape.Length - 1 : dim;
var outerSize = 1;
var innerSize = 1;
var dimSize = input.Shape[_dim];
for (var i = 0; i < _dim; ++i)
{
outerSize *= input.Shape[i];
}
for (var i = _dim + 1; i < input.Shape.Length; ++i)
{
innerSize *= input.Shape[i];
}
var dimStride = innerSize;
var outerStride = dimSize * dimStride;
var output = input.Copy();
var nCpu = SystemInfo.processorCount;
Parallel.For(0, nCpu, workerId =>
{
var max = (outerSize * innerSize) * (workerId + 1) / nCpu;
for (var i = (outerSize * innerSize) * workerId / nCpu; i < max; i++)
{
int outerIdx = i / innerSize;
int innerIdx = i % innerSize;
// works for contiguous!!
var index = outerIdx * outerStride + innerIdx;
var inputMax = float.MinValue;
for (var d = 0; d < dimSize; d++)
{
if (output.Data[d * dimStride] >= inputMax)
{
inputMax = output.Data[d * dimStride];
}
}
float sum = 0;
for (var d = 0; d < dimSize; d++)
{
var z = (float)Math.Exp(output.Data[index + d * dimStride] - inputMax);
output.Data[index + d * dimStride] = z;
sum += z;
}
float invSum = 1 / sum;
for (var d = 0; d < dimSize; d++)
{
output.Data[index + d * dimStride] = output.Data[index + d * dimStride] * invSum;
}
}
});
if (gpu)
{
output.Gpu(input.Shader);
}
output = input.HookAutograd(ref output, "softmax-" + _dim.ToString(), false);
return(output);
}
