Ranges when condition gives a zero length tensor about torchsharp

[NiklasGustafsson]

Hmmm. It works as I had expected it to do.

Here's my repro:

        [Fact]
        public void Validate_1187()
        {
            var target = 1.5f;
            using var h = torch.rand(10);
            using var expected = h.clone();

            // This should do nothing, including blow up.
            h[h > target] = 1.0f;

            Assert.Equal(expected, h);
        }

What was your expected result?

[Sprinzl]

Ok Niklas got the solution:
h[h > target] = h[h > target] .copy_(1);
The problem arises when the h[h > target] has zero length or is null. (Think......)

Thanks Niklas

The goal of the hole script us to get the first non-zero of the row and when hot-encode it. Whats for labeling for stock market data. I have an LLM Encoder with next step prediction self-subervised training. I want fine-tune as follows profit - neutral - stop loss.
Something like this:
from:
0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 1 1 2 0 0 0 0 0 0
0 0 0 1 1 1 2 0 0 0 0 0 0 0
0 0 0 1 1 2 0 0 0 0 0 0 0 0
0 0 1 1 2 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 2 2 1
0 0 0 0 2 2 0 0 0 2 2 2 1 1
0 0 0 2 2 0 0 0 2 2 2 2 2 1
0 0 0 0 0 0 0 0 0 2 2 0 2 2
to:
1 0 0
0 1 0
0 1 0
0 1 0
0 1 0
1 0 0
0 0 1
0 0 1
0 0 1
0 0 1
I use Rhino3d as visualization for tensors.....

[NiklasGustafsson]

Is the behavior different from PyTorch? If the assignment is supposed to do what your workaround does, can you give me a simple example in Python that illustrates it?

[Sprinzl]

It is a custom made code. All c#. So i cannot say. I have also a runtime-compiler plugin in the CAD-System which is compiling a vs-project and serializes it before. (So you do not to need to restart the programm - something what if you attach an dll in VS-Code does not work) So where can be a series of problems. I changed from
h[h > target] = 1.0f; to h[h > target] = h[h > target].copy_(1);
All tensors in cuda. Where is the spagetti-monster code for what task:

private Tensor LabelEncoder(Tensor bar, double target, int steps) 

{
//Risk-Reward 1:1
//bar Sequence -- Open|H|L|C|WAP|Volume|Ratio|YearMonthDay|HourMinutesSeconds
Func<Tensor, int, int,Tensor> hotselect = (x, dim, label) =>
{
var max = torch.max(x != 0, dim: dim).indexes;
max = max + arange(x.numel() / x.size(dim), dtype: ScalarType.Int64, device: x.device) * x.size(dim);
max = torch.nn.functional.one_hot(x.flatten()[max].to(ScalarType.Int64), label);
var n0 = max[.., 0];
var n1 = max[.., 1];
max[.., 0] = n1;
max[.., 1] = n0;
return max;
};
int high = 1;
int low = 2;
int close = 3;
var v = bar.clone();
v = v.unsqueeze(0);
v = v.unfold(dimension: 1, size: steps, step: 1)[0, .., .., ..].permute(0, 2, 1);
//[1269674x15x9]
var h = (v[.., 1.., high].permute(1, 0) / v[.., 0, close]).permute(1, 0) - 1.0;
h[h > target] = h[h > target].copy(1);
h[h != 1] = h[h != 1].copy(0);
var l = ((v[.., 1.., low].permute(1, 0) / v[.., 0, close]).permute(1, 0) - 1.0) * -1.0;
l[l > target] = l[l > target].copy( 1);
l[l != 1] = l[l != 1].copy(0);
var mix = torch.zeros_like(h, device: bar.device);
mix[(l == 1)] = mix[(l == 1)].copy_(1);
mix[(h == 1)] = mix[(h == 1)].copy_(2);
var sum = mix.sum(1);
var idx = hotselect(mix, 1, 3);
var extend = torch.zeros(new long[] { steps - 1, 3 }, dtype: idx.dtype, device: idx.device);
extend[.., 1] = 1.0;
idx = torch.cat(new List() { idx, extend }, dim: 0L);
return idx;
}

[Sprinzl]

I cannot reproduce the error... no idea. Sorry. The mistake was some where else.