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Add a simple implementation of cumsum. (#1334)
* Add a simple implementation of cumsum. * Add another test.
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@ -2474,6 +2474,28 @@ impl Tensor {
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let t2 = t.reshape((n, 1))?.broadcast_as((n, n))?;
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t1.eq(&t2)?.to_dtype(dtype)
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}
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/// Returns the cumulative sum of elements of the input tensor summed over the specified
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/// dimension.
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///
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/// This operation is most efficient when dim is the last dimension of the tensor.
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pub fn cumsum<D: Dim>(&self, dim: D) -> Result<Self> {
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let dim = dim.to_index(self.shape(), "cumsum")?;
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let rank = self.rank();
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if rank == 0 {
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return Ok(self.clone());
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}
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let n_axis = self.dim(dim)?;
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let triu = Tensor::triu2(n_axis, self.dtype(), self.device())?;
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if rank == 1 {
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self.unsqueeze(0)?.matmul(&triu)?.squeeze(0)
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} else {
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let last = rank - 1;
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let t = self.transpose(dim, last)?;
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let t = t.broadcast_matmul(&triu)?;
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t.transpose(dim, last)
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}
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}
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}
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macro_rules! bin_trait {
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@ -1169,3 +1169,30 @@ fn tril_triu_eye() -> Result<()> {
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);
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Ok(())
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}
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#[test]
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fn cumsum() -> Result<()> {
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let t = &[3f32, 1., 4., 1., 5.];
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let t = Tensor::new(t, &Device::Cpu)?;
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assert_eq!(t.cumsum(0)?.to_vec1::<f32>()?, [3., 4., 8., 9., 14.]);
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let t = t.unsqueeze(1)?;
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assert_eq!(
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t.cumsum(0)?.to_vec2::<f32>()?,
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[[3.0], [4.0], [8.0], [9.0], [14.0]]
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);
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assert_eq!(
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t.cumsum(1)?.to_vec2::<f32>()?,
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[[3.0], [1.0], [4.0], [1.0], [5.0]]
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);
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let t = &[[3f32, 1., 4., 1., 5.], [2., 1., 7., 8., 2.]];
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let t = Tensor::new(t, &Device::Cpu)?;
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assert_eq!(
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t.cumsum(1)?.to_vec2::<f32>()?,
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[[3.0, 4.0, 8.0, 9.0, 14.0], [2.0, 3.0, 10.0, 18.0, 20.0]],
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);
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assert_eq!(
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t.cumsum(0)?.to_vec2::<f32>()?,
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[[3.0, 1.0, 4.0, 1.0, 5.0], [5.0, 2.0, 11.0, 9.0, 7.0]]
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);
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Ok(())
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}
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