Migrate/jit/cat #1457
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Migrate/jit/cat #1457
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Codecov ReportAll modified and coverable lines are covered by tests ✅
Additional details and impacted files@@ Coverage Diff @@
## main #1457 +/- ##
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- Coverage 85.79% 85.78% -0.02%
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Files 647 649 +2
Lines 72106 72414 +308
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+ Hits 61866 62119 +253
- Misses 10240 10295 +55 ☔ View full report in Codecov by Sentry. |
Comment on lines
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| let first_tensor = tensors.first().expect("Tensors should not be empty"); | ||
| let mut shape = B::float_shape(first_tensor); | ||
| let device = &B::float_device(first_tensor); | ||
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| let output_dim_length: usize = tensors | ||
| .iter() | ||
| .map(|tensor: &FloatTensor<B, D>| B::float_shape(tensor).dims[dim]) | ||
| .sum(); | ||
| shape.dims[dim] = output_dim_length; | ||
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| let mut tensor_output = B::float_empty(shape.clone(), device); | ||
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| let mut i = 0; | ||
| let indices_select_all = [0; D].map(|_| { | ||
| let start = 0; | ||
| let end = shape.dims[i]; | ||
| i += 1; | ||
| start..end | ||
| }); | ||
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| let mut output_index = 0; | ||
| for tensor in tensors { | ||
| let mut indices = indices_select_all.clone(); | ||
| let tensor_dim_length = B::float_shape(&tensor).dims[dim]; | ||
| indices[dim] = output_index..tensor_dim_length; | ||
| output_index += tensor_dim_length; | ||
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| tensor_output = B::float_slice_assign(tensor_output, indices, tensor) | ||
| } | ||
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| tensor_output |
There was a problem hiding this comment.
I think it would be better to implement this function in a specific module that work with the Tensor struct instead of the backend API. Here we could simply:
fn float_cat<const D: usize>(tensors: Vec<FloatTensor<B, D>>, dim: usize) -> FloatTensor<B, D> {
cat_with_slice_assign(tensors.into_iter().map(Tensor::from_primitive).collect(), dim).into_primitive()
}
nathanielsimard
approved these changes
Mar 17, 2024
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Cat kernel for #1422
Note: it was chosen to rely on a default implementation based on slice assign instead of making a kernel which would have the same performance anyway because of the dynamic number of inputs.