joddiy edited a comment on pull request #760:
URL: https://github.com/apache/singa/pull/760#issuecomment-657401578
> Hi @joddiy,
>
> Would we be considering only 2 dimensions for the scatter_elements
operation?
> For backwards operation with scatter_elements can we store the indexes we
process during the forward pass, this can help us reducing the time complexity
for the operation?
> Also for implementing the advanced indexing operation, we can implement it
as a suboptimal operation for processing the index as a list of tuples and
further indexing only based on those list of tuples? This can save complexity
of iterating over dimension elements multiple times
>
> Thanks and Regards,
> Shashank Nigam
Hi, Shashank
> Would we be considering only 2 dimensions for the scatter_elements
operation?
For now, all test cases are 2-d inputs, so we may only need to support the
2d inputs.
> For backwards operation with scatter_elements can we store the indexes we
process during the forward pass, this can help us reducing the time complexity
for the operation?
Sure, of course, however, even we store the indexes, we still iterate
through all these indexes when we use them, right? it cannot reduce the time
complexity.
> Also for implementing the advanced indexing operation, we can implement it
as a suboptimal operation for processing the index as a list of tuples and
further indexing only based on those list of tuples? This can save complexity
of iterating over dimension elements multiple times
Let us see this code, actually, for elements in the `self.updates` tensor,
we use each of them for only one time, right? even we stores these indexes as a
list of tuple, we still use each of time for one time, right? how can we save
complexity of iterating?
```
u_shape = self.updates.shape
y = _x.copy()
for i in range(u_shape[0]):
for j in range(u_shape[1]):
idx = int(self.indices[i][j])
if self.axis == 0:
y[idx][j] = self.updates[i][j]
else:
y[i][idx] = self.updates[i][j]
```
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