[Relay][Pytorch] Add support for aten::linalg_vector_norm
#16123
Conversation
mshr-h
force-pushed
the
pytorch-linalg_vector_norm
branch
from
b4e0e40 to
f2e6107
Compare
| return _op.scatter_nd(source, indices, values, mode) | ||
|
|
||
| def linalg_vector_norm(self, inputs, input_types): | ||
| data = inputs[0] |
There was a problem hiding this comment.
It looks like this method is based on torch.linalg.vector_norm. The latter assums that input data type is float, double or complex one; dtype also should be real or complex. Would you check it?
There was a problem hiding this comment.
Thanks for your review. That's true. It's based on torch.linalg.vector_norm and it supports float, double and complex dtypes as input. Seems like PyTorch doesn't support complex data type. convert_pt_to_tvm_type
There was a problem hiding this comment.
Hello @mshr-h! My idea was to add something like assert data.dtype == float or data.dtype == double. And may be add TODO for further support complex values, but I do not think it is needed just now
There was a problem hiding this comment.
Hi @vvchernov ! I've added assertion and testcases for double-precision input data.
vvchernov
left a comment
vvchernov
left a comment
There was a problem hiding this comment.
Thank you for your work! LGTM, see my small comment
|
|
||
| class VectorNorm5(torch.nn.Module): | ||
| def forward(self, x): | ||
| return torch.linalg.vector_norm(x, ord=0) |
There was a problem hiding this comment.
if you only need different ord for each test case, you don't need to create a different class for each. Please clean them up.
There was a problem hiding this comment.
@masahi Revised as suggested. Please review. Thanks!
3 participants
Fix #16096
Support torch.linalg.vector_norm.
cc @jikechao @vvchernov @Hzfengsy @junrushao