feat: implementation of torch.cdist function to pytorch frontend. #27004
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@joyios1 Thanks for making the PR! I've left a couple of minor comments but otherwise this looks good, so if we can get these addressed then we should be good to merge.
ivy_tests/test_ivy/test_frontends/test_torch/test_miscellaneous_ops.py
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oh, yes, I forgot it. Co-authored-by: Sam Armstrong <88863522+Sam-Armstrong@users.noreply.github.com>
…s_ops.py Yes that is better. I did the change and the tests pass, so it is fine! Co-authored-by: Sam Armstrong <88863522+Sam-Armstrong@users.noreply.github.com>
PR Description
Hello,
feat: implementation of torch.cdist function to pytorch frontend.
The shape of the two input arrays 'x1' and 'x2' is three dimensional tensors (BxRxM), so I check if the shape of both tensors is 3 otherwise an exception occurs.
the torch.cdist function does not accept float16 as a type therefore float32 and float 64 types were used.
the parameter 'p' recieves values from 0 to inf so I set minimum number 0 and maximum number 1 million. Is that correct? Maybe I need to use math.inf.
The parameter 'compute mode' is a tricky one. It handles the special case of p=2 (euclidean distance) and it specifies whether or not matrix multiplication (mm) will be used for the calculation of the distances . Therefore, I implemented both ways, the mm way with ivy.vector_norm and the not_mm way with 3 for loops. I am sure if this is the correct way but the tests pass for all five (5) backends.
Thank you for your time, I am looking forward for your response.
Close #26797
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