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jax.numpy
delivers ediff1d
on mathematical functions
#11801
Conversation
you are supposed to add a frontend function for ediff1d of jax right? why did you add those backend experimental functions instead? |
Hi, Daniel. Because some unit tests case didn't pass. (Can't reach 100% pass) It's related to the backend. So I did it. Can I implement the backend together or separate it into two PR? |
well, I need to see your frontend version of ediff1d first |
also, you can not just add functions to our experimental module, all the functions that we plan to have there is listed here: #3856 |
Okay, thanks. I will delete the code related to the backend. And make a new PR. How about my frontend functions? Does it have any problems? |
It looks find to me, but I suppose it only works with your backend code right? |
Yes, If I delete the backend functions, it will fail the current test case. If possible, can I only test the frontend functions? |
This is my test case. I refer to
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ok, just to make sure, have you tried using the existing functions in Ivy's module directly or compositionally to achieve the desired behavior? because we might be about to skip the problem of adding a new set of backend functions and tests |
Yes, I didn't use ivy's functions on my implementation function. What should I do now? Delete the backend functions and make a new PR, right? |
well you can try remove the unnecessary changes on this branch if you know how to. but you are free to create a new branch and pull request |
Great, thank you |
I have deleted four functions about the backend of |
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check the comments below, thanks
ivy_tests/test_ivy/test_frontends/test_jax/test_jax_numpy_math.py
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I think it is good enough. Thank you!
Thanks for your review. |
Hi, Because the force push caused conflicts, so I make a new PR again. Please help me to review my code. Thank you
Close #11072