Added asin function and test for frontend

[VladZetta]

close #15799

[VladZetta]

Hi @hello-fri-end !
Could you please review my PR? I get some checks failed ( 1 for intelligent-test-pr(6) and 1 for formatting). I just cannot find an error. Sorry for running too much test, but I really want to find the error :)

Thanks!

[VladZetta]

Changed Dtypes

[hello-fri-end]

Hi @hello-fri-end ! Could you please review my PR? I get some checks failed ( 1 for intelligent-test-pr(6) and 1 for formatting). I just cannot find an error. Sorry for running too much test, but I really want to find the error :)

Thanks!

Hey @VladZetta ! The documentation of paddle.asin seems to be wrong as it's throwing an error when float16 is used. You can confirm the same by running the following code:

dtypes_list = ["int8", "int16", "int32", "int64", "uint8", "float16", "float32", "float64", "complex64", "complex128", "bool",]
unsupported = []
supported = []
for dtype in dtypes_list:
    try:
        a = paddle.to_tensor([1.,2,3],dtype=dtype)
        # insert the function you want to test in the following line
        #paddle.normal(a,a)
        paddle.asin(a)
        supported.append(a.dtype)
    except:
        # use this to generate native dtype
        unsupported.append(a.dtype)
        # use this to generate string dtype
        #unsupported.append(dtype)
print(unsupported)
print(supported)

If you remove float16 from the supported dtypes, I think the tests will pass. Let me know if you have any other questions :)

P.S: Could you please link the issue in the description that this PR closes.

[VladZetta]

Hi @hello-fri-end ! Could you please review my PR? I get some checks failed ( 1 for intelligent-test-pr(6) and 1 for formatting). I just cannot find an error. Sorry for running too much test, but I really want to find the error :)
Thanks!

Hey @VladZetta ! The documentation of paddle.asin seems to be wrong as it's throwing an error when float16 is used. You can confirm the same by running the following code:

dtypes_list = ["int8", "int16", "int32", "int64", "uint8", "float16", "float32", "float64", "complex64", "complex128", "bool",]
unsupported = []
supported = []
for dtype in dtypes_list:
    try:
        a = paddle.to_tensor([1.,2,3],dtype=dtype)
        # insert the function you want to test in the following line
        #paddle.normal(a,a)
        paddle.asin(a)
        supported.append(a.dtype)
    except:
        # use this to generate native dtype
        unsupported.append(a.dtype)
        # use this to generate string dtype
        #unsupported.append(dtype)
print(unsupported)
print(supported)

If you remove float16 from the supported dtypes, I think the tests will pass. Let me know if you have any other questions :)

P.S: Could you please link the issue in the description that this PR closes.

@hello-fri-end
Well, indeed float16 is not supported, changed it! and added link in description to the issue

But tests are still failing. I don't get why you suggested using get_dtypes("valid"). Is there a way to make it return only float32 and float64 ? I'm just guessing

[hello-fri-end]

The changes look good to me! The errors in the CI are unrelated to your PR. Thanks for the contribution :)

[VladZetta]

The changes look good to me! The errors in the CI are unrelated to your PR. Thanks for the contribution :)

Thanks for helping out! So PR will be accepted? Because it seems like I need to send the link of PR to go further :)

[hello-fri-end]

Hi @hello-fri-end ! Could you please review my PR? I get some checks failed ( 1 for intelligent-test-pr(6) and 1 for formatting). I just cannot find an error. Sorry for running too much test, but I really want to find the error :)
Thanks!

Hey @VladZetta ! The documentation of paddle.asin seems to be wrong as it's throwing an error when float16 is used. You can confirm the same by running the following code:

dtypes_list = ["int8", "int16", "int32", "int64", "uint8", "float16", "float32", "float64", "complex64", "complex128", "bool",]
unsupported = []
supported = []
for dtype in dtypes_list:
    try:
        a = paddle.to_tensor([1.,2,3],dtype=dtype)
        # insert the function you want to test in the following line
        #paddle.normal(a,a)
        paddle.asin(a)
        supported.append(a.dtype)
    except:
        # use this to generate native dtype
        unsupported.append(a.dtype)
        # use this to generate string dtype
        #unsupported.append(dtype)
print(unsupported)
print(supported)

If you remove float16 from the supported dtypes, I think the tests will pass. Let me know if you have any other questions :)
P.S: Could you please link the issue in the description that this PR closes.

@hello-fri-end Well, indeed float16 is not supported, changed it! and added link in description to the issue

But tests are still failing. I don't get why you suggested using get_dtypes("valid"). Is there a way to make it return only float32 and float64 ? I'm just guessing

get_dtypes("valid") automatically finds all the dtypes that are supported by the function, so, even if the function only supports floats, that's what valid will return :)

[hello-fri-end]

Frontend Task Checklist

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6. • ❌: In case a frontend function is being added:
     1. • ❌: It is a composition of ivy functions.
     2. • ❌: In case the needed composition is long (using numerous ivy functions), a Missing Function Suggestion issue has been opened to suggest a new ivy function should be added to shorten the frontend implementation.
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7. • ❌: In case a frontend method is being added:
     1. • ❌: It is composed of existing frontend functions or methods.
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          • ❌: @to_ivy_arrays_and_back has been added to the method.
          • ❌: A ToDo comment has been made prompting to remove the decorator and update the implementation as soon as the missing function has been added.
8. • ❌: The function/method's test has been added (except in the alias case mentioned in <2>):
     1. • ❌: All supported arguments are being generated in handle_frontend_test/handle_frontend_method and passed to test_frontend_function/test_frontend_method.
     2. • ❌: The argument generation covers all possible supported values. Array sizes, dimensions, and axes adhere to the full supported set of the original function/method.
     3. • ❌: The available_dtypes parameter passed to the helper generating the function/method's input array is set to helpers.get_dtypes("valid"). If there are unsupported dtypes that cause the test to fail, they should be handled by adding @with_supported_dtypes/@with_unsupported_dtype to the function/method.
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10. • ❌: The PR closes a Sub Task issue linked to one of the open frontend ToDo lists.
11. • ❌: The function/method and its test have been added to the correct .py files corresponding to the addressed ToDo list.
12. • ❌: The PR only contains changes relevant to the addressed subtask.

[hello-fri-end]

The changes look good to me! The errors in the CI are unrelated to your PR. Thanks for the contribution :)

Thanks for helping out! So PR will be accepted? Because it seems like I need to send the link of PR to go further :)

Merged it 🚀