Fix Frontend Failing Test: torch - blas_and_lapack_ops.cholesky and torch - linalg.cholesky

[Daniel4078]

No description provided.

[Daniel4078]

It seems like there are two cholesky function under the ivy/functional/frontend/torch. one is in ivy/functional/frontends/torch/linalg.py#L14, and the other is in /ivy/functional/frontends/torch/blas_and_lapack_ops.py#L93. Both frontend function uses the same ivy function ivy.cholesky, and the tests for both frontend functions fail for now for the same "AssertionError: returned dtype = float32, ground-truth returned dtype = float64" reason.

[Daniel4078]

though for the similiar ivy/functional/frontends/torch/linalg/cholesky_ex frontend function, it false with "AttributeError: 'list' object has no attribute 'T'" instead. which seems to be caused by the extra error check in the cholesky_ex function

[Daniel4078]

it seems like for torch, even if the input dtype is float32, the output dtype is expected to be float64, instead of keeping at float32 like what ivy did.

[Daniel4078]

I found a related new failure from the frontend test of torch.linalg.cholesky that is different:
E torch._C._LinAlgError: cholesky: The factorization could not be completed because the input is not positive-definite (the leading minor of order 1 is not positive-definite).
E Falsifying example: test_torch_cholesky(
E on_device='cpu',
E frontend='torch',
E backend_fw='tensorflow',
E dtype_and_x=(['complex64'],
E [array([[-8.0909090e+00+7.5864944j , 6.5512314e+00-8.873394j ,
E -1.6476854e+00-0.5j , -9.9999997e-05+9.090909j ,
E -1.9000000e+00+2.7267523j ],
E [-2.0000100e+00-9.090909j , -1.0001000e+00-1.7673124j ,
E 5.0000000e-01-1.1j , -1.9689250e+00+2.00001j ,
E -1.9000000e+00+9.090909j ],
E [ 5.0000000e-01+1.5j , -3.6476414e+00+0.99999j ,
E -3.3333334e-01+9.090909j , 9.0909090e+00+8.090909j ,
E -8.5043573e+00-2.198202j ],
E [ 1.9000000e+00+0.33333334j, 9.0909090e+00+9.090909j ,
E 1.0001000e+00+9.090909j , -1.1000000e+00-8.090909j ,
E -2.0000100e+00+9.090909j ],
E [ 6.8268528e+00-5.307268j , 3.3333334e-01-5.0536847j ,
E -5.0000000e-01+4.6941276j , 2.3796415e+00+9.090909j ,
E 1.9000000e+00+9.090909j ]], dtype=complex64)]),
E upper=False,
E test_flags=FrontendFunctionTestFlags(
E num_positional_args=1,
E with_out=True,
E with_copy=False,
E inplace=False,
E as_variable=[False],
E native_arrays=[True],
E test_trace=False,
E test_trace_each=False,
E generate_frontend_arrays=True,
E transpile=False,
E precision_mode=True,
E ),
E fn_tree='ivy.functional.frontends.torch.cholesky',
E )
E
E You can reproduce this example by temporarily adding @reproduce_failure('6.98.10', b'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') as a decorator on your test case