Exclude code from coverage

[vlad-perevezentsev]

This PR continues #2268 and suggests excluding some lines of code from coverage using pragma:: no cover to increase the coverage statistic

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• Have you added a test, reproducer or referred to issue with a reproducer?
• Have you tested your changes locally for CPU and GPU devices?
• Have you made sure that new changes do not introduce compiler warnings?
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[coveralls]

coverage: 71.181% (+0.2%) from 70.965%
when pulling f52bbbe on exclude_coverage_code
into 5649b06 on master.

[github-actions]

Array API standard conformance tests for dpnp=0.17.0dev4=py312he4f9c94_19 ran successfully.
Passed: 954
Failed: 0
Skipped: 46

[antonwolfy]

LGTM! Thank you @vlad-perevezentsev

[vlad-perevezentsev]

Regarding the rest of the uncovered lines

dpnp/dpnp_array.py

	def __rmatmul__(self, other): unsupported 

dpnp/dpnp_iface.py

	def get_dpnp_descriptor # partially covered, to be removed in the future -> 466, 495, 511->514, 530
	def is_supported_array_or_scalar(a):  # not used in code
	def synchronize_array_data(a):  # not used in code

dpnp/dpnp_iface_indexing.py

	def choose(x1, choices, out=None, mode="raise"):  #Not covered conditions for fallback numpy. Will be updated soon 
         # -> 142, 147, 149, 153, 155, 165,

	def putmask(x1, mask, values):
	return call_origin(numpy.putmask, x1, mask, values, dpnp_inplace=True)  # not used in tests

dpnp/dpnp_iface_libmath.py

	def erf(in_array1):
		result = create_output_descriptor_py(
		        in_array1.shape, in_array1.dtype, None
		    ).get_pyobj()
		    for i in range(result.size):
		        result[i] = math.erf(in_array1[i])
		    return result # not used in tests

dpnp/dpnp_iface_mathematical.py

	def modf(x1, **kwargs):
	if kwargs:  # not used in tests
	return call_origin(numpy.modf, x1, **kwargs) # not used in tests

dpnp/dpnp_iface_sorting.py

	def partition(x1, kth, axis=-1, kind="introselect", order=None): # Not covered conditions for fallback numpy. Will be updated soon
        # -> 232, 234

dpnp/dpnp_iface_statistics.py

	def cov(  # Not covered conditions for fallback numpy
         # ->  664, 672, 674

dpnp/dpnp_iface_types.py

     def is_type_supported(obj_type):  # deprecated function used only `def shuffle(x1)`, partially covered

[antonwolfy]

def __rmatmul__(self, other): unsupported

It is not actually true. It might be covered, like:

a = dpnp.ones(10)
b = numpy.ones(10)

b @ a
---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
Cell In[4], line 1
----> 1 b @ a

File ~/code/dpnp/dpnp/dpnp_array.py:547, in dpnp_array.__rmatmul__(self, other)
    545 def __rmatmul__(self, other):
    546     """Return ``value@self``."""
--> 547     return dpnp.matmul(other, self)

File ~/code/dpnp/dpnp/dpnp_iface_linearalgebra.py:857, in matmul(x1, x2, out, casting, order, dtype, subok, signature, axes, axis)
    852 if axis is not None:
    853     raise NotImplementedError(
    854         "axis keyword argument is only supported by its default value."
    855     )
--> 857 return dpnp_matmul(
    858     x1,
    859     x2,
    860     out=out,
    861     casting=casting,
    862     order=order,
    863     dtype=dtype,
    864     axes=axes,
    865 )

File ~/code/dpnp/dpnp/dpnp_utils/dpnp_utils_linearalgebra.py:765, in dpnp_matmul(x1, x2, out, casting, order, dtype, axes)
    746 def dpnp_matmul(
    747     x1,
    748     x2,
   (...)
    755     axes=None,
    756 ):
    757     """
    758     Return the matrix product of two arrays.
    759
   (...)
    762
    763     """
--> 765     dpnp.check_supported_arrays_type(x1, x2)
    766     res_usm_type, exec_q = get_usm_allocations([x1, x2])
    767     _validate_out_array(out, exec_q)

File ~/code/dpnp/dpnp/dpnp_iface.py:400, in check_supported_arrays_type(scalar_type, all_scalars, *arrays)
    397     if scalar_type and dpnp.isscalar(a):
    398         continue
--> 400     raise TypeError(
    401         f"An array must be any of supported type, but got {type(a)}"
    402     )
    404 if len(arrays) > 0 and not (all_scalars or any_is_array):
    405     raise TypeError(
    406         "At least one input must be of supported array type, "
    407         "but got all scalars."
    408     )

TypeError: An array must be any of supported type, but got <class 'numpy.ndarray'>

As you can see from the call stack there is __rmatmul__ called.

For testing of positive use case you need something like:

a = dpnp.ones(10)
b = dpnp.ones(10)

class A(dpnp.ndarray):
     def __init__(self, x):
         self._array_obj = x.get_array()

     def __matmul__(self, other):
         return NotImplemented

a1 = A(a)

a1 @ b
# Out: array(10.)

[antonwolfy]

def is_supported_array_or_scalar(a): # not used in code
def synchronize_array_data(a): # not used in code

We need to add tests for them. It is an interface functions.

[antonwolfy]

The rest uncovered functions can be left unchanged, because we will rework them when remove the backend.