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Add core_tests/test_ufunc_methods.py
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‎dpnp/tests/third_party/cupy/core_tests/test_ufunc_methods.py‎

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import numpy
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import pytest
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import dpnp as cupy
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from dpnp.tests.third_party.cupy import testing
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class TestUfuncOuter:
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@testing.numpy_cupy_array_equal()
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def test_add_outer(self, xp):
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x = testing.shaped_random((2, 3), xp=xp, dtype=numpy.int32, seed=0)
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y = testing.shaped_random((4, 1, 5), xp=xp, dtype=numpy.int32, seed=1)
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return xp.add.outer(x, y)
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@pytest.mark.skip("Scalar input is not supported")
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@testing.numpy_cupy_array_equal()
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def test_add_outer_scalar(self, xp):
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return xp.add.outer(2, 3)
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@pytest.mark.skip("at() method is not supported")
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class TestUfuncAtAtomicOps:
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@testing.for_dtypes("iIQefd")
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@testing.numpy_cupy_array_equal()
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def test_at_add(self, xp, dtype):
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if cupy.cuda.runtime.is_hip and dtype == numpy.float16:
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pytest.skip("atomicAdd does not support float16 in HIP")
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shape = (50,)
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x = testing.shaped_random(shape, xp=xp, dtype=dtype, seed=0)
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mask = testing.shaped_random(shape, xp=xp, dtype=bool, seed=1)
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indices = xp.nonzero(mask)[0]
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xp.add.at(x, indices, 3)
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return x
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@testing.for_dtypes("iIQefd")
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@testing.numpy_cupy_array_equal()
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def test_at_add_duplicate_indices(self, xp, dtype):
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if cupy.cuda.runtime.is_hip and dtype == numpy.float16:
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pytest.skip("atomicAdd does not support float16 in HIP")
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shape = (50,)
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x = testing.shaped_random(shape, xp=xp, dtype=dtype, seed=0)
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indices = testing.shaped_random(
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shape, xp=xp, dtype=numpy.int32, scale=shape[0], seed=1
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)
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xp.add.at(x, indices, 3)
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return x
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@testing.for_dtypes("iI")
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@testing.numpy_cupy_array_equal()
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def test_at_subtract(self, xp, dtype):
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shape = (50,)
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x = testing.shaped_random(shape, xp=xp, dtype=dtype, seed=0)
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mask = testing.shaped_random(shape, xp=xp, dtype=bool, seed=1)
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indices = xp.nonzero(mask)[0]
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xp.subtract.at(x, indices, 3)
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return x
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@testing.for_dtypes("iI")
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@testing.numpy_cupy_array_equal()
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def test_at_subtract_duplicate_indices(self, xp, dtype):
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shape = (50,)
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x = testing.shaped_random(shape, xp=xp, dtype=dtype, seed=0)
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indices = testing.shaped_random(
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shape, xp=xp, dtype=numpy.int32, scale=shape[0], seed=1
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)
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xp.subtract.at(x, indices, 3)
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return x
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@testing.for_dtypes("iIQfd")
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@testing.numpy_cupy_allclose()
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def test_at_min(self, xp, dtype):
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shape = (50,)
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x = testing.shaped_random(shape, xp=xp, dtype=dtype, seed=0)
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mask = testing.shaped_random(shape, xp=xp, dtype=bool, seed=1)
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indices = xp.nonzero(mask)[0]
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xp.minimum.at(x, indices, 3)
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return x
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@testing.for_dtypes("iIQfd")
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@testing.numpy_cupy_allclose()
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def test_at_min_duplicate_indices(self, xp, dtype):
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shape = (50,)
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x = testing.shaped_random(shape, xp=xp, dtype=dtype, seed=0)
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indices = testing.shaped_random(
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shape, xp=xp, dtype=numpy.int32, scale=shape[0], seed=1
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)
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values = testing.shaped_random(
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indices.shape, xp=xp, dtype=dtype, seed=2
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)
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xp.minimum.at(x, indices, values)
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return x
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@testing.for_dtypes("iIQfd")
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@testing.numpy_cupy_allclose()
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def test_at_max(self, xp, dtype):
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shape = (50,)
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x = testing.shaped_random(shape, xp=xp, dtype=dtype, seed=0)
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mask = testing.shaped_random(shape, xp=xp, dtype=bool, seed=1)
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indices = xp.nonzero(mask)[0]
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xp.maximum.at(x, indices, 3)
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return x
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@testing.for_dtypes("iIQfd")
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@testing.numpy_cupy_allclose()
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def test_at_max_duplicate_indices(self, xp, dtype):
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shape = (50,)
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x = testing.shaped_random(shape, xp=xp, dtype=dtype, seed=0)
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indices = testing.shaped_random(
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shape, xp=xp, dtype=numpy.int32, scale=shape[0], seed=1
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)
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values = testing.shaped_random(
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indices.shape, xp=xp, dtype=dtype, seed=2
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)
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xp.maximum.at(x, indices, values)
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return x
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@testing.for_dtypes("iIlLqQ")
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@testing.numpy_cupy_array_equal()
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def test_at_bitwise_and(self, xp, dtype):
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if cupy.cuda.runtime.is_hip and numpy.dtype(dtype).char in "lq":
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pytest.skip("atomicOr does not support int64 in HIP")
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shape = (50,)
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x = testing.shaped_random(shape, xp=xp, dtype=dtype, seed=0)
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indices = testing.shaped_random(
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shape, xp=xp, dtype=numpy.int32, scale=shape[0], seed=1
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)
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values = testing.shaped_random(
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indices.shape, xp=xp, dtype=dtype, seed=2
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)
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xp.bitwise_and.at(x, indices, values)
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return x
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@testing.for_dtypes("iIlLqQ")
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@testing.numpy_cupy_array_equal()
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def test_at_bitwise_or(self, xp, dtype):
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if cupy.cuda.runtime.is_hip and numpy.dtype(dtype).char in "lq":
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pytest.skip("atomicOr does not support int64 in HIP")
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shape = (50,)
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x = testing.shaped_random(shape, xp=xp, dtype=dtype, seed=0)
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indices = testing.shaped_random(
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shape, xp=xp, dtype=numpy.int32, scale=shape[0], seed=1
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)
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values = testing.shaped_random(
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indices.shape, xp=xp, dtype=dtype, seed=2
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)
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xp.bitwise_or.at(x, indices, values)
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return x
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@testing.for_dtypes("iIlLqQ")
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@testing.numpy_cupy_array_equal()
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def test_at_bitwise_xor(self, xp, dtype):
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if cupy.cuda.runtime.is_hip and numpy.dtype(dtype).char in "lq":
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pytest.skip("atomicXor does not support int64 in HIP")
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shape = (50,)
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x = testing.shaped_random(shape, xp=xp, dtype=dtype, seed=0)
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indices = testing.shaped_random(
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shape, xp=xp, dtype=numpy.int32, scale=shape[0], seed=1
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)
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values = testing.shaped_random(
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indices.shape, xp=xp, dtype=dtype, seed=2
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)
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xp.bitwise_xor.at(x, indices, values)
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return x
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@testing.for_dtypes("iIQefd")
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@testing.numpy_cupy_array_equal()
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def test_at_boolean_mask(self, xp, dtype):
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shape = (50,)
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x = testing.shaped_random(shape, xp=xp, dtype=dtype, seed=0)
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mask = testing.shaped_random(shape, xp=xp, dtype=bool, seed=1)
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xp.add.at(x, mask, 3)
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return x
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@testing.for_dtypes("iIQefd")
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@testing.numpy_cupy_array_equal()
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def test_at_array_values(self, xp, dtype):
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if cupy.cuda.runtime.is_hip and dtype == numpy.float16:
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pytest.skip("atomicAdd does not support float16 in HIP")
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shape = (50,)
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x = testing.shaped_random(shape, xp=xp, dtype=dtype, seed=0)
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mask = testing.shaped_random(shape, xp=xp, dtype=bool, seed=1)
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indices = xp.nonzero(mask)[0]
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values = testing.shaped_random(
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indices.shape, xp=xp, dtype=numpy.int32, seed=2
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)
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xp.add.at(x, indices, values)
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return x
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@testing.for_dtypes("iIQefd")
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@testing.numpy_cupy_array_equal()
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def test_at_multi_dimensional(self, xp, dtype):
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if cupy.cuda.runtime.is_hip and dtype == numpy.float16:
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pytest.skip("atomicAdd does not support float16 in HIP")
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shape = (20, 30)
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x = testing.shaped_random(shape, xp=xp, dtype=dtype, seed=0)
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mask = testing.shaped_random(shape, xp=xp, dtype=bool, seed=1)
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indices = xp.nonzero(mask)
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xp.add.at(x, indices, 3)
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return x
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@pytest.mark.skip("reduce() method is not supported")
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class TestUfuncReduce:
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@testing.for_all_dtypes()
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@testing.numpy_cupy_allclose(rtol={numpy.float16: 1e-3, "default": 1e-6})
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def test_reduce_add(self, xp, dtype):
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x = testing.shaped_random((3, 4), xp=xp, dtype=dtype, seed=0)
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return xp.add.reduce(x, axis=-1)
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@testing.for_all_dtypes()
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@testing.numpy_cupy_allclose(rtol={numpy.float16: 1e-3, "default": 1e-6})
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def test_multiply_add(self, xp, dtype):
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x = testing.shaped_random((3, 4), xp=xp, dtype=dtype, seed=0)
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return xp.multiply.reduce(x, axis=-1)
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@pytest.mark.skip("accumulate() method is not supported")
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class TestUfuncAccumulate:
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@testing.for_all_dtypes()
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@testing.numpy_cupy_allclose(rtol={numpy.float16: 1e-3, "default": 1e-6})
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def test_reduce_add(self, xp, dtype):
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x = testing.shaped_random((3, 4), xp=xp, dtype=dtype, seed=0)
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return xp.add.accumulate(x, axis=-1)
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@testing.for_all_dtypes()
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@testing.numpy_cupy_allclose(rtol={numpy.float16: 1e-3, "default": 1e-6})
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def test_multiply_add(self, xp, dtype):
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x = testing.shaped_random((3, 4), xp=xp, dtype=dtype, seed=0)
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return xp.multiply.accumulate(x, axis=-1)
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@pytest.mark.skip("reduceat() method is not supported")
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class TestUfuncReduceAt:
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@testing.for_all_dtypes(no_float16=True)
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@testing.numpy_cupy_allclose(rtol=1e-6)
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def test_reduce_add(self, xp, dtype):
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x = testing.shaped_random((3, 4, 5), xp=xp, dtype=dtype, seed=0)
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indices = testing.shaped_random(
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(20,), xp=xp, dtype=numpy.int32, scale=4, seed=1
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)
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return xp.add.reduceat(x, indices, axis=1)

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