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in place divide and floor_divide
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@vtavana
vtavana committed Oct 11, 2023
1 parent 76e3f87 commit f8f1ff7
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Showing 2 changed files with 123 additions and 22 deletions.
2 changes: 2 additions & 0 deletions dpnp/dpnp_algo/dpnp_elementwise_common.py
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Original file line number Diff line number Diff line change
Expand Up @@ -1072,6 +1072,7 @@ def _call_divide(src1, src2, dst, sycl_queue, depends=None):
ti._divide_result_type,
_call_divide,
_divide_docstring_,
ti._divide_inplace,
)


Expand Down Expand Up @@ -1218,6 +1219,7 @@ def dpnp_floor(x, out=None, order="K"):
ti._floor_divide_result_type,
ti._floor_divide,
_floor_divide_docstring_,
ti._floor_divide_inplace,
)


Expand Down
143 changes: 121 additions & 22 deletions tests/test_mathematical.py
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Original file line number Diff line number Diff line change
Expand Up @@ -965,9 +965,9 @@ def test_invalid_out(self, out):
assert_raises(TypeError, numpy.add, a.asnumpy(), 2, out)


class TestHypot:
@pytest.mark.parametrize("dtype", get_float_dtypes())
def test_hypot(self, dtype):
class TestDivide:
@pytest.mark.parametrize("dtype", get_float_dtypes() + get_complex_dtypes())
def test_divide(self, dtype):
array1_data = numpy.arange(10)
array2_data = numpy.arange(5, 15)
out = numpy.empty(10, dtype=dtype)
Expand All @@ -976,56 +976,71 @@ def test_hypot(self, dtype):
dp_array1 = dpnp.array(array1_data, dtype=dtype)
dp_array2 = dpnp.array(array2_data, dtype=dtype)
dp_out = dpnp.array(out, dtype=dtype)
result = dpnp.hypot(dp_array1, dp_array2, out=dp_out)
result = dpnp.divide(dp_array1, dp_array2, out=dp_out)

# original
np_array1 = numpy.array(array1_data, dtype=dtype)
np_array2 = numpy.array(array2_data, dtype=dtype)
expected = numpy.hypot(np_array1, np_array2, out=out)
expected = numpy.divide(np_array1, np_array2, out=out)

assert_allclose(expected, result)
assert_allclose(out, dp_out)
tol = 1e-07
assert_allclose(expected, result, rtol=tol, atol=tol)
assert_allclose(out, dp_out, rtol=tol, atol=tol)

@pytest.mark.parametrize("dtype", get_float_dtypes())
@pytest.mark.usefixtures("suppress_divide_invalid_numpy_warnings")
@pytest.mark.parametrize("dtype", get_float_dtypes() + get_complex_dtypes())
def test_out_dtypes(self, dtype):
size = 10

np_array1 = numpy.arange(size, 2 * size, dtype=dtype)
np_array2 = numpy.arange(size, dtype=dtype)
np_out = numpy.empty(size, dtype=numpy.float32)
expected = numpy.hypot(np_array1, np_array2, out=np_out)
np_out = numpy.empty(size, dtype=numpy.complex64)
expected = numpy.divide(np_array1, np_array2, out=np_out)

dp_array1 = dpnp.arange(size, 2 * size, dtype=dtype)
dp_array2 = dpnp.arange(size, dtype=dtype)

dp_out = dpnp.empty(size, dtype=dpnp.float32)
if dtype != dpnp.float32:
dp_out = dpnp.empty(size, dtype=dpnp.complex64)
if dtype != dpnp.complex64:
# dtype of out mismatches types of input arrays
with pytest.raises(TypeError):
dpnp.hypot(dp_array1, dp_array2, out=dp_out)
dpnp.divide(dp_array1, dp_array2, out=dp_out)

# allocate new out with expected type
dp_out = dpnp.empty(size, dtype=dtype)

result = dpnp.hypot(dp_array1, dp_array2, out=dp_out)
result = dpnp.divide(dp_array1, dp_array2, out=dp_out)

tol = numpy.finfo(numpy.float32).resolution
tol = 1e-07
assert_allclose(expected, result, rtol=tol, atol=tol)

@pytest.mark.parametrize("dtype", get_float_dtypes())
@pytest.mark.usefixtures("suppress_divide_invalid_numpy_warnings")
@pytest.mark.parametrize("dtype", get_float_dtypes() + get_complex_dtypes())
def test_out_overlap(self, dtype):
size = 15
# DPNP
dp_a = dpnp.arange(2 * size, dtype=dtype)
dpnp.hypot(dp_a[size::], dp_a[::2], out=dp_a[:size:])
dpnp.divide(dp_a[size::], dp_a[::2], out=dp_a[:size:])

# original
np_a = numpy.arange(2 * size, dtype=dtype)
numpy.hypot(np_a[size::], np_a[::2], out=np_a[:size:])
numpy.divide(np_a[size::], np_a[::2], out=np_a[:size:])

tol = numpy.finfo(numpy.float32).resolution
tol = 1e-07
assert_allclose(np_a, dp_a, rtol=tol, atol=tol)

@pytest.mark.parametrize("dtype", get_float_dtypes() + get_complex_dtypes())
def test_inplace_strided_out(self, dtype):
size = 21

np_a = numpy.arange(size, dtype=dtype)
np_a[::3] /= 4

dp_a = dpnp.arange(size, dtype=dtype)
dp_a[::3] /= 4

assert_allclose(dp_a, np_a)

@pytest.mark.parametrize(
"shape", [(0,), (15,), (2, 2)], ids=["(0,)", "(15, )", "(2,2)"]
)
Expand All @@ -1035,7 +1050,7 @@ def test_invalid_shape(self, shape):
dp_out = dpnp.empty(shape)

with pytest.raises(ValueError):
dpnp.hypot(dp_array1, dp_array2, out=dp_out)
dpnp.divide(dp_array1, dp_array2, out=dp_out)

@pytest.mark.parametrize(
"out",
Expand All @@ -1045,8 +1060,8 @@ def test_invalid_shape(self, shape):
def test_invalid_out(self, out):
a = dpnp.arange(10)

assert_raises(TypeError, dpnp.hypot, a, 2, out)
assert_raises(TypeError, numpy.hypot, a.asnumpy(), 2, out)
assert_raises(TypeError, dpnp.divide, a, 2, out)
assert_raises(TypeError, numpy.divide, a.asnumpy(), 2, out)


class TestFmax:
Expand Down Expand Up @@ -1211,6 +1226,90 @@ def test_invalid_out(self, out):
assert_raises(TypeError, numpy.fmin, a.asnumpy(), 2, out)


class TestHypot:
@pytest.mark.parametrize("dtype", get_float_dtypes())
def test_hypot(self, dtype):
array1_data = numpy.arange(10)
array2_data = numpy.arange(5, 15)
out = numpy.empty(10, dtype=dtype)

# DPNP
dp_array1 = dpnp.array(array1_data, dtype=dtype)
dp_array2 = dpnp.array(array2_data, dtype=dtype)
dp_out = dpnp.array(out, dtype=dtype)
result = dpnp.hypot(dp_array1, dp_array2, out=dp_out)

# original
np_array1 = numpy.array(array1_data, dtype=dtype)
np_array2 = numpy.array(array2_data, dtype=dtype)
expected = numpy.hypot(np_array1, np_array2, out=out)

assert_allclose(expected, result)
assert_allclose(out, dp_out)

@pytest.mark.parametrize("dtype", get_float_dtypes())
def test_out_dtypes(self, dtype):
size = 10

np_array1 = numpy.arange(size, 2 * size, dtype=dtype)
np_array2 = numpy.arange(size, dtype=dtype)
np_out = numpy.empty(size, dtype=numpy.float32)
expected = numpy.hypot(np_array1, np_array2, out=np_out)

dp_array1 = dpnp.arange(size, 2 * size, dtype=dtype)
dp_array2 = dpnp.arange(size, dtype=dtype)

dp_out = dpnp.empty(size, dtype=dpnp.float32)
if dtype != dpnp.float32:
# dtype of out mismatches types of input arrays
with pytest.raises(TypeError):
dpnp.hypot(dp_array1, dp_array2, out=dp_out)

# allocate new out with expected type
dp_out = dpnp.empty(size, dtype=dtype)

result = dpnp.hypot(dp_array1, dp_array2, out=dp_out)

tol = numpy.finfo(numpy.float32).resolution
assert_allclose(expected, result, rtol=tol, atol=tol)

@pytest.mark.parametrize("dtype", get_float_dtypes())
def test_out_overlap(self, dtype):
size = 15
# DPNP
dp_a = dpnp.arange(2 * size, dtype=dtype)
dpnp.hypot(dp_a[size::], dp_a[::2], out=dp_a[:size:])

# original
np_a = numpy.arange(2 * size, dtype=dtype)
numpy.hypot(np_a[size::], np_a[::2], out=np_a[:size:])

tol = numpy.finfo(numpy.float32).resolution
assert_allclose(np_a, dp_a, rtol=tol, atol=tol)

@pytest.mark.parametrize(
"shape", [(0,), (15,), (2, 2)], ids=["(0,)", "(15, )", "(2,2)"]
)
def test_invalid_shape(self, shape):
dp_array1 = dpnp.arange(10)
dp_array2 = dpnp.arange(5, 15)
dp_out = dpnp.empty(shape)

with pytest.raises(ValueError):
dpnp.hypot(dp_array1, dp_array2, out=dp_out)

@pytest.mark.parametrize(
"out",
[4, (), [], (3, 7), [2, 4]],
ids=["4", "()", "[]", "(3, 7)", "[2, 4]"],
)
def test_invalid_out(self, out):
a = dpnp.arange(10)

assert_raises(TypeError, dpnp.hypot, a, 2, out)
assert_raises(TypeError, numpy.hypot, a.asnumpy(), 2, out)


class TestMaximum:
@pytest.mark.parametrize("dtype", get_all_dtypes(no_none=True))
def test_maximum(self, dtype):
Expand Down

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