def parametrize_dtype_specifier(argname, dtypes=None, additional_args=None):
"""Parametrizes a test with various arguments that can be used as dtypes.
Args:
argname(str): Argument name to pass the value that can be used as a
dtype.
dtypes(list of strs): List of dtype names.
additional_args(tuple of list): Additional values to be included.
"""
if dtypes is None:
dtypes = all_dtypes
assert isinstance(argname, str)
assert isinstance(dtypes, (tuple, list))
assert all(isinstance(dt, str) for dt in dtypes)
lst = []
# dtype names
lst += list(dtypes)
# numpy dtypes
lst += [numpy.dtype(dt) for dt in dtypes]
# char codes
lst += [chainerx.dtype(dt).char for dt in dtypes]
# User-specified args
if additional_args is not None:
assert isinstance(additional_args, (tuple, list))
lst += list(additional_args)
return pytest.mark.parametrize(argname, lst)
def parametrize_dtype_specifier(argname, dtypes=None, additional_args=None):
"""Parametrizes a test with various arguments that can be used as dtypes.
Args:
argname(str): Argument name to pass the value that can be used as a
dtype.
dtypes(list of strs): List of dtype names.
additional_args(tuple of list): Additional values to be included.
"""
if dtypes is None:
dtypes = all_dtypes
assert isinstance(argname, str)
assert isinstance(dtypes, (tuple, list))
assert all(isinstance(dt, str) for dt in dtypes)
lst = []
# dtype names
lst += list(dtypes)
# numpy dtypes
lst += [numpy.dtype(dt) for dt in dtypes]
# char codes
lst += [chainerx.dtype(dt).char for dt in dtypes]
# User-specified args
if additional_args is not None:
assert isinstance(additional_args, (tuple, list))
lst += list(additional_args)
return pytest.mark.parametrize(argname, lst)
def test_array_from_tuple_or_list(xp, obj, dtype_spec, device):
if xp is numpy and isinstance(dtype_spec, chainerx.dtype):
dtype_spec = dtype_spec.name
# Skip nan/inf -> integer conversion that would cause a cast error.
if (not _is_all_finite(obj) and dtype_spec not in (None, Unspecified)
and chainerx.dtype(dtype_spec).kind not in ('f', 'c')):
return chainerx.testing.ignore()
if dtype_spec is Unspecified:
return xp.array(obj)
else:
return xp.array(obj, dtype_spec)
def _check_array_from_chainerx_array_with_dtype(
shape, src_dtype, dst_dtype_spec, copy, device=None):
t = array_utils.create_dummy_ndarray(
chainerx, shape, src_dtype, device=device)
a = chainerx.array(t, dtype=dst_dtype_spec, copy=copy)
src_dtype = chainerx.dtype(src_dtype)
dst_dtype = src_dtype if dst_dtype_spec is None else chainerx.dtype(
dst_dtype_spec)
device = chainerx.get_device(device)
if (not copy
and src_dtype == dst_dtype
and device is chainerx.get_default_device()):
assert t is a
else:
assert t is not a
chainerx.testing.assert_array_equal_ex(a, t.astype(dst_dtype))
assert a.dtype == dst_dtype
assert a.device is chainerx.get_default_device()
def _check_array_from_chainerx_array_with_dtype(
shape, src_dtype, dst_dtype_spec, copy, device=None):
t = array_utils.create_dummy_ndarray(
chainerx, shape, src_dtype, device=device)
a = chainerx.array(t, dtype=dst_dtype_spec, copy=copy)
src_dtype = chainerx.dtype(src_dtype)
dst_dtype = src_dtype if dst_dtype_spec is None else chainerx.dtype(
dst_dtype_spec)
device = chainerx.get_device(device)
if (not copy
and src_dtype == dst_dtype
and device is chainerx.get_default_device()):
assert t is a
else:
assert t is not a
chainerx.testing.assert_array_equal_ex(a, t.astype(dst_dtype))
assert a.dtype == dst_dtype
assert a.device is chainerx.get_default_device()
def test_array_from_tuple_or_list(xp, obj, dtype_spec, device):
if xp is numpy and isinstance(dtype_spec, chainerx.dtype):
dtype_spec = dtype_spec.name
# Skip nan/inf -> integer conversion that would cause a cast error.
if (not _is_all_finite(obj)
and dtype_spec not in (None, Unspecified)
and chainerx.dtype(dtype_spec).kind not in ('f', 'c')):
return chainerx.testing.ignore()
if dtype_spec is Unspecified:
return xp.array(obj)
else:
return xp.array(obj, dtype_spec)
def test_array_from_chainerx_array_with_device(
src_dtype, dst_dtype, copy, device, dst_device_spec):
t = array_utils.create_dummy_ndarray(
chainerx, (2,), src_dtype, device=device)
a = chainerx.array(t, dtype=dst_dtype, copy=copy, device=dst_device_spec)
dst_device = chainerx.get_device(dst_device_spec)
if not copy and src_dtype == dst_dtype and device is dst_device:
assert t is a
else:
assert t is not a
chainerx.testing.assert_array_equal_ex(
a, t.to_device(dst_device).astype(dst_dtype))
assert a.dtype == chainerx.dtype(dst_dtype)
assert a.device is dst_device
def test_array_from_chainerx_array_with_device(
src_dtype, dst_dtype, copy, device, dst_device_spec):
t = array_utils.create_dummy_ndarray(
chainerx, (2,), src_dtype, device=device)
a = chainerx.array(t, dtype=dst_dtype, copy=copy, device=dst_device_spec)
dst_device = chainerx.get_device(dst_device_spec)
if not copy and src_dtype == dst_dtype and device is dst_device:
assert t is a
else:
assert t is not a
chainerx.testing.assert_array_equal_ex(
a, t.to_device(dst_device).astype(dst_dtype))
assert a.dtype == chainerx.dtype(dst_dtype)
assert a.device is dst_device
def _check_array(
array, expected_dtype, expected_shape, expected_data_list=None,
device=None):
expected_dtype = chainerx.dtype(expected_dtype)
assert isinstance(array.dtype, chainerx.dtype)
assert isinstance(array.shape, tuple)
assert array.dtype == expected_dtype
assert array.shape == expected_shape
assert array.itemsize == expected_dtype.itemsize
assert array.size == array_utils.total_size(expected_shape)
assert array.nbytes == expected_dtype.itemsize * \
array_utils.total_size(expected_shape)
if expected_data_list is not None:
assert array._debug_flat_data == expected_data_list
assert array.is_contiguous
array_utils.check_device(array, device)
def _check_array(
array, expected_dtype, expected_shape, expected_data_list=None,
device=None):
expected_dtype = chainerx.dtype(expected_dtype)
assert isinstance(array.dtype, chainerx.dtype)
assert isinstance(array.shape, tuple)
assert array.dtype == expected_dtype
assert array.shape == expected_shape
assert array.itemsize == expected_dtype.itemsize
assert array.size == array_utils.total_size(expected_shape)
assert array.nbytes == expected_dtype.itemsize * \
array_utils.total_size(expected_shape)
if expected_data_list is not None:
assert array._debug_flat_data == expected_data_list
assert array.is_contiguous
array_utils.check_device(array, device)
def test_asarray_from_chainerx_array_with_device(src_dtype, dst_dtype, device,
dst_device_spec):
t = array_utils.create_dummy_ndarray(chainerx, (2, ),
src_dtype,
device=device)
a = chainerx.asarray(t, dtype=dst_dtype, device=dst_device_spec)
dst_device = chainerx.get_device(dst_device_spec)
if ((dst_dtype is None or src_dtype == dst_dtype)
and (dst_device_spec is None or device is dst_device)):
assert t is a
else:
assert t is not a
if dst_dtype is None:
dst_dtype = t.dtype
chainerx.testing.assert_array_equal_ex(
a,
t.to_device(dst_device).astype(dst_dtype))
assert a.dtype == chainerx.dtype(dst_dtype)
assert a.device is dst_device
def test_parametrize_dtype_specifier_with_dtypes(spec):
assert chainerx.dtype(spec).name in ('int32', 'float64')
def create_dummy_ndarray(xp,
shape,
dtype,
device=None,
pattern=1,
padding=True,
start=None):
dtype = chainerx.dtype(dtype).name
size = total_size(shape)
if dtype in ('bool', 'bool_'):
if pattern == 1:
data = [i % 2 == 1 for i in range(size)]
else:
data = [i % 3 == 0 for i in range(size)]
else:
if start is None:
if dtype in chainerx.testing.unsigned_dtypes:
start = 0 if pattern == 1 else 1
else:
start = -1 if pattern == 1 else -2
data = list(range(start, size + start))
if padding is True:
padding = 1
elif padding is False:
padding = 0
# Unpadded array
a_unpad = numpy.array(data, dtype=dtype).reshape(shape)
if padding == 0:
a_np = a_unpad
else:
# Create possibly padded (non-contiguous) array.
# Elements in each axis will be spaced with corresponding padding.
# The padding for axis `i` is computed as `itemsize * padding[i]`.
if numpy.isscalar(padding):
padding = (padding, ) * len(shape)
assert len(padding) == len(shape)
# Allocate 1-dim raw buffer
buf_nitems = 1
for dim, pad in zip((1, ) + shape[::-1], padding[::-1] + (0, )):
buf_nitems = buf_nitems * dim + pad
# intentionally using uninitialized padding values
buf_a = numpy.empty((buf_nitems, ), dtype=dtype)
# Compute strides
strides = []
st = 1
itemsize = buf_a.itemsize
for dim, pad in zip(shape[::-1], padding[::-1]):
st += pad
strides.append(st * itemsize)
st *= dim
strides = tuple(strides[::-1])
# Create strided array and copy data
a_np = numpy.asarray(
numpy.lib.stride_tricks.as_strided(buf_a, shape, strides))
a_np[...] = a_unpad
numpy.testing.assert_array_equal(a_np, a_unpad)
# Convert to NumPy or chainerx array
if xp is chainerx:
a = chainerx.testing._fromnumpy(a_np, keepstrides=True, device=device)
assert a.strides == a_np.strides
else:
a = a_np
# Checks
if padding == 0 or all(pad == 0 for pad in padding):
if xp is chainerx:
assert a.is_contiguous
else:
assert a.flags.c_contiguous
assert a.shape == shape
assert a.dtype.name == dtype
return a
def _is_bool_spec(dtype_spec):
# Used in arange tests
if dtype_spec is None:
return False
return chainerx.dtype(dtype_spec) == chainerx.bool_
def test_init_with_dtype(value, dtype_spec):
expected_dtype = chainerx.dtype(dtype_spec)
scalar = chainerx.Scalar(value, dtype_spec)
assert scalar.dtype == expected_dtype
assert scalar == chainerx.Scalar(value, expected_dtype)
def test_init_with_dtype(value, dtype_spec):
expected_dtype = chainerx.dtype(dtype_spec)
scalar = chainerx.Scalar(value, dtype_spec)
assert scalar.dtype == expected_dtype
assert scalar == chainerx.Scalar(value, expected_dtype)
def create_dummy_ndarray(
xp, shape, dtype, device=None, pattern=1, padding=True, start=None):
dtype = chainerx.dtype(dtype).name
size = total_size(shape)
if dtype in ('bool', 'bool_'):
if pattern == 1:
data = [i % 2 == 1 for i in range(size)]
else:
data = [i % 3 == 0 for i in range(size)]
else:
if start is None:
if dtype in chainerx.testing.unsigned_dtypes:
start = 0 if pattern == 1 else 1
else:
start = -1 if pattern == 1 else -2
data = list(range(start, size + start))
if padding is True:
padding = 1
elif padding is False:
padding = 0
# Unpadded array
a_unpad = numpy.array(data, dtype=dtype).reshape(shape)
if padding == 0:
a_np = a_unpad
else:
# Create possibly padded (non-contiguous) array.
# Elements in each axis will be spaced with corresponding padding.
# The padding for axis `i` is computed as `itemsize * padding[i]`.
if numpy.isscalar(padding):
padding = (padding,) * len(shape)
assert len(padding) == len(shape)
# Allocate 1-dim raw buffer
buf_nitems = 1
for dim, pad in zip((1,) + shape[::-1], padding[::-1] + (0,)):
buf_nitems = buf_nitems * dim + pad
# intentionally using uninitialized padding values
buf_a = numpy.empty((buf_nitems,), dtype=dtype)
# Compute strides
strides = []
st = 1
itemsize = buf_a.itemsize
for dim, pad in zip(shape[::-1], padding[::-1]):
st += pad
strides.append(st * itemsize)
st *= dim
strides = tuple(strides[::-1])
# Create strided array and copy data
a_np = numpy.asarray(
numpy.lib.stride_tricks.as_strided(buf_a, shape, strides))
a_np[...] = a_unpad
numpy.testing.assert_array_equal(a_np, a_unpad)
# Convert to NumPy or chainerx array
if xp is chainerx:
a = chainerx.testing._fromnumpy(a_np, keepstrides=True, device=device)
assert a.strides == a_np.strides
else:
a = a_np
# Checks
if padding == 0 or all(pad == 0 for pad in padding):
if xp is chainerx:
assert a.is_contiguous
else:
assert a.flags.c_contiguous
assert a.shape == shape
assert a.dtype.name == dtype
return a
def _is_bool_spec(dtype_spec):
# Used in arange tests
if dtype_spec is None:
return False
return chainerx.dtype(dtype_spec) == chainerx.bool_
def test_parametrize_dtype_specifier_with_dtypes(spec):
assert chainerx.dtype(spec).name in ('int32', 'float64')
