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[API Enhancement] No.1 support the any number of times in paddle.diff…
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… [used AI Studio] (PaddlePaddle#56681)

* Fix: The number of times to recursively compute the difference supports any number

* Fix: use pre-commit to format code
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@XavierZXY @lxd-cumt
XavierZXY authored and lxd-cumt committed Aug 28, 2023
1 parent a67a81b commit c274162
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Showing 2 changed files with 138 additions and 112 deletions.
241 changes: 129 additions & 112 deletions python/paddle/tensor/math.py
View file
Original file line number Diff line number Diff line change
Expand Up @@ -5565,125 +5565,142 @@ def diff(x, n=1, axis=-1, prepend=None, append=None, name=None):
[[1, 1],
[1, 1]])
"""

if axis < 0:
axis = axis + len(x.shape)
if axis > len(x.shape):
axis = len(x.shape)
if axis < 0:
axis = 0
dtype = x.dtype
axes = [axis]
infer_flags = [1 for i in range(len(axes))]
if in_dynamic_mode():
has_pend = False
input_list = []
if prepend is not None and append is not None:
input_list = [prepend, x, append]
has_pend = True
elif prepend is not None:
input_list = [prepend, x]
has_pend = True
elif append is not None:
input_list = [x, append]
has_pend = True
if has_pend:
new_input = _C_ops.concat(input_list, axis)
else:
new_input = x

attrs_1 = ()
attrs_2 = ()

dim_len = new_input.shape[axis]

starts_1 = [0]
attrs_1 += ('starts', starts_1)
ends_1 = [dim_len - 1]
attrs_1 += ('ends', ends_1)
input_front = _C_ops.slice(
new_input, axes, starts_1, ends_1, infer_flags, []
)
starts_2 = [1]
attrs_2 += ('starts', starts_2)
ends_2 = [dim_len]
attrs_2 += ('ends', ends_2)
input_back = _C_ops.slice(
new_input, axes, starts_2, ends_2, infer_flags, []
if n < 1:
raise ValueError(
"Diff expects input to be at least one-dimensional but got {}".format(
n
)
)

if x.dtype == paddle.bool:
return _C_ops.logical_xor(input_back, input_front)
def _diff_handler(x, n=1, axis=-1, prepend=None, append=None, name=None):
if axis < 0:
axis = axis + len(x.shape)
if axis > len(x.shape):
axis = len(x.shape)
if axis < 0:
axis = 0
dtype = x.dtype
axes = [axis]
infer_flags = [1 for i in range(len(axes))]
if in_dynamic_mode():
has_pend = False
input_list = []
if prepend is not None and append is not None:
input_list = [prepend, x, append]
has_pend = True
elif prepend is not None:
input_list = [prepend, x]
has_pend = True
elif append is not None:
input_list = [x, append]
has_pend = True
if has_pend:
new_input = _C_ops.concat(input_list, axis)
else:
new_input = x

attrs_1 = ()
attrs_2 = ()

dim_len = new_input.shape[axis]

starts_1 = [0]
attrs_1 += ('starts', starts_1)
ends_1 = [dim_len - 1]
attrs_1 += ('ends', ends_1)
input_front = _C_ops.slice(
new_input, axes, starts_1, ends_1, infer_flags, []
)
starts_2 = [1]
attrs_2 += ('starts', starts_2)
ends_2 = [dim_len]
attrs_2 += ('ends', ends_2)
input_back = _C_ops.slice(
new_input, axes, starts_2, ends_2, infer_flags, []
)

if x.dtype == paddle.bool:
return _C_ops.logical_xor(input_back, input_front)
else:
return _C_ops.subtract(input_back, input_front)
else:
return _C_ops.subtract(input_back, input_front)
else:
check_variable_and_dtype(
x,
'x',
['float16', 'float32', 'float64', 'bool', 'int32', 'int64'],
'diff',
)
check_type(axis, 'axis', (int), 'diff')
helper = LayerHelper('diff', **locals())
has_pend = False
input_list = []
if prepend is not None and append is not None:
input_list = [prepend, x, append]
has_pend = True
elif prepend is not None:
input_list = [prepend, x]
has_pend = True
elif append is not None:
input_list = [x, append]
has_pend = True

if has_pend:
new_input = helper.create_variable_for_type_inference(dtype)
check_variable_and_dtype(
x,
'x',
['float16', 'float32', 'float64', 'bool', 'int32', 'int64'],
'diff',
)
check_type(axis, 'axis', (int), 'diff')
helper = LayerHelper('diff', **locals())
has_pend = False
input_list = []
if prepend is not None and append is not None:
input_list = [prepend, x, append]
has_pend = True
elif prepend is not None:
input_list = [prepend, x]
has_pend = True
elif append is not None:
input_list = [x, append]
has_pend = True

if has_pend:
new_input = helper.create_variable_for_type_inference(dtype)
helper.append_op(
type='concat',
inputs={'X': input_list},
outputs={'Out': [new_input]},
attrs={'axis': axis},
)
else:
new_input = x

dim_len = new_input.shape[axis]
attrs_1 = {'axes': axes}
starts_1 = [0]
ends_1 = [dim_len - 1]
attrs_1['starts'] = starts_1
attrs_1['ends'] = ends_1
input_front = helper.create_variable_for_type_inference(dtype)
helper.append_op(
type='concat',
inputs={'X': input_list},
outputs={'Out': [new_input]},
attrs={'axis': axis},
type='slice',
inputs={'Input': new_input},
attrs=attrs_1,
outputs={'Out': input_front},
)
else:
new_input = x

dim_len = new_input.shape[axis]
attrs_1 = {'axes': axes}
starts_1 = [0]
ends_1 = [dim_len - 1]
attrs_1['starts'] = starts_1
attrs_1['ends'] = ends_1
input_front = helper.create_variable_for_type_inference(dtype)
helper.append_op(
type='slice',
inputs={'Input': new_input},
attrs=attrs_1,
outputs={'Out': input_front},
)
attrs_2 = {'axes': axes}
starts_2 = [1]
ends_2 = [dim_len]
attrs_2['starts'] = starts_2
attrs_2['ends'] = ends_2
input_back = helper.create_variable_for_type_inference(dtype)
helper.append_op(
type='slice',
inputs={'Input': new_input},
attrs=attrs_2,
outputs={'Out': input_back},
)

if dtype == paddle.bool:
out = helper.create_variable_for_type_inference(dtype)
attrs_2 = {'axes': axes}
starts_2 = [1]
ends_2 = [dim_len]
attrs_2['starts'] = starts_2
attrs_2['ends'] = ends_2
input_back = helper.create_variable_for_type_inference(dtype)
helper.append_op(
type='logical_xor',
inputs={"X": input_back, "Y": input_front},
outputs={"Out": out},
type='slice',
inputs={'Input': new_input},
attrs=attrs_2,
outputs={'Out': input_back},
)
else:
out = paddle.tensor.math.subtract(input_back, input_front)
return out

if dtype == paddle.bool:
out = helper.create_variable_for_type_inference(dtype)
helper.append_op(
type='logical_xor',
inputs={"X": input_back, "Y": input_front},
outputs={"Out": out},
)
else:
out = paddle.tensor.math.subtract(input_back, input_front)
return out

out = _diff_handler(
x, n=1, axis=axis, prepend=prepend, append=append, name=name
)
if n > 1:
for _ in range(n - 1):
out = _diff_handler(
out, n=1, axis=axis, prepend=prepend, append=append, name=name
)
return out


def angle(x, name=None):
Expand Down
9 changes: 9 additions & 0 deletions test/legacy_test/test_diff_op.py
View file
Original file line number Diff line number Diff line change
Expand Up @@ -145,6 +145,15 @@ def test_grad(self):
self.func_grad()


class TestDiffOpN(TestDiffOp):
def set_args(self):
self.input = np.array([1, 4, 5, 2]).astype('float32')
self.n = 2
self.axis = 0
self.prepend = None
self.append = None


class TestDiffOpAxis(TestDiffOp):
def set_args(self):
self.input = np.array([[1, 4, 5, 2], [1, 5, 4, 2]]).astype('float32')
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