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【Hackathon 7th No.28】为 paddle.clip 进行功能增强 #69109

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【Hackathon 7th No.28】为 paddle.clip 进行功能增强 #69109

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ff4137e
update code
a162837 Nov 1, 2024
d2d2a6a
Merge branch 'PaddlePaddle:develop' into NEW
a162837 Nov 1, 2024
8de222e
fix code
a162837 Nov 1, 2024
1ff5b4b
Merge branch 'NEW' of https://github.com/a162837/Paddle into NEW
a162837 Nov 1, 2024
929415d
fix code
a162837 Nov 1, 2024
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204 changes: 142 additions & 62 deletions python/paddle/tensor/math.py
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Original file line number Diff line number Diff line change
Expand Up @@ -3714,10 +3714,33 @@ def log10_(x: Tensor, name: str | None = None) -> Tensor:
return _C_ops.log10_(x)


def check_clip_tensor(c_x, value, re_value, value_type, name):
if value is None:
value = paddle.full_like(c_x, re_value, value_type)
else:
if isinstance(value, (Variable, paddle.pir.Value, paddle.Tensor)):
if len(value.shape) == 1 and value.shape[-1] == 0:
raise ValueError(
f"The {name} dimension should be equal to the inner dimension of the x, but the {name} dimension is {value.shape}"
)
elif (
len(value.shape) != 0
and value.shape != c_x.shape[-len(value.shape) :]
and value.shape != [1]
and value.shape != (1,)
):
raise ValueError(
f"The {name} dimension should be equal to the inner dimension of the x, but the {name} dimension is {value.shape} and the x dimension is {c_x.shape[-len(value.shape):]}."
)
else:
value = paddle.full_like(c_x, value, value_type)
return value


def clip(
x: Tensor,
min: float | None = None,
max: float | None = None,
min: float | Tensor | None = None,
max: float | Tensor | None = None,
name: str | None = None,
) -> Tensor:
"""
Expand Down Expand Up @@ -3761,84 +3784,119 @@ def clip(
if x_dtype == 'paddle.int32':
min_ = np.iinfo(np.int32).min
max_ = np.iinfo(np.int32).max - 2**7
value_dtype = 'int32'
elif x_dtype == 'paddle.int64':
min_ = np.iinfo(np.int64).min
max_ = np.iinfo(np.int64).max - 2**39
value_dtype = 'int64'
elif x_dtype == 'paddle.float16':
min_ = float(np.finfo(np.float16).min)
max_ = float(np.finfo(np.float16).max)
value_dtype = 'float16'
else:
min_ = float(np.finfo(np.float32).min)
max_ = float(np.finfo(np.float32).max)
value_dtype = 'float32'

if (
isinstance(min, (Variable, paddle.pir.Value, paddle.Tensor))
and (
(len(min.shape) == 1 and min.shape[-1] not in [1, 0])
or len(min.shape) > 1
)
) or (
isinstance(max, (Variable, paddle.pir.Value, paddle.Tensor))
and (
(len(max.shape) == 1 and max.shape[-1] not in [1, 0])
or len(max.shape) > 1
)
):
min_n = check_clip_tensor(x, min, min_, value_dtype, 'min')
max_n = check_clip_tensor(x, max, max_, value_dtype, 'max')

min_n = (
paddle.broadcast_to(min_n, x.shape) if min_n.shape != x.shape else min_n
)
max_n = (
paddle.broadcast_to(max_n, x.shape) if max_n.shape != x.shape else max_n
)

output_min = paddle.where(x < min_n, min_n, x)
output = paddle.where(output_min > max_n, max_n, output_min)
return output

if in_dynamic_or_pir_mode():
if isinstance(min, Variable):
min = min.item(0)
if isinstance(max, Variable):
max = max.item(0)
min = min_ if min is None else min
max = max_ if max is None else max
return _C_ops.clip(x, min, max)
else:
if min is not None:
check_type(min, 'min', (float, int, Variable), 'clip')
if in_dynamic_or_pir_mode():
if isinstance(min, Variable):
check_dtype(
min.dtype,
'min',
['float16', 'float32', 'float64', 'int32', 'uint16'],
'clip',
'(When the type of min in clip is Variable.)',
)
if max is not None:
check_type(max, 'max', (float, int, Variable), 'clip')
min = min.item(0)
if isinstance(max, Variable):
check_dtype(
max.dtype,
'max',
['float16', 'float32', 'float64', 'int32', 'uint16'],
'clip',
'(When the type of max in clip is Variable.)',
)

check_variable_and_dtype(
x,
'x',
['float16', 'float32', 'float64', 'int32', 'int64', 'uint16'],
'clip',
)
max = max.item(0)
min = min_ if min is None else min
max = max_ if max is None else max
return _C_ops.clip(x, min, max)
else:
if min is not None:
check_type(min, 'min', (float, int, Variable), 'clip')
if isinstance(min, Variable):
check_dtype(
min.dtype,
'min',
['float16', 'float32', 'float64', 'int32', 'uint16'],
'clip',
'(When the type of min in clip is Variable.)',
)
if max is not None:
check_type(max, 'max', (float, int, Variable), 'clip')
if isinstance(max, Variable):
check_dtype(
max.dtype,
'max',
['float16', 'float32', 'float64', 'int32', 'uint16'],
'clip',
'(When the type of max in clip is Variable.)',
)

inputs = {'X': x}
attrs = {'min': min_, 'max': max_}
check_variable_and_dtype(
x,
'x',
['float16', 'float32', 'float64', 'int32', 'int64', 'uint16'],
'clip',
)

if isinstance(min, Variable):
min.stop_gradient = True
inputs['Min'] = min
elif min is not None:
attrs['min'] = min
inputs = {'X': x}
attrs = {'min': min_, 'max': max_}

if isinstance(max, Variable):
max.stop_gradient = True
inputs['Max'] = max
elif max is not None:
attrs['max'] = max
if isinstance(min, Variable):
min.stop_gradient = True
inputs['Min'] = min
elif min is not None:
attrs['min'] = min

helper = LayerHelper('clip', **locals())
output = helper.create_variable_for_type_inference(
dtype=helper.input_dtype('x')
)
helper.append_op(
type='clip', inputs=inputs, outputs={'Out': [output]}, attrs=attrs
)
if isinstance(max, Variable):
max.stop_gradient = True
inputs['Max'] = max
elif max is not None:
attrs['max'] = max

helper = LayerHelper('clip', **locals())
output = helper.create_variable_for_type_inference(
dtype=helper.input_dtype('x')
)
helper.append_op(
type='clip',
inputs=inputs,
outputs={'Out': [output]},
attrs=attrs,
)

return output
return output


@inplace_apis_in_dygraph_only
def clip_(
x: Tensor,
min: float | None = None,
max: float | None = None,
min: float | Tensor | None = None,
max: float | Tensor | None = None,
name: str | None = None,
) -> Tensor:
"""
Expand All @@ -3847,15 +3905,37 @@ def clip_(
"""
fmin = float(np.finfo(np.float32).min)
fmax = float(np.finfo(np.float32).max)
if isinstance(min, Variable):
min = min.item(0)
if isinstance(max, Variable):
max = max.item(0)
min = fmin if min is None else min
max = fmax if max is None else max

if in_dynamic_mode():
return _C_ops.clip_(x, min, max)
if (
isinstance(min, (Variable, paddle.pir.Value, paddle.Tensor))
and (
(len(min.shape) == 1 and min.shape[-1] not in [1, 0])
or len(min.shape) > 1
)
) or (
isinstance(max, (Variable, paddle.pir.Value, paddle.Tensor))
and (
(len(max.shape) == 1 and max.shape[-1] not in [1, 0])
or len(max.shape) > 1
)
):
max = check_clip_tensor(x, max, fmin, x.dtype, 'max')
min = check_clip_tensor(x, min, fmin, x.dtype, 'min')

max_expand = (
paddle.broadcast_to(max, x.shape) if max.shape != x.shape else max
)
min_expand = (
paddle.broadcast_to(min, x.shape) if min.shape != x.shape else min
)

paddle.where_(x > min_expand, x, min_expand)
return paddle.where_(x < max_expand, x, max_expand)
else:
return _C_ops.clip_(x, min, max)


def trace(
Expand Down
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