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【Hackathon 7th No.28】为 paddle.clip 进行功能增强 #68819

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【Hackathon 7th No.28】为 paddle.clip 进行功能增强 #68819

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932f9c6
为 paddle.clip 进行功能增强
a162837 Oct 18, 2024
df55a35
test 1-D
a162837 Oct 19, 2024
554c302
use python
a162837 Oct 22, 2024
cead838
use python
a162837 Oct 22, 2024
33ae2dc
use python
a162837 Oct 22, 2024
bee6bb1
test tensor clip
a162837 Oct 22, 2024
3d13805
test tensor clip
a162837 Oct 22, 2024
2284964
test tensor
a162837 Oct 23, 2024
065a9dd
all code
a162837 Oct 23, 2024
f766ab4
fix code
a162837 Oct 26, 2024
bc3b43c
fix dygraph code
a162837 Oct 26, 2024
83184f3
fix static code
a162837 Oct 30, 2024
40bfcd5
fix static code
a162837 Oct 30, 2024
01cd260
fix static code
a162837 Oct 30, 2024
32ede97
fix static code
a162837 Oct 30, 2024
e930f72
fix static code
a162837 Oct 30, 2024
9ec1bba
fix static code
a162837 Oct 31, 2024
199d74e
fix static code
a162837 Oct 31, 2024
2bec116
Merge branch 'PaddlePaddle:develop' into clip
a162837 Nov 1, 2024
0cd7fb3
fix static code
a162837 Nov 2, 2024
930ecc0
Merge branch 'clip' of https://github.com/a162837/Paddle into clip
a162837 Nov 2, 2024
8f45f09
fix code_style
a162837 Nov 2, 2024
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218 changes: 155 additions & 63 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 +3784,127 @@ 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 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 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.)',
)
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')

check_variable_and_dtype(
x,
'x',
['float16', 'float32', 'float64', 'int32', 'int64', 'uint16'],
'clip',
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
)

inputs = {'X': x}
attrs = {'min': min_, 'max': max_}
output_min = paddle.where(x < min_n, min_n, x)
output = paddle.where(output_min > max_n, max_n, output_min)
return output

if isinstance(min, Variable):
min.stop_gradient = True
inputs['Min'] = min
elif min is not None:
attrs['min'] = min
else:
if in_dynamic_or_pir_mode():
if isinstance(min, (Variable, paddle.pir.Value)):
min = min.item()
if isinstance(max, (Variable, paddle.pir.Value)):
max = max.item()
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, paddle.Tensor), 'clip'
)
if isinstance(min, (Variable, paddle.Tensor, paddle.pir.Value)):
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, paddle.Tensor), 'clip'
)
if isinstance(max, (Variable, paddle.Tensor, paddle.pir.Value)):
check_dtype(
max.dtype,
'max',
['float16', 'float32', 'float64', 'int32', 'uint16'],
'clip',
'(When the type of max in clip is Variable.)',
)

if isinstance(max, Variable):
max.stop_gradient = True
inputs['Max'] = max
elif max is not None:
attrs['max'] = max
check_variable_and_dtype(
x,
'x',
['float16', 'float32', 'float64', 'int32', 'int64', 'uint16'],
'clip',
)

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
)
inputs = {'X': x}
attrs = {'min': min_, 'max': max_}

if isinstance(min, (Variable, paddle.Tensor, paddle.pir.Value)):
min.stop_gradient = True
inputs['Min'] = min
elif min is not None:
attrs['min'] = min

if isinstance(max, (Variable, paddle.Tensor, paddle.pir.Value)):
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 +3913,41 @@ 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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