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30122212
编写于
8月 18, 2022
作者:
F
feifei-111
提交者:
GitHub
8月 18, 2022
浏览文件
操作
浏览文件
下载
电子邮件补丁
差异文件
[API]Support static branch in paddle.to_tensor (#45164)
* fix_shape
上级
bb6bd223
变更
6
隐藏空白更改
内联
并排
Showing
6 changed file
with
255 addition
and
71 deletion
+255
-71
python/paddle/fluid/dygraph/dygraph_to_static/basic_api_transformer.py
.../fluid/dygraph/dygraph_to_static/basic_api_transformer.py
+0
-3
python/paddle/fluid/dygraph/dygraph_to_static/partial_program.py
...paddle/fluid/dygraph/dygraph_to_static/partial_program.py
+1
-0
python/paddle/fluid/tests/unittests/dygraph_to_static/test_convert_call_generator.py
...nittests/dygraph_to_static/test_convert_call_generator.py
+1
-1
python/paddle/fluid/tests/unittests/dygraph_to_static/test_tensor_shape.py
...id/tests/unittests/dygraph_to_static/test_tensor_shape.py
+10
-8
python/paddle/fluid/tests/unittests/dygraph_to_static/test_to_tensor.py
...fluid/tests/unittests/dygraph_to_static/test_to_tensor.py
+124
-0
python/paddle/tensor/creation.py
python/paddle/tensor/creation.py
+119
-59
未找到文件。
python/paddle/fluid/dygraph/dygraph_to_static/basic_api_transformer.py
浏览文件 @
30122212
...
@@ -129,9 +129,6 @@ def is_to_variable(node):
...
@@ -129,9 +129,6 @@ def is_to_variable(node):
if
utils
.
is_dygraph_api
(
node
):
if
utils
.
is_dygraph_api
(
node
):
return
api_name
.
endswith
(
"to_variable"
)
return
api_name
.
endswith
(
"to_variable"
)
if
utils
.
is_paddle_api
(
node
):
return
api_name
.
endswith
(
"to_tensor"
)
return
False
return
False
...
...
python/paddle/fluid/dygraph/dygraph_to_static/partial_program.py
浏览文件 @
30122212
...
@@ -556,6 +556,7 @@ class PartialProgramLayer:
...
@@ -556,6 +556,7 @@ class PartialProgramLayer:
var_base
=
core
.
eager
.
Tensor
(
var_desc
.
dtype
(),
var_desc
.
shape
(),
var_base
=
core
.
eager
.
Tensor
(
var_desc
.
dtype
(),
var_desc
.
shape
(),
var_desc
.
name
(),
var_desc
.
type
(),
var_desc
.
name
(),
var_desc
.
type
(),
False
)
False
)
var_base
.
stop_gradient
=
var
.
stop_gradient
out_varbase_map
[
var_desc
.
name
()]
=
var_base
out_varbase_map
[
var_desc
.
name
()]
=
var_base
return
var_base
return
var_base
...
...
python/paddle/fluid/tests/unittests/dygraph_to_static/test_convert_call_generator.py
浏览文件 @
30122212
...
@@ -29,7 +29,7 @@ from paddle.jit import to_static
...
@@ -29,7 +29,7 @@ from paddle.jit import to_static
def
dyfunc_generator
():
def
dyfunc_generator
():
for
i
in
range
(
100
):
for
i
in
range
(
100
):
yield
paddle
.
to_tensor
([
i
]
*
10
)
yield
paddle
.
fluid
.
dygraph
.
to_variable
([
i
]
*
10
)
def
main_func
():
def
main_func
():
...
...
python/paddle/fluid/tests/unittests/dygraph_to_static/test_tensor_shape.py
浏览文件 @
30122212
...
@@ -12,6 +12,8 @@
...
@@ -12,6 +12,8 @@
# See the License for the specific language governing permissions and
# See the License for the specific language governing permissions and
# limitations under the License.
# limitations under the License.
# to_tensor api will create 1 less op now, this test was changed
from
__future__
import
print_function
from
__future__
import
print_function
import
numpy
as
np
import
numpy
as
np
...
@@ -298,7 +300,7 @@ class TestTensorShapeBasic2(TestTensorShapeBasic):
...
@@ -298,7 +300,7 @@ class TestTensorShapeBasic2(TestTensorShapeBasic):
self
.
dygraph_func
=
dyfunc_tensor_shape_2
self
.
dygraph_func
=
dyfunc_tensor_shape_2
def
_set_expected_op_num
(
self
):
def
_set_expected_op_num
(
self
):
self
.
expected_op_num
=
2
self
.
expected_op_num
=
1
self
.
expected_shape_op_num
=
0
self
.
expected_shape_op_num
=
0
self
.
expected_slice_op_num
=
0
self
.
expected_slice_op_num
=
0
...
@@ -347,7 +349,7 @@ class TestTupleShape1(TestTensorShapeBasic):
...
@@ -347,7 +349,7 @@ class TestTupleShape1(TestTensorShapeBasic):
self
.
dygraph_func
=
dyfunc_tuple_shape_1
self
.
dygraph_func
=
dyfunc_tuple_shape_1
def
_set_expected_op_num
(
self
):
def
_set_expected_op_num
(
self
):
self
.
expected_op_num
=
5
self
.
expected_op_num
=
4
self
.
expected_shape_op_num
=
1
self
.
expected_shape_op_num
=
1
self
.
expected_slice_op_num
=
2
self
.
expected_slice_op_num
=
2
...
@@ -362,7 +364,7 @@ class TestTupleShape2(TestTensorShapeBasic):
...
@@ -362,7 +364,7 @@ class TestTupleShape2(TestTensorShapeBasic):
self
.
dygraph_func
=
dyfunc_tuple_shape_2
self
.
dygraph_func
=
dyfunc_tuple_shape_2
def
_set_expected_op_num
(
self
):
def
_set_expected_op_num
(
self
):
self
.
expected_op_num
=
5
self
.
expected_op_num
=
4
self
.
expected_shape_op_num
=
1
self
.
expected_shape_op_num
=
1
self
.
expected_slice_op_num
=
1
self
.
expected_slice_op_num
=
1
...
@@ -375,7 +377,7 @@ class TestTupleShape3(TestTensorShapeBasic):
...
@@ -375,7 +377,7 @@ class TestTupleShape3(TestTensorShapeBasic):
self
.
dygraph_func
=
dyfunc_tuple_shape_3
self
.
dygraph_func
=
dyfunc_tuple_shape_3
def
_set_expected_op_num
(
self
):
def
_set_expected_op_num
(
self
):
self
.
expected_op_num
=
5
self
.
expected_op_num
=
4
self
.
expected_shape_op_num
=
1
self
.
expected_shape_op_num
=
1
self
.
expected_slice_op_num
=
2
self
.
expected_slice_op_num
=
2
...
@@ -388,7 +390,7 @@ class TestPaddleShapeApi(TestTensorShapeBasic):
...
@@ -388,7 +390,7 @@ class TestPaddleShapeApi(TestTensorShapeBasic):
self
.
dygraph_func
=
dyfunc_paddle_shape_api
self
.
dygraph_func
=
dyfunc_paddle_shape_api
def
_set_expected_op_num
(
self
):
def
_set_expected_op_num
(
self
):
self
.
expected_op_num
=
6
self
.
expected_op_num
=
5
self
.
expected_shape_op_num
=
2
self
.
expected_shape_op_num
=
2
self
.
expected_slice_op_num
=
2
self
.
expected_slice_op_num
=
2
...
@@ -490,7 +492,7 @@ class TestTensorShapeInWhile4(TestTensorShapeBasic):
...
@@ -490,7 +492,7 @@ class TestTensorShapeInWhile4(TestTensorShapeBasic):
self
.
dygraph_func
=
dyfunc_with_while_4
self
.
dygraph_func
=
dyfunc_with_while_4
def
_set_expected_op_num
(
self
):
def
_set_expected_op_num
(
self
):
self
.
expected_op_num
=
5
self
.
expected_op_num
=
4
self
.
expected_shape_op_num
=
0
self
.
expected_shape_op_num
=
0
self
.
expected_slice_op_num
=
0
self
.
expected_slice_op_num
=
0
...
@@ -554,7 +556,7 @@ class TestOpNumWithTensorShapeTuple1(TestOpNumBasicWithTensorShape):
...
@@ -554,7 +556,7 @@ class TestOpNumWithTensorShapeTuple1(TestOpNumBasicWithTensorShape):
self
.
dygraph_func
=
dyfunc_tuple_shape_1
self
.
dygraph_func
=
dyfunc_tuple_shape_1
def
_set_expected_op_num
(
self
):
def
_set_expected_op_num
(
self
):
self
.
expected_op_num
=
5
self
.
expected_op_num
=
4
self
.
expected_shape_op_num
=
1
self
.
expected_shape_op_num
=
1
self
.
expected_slice_op_num
=
1
self
.
expected_slice_op_num
=
1
...
@@ -602,7 +604,7 @@ class TestChangeShapeAfterAssign(TestTensorShapeBasic):
...
@@ -602,7 +604,7 @@ class TestChangeShapeAfterAssign(TestTensorShapeBasic):
self
.
dygraph_func
=
dyfunc_change_shape_after_assign
self
.
dygraph_func
=
dyfunc_change_shape_after_assign
def
_set_expected_op_num
(
self
):
def
_set_expected_op_num
(
self
):
self
.
expected_op_num
=
6
self
.
expected_op_num
=
5
self
.
expected_shape_op_num
=
1
self
.
expected_shape_op_num
=
1
self
.
expected_slice_op_num
=
1
self
.
expected_slice_op_num
=
1
...
...
python/paddle/fluid/tests/unittests/dygraph_to_static/test_to_tensor.py
0 → 100644
浏览文件 @
30122212
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from
__future__
import
print_function
import
numpy
import
paddle
import
unittest
import
os
import
tempfile
import
paddle.inference
as
paddle_infer
from
paddle.fluid.framework
import
program_guard
,
Program
import
numpy
as
np
from
paddle.fluid
import
core
def
case0
(
x
):
a
=
paddle
.
to_tensor
([
1.0
,
2.0
,
3.0
],
dtype
=
"int64"
)
return
a
def
case1
(
x
):
paddle
.
set_default_dtype
(
"float64"
)
a
=
paddle
.
to_tensor
([
1.0
,
2.0
,
3.0
],
stop_gradient
=
False
)
return
a
def
case2
(
x
):
if
core
.
is_compiled_with_cuda
():
place
=
paddle
.
CUDAPlace
(
0
)
else
:
place
=
paddle
.
CPUPlace
()
a
=
paddle
.
to_tensor
([
1.0
,
2.0
,
3.0
],
place
=
place
,
dtype
=
"int64"
,
stop_gradient
=
False
)
return
a
def
case3
(
x
):
paddle
.
set_default_dtype
(
"float64"
)
if
core
.
is_compiled_with_cuda
():
place
=
paddle
.
CUDAPlace
(
0
)
else
:
place
=
paddle
.
CPUPlace
()
a
=
paddle
.
to_tensor
([
1.0
,
2.0
,
3.0
],
place
=
place
)
return
a
class
TestToTensorReturnVal
(
unittest
.
TestCase
):
def
test_to_tensor_badreturn
(
self
):
paddle
.
disable_static
()
x
=
paddle
.
to_tensor
([
3
])
a
=
paddle
.
jit
.
to_static
(
case0
)(
x
)
b
=
case0
(
x
)
self
.
assertTrue
(
a
.
dtype
==
b
.
dtype
)
self
.
assertTrue
(
a
.
stop_gradient
==
b
.
stop_gradient
)
self
.
assertTrue
(
a
.
place
.
_equals
(
b
.
place
))
a
=
paddle
.
jit
.
to_static
(
case1
)(
x
)
b
=
case1
(
x
)
self
.
assertTrue
(
a
.
dtype
==
b
.
dtype
)
self
.
assertTrue
(
a
.
stop_gradient
==
b
.
stop_gradient
)
self
.
assertTrue
(
a
.
place
.
_equals
(
b
.
place
))
a
=
paddle
.
jit
.
to_static
(
case2
)(
x
)
b
=
case2
(
x
)
self
.
assertTrue
(
a
.
dtype
==
b
.
dtype
)
self
.
assertTrue
(
a
.
stop_gradient
==
b
.
stop_gradient
)
self
.
assertTrue
(
a
.
place
.
_equals
(
b
.
place
))
a
=
paddle
.
jit
.
to_static
(
case3
)(
x
)
b
=
case3
(
x
)
self
.
assertTrue
(
a
.
dtype
==
b
.
dtype
)
self
.
assertTrue
(
a
.
stop_gradient
==
b
.
stop_gradient
)
self
.
assertTrue
(
a
.
place
.
_equals
(
b
.
place
))
class
TestStatic
(
unittest
.
TestCase
):
def
test_static
(
self
):
paddle
.
enable_static
()
main_prog
=
Program
()
starup_prog
=
Program
()
with
program_guard
(
main_prog
,
starup_prog
):
if
core
.
is_compiled_with_cuda
():
place
=
paddle
.
CUDAPlace
(
0
)
else
:
place
=
paddle
.
CPUPlace
()
x
=
paddle
.
to_tensor
(
paddle
.
randn
([
5
,
2
]),
dtype
=
'float64'
,
stop_gradient
=
False
,
place
=
place
)
out
=
paddle
.
static
.
nn
.
fc
(
x
,
1
)
sgd
=
paddle
.
optimizer
.
SGD
()
sgd
.
minimize
(
paddle
.
mean
(
out
))
exe
=
paddle
.
static
.
Executor
()
exe
.
run
(
starup_prog
)
res
=
exe
.
run
(
fetch_list
=
[
x
,
out
])
if
__name__
==
'__main__'
:
unittest
.
main
()
python/paddle/tensor/creation.py
浏览文件 @
30122212
...
@@ -270,65 +270,7 @@ def logspace(start, stop, num, base=10.0, dtype=None, name=None):
...
@@ -270,65 +270,7 @@ def logspace(start, stop, num, base=10.0, dtype=None, name=None):
return
out
return
out
@
dygraph_only
def
_to_tensor_non_static
(
data
,
dtype
=
None
,
place
=
None
,
stop_gradient
=
True
):
def
to_tensor
(
data
,
dtype
=
None
,
place
=
None
,
stop_gradient
=
True
):
r
"""
Constructs a ``paddle.Tensor`` from ``data`` ,
which can be scalar, tuple, list, numpy\.ndarray, paddle\.Tensor.
If the ``data`` is already a Tensor, copy will be performed and return a new tensor.
If you only want to change stop_gradient property, please call ``Tensor.stop_gradient = stop_gradient`` directly.
Args:
data(scalar|tuple|list|ndarray|Tensor): Initial data for the tensor.
Can be a scalar, list, tuple, numpy\.ndarray, paddle\.Tensor.
dtype(str|np.dtype, optional): The desired data type of returned tensor. Can be 'bool' , 'float16' ,
'float32' , 'float64' , 'int8' , 'int16' , 'int32' , 'int64' , 'uint8',
'complex64' , 'complex128'. Default: None, infers dtype from ``data``
except for python float number which gets dtype from ``get_default_type`` .
place(CPUPlace|CUDAPinnedPlace|CUDAPlace|str, optional): The place to allocate Tensor. Can be
CPUPlace, CUDAPinnedPlace, CUDAPlace. Default: None, means global place. If ``place`` is
string, It can be ``cpu``, ``gpu:x`` and ``gpu_pinned``, where ``x`` is the index of the GPUs.
stop_gradient(bool, optional): Whether to block the gradient propagation of Autograd. Default: True.
Returns:
Tensor: A Tensor constructed from ``data`` .
Examples:
.. code-block:: python
import paddle
type(paddle.to_tensor(1))
# <class 'paddle.Tensor'>
paddle.to_tensor(1)
# Tensor(shape=[1], dtype=int64, place=CPUPlace, stop_gradient=True,
# [1])
x = paddle.to_tensor(1, stop_gradient=False)
print(x)
# Tensor(shape=[1], dtype=int64, place=CPUPlace, stop_gradient=False,
# [1])
paddle.to_tensor(x) # A new tensor will be created with default stop_gradient=True
# Tensor(shape=[1], dtype=int64, place=CPUPlace, stop_gradient=True,
# [1])
paddle.to_tensor([[0.1, 0.2], [0.3, 0.4]], place=paddle.CPUPlace(), stop_gradient=False)
# Tensor(shape=[2, 2], dtype=float32, place=CPUPlace, stop_gradient=False,
# [[0.10000000, 0.20000000],
# [0.30000001, 0.40000001]])
type(paddle.to_tensor([[1+1j, 2], [3+2j, 4]], dtype='complex64'))
# <class 'paddle.Tensor'>
paddle.to_tensor([[1+1j, 2], [3+2j, 4]], dtype='complex64')
# Tensor(shape=[2, 2], dtype=complex64, place=CPUPlace, stop_gradient=True,
# [[(1+1j), (2+0j)],
# [(3+2j), (4+0j)]])
"""
place
=
_get_paddle_place
(
place
)
place
=
_get_paddle_place
(
place
)
if
place
is
None
:
if
place
is
None
:
place
=
_current_expected_place
()
place
=
_current_expected_place
()
...
@@ -417,6 +359,124 @@ def to_tensor(data, dtype=None, place=None, stop_gradient=True):
...
@@ -417,6 +359,124 @@ def to_tensor(data, dtype=None, place=None, stop_gradient=True):
stop_gradient
=
stop_gradient
)
stop_gradient
=
stop_gradient
)
def
to_tensor
(
data
,
dtype
=
None
,
place
=
None
,
stop_gradient
=
True
):
r
"""
Constructs a ``paddle.Tensor`` from ``data`` ,
which can be scalar, tuple, list, numpy\.ndarray, paddle\.Tensor.
If the ``data`` is already a Tensor, copy will be performed and return a new tensor.
If you only want to change stop_gradient property, please call ``Tensor.stop_gradient = stop_gradient`` directly.
Args:
data(scalar|tuple|list|ndarray|Tensor): Initial data for the tensor.
Can be a scalar, list, tuple, numpy\.ndarray, paddle\.Tensor.
dtype(str|np.dtype, optional): The desired data type of returned tensor. Can be 'bool' , 'float16' ,
'float32' , 'float64' , 'int8' , 'int16' , 'int32' , 'int64' , 'uint8',
'complex64' , 'complex128'. Default: None, infers dtype from ``data``
except for python float number which gets dtype from ``get_default_type`` .
place(CPUPlace|CUDAPinnedPlace|CUDAPlace|str, optional): The place to allocate Tensor. Can be
CPUPlace, CUDAPinnedPlace, CUDAPlace. Default: None, means global place. If ``place`` is
string, It can be ``cpu``, ``gpu:x`` and ``gpu_pinned``, where ``x`` is the index of the GPUs.
stop_gradient(bool, optional): Whether to block the gradient propagation of Autograd. Default: True.
Returns:
Tensor: A Tensor constructed from ``data`` .
Examples:
.. code-block:: python
import paddle
type(paddle.to_tensor(1))
# <class 'paddle.Tensor'>
paddle.to_tensor(1)
# Tensor(shape=[1], dtype=int64, place=CPUPlace, stop_gradient=True,
# [1])
x = paddle.to_tensor(1, stop_gradient=False)
print(x)
# Tensor(shape=[1], dtype=int64, place=CPUPlace, stop_gradient=False,
# [1])
paddle.to_tensor(x) # A new tensor will be created with default stop_gradient=True
# Tensor(shape=[1], dtype=int64, place=CPUPlace, stop_gradient=True,
# [1])
paddle.to_tensor([[0.1, 0.2], [0.3, 0.4]], place=paddle.CPUPlace(), stop_gradient=False)
# Tensor(shape=[2, 2], dtype=float32, place=CPUPlace, stop_gradient=False,
# [[0.10000000, 0.20000000],
# [0.30000001, 0.40000001]])
type(paddle.to_tensor([[1+1j, 2], [3+2j, 4]], dtype='complex64'))
# <class 'paddle.Tensor'>
paddle.to_tensor([[1+1j, 2], [3+2j, 4]], dtype='complex64')
# Tensor(shape=[2, 2], dtype=complex64, place=CPUPlace, stop_gradient=True,
# [[(1+1j), (2+0j)],
# [(3+2j), (4+0j)]])
"""
if
_non_static_mode
():
return
_to_tensor_non_static
(
data
,
dtype
,
place
,
stop_gradient
)
# call assign for static graph
else
:
def
call_assign
(
data
,
dtype
=
None
,
stop_grandient
=
None
):
if
isinstance
(
data
,
(
Variable
,
core
.
VarBase
))
and
(
dtype
is
None
or
dtype
==
data
.
dtype
):
output
=
data
else
:
if
dtype
:
target_dtype
=
convert_dtype
(
dtype
)
elif
hasattr
(
data
,
'dtype'
):
target_dtype
=
convert_dtype
(
data
.
dtype
)
else
:
target_dtype
=
convert_dtype
(
paddle
.
get_default_dtype
())
if
not
isinstance
(
data
,
np
.
ndarray
):
if
np
.
isscalar
(
data
)
and
not
isinstance
(
data
,
str
):
data
=
np
.
array
([
data
])
elif
isinstance
(
data
,
(
list
,
tuple
)):
if
any
(
isinstance
(
x
,
Variable
)
for
x
in
data
):
to_stack_list
=
[
None
]
*
len
(
data
)
for
idx
,
d
in
enumerate
(
data
):
to_stack_list
[
idx
]
=
call_assign
(
d
,
dtype
,
stop_gradient
)
data
=
paddle
.
stack
(
to_stack_list
)
data
=
paddle
.
squeeze
(
data
,
-
1
)
output
=
assign
(
data
)
if
target_dtype
is
not
None
and
convert_dtype
(
output
.
dtype
)
!=
target_dtype
:
output
=
paddle
.
cast
(
output
,
target_dtype
)
output
.
stop_gradient
=
stop_gradient
return
output
place
=
_get_paddle_place
(
place
)
if
place
is
None
:
place
=
_current_expected_place
()
elif
not
isinstance
(
place
,
(
core
.
Place
,
core
.
CPUPlace
,
core
.
CUDAPinnedPlace
,
core
.
CUDAPlace
,
core
.
NPUPlace
,
core
.
XPUPlace
,
core
.
MLUPlace
,
core
.
CustomPlace
)):
raise
ValueError
(
"'place' must be any of paddle.Place, paddle.CPUPlace, paddle.CUDAPinnedPlace, paddle.CUDAPlace, paddle.NPUPlace, paddle.XPUPlace, paddle.MLUPlace, paddle.CustomPlace"
)
import
re
re_exp
=
re
.
compile
(
r
'[(](.*?)[)]'
,
re
.
S
)
place_str
=
re
.
findall
(
re_exp
,
str
(
place
))[
0
]
with
paddle
.
static
.
device_guard
(
place_str
):
return
call_assign
(
data
,
dtype
,
stop_gradient
)
def
full_like
(
x
,
fill_value
,
dtype
=
None
,
name
=
None
):
def
full_like
(
x
,
fill_value
,
dtype
=
None
,
name
=
None
):
"""
"""
...
...
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