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73df2b1e
编写于
3月 29, 2023
作者:
zhouweiwei2014
提交者:
GitHub
3月 29, 2023
浏览文件
操作
浏览文件
下载
电子邮件补丁
差异文件
[Zero-Dim] change Tensor.numpy() usage to other equivalent usage, avoid hack (#52197)
上级
d966301e
变更
19
显示空白变更内容
内联
并排
Showing
19 changed file
with
46 addition
and
47 deletion
+46
-47
paddle/fluid/pybind/eager_method.cc
paddle/fluid/pybind/eager_method.cc
+4
-3
python/paddle/distributed/fleet/meta_parallel/pp_utils/p2p_communication.py
...ributed/fleet/meta_parallel/pp_utils/p2p_communication.py
+1
-1
python/paddle/fluid/dataloader/dataset.py
python/paddle/fluid/dataloader/dataset.py
+1
-1
python/paddle/fluid/dygraph/math_op_patch.py
python/paddle/fluid/dygraph/math_op_patch.py
+4
-4
python/paddle/fluid/dygraph/varbase_patch_methods.py
python/paddle/fluid/dygraph/varbase_patch_methods.py
+4
-4
python/paddle/fluid/layers/control_flow.py
python/paddle/fluid/layers/control_flow.py
+2
-2
python/paddle/framework/io.py
python/paddle/framework/io.py
+4
-4
python/paddle/framework/io_utils.py
python/paddle/framework/io_utils.py
+1
-3
python/paddle/hapi/model.py
python/paddle/hapi/model.py
+1
-1
python/paddle/metric/metrics.py
python/paddle/metric/metrics.py
+7
-7
python/paddle/nn/decode.py
python/paddle/nn/decode.py
+1
-1
python/paddle/nn/functional/common.py
python/paddle/nn/functional/common.py
+1
-1
python/paddle/nn/functional/pooling.py
python/paddle/nn/functional/pooling.py
+2
-2
python/paddle/optimizer/optimizer.py
python/paddle/optimizer/optimizer.py
+2
-2
python/paddle/quantization/imperative/ptq_quantizer.py
python/paddle/quantization/imperative/ptq_quantizer.py
+4
-4
python/paddle/static/nn/metric.py
python/paddle/static/nn/metric.py
+1
-1
python/paddle/tensor/array.py
python/paddle/tensor/array.py
+2
-2
python/paddle/tensor/manipulation.py
python/paddle/tensor/manipulation.py
+2
-2
python/paddle/utils/layers_utils.py
python/paddle/utils/layers_utils.py
+2
-2
未找到文件。
paddle/fluid/pybind/eager_method.cc
浏览文件 @
73df2b1e
...
...
@@ -141,10 +141,11 @@ static PyObject* tensor_method_numpy(TensorObject* self,
"order to avoid this problem, "
"0D Tensor will be changed to 1D numpy currently, but it's not "
"correct and will be "
"removed in future. Please modify "
" 'Tensor.numpy()[0]' to 'float(Tensor)' as soon as "
"removed in future. For Tensor contain only one element, Please "
"modify "
" 'Tensor.numpy()[0]' to 'Tensor.item()' as soon as "
"possible, "
"otherwise 'Tensor.numpy()[0]' will raise error"
;
"otherwise 'Tensor.numpy()[0]' will raise error
in future.
"
;
py_rank
=
1
;
py_dims
[
0
]
=
1
;
py_strides
[
0
]
=
sizeof_dtype
*
numel
;
...
...
python/paddle/distributed/fleet/meta_parallel/pp_utils/p2p_communication.py
浏览文件 @
73df2b1e
...
...
@@ -83,7 +83,7 @@ class SendRecvMeta:
# recv stop_gradient
stop_grad
=
paddle
.
to_tensor
([
0
])
paddle
.
distributed
.
recv
(
stop_grad
,
src
=
src_rank
,
group
=
group
)
return
shape
.
numpy
().
tolist
(),
dtype
.
item
(),
stop_grad
.
item
()
return
shape
.
tolist
(),
dtype
.
item
(),
stop_grad
.
item
()
def
recv_meta
(
self
,
group
):
tensor_type
=
paddle
.
to_tensor
([
0
])
...
...
python/paddle/fluid/dataloader/dataset.py
浏览文件 @
73df2b1e
...
...
@@ -514,7 +514,7 @@ def random_split(dataset, lengths, generator=None):
)
# TODO(@Joejiong): support Variable or Tensor type with .tolist class member function.
# For example var.item() and var.tolist()
indices
=
paddle
.
randperm
(
sum
(
lengths
)).
numpy
().
tolist
()
indices
=
paddle
.
randperm
(
sum
(
lengths
)).
tolist
()
return
[
Subset
(
dataset
,
indices
[
offset
-
length
:
offset
])
for
offset
,
length
in
zip
(
_accumulate
(
lengths
),
lengths
)
...
...
python/paddle/fluid/dygraph/math_op_patch.py
浏览文件 @
73df2b1e
...
...
@@ -140,21 +140,21 @@ def monkey_patch_math_varbase():
),
"only one element variable can be converted to float."
tensor
=
var
.
value
().
get_tensor
()
assert
tensor
.
_is_initialized
(),
"variable's tensor is not initialized"
return
float
(
var
.
numpy
().
flatten
()[
0
]
)
return
float
(
var
.
item
()
)
def
_long_
(
var
):
numel
=
np
.
prod
(
var
.
shape
)
assert
numel
==
1
,
"only one element variable can be converted to long."
tensor
=
var
.
value
().
get_tensor
()
assert
tensor
.
_is_initialized
(),
"variable's tensor is not initialized"
return
int
(
var
.
numpy
().
flatten
()[
0
]
)
return
int
(
var
.
item
()
)
def
_int_
(
var
):
numel
=
np
.
prod
(
var
.
shape
)
assert
numel
==
1
,
"only one element variable can be converted to int."
tensor
=
var
.
value
().
get_tensor
()
assert
tensor
.
_is_initialized
(),
"variable's tensor is not initialized"
return
int
(
var
.
numpy
().
flatten
()[
0
]
)
return
int
(
var
.
item
()
)
def
_len_
(
var
):
assert
var
.
ndim
>
0
,
"len() of a 0D tensor is wrong"
...
...
@@ -172,7 +172,7 @@ def monkey_patch_math_varbase():
),
"only one element variable can be converted to python index."
tensor
=
var
.
value
().
get_tensor
()
assert
tensor
.
_is_initialized
(),
"variable's tensor is not initialized"
return
int
(
var
.
numpy
().
flatten
()[
0
]
)
return
int
(
var
.
item
()
)
@
property
def
_ndim_
(
var
):
...
...
python/paddle/fluid/dygraph/varbase_patch_methods.py
浏览文件 @
73df2b1e
...
...
@@ -379,8 +379,8 @@ def monkey_patch_varbase():
if
self
.
grad
is
None
:
return
None
if
self
.
grad
.
is_selected_rows
():
return
(
np
.
array
(
self
.
grad
.
numpy
()
),
np
.
array
(
self
.
grad
.
rows
()))
return
self
.
grad
.
numpy
(
)
return
(
np
.
array
(
self
.
grad
),
np
.
array
(
self
.
grad
.
rows
()))
return
np
.
array
(
self
.
grad
)
else
:
if
self
.
_grad_ivar
()
is
None
:
return
None
...
...
@@ -735,11 +735,11 @@ def monkey_patch_varbase():
),
"When Variable is used as the condition of if/while , Variable can only contain one element."
if
framework
.
global_var
.
_in_eager_mode_
:
assert
self
.
_is_initialized
(),
"tensor not initialized"
return
bool
(
np
.
all
(
self
.
numpy
()
>
0
)
)
return
bool
(
self
.
item
()
>
0
)
else
:
tensor
=
self
.
value
().
get_tensor
()
assert
tensor
.
_is_initialized
(),
"tensor not initialized"
return
bool
(
np
.
all
(
tensor
.
__array__
()
>
0
)
)
return
bool
(
self
.
item
()
>
0
)
def
__bool__
(
self
):
return
self
.
__nonzero__
()
...
...
python/paddle/fluid/layers/control_flow.py
浏览文件 @
73df2b1e
...
...
@@ -1150,7 +1150,7 @@ def while_loop(cond, body, loop_vars, is_test=False, name=None):
)
if
in_dygraph_mode
():
now_cond
=
pre_cond
.
numpy
().
item
()
now_cond
=
pre_cond
.
item
()
while
now_cond
:
output_vars
=
body
(
*
loop_vars
)
if
not
isinstance
(
output_vars
,
(
list
,
tuple
)):
...
...
@@ -1160,7 +1160,7 @@ def while_loop(cond, body, loop_vars, is_test=False, name=None):
"body in while_loop should return the same arity "
"(length and structure) and types as loop_vars"
)
now_cond
=
cond
(
*
output_vars
).
numpy
().
item
()
now_cond
=
cond
(
*
output_vars
).
item
()
map_structure
(
assign_skip_lod_tensor_array
,
output_vars
,
loop_vars
)
return
loop_vars
else
:
...
...
python/paddle/framework/io.py
浏览文件 @
73df2b1e
...
...
@@ -63,7 +63,7 @@ def _build_saved_state_dict(state_dict):
raise
ValueError
(
"The saved tensor is not initialized. If you used group sharded, please use save_group_sharded_model."
)
save_dict
[
key
]
=
value
.
numpy
(
)
save_dict
[
key
]
=
np
.
array
(
value
)
name_table
[
key
]
=
value
.
name
else
:
save_dict
[
key
]
=
value
...
...
@@ -92,7 +92,7 @@ def _load_state_dict_from_save_inference_model(model_path, config):
# 3. construct state_dict
load_param_dict
=
{}
for
var_name
in
persistable_var_dict
:
load_param_dict
[
var_name
]
=
persistable_var_dict
[
var_name
].
numpy
(
)
load_param_dict
[
var_name
]
=
np
.
array
(
persistable_var_dict
[
var_name
]
)
# if *.info exists, we can recover structured_name
var_info_filename
=
str
(
config
.
params_filename
)
+
".info"
...
...
@@ -146,7 +146,7 @@ def _load_state_dict_from_save_params(model_path):
# 3. construct state_dict
load_param_dict
=
{}
for
var
in
load_var_list
:
load_param_dict
[
var
.
name
]
=
var
.
numpy
(
)
load_param_dict
[
var
.
name
]
=
np
.
array
(
var
)
return
load_param_dict
...
...
@@ -291,7 +291,7 @@ def _pickle_save(obj, f, protocol):
)
def
reduce_varbase
(
self
):
data
=
self
.
numpy
(
)
data
=
np
.
array
(
self
)
name
=
self
.
name
return
(
tuple
,
((
name
,
data
),))
...
...
python/paddle/framework/io_utils.py
浏览文件 @
73df2b1e
...
...
@@ -180,9 +180,7 @@ def _load_program_scope(main=None, startup=None, scope=None):
@
static_only
def
_legacy_static_save
(
param_dict
,
model_path
,
protocol
=
2
):
def
get_tensor
(
var
):
if
isinstance
(
var
,
(
core
.
VarBase
,
core
.
eager
.
Tensor
)):
return
var
.
numpy
()
elif
isinstance
(
var
,
core
.
LoDTensor
):
if
isinstance
(
var
,
(
core
.
VarBase
,
core
.
eager
.
Tensor
,
core
.
LoDTensor
)):
return
np
.
array
(
var
)
return
var
...
...
python/paddle/hapi/model.py
浏览文件 @
73df2b1e
...
...
@@ -61,7 +61,7 @@ def to_numpy(var):
var
,
(
Variable
,
fluid
.
core
.
VarBase
,
fluid
.
core
.
eager
.
Tensor
)
),
"not a variable"
if
isinstance
(
var
,
(
fluid
.
core
.
VarBase
,
fluid
.
core
.
eager
.
Tensor
)):
return
var
.
numpy
(
)
return
np
.
array
(
var
)
t
=
global_scope
().
find_var
(
var
.
name
).
get_tensor
()
return
np
.
array
(
t
)
...
...
python/paddle/metric/metrics.py
浏览文件 @
73df2b1e
...
...
@@ -292,7 +292,7 @@ class Accuracy(Metric):
Tensor: the accuracy of current step.
"""
if
isinstance
(
correct
,
(
paddle
.
Tensor
,
paddle
.
fluid
.
core
.
eager
.
Tensor
)):
correct
=
correct
.
numpy
(
)
correct
=
np
.
array
(
correct
)
num_samples
=
np
.
prod
(
np
.
array
(
correct
.
shape
[:
-
1
]))
accs
=
[]
for
i
,
k
in
enumerate
(
self
.
topk
):
...
...
@@ -420,12 +420,12 @@ class Precision(Metric):
The data type is 'int32' or 'int64'.
"""
if
isinstance
(
preds
,
(
paddle
.
Tensor
,
paddle
.
fluid
.
core
.
eager
.
Tensor
)):
preds
=
preds
.
numpy
(
)
preds
=
np
.
array
(
preds
)
elif
not
_is_numpy_
(
preds
):
raise
ValueError
(
"The 'preds' must be a numpy ndarray or Tensor."
)
if
isinstance
(
labels
,
(
paddle
.
Tensor
,
paddle
.
fluid
.
core
.
eager
.
Tensor
)):
labels
=
labels
.
numpy
(
)
labels
=
np
.
array
(
labels
)
elif
not
_is_numpy_
(
labels
):
raise
ValueError
(
"The 'labels' must be a numpy ndarray or Tensor."
)
...
...
@@ -553,12 +553,12 @@ class Recall(Metric):
Shape: [batch_size, 1], Dtype: 'int32' or 'int64'.
"""
if
isinstance
(
preds
,
(
paddle
.
Tensor
,
paddle
.
fluid
.
core
.
eager
.
Tensor
)):
preds
=
preds
.
numpy
(
)
preds
=
np
.
array
(
preds
)
elif
not
_is_numpy_
(
preds
):
raise
ValueError
(
"The 'preds' must be a numpy ndarray or Tensor."
)
if
isinstance
(
labels
,
(
paddle
.
Tensor
,
paddle
.
fluid
.
core
.
eager
.
Tensor
)):
labels
=
labels
.
numpy
(
)
labels
=
np
.
array
(
labels
)
elif
not
_is_numpy_
(
labels
):
raise
ValueError
(
"The 'labels' must be a numpy ndarray or Tensor."
)
...
...
@@ -705,12 +705,12 @@ class Auc(Metric):
representing the label of the instance i.
"""
if
isinstance
(
labels
,
(
paddle
.
Tensor
,
paddle
.
fluid
.
core
.
eager
.
Tensor
)):
labels
=
labels
.
numpy
(
)
labels
=
np
.
array
(
labels
)
elif
not
_is_numpy_
(
labels
):
raise
ValueError
(
"The 'labels' must be a numpy ndarray or Tensor."
)
if
isinstance
(
preds
,
(
paddle
.
Tensor
,
paddle
.
fluid
.
core
.
eager
.
Tensor
)):
preds
=
preds
.
numpy
(
)
preds
=
np
.
array
(
preds
)
elif
not
_is_numpy_
(
preds
):
raise
ValueError
(
"The 'preds' must be a numpy ndarray or Tensor."
)
...
...
python/paddle/nn/decode.py
浏览文件 @
73df2b1e
...
...
@@ -712,7 +712,7 @@ def _dynamic_decode_imperative(
step_idx
=
0
step_idx_tensor
=
paddle
.
full
(
shape
=
[
1
],
fill_value
=
step_idx
,
dtype
=
"int64"
)
while
cond
.
numpy
():
while
cond
.
item
():
(
step_outputs
,
next_states
,
next_inputs
,
next_finished
)
=
decoder
.
step
(
step_idx_tensor
,
inputs
,
states
,
**
kwargs
)
...
...
python/paddle/nn/functional/common.py
浏览文件 @
73df2b1e
...
...
@@ -490,7 +490,7 @@ def interpolate(
else
:
if
in_dynamic_mode
():
if
isinstance
(
out_shape
,
Variable
):
out_shape
=
list
(
out_shape
.
numpy
())
out_shape
=
list
(
out_shape
.
numpy
(
False
))
else
:
out_shape
=
list
(
out_shape
)
...
...
python/paddle/nn/functional/pooling.py
浏览文件 @
73df2b1e
...
...
@@ -706,7 +706,7 @@ def _unpool_output_size(x, kernel_size, stride, padding, output_size):
else
:
for
i
,
var
in
enumerate
(
output_size
):
if
isinstance
(
var
,
Variable
):
output_size
[
i
]
=
var
.
numpy
().
item
()
output_size
[
i
]
=
var
.
item
()
if
len
(
output_size
)
==
len
(
kernel_size
)
+
2
:
output_size
=
output_size
[
2
:]
...
...
@@ -1609,7 +1609,7 @@ def adaptive_avg_pool2d(x, output_size, data_format='NCHW', name=None):
if
in_dygraph_mode
():
output_size
=
[
item
.
numpy
().
item
(
0
)
if
isinstance
(
item
,
Variable
)
else
item
item
.
item
(
0
)
if
isinstance
(
item
,
Variable
)
else
item
for
item
in
output_size
]
# output_size support Variable in static graph mode
...
...
python/paddle/optimizer/optimizer.py
浏览文件 @
73df2b1e
...
...
@@ -382,9 +382,9 @@ class Optimizer:
load_para
=
state_dict
[
var_tmp
.
name
]
if
isinstance
(
load_para
,
Variable
):
load_para_np
=
load_para
.
numpy
(
)
load_para_np
=
np
.
array
(
load_para
)
elif
isinstance
(
load_para
,
core
.
VarBase
):
load_para_np
=
load_para
.
numpy
(
)
load_para_np
=
np
.
array
(
load_para
)
elif
isinstance
(
load_para
,
np
.
ndarray
):
load_para_np
=
load_para
else
:
...
...
python/paddle/quantization/imperative/ptq_quantizer.py
浏览文件 @
73df2b1e
...
...
@@ -54,13 +54,13 @@ def combine_abs_max_and_hist(
return
origin_max
,
origin_hist
elif
origin_max
==
0.0
:
new_hist
,
_
=
np
.
histogram
(
paddle
.
abs
(
tensor
).
numpy
(),
range
=
(
0
,
new_max
),
bins
=
bins
paddle
.
abs
(
tensor
).
numpy
(
False
),
range
=
(
0
,
new_max
),
bins
=
bins
)
new_hist
=
new_hist
.
astype
(
np
.
float32
)
return
new_max
,
new_hist
elif
new_max
<=
origin_max
:
new_hist
,
_
=
np
.
histogram
(
paddle
.
abs
(
tensor
).
numpy
(),
range
=
(
0
,
origin_max
),
bins
=
bins
paddle
.
abs
(
tensor
).
numpy
(
False
),
range
=
(
0
,
origin_max
),
bins
=
bins
)
new_hist
=
new_hist
.
astype
(
np
.
float32
)
new_hist
+=
origin_hist
...
...
@@ -84,7 +84,7 @@ def combine_abs_max_and_hist(
sampled_hist
=
sampled_hist
.
astype
(
np
.
float32
)
new_hist
,
_
=
np
.
histogram
(
paddle
.
abs
(
tensor
).
numpy
(),
range
=
(
0
,
new_max
),
bins
=
bins
paddle
.
abs
(
tensor
).
numpy
(
False
),
range
=
(
0
,
new_max
),
bins
=
bins
)
new_hist
=
new_hist
.
astype
(
np
.
float32
)
new_hist
+=
sampled_hist
...
...
@@ -189,7 +189,7 @@ class BaseHistQuantizer(BaseQuantizer, metaclass=abc.ABCMeta):
self
.
hists
.
append
(
None
)
else
:
hist
,
_
=
np
.
histogram
(
paddle
.
abs
(
tensor
).
numpy
(),
paddle
.
abs
(
tensor
).
numpy
(
False
),
range
=
(
0.0
,
abs_max_vals
[
idx
]),
bins
=
self
.
bins
,
)
...
...
python/paddle/static/nn/metric.py
浏览文件 @
73df2b1e
...
...
@@ -76,7 +76,7 @@ def accuracy(input, label, k=1, correct=None, total=None):
if
total
is
None
:
total
=
_varbase_creator
(
dtype
=
"int32"
)
_k
=
k
.
numpy
().
item
(
0
)
if
isinstance
(
k
,
Variable
)
else
k
_k
=
k
.
item
(
0
)
if
isinstance
(
k
,
Variable
)
else
k
topk_out
,
topk_indices
=
_legacy_C_ops
.
top_k_v2
(
input
,
'k'
,
_k
,
'sorted'
,
False
)
...
...
python/paddle/tensor/array.py
浏览文件 @
73df2b1e
...
...
@@ -119,7 +119,7 @@ def array_read(array, i):
assert
i
.
shape
==
[
1
],
"The shape of index 'i' should be [1] in dygraph mode"
i
=
i
.
numpy
().
item
(
0
)
i
=
i
.
item
(
0
)
return
array
[
i
]
else
:
check_variable_and_dtype
(
i
,
'i'
,
[
'int64'
],
'array_read'
)
...
...
@@ -179,7 +179,7 @@ def array_write(x, i, array=None):
assert
i
.
shape
==
[
1
],
"The shape of index 'i' should be [1] in dygraph mode"
i
=
i
.
numpy
().
item
(
0
)
i
=
i
.
item
(
0
)
if
array
is
None
:
array
=
create_array
(
x
.
dtype
)
assert
isinstance
(
...
...
python/paddle/tensor/manipulation.py
浏览文件 @
73df2b1e
...
...
@@ -334,7 +334,7 @@ def slice(input, axes, starts, ends):
for
item
in
starts
]
elif
isinstance
(
starts
,
tmp_tensor_type
):
tensor_t
=
starts
.
numpy
()
tensor_t
=
starts
.
numpy
(
False
)
starts
=
[
ele
for
ele
in
tensor_t
]
infer_flags
=
[
-
1
for
i
in
range
(
len
(
axes
))]
...
...
@@ -344,7 +344,7 @@ def slice(input, axes, starts, ends):
for
item
in
ends
]
elif
isinstance
(
ends
,
tmp_tensor_type
):
tensor_t
=
ends
.
numpy
()
tensor_t
=
ends
.
numpy
(
False
)
ends
=
[
ele
for
ele
in
tensor_t
]
infer_flags
=
[
-
1
for
i
in
range
(
len
(
axes
))]
...
...
python/paddle/utils/layers_utils.py
浏览文件 @
73df2b1e
...
...
@@ -456,12 +456,12 @@ def convert_shape_to_list(shape):
if
isinstance
(
shape
,
(
list
,
tuple
)):
shape
=
list
(
map
(
lambda
x
:
x
.
numpy
().
flat
[
0
]
if
isinstance
(
x
,
Variable
)
else
x
,
lambda
x
:
x
.
item
(
0
)
if
isinstance
(
x
,
Variable
)
else
x
,
shape
,
)
)
else
:
shape
=
shape
.
numpy
().
astype
(
int
).
tolist
()
shape
=
shape
.
astype
(
int
).
tolist
()
return
shape
...
...
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