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体验新版 GitCode,发现更多精彩内容 >>
提交
701af439
编写于
2月 26, 2019
作者:
M
minqiyang
提交者:
ceci3
3月 04, 2019
浏览文件
操作
浏览文件
下载
电子邮件补丁
差异文件
Fix bugs
test=develop
上级
4f43e981
变更
5
隐藏空白更改
内联
并排
Showing
5 changed file
with
71 addition
and
89 deletion
+71
-89
paddle/fluid/imperative/layer.cc
paddle/fluid/imperative/layer.cc
+3
-18
paddle/fluid/imperative/layer.h
paddle/fluid/imperative/layer.h
+0
-6
paddle/fluid/imperative/tracer.cc
paddle/fluid/imperative/tracer.cc
+3
-1
paddle/fluid/pybind/pybind.cc
paddle/fluid/pybind/pybind.cc
+0
-1
python/paddle/fluid/tests/unittests/test_imperative_optimizer.py
...paddle/fluid/tests/unittests/test_imperative_optimizer.py
+65
-63
未找到文件。
paddle/fluid/imperative/layer.cc
浏览文件 @
701af439
...
...
@@ -118,19 +118,16 @@ class Autograd {
while
(
!
ready
.
empty
())
{
OpBase
*
ready_op
=
ready
.
front
();
ready
.
pop_front
();
LOG
(
ERROR
)
<<
"ApplyGrad Start"
;
std
::
map
<
std
::
string
,
std
::
vector
<
VarBase
*>>
input_grads
=
ready_op
->
ApplyGrad
();
for
(
auto
it
:
input_grads
)
{
const
std
::
vector
<
VarBase
*>&
ingrads
=
it
.
second
;
LOG
(
ERROR
)
<<
"XX"
;
for
(
size_t
i
=
0
;
i
<
ingrads
.
size
();
++
i
)
{
if
(
!
ingrads
[
i
])
continue
;
if
(
ready_op
->
input_vars_
[
it
.
first
][
i
]
->
IsStopGradient
())
{
continue
;
}
LOG
(
ERROR
)
<<
"XX"
;
OpBase
*
pre_op
=
ready_op
->
pre_ops_
[
it
.
first
][
i
];
if
(
!
pre_op
)
continue
;
...
...
@@ -140,13 +137,10 @@ class Autograd {
if
(
pre_op_ready
)
{
ready
.
push_back
(
pre_op
);
}
LOG
(
ERROR
)
<<
"XX"
;
}
}
ready_op
->
InvokeBackwardHooks
();
LOG
(
ERROR
)
<<
"ApplyGrad End"
;
}
}
...
...
@@ -219,6 +213,7 @@ std::map<std::string, std::vector<VarBase*>> OpBase::ApplyGrad() {
return
{};
}
VLOG
(
3
)
<<
"apply op grad: "
<<
op_desc_
->
Type
();
std
::
vector
<
framework
::
VariableValueMap
>
grad_outputs
;
if
(
backward_id_
>
0
)
{
VLOG
(
3
)
<<
"py_layer_grad"
;
...
...
@@ -229,10 +224,8 @@ std::map<std::string, std::vector<VarBase*>> OpBase::ApplyGrad() {
grad_input_vars_
[
0
][
framework
::
GradVarName
(
PyLayer
::
kFwdInp
)]);
}
else
{
grad_outputs
.
resize
(
grad_op_descs_
.
size
());
LOG
(
ERROR
)
<<
"ApplyGrad "
<<
grad_op_descs_
.
size
();
for
(
size_t
k
=
0
;
k
<
grad_op_descs_
.
size
();
++
k
)
{
framework
::
OpDesc
*
grad_op_desc
=
grad_op_descs_
[
k
];
LOG
(
ERROR
)
<<
"op grad "
<<
grad_op_desc
->
Type
();
VLOG
(
3
)
<<
"op grad "
<<
grad_op_desc
->
Type
();
for
(
auto
it
:
grad_output_vars_
[
k
])
{
auto
&
outputs
=
grad_outputs
[
k
][
it
.
first
];
...
...
@@ -244,16 +237,12 @@ std::map<std::string, std::vector<VarBase*>> OpBase::ApplyGrad() {
}
}
LOG
(
ERROR
)
<<
"op grad "
<<
grad_op_desc
->
Type
();
framework
::
RuntimeContext
ctx
(
grad_input_vars_
[
k
],
grad_outputs
[
k
]);
// No need to do compile time infer shape here.
// grad_op_desc_->InferShape(*block_);
grad_op_desc
->
InferVarType
(
block_
);
LOG
(
ERROR
)
<<
"op grad "
<<
grad_op_desc
->
Type
();
std
::
unique_ptr
<
framework
::
OperatorBase
>
opbase
=
framework
::
OpRegistry
::
CreateOp
(
*
grad_op_desc
);
framework
::
OperatorWithKernel
*
op_kernel
=
...
...
@@ -268,8 +257,6 @@ std::map<std::string, std::vector<VarBase*>> OpBase::ApplyGrad() {
}
}
LOG
(
ERROR
)
<<
"delete grad start "
;
for
(
size_t
k
=
0
;
k
<
grad_output_vars_
.
size
();
++
k
)
{
for
(
auto
it
:
grad_output_vars_
[
k
])
{
auto
&
outputs
=
grad_outputs
[
k
][
it
.
first
];
...
...
@@ -289,18 +276,16 @@ std::map<std::string, std::vector<VarBase*>> OpBase::ApplyGrad() {
}
void
OpBase
::
InvokeBackwardHooks
()
{
LOG
(
ERROR
)
<<
"call backward start "
;
VLOG
(
3
)
<<
"call backward hooks, hooks num: "
<<
backward_hooks_
.
size
()
;
// call backward hooks
for
(
py
::
object
&
callable
:
backward_hooks_
)
{
callable
(
this
);
}
LOG
(
ERROR
)
<<
"call backward end "
;
}
void
OpBase
::
RegisterBackwardHooks
(
const
py
::
object
&
callable
)
{
LOG
(
ERROR
)
<<
"Register backward hooks "
<<
trace_id_
;
VLOG
(
3
)
<<
"Register backward hooks "
<<
trace_id_
;
// TODO(minqiyang): check the callable format
backward_hooks_
.
push_back
(
callable
);
...
...
paddle/fluid/imperative/layer.h
浏览文件 @
701af439
...
...
@@ -134,8 +134,6 @@ class VarBase {
public:
virtual
~
VarBase
()
{
LOG
(
ERROR
)
<<
"remove var "
<<
name_
.
c_str
();
if
(
block_
)
{
block_
->
RemoveVar
(
name_
);
}
...
...
@@ -225,13 +223,9 @@ class PYBIND11_HIDDEN OpBase {
delete
desc
;
}
LOG
(
ERROR
)
<<
"remove op "
<<
op_desc_
->
Type
()
<<
" id "
<<
trace_id_
;
if
(
block_
)
{
block_
->
RemoveOpInternal
(
op_desc_
);
}
LOG
(
ERROR
)
<<
"remove op end "
<<
trace_id_
;
}
std
::
map
<
std
::
string
,
std
::
vector
<
VarBase
*>>
ApplyGrad
();
...
...
paddle/fluid/imperative/tracer.cc
浏览文件 @
701af439
...
...
@@ -155,6 +155,7 @@ std::set<std::string> Tracer::Trace(OpBase* op, const VarBasePtrMap& inputs,
op
->
grad_input_vars_
.
resize
(
op
->
grad_op_descs_
.
size
());
op
->
grad_output_vars_
.
resize
(
op
->
grad_op_descs_
.
size
());
for
(
size_t
i
=
0
;
i
<
op
->
grad_op_descs_
.
size
();
++
i
)
{
framework
::
OpDesc
*
grad_op_desc
=
op
->
grad_op_descs_
[
i
];
for
(
auto
it
:
grad_op_desc
->
Inputs
())
{
...
...
@@ -167,7 +168,6 @@ std::set<std::string> Tracer::Trace(OpBase* op, const VarBasePtrMap& inputs,
PADDLE_ENFORCE
(
fwd_var_it
!=
vars
.
end
());
// Forward inputs or outputs.
grad_in_vars
.
push_back
(
fwd_var_it
->
second
->
var_
);
vars_saved_for_backward
.
insert
(
it
.
first
);
}
else
{
VarBase
*
var
=
vars
[
var_it
->
second
];
if
(
!
var
->
grads_
->
var_
->
IsInitialized
())
{
...
...
@@ -177,6 +177,8 @@ std::set<std::string> Tracer::Trace(OpBase* op, const VarBasePtrMap& inputs,
// Douts.
grad_in_vars
.
push_back
(
var
->
grads_
->
var_
);
}
vars_saved_for_backward
.
insert
(
it
.
first
);
}
}
...
...
paddle/fluid/pybind/pybind.cc
浏览文件 @
701af439
...
...
@@ -173,7 +173,6 @@ PYBIND11_MODULE(core, m) {
[](
const
imperative
::
VarBase
&
self
)
{
return
self
.
name_
;
},
[](
imperative
::
VarBase
&
self
,
const
std
::
string
&
name
)
{
self
.
name_
=
name
;
LOG
(
ERROR
)
<<
"create ivar name "
<<
self
.
name_
;
})
.
def_property
(
"block"
,
[](
const
imperative
::
VarBase
&
self
)
{
return
self
.
block_
;
},
...
...
python/paddle/fluid/tests/unittests/test_imperative_optimizer.py
浏览文件 @
701af439
...
...
@@ -12,6 +12,8 @@
# See the License for the specific language governing permissions and
# limitations under the License.
from
__future__
import
print_function
import
contextlib
import
unittest
import
numpy
as
np
...
...
@@ -146,69 +148,69 @@ class TestImperativeMnist(unittest.TestCase):
for
param
in
mnist
.
parameters
():
dy_param_value
[
param
.
name
]
=
param
.
_numpy
()
#
with new_program_scope():
#
fluid.default_startup_program().random_seed = seed
#
fluid.default_main_program().random_seed = seed
#
exe = fluid.Executor(fluid.CPUPlace(
#
) if not core.is_compiled_with_cuda() else fluid.CUDAPlace(0))
#
mnist = MNIST("mnist")
#
sgd = SGDOptimizer(learning_rate=1e-3)
#
train_reader = paddle.batch(
#
paddle.dataset.mnist.train(), batch_size=128, drop_last=True)
#
img = fluid.layers.data(
#
name='pixel', shape=[1, 28, 28], dtype='float32')
#
label = fluid.layers.data(name='label', shape=[1], dtype='int64')
#
cost = mnist(img)
#
loss = fluid.layers.cross_entropy(cost, label)
#
avg_loss = fluid.layers.mean(loss)
#
sgd.minimize(avg_loss)
#
# initialize params and fetch them
#
static_param_init_value = {}
#
static_param_name_list = []
#
for param in mnist.parameters():
#
static_param_name_list.append(param.name)
#
out = exe.run(fluid.default_startup_program(),
#
fetch_list=static_param_name_list)
#
for i in range(len(static_param_name_list)):
#
static_param_init_value[static_param_name_list[i]] = out[i]
#
for epoch in range(epoch_num):
#
for batch_id, data in enumerate(train_reader()):
#
static_x_data = np.array(
#
[x[0].reshape(1, 28, 28)
#
for x in data]).astype('float32')
#
y_data = np.array(
#
[x[1] for x in data]).astype('int64').reshape([128, 1])
#
fetch_list = [avg_loss.name]
#
fetch_list.extend(static_param_name_list)
#
out = exe.run(
#
fluid.default_main_program(),
#
feed={"pixel": static_x_data,
#
"label": y_data},
#
fetch_list=fetch_list)
#
static_param_value = {}
#
static_out = out[0]
#
for i in range(1, len(out)):
#
static_param_value[static_param_name_list[i - 1]] = out[
#
i]
#
self.assertTrue(np.allclose(dy_x_data.all(), static_x_data.all()))
#
for key, value in six.iteritems(static_param_init_value):
#
self.assertTrue(np.allclose(value, dy_param_init_value[key]))
#
self.assertTrue(np.allclose(static_out, dy_out))
#
for key, value in six.iteritems(static_param_value):
#
self.assertTrue(np.allclose(value, dy_param_value[key], atol=1e-5))
with
new_program_scope
():
fluid
.
default_startup_program
().
random_seed
=
seed
fluid
.
default_main_program
().
random_seed
=
seed
exe
=
fluid
.
Executor
(
fluid
.
CPUPlace
(
)
if
not
core
.
is_compiled_with_cuda
()
else
fluid
.
CUDAPlace
(
0
))
mnist
=
MNIST
(
"mnist"
)
sgd
=
SGDOptimizer
(
learning_rate
=
1e-3
)
train_reader
=
paddle
.
batch
(
paddle
.
dataset
.
mnist
.
train
(),
batch_size
=
128
,
drop_last
=
True
)
img
=
fluid
.
layers
.
data
(
name
=
'pixel'
,
shape
=
[
1
,
28
,
28
],
dtype
=
'float32'
)
label
=
fluid
.
layers
.
data
(
name
=
'label'
,
shape
=
[
1
],
dtype
=
'int64'
)
cost
=
mnist
(
img
)
loss
=
fluid
.
layers
.
cross_entropy
(
cost
,
label
)
avg_loss
=
fluid
.
layers
.
mean
(
loss
)
sgd
.
minimize
(
avg_loss
)
# initialize params and fetch them
static_param_init_value
=
{}
static_param_name_list
=
[]
for
param
in
mnist
.
parameters
():
static_param_name_list
.
append
(
param
.
name
)
out
=
exe
.
run
(
fluid
.
default_startup_program
(),
fetch_list
=
static_param_name_list
)
for
i
in
range
(
len
(
static_param_name_list
)):
static_param_init_value
[
static_param_name_list
[
i
]]
=
out
[
i
]
for
epoch
in
range
(
epoch_num
):
for
batch_id
,
data
in
enumerate
(
train_reader
()):
static_x_data
=
np
.
array
(
[
x
[
0
].
reshape
(
1
,
28
,
28
)
for
x
in
data
]).
astype
(
'float32'
)
y_data
=
np
.
array
(
[
x
[
1
]
for
x
in
data
]).
astype
(
'int64'
).
reshape
([
128
,
1
])
fetch_list
=
[
avg_loss
.
name
]
fetch_list
.
extend
(
static_param_name_list
)
out
=
exe
.
run
(
fluid
.
default_main_program
(),
feed
=
{
"pixel"
:
static_x_data
,
"label"
:
y_data
},
fetch_list
=
fetch_list
)
static_param_value
=
{}
static_out
=
out
[
0
]
for
i
in
range
(
1
,
len
(
out
)):
static_param_value
[
static_param_name_list
[
i
-
1
]]
=
out
[
i
]
self
.
assertTrue
(
np
.
allclose
(
dy_x_data
.
all
(),
static_x_data
.
all
()))
for
key
,
value
in
six
.
iteritems
(
static_param_init_value
):
self
.
assertTrue
(
np
.
allclose
(
value
,
dy_param_init_value
[
key
]))
self
.
assertTrue
(
np
.
allclose
(
static_out
,
dy_out
))
for
key
,
value
in
six
.
iteritems
(
static_param_value
):
self
.
assertTrue
(
np
.
allclose
(
value
,
dy_param_value
[
key
],
atol
=
1e-5
))
if
__name__
==
'__main__'
:
...
...
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