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c62674f4
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体验新版 GitCode,发现更多精彩内容 >>
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c62674f4
编写于
4月 12, 2019
作者:
C
chengduo
提交者:
GitHub
4月 12, 2019
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差异文件
Refine StaticRnn (#16707)
* enable recurrent op test=develop
上级
e9409665
变更
6
显示空白变更内容
内联
并排
Showing
6 changed file
with
262 addition
and
136 deletion
+262
-136
paddle/fluid/API.spec
paddle/fluid/API.spec
+6
-6
paddle/fluid/operators/recurrent_op.cc
paddle/fluid/operators/recurrent_op.cc
+106
-64
paddle/fluid/operators/rnn_memory_helper_op.cc
paddle/fluid/operators/rnn_memory_helper_op.cc
+9
-2
python/paddle/fluid/layers/control_flow.py
python/paddle/fluid/layers/control_flow.py
+108
-9
python/paddle/fluid/tests/unittests/CMakeLists.txt
python/paddle/fluid/tests/unittests/CMakeLists.txt
+0
-1
python/paddle/fluid/tests/unittests/test_recurrent_op.py
python/paddle/fluid/tests/unittests/test_recurrent_op.py
+33
-54
未找到文件。
paddle/fluid/API.spec
浏览文件 @
c62674f4
...
...
@@ -301,12 +301,12 @@ paddle.fluid.layers.DynamicRNN.static_input (ArgSpec(args=['self', 'x'], varargs
paddle.fluid.layers.DynamicRNN.step_input (ArgSpec(args=['self', 'x', 'level'], varargs=None, keywords=None, defaults=(0,)), ('document', '7568c5ac7622a10288d3307a94134655'))
paddle.fluid.layers.DynamicRNN.update_memory (ArgSpec(args=['self', 'ex_mem', 'new_mem'], varargs=None, keywords=None, defaults=None), ('document', '5d83987da13b98363d6a807a52d8024f'))
paddle.fluid.layers.StaticRNN.__init__ (ArgSpec(args=['self', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
paddle.fluid.layers.StaticRNN.memory (ArgSpec(args=['self', 'init', 'shape', 'batch_ref', 'init_value', 'init_batch_dim_idx', 'ref_batch_dim_idx'], varargs=None, keywords=None, defaults=(None, None, None, 0.0, 0, 1)), ('document', '
c24e368e23afac1ed91a78a639d7a9c7
'))
paddle.fluid.layers.StaticRNN.output (ArgSpec(args=['self'], varargs='outputs', keywords=None, defaults=None), ('document', '
6adf97f83acf6453d4a6a4b1070f3754
'))
paddle.fluid.layers.StaticRNN.step (ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None), ('document', '6
adf97f83acf6453d4a6a4b1070f3754
'))
paddle.fluid.layers.StaticRNN.step_input (ArgSpec(args=['self', 'x'], varargs=None, keywords=None, defaults=None), ('document', '
6adf97f83acf6453d4a6a4b1070f3754
'))
paddle.fluid.layers.StaticRNN.step_output (ArgSpec(args=['self', 'o'], varargs=None, keywords=None, defaults=None), ('document', '
6adf97f83acf6453d4a6a4b1070f3754
'))
paddle.fluid.layers.StaticRNN.update_memory (ArgSpec(args=['self', 'mem', 'var'], varargs=None, keywords=None, defaults=None), ('document', '
6adf97f83acf6453d4a6a4b1070f3754
'))
paddle.fluid.layers.StaticRNN.memory (ArgSpec(args=['self', 'init', 'shape', 'batch_ref', 'init_value', 'init_batch_dim_idx', 'ref_batch_dim_idx'], varargs=None, keywords=None, defaults=(None, None, None, 0.0, 0, 1)), ('document', '
72530f299d6451a567cf4a12dc3fb1ff
'))
paddle.fluid.layers.StaticRNN.output (ArgSpec(args=['self'], varargs='outputs', keywords=None, defaults=None), ('document', '
df6ceab6e6c9bd31e97914d7e7538137
'))
paddle.fluid.layers.StaticRNN.step (ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None), ('document', '6
d3e0a5d9aa519a9773a36e1620ea9b7
'))
paddle.fluid.layers.StaticRNN.step_input (ArgSpec(args=['self', 'x'], varargs=None, keywords=None, defaults=None), ('document', '
903387ec11f3d0bf46821d31a68cffa5
'))
paddle.fluid.layers.StaticRNN.step_output (ArgSpec(args=['self', 'o'], varargs=None, keywords=None, defaults=None), ('document', '
252890d4c3199a7623ab8667e13fd837
'))
paddle.fluid.layers.StaticRNN.update_memory (ArgSpec(args=['self', 'mem', 'var'], varargs=None, keywords=None, defaults=None), ('document', '
7a0000520f179f35239956a5ba55119f
'))
paddle.fluid.layers.reorder_lod_tensor_by_rank (ArgSpec(args=['x', 'rank_table'], varargs=None, keywords=None, defaults=None), ('document', '3545f529ef04e8f6ecb76b47fa3df01a'))
paddle.fluid.layers.Print (ArgSpec(args=['input', 'first_n', 'message', 'summarize', 'print_tensor_name', 'print_tensor_type', 'print_tensor_shape', 'print_tensor_lod', 'print_phase'], varargs=None, keywords=None, defaults=(-1, None, -1, True, True, True, True, 'both')), ('document', '5fef91b0e21c93610785f2b1f7161732'))
paddle.fluid.layers.is_empty (ArgSpec(args=['x', 'cond'], varargs=None, keywords=None, defaults=(None,)), ('document', 'bbe578dbb49ad13e15b014e98c22b519'))
...
...
paddle/fluid/operators/recurrent_op.cc
浏览文件 @
c62674f4
...
...
@@ -23,6 +23,7 @@ constexpr char kInitialStates[] = "initial_states";
constexpr
char
kParameters
[]
=
"parameters"
;
constexpr
char
kOutputs
[]
=
"outputs"
;
constexpr
char
kStepScopes
[]
=
"step_scopes"
;
constexpr
char
kHasStates
[]
=
"has_states"
;
constexpr
char
kExStates
[]
=
"ex_states"
;
constexpr
char
kStates
[]
=
"states"
;
constexpr
char
kStepBlock
[]
=
"sub_block"
;
...
...
@@ -241,11 +242,16 @@ class RecurrentOp : public RecurrentBase {
private:
void
RunImpl
(
const
framework
::
Scope
&
scope
,
const
platform
::
Place
&
place
)
const
override
{
bool
has_state
=
Attr
<
bool
>
(
kHasStates
);
auto
seq_len
=
static_cast
<
size_t
>
(
this
->
GetSequenceLength
(
scope
));
VLOG
(
3
)
<<
"Static RNN input sequence length = "
<<
seq_len
;
StepScopes
scopes
=
CreateStepScopes
(
scope
,
seq_len
);
auto
reverse
=
Attr
<
bool
>
(
kReverse
);
// get device context from pool
platform
::
DeviceContextPool
&
pool
=
platform
::
DeviceContextPool
::
Instance
();
auto
&
dev_ctx
=
*
pool
.
Get
(
place
);
framework
::
Executor
executor
(
place
);
auto
*
block
=
Attr
<
framework
::
BlockDesc
*>
(
kStepBlock
);
...
...
@@ -269,6 +275,7 @@ class RecurrentOp : public RecurrentBase {
inside
->
Resize
(
framework
::
make_ddim
(
dims
));
});
if
(
has_state
)
{
if
(
i
==
0
)
{
// Link initial states --> ex_states
LinkTensor
(
scope
,
Inputs
(
kInitialStates
),
&
cur_scope
,
...
...
@@ -279,6 +286,7 @@ class RecurrentOp : public RecurrentBase {
LinkTensor
(
ex_scope
,
Attr
<
std
::
vector
<
std
::
string
>>
(
kStates
),
&
cur_scope
,
Attr
<
std
::
vector
<
std
::
string
>>
(
kExStates
));
}
}
// Every inputs are linked now, execute!
executor
.
Run
(
*
program
,
&
cur_scope
,
block
->
ID
(),
...
...
@@ -286,11 +294,6 @@ class RecurrentOp : public RecurrentBase {
std
::
vector
<
std
::
string
>
()
/*skip_ref_cnt_vars*/
,
true
/*force_disable_gc*/
);
// get device context from pool
platform
::
DeviceContextPool
&
pool
=
platform
::
DeviceContextPool
::
Instance
();
auto
&
dev_ctx
=
*
pool
.
Get
(
place
);
// Copy inside::output -> outside::output
// outside::output[seq_offset: seq_offset + 1] = inside::output
this
->
LinkTensorWithCallback
(
...
...
@@ -333,13 +336,13 @@ class RecurrentGradOp : public RecurrentBase {
private:
void
RunImpl
(
const
framework
::
Scope
&
scope
,
const
platform
::
Place
&
place
)
const
override
{
auto
seq_len
=
static_cast
<
size_t
>
(
GetSequenceLength
(
scope
));
bool
has_state
=
Attr
<
bool
>
(
kHasStates
);
const
size_t
seq_len
=
static_cast
<
size_t
>
(
GetSequenceLength
(
scope
));
StepScopes
scopes
=
CreateStepScopes
(
scope
,
seq_len
);
auto
reverse
=
Attr
<
bool
>
(
kReverse
);
framework
::
Executor
executor
(
place
);
auto
*
block
=
Attr
<
framework
::
BlockDesc
*>
(
kStepBlock
);
auto
*
program
=
block
->
Program
();
// get device context from pool
...
...
@@ -350,6 +353,7 @@ class RecurrentGradOp : public RecurrentBase {
size_t
seq_offset
=
reverse
?
step_id
:
seq_len
-
step_id
-
1
;
VLOG
(
3
)
<<
"Recurrent backward operate at the time step "
<<
seq_offset
;
auto
&
cur_scope
=
scopes
.
CurScope
();
// Link outside::output_grads --> inside::output_grads
// inside::output_grad = outside::output_grad[seq_offset:seq_offset+1]
LinkTensorWithCallback
(
...
...
@@ -370,6 +374,7 @@ class RecurrentGradOp : public RecurrentBase {
VLOG
(
10
)
<<
" RNN output gradients = ["
<<
sout
.
str
()
<<
"]"
;
}
if
(
has_state
)
{
// Link states
// if cur_scope::cur_state_grad in out_grads:
// cur_scope::cur_state_grad += ex_scope::ex_state_grad
...
...
@@ -396,6 +401,7 @@ class RecurrentGradOp : public RecurrentBase {
framework
::
TensorCopy
(
ex_tensor
,
place
,
dev_ctx
,
cur_grad_tensor
);
}
}
}
VLOG
(
5
)
<<
"Recurrent memory linking finished "
;
// Run step block with cur_scope
...
...
@@ -442,8 +448,8 @@ class RecurrentGradOp : public RecurrentBase {
}
auto
new_inside_name
=
cur_scope
.
Rename
(
inside_grad_name
);
// sum gradient
// sum gradient
auto
sum_op
=
framework
::
OpRegistry
::
CreateOp
(
"sum"
,
{{
"X"
,
{
pg_names
[
param_id
],
new_inside_name
}}},
{{
"Out"
,
{
pg_names
[
param_id
]}}},
...
...
@@ -475,11 +481,13 @@ class RecurrentGradOp : public RecurrentBase {
true
/*is_backward*/
);
VLOG
(
5
)
<<
"Link outside gradient finished "
;
if
(
has_state
)
{
if
(
step_id
+
1
==
seq_len
)
{
// at_end
// copy initialize states gradient from inside to outside
LinkTensorWithCallback
(
cur_scope
,
GradVarLists
(
Attr
<
std
::
vector
<
std
::
string
>>
(
kExStates
)),
scope
,
Outputs
(
kInitStateGrads
),
cur_scope
,
GradVarLists
(
Attr
<
std
::
vector
<
std
::
string
>>
(
kExStates
)),
scope
,
Outputs
(
kInitStateGrads
),
[
&
](
const
framework
::
LoDTensor
&
inside
,
framework
::
LoDTensor
*
outside
)
{
outside
->
Resize
(
inside
.
dims
());
...
...
@@ -489,8 +497,17 @@ class RecurrentGradOp : public RecurrentBase {
true
/*is_backward*/
);
VLOG
(
5
)
<<
"Link initialize state gradient finished "
;
}
}
scopes
.
Next
();
}
// Delete the scope of StepScopes
dev_ctx
.
Wait
();
auto
*
var
=
scope
.
FindVar
(
Input
(
kStepScopes
));
PADDLE_ENFORCE
(
var
!=
nullptr
);
auto
step_scopes
=
var
->
GetMutable
<
StepScopeVar
>
();
for
(
auto
*
sub_scope
:
*
step_scopes
)
{
const_cast
<
framework
::
Scope
&>
(
scope
).
DeleteScope
(
sub_scope
);
}
}
private:
...
...
@@ -541,6 +558,7 @@ class RecurrentOpProtoMaker : public framework::OpProtoAndCheckerMaker {
.
AsDuplicable
();
AddOutput
(
kStepScopes
,
"StepScopes contain all local variables in each time step."
);
AddAttr
<
bool
>
(
kHasStates
,
"Whether has states."
).
SetDefault
(
false
);
AddAttr
<
std
::
vector
<
std
::
string
>>
(
kExStates
,
string
::
Sprintf
(
R"DOC(The ex-state variable names.
...
...
@@ -624,20 +642,44 @@ class RecurrentGradOpDescMaker : public framework::SingleGradOpDescMaker {
class
RecurrentGradOpShapeInference
:
public
framework
::
InferShapeBase
{
public:
void
operator
()(
framework
::
InferShapeContext
*
ctx
)
const
override
{
std
::
vector
<
std
::
string
>
input
{
kInputs
,
kInitialStates
};
std
::
vector
<
std
::
string
>
output
{
kOutputs
};
for
(
auto
&
s
:
input
)
{
// NOTE(zcd): In some case, some of kInputs doesn't have gradient.
PADDLE_ENFORCE
(
ctx
->
HasInputs
(
s
));
}
for
(
auto
&
s
:
output
)
{
PADDLE_ENFORCE
(
ctx
->
HasInputs
(
s
));
}
for
(
auto
&
s
:
input
)
{
ctx
->
SetOutputsDim
(
framework
::
GradVarName
(
s
),
ctx
->
GetInputsDim
(
s
));
}
// In some case the kInitialStates is empty.
// If the kInitialStates is empty, all the states should be empty.
if
(
!
ctx
->
HasInputs
(
kInitialStates
))
{
PADDLE_ENFORCE_EQ
(
ctx
->
Attrs
().
Get
<
std
::
vector
<
std
::
string
>>
(
kExStates
).
size
(),
0
,
"The Attr(%s) should be empty."
,
kExStates
);
PADDLE_ENFORCE_EQ
(
ctx
->
Attrs
().
Get
<
std
::
vector
<
std
::
string
>>
(
kStates
).
size
(),
0
,
"The Attr(%s) should be empty."
,
kStates
);
}
PADDLE_ENFORCE
(
ctx
->
HasInputs
(
kInputs
),
"The input(%s) should not be empty."
,
kInputs
);
PADDLE_ENFORCE
(
ctx
->
HasInputs
(
kOutputs
),
"The input(%s) should not be empty."
,
kOutputs
);
// In some case the kInitialStates is empty.
if
(
ctx
->
HasInputs
(
kInitialStates
))
{
PADDLE_ENFORCE
(
ctx
->
HasOutputs
(
framework
::
GradVarName
(
kInitialStates
)),
"The output of(%s) should not be empty."
,
framework
::
GradVarName
(
kInitialStates
));
ctx
->
SetOutputsDim
(
framework
::
GradVarName
(
kInitialStates
),
ctx
->
GetInputsDim
(
kInitialStates
));
}
PADDLE_ENFORCE
(
ctx
->
HasOutputs
(
framework
::
GradVarName
(
kInputs
)),
"The output of(%s) should not be empty."
,
framework
::
GradVarName
(
kInputs
));
ctx
->
SetOutputsDim
(
framework
::
GradVarName
(
kInputs
),
ctx
->
GetInputsDim
(
kInputs
));
// In some case the kParameters is empty.
if
(
ctx
->
HasInputs
(
kParameters
))
{
PADDLE_ENFORCE
(
ctx
->
HasOutputs
(
framework
::
GradVarName
(
kParameters
)));
PADDLE_ENFORCE
(
ctx
->
HasOutputs
(
framework
::
GradVarName
(
kParameters
)),
"The output of(%s) should not be empty."
,
framework
::
GradVarName
(
kParameters
));
ctx
->
SetOutputsDim
(
framework
::
GradVarName
(
kParameters
),
ctx
->
GetInputsDim
(
kParameters
));
}
...
...
paddle/fluid/operators/rnn_memory_helper_op.cc
浏览文件 @
c62674f4
...
...
@@ -40,9 +40,12 @@ class RNNMemoryHelperOp : public framework::OperatorBase {
"Cannot find out_var in scope, out_var_name is %s"
,
out_name
);
platform
::
DeviceContextPool
&
pool
=
platform
::
DeviceContextPool
::
Instance
();
auto
&
dev_ctx
=
*
pool
.
Get
(
dev_place
);
auto
*
out_tensor
=
out_var
->
GetMutable
<
framework
::
LoDTensor
>
();
auto
&
mem_tensor
=
mem_var
->
Get
<
framework
::
LoDTensor
>
();
framework
::
TensorCopy
Sync
(
mem_tensor
,
dev_place
,
out_tensor
);
framework
::
TensorCopy
(
mem_tensor
,
dev_place
,
dev_ctx
,
out_tensor
);
out_tensor
->
set_lod
(
mem_tensor
.
lod
());
}
};
...
...
@@ -92,6 +95,9 @@ class RNNMemoryHelperGradOp : public framework::OperatorBase {
"Cannot find in_grad_var in scope, name is %s"
,
in_grad_var_name
);
platform
::
DeviceContextPool
&
pool
=
platform
::
DeviceContextPool
::
Instance
();
auto
&
dev_ctx
=
*
pool
.
Get
(
dev_place
);
if
(
out_grad_var
==
nullptr
)
{
VLOG
(
5
)
<<
"Using fill constant 0 as starting gradient"
;
auto
in_var_name
=
Input
(
"X"
);
...
...
@@ -109,7 +115,8 @@ class RNNMemoryHelperGradOp : public framework::OperatorBase {
}
else
{
auto
&
out_grad_tensor
=
out_grad_var
->
Get
<
framework
::
LoDTensor
>
();
auto
*
in_grad_tensor
=
in_grad_var
->
GetMutable
<
framework
::
LoDTensor
>
();
framework
::
TensorCopySync
(
out_grad_tensor
,
dev_place
,
in_grad_tensor
);
framework
::
TensorCopy
(
out_grad_tensor
,
dev_place
,
dev_ctx
,
in_grad_tensor
);
in_grad_tensor
->
set_lod
(
out_grad_tensor
.
lod
());
}
}
...
...
python/paddle/fluid/layers/control_flow.py
浏览文件 @
c62674f4
...
...
@@ -267,8 +267,44 @@ class StaticRNN(object):
"""
StaticRNN class.
StaticRNN class is used to create a StaticRNN. The RNN will have its
own parameters like inputs, outputs, memories, status and length.
The StaticRNN can process a batch of sequence data. The length of each
sample sequence must be equal. The StaticRNN will have its own parameters
like inputs, outputs, memories. **Note that the first dimension of inputs
represents sequence length, and all the sequence length of inputs must be
the same. And the meaning of each axis of input and output are the same.**
Examples:
>>> import paddle.fluid as fluid
>>> import paddle.fluid.layers as layers
>>>
>>> vocab_size, hidden_size=10000, 200
>>> x = layers.data(name="x", shape=[-1, 1, 1], dtype='int64')
>>> x_emb = layers.embedding(
>>> input=x,
>>> size=[vocab_size, hidden_size],
>>> dtype='float32',
>>> is_sparse=False)
>>> x_emb = layers.transpose(x_emb, perm=[1, 0, 2])
>>>
>>> rnn = fluid.layers.StaticRNN()
>>> with rnn.step():
>>> word = rnn.step_input(x_emb)
>>> prev = rnn.memory(shape=[-1, hidden_size], batch_ref = word)
>>> hidden = fluid.layers.fc(input=[word, prev], size=hidden_size, act='relu')
>>> rnn.update_memory(prev, hidden) # set prev to hidden
>>> rnn.step_output(hidden)
>>>
>>> result = rnn()
The StaticRNN will unfold sequence into time steps. Users need to define
how to process each time step during the :code:`with` step.
The :code:`memory` is used as a staging data cross time step. The initial
value of memory can be a variable that is filled with a constant value or
a specified variable.
The StaticRNN can mark multiple variables as its output. Use `rnn()` to
get the output sequence.
"""
BEFORE_RNN_BLOCK
=
0
IN_RNN_BLOCK
=
1
...
...
@@ -284,6 +320,9 @@ class StaticRNN(object):
self
.
seq_len
=
None
def
step
(
self
):
"""
The block for user to define operators in RNN.
"""
return
BlockGuardWithCompletion
(
self
)
def
_assert_in_rnn_block_
(
self
,
method
):
...
...
@@ -298,13 +337,28 @@ class StaticRNN(object):
init_batch_dim_idx
=
0
,
ref_batch_dim_idx
=
1
):
"""
Create a memory variable for static rnn.
If the :code:`init` is not None, :code:`memory` will be initialized by
this Variable. If the :code:`init` is None, :code:`shape` and :code:`batch_ref`
must be set, and this function will initialize a :code:`init` Variable.
Args:
init: boot memory, if not set, a shape, batch_ref must be provided
shape: shape of the boot memory
batch_ref: batch size reference variable
init_value: the init value of boot memory
init_batch_dim_idx: the index of batch size in init's dimension
ref_batch_dim_idx: the index of batch size in batch_ref's dimension
init(Variable|None): The initialized variable. If it is not set,
:code:`shape` and :code:`batch_ref` must be provided.
Default: None.
shape(list|tuple): The shape of the boot memory. NOTE the shape
does not contain batch_size. Default: None.
batch_ref(Variable|None): The batch size reference Variable.
Default: None.
init_value(float): the init value of boot memory. Default: 0.0.
init_batch_dim_idx(int): the batch_size axis of the
:code:`init` Variable. Default: 0.
ref_batch_dim_idx(int): the batch_size axis of the
:code:`batch_ref` Variable. Default: 1.
Returns:
The memory variable.
"""
self
.
_assert_in_rnn_block_
(
'memory'
)
if
init
is
None
:
...
...
@@ -343,6 +397,16 @@ class StaticRNN(object):
return
pre_mem
def
step_input
(
self
,
x
):
"""
Mark a sequence as a StaticRNN input.
Args:
x(Variable): The input sequence, the shape of x
should be [seq_len, ...].
Returns:
The current time step in the input sequence.
"""
self
.
_assert_in_rnn_block_
(
'step_input'
)
if
not
isinstance
(
x
,
Variable
):
raise
TypeError
(
"step input takes a Variable"
)
...
...
@@ -357,6 +421,15 @@ class StaticRNN(object):
return
ipt
def
step_output
(
self
,
o
):
"""
Mark a sequence as a StaticRNN output.
Args:
o(Variable): The output sequence.
Returns:
None.
"""
self
.
_assert_in_rnn_block_
(
'step_output'
)
if
not
isinstance
(
o
,
Variable
):
raise
TypeError
(
"step output takes a Variable"
)
...
...
@@ -376,10 +449,30 @@ class StaticRNN(object):
self
.
outputs
.
append
(
out_var
)
def
output
(
self
,
*
outputs
):
"""
Mark the StaticRNN output variables.
Args:
outputs: The output Variables.
Returns:
None
"""
for
each
in
outputs
:
self
.
step_output
(
each
)
def
update_memory
(
self
,
mem
,
var
):
"""
Update the memory from ex_mem to new_mem. NOTE that the shape and data
type of :code:`ex_mem` and :code:`new_mem` must be same.
Args:
mem(Variable): the memory variable.
var(Variable): the plain variable generated in RNN block.
Returns:
None
"""
if
not
isinstance
(
mem
,
Variable
)
or
not
isinstance
(
var
,
Variable
):
raise
TypeError
(
"update memory should take variables"
)
self
.
memories
[
mem
.
name
].
mem
=
var
...
...
@@ -419,6 +512,9 @@ class StaticRNN(object):
for
m
in
self
.
memories
:
local_inputs
.
add
(
m
)
# NOTE(zcd): the params have two categories of variables.
# - the variables that are the out of StaticRnn.
# - the variables that are the parameters of some layers, for example, conv2d.
params
=
list
()
for
op
in
rnn_block
.
ops
:
assert
isinstance
(
op
,
Operator
)
...
...
@@ -435,17 +531,19 @@ class StaticRNN(object):
inlinks
=
[
parent_block
.
var
(
i
.
name
)
for
i
in
self
.
inputs
]
outlinks
=
self
.
outputs
# NOTE(zcd): the states maybe empty in some case.
boot_memories
=
[]
pre_memories
=
[]
memories
=
[]
for
_
,
mem
in
six
.
iteritems
(
self
.
memories
):
boot_memories
.
append
(
mem
.
init
)
pre_memories
.
append
(
mem
.
pre_mem
.
name
)
assert
mem
.
mem
is
not
None
,
"%s should be updated in every step."
%
(
mem
.
init
.
name
)
mem_var
=
rnn_block
.
var
(
mem
.
mem
.
name
)
assert
isinstance
(
mem_var
,
Variable
)
new_mem
=
self
.
helper
.
create_variable_for_type_inference
(
dtype
=
mem_var
.
dtype
)
rnn_block
.
append_op
(
type
=
'rnn_memory_helper'
,
inputs
=
{
'X'
:
[
mem_var
]},
...
...
@@ -464,6 +562,7 @@ class StaticRNN(object):
outputs
=
{
'outputs'
:
outlinks
,
'step_scopes'
:
[
step_scope
]},
attrs
=
{
'has_states'
:
len
(
pre_memories
)
>
0
,
'ex_states'
:
pre_memories
,
'states'
:
memories
,
'sub_block'
:
rnn_block
...
...
python/paddle/fluid/tests/unittests/CMakeLists.txt
浏览文件 @
c62674f4
...
...
@@ -25,7 +25,6 @@ endif()
list
(
REMOVE_ITEM TEST_OPS test_seq_concat_op
)
# FIXME(helin): https://github.com/PaddlePaddle/Paddle/issues/8290
list
(
REMOVE_ITEM TEST_OPS test_modified_huber_loss_op
)
# FIXME(qijun) https://github.com/PaddlePaddle/Paddle/issues/5184
list
(
REMOVE_ITEM TEST_OPS test_lstm_unit_op
)
# # FIXME(qijun) https://github.com/PaddlePaddle/Paddle/issues/5185
list
(
REMOVE_ITEM TEST_OPS test_recurrent_op
)
# FIXME(qijun) https://github.com/PaddlePaddle/Paddle/issues/6152
list
(
REMOVE_ITEM TEST_OPS test_cond_op
)
# FIXME(qijun): https://github.com/PaddlePaddle/Paddle/issues/5101#issuecomment-339814957
list
(
REMOVE_ITEM TEST_OPS op_test
)
# op_test is a helper python file, not a test
...
...
python/paddle/fluid/tests/unittests/test_recurrent_op.py
浏览文件 @
c62674f4
...
...
@@ -15,7 +15,7 @@
from
__future__
import
print_function
import
unittest
import
paddle.fluid
as
fluid
import
paddle.fluid.layers
as
layers
from
paddle.fluid.framework
import
Program
,
grad_var_name
from
paddle.fluid.executor
import
Executor
...
...
@@ -115,10 +115,6 @@ class RecurrentOpTest1(unittest.TestCase):
def
setup_program
(
self
):
self
.
main_program
=
Program
()
self
.
startup_program
=
Program
()
self
.
p_info
=
{
"main_program"
:
self
.
main_program
,
"startup_program"
:
self
.
startup_program
}
self
.
place
=
core
.
CPUPlace
()
def
setUp
(
self
):
...
...
@@ -129,33 +125,29 @@ class RecurrentOpTest1(unittest.TestCase):
self
.
output_shape
=
(
self
.
sent_len
,
self
.
batch_size
,
self
.
input_dim
)
self
.
py_rnn
=
PySimpleRNN1
(
self
.
input_shape
,
self
.
output_shape
)
self
.
output
=
layers
.
mean
(
self
.
create_rnn_op
(),
**
self
.
p_info
)
with
fluid
.
program_guard
(
self
.
main_program
,
self
.
startup_program
):
self
.
output
=
layers
.
mean
(
self
.
create_rnn_op
())
def
create_rnn_op
(
self
):
x
=
layers
.
data
(
shape
=
[
self
.
sent_len
,
self
.
batch_size
,
self
.
input_dim
],
dtype
=
'float32'
,
name
=
'x'
,
append_batch_size
=
False
,
**
self
.
p_info
)
append_batch_size
=
False
)
x
.
stop_gradient
=
False
h_boot
=
layers
.
data
(
shape
=
[
self
.
input_dim
],
dtype
=
'float32'
,
name
=
'h_boot'
,
**
self
.
p_info
)
shape
=
[
self
.
input_dim
],
dtype
=
'float32'
,
name
=
'h_boot'
)
h_boot
.
stop_gradient
=
False
rnn
=
layers
.
StaticRNN
(
main_program
=
self
.
main_program
)
rnn
=
layers
.
StaticRNN
()
with
rnn
.
step
():
h_pre
=
rnn
.
memory
(
init
=
h_boot
)
x_t
=
rnn
.
step_input
(
x
)
h
=
layers
.
scale
(
x
=
layers
.
elementwise_add
(
x
=
h_pre
,
y
=
x_t
,
**
self
.
p_info
),
scale
=
self
.
py_rnn
.
scale
,
**
self
.
p_info
)
x
=
h_pre
,
y
=
x_t
),
scale
=
self
.
py_rnn
.
scale
)
rnn
.
update_memory
(
h_pre
,
h
)
rnn
.
output
(
h
)
...
...
@@ -193,6 +185,7 @@ class RecurrentOpTest1(unittest.TestCase):
def
test_backward
(
self
):
self
.
check_forward
()
with
fluid
.
program_guard
(
self
.
main_program
,
self
.
startup_program
):
append_backward
(
self
.
output
)
ana_grad
=
[
np
.
array
(
x
)
for
x
in
self
.
backward
()]
...
...
@@ -205,12 +198,8 @@ class RecurrentOpTest1(unittest.TestCase):
num_grad
[
idx
],
ana_grad
[
idx
],
rtol
=
0.1
).
all
())
def
check_forward
(
self
):
print
(
'test recurrent op forward'
)
pd_output
=
self
.
forward
()
py_output
=
self
.
py_rnn
.
forward
()
print
(
'pd_output'
,
pd_output
)
print
print
(
'py_output'
,
py_output
)
self
.
assertEqual
(
pd_output
.
shape
,
py_output
.
shape
)
self
.
assertTrue
(
np
.
isclose
(
pd_output
,
py_output
,
rtol
=
0.1
).
all
())
...
...
@@ -263,24 +252,21 @@ class RecurrentOpTest2(RecurrentOpTest1):
self
.
output_shape
=
(
self
.
sent_len
,
self
.
batch_size
,
self
.
input_dim
)
self
.
py_rnn
=
PySimpleRNN2
(
self
.
input_shape
,
self
.
output_shape
)
self
.
output
=
layers
.
mean
(
self
.
create_rnn_op
(),
**
self
.
p_info
)
with
fluid
.
program_guard
(
self
.
main_program
,
self
.
startup_program
):
self
.
output
=
layers
.
mean
(
self
.
create_rnn_op
())
def
create_rnn_op
(
self
):
x
=
layers
.
data
(
shape
=
[
self
.
sent_len
,
self
.
batch_size
,
self
.
input_dim
],
dtype
=
'float32'
,
name
=
'x'
,
append_batch_size
=
False
,
**
self
.
p_info
)
append_batch_size
=
False
)
x
.
stop_gradient
=
False
h_boot
=
layers
.
data
(
shape
=
[
self
.
input_dim
],
dtype
=
'float32'
,
name
=
'h_boot'
,
**
self
.
p_info
)
shape
=
[
self
.
input_dim
],
dtype
=
'float32'
,
name
=
'h_boot'
)
h_boot
.
stop_gradient
=
False
rnn
=
layers
.
StaticRNN
(
main_program
=
self
.
main_program
)
rnn
=
layers
.
StaticRNN
()
with
rnn
.
step
():
h_pre
=
rnn
.
memory
(
init
=
h_boot
)
x_t
=
rnn
.
step_input
(
x
)
...
...
@@ -288,18 +274,13 @@ class RecurrentOpTest2(RecurrentOpTest1):
temp_l
=
layers
.
fc
(
input
=
x_t
,
size
=
self
.
input_dim
,
param_attr
=
'W'
,
bias_attr
=
False
,
**
self
.
p_info
)
bias_attr
=
False
)
temp_r
=
layers
.
fc
(
input
=
h_pre
,
size
=
self
.
input_dim
,
param_attr
=
'U'
,
bias_attr
=
False
,
**
self
.
p_info
)
bias_attr
=
False
)
h
=
layers
.
sigmoid
(
x
=
layers
.
elementwise_add
(
x
=
temp_l
,
y
=
temp_r
,
**
self
.
p_info
),
**
self
.
p_info
)
h
=
layers
.
sigmoid
(
x
=
layers
.
elementwise_add
(
x
=
temp_l
,
y
=
temp_r
))
rnn
.
update_memory
(
h_pre
,
h
)
rnn
.
output
(
h
)
...
...
@@ -362,40 +343,38 @@ class RecurrentOpMultipleMemoryTest(RecurrentOpTest1):
self
.
py_rnn
=
RecurrentOpMultipleMemoryTest
.
PySimpleRNN3
(
self
.
input_shape
,
self
.
output_shape
)
self
.
output
=
layers
.
mean
(
self
.
create_rnn_op
(),
**
self
.
p_info
)
with
fluid
.
program_guard
(
self
.
main_program
,
self
.
startup_program
):
self
.
output
=
layers
.
mean
(
self
.
create_rnn_op
())
def
create_rnn_op
(
self
):
x
=
layers
.
data
(
shape
=
[
self
.
sent_len
,
self
.
batch_size
,
self
.
input_dim
],
dtype
=
'float32'
,
name
=
'x'
,
append_batch_size
=
False
,
**
self
.
p_info
)
append_batch_size
=
False
)
x
.
stop_gradient
=
False
h_boot1
=
layers
.
data
(
shape
=
[
self
.
batch_size
,
self
.
input_dim
],
dtype
=
'float32'
,
name
=
'h_boot1'
,
append_batch_size
=
False
,
**
self
.
p_info
)
append_batch_size
=
False
)
h_boot1
.
stop_gradient
=
False
h_boot2
=
layers
.
data
(
shape
=
[
self
.
batch_size
,
self
.
input_dim
],
dtype
=
'float32'
,
name
=
'h_boot2'
,
append_batch_size
=
False
,
**
self
.
p_info
)
append_batch_size
=
False
)
h_boot2
.
stop_gradient
=
False
rnn
=
layers
.
StaticRNN
(
main_program
=
self
.
main_program
)
rnn
=
layers
.
StaticRNN
()
with
rnn
.
step
():
h_pre1
=
rnn
.
memory
(
init
=
h_boot1
)
h_pre2
=
rnn
.
memory
(
init
=
h_boot2
)
x_t
=
rnn
.
step_input
(
x
)
mem1
=
layers
.
scale
(
x
=
h_pre1
,
scale
=
1.0
,
**
self
.
p_info
)
mem2
=
layers
.
scale
(
x
=
h_pre2
,
scale
=
1.0
,
**
self
.
p_info
)
out
=
layers
.
sums
(
input
=
[
mem1
,
x_t
,
mem2
]
,
**
self
.
p_info
)
mem1
=
layers
.
scale
(
x
=
h_pre1
,
scale
=
1.0
)
mem2
=
layers
.
scale
(
x
=
h_pre2
,
scale
=
1.0
)
out
=
layers
.
sums
(
input
=
[
mem1
,
x_t
,
mem2
])
rnn
.
update_memory
(
h_pre1
,
mem1
)
rnn
.
update_memory
(
h_pre2
,
mem2
)
...
...
@@ -446,23 +425,23 @@ class RecurrentOpNoMemBootTest(RecurrentOpTest1):
self
.
output_shape
=
(
self
.
sent_len
,
self
.
batch_size
,
self
.
input_dim
)
self
.
py_rnn
=
RecurrentOpNoMemBootTest
.
PySimpleRNN4
(
self
.
input_shape
,
self
.
output_shape
)
self
.
output
=
layers
.
mean
(
self
.
create_rnn_op
(),
**
self
.
p_info
)
print
(
self
.
main_program
)
with
fluid
.
program_guard
(
self
.
main_program
,
self
.
startup_program
):
self
.
output
=
layers
.
mean
(
self
.
create_rnn_op
())
def
create_rnn_op
(
self
):
x
=
layers
.
data
(
shape
=
[
self
.
sent_len
,
self
.
batch_size
,
self
.
input_dim
],
dtype
=
'float32'
,
name
=
'x'
,
append_batch_size
=
False
,
**
self
.
p_info
)
append_batch_size
=
False
)
x
.
stop_gradient
=
False
rnn
=
layers
.
StaticRNN
(
main_program
=
self
.
main_program
)
rnn
=
layers
.
StaticRNN
()
with
rnn
.
step
():
mem_pre
=
rnn
.
memory
(
shape
=
[
-
1
,
self
.
input_dim
],
batch_ref
=
x
)
x_t
=
rnn
.
step_input
(
x
)
mem
=
layers
.
elementwise_add
(
x
=
mem_pre
,
y
=
x_t
,
**
self
.
p_info
)
mem
=
layers
.
elementwise_add
(
x
=
mem_pre
,
y
=
x_t
)
rnn
.
update_memory
(
mem_pre
,
mem
)
rnn
.
output
(
mem
)
...
...
编辑
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