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d080d3e6
编写于
8月 10, 2018
作者:
Q
qiaolongfei
浏览文件
操作
浏览文件
下载
差异文件
Merge branch 'develop' of
https://github.com/PaddlePaddle/Paddle
into timeline-support-pure-cpu
上级
e008600b
cf799a6a
变更
14
隐藏空白更改
内联
并排
Showing
14 changed file
with
369 addition
and
47 deletion
+369
-47
paddle/fluid/inference/analysis/data_flow_graph.cc
paddle/fluid/inference/analysis/data_flow_graph.cc
+28
-0
paddle/fluid/inference/analysis/data_flow_graph.h
paddle/fluid/inference/analysis/data_flow_graph.h
+1
-0
paddle/fluid/inference/analysis/data_flow_graph_to_fluid_pass.cc
...fluid/inference/analysis/data_flow_graph_to_fluid_pass.cc
+1
-0
paddle/fluid/inference/analysis/dfg_graphviz_draw_pass.cc
paddle/fluid/inference/analysis/dfg_graphviz_draw_pass.cc
+3
-3
paddle/fluid/inference/api/api.cc
paddle/fluid/inference/api/api.cc
+22
-4
paddle/fluid/inference/api/paddle_inference_api.h
paddle/fluid/inference/api/paddle_inference_api.h
+2
-1
paddle/fluid/operators/elementwise_op_function.h
paddle/fluid/operators/elementwise_op_function.h
+6
-6
paddle/fluid/operators/softmax_with_cross_entropy_op.cu
paddle/fluid/operators/softmax_with_cross_entropy_op.cu
+209
-9
python/paddle/dataset/conll05.py
python/paddle/dataset/conll05.py
+4
-4
python/paddle/dataset/wmt14.py
python/paddle/dataset/wmt14.py
+1
-1
python/paddle/fluid/tests/unittests/test_dist_transpiler.py
python/paddle/fluid/tests/unittests/test_dist_transpiler.py
+78
-6
python/paddle/fluid/transpiler/distribute_transpiler.py
python/paddle/fluid/transpiler/distribute_transpiler.py
+9
-8
python/paddle/v2/dataset/conll05.py
python/paddle/v2/dataset/conll05.py
+4
-4
python/paddle/v2/dataset/wmt14.py
python/paddle/v2/dataset/wmt14.py
+1
-1
未找到文件。
paddle/fluid/inference/analysis/data_flow_graph.cc
浏览文件 @
d080d3e6
...
...
@@ -337,6 +337,34 @@ ExtractInputAndOutputOfSubGraph(std::vector<Node *> &graph) { // NOLINT
std
::
vector
<
Node
*>
(
outputs
.
begin
(),
outputs
.
end
()));
}
void
FilterRedundantOutputOfSubGraph
(
DataFlowGraph
*
graph
)
{
std
::
vector
<
Node
*>
op_nodes
;
for
(
auto
&
node
:
GraphTraits
<
DataFlowGraph
>
(
graph
).
nodes_in_TS
())
{
if
(
node
.
type
()
==
Node
::
Type
::
kValue
||
node
.
deleted
())
{
continue
;
}
op_nodes
.
push_back
(
&
node
);
}
size_t
op_num
=
op_nodes
.
size
();
for
(
size_t
i
=
0
;
i
<
op_num
;
i
++
)
{
if
(
op_nodes
[
i
]
->
type
()
==
Node
::
Type
::
kFunction
)
continue
;
std
::
unordered_set
<
std
::
string
>
follow_up_input_names
;
for
(
size_t
j
=
i
+
1
;
j
<
op_num
;
j
++
)
{
for
(
auto
*
in
:
op_nodes
[
j
]
->
inlinks
)
{
follow_up_input_names
.
insert
(
in
->
name
());
}
}
std
::
vector
<
Node
*>
filtered_subgraph_outlinks
;
for
(
auto
*
out
:
op_nodes
[
i
]
->
outlinks
)
{
if
(
follow_up_input_names
.
count
(
out
->
name
()))
{
filtered_subgraph_outlinks
.
push_back
(
out
);
}
}
PADDLE_ENFORCE_GE
(
filtered_subgraph_outlinks
.
size
(),
1UL
);
op_nodes
[
i
]
->
outlinks
=
filtered_subgraph_outlinks
;
}
}
}
// namespace analysis
}
// namespace inference
}
// namespace paddle
paddle/fluid/inference/analysis/data_flow_graph.h
浏览文件 @
d080d3e6
...
...
@@ -178,6 +178,7 @@ struct GraphTraits<DataFlowGraph> {
std
::
pair
<
std
::
vector
<
Node
*>
,
std
::
vector
<
Node
*>>
ExtractInputAndOutputOfSubGraph
(
std
::
vector
<
Node
*>
&
graph
);
// NOLINT
void
FilterRedundantOutputOfSubGraph
(
DataFlowGraph
*
graph
);
}
// namespace analysis
}
// namespace inference
}
// namespace paddle
paddle/fluid/inference/analysis/data_flow_graph_to_fluid_pass.cc
浏览文件 @
d080d3e6
...
...
@@ -52,6 +52,7 @@ bool DataFlowGraphToFluidPass::Initialize(Argument *argument) {
bool
DataFlowGraphToFluidPass
::
Finalize
()
{
return
true
;
}
void
DataFlowGraphToFluidPass
::
Run
(
DataFlowGraph
*
graph
)
{
FilterRedundantOutputOfSubGraph
(
graph
);
LOG
(
INFO
)
<<
"graph.inputs "
<<
graph
->
inputs
.
size
();
for
(
auto
&
node
:
GraphTraits
<
DataFlowGraph
>
(
graph
).
nodes_in_TS
())
{
if
(
node
.
deleted
())
continue
;
...
...
paddle/fluid/inference/analysis/dfg_graphviz_draw_pass.cc
浏览文件 @
d080d3e6
...
...
@@ -46,9 +46,9 @@ std::string DFG_GraphvizDrawPass::Draw(DataFlowGraph *graph) {
for
(
size_t
i
=
0
;
i
<
graph
->
nodes
.
size
();
i
++
)
{
const
Node
&
node
=
graph
->
nodes
.
Get
(
i
);
if
(
!
config_
.
display_deleted_node
&&
node
.
deleted
())
continue
;
for
(
auto
&
in
:
node
.
in
links
)
{
if
(
!
config_
.
display_deleted_node
&&
in
->
deleted
())
continue
;
dot
.
AddEdge
(
in
->
repr
(),
node
.
repr
(),
{});
for
(
auto
&
out
:
node
.
out
links
)
{
if
(
!
config_
.
display_deleted_node
&&
out
->
deleted
())
continue
;
dot
.
AddEdge
(
node
.
repr
(),
out
->
repr
(),
{});
}
}
return
dot
.
Build
();
...
...
paddle/fluid/inference/api/api.cc
浏览文件 @
d080d3e6
...
...
@@ -12,6 +12,7 @@ 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. */
#include <glog/logging.h>
#include "paddle/fluid/inference/api/paddle_inference_api.h"
namespace
paddle
{
...
...
@@ -40,19 +41,36 @@ PaddleBuf::PaddleBuf(PaddleBuf&& other)
PaddleBuf
::
PaddleBuf
(
const
PaddleBuf
&
other
)
{
*
this
=
other
;
}
PaddleBuf
&
PaddleBuf
::
operator
=
(
const
PaddleBuf
&
other
)
{
if
(
!
other
.
memory_owned_
)
{
data_
=
other
.
data_
;
length_
=
other
.
length_
;
memory_owned_
=
other
.
memory_owned_
;
}
else
{
Resize
(
other
.
length
());
memcpy
(
data_
,
other
.
data
(),
other
.
length
());
length_
=
other
.
length
();
memory_owned_
=
true
;
}
return
*
this
;
}
PaddleBuf
&
PaddleBuf
::
operator
=
(
PaddleBuf
&&
other
)
{
// only the buffer with external memory can be copied
assert
(
!
other
.
memory_owned_
);
data_
=
other
.
data_
;
length_
=
other
.
length_
;
memory_owned_
=
other
.
memory_owned_
;
other
.
data_
=
nullptr
;
other
.
length_
=
0
;
other
.
memory_owned_
=
false
;
return
*
this
;
}
void
PaddleBuf
::
Resize
(
size_t
length
)
{
// Only the owned memory can be reset, the external memory can't be changed.
if
(
length_
==
length
)
return
;
assert
(
memory_owned_
);
Free
();
if
(
memory_owned_
)
{
Free
();
}
data_
=
new
char
[
length
];
length_
=
length
;
memory_owned_
=
true
;
...
...
@@ -68,7 +86,7 @@ void PaddleBuf::Reset(void* data, size_t length) {
void
PaddleBuf
::
Free
()
{
if
(
memory_owned_
&&
data_
)
{
assert
(
length_
>
0
);
delete
static_cast
<
char
*>
(
data_
);
delete
[]
static_cast
<
char
*>
(
data_
);
data_
=
nullptr
;
length_
=
0
;
}
...
...
paddle/fluid/inference/api/paddle_inference_api.h
浏览文件 @
d080d3e6
...
...
@@ -40,11 +40,12 @@ class PaddleBuf {
// Copy only available when memory is managed externally.
explicit
PaddleBuf
(
const
PaddleBuf
&
);
PaddleBuf
&
operator
=
(
const
PaddleBuf
&
);
PaddleBuf
&
operator
=
(
PaddleBuf
&&
);
// Do not own the memory.
PaddleBuf
(
void
*
data
,
size_t
length
)
:
data_
(
data
),
length_
(
length
),
memory_owned_
{
false
}
{}
// Own memory.
explicit
PaddleBuf
(
size_t
length
)
PaddleBuf
(
size_t
length
)
:
data_
(
new
char
[
length
]),
length_
(
length
),
memory_owned_
(
true
)
{}
// Resize to `length` bytes.
void
Resize
(
size_t
length
);
...
...
paddle/fluid/operators/elementwise_op_function.h
浏览文件 @
d080d3e6
...
...
@@ -534,8 +534,8 @@ void ElemwiseGradCompute(const framework::ExecutionContext& ctx,
const
framework
::
Tensor
&
dout
,
int
axis
,
framework
::
Tensor
*
dx
,
framework
::
Tensor
*
dy
,
DX_OP
dx_op
,
DY_OP
dy_op
)
{
const
framework
::
DDim
x_dim
=
x
.
dims
();
const
framework
::
DDim
y_dim
=
y
.
dims
();
const
framework
::
DDim
&
x_dim
=
x
.
dims
();
const
framework
::
DDim
&
y_dim
=
y
.
dims
();
if
(
x
.
dims
()
==
y
.
dims
())
{
ElemwiseGradComputeNoBroadcast
<
DeviceContext
,
T
,
DX_OP
,
DY_OP
>
(
ctx
,
x_dim
,
y_dim
,
x
,
y
,
out
,
dout
,
axis
,
dx
,
dy
,
dx_op
,
dy_op
);
...
...
@@ -558,19 +558,19 @@ void ElemwiseExplicitGradCompute(const framework::ExecutionContext& ctx,
framework
::
Tensor
*
dx
,
framework
::
Tensor
*
dy
,
DX_OP
dx_op
,
DY_OP
dy_op
)
{
if
(
dy
==
nullptr
)
{
const
framework
::
DDim
dx_dims
=
dout
.
dims
();
const
framework
::
DDim
&
dx_dims
=
dout
.
dims
();
auto
dy_dims
=
dx_dims
;
ElemwiseGradComputeNoBroadcast
<
DeviceContext
,
T
,
DX_OP
,
DY_OP
>
(
ctx
,
dx_dims
,
dy_dims
,
x
,
y
,
out
,
dout
,
axis
,
dx
,
dy
,
dx_op
,
dy_op
);
}
else
{
if
(
dout
.
dims
()
==
dy
->
dims
())
{
const
framework
::
DDim
dx_dims
=
dout
.
dims
();
const
framework
::
DDim
dy_dims
=
dy
->
dims
();
const
framework
::
DDim
&
dx_dims
=
dout
.
dims
();
const
framework
::
DDim
&
dy_dims
=
dy
->
dims
();
ElemwiseGradComputeNoBroadcast
<
DeviceContext
,
T
,
DX_OP
,
DY_OP
>
(
ctx
,
dx_dims
,
dy_dims
,
x
,
y
,
out
,
dout
,
axis
,
dx
,
dy
,
dx_op
,
dy_op
);
}
else
{
// Y is a scalar
auto
dx_dims
=
dout
.
dims
();
const
framework
::
DDim
dy_dims
=
dy
->
dims
();
const
framework
::
DDim
&
dy_dims
=
dy
->
dims
();
ElemwiseGradComputeWithBroadcast
<
DeviceContext
,
T
,
DX_OP
,
DY_OP
>
(
ctx
,
dx_dims
,
dy_dims
,
x
,
y
,
out
,
dout
,
axis
,
dx
,
dy
,
dx_op
,
dy_op
);
}
...
...
paddle/fluid/operators/softmax_with_cross_entropy_op.cu
浏览文件 @
d080d3e6
/* Copyright (c) 201
6
PaddlePaddle Authors. All Rights Reserved.
/* Copyright (c) 201
8
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.
...
...
@@ -14,6 +14,8 @@ limitations under the License. */
#define EIGEN_USE_GPU
#include <cub/cub.cuh>
#include "paddle/fluid/operators/math/cross_entropy.h"
#include "paddle/fluid/operators/softmax_with_cross_entropy_op.h"
namespace
paddle
{
...
...
@@ -53,8 +55,196 @@ __global__ void SoftCrossEntropyGradientKernel(T* logit_grad,
logit_grad
[
ids
]
=
loss_grad
[
row_ids
]
*
(
logit_grad
[
ids
]
-
labels
[
ids
]);
}
}
}
// namespace
static
__device__
__forceinline__
float
real_exp
(
float
x
)
{
return
expf
(
x
);
}
static
__device__
__forceinline__
double
real_exp
(
double
x
)
{
return
exp
(
x
);
}
static
__device__
__forceinline__
float
real_log
(
float
x
)
{
return
math
::
TolerableValue
<
float
>
()(
logf
(
x
));
}
static
__device__
__forceinline__
double
real_log
(
double
x
)
{
return
math
::
TolerableValue
<
double
>
()(
log
(
x
));
}
/** In the following codes, 3 CUDA kernels are implemented to calculate softmax
* and loss **/
/*
Supposing the x is `logits` and y is `labels`, the equations are as
followings:
cross\_entropy_i = \sum_{j}[- y_i_j * log({e^{x_i_j}/\sum_{j}e^{x_i_j}})]
= \sum_{j}[- y_i_j * log({e^{x_i_j - max_i}/\sum_{j}e^{x_i_j-max_i}})]
= \sum_{j}[-y_i_j * (x_i_j - max_i - log\sum_{j}e^{x_i_j - max_i})]
= \sum_{j}[-y_i_j * (x_i_j - max_i - logDiffMaxSum_i)]
= \sum_{j}(-y_i_j * tmp_i_j)
softmax_i_j = e^{tmp_i_j}
where:
max_i = \max_{j}{x_i_j}
logDiffMaxSum_i = log\sum_{j}e^{x_i_j - max_i}
tmp_i_j = x_i_j - max_i - logDiffMaxSum_i
Therefore, the calculation can be separated into 3 steps:
Step 1: row-wise operation to calculate max_i
Step 2: row-wise operation to calculate logDiffMaxSum_i
Step 3: caculate tmp_i_j, and finally get softmax_i_j and cross\_entropy_i
To save memory, we can share memory among max_i, logDiffMaxSum_i and
cross\_entropy_i.
In this way, the 3 steps should be changed to:
Step 1 (RowReductionForMax): row-wise operation to calculate max_i
Step 2 (RowReductionForDiffMaxSum): calculate immediate result of softmax'_i_j =
x_i_j - max_i, and row-wise operation to calculate logDiffMaxSum_i
Step 3 (RowReductionForSoftmaxAndCrossEntropy): calculate tmp_i_j = softmax'_i_j
- logDiffMaxSum_i, and finally get softmax_i_j and cross\_entropy_i
*/
// There are 3 kinds of reduce algorithms in cub:
// BLOCK_REDUCE_RAKING_COMMUTATIVE_ONLY
// BLOCK_REDUCE_RAKING
// BLOCK_REDUCE_WARP_REDUCTIONS (default)
template
<
typename
T
,
int
BlockDim
>
using
BlockReduce
=
cub
::
BlockReduce
<
T
,
BlockDim
/*, cub::BLOCK_REDUCE_WARP_REDUCTIONS*/
>
;
template
<
typename
T
,
int
BlockDim
>
using
BlockReduceTempStorage
=
typename
BlockReduce
<
T
,
BlockDim
>::
TempStorage
;
// Make sure that BlockDim <= feature_size
// This kernel is used to calculate the max element of each row
template
<
typename
T
,
int
BlockDim
>
__global__
void
RowReductionForMax
(
const
T
*
logits_data
,
T
*
max_data
,
int
feature_size
)
{
__shared__
BlockReduceTempStorage
<
T
,
BlockDim
>
temp_storage
;
auto
beg_idx
=
feature_size
*
blockIdx
.
x
+
threadIdx
.
x
;
auto
end_idx
=
feature_size
*
(
blockIdx
.
x
+
1
);
T
cur_max
=
logits_data
[
beg_idx
];
beg_idx
+=
BlockDim
;
while
(
beg_idx
<
end_idx
)
{
if
(
cur_max
<
logits_data
[
beg_idx
])
{
cur_max
=
logits_data
[
beg_idx
];
}
beg_idx
+=
BlockDim
;
}
cur_max
=
BlockReduce
<
T
,
BlockDim
>
(
temp_storage
).
Reduce
(
cur_max
,
cub
::
Max
());
if
(
threadIdx
.
x
==
0
)
{
max_data
[
blockIdx
.
x
]
=
cur_max
<
-
64
?
-
64
:
cur_max
;
}
}
// Make sure that BlockDim <= feature_size
template
<
typename
T
,
int
BlockDim
>
__global__
void
RowReductionForDiffMaxSum
(
const
T
*
logits_data
,
T
*
max_data
,
T
*
softmax
,
int
feature_size
)
{
__shared__
BlockReduceTempStorage
<
T
,
BlockDim
>
temp_storage
;
auto
beg_idx
=
feature_size
*
blockIdx
.
x
+
threadIdx
.
x
;
auto
end_idx
=
feature_size
*
(
blockIdx
.
x
+
1
);
auto
block_max
=
max_data
[
blockIdx
.
x
];
softmax
[
beg_idx
]
=
logits_data
[
beg_idx
]
-
block_max
;
T
diff_max_sum
=
real_exp
(
softmax
[
beg_idx
]);
beg_idx
+=
BlockDim
;
while
(
beg_idx
<
end_idx
)
{
softmax
[
beg_idx
]
=
logits_data
[
beg_idx
]
-
block_max
;
diff_max_sum
+=
real_exp
(
softmax
[
beg_idx
]);
beg_idx
+=
BlockDim
;
}
diff_max_sum
=
BlockReduce
<
T
,
BlockDim
>
(
temp_storage
).
Reduce
(
diff_max_sum
,
cub
::
Sum
());
if
(
threadIdx
.
x
==
0
)
max_data
[
blockIdx
.
x
]
=
real_log
(
diff_max_sum
);
}
// Make sure that BlockDim <= feature_size
template
<
typename
T
,
int
BlockDim
>
__global__
void
RowReductionForSoftmaxAndCrossEntropy
(
const
T
*
logits_data
,
const
T
*
labels_data
,
T
*
loss_data
,
T
*
softmax
,
int
feature_size
)
{
__shared__
BlockReduceTempStorage
<
T
,
BlockDim
>
temp_storage
;
auto
beg_idx
=
feature_size
*
blockIdx
.
x
+
threadIdx
.
x
;
auto
end_idx
=
feature_size
*
(
blockIdx
.
x
+
1
);
// log_diff_max_sum shares memory with loss
auto
block_log_diff_max_sum
=
loss_data
[
blockIdx
.
x
];
auto
tmp
=
softmax
[
beg_idx
]
-
block_log_diff_max_sum
;
softmax
[
beg_idx
]
=
real_exp
(
tmp
);
auto
loss
=
-
labels_data
[
beg_idx
]
*
tmp
;
beg_idx
+=
BlockDim
;
while
(
beg_idx
<
end_idx
)
{
tmp
=
softmax
[
beg_idx
]
-
block_log_diff_max_sum
;
softmax
[
beg_idx
]
=
real_exp
(
tmp
);
loss
-=
(
labels_data
[
beg_idx
]
*
tmp
);
beg_idx
+=
BlockDim
;
}
loss
=
BlockReduce
<
T
,
BlockDim
>
(
temp_storage
).
Reduce
(
loss
,
cub
::
Sum
());
if
(
threadIdx
.
x
==
0
)
loss_data
[
blockIdx
.
x
]
=
loss
;
}
template
<
typename
T
>
__global__
void
SetSoftmaxToOneWhenFeatureSizeIsOne
(
T
*
out
,
int
batch_size
)
{
auto
idx
=
threadIdx
.
x
+
blockIdx
.
x
*
blockDim
.
x
;
if
(
idx
<
batch_size
)
out
[
idx
]
=
static_cast
<
T
>
(
1
);
}
template
<
typename
T
>
static
void
SoftmaxWithCrossEntropyFusedKernel
(
const
T
*
logits_data
,
const
T
*
labels_data
,
T
*
softmax_data
,
T
*
loss_data
,
int
batch_size
,
int
feature_size
,
cudaStream_t
stream
)
{
constexpr
int
kMaxBlockDim
=
512
;
int
block_dim
=
feature_size
>=
kMaxBlockDim
?
kMaxBlockDim
:
(
1
<<
static_cast
<
int
>
(
std
::
log2
(
feature_size
)));
#define CALL_SOFTMAX_WITH_CROSS_ENTROPY_FUSED_KERNEL(BlockDim) \
case BlockDim: \
RowReductionForMax<T, BlockDim><<<batch_size, BlockDim, 0, stream>>>( \
logits_data, loss_data, feature_size); \
RowReductionForDiffMaxSum<T, \
BlockDim><<<batch_size, BlockDim, 0, stream>>>( \
logits_data, loss_data, softmax_data, feature_size); \
RowReductionForSoftmaxAndCrossEntropy< \
T, BlockDim><<<batch_size, BlockDim, 0, stream>>>( \
logits_data, labels_data, loss_data, softmax_data, feature_size); \
break
switch
(
block_dim
)
{
CALL_SOFTMAX_WITH_CROSS_ENTROPY_FUSED_KERNEL
(
512
);
CALL_SOFTMAX_WITH_CROSS_ENTROPY_FUSED_KERNEL
(
256
);
CALL_SOFTMAX_WITH_CROSS_ENTROPY_FUSED_KERNEL
(
128
);
CALL_SOFTMAX_WITH_CROSS_ENTROPY_FUSED_KERNEL
(
64
);
CALL_SOFTMAX_WITH_CROSS_ENTROPY_FUSED_KERNEL
(
32
);
CALL_SOFTMAX_WITH_CROSS_ENTROPY_FUSED_KERNEL
(
16
);
CALL_SOFTMAX_WITH_CROSS_ENTROPY_FUSED_KERNEL
(
8
);
CALL_SOFTMAX_WITH_CROSS_ENTROPY_FUSED_KERNEL
(
4
);
CALL_SOFTMAX_WITH_CROSS_ENTROPY_FUSED_KERNEL
(
2
);
case
1
:
SetSoftmaxToOneWhenFeatureSizeIsOne
<<<
(
batch_size
+
kMaxBlockDim
-
1
)
/
kMaxBlockDim
,
kMaxBlockDim
,
0
,
stream
>>>
(
softmax_data
,
batch_size
);
cudaMemsetAsync
(
loss_data
,
0
,
batch_size
,
stream
);
break
;
default:
PADDLE_THROW
(
"BlockDim must be 2^n in softmax_with_cross_entropy_op"
);
break
;
}
#undef CALL_SOFTMAX_WITH_CROSS_ENTROPY_FUSED_KERNEL
}
template
<
typename
T
>
class
SoftmaxWithCrossEntropyCUDAKernel
:
public
framework
::
OpKernel
<
T
>
{
public:
...
...
@@ -66,14 +256,24 @@ class SoftmaxWithCrossEntropyCUDAKernel : public framework::OpKernel<T> {
Tensor
*
softmax
=
context
.
Output
<
Tensor
>
(
"Softmax"
);
Tensor
*
loss
=
context
.
Output
<
Tensor
>
(
"Loss"
);
softmax
->
mutable_data
<
T
>
(
context
.
GetPlace
());
loss
->
mutable_data
<
T
>
(
context
.
GetPlace
());
math
::
SoftmaxFunctor
<
platform
::
CUDADeviceContext
,
T
>
()(
context
.
cuda_device_context
(),
logits
,
softmax
);
math
::
CrossEntropyFunctor
<
platform
::
CUDADeviceContext
,
T
>
()(
context
.
cuda_device_context
(),
loss
,
softmax
,
labels
,
context
.
Attr
<
bool
>
(
"soft_label"
));
auto
*
softmax_data
=
softmax
->
mutable_data
<
T
>
(
context
.
GetPlace
());
auto
*
loss_data
=
loss
->
mutable_data
<
T
>
(
context
.
GetPlace
());
auto
soft_label
=
context
.
Attr
<
bool
>
(
"soft_label"
);
if
(
soft_label
)
{
int
batch_size
=
logits
->
dims
()[
0
];
int
feature_size
=
logits
->
dims
()[
1
];
auto
*
logits_data
=
logits
->
data
<
T
>
();
auto
*
labels_data
=
labels
->
data
<
T
>
();
SoftmaxWithCrossEntropyFusedKernel
(
logits_data
,
labels_data
,
softmax_data
,
loss_data
,
batch_size
,
feature_size
,
context
.
cuda_device_context
().
stream
());
}
else
{
math
::
SoftmaxCUDNNFunctor
<
T
>
()(
context
.
cuda_device_context
(),
logits
,
softmax
);
math
::
CrossEntropyFunctor
<
platform
::
CUDADeviceContext
,
T
>
()(
context
.
cuda_device_context
(),
loss
,
softmax
,
labels
,
false
);
}
}
};
...
...
python/paddle/dataset/conll05.py
浏览文件 @
d080d3e6
...
...
@@ -29,13 +29,13 @@ __all__ = ['test, get_dict', 'get_embedding', 'convert']
DATA_URL
=
'http://www.cs.upc.edu/~srlconll/conll05st-tests.tar.gz'
DATA_MD5
=
'387719152ae52d60422c016e92a742fc'
WORDDICT_URL
=
'http://paddle
paddle.bj.bcebos.com/demo/srl_dict_and_embedding/
wordDict.txt'
WORDDICT_URL
=
'http://paddle
models.bj.bcebos.com/conll05st%2F
wordDict.txt'
WORDDICT_MD5
=
'ea7fb7d4c75cc6254716f0177a506baa'
VERBDICT_URL
=
'http://paddle
paddle.bj.bcebos.com/demo/srl_dict_and_embedding/
verbDict.txt'
VERBDICT_URL
=
'http://paddle
models.bj.bcebos.com/conll05st%2F
verbDict.txt'
VERBDICT_MD5
=
'0d2977293bbb6cbefab5b0f97db1e77c'
TRGDICT_URL
=
'http://paddle
paddle.bj.bcebos.com/demo/srl_dict_and_embedding/
targetDict.txt'
TRGDICT_URL
=
'http://paddle
models.bj.bcebos.com/conll05st%2F
targetDict.txt'
TRGDICT_MD5
=
'd8c7f03ceb5fc2e5a0fa7503a4353751'
EMB_URL
=
'http://paddle
paddle.bj.bcebos.com/demo/srl_dict_and_embedding/
emb'
EMB_URL
=
'http://paddle
models.bj.bcebos.com/conll05st%2F
emb'
EMB_MD5
=
'bf436eb0faa1f6f9103017f8be57cdb7'
UNK_IDX
=
0
...
...
python/paddle/dataset/wmt14.py
浏览文件 @
d080d3e6
...
...
@@ -40,7 +40,7 @@ URL_TRAIN = ('http://paddlepaddle.cdn.bcebos.com/demo/'
'wmt_shrinked_data/wmt14.tgz'
)
MD5_TRAIN
=
'0791583d57d5beb693b9414c5b36798c'
# BLEU of this trained model is 26.92
URL_MODEL
=
'http://paddle
paddle.bj.bcebos.com/demo/wmt_14/wmt14_model.tar.
gz'
URL_MODEL
=
'http://paddle
models.bj.bcebos.com/wmt%2Fwmt14.t
gz'
MD5_MODEL
=
'0cb4a5366189b6acba876491c8724fa3'
START
=
"<s>"
...
...
python/paddle/fluid/tests/unittests/test_dist_transpiler.py
浏览文件 @
d080d3e6
...
...
@@ -51,17 +51,17 @@ class TranspilerTest(unittest.TestCase):
self
.
origin_prog
=
main
.
clone
()
return
main
def
get_trainer
(
self
,
config
=
None
):
t
=
self
.
_transpiler_instance
(
config
)
def
get_trainer
(
self
,
config
=
None
,
sync_mode
=
True
):
t
=
self
.
_transpiler_instance
(
config
,
sync_mode
)
return
t
.
get_trainer_program
()
def
get_pserver
(
self
,
ep
,
config
=
None
):
t
=
self
.
_transpiler_instance
(
config
)
def
get_pserver
(
self
,
ep
,
config
=
None
,
sync_mode
=
True
):
t
=
self
.
_transpiler_instance
(
config
,
sync_mode
)
pserver
=
t
.
get_pserver_program
(
ep
)
startup
=
t
.
get_startup_program
(
ep
,
pserver
)
return
pserver
,
startup
def
_transpiler_instance
(
self
,
config
=
None
):
def
_transpiler_instance
(
self
,
config
=
None
,
sync_mode
=
True
):
if
not
self
.
transpiler
:
main
=
self
.
get_main_program
()
self
.
transpiler
=
fluid
.
DistributeTranspiler
(
config
=
config
)
...
...
@@ -69,7 +69,8 @@ class TranspilerTest(unittest.TestCase):
self
.
trainer_id
,
program
=
main
,
pservers
=
self
.
pserver_eps
,
trainers
=
self
.
trainers
)
trainers
=
self
.
trainers
,
sync_mode
=
sync_mode
)
return
self
.
transpiler
...
...
@@ -464,5 +465,76 @@ class TestDistLookupTable(TestDistLookupTableBase):
self
.
assertEqual
([
op
.
type
for
op
in
trainer
.
blocks
[
0
].
ops
],
ops
)
class
TestAsyncLocalLookupTable
(
TestDistLookupTableBase
):
def
net_conf
(
self
):
self
.
network_with_table
(
is_sparse
=
True
,
is_distributed
=
False
)
def
transpiler_test_impl
(
self
):
config
=
fluid
.
DistributeTranspilerConfig
()
pserver1
,
startup1
=
self
.
get_pserver
(
self
.
pserver1_ep
,
config
,
False
)
self
.
assertEqual
(
len
(
pserver1
.
blocks
),
3
)
# 0 listen_and_serv
# 1 optimize for fc_w or fc_b adam
self
.
assertEqual
([
op
.
type
for
op
in
pserver1
.
blocks
[
1
].
ops
],
[
"adam"
,
"scale"
,
"scale"
])
# 2 optimize for table adam
# NOTE: if param is not selected rows, the grad will scaled to grad / trainer_num
self
.
assertEqual
([
op
.
type
for
op
in
pserver1
.
blocks
[
2
].
ops
],
[
"adam"
,
"scale"
,
"scale"
])
trainer
=
self
.
get_trainer
(
config
)
self
.
assertEqual
(
len
(
trainer
.
blocks
),
1
)
ops
=
[
'lookup_table'
,
'sequence_pool'
,
'lookup_table'
,
'sequence_pool'
,
'concat'
,
'mul'
,
'elementwise_add'
,
'cross_entropy'
,
'mean'
,
'fill_constant'
,
'mean_grad'
,
'cross_entropy_grad'
,
'elementwise_add_grad'
,
'send'
,
'mul_grad'
,
'send'
,
'concat_grad'
,
'sequence_pool_grad'
,
'lookup_table_grad'
,
'sequence_pool_grad'
,
'lookup_table_grad'
,
'sum'
,
'split_selected_rows'
,
'send'
,
'recv'
,
'recv'
,
'recv'
,
'concat'
]
self
.
assertEqual
([
op
.
type
for
op
in
trainer
.
blocks
[
0
].
ops
],
ops
)
class
TestAsyncDistLookupTable
(
TestDistLookupTableBase
):
def
net_conf
(
self
):
self
.
network_with_table
(
is_sparse
=
True
,
is_distributed
=
True
)
def
transpiler_test_impl
(
self
):
config
=
fluid
.
DistributeTranspilerConfig
()
pserver1
,
startup1
=
self
.
get_pserver
(
self
.
pserver1_ep
,
config
,
False
)
self
.
assertEqual
(
len
(
pserver1
.
blocks
),
6
)
# 0 listen_and_serv
# 1 optimize for fc_w or fc_b adam
self
.
assertEqual
([
op
.
type
for
op
in
pserver1
.
blocks
[
1
].
ops
],
[
"adam"
,
"scale"
,
"scale"
])
# 2 optimize for table sgd
self
.
assertEqual
([
op
.
type
for
op
in
pserver1
.
blocks
[
2
].
ops
],
[
"sgd"
])
# 3 prefetch -> lookup_sparse_table for data0
self
.
assertEqual
([
op
.
type
for
op
in
pserver1
.
blocks
[
3
].
ops
],
[
"lookup_sparse_table"
])
# 4 prefetch -> lookup_sparse_table for data1
self
.
assertEqual
([
op
.
type
for
op
in
pserver1
.
blocks
[
4
].
ops
],
[
"lookup_sparse_table"
])
# 5 save table
self
.
assertEqual
([
op
.
type
for
op
in
pserver1
.
blocks
[
5
].
ops
],
[
"save"
])
trainer
=
self
.
get_trainer
(
config
)
self
.
assertEqual
(
len
(
trainer
.
blocks
),
1
)
ops
=
[
'split_ids'
,
'prefetch'
,
'merge_ids'
,
'sequence_pool'
,
'split_ids'
,
'prefetch'
,
'merge_ids'
,
'sequence_pool'
,
'concat'
,
'mul'
,
'elementwise_add'
,
'cross_entropy'
,
'mean'
,
'fill_constant'
,
'mean_grad'
,
'cross_entropy_grad'
,
'elementwise_add_grad'
,
'send'
,
'mul_grad'
,
'send'
,
'concat_grad'
,
'sequence_pool_grad'
,
'lookup_table_grad'
,
'sequence_pool_grad'
,
'lookup_table_grad'
,
'sum'
,
'split_ids'
,
'send'
,
'recv'
,
'recv'
]
self
.
assertEqual
([
op
.
type
for
op
in
trainer
.
blocks
[
0
].
ops
],
ops
)
if
__name__
==
"__main__"
:
unittest
.
main
()
python/paddle/fluid/transpiler/distribute_transpiler.py
浏览文件 @
d080d3e6
...
...
@@ -293,14 +293,15 @@ class DistributeTranspiler(object):
RPC_OP_ROLE_ATTR_NAME
:
RPC_OP_ROLE_ATTR_VALUE
})
program
.
global_block
().
append_op
(
type
=
"fetch_barrier"
,
inputs
=
{},
outputs
=
{},
attrs
=
{
"endpoints"
:
pserver_endpoints
,
RPC_OP_ROLE_ATTR_NAME
:
RPC_OP_ROLE_ATTR_VALUE
})
if
self
.
sync_mode
:
program
.
global_block
().
append_op
(
type
=
"fetch_barrier"
,
inputs
=
{},
outputs
=
{},
attrs
=
{
"endpoints"
:
pserver_endpoints
,
RPC_OP_ROLE_ATTR_NAME
:
RPC_OP_ROLE_ATTR_VALUE
})
for
varname
,
splited_var
in
self
.
param_var_mapping
.
iteritems
():
if
len
(
splited_var
)
<=
1
:
...
...
python/paddle/v2/dataset/conll05.py
浏览文件 @
d080d3e6
...
...
@@ -29,13 +29,13 @@ __all__ = ['test, get_dict', 'get_embedding', 'convert']
DATA_URL
=
'http://www.cs.upc.edu/~srlconll/conll05st-tests.tar.gz'
DATA_MD5
=
'387719152ae52d60422c016e92a742fc'
WORDDICT_URL
=
'http://paddle
paddle.bj.bcebos.com/demo/srl_dict_and_embedding/
wordDict.txt'
WORDDICT_URL
=
'http://paddle
models.bj.bcebos.com/conll05st%2F
wordDict.txt'
WORDDICT_MD5
=
'ea7fb7d4c75cc6254716f0177a506baa'
VERBDICT_URL
=
'http://paddle
paddle.bj.bcebos.com/demo/srl_dict_and_embedding/
verbDict.txt'
VERBDICT_URL
=
'http://paddle
models.bj.bcebos.com/conll05st%2F
verbDict.txt'
VERBDICT_MD5
=
'0d2977293bbb6cbefab5b0f97db1e77c'
TRGDICT_URL
=
'http://paddle
paddle.bj.bcebos.com/demo/srl_dict_and_embedding/
targetDict.txt'
TRGDICT_URL
=
'http://paddle
models.bj.bcebos.com/conll05st%2F
targetDict.txt'
TRGDICT_MD5
=
'd8c7f03ceb5fc2e5a0fa7503a4353751'
EMB_URL
=
'http://paddle
paddle.bj.bcebos.com/demo/srl_dict_and_embedding/
emb'
EMB_URL
=
'http://paddle
models.bj.bcebos.com/conll05st%2F
emb'
EMB_MD5
=
'bf436eb0faa1f6f9103017f8be57cdb7'
UNK_IDX
=
0
...
...
python/paddle/v2/dataset/wmt14.py
浏览文件 @
d080d3e6
...
...
@@ -41,7 +41,7 @@ URL_TRAIN = ('http://paddlepaddle.cdn.bcebos.com/demo/'
'wmt_shrinked_data/wmt14.tgz'
)
MD5_TRAIN
=
'0791583d57d5beb693b9414c5b36798c'
# BLEU of this trained model is 26.92
URL_MODEL
=
'http://paddle
paddle.bj.bcebos.com/demo/wmt_14/wmt14_model.tar.
gz'
URL_MODEL
=
'http://paddle
models.bj.bcebos.com/wmt%2Fwmt14.t
gz'
MD5_MODEL
=
'0cb4a5366189b6acba876491c8724fa3'
START
=
"<s>"
...
...
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