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bbc818a5
编写于
11月 02, 2018
作者:
S
sneaxiy
浏览文件
操作
浏览文件
下载
差异文件
test=develop
上级
203027ca
57c90e95
变更
26
隐藏空白更改
内联
并排
Showing
26 changed file
with
1554 addition
and
51 deletion
+1554
-51
paddle/fluid/API.spec
paddle/fluid/API.spec
+1
-0
paddle/fluid/framework/ir/CMakeLists.txt
paddle/fluid/framework/ir/CMakeLists.txt
+2
-0
paddle/fluid/framework/ir/conv_relu_mkldnn_fuse_pass.h
paddle/fluid/framework/ir/conv_relu_mkldnn_fuse_pass.h
+2
-1
paddle/fluid/framework/ir/conv_relu_mkldnn_fuse_pass_tester.cc
...e/fluid/framework/ir/conv_relu_mkldnn_fuse_pass_tester.cc
+3
-0
paddle/fluid/framework/ir/depthwise_conv_mkldnn_pass.cc
paddle/fluid/framework/ir/depthwise_conv_mkldnn_pass.cc
+58
-0
paddle/fluid/framework/ir/depthwise_conv_mkldnn_pass.h
paddle/fluid/framework/ir/depthwise_conv_mkldnn_pass.h
+34
-0
paddle/fluid/framework/ir/depthwise_conv_mkldnn_pass_tester.cc
...e/fluid/framework/ir/depthwise_conv_mkldnn_pass_tester.cc
+123
-0
paddle/fluid/framework/ir/fc_fuse_pass_tester.cc
paddle/fluid/framework/ir/fc_fuse_pass_tester.cc
+3
-0
paddle/fluid/framework/ir/graph.cc
paddle/fluid/framework/ir/graph.cc
+54
-0
paddle/fluid/framework/tensor_util.cc
paddle/fluid/framework/tensor_util.cc
+6
-0
paddle/fluid/inference/analysis/analyzer.h
paddle/fluid/inference/analysis/analyzer.h
+1
-0
paddle/fluid/inference/analysis/data_flow_graph_tester.cc
paddle/fluid/inference/analysis/data_flow_graph_tester.cc
+3
-0
paddle/fluid/operators/affine_grid_cudnn_op.cu.cc
paddle/fluid/operators/affine_grid_cudnn_op.cu.cc
+112
-0
paddle/fluid/operators/affine_grid_op.cc
paddle/fluid/operators/affine_grid_op.cc
+233
-0
paddle/fluid/operators/affine_grid_op.h
paddle/fluid/operators/affine_grid_op.h
+190
-0
paddle/fluid/operators/delete_var_op.cc
paddle/fluid/operators/delete_var_op.cc
+7
-1
paddle/fluid/platform/cudnn_helper.h
paddle/fluid/platform/cudnn_helper.h
+22
-0
paddle/fluid/platform/dynload/cudnn.h
paddle/fluid/platform/dynload/cudnn.h
+45
-38
python/paddle/fluid/io.py
python/paddle/fluid/io.py
+6
-2
python/paddle/fluid/layers/nn.py
python/paddle/fluid/layers/nn.py
+157
-6
python/paddle/fluid/tests/unittests/dist_save_load.py
python/paddle/fluid/tests/unittests/dist_save_load.py
+174
-0
python/paddle/fluid/tests/unittests/test_affine_grid_op.py
python/paddle/fluid/tests/unittests/test_affine_grid_op.py
+79
-0
python/paddle/fluid/tests/unittests/test_dist_save_load.py
python/paddle/fluid/tests/unittests/test_dist_save_load.py
+90
-0
python/paddle/fluid/tests/unittests/test_layers.py
python/paddle/fluid/tests/unittests/test_layers.py
+16
-0
python/paddle/fluid/tests/unittests/test_py_reader_pin_memory.py
...paddle/fluid/tests/unittests/test_py_reader_pin_memory.py
+130
-0
python/paddle/fluid/transpiler/distribute_transpiler.py
python/paddle/fluid/transpiler/distribute_transpiler.py
+3
-3
未找到文件。
paddle/fluid/API.spec
浏览文件 @
bbc818a5
...
...
@@ -174,6 +174,7 @@ paddle.fluid.layers.mean ArgSpec(args=['x', 'name'], varargs=None, keywords=None
paddle.fluid.layers.mul ArgSpec(args=['x', 'y', 'x_num_col_dims', 'y_num_col_dims', 'name'], varargs=None, keywords=None, defaults=(1, 1, None))
paddle.fluid.layers.sigmoid_cross_entropy_with_logits ArgSpec(args=['x', 'label', 'name'], varargs=None, keywords=None, defaults=(None,))
paddle.fluid.layers.maxout ArgSpec(args=['x', 'groups', 'name'], varargs=None, keywords=None, defaults=(None,))
paddle.fluid.layers.affine_grid ArgSpec(args=['theta', 'out_shape', 'name'], varargs=None, keywords=None, defaults=(None,))
paddle.fluid.layers.sequence_reverse ArgSpec(args=['x', 'name'], varargs=None, keywords=None, defaults=(None,))
paddle.fluid.layers.affine_channel ArgSpec(args=['x', 'scale', 'bias', 'data_layout', 'name'], varargs=None, keywords=None, defaults=(None, None, 'NCHW', None))
paddle.fluid.layers.hash ArgSpec(args=['input', 'hash_size', 'num_hash', 'name'], varargs=None, keywords=None, defaults=(1, None))
...
...
paddle/fluid/framework/ir/CMakeLists.txt
浏览文件 @
bbc818a5
...
...
@@ -41,6 +41,7 @@ pass_library(conv_bn_fuse_pass inference)
pass_library
(
seqconv_eltadd_relu_fuse_pass inference
)
if
(
WITH_MKLDNN
)
pass_library
(
mkldnn_placement_pass base
)
pass_library
(
depthwise_conv_mkldnn_pass base
)
pass_library
(
conv_bias_mkldnn_fuse_pass inference
)
pass_library
(
conv_relu_mkldnn_fuse_pass inference
)
pass_library
(
conv_elementwise_add_mkldnn_fuse_pass inference
)
...
...
@@ -59,6 +60,7 @@ cc_test(graph_to_program_pass_test SRCS graph_to_program_pass_test.cc DEPS graph
cc_test
(
test_graph_pattern_detector SRCS graph_pattern_detector_tester.cc DEPS graph_pattern_detector
)
cc_test
(
test_fc_fuse_pass SRCS fc_fuse_pass_tester.cc DEPS fc_fuse_pass framework_proto
)
if
(
WITH_MKLDNN
)
cc_test
(
test_depthwise_conv_mkldnn_pass SRCS depthwise_conv_mkldnn_pass_tester.cc DEPS depthwise_conv_mkldnn_pass
)
cc_test
(
test_conv_relu_mkldnn_fuse_pass SRCS conv_relu_mkldnn_fuse_pass_tester.cc DEPS conv_relu_mkldnn_fuse_pass
)
cc_test
(
test_conv_elementwise_add_mkldnn_fuse_pass SRCS conv_elementwise_add_mkldnn_fuse_pass_tester.cc DEPS conv_elementwise_add_mkldnn_fuse_pass
)
endif
()
paddle/fluid/framework/ir/conv_relu_mkldnn_fuse_pass.h
浏览文件 @
bbc818a5
...
...
@@ -31,7 +31,8 @@ class ConvReLUFusePass : public FusePassBase {
virtual
~
ConvReLUFusePass
()
{}
protected:
std
::
unique_ptr
<
ir
::
Graph
>
ApplyImpl
(
std
::
unique_ptr
<
ir
::
Graph
>
graph
)
const
;
std
::
unique_ptr
<
ir
::
Graph
>
ApplyImpl
(
std
::
unique_ptr
<
ir
::
Graph
>
graph
)
const
override
;
};
}
// namespace ir
...
...
paddle/fluid/framework/ir/conv_relu_mkldnn_fuse_pass_tester.cc
浏览文件 @
bbc818a5
...
...
@@ -15,6 +15,7 @@
#include "paddle/fluid/framework/ir/conv_relu_mkldnn_fuse_pass.h"
#include <gtest/gtest.h>
#include "paddle/fluid/framework/op_proto_maker.h"
namespace
paddle
{
namespace
framework
{
...
...
@@ -36,6 +37,8 @@ void SetOp(ProgramDesc* prog, const std::string& type, const std::string& name,
op
->
SetInput
(
"X"
,
inputs
);
}
op
->
SetOutput
(
"Out"
,
outputs
);
op
->
SetAttr
(
OpProtoAndCheckerMaker
::
OpRoleAttrName
(),
static_cast
<
int
>
(
OpRole
::
kForward
));
}
// a->OP0->b
...
...
paddle/fluid/framework/ir/depthwise_conv_mkldnn_pass.cc
0 → 100644
浏览文件 @
bbc818a5
/* Copyright (c) 2018 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. */
#include "paddle/fluid/framework/ir/depthwise_conv_mkldnn_pass.h"
#include "paddle/fluid/framework/ir/graph_pattern_detector.h"
namespace
paddle
{
namespace
framework
{
namespace
ir
{
#define GET_NODE(id, pattern) \
PADDLE_ENFORCE(subgraph.count(pattern.RetrieveNode(#id)), \
"pattern has no Node called %s", #id); \
auto* id = subgraph.at(pattern.RetrieveNode(#id)); \
PADDLE_ENFORCE_NOT_NULL(id, "subgraph has no node %s", #id);
std
::
unique_ptr
<
ir
::
Graph
>
DepthwiseConvMKLDNNPass
::
ApplyImpl
(
std
::
unique_ptr
<
ir
::
Graph
>
graph
)
const
{
PADDLE_ENFORCE
(
graph
.
get
());
FusePassBase
::
Init
(
"depthwise_conv_mkldnn_pass"
,
graph
.
get
());
GraphPatternDetector
gpd
;
auto
*
pattern
=
gpd
.
mutable_pattern
();
pattern
->
NewNode
(
"depthwise_conv"
)
->
assert_is_op
(
"depthwise_conv2d"
)
->
assert_op_attr
(
"use_mkldnn"
,
true
);
int
found_depthwise_conv_mkldnn_count
=
0
;
auto
handler
=
[
&
](
const
GraphPatternDetector
::
subgraph_t
&
subgraph
,
Graph
*
g
)
{
VLOG
(
3
)
<<
"handle DepthwiseConvMKLDNN fuse"
;
GET_NODE
(
depthwise_conv
,
(
*
pattern
));
depthwise_conv
->
Op
()
->
SetType
(
"conv2d"
);
found_depthwise_conv_mkldnn_count
++
;
};
gpd
(
graph
.
get
(),
handler
);
AddStatis
(
found_depthwise_conv_mkldnn_count
);
return
graph
;
}
}
// namespace ir
}
// namespace framework
}
// namespace paddle
REGISTER_PASS
(
depthwise_conv_mkldnn_pass
,
paddle
::
framework
::
ir
::
DepthwiseConvMKLDNNPass
);
paddle/fluid/framework/ir/depthwise_conv_mkldnn_pass.h
0 → 100644
浏览文件 @
bbc818a5
/* Copyright (c) 2018 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. */
#pragma once
#include "paddle/fluid/framework/ir/fuse_pass_base.h"
namespace
paddle
{
namespace
framework
{
namespace
ir
{
class
DepthwiseConvMKLDNNPass
:
public
FusePassBase
{
public:
virtual
~
DepthwiseConvMKLDNNPass
()
{}
protected:
std
::
unique_ptr
<
ir
::
Graph
>
ApplyImpl
(
std
::
unique_ptr
<
ir
::
Graph
>
graph
)
const
override
;
};
}
// namespace ir
}
// namespace framework
}
// namespace paddle
paddle/fluid/framework/ir/depthwise_conv_mkldnn_pass_tester.cc
0 → 100644
浏览文件 @
bbc818a5
// Copyright (c) 2018 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.
#include "paddle/fluid/framework/ir/depthwise_conv_mkldnn_pass.h"
#include <gtest/gtest.h>
namespace
paddle
{
namespace
framework
{
namespace
ir
{
void
SetOp
(
ProgramDesc
*
prog
,
const
std
::
string
&
type
,
const
std
::
string
&
name
,
const
std
::
vector
<
std
::
string
>&
inputs
,
const
std
::
vector
<
std
::
string
>&
outputs
,
bool
use_mkldnn
=
false
)
{
auto
*
op
=
prog
->
MutableBlock
(
0
)
->
AppendOp
();
op
->
SetType
(
type
);
op
->
SetAttr
(
"use_mkldnn"
,
use_mkldnn
);
op
->
SetAttr
(
"name"
,
name
);
op
->
SetInput
(
"Input"
,
{
inputs
[
0
]});
op
->
SetInput
(
"Filter"
,
{
inputs
[
1
]});
op
->
SetInput
(
"Bias"
,
{
inputs
[
2
]});
op
->
SetOutput
(
"Out"
,
outputs
);
}
// (a, weights, bias)->depthwise conv mkldnn->b
// (b, weights2, bias2)->depthwise conv no mkldnn->c
// (c, weights3, bias3)->conv mkldnn->d
// (d, weights3, bias3)->conv no mkldnn->e
ProgramDesc
BuildProgramDesc
()
{
ProgramDesc
prog
;
for
(
auto
&
v
:
std
::
vector
<
std
::
string
>
(
{
"a"
,
"b"
,
"c"
,
"d"
,
"e"
,
"weights"
,
"bias"
,
"weights2"
,
"bias2"
,
"weights3"
,
"bias3"
,
"weights4"
,
"bias4"
}))
{
auto
*
var
=
prog
.
MutableBlock
(
0
)
->
Var
(
v
);
var
->
SetType
(
proto
::
VarType
::
SELECTED_ROWS
);
if
(
v
==
"weights"
||
v
==
"bias"
||
v
==
"weights2"
||
v
==
"bias2"
||
v
==
"weights3"
||
v
==
"bias3"
||
v
==
"weights4"
||
v
==
"bias4"
)
{
var
->
SetPersistable
(
true
);
}
}
// depthwise conv with MKL-DNN
SetOp
(
&
prog
,
"depthwise_conv2d"
,
"conv1"
,
std
::
vector
<
std
::
string
>
({
"a"
,
"weights"
,
"bias"
}),
std
::
vector
<
std
::
string
>
({
"b"
}),
true
);
// depthwise conv without MKL-DNN
SetOp
(
&
prog
,
"depthwise_conv2d"
,
"conv2"
,
std
::
vector
<
std
::
string
>
({
"b"
,
"weights2"
,
"bias2"
}),
std
::
vector
<
std
::
string
>
({
"c"
}),
false
);
// conv with MKL-DNN
SetOp
(
&
prog
,
"conv2d"
,
"conv3"
,
std
::
vector
<
std
::
string
>
({
"c"
,
"weights3"
,
"bias3"
}),
std
::
vector
<
std
::
string
>
({
"d"
}),
true
);
// conv without MKL-dNN
SetOp
(
&
prog
,
"conv2d"
,
"conv4"
,
std
::
vector
<
std
::
string
>
({
"d"
,
"weights4"
,
"bias4"
}),
std
::
vector
<
std
::
string
>
({
"e"
}),
false
);
return
prog
;
}
TEST
(
DepthwiseConvMKLDNNPass
,
basic
)
{
auto
prog
=
BuildProgramDesc
();
std
::
unique_ptr
<
ir
::
Graph
>
graph
(
new
ir
::
Graph
(
prog
));
auto
pass
=
PassRegistry
::
Instance
().
Get
(
"depthwise_conv_mkldnn_pass"
);
struct
counters
{
int
mkldnn_depthwise_conv_nodes
;
int
other_depthwise_conv_nodes
;
int
mkldnn_conv_nodes
;
int
other_conv_nodes
;
};
counters
before
{
1
,
1
,
1
,
1
};
graph
=
pass
->
Apply
(
std
::
move
(
graph
));
// initialize counters before loop
counters
after
{
0
,
0
,
0
,
0
};
for
(
auto
*
node
:
graph
->
Nodes
())
{
if
(
node
->
IsOp
())
{
auto
*
op
=
node
->
Op
();
if
(
op
->
Type
()
==
"conv2d"
)
{
if
(
boost
::
get
<
bool
>
(
op
->
GetAttr
(
"use_mkldnn"
)))
after
.
mkldnn_conv_nodes
++
;
else
after
.
other_conv_nodes
++
;
}
else
if
(
op
->
Type
()
==
"depthwise_conv2d"
)
{
if
(
boost
::
get
<
bool
>
(
op
->
GetAttr
(
"use_mkldnn"
)))
after
.
mkldnn_depthwise_conv_nodes
++
;
else
after
.
other_depthwise_conv_nodes
++
;
}
}
}
EXPECT_EQ
(
after
.
other_depthwise_conv_nodes
,
before
.
other_depthwise_conv_nodes
);
EXPECT_EQ
(
after
.
other_conv_nodes
,
before
.
other_conv_nodes
);
EXPECT_EQ
(
after
.
mkldnn_depthwise_conv_nodes
,
before
.
mkldnn_depthwise_conv_nodes
-
1
);
EXPECT_EQ
(
after
.
mkldnn_conv_nodes
,
before
.
mkldnn_conv_nodes
+
1
);
}
}
// namespace ir
}
// namespace framework
}
// namespace paddle
USE_PASS
(
depthwise_conv_mkldnn_pass
);
paddle/fluid/framework/ir/fc_fuse_pass_tester.cc
浏览文件 @
bbc818a5
...
...
@@ -15,6 +15,7 @@
#include "paddle/fluid/framework/ir/fc_fuse_pass.h"
#include <gtest/gtest.h>
#include "paddle/fluid/framework/op_proto_maker.h"
namespace
paddle
{
namespace
framework
{
...
...
@@ -32,6 +33,8 @@ void SetOp(ProgramDesc* prog, const std::string& type,
op
->
SetInput
(
"X"
,
inputs
);
}
op
->
SetOutput
(
"Out"
,
outputs
);
op
->
SetAttr
(
OpProtoAndCheckerMaker
::
OpRoleAttrName
(),
static_cast
<
int
>
(
OpRole
::
kForward
));
}
// a->OP0->b
...
...
paddle/fluid/framework/ir/graph.cc
浏览文件 @
bbc818a5
...
...
@@ -23,8 +23,62 @@ limitations under the License. */
namespace
paddle
{
namespace
framework
{
namespace
ir
{
namespace
{
void
CheckProgram
(
const
ProgramDesc
&
program
)
{
std
::
map
<
int
,
bool
>
visit
;
#define _INT(role) static_cast<int>(role)
for
(
size_t
i
=
0
;
i
<
program
.
Size
();
++
i
)
{
for
(
OpDesc
*
op
:
program
.
Block
(
i
).
AllOps
())
{
// For backward compatibility, some program doesn't have role added.
if
(
!
op
->
HasAttr
(
OpProtoAndCheckerMaker
::
OpRoleAttrName
()))
continue
;
int
role_id
=
boost
::
get
<
int
>
(
op
->
GetAttr
(
OpProtoAndCheckerMaker
::
OpRoleAttrName
()));
visit
[
role_id
]
=
true
;
switch
(
role_id
)
{
case
_INT
(
OpRole
::
kForward
):
PADDLE_ENFORCE
(
visit
.
find
(
_INT
(
OpRole
::
kBackward
))
==
visit
.
end
(),
"Cannot add forward operator before backward operator."
);
break
;
case
_INT
(
OpRole
::
kBackward
):
case
_INT
(
OpRole
::
kBackward
)
|
_INT
(
OpRole
::
kLoss
):
PADDLE_ENFORCE
(
visit
.
find
(
_INT
(
OpRole
::
kOptimize
))
==
visit
.
end
(),
"Cannot add backward operator before optimize operator."
);
break
;
case
_INT
(
OpRole
::
kForward
)
|
_INT
(
OpRole
::
kLoss
):
PADDLE_ENFORCE
(
visit
.
find
(
_INT
(
OpRole
::
kBackward
)
|
_INT
(
OpRole
::
kLoss
))
==
visit
.
end
(),
"Cannot add backward|loss operator before "
"forward|loss operator."
);
PADDLE_ENFORCE
(
visit
.
find
(
_INT
(
OpRole
::
kOptimize
))
==
visit
.
end
(),
"Cannot add backward operator before optimize operator."
);
break
;
case
_INT
(
OpRole
::
kOptimize
):
case
_INT
(
OpRole
::
kOptimize
)
|
_INT
(
OpRole
::
kLRSched
):
PADDLE_ENFORCE
(
visit
.
find
(
_INT
(
OpRole
::
kBackward
))
!=
visit
.
end
(),
"Optimize operators must follow backward operator."
);
break
;
case
_INT
(
OpRole
::
kLRSched
):
case
_INT
(
OpRole
::
kDist
):
case
_INT
(
OpRole
::
kRPC
):
case
_INT
(
OpRole
::
kNotSpecified
):
break
;
default:
LOG
(
FATAL
)
<<
"Unknown operator role. Don't add new role because "
"you don't know what you are doing."
;
}
}
}
#undef _INT
}
}
// namespace
Graph
::
Graph
(
const
ProgramDesc
&
program
)
:
program_
(
program
)
{
CheckProgram
(
program_
);
// Make the nodes id start from 0.
Node
::
ResetId
();
auto
var_nodes
=
InitFromProgram
(
program_
);
...
...
paddle/fluid/framework/tensor_util.cc
浏览文件 @
bbc818a5
...
...
@@ -153,6 +153,12 @@ void TensorCopySync(const Tensor& src, const platform::Place& dst_place,
auto
src_gpu_place
=
boost
::
get
<
platform
::
CUDAPlace
>
(
src_place
);
auto
dst_gpu_place
=
boost
::
get
<
platform
::
CUDAPlace
>
(
dst_place
);
memory
::
Copy
(
dst_gpu_place
,
dst_ptr
,
src_gpu_place
,
src_ptr
,
size
,
nullptr
);
}
else
if
(
platform
::
is_cuda_pinned_place
(
src_place
)
&&
platform
::
is_gpu_place
(
dst_place
))
{
auto
src_pinned_place
=
boost
::
get
<
platform
::
CUDAPinnedPlace
>
(
src_place
);
auto
dst_gpu_place
=
boost
::
get
<
platform
::
CUDAPlace
>
(
dst_place
);
memory
::
Copy
(
dst_gpu_place
,
dst_ptr
,
src_pinned_place
,
src_ptr
,
size
,
nullptr
);
}
#endif
}
...
...
paddle/fluid/inference/analysis/analyzer.h
浏览文件 @
bbc818a5
...
...
@@ -79,6 +79,7 @@ class Analyzer : public OrderedRegistry<PassManager> {
"conv_bn_fuse_pass"
,
//
"conv_eltwiseadd_bn_fuse_pass"
,
//
#ifdef PADDLE_WITH_MKLDNN
"depthwise_conv_mkldnn_pass"
,
//
"conv_bias_mkldnn_fuse_pass"
,
//
"conv_relu_mkldnn_fuse_pass"
,
//
"conv_elementwise_add_mkldnn_fuse_pass"
,
//
...
...
paddle/fluid/inference/analysis/data_flow_graph_tester.cc
浏览文件 @
bbc818a5
...
...
@@ -13,6 +13,7 @@ See the License for the specific language governing permissions and
limitations under the License. */
#include "paddle/fluid/inference/analysis/data_flow_graph.h"
#include "paddle/fluid/framework/op_proto_maker.h"
#include "paddle/fluid/framework/program_desc.h"
#include "paddle/fluid/inference/analysis/ut_helper.h"
...
...
@@ -130,6 +131,8 @@ void SetOp(framework::ProgramDesc* prog, const std::string& type,
op
->
SetType
(
type
);
op
->
SetInput
(
"Xs"
,
inputs
);
op
->
SetOutput
(
"Xs"
,
outputs
);
op
->
SetAttr
(
framework
::
OpProtoAndCheckerMaker
::
OpRoleAttrName
(),
static_cast
<
int
>
(
framework
::
OpRole
::
kForward
));
}
TEST
(
DataFlowGraph
,
Build_IR_Graph
)
{
...
...
paddle/fluid/operators/affine_grid_cudnn_op.cu.cc
0 → 100644
浏览文件 @
bbc818a5
/* Copyright (c) 2018 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. */
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/platform/cudnn_helper.h"
namespace
paddle
{
namespace
operators
{
using
Tensor
=
framework
::
Tensor
;
using
ScopedSpatialTransformerDescriptor
=
platform
::
ScopedSpatialTransformerDescriptor
;
template
<
typename
T
>
class
CUDNNAffineGridOpKernel
:
public
framework
::
OpKernel
<
T
>
{
public:
void
Compute
(
const
framework
::
ExecutionContext
&
ctx
)
const
override
{
PADDLE_ENFORCE
(
platform
::
is_gpu_place
(
ctx
.
GetPlace
()),
"It must use CUDAPlace."
);
auto
&
dev_ctx
=
ctx
.
template
device_context
<
platform
::
CUDADeviceContext
>();
auto
handle
=
dev_ctx
.
cudnn_handle
();
auto
*
theta
=
ctx
.
Input
<
Tensor
>
(
"Theta"
);
auto
*
output
=
ctx
.
Output
<
Tensor
>
(
"Output"
);
const
T
*
theta_data
=
theta
->
data
<
T
>
();
int
n
=
theta
->
dims
()[
0
];
auto
size_attr
=
ctx
.
Attr
<
std
::
vector
<
int
>>
(
"output_shape"
);
Tensor
h_sizes
;
int
*
h_size_data
;
if
(
size_attr
.
size
()
==
0
)
{
auto
*
output_shape
=
ctx
.
Input
<
Tensor
>
(
"OutputShape"
);
framework
::
TensorCopy
(
*
output_shape
,
platform
::
CPUPlace
(),
&
h_sizes
);
h_size_data
=
h_sizes
.
data
<
int
>
();
}
else
{
h_size_data
=
h_sizes
.
mutable_data
<
int
>
({
4
},
platform
::
CPUPlace
());
h_size_data
[
0
]
=
n
;
h_size_data
[
1
]
=
size_attr
[
1
];
h_size_data
[
2
]
=
size_attr
[
2
];
h_size_data
[
3
]
=
size_attr
[
3
];
}
T
*
output_data
=
output
->
mutable_data
<
T
>
(
{
n
,
h_size_data
[
2
],
h_size_data
[
3
],
2
},
ctx
.
GetPlace
());
ScopedSpatialTransformerDescriptor
st_desc
;
cudnnSpatialTransformerDescriptor_t
cudnn_st_desc
=
st_desc
.
descriptor
<
T
>
(
4
,
h_size_data
);
PADDLE_ENFORCE
(
platform
::
dynload
::
cudnnSpatialTfGridGeneratorForward
(
handle
,
cudnn_st_desc
,
theta_data
,
output_data
));
}
};
template
<
typename
T
>
class
CUDNNAffineGridGradOpKernel
:
public
framework
::
OpKernel
<
T
>
{
public:
void
Compute
(
const
framework
::
ExecutionContext
&
ctx
)
const
override
{
PADDLE_ENFORCE
(
platform
::
is_gpu_place
(
ctx
.
GetPlace
()),
"It must use CUDAPlace."
);
auto
&
dev_ctx
=
ctx
.
template
device_context
<
platform
::
CUDADeviceContext
>();
auto
handle
=
dev_ctx
.
cudnn_handle
();
auto
output_grad
=
ctx
.
Input
<
Tensor
>
(
framework
::
GradVarName
(
"Output"
));
auto
theta_grad
=
ctx
.
Output
<
Tensor
>
(
framework
::
GradVarName
(
"Theta"
));
int
n
=
output_grad
->
dims
()[
0
];
auto
size_attr
=
ctx
.
Attr
<
std
::
vector
<
int
>>
(
"output_shape"
);
Tensor
h_sizes
;
int
*
h_size_data
;
if
(
size_attr
.
size
()
==
0
)
{
auto
*
output_shape
=
ctx
.
Input
<
Tensor
>
(
"OutputShape"
);
framework
::
TensorCopy
(
*
output_shape
,
platform
::
CPUPlace
(),
&
h_sizes
);
h_size_data
=
h_sizes
.
data
<
int
>
();
}
else
{
h_size_data
=
h_sizes
.
mutable_data
<
int
>
({
4
},
platform
::
CPUPlace
());
h_size_data
[
0
]
=
n
;
h_size_data
[
1
]
=
size_attr
[
1
];
h_size_data
[
2
]
=
size_attr
[
2
];
h_size_data
[
3
]
=
size_attr
[
3
];
}
ScopedSpatialTransformerDescriptor
st_desc
;
cudnnSpatialTransformerDescriptor_t
cudnn_st_desc
=
st_desc
.
descriptor
<
T
>
(
4
,
h_size_data
);
const
T
*
output_grad_data
=
output_grad
->
data
<
T
>
();
T
*
theta_grad_data
=
theta_grad
->
mutable_data
<
T
>
(
ctx
.
GetPlace
());
PADDLE_ENFORCE
(
platform
::
dynload
::
cudnnSpatialTfGridGeneratorBackward
(
handle
,
cudnn_st_desc
,
output_grad_data
,
theta_grad_data
));
}
};
}
// namespace operators
}
// namespace paddle
namespace
plat
=
paddle
::
platform
;
REGISTER_OP_KERNEL
(
affine_grid
,
CUDNN
,
plat
::
CUDAPlace
,
paddle
::
operators
::
CUDNNAffineGridOpKernel
<
float
>
,
paddle
::
operators
::
CUDNNAffineGridOpKernel
<
double
>
);
REGISTER_OP_KERNEL
(
affine_grid_grad
,
CUDNN
,
plat
::
CUDAPlace
,
paddle
::
operators
::
CUDNNAffineGridGradOpKernel
<
float
>
,
paddle
::
operators
::
CUDNNAffineGridGradOpKernel
<
double
>
);
paddle/fluid/operators/affine_grid_op.cc
0 → 100644
浏览文件 @
bbc818a5
/* Copyright (c) 2018 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. */
#include "paddle/fluid/operators/affine_grid_op.h"
#include <string>
#include "paddle/fluid/framework/op_registry.h"
#ifdef PADDLE_WITH_CUDA
#include "paddle/fluid/platform/cudnn_helper.h"
#endif
namespace
paddle
{
namespace
operators
{
using
Tensor
=
framework
::
Tensor
;
template
<
typename
T
>
struct
Linspace
<
paddle
::
platform
::
CPUDeviceContext
,
T
>
{
framework
::
Tensor
operator
()(
T
start
,
T
end
,
int
count
,
const
framework
::
ExecutionContext
&
ctx
)
{
Tensor
numbers
;
T
*
number_data
=
numbers
.
mutable_data
<
T
>
({
count
},
platform
::
CPUPlace
());
T
slice
=
(
end
-
start
)
/
(
T
)(
count
-
1
);
for
(
int
i
=
0
;
i
<
count
;
++
i
)
{
number_data
[
i
]
=
start
+
(
T
)
i
*
slice
;
}
return
numbers
;
}
};
class
AffineGridOp
:
public
framework
::
OperatorWithKernel
{
public:
using
framework
::
OperatorWithKernel
::
OperatorWithKernel
;
void
InferShape
(
framework
::
InferShapeContext
*
ctx
)
const
override
{
PADDLE_ENFORCE
(
ctx
->
HasInput
(
"Theta"
),
"Input(Theta) of AffineGridOp should not be null."
);
PADDLE_ENFORCE
(
ctx
->
HasOutput
(
"Output"
),
"Output(Output) of AffineGridOp should not be null."
);
auto
theta_dims
=
ctx
->
GetInputDim
(
"Theta"
);
PADDLE_ENFORCE
(
theta_dims
.
size
()
==
3
,
"AffineGrid's Input(Theta) should be 3-D tensor."
);
auto
output_shape
=
ctx
->
Attrs
().
Get
<
std
::
vector
<
int
>>
(
"output_shape"
);
if
(
output_shape
.
size
()
==
0
)
{
PADDLE_ENFORCE
(
ctx
->
HasInput
(
"OutputShape"
),
"Input(OutputShape) of AffineGridOp should not be null if "
"attr(output_shape) is not configured."
);
auto
output_shape_dims
=
ctx
->
GetInputDim
(
"OutputShape"
);
PADDLE_ENFORCE
(
output_shape_dims
.
size
()
==
1
,
"AffineGrid's Input(OutputShape) should be 1-D tensor."
);
}
else
{
PADDLE_ENFORCE
(
output_shape
.
size
()
==
4
,
"The size of attr(output_shape) should be 4."
);
}
PADDLE_ENFORCE
(
theta_dims
[
1
]
==
2
,
"Input(theta) dims[1] should be 2."
);
PADDLE_ENFORCE
(
theta_dims
[
2
]
==
3
,
"Input(theta) dims[2] should be 3."
);
// N * H * W * 2
ctx
->
SetOutputDim
(
"Output"
,
framework
::
make_ddim
({
theta_dims
[
0
],
-
1
,
-
1
,
2
}));
ctx
->
ShareLoD
(
"Theta"
,
"Output"
);
}
protected:
framework
::
OpKernelType
GetExpectedKernelType
(
const
framework
::
ExecutionContext
&
ctx
)
const
override
{
framework
::
LibraryType
library
{
framework
::
LibraryType
::
kPlain
};
#ifdef PADDLE_WITH_CUDA
if
(
platform
::
CanCUDNNBeUsed
(
ctx
))
{
library
=
framework
::
LibraryType
::
kCUDNN
;
}
#endif
auto
data_type
=
framework
::
ToDataType
(
ctx
.
Input
<
Tensor
>
(
"Theta"
)
->
type
());
return
framework
::
OpKernelType
(
data_type
,
ctx
.
GetPlace
(),
framework
::
DataLayout
::
kAnyLayout
,
library
);
}
};
class
AffineGridOpMaker
:
public
framework
::
OpProtoAndCheckerMaker
{
public:
void
Make
()
override
{
AddInput
(
"Theta"
,
"(Tensor) A batch of affine transform parameters with shape [N, 2, 3]. "
"It is used to transform coordinate (x_0, y_0) to coordinate (x_1, "
"y_1)."
);
AddInput
(
"OutputShape"
,
"(Tensor) The shape of target image with format [N, C, H, W]."
)
.
AsDispensable
();
AddOutput
(
"Output"
,
"(Tensor) Output Tensor with shape [N, H, W, 2]."
);
AddAttr
<
bool
>
(
"use_cudnn"
,
"(bool, default false) Only used in cudnn kernel, need install cudnn"
)
.
SetDefault
(
true
);
AddAttr
<
std
::
vector
<
int
>>
(
"output_shape"
,
"The target output image shape with format [N, C, H, W]."
)
.
SetDefault
(
std
::
vector
<
int
>
());
AddComment
(
R"DOC(
It generates a grid of (x,y) coordinates using the parameters of the
affine transformation that correspond to a set of points where the input
feature map should be sampled to produce the transformed output feature map.
Given:
Theta = [[[x_11, x_12, x_13]
[x_14, x_15, x_16]]
[[x_21, x_22, x_23]
[x_24, x_25, x_26]]]
OutputShape = [2, 3, 5, 5]
Step 1:
Generate relative coordinates according to OutputShape.
The values of relative coordinates are in the interval between -1 and 1.
The shape of the relative coordinates is [2, H, W] as below:
C = [[[-1. -1. -1. -1. -1. ]
[-0.5 -0.5 -0.5 -0.5 -0.5]
[ 0. 0. 0. 0. 0. ]
[ 0.5 0.5 0.5 0.5 0.5]
[ 1. 1. 1. 1. 1. ]]
[[-1. -0.5 0. 0.5 1. ]
[-1. -0.5 0. 0.5 1. ]
[-1. -0.5 0. 0.5 1. ]
[-1. -0.5 0. 0.5 1. ]
[-1. -0.5 0. 0.5 1. ]]]
C[0] is the coordinates in height axis and C[1] is the coordinates in width axis.
Step2:
Tanspose and reshape C to shape [H * W, 2] and append ones to last dimension. The we get:
C_ = [[-1. -1. 1. ]
[-0.5 -1. 1. ]
[ 0. -1. 1. ]
[ 0.5 -1. 1. ]
[ 1. -1. 1. ]
[-1. -0.5 1. ]
[-0.5 -0.5 1. ]
[ 0. -0.5 1. ]
[ 0.5 -0.5 1. ]
[ 1. -0.5 1. ]
[-1. 0. 1. ]
[-0.5 0. 1. ]
[ 0. 0. 1. ]
[ 0.5 0. 1. ]
[ 1. 0. 1. ]
[-1. 0.5 1. ]
[-0.5 0.5 1. ]
[ 0. 0.5 1. ]
[ 0.5 0.5 1. ]
[ 1. 0.5 1. ]
[-1. 1. 1. ]
[-0.5 1. 1. ]
[ 0. 1. 1. ]
[ 0.5 1. 1. ]
[ 1. 1. 1. ]]
Step3:
Compute output by equation $$Output[i] = C_ * Theta[i]^T$$
)DOC"
);
}
};
class
AffineGridOpGrad
:
public
framework
::
OperatorWithKernel
{
public:
using
framework
::
OperatorWithKernel
::
OperatorWithKernel
;
void
InferShape
(
framework
::
InferShapeContext
*
ctx
)
const
override
{
auto
theta_dims
=
ctx
->
GetInputDim
(
"Theta"
);
if
(
ctx
->
HasOutput
(
framework
::
GradVarName
(
"Theta"
)))
{
ctx
->
SetOutputDim
(
framework
::
GradVarName
(
"Theta"
),
theta_dims
);
}
}
protected:
framework
::
OpKernelType
GetExpectedKernelType
(
const
framework
::
ExecutionContext
&
ctx
)
const
override
{
framework
::
LibraryType
library_
{
framework
::
LibraryType
::
kPlain
};
#ifdef PADDLE_WITH_CUDA
if
(
platform
::
CanCUDNNBeUsed
(
ctx
))
{
library_
=
framework
::
LibraryType
::
kCUDNN
;
}
#endif
return
framework
::
OpKernelType
(
framework
::
ToDataType
(
ctx
.
Input
<
Tensor
>
(
"Theta"
)
->
type
()),
ctx
.
GetPlace
(),
framework
::
DataLayout
::
kAnyLayout
,
library_
);
}
};
class
AffineGridGradMaker
:
public
framework
::
SingleGradOpDescMaker
{
public:
using
framework
::
SingleGradOpDescMaker
::
SingleGradOpDescMaker
;
protected:
std
::
unique_ptr
<
framework
::
OpDesc
>
Apply
()
const
override
{
auto
*
op
=
new
framework
::
OpDesc
();
op
->
SetType
(
"affine_grid_grad"
);
op
->
SetInput
(
"Theta"
,
Input
(
"Theta"
));
op
->
SetInput
(
"OutputShape"
,
Input
(
"OutputShape"
));
op
->
SetInput
(
framework
::
GradVarName
(
"Output"
),
OutputGrad
(
"Output"
));
op
->
SetAttrMap
(
Attrs
());
op
->
SetOutput
(
framework
::
GradVarName
(
"Theta"
),
InputGrad
(
"Theta"
));
return
std
::
unique_ptr
<
framework
::
OpDesc
>
(
op
);
}
};
}
// namespace operators
}
// namespace paddle
namespace
ops
=
paddle
::
operators
;
REGISTER_OPERATOR
(
affine_grid
,
ops
::
AffineGridOp
,
ops
::
AffineGridOpMaker
,
ops
::
AffineGridGradMaker
);
REGISTER_OPERATOR
(
affine_grid_grad
,
ops
::
AffineGridOpGrad
);
REGISTER_OP_CPU_KERNEL
(
affine_grid
,
ops
::
AffineGridOpKernel
<
paddle
::
platform
::
CPUDeviceContext
,
float
>
,
ops
::
AffineGridOpKernel
<
paddle
::
platform
::
CPUDeviceContext
,
double
>
);
REGISTER_OP_CPU_KERNEL
(
affine_grid_grad
,
ops
::
AffineGridGradOpKernel
<
paddle
::
platform
::
CPUDeviceContext
,
float
>
,
ops
::
AffineGridGradOpKernel
<
paddle
::
platform
::
CPUDeviceContext
,
double
>
);
paddle/fluid/operators/affine_grid_op.h
0 → 100644
浏览文件 @
bbc818a5
/* Copyright (c) 2018 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. */
#pragma once
#include <vector>
#include "paddle/fluid/framework/eigen.h"
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/operators/math/blas.h"
#include "paddle/fluid/operators/math/math_function.h"
namespace
paddle
{
namespace
operators
{
using
Tensor
=
framework
::
Tensor
;
template
<
typename
T
,
size_t
D
,
int
MajorType
=
Eigen
::
RowMajor
,
typename
IndexType
=
Eigen
::
DenseIndex
>
using
EigenTensor
=
framework
::
EigenTensor
<
T
,
D
,
MajorType
,
IndexType
>
;
using
Array1
=
Eigen
::
DSizes
<
int64_t
,
1
>
;
using
Array2
=
Eigen
::
DSizes
<
int64_t
,
2
>
;
using
Array3
=
Eigen
::
DSizes
<
int64_t
,
3
>
;
using
Array4
=
Eigen
::
DSizes
<
int64_t
,
4
>
;
/**
*Return a tensor with evenly spaced numbers over a specified interval.
*/
template
<
typename
DeviceContext
,
typename
T
>
struct
Linspace
{
framework
::
Tensor
operator
()(
T
start
,
T
end
,
int
count
,
const
framework
::
ExecutionContext
&
ctx
);
};
template
<
typename
DeviceContext
,
typename
T
>
class
AffineGridOpKernel
:
public
framework
::
OpKernel
<
T
>
{
public:
void
Compute
(
const
framework
::
ExecutionContext
&
ctx
)
const
override
{
auto
&
place
=
*
ctx
.
template
device_context
<
DeviceContext
>().
eigen_device
();
auto
*
theta
=
ctx
.
Input
<
Tensor
>
(
"Theta"
);
int
n
=
theta
->
dims
()[
0
];
auto
size_attr
=
ctx
.
Attr
<
std
::
vector
<
int
>>
(
"output_shape"
);
int
h
=
0
;
int
w
=
0
;
if
(
size_attr
.
size
()
==
0
)
{
auto
*
output_shape
=
ctx
.
Input
<
Tensor
>
(
"OutputShape"
);
Tensor
h_sizes
;
framework
::
TensorCopy
(
*
output_shape
,
platform
::
CPUPlace
(),
&
h_sizes
);
const
int
*
h_size_data
=
h_sizes
.
data
<
int
>
();
h
=
h_size_data
[
2
];
w
=
h_size_data
[
3
];
}
else
{
h
=
size_attr
[
2
];
w
=
size_attr
[
3
];
}
auto
*
output
=
ctx
.
Output
<
Tensor
>
(
"Output"
);
output
->
mutable_data
<
T
>
({
n
,
h
,
w
,
2
},
ctx
.
GetPlace
());
math
::
SetConstant
<
DeviceContext
,
T
>
()(
ctx
.
template
device_context
<
DeviceContext
>(),
output
,
static_cast
<
T
>
(
0
));
Linspace
<
DeviceContext
,
T
>
linspace
;
// Get indexes of height with shape [height, width, 1]
auto
h_idx
=
linspace
((
T
)
-
1
,
(
T
)
1
,
h
,
ctx
);
auto
h_idx_t
=
EigenTensor
<
T
,
1
>::
From
(
h_idx
);
// Get indexes of width with shape [height, width, 1]
auto
w_idx
=
linspace
((
T
)
-
1
,
(
T
)
1
,
w
,
ctx
);
auto
w_idx_t
=
EigenTensor
<
T
,
1
>::
From
(
w_idx
);
// Get constant ones tensor with shape [height, width, 1]
Tensor
ones
;
ones
.
mutable_data
<
T
>
({
h
,
w
,
1
},
ctx
.
GetPlace
());
auto
ones_t
=
EigenTensor
<
T
,
3
>::
From
(
ones
).
setConstant
((
T
)
1
);
// Get grid tensor with shape [n, h, w, 3] by concatenating h_idx, w_idx and
// ones
Tensor
grid
;
grid
.
mutable_data
<
T
>
({
n
,
h
,
w
,
3
},
ctx
.
GetPlace
());
auto
grid_t
=
EigenTensor
<
T
,
4
>::
From
(
grid
);
grid_t
.
device
(
place
)
=
w_idx_t
.
reshape
(
Array2
(
1
,
w
))
.
broadcast
(
Array2
(
h
,
1
))
.
reshape
(
Array3
(
h
,
w
,
1
))
.
concatenate
(
h_idx_t
.
reshape
(
Array2
(
1
,
h
))
.
broadcast
(
Array2
(
w
,
1
))
.
shuffle
(
Array2
(
1
,
0
))
.
reshape
(
Array3
(
h
,
w
,
1
)),
2
)
.
eval
()
.
concatenate
(
ones_t
,
2
)
.
reshape
(
Array4
(
1
,
h
,
w
,
3
))
.
broadcast
(
Array4
(
n
,
1
,
1
,
1
));
// output = grid * theta.T
// TODO(wanghaoshuang): Refine batched matrix multiply
auto
blas
=
math
::
GetBlas
<
DeviceContext
,
T
>
(
ctx
);
for
(
int
i
=
0
;
i
<
n
;
++
i
)
{
Tensor
sliced_grid
=
grid
.
Slice
(
i
,
i
+
1
).
Resize
({
h
*
w
,
3
});
Tensor
sliced_theta
=
theta
->
Slice
(
i
,
i
+
1
).
Resize
({
2
,
3
});
Tensor
sliced_out
=
output
->
Slice
(
i
,
i
+
1
).
Resize
({
h
*
w
,
2
});
blas
.
MatMul
(
sliced_grid
,
false
,
sliced_theta
,
true
,
T
(
1
),
&
sliced_out
,
T
(
0
));
}
}
};
template
<
typename
DeviceContext
,
typename
T
>
class
AffineGridGradOpKernel
:
public
framework
::
OpKernel
<
T
>
{
public:
void
Compute
(
const
framework
::
ExecutionContext
&
ctx
)
const
override
{
auto
&
place
=
*
ctx
.
template
device_context
<
DeviceContext
>().
eigen_device
();
auto
output_grad
=
ctx
.
Input
<
Tensor
>
(
framework
::
GradVarName
(
"Output"
));
auto
theta_grad
=
ctx
.
Output
<
Tensor
>
(
framework
::
GradVarName
(
"Theta"
));
int
n
=
output_grad
->
dims
()[
0
];
auto
size_attr
=
ctx
.
Attr
<
std
::
vector
<
int
>>
(
"output_shape"
);
int
h
=
0
;
int
w
=
0
;
if
(
size_attr
.
size
()
==
0
)
{
auto
*
output_shape
=
ctx
.
Input
<
Tensor
>
(
"OutputShape"
);
Tensor
h_sizes
;
framework
::
TensorCopy
(
*
output_shape
,
platform
::
CPUPlace
(),
&
h_sizes
);
const
int
*
h_size_data
=
h_sizes
.
data
<
int
>
();
h
=
h_size_data
[
2
];
w
=
h_size_data
[
3
];
}
else
{
h
=
size_attr
[
2
];
w
=
size_attr
[
3
];
}
theta_grad
->
mutable_data
<
T
>
({
n
,
2
,
3
},
ctx
.
GetPlace
());
math
::
SetConstant
<
DeviceContext
,
T
>
()(
ctx
.
template
device_context
<
DeviceContext
>(),
theta_grad
,
static_cast
<
T
>
(
0
));
Linspace
<
DeviceContext
,
T
>
linspace
;
// Get indexes of height with shape [height, width, 1]
auto
h_idx
=
linspace
((
T
)
-
1
,
(
T
)
1
,
h
,
ctx
);
auto
h_idx_t
=
EigenTensor
<
T
,
1
>::
From
(
h_idx
);
// Get indexes of width with shape [height, width, 1]
auto
w_idx
=
linspace
((
T
)
-
1
,
(
T
)
1
,
w
,
ctx
);
auto
w_idx_t
=
EigenTensor
<
T
,
1
>::
From
(
w_idx
);
// Get constant ones tensor with shape [height, width, 1]
Tensor
ones
;
ones
.
mutable_data
<
T
>
({
h
,
w
,
1
},
ctx
.
GetPlace
());
auto
ones_t
=
EigenTensor
<
T
,
3
>::
From
(
ones
).
setConstant
((
T
)
1
);
// Get grid tensor with shape [n, h, w, 3] by concatenating h_idx, w_idx and
// ones
Tensor
grid
;
grid
.
mutable_data
<
T
>
({
n
,
h
,
w
,
3
},
ctx
.
GetPlace
());
auto
grid_t
=
EigenTensor
<
T
,
4
>::
From
(
grid
);
grid_t
.
device
(
place
)
=
w_idx_t
.
reshape
(
Array2
(
1
,
w
))
.
broadcast
(
Array2
(
h
,
1
))
.
reshape
(
Array3
(
h
,
w
,
1
))
.
concatenate
(
h_idx_t
.
reshape
(
Array2
(
1
,
h
))
.
broadcast
(
Array2
(
w
,
1
))
.
shuffle
(
Array2
(
1
,
0
))
.
reshape
(
Array3
(
h
,
w
,
1
)),
2
)
.
eval
()
.
concatenate
(
ones_t
,
2
)
.
reshape
(
Array4
(
1
,
h
,
w
,
3
))
.
broadcast
(
Array4
(
n
,
1
,
1
,
1
));
// output = grid * theta.T
// TODO(wanghaoshuang): Refine batched matrix multiply
auto
blas
=
math
::
GetBlas
<
DeviceContext
,
T
>
(
ctx
);
for
(
int
i
=
0
;
i
<
n
;
++
i
)
{
Tensor
sliced_grid
=
grid
.
Slice
(
i
,
i
+
1
).
Resize
({
h
*
w
,
3
});
Tensor
sliced_out_grad
=
output_grad
->
Slice
(
i
,
i
+
1
).
Resize
({
h
*
w
,
2
});
Tensor
sliced_theta_grad
=
theta_grad
->
Slice
(
i
,
i
+
1
).
Resize
({
2
,
3
});
blas
.
MatMul
(
sliced_out_grad
,
true
,
sliced_grid
,
false
,
T
(
1
),
&
sliced_theta_grad
,
T
(
0
));
}
}
};
}
// namespace operators
}
// namespace paddle
paddle/fluid/operators/delete_var_op.cc
浏览文件 @
bbc818a5
...
...
@@ -32,6 +32,11 @@ class DeleteVarOp : public framework::OperatorBase {
}
};
class
DeleteVarOpShapeInference
:
public
framework
::
InferShapeBase
{
public:
void
operator
()(
framework
::
InferShapeContext
*
ctx
)
const
override
{}
};
class
DeleteVarOpInfoMaker
:
public
framework
::
OpProtoAndCheckerMaker
{
public:
void
Make
()
override
{
...
...
@@ -48,4 +53,5 @@ It should not be configured by users directly.
REGISTER_OPERATOR
(
delete_var
,
paddle
::
operators
::
DeleteVarOp
,
paddle
::
framework
::
EmptyGradOpMaker
,
paddle
::
operators
::
DeleteVarOpInfoMaker
);
paddle
::
operators
::
DeleteVarOpInfoMaker
,
paddle
::
operators
::
DeleteVarOpShapeInference
);
paddle/fluid/platform/cudnn_helper.h
浏览文件 @
bbc818a5
...
...
@@ -341,6 +341,28 @@ class ScopedPoolingDescriptor {
DISABLE_COPY_AND_ASSIGN
(
ScopedPoolingDescriptor
);
};
class
ScopedSpatialTransformerDescriptor
{
public:
ScopedSpatialTransformerDescriptor
()
{
PADDLE_ENFORCE
(
dynload
::
cudnnCreateSpatialTransformerDescriptor
(
&
desc_
));
}
~
ScopedSpatialTransformerDescriptor
()
{
PADDLE_ENFORCE
(
dynload
::
cudnnDestroySpatialTransformerDescriptor
(
desc_
));
}
template
<
typename
T
>
inline
cudnnSpatialTransformerDescriptor_t
descriptor
(
const
int
nbDims
,
const
int
dimA
[])
{
PADDLE_ENFORCE
(
dynload
::
cudnnSetSpatialTransformerNdDescriptor
(
desc_
,
CUDNN_SAMPLER_BILINEAR
,
CudnnDataType
<
T
>::
type
,
nbDims
,
dimA
));
return
desc_
;
}
private:
cudnnSpatialTransformerDescriptor_t
desc_
;
DISABLE_COPY_AND_ASSIGN
(
ScopedSpatialTransformerDescriptor
);
};
inline
bool
CanCUDNNBeUsed
(
const
framework
::
ExecutionContext
&
ctx
)
{
bool
use_cudnn
=
ctx
.
Attr
<
bool
>
(
"use_cudnn"
);
use_cudnn
&=
paddle
::
platform
::
is_gpu_place
(
ctx
.
GetPlace
());
...
...
paddle/fluid/platform/dynload/cudnn.h
浏览文件 @
bbc818a5
...
...
@@ -65,44 +65,51 @@ extern void EnforceCUDNNLoaded(const char* fn_name);
* include all needed cudnn functions in HPPL
* different cudnn version has different interfaces
**/
#define CUDNN_DNN_ROUTINE_EACH(__macro) \
__macro(cudnnSetTensor4dDescriptor); \
__macro(cudnnSetTensor4dDescriptorEx); \
__macro(cudnnSetTensorNdDescriptor); \
__macro(cudnnGetTensorNdDescriptor); \
__macro(cudnnGetConvolutionNdForwardOutputDim); \
__macro(cudnnGetConvolutionForwardAlgorithm); \
__macro(cudnnCreateTensorDescriptor); \
__macro(cudnnDestroyTensorDescriptor); \
__macro(cudnnCreateFilterDescriptor); \
__macro(cudnnSetFilter4dDescriptor); \
__macro(cudnnSetFilterNdDescriptor); \
__macro(cudnnGetFilterNdDescriptor); \
__macro(cudnnSetPooling2dDescriptor); \
__macro(cudnnSetPoolingNdDescriptor); \
__macro(cudnnGetPoolingNdDescriptor); \
__macro(cudnnDestroyFilterDescriptor); \
__macro(cudnnCreateConvolutionDescriptor); \
__macro(cudnnCreatePoolingDescriptor); \
__macro(cudnnDestroyPoolingDescriptor); \
__macro(cudnnSetConvolution2dDescriptor); \
__macro(cudnnDestroyConvolutionDescriptor); \
__macro(cudnnSetConvolutionNdDescriptor); \
__macro(cudnnGetConvolutionNdDescriptor); \
__macro(cudnnDeriveBNTensorDescriptor); \
__macro(cudnnCreate); \
__macro(cudnnDestroy); \
__macro(cudnnSetStream); \
__macro(cudnnActivationForward); \
__macro(cudnnConvolutionForward); \
__macro(cudnnConvolutionBackwardBias); \
__macro(cudnnGetConvolutionForwardWorkspaceSize); \
__macro(cudnnTransformTensor); \
__macro(cudnnPoolingForward); \
__macro(cudnnPoolingBackward); \
__macro(cudnnSoftmaxBackward); \
__macro(cudnnSoftmaxForward); \
__macro(cudnnGetVersion); \
#define CUDNN_DNN_ROUTINE_EACH(__macro) \
__macro(cudnnSetTensor4dDescriptor); \
__macro(cudnnSetTensor4dDescriptorEx); \
__macro(cudnnSetTensorNdDescriptor); \
__macro(cudnnGetTensorNdDescriptor); \
__macro(cudnnGetConvolutionNdForwardOutputDim); \
__macro(cudnnGetConvolutionForwardAlgorithm); \
__macro(cudnnCreateTensorDescriptor); \
__macro(cudnnDestroyTensorDescriptor); \
__macro(cudnnCreateFilterDescriptor); \
__macro(cudnnSetFilter4dDescriptor); \
__macro(cudnnSetFilterNdDescriptor); \
__macro(cudnnGetFilterNdDescriptor); \
__macro(cudnnSetPooling2dDescriptor); \
__macro(cudnnSetPoolingNdDescriptor); \
__macro(cudnnGetPoolingNdDescriptor); \
__macro(cudnnDestroyFilterDescriptor); \
__macro(cudnnCreateConvolutionDescriptor); \
__macro(cudnnCreatePoolingDescriptor); \
__macro(cudnnDestroyPoolingDescriptor); \
__macro(cudnnSetConvolution2dDescriptor); \
__macro(cudnnDestroyConvolutionDescriptor); \
__macro(cudnnSetConvolutionNdDescriptor); \
__macro(cudnnGetConvolutionNdDescriptor); \
__macro(cudnnDeriveBNTensorDescriptor); \
__macro(cudnnCreateSpatialTransformerDescriptor); \
__macro(cudnnSetSpatialTransformerNdDescriptor); \
__macro(cudnnDestroySpatialTransformerDescriptor); \
__macro(cudnnSpatialTfGridGeneratorForward); \
__macro(cudnnSpatialTfGridGeneratorBackward); \
__macro(cudnnSpatialTfSamplerForward); \
__macro(cudnnSpatialTfSamplerBackward); \
__macro(cudnnCreate); \
__macro(cudnnDestroy); \
__macro(cudnnSetStream); \
__macro(cudnnActivationForward); \
__macro(cudnnConvolutionForward); \
__macro(cudnnConvolutionBackwardBias); \
__macro(cudnnGetConvolutionForwardWorkspaceSize); \
__macro(cudnnTransformTensor); \
__macro(cudnnPoolingForward); \
__macro(cudnnPoolingBackward); \
__macro(cudnnSoftmaxBackward); \
__macro(cudnnSoftmaxForward); \
__macro(cudnnGetVersion); \
__macro(cudnnGetErrorString);
CUDNN_DNN_ROUTINE_EACH
(
DECLARE_DYNAMIC_LOAD_CUDNN_WRAP
)
...
...
python/paddle/fluid/io.py
浏览文件 @
bbc818a5
...
...
@@ -884,12 +884,13 @@ def _load_slice_up_vars(executor, dirname, slice_vars_and_attrs):
load_prog
=
Program
()
load_block
=
load_prog
.
global_block
()
need_delete_vars
=
[]
for
var_tuple
in
slice_vars_and_attrs
:
orig_var
=
var_tuple
[
0
]
start
=
var_tuple
[
1
]
slice_var
=
var_tuple
[
2
]
end
=
start
+
reduce
(
lambda
x
,
y
:
x
*
y
,
slice_var
.
shape
)
end
=
start
+
slice_var
.
shape
[
0
]
clone_orig_var
=
load_block
.
create_var
(
name
=
orig_var
.
name
,
...
...
@@ -917,5 +918,8 @@ def _load_slice_up_vars(executor, dirname, slice_vars_and_attrs):
attrs
=
{
'axes'
:
[
0
],
'starts'
:
[
start
],
'ends'
:
[
end
]})
need_delete_vars
.
append
(
clone_orig_var
)
load_block
.
append_op
(
type
=
'delete_var'
,
inputs
=
{
'X'
:
need_delete_vars
},
)
executor
.
run
(
load_prog
)
python/paddle/fluid/layers/nn.py
浏览文件 @
bbc818a5
...
...
@@ -154,6 +154,7 @@ __all__ = [
'mul'
,
'sigmoid_cross_entropy_with_logits'
,
'maxout'
,
'affine_grid'
,
'sequence_reverse'
,
'affine_channel'
,
'hash'
,
...
...
@@ -710,8 +711,18 @@ def dynamic_gru(input,
The first part are weights of the update gate and reset gate with
shape :math:`(D
\\
times 2D)`, and the second part are weights for
candidate hidden state with shape :math:`(D
\\
times D)`.
bias_attr(ParamAttr): The parameter attribute for learnable the
hidden-hidden bias.
If it is set to None or one attribute of ParamAttr, dynamic_gru will
create ParamAttr as param_attr. If the Initializer of the param_attr
is not set, the parameter is initialized with Xavier. Default: None.
bias_attr (ParamAttr|bool|None): The parameter attribute for the bias
of GRU. Note that the bias with :math:`(1
\\
times 3D)` concatenates
the bias in the update gate, reset gate and candidate calculations.
If it is set to False, no bias will be applied to the update gate,
reset gate and candidate calculations. If it is set to None or one
attribute of ParamAttr, dynamic_gru will create ParamAttr as
bias_attr. If the Initializer of the bias_attr is not set, the bias
is initialized zero. Default: None.
is_reverse(bool): Whether to compute reversed GRU, default
:attr:`False`.
gate_activation(str): The activation for update gate and reset gate.
...
...
@@ -810,10 +821,29 @@ def gru_unit(input,
Args:
input (Variable): The fc transformed input value of current step.
hidden (Variable): The hidden value of
lstm
unit from previous step.
hidden (Variable): The hidden value of
gru
unit from previous step.
size (integer): The input dimension value.
param_attr (ParamAttr): The weight parameters for gru unit. Default: None
bias_attr (ParamAttr): The bias parameters for gru unit. Default: None
param_attr(ParamAttr|None): The parameter attribute for the learnable
hidden-hidden weight matrix. Note:
- The shape of the weight matrix is :math:`(T
\\
times 3D)`, where
:math:`D` is the hidden size.
- All elements in the weight matrix can be divided into two parts.
The first part are weights of the update gate and reset gate with
shape :math:`(D
\\
times 2D)`, and the second part are weights for
candidate hidden state with shape :math:`(D
\\
times D)`.
If it is set to None or one attribute of ParamAttr, gru_unit will
create ParamAttr as param_attr. If the Initializer of the param_attr
is not set, the parameter is initialized with Xavier. Default: None.
bias_attr (ParamAttr|bool|None): The parameter attribute for the bias
of GRU. Note that the bias with :math:`(1
\\
times 3D)` concatenates
the bias in the update gate, reset gate and candidate calculations.
If it is set to False, no bias will be applied to the update gate,
reset gate and candidate calculations. If it is set to None or one
attribute of ParamAttr, gru_unit will create ParamAttr as
bias_attr. If the Initializer of the bias_attr is not set, the bias
is initialized zero. Default: None.
activation (string): The activation type for cell (actNode).
Default: 'tanh'
gate_activation (string): The activation type for gates (actGate).
...
...
@@ -4443,7 +4473,10 @@ def transpose(x, perm, name=None):
Examples:
.. code-block:: python
x = fluid.layers.data(name='x', shape=[5, 10, 15], dtype='float32')
# use append_batch_size=False to avoid prepending extra
# batch size in shape
x = fluid.layers.data(name='x', shape=[5, 10, 15],
dtype='float32', append_batch_size=False)
x_transposed = layers.transpose(x, perm=[1, 0, 2])
"""
...
...
@@ -6108,6 +6141,124 @@ def crop(x, shape=None, offsets=None, name=None):
return
out
def
affine_grid
(
theta
,
out_shape
,
name
=
None
):
"""
It generates a grid of (x,y) coordinates using the parameters of
the affine transformation that correspond to a set of points where
the input feature map should be sampled to produce the transformed
output feature map.
.. code-block:: text
* Case 1:
Given:
theta = [[[x_11, x_12, x_13]
[x_14, x_15, x_16]]
[[x_21, x_22, x_23]
[x_24, x_25, x_26]]]
out_shape = [2, 3, 5, 5]
Step 1:
Generate normalized coordinates according to out_shape.
The values of the normalized coordinates are in the interval between -1 and 1.
The shape of the normalized coordinates is [2, H, W] as below:
C = [[[-1. -1. -1. -1. -1. ]
[-0.5 -0.5 -0.5 -0.5 -0.5]
[ 0. 0. 0. 0. 0. ]
[ 0.5 0.5 0.5 0.5 0.5]
[ 1. 1. 1. 1. 1. ]]
[[-1. -0.5 0. 0.5 1. ]
[-1. -0.5 0. 0.5 1. ]
[-1. -0.5 0. 0.5 1. ]
[-1. -0.5 0. 0.5 1. ]
[-1. -0.5 0. 0.5 1. ]]]
C[0] is the coordinates in height axis and C[1] is the coordinates in width axis.
Step2:
Tanspose and reshape C to shape [H * W, 2] and append ones to last dimension. The we get:
C_ = [[-1. -1. 1. ]
[-0.5 -1. 1. ]
[ 0. -1. 1. ]
[ 0.5 -1. 1. ]
[ 1. -1. 1. ]
[-1. -0.5 1. ]
[-0.5 -0.5 1. ]
[ 0. -0.5 1. ]
[ 0.5 -0.5 1. ]
[ 1. -0.5 1. ]
[-1. 0. 1. ]
[-0.5 0. 1. ]
[ 0. 0. 1. ]
[ 0.5 0. 1. ]
[ 1. 0. 1. ]
[-1. 0.5 1. ]
[-0.5 0.5 1. ]
[ 0. 0.5 1. ]
[ 0.5 0.5 1. ]
[ 1. 0.5 1. ]
[-1. 1. 1. ]
[-0.5 1. 1. ]
[ 0. 1. 1. ]
[ 0.5 1. 1. ]
[ 1. 1. 1. ]]
Step3:
Compute output by equation $$Output[i] = C_ * Theta[i]^T$$
Args:
theta (Variable): A batch of affine transform parameters with shape [N, 2, 3].
out_shape (Variable | list | tuple): The shape of target output with format [N, C, H, W].
out_shape can be a Variable or a list or tuple.
name(str|None): A name for this layer(optional). If set None, the layer
will be named automatically.
Returns:
Variable: The output with shape [N, H, W, 2].
Raises:
ValueError: If the type of arguments is not supported.
Examples:
.. code-block:: python
theta = fluid.layers.data(name="x", shape=[2, 3], dtype="float32")
out_shape = fluid.layers.data(name="y", shape=[-1], dtype="float32")
data = fluid.layers.affine_grid(theta, out_shape)
# or
data = fluid.layers.affine_grid(theta, [5, 3, 28, 28])
"""
helper
=
LayerHelper
(
'affine_grid'
)
if
not
(
isinstance
(
out_shape
,
list
)
or
isinstance
(
out_shape
,
tuple
)
or
\
isinstance
(
out_shape
,
Variable
)):
raise
ValueError
(
"The out_shape should be a list, tuple or Variable."
)
if
not
isinstance
(
theta
,
Variable
):
raise
ValueError
(
"The theta should be a Variable."
)
out
=
helper
.
create_variable_for_type_inference
(
theta
.
dtype
)
ipts
=
{
'Theta'
:
theta
}
attrs
=
{}
if
isinstance
(
out_shape
,
Variable
):
ipts
[
'OutputShape'
]
=
out_shape
else
:
attrs
[
'output_shape'
]
=
out_shape
helper
.
append_op
(
type
=
'affine_grid'
,
inputs
=
ipts
,
outputs
=
{
'Output'
:
out
},
attrs
=
None
if
len
(
attrs
)
==
0
else
attrs
)
return
out
def
rank_loss
(
label
,
left
,
right
,
name
=
None
):
"""
**Rank loss layer for RankNet**
...
...
python/paddle/fluid/tests/unittests/dist_save_load.py
0 → 100644
浏览文件 @
bbc818a5
# Copyright (c) 2018 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
os
import
sys
import
signal
import
subprocess
import
argparse
import
time
import
math
import
random
from
multiprocessing
import
Process
from
functools
import
reduce
import
numpy
as
np
import
unittest
import
six
import
paddle
import
paddle.fluid
as
fluid
from
paddle.fluid
import
core
from
paddle.fluid
import
io
from
test_dist_base
import
TestDistRunnerBase
,
runtime_main
,
RUN_STEP
from
dist_simnet_bow
import
TestDistSimnetBow2x2
,
DATA_URL
,
DATA_MD5
class
TestDistSaveLoad2x2
(
TestDistSimnetBow2x2
):
def
_load_persistable_vars
(
self
,
executor
,
dirname
,
program
):
def
_is_checkpoint_var
(
var
):
"""
the checkpoint will not save or load all the variables.
var type is FEED_MINIBATCH/FETCH_LIST/RAW or var name ends with @GRAD are discarded.
: param var(Variable)
"""
if
var
.
desc
.
type
()
==
core
.
VarDesc
.
VarType
.
FEED_MINIBATCH
or
\
var
.
desc
.
type
()
==
core
.
VarDesc
.
VarType
.
FETCH_LIST
or
\
var
.
desc
.
type
()
==
core
.
VarDesc
.
VarType
.
RAW
:
return
False
# @GRAD are named for gradient variables, checkpoint will not save it.
if
"@GRAD"
in
var
.
name
:
return
False
# .trainer_ are named for distribute train variables, checkpoint will not save it.
if
".trainer_"
in
var
.
name
:
return
False
# .block is named for distribute train variables, checkpoint will not save it.
if
".block"
in
var
.
name
:
return
False
if
"tmp_"
in
var
.
name
:
return
False
return
var
.
persistable
io
.
load_vars
(
executor
,
dirname
=
dirname
,
main_program
=
program
,
predicate
=
_is_checkpoint_var
,
filename
=
None
)
def
run_pserver
(
self
,
args
):
self
.
get_model
(
batch_size
=
2
)
# NOTE: pserver should not call memory optimize
t
=
self
.
get_transpiler
(
args
.
trainer_id
,
fluid
.
default_main_program
(),
args
.
endpoints
,
args
.
trainers
,
args
.
sync_mode
)
pserver_prog
=
t
.
get_pserver_program
(
args
.
current_endpoint
)
startup_prog
=
t
.
get_startup_program
(
args
.
current_endpoint
,
pserver_prog
)
need_load
=
bool
(
int
(
os
.
getenv
(
"LOAD"
,
"0"
)))
model_dir
=
os
.
getenv
(
"MODEL_DIR"
,
""
)
place
=
fluid
.
CPUPlace
()
exe
=
fluid
.
Executor
(
place
)
exe
.
run
(
startup_prog
)
if
need_load
and
model_dir
:
self
.
_load_persistable_vars
(
exe
,
model_dir
,
startup_prog
)
exe
.
run
(
pserver_prog
)
def
run_trainer
(
self
,
args
):
test_program
,
avg_cost
,
train_reader
,
test_reader
,
batch_acc
,
predict
=
\
self
.
get_model
(
batch_size
=
2
)
if
args
.
mem_opt
:
fluid
.
memory_optimize
(
fluid
.
default_main_program
(),
skip_grads
=
True
)
if
args
.
is_dist
:
t
=
self
.
get_transpiler
(
args
.
trainer_id
,
fluid
.
default_main_program
(),
args
.
endpoints
,
args
.
trainers
,
args
.
sync_mode
)
trainer_prog
=
t
.
get_trainer_program
()
else
:
trainer_prog
=
fluid
.
default_main_program
()
if
args
.
use_cuda
:
place
=
fluid
.
CUDAPlace
(
0
)
else
:
place
=
fluid
.
CPUPlace
()
startup_exe
=
fluid
.
Executor
(
place
)
startup_exe
.
run
(
fluid
.
default_startup_program
())
strategy
=
fluid
.
ExecutionStrategy
()
strategy
.
num_threads
=
1
strategy
.
allow_op_delay
=
False
build_stra
=
fluid
.
BuildStrategy
()
if
args
.
use_reduce
:
build_stra
.
reduce_strategy
=
fluid
.
BuildStrategy
.
ReduceStrategy
.
Reduce
else
:
build_stra
.
reduce_strategy
=
fluid
.
BuildStrategy
.
ReduceStrategy
.
AllReduce
exe
=
fluid
.
ParallelExecutor
(
args
.
use_cuda
,
loss_name
=
avg_cost
.
name
,
exec_strategy
=
strategy
,
build_strategy
=
build_stra
)
feed_var_list
=
[
var
for
var
in
trainer_prog
.
global_block
().
vars
.
values
()
if
var
.
is_data
]
feeder
=
fluid
.
DataFeeder
(
feed_var_list
,
place
)
reader_generator
=
train_reader
()
def
get_data
():
origin_batch
=
next
(
reader_generator
)
if
args
.
is_dist
and
args
.
use_reader_alloc
:
new_batch
=
[]
for
offset
,
item
in
enumerate
(
origin_batch
):
if
offset
%
2
==
args
.
trainer_id
:
new_batch
.
append
(
item
)
return
new_batch
else
:
return
origin_batch
need_save
=
bool
(
int
(
os
.
getenv
(
"SAVE"
,
"0"
)))
model_dir
=
os
.
getenv
(
"MODEL_DIR"
,
""
)
if
need_save
:
for
_
in
six
.
moves
.
xrange
(
RUN_STEP
):
loss
,
=
exe
.
run
(
fetch_list
=
[
avg_cost
.
name
],
feed
=
feeder
.
feed
(
get_data
()))
if
need_save
and
model_dir
:
io
.
save_persistables
(
startup_exe
,
model_dir
,
trainer_prog
)
var
=
np
.
array
(
fluid
.
global_scope
().
find_var
(
'__fc_b__'
).
get_tensor
())
print
(
np
.
ravel
(
var
).
tolist
())
if
__name__
==
"__main__"
:
paddle
.
dataset
.
common
.
download
(
DATA_URL
,
'simnet'
,
DATA_MD5
,
"train"
)
runtime_main
(
TestDistSaveLoad2x2
)
python/paddle/fluid/tests/unittests/test_affine_grid_op.py
0 → 100644
浏览文件 @
bbc818a5
# Copyright (c) 2018 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.
import
unittest
import
numpy
as
np
from
op_test
import
OpTest
def
AffineGrid
(
theta
,
size
):
n
=
size
[
0
]
w
=
size
[
3
]
h
=
size
[
2
]
h_idx
=
np
.
repeat
(
np
.
linspace
(
-
1
,
1
,
h
)[
np
.
newaxis
,
:],
w
,
axis
=
0
).
T
[:,
:,
np
.
newaxis
]
w_idx
=
np
.
repeat
(
np
.
linspace
(
-
1
,
1
,
w
)[
np
.
newaxis
,
:],
h
,
axis
=
0
)[:,
:,
np
.
newaxis
]
grid
=
np
.
concatenate
(
[
w_idx
,
h_idx
,
np
.
ones
([
h
,
w
,
1
])],
axis
=
2
)
# h * w * 3
grid
=
np
.
repeat
(
grid
[
np
.
newaxis
,
:],
size
[
0
],
axis
=
0
)
# n * h * w *3
ret
=
np
.
zeros
([
n
,
h
*
w
,
2
])
theta
=
theta
.
transpose
([
0
,
2
,
1
])
for
i
in
range
(
len
(
theta
)):
ret
[
i
]
=
np
.
dot
(
grid
[
i
].
reshape
([
h
*
w
,
3
]),
theta
[
i
])
# print ret.reshape([h * w, 2]).astype("float32")
return
ret
.
reshape
([
n
,
h
,
w
,
2
]).
astype
(
"float32"
)
class
TestAffineGridOp
(
OpTest
):
def
setUp
(
self
):
self
.
initTestCase
()
self
.
op_type
=
"affine_grid"
theta
=
np
.
random
.
randint
(
1
,
3
,
self
.
theta_shape
).
astype
(
"float32"
)
theta
=
np
.
ones
(
self
.
theta_shape
).
astype
(
"float32"
)
self
.
inputs
=
{
'Theta'
:
theta
}
self
.
attrs
=
{
"use_cudnn"
:
True
}
if
self
.
dynamic_shape
:
self
.
inputs
[
'OutputShape'
]
=
self
.
output_shape
else
:
self
.
attrs
[
'output_shape'
]
=
self
.
output_shape
self
.
outputs
=
{
'Output'
:
AffineGrid
(
theta
,
self
.
output_shape
)}
def
test_check_output
(
self
):
self
.
check_output
()
def
test_check_grad_normal
(
self
):
self
.
check_grad
(
[
'Theta'
],
'Output'
,
no_grad_set
=
[
'OutputShape'
],
max_relative_error
=
0.006
)
def
initTestCase
(
self
):
self
.
theta_shape
=
(
3
,
2
,
3
)
self
.
output_shape
=
np
.
array
([
3
,
2
,
5
,
7
]).
astype
(
"int32"
)
self
.
dynamic_shape
=
False
class
TestAffineGridOpCase1
(
TestAffineGridOp
):
def
initTestCase
(
self
):
self
.
theta_shape
=
(
3
,
2
,
3
)
self
.
output_shape
=
np
.
array
([
3
,
2
,
5
,
7
]).
astype
(
"int32"
)
self
.
dynamic_shape
=
True
if
__name__
==
'__main__'
:
unittest
.
main
()
python/paddle/fluid/tests/unittests/test_dist_save_load.py
0 → 100644
浏览文件 @
bbc818a5
# Copyright (c) 2018 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
os
import
shutil
import
unittest
import
tempfile
import
numpy
as
np
from
test_dist_base
import
TestDistBase
,
RUN_STEP
class
TestDistSaveLoadDense2x2
(
TestDistBase
):
def
_setup_config
(
self
):
self
.
_sync_mode
=
True
self
.
_enforce_place
=
"CPU"
def
check_with_place
(
self
,
model_file
,
delta
=
1e-3
,
check_error_log
=
False
,
need_envs
=
{}):
required_envs
=
{
"PATH"
:
os
.
getenv
(
"PATH"
,
""
),
"PYTHONPATH"
:
os
.
getenv
(
"PYTHONPATH"
,
""
),
"LD_LIBRARY_PATH"
:
os
.
getenv
(
"LD_LIBRARY_PATH"
,
""
),
"http_proxy"
:
""
}
required_envs
.
update
(
need_envs
)
if
check_error_log
:
required_envs
[
"GLOG_v"
]
=
"7"
required_envs
[
"GLOG_logtostderr"
]
=
"1"
model_dir
=
tempfile
.
mkdtemp
()
local_env
=
{}
local_env
[
"SAVE"
]
=
"1"
local_env
[
"MODEL_DIR"
]
=
model_dir
local_env
.
update
(
required_envs
)
cluster_env
=
{}
cluster_env
[
"LOAD"
]
=
"1"
cluster_env
[
"MODEL_DIR"
]
=
model_dir
cluster_env
.
update
(
required_envs
)
local_var
=
self
.
_run_local
(
model_file
,
local_env
,
check_error_log
)
tr0_var
,
tr1_var
=
self
.
_run_cluster
(
model_file
,
cluster_env
,
check_error_log
)
shutil
.
rmtree
(
model_dir
)
local_np
=
np
.
array
(
eval
(
local_var
[
0
]))
train0_np
=
np
.
array
(
eval
(
tr0_var
[
0
]))
train1_np
=
np
.
array
(
eval
(
tr1_var
[
0
]))
self
.
assertAlmostEqual
(
local_np
.
all
(),
train0_np
.
all
(),
delta
=
delta
)
self
.
assertAlmostEqual
(
local_np
.
all
(),
train1_np
.
all
(),
delta
=
delta
)
self
.
assertAlmostEqual
(
train0_np
.
all
(),
train1_np
.
all
(),
delta
=
delta
)
@
unittest
.
skip
(
reason
=
"CI fail"
)
def
test_dist
(
self
):
need_envs
=
{
"IS_DISTRIBUTED"
:
'0'
,
"IS_SPARSE"
:
'0'
,
'IS_SELF_CONTAINED_LR'
:
'1'
}
self
.
check_with_place
(
"dist_save_load.py"
,
delta
=
0
,
check_error_log
=
False
,
need_envs
=
need_envs
)
if
__name__
==
"__main__"
:
unittest
.
main
()
python/paddle/fluid/tests/unittests/test_layers.py
浏览文件 @
bbc818a5
...
...
@@ -865,6 +865,22 @@ class TestBook(unittest.TestCase):
self
.
assertIsNotNone
(
out
)
print
(
str
(
program
))
def
test_affine_grid
(
self
):
program
=
Program
()
with
program_guard
(
program
):
data
=
layers
.
data
(
name
=
'data'
,
shape
=
[
2
,
3
,
3
],
dtype
=
"float32"
)
out
,
ids
=
layers
.
argsort
(
input
=
data
,
axis
=
1
)
theta
=
layers
.
data
(
name
=
"theta"
,
shape
=
[
2
,
3
],
dtype
=
"float32"
)
out_shape
=
layers
.
data
(
name
=
"out_shape"
,
shape
=
[
-
1
],
dtype
=
"float32"
)
data_0
=
layers
.
affine_grid
(
theta
,
out_shape
)
data_1
=
layers
.
affine_grid
(
theta
,
[
5
,
3
,
28
,
28
])
self
.
assertIsNotNone
(
data_0
)
self
.
assertIsNotNone
(
data_1
)
print
(
str
(
program
))
if
__name__
==
'__main__'
:
unittest
.
main
()
python/paddle/fluid/tests/unittests/test_py_reader_pin_memory.py
0 → 100644
浏览文件 @
bbc818a5
# Copyright (c) 2018 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
unittest
import
paddle
import
paddle.fluid
as
fluid
import
paddle.fluid.core
as
core
import
numpy
as
np
from
threading
import
Thread
def
user_reader
(
inputs
):
def
_reader
():
for
d
in
inputs
:
yield
d
return
_reader
def
batch_feeder
(
batch_reader
,
pin_memory
=
False
,
img_dtype
=
"float32"
):
def
_feeder
():
for
batch_data
in
batch_reader
():
sample_batch
=
[]
label_batch
=
[]
for
sample
,
label
in
batch_data
:
sample_batch
.
append
(
sample
)
label_batch
.
append
([
label
])
tensor
=
core
.
LoDTensor
()
label
=
core
.
LoDTensor
()
place
=
core
.
CUDAPinnedPlace
()
if
pin_memory
else
core
.
CPUPlace
()
tensor
.
set
(
np
.
array
(
sample_batch
,
dtype
=
img_dtype
),
place
)
label
.
set
(
np
.
array
(
label_batch
,
dtype
=
"int64"
),
place
)
yield
[
tensor
,
label
]
return
_feeder
class
TestPyReader
(
unittest
.
TestCase
):
def
setUp
(
self
):
self
.
capacity
=
10
self
.
shapes
=
[(
-
1
,
3
,
2
,
1
),
(
-
1
,
1
)]
self
.
lod_levels
=
[
0
,
0
]
self
.
dtypes
=
[
'float32'
,
'int64'
]
def
test_pin_memory_pyreader
(
self
):
with
fluid
.
program_guard
(
fluid
.
Program
(),
fluid
.
Program
()):
place
=
fluid
.
CUDAPlace
(
0
)
if
fluid
.
core
.
is_compiled_with_cuda
(
)
else
fluid
.
CPUPlace
()
executor
=
fluid
.
Executor
(
place
)
data_file
=
fluid
.
layers
.
py_reader
(
capacity
=
self
.
capacity
,
dtypes
=
self
.
dtypes
,
lod_levels
=
self
.
lod_levels
,
shapes
=
self
.
shapes
)
# feed_queue = data_file.queue
read_out_data
=
fluid
.
layers
.
read_file
(
data_file
)
self
.
inputs
=
[]
for
_
in
range
(
10
):
sample
=
np
.
random
.
uniform
(
low
=
0
,
high
=
1
,
size
=
[
3
,
2
,
1
]).
astype
(
"float32"
)
label
=
np
.
random
.
uniform
(
low
=
0
,
high
=
10
,
size
=
[
1
]).
astype
(
"int64"
)
self
.
inputs
.
append
((
sample
,
label
))
self
.
input_tensors
=
[]
for
d
,
l
in
batch_feeder
(
paddle
.
batch
(
user_reader
(
self
.
inputs
),
batch_size
=
2
),
pin_memory
=
True
if
fluid
.
core
.
is_compiled_with_cuda
()
else
False
)():
ta
=
fluid
.
LoDTensorArray
()
ta
.
append
(
d
)
ta
.
append
(
l
)
self
.
input_tensors
.
append
(
ta
)
self
.
batched_inputs
=
[]
for
batch
in
paddle
.
batch
(
user_reader
(
self
.
inputs
),
batch_size
=
2
)():
feed_d
=
[]
feed_l
=
[]
for
d
,
l
in
batch
:
feed_d
.
append
(
d
)
feed_l
.
append
([
l
])
self
.
batched_inputs
.
append
([
feed_d
,
feed_l
])
data_file
.
decorate_tensor_provider
(
batch_feeder
(
paddle
.
batch
(
user_reader
(
self
.
inputs
),
batch_size
=
2
),
pin_memory
=
True
if
fluid
.
core
.
is_compiled_with_cuda
()
else
False
))
executor
.
run
(
fluid
.
default_startup_program
())
self
.
outputs
=
[]
data_file
.
start
()
for
_
in
self
.
input_tensors
:
self
.
outputs
.
append
(
executor
.
run
(
fetch_list
=
list
(
read_out_data
)))
data_file
.
reset
()
self
.
validate
()
def
validate
(
self
):
self
.
assertEqual
(
len
(
self
.
batched_inputs
),
len
(
self
.
outputs
))
for
in_data_list
,
out_data_list
in
zip
(
self
.
batched_inputs
,
self
.
outputs
):
self
.
assertEqual
(
len
(
in_data_list
),
len
(
out_data_list
))
in_data_list_np
=
[
np
.
array
(
in_lod_tensor
)
for
in_lod_tensor
in
in_data_list
]
for
in_data
,
out_data
in
zip
(
in_data_list_np
,
out_data_list
):
self
.
assertTrue
((
in_data
==
out_data
).
all
())
if
__name__
==
'__main__'
:
unittest
.
main
()
python/paddle/fluid/transpiler/distribute_transpiler.py
浏览文件 @
bbc818a5
...
...
@@ -920,11 +920,11 @@ to transpile() call.")
block_idx
=
int
(
block_name
.
split
(
block_suffix
)[
1
])
orig_var
=
self
.
origin_program
.
global_block
().
vars
[
orig_var_name
]
skip_
numel
=
0
skip_
dim0
=
0
slice_vars
=
self
.
param_var_mapping
[
orig_var_name
]
for
slice_var
in
slice_vars
[:
block_idx
]:
skip_
numel
+=
reduce
(
lambda
x
,
y
:
x
*
y
,
slice_var
.
shape
)
slice_vars_and_attrs
.
append
([
orig_var
,
skip_
numel
,
param
])
skip_
dim0
+=
slice_var
.
shape
[
0
]
slice_vars_and_attrs
.
append
([
orig_var
,
skip_
dim0
,
param
])
return
slice_vars_and_attrs
...
...
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