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体验新版 GitCode,发现更多精彩内容 >>
提交
da87f7a6
编写于
12月 20, 2018
作者:
T
typhoonzero
浏览文件
操作
浏览文件
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电子邮件补丁
差异文件
Revert "[Feature] Fp16 training for resnet50 (#14850)"
This reverts commit
3d750f9c
.
上级
3babc801
变更
25
隐藏空白更改
内联
并排
Showing
25 changed file
with
141 addition
and
356 deletion
+141
-356
paddle/fluid/framework/details/multi_devices_graph_pass.cc
paddle/fluid/framework/details/multi_devices_graph_pass.cc
+3
-5
paddle/fluid/framework/details/multi_devices_graph_pass.h
paddle/fluid/framework/details/multi_devices_graph_pass.h
+1
-2
paddle/fluid/framework/details/scale_loss_grad_op_handle.cc
paddle/fluid/framework/details/scale_loss_grad_op_handle.cc
+17
-44
paddle/fluid/framework/details/scale_loss_grad_op_handle.h
paddle/fluid/framework/details/scale_loss_grad_op_handle.h
+2
-3
paddle/fluid/operators/elementwise/elementwise_div_op.cu
paddle/fluid/operators/elementwise/elementwise_div_op.cu
+0
-5
paddle/fluid/operators/elementwise/elementwise_mul_op.cu
paddle/fluid/operators/elementwise/elementwise_mul_op.cu
+10
-12
paddle/fluid/operators/fill_zeros_like_op.cu.cc
paddle/fluid/operators/fill_zeros_like_op.cu.cc
+0
-3
paddle/fluid/operators/metrics/accuracy_op.cu
paddle/fluid/operators/metrics/accuracy_op.cu
+3
-5
paddle/fluid/operators/optimizers/momentum_op.cu
paddle/fluid/operators/optimizers/momentum_op.cu
+1
-4
paddle/fluid/operators/optimizers/momentum_op.h
paddle/fluid/operators/optimizers/momentum_op.h
+2
-4
paddle/fluid/operators/top_k_op.cu
paddle/fluid/operators/top_k_op.cu
+6
-9
paddle/fluid/platform/nccl_helper.h
paddle/fluid/platform/nccl_helper.h
+0
-3
python/paddle/fluid/data_feeder.py
python/paddle/fluid/data_feeder.py
+0
-2
python/paddle/fluid/initializer.py
python/paddle/fluid/initializer.py
+4
-50
python/paddle/fluid/layers/learning_rate_scheduler.py
python/paddle/fluid/layers/learning_rate_scheduler.py
+75
-100
python/paddle/fluid/layers/nn.py
python/paddle/fluid/layers/nn.py
+2
-6
python/paddle/fluid/optimizer.py
python/paddle/fluid/optimizer.py
+4
-13
python/paddle/fluid/tests/unittests/op_test.py
python/paddle/fluid/tests/unittests/op_test.py
+0
-2
python/paddle/fluid/tests/unittests/test_accuracy_op.py
python/paddle/fluid/tests/unittests/test_accuracy_op.py
+2
-15
python/paddle/fluid/tests/unittests/test_elementwise_div_op.py
...n/paddle/fluid/tests/unittests/test_elementwise_div_op.py
+2
-23
python/paddle/fluid/tests/unittests/test_elementwise_mul_op.py
...n/paddle/fluid/tests/unittests/test_elementwise_mul_op.py
+0
-5
python/paddle/fluid/tests/unittests/test_fill_zeros_like_op.py
...n/paddle/fluid/tests/unittests/test_fill_zeros_like_op.py
+1
-11
python/paddle/fluid/tests/unittests/test_learning_rate_scheduler.py
...dle/fluid/tests/unittests/test_learning_rate_scheduler.py
+1
-1
python/paddle/fluid/tests/unittests/test_momentum_op.py
python/paddle/fluid/tests/unittests/test_momentum_op.py
+4
-17
python/paddle/fluid/tests/unittests/test_top_k_op.py
python/paddle/fluid/tests/unittests/test_top_k_op.py
+1
-12
未找到文件。
paddle/fluid/framework/details/multi_devices_graph_pass.cc
浏览文件 @
da87f7a6
...
...
@@ -355,9 +355,7 @@ std::unique_ptr<ir::Graph> MultiDevSSAGraphBuilder::ApplyImpl(
BuildStrategy
::
GradientScaleStrategy
::
kCustomized
)
{
// TODO(paddle-dev): Why is there no input for this op_handle?
auto
loss_grad_name
=
node
->
Op
()
->
OutputArgumentNames
()[
0
];
auto
out_dtype
=
all_vars_
.
at
(
loss_grad_name
)
->
GetDataType
();
CreateScaleLossGradOp
(
&
result
,
loss_grad_name
,
node
->
outputs
[
0
],
out_dtype
);
CreateScaleLossGradOp
(
&
result
,
loss_grad_name
,
node
->
outputs
[
0
]);
}
// This assumes the backward generating code will ensure IsScaleLossOp
// is true only for the op that scale the final scalar loss.
...
...
@@ -660,13 +658,13 @@ int MultiDevSSAGraphBuilder::GetVarDeviceID(
void
MultiDevSSAGraphBuilder
::
CreateScaleLossGradOp
(
ir
::
Graph
*
result
,
const
std
::
string
&
loss_grad_name
,
ir
::
Node
*
out_var_node
,
proto
::
VarType
::
Type
dtype
)
const
{
ir
::
Node
*
out_var_node
)
const
{
for
(
size_t
i
=
0
;
i
<
places_
.
size
();
++
i
)
{
// Insert ScaleCost OpHandle
auto
*
dev_ctx
=
platform
::
DeviceContextPool
::
Instance
().
Get
(
places_
[
i
]);
auto
*
op_handle
=
new
ScaleLossGradOpHandle
(
result
->
CreateEmptyNode
(
"scale_loss_grad"
,
ir
::
Node
::
Type
::
kOperation
),
local_scopes_
.
size
(),
local_scopes_
[
i
],
places_
[
i
],
dev_ctx
,
dtype
);
local_scopes_
.
size
(),
local_scopes_
[
i
],
places_
[
i
],
dev_ctx
);
result
->
Get
<
GraphOps
>
(
kGraphOps
).
emplace_back
(
op_handle
);
// FIXME: Currently ScaleLossGradOp only use device_count as scale
...
...
paddle/fluid/framework/details/multi_devices_graph_pass.h
浏览文件 @
da87f7a6
...
...
@@ -68,8 +68,7 @@ class MultiDevSSAGraphBuilder : public ir::Pass {
void
CreateScaleLossGradOp
(
ir
::
Graph
*
result
,
const
std
::
string
&
loss_grad_name
,
ir
::
Node
*
out_var_node
,
proto
::
VarType
::
Type
dtype
)
const
;
ir
::
Node
*
out_var_node
)
const
;
VarHandle
*
CreateReduceOp
(
ir
::
Graph
*
result
,
const
std
::
string
&
og
,
int
dst_dev_id
)
const
;
...
...
paddle/fluid/framework/details/scale_loss_grad_op_handle.cc
浏览文件 @
da87f7a6
...
...
@@ -22,66 +22,39 @@ namespace details {
ScaleLossGradOpHandle
::
ScaleLossGradOpHandle
(
ir
::
Node
*
node
,
size_t
num_dev
,
Scope
*
scope
,
platform
::
Place
place
,
platform
::
DeviceContext
*
dev_ctx
,
proto
::
VarType
::
Type
dtype
)
platform
::
DeviceContext
*
dev_ctx
)
:
OpHandleBase
(
node
),
coeff_
(
static_cast
<
float
>
(
1.0
/
num_dev
)),
scope_
(
scope
),
place_
(
place
),
out_dtype_
(
dtype
)
{
place_
(
place
)
{
this
->
SetDeviceContext
(
place_
,
dev_ctx
);
}
ScaleLossGradOpHandle
::~
ScaleLossGradOpHandle
()
{}
struct
ScaleLossGradFunctor
{
float
coeff_
;
Tensor
*
out_
;
platform
::
Place
place_
;
OpHandleBase
*
op_handle_
;
proto
::
VarType
::
Type
out_dtype_
;
platform
::
DeviceContext
*
ctx_
;
ScaleLossGradFunctor
(
float
coeff
,
Tensor
*
out
,
platform
::
Place
place
,
OpHandleBase
*
op_handle
,
proto
::
VarType
::
Type
dtype
,
platform
::
DeviceContext
*
ctx
)
:
coeff_
(
coeff
),
out_
(
out
),
place_
(
place
),
out_dtype_
(
dtype
),
ctx_
(
ctx
)
{}
template
<
typename
OutT
>
void
apply
()
const
{
auto
*
out_data
=
out_
->
mutable_data
<
OutT
>
(
place_
);
if
(
platform
::
is_cpu_place
(
place_
))
{
*
out_data
=
static_cast
<
OutT
>
(
coeff_
);
}
else
{
#ifdef PADDLE_WITH_CUDA
OutT
cast_coeff
=
static_cast
<
OutT
>
(
coeff_
);
auto
stream
=
static_cast
<
platform
::
CUDADeviceContext
*>
(
ctx_
)
->
stream
();
memory
::
Copy
(
boost
::
get
<
platform
::
CUDAPlace
>
(
place_
),
out_data
,
platform
::
CPUPlace
(),
&
cast_coeff
,
SizeOfType
(
out_dtype_
),
stream
);
VLOG
(
10
)
<<
place_
<<
"RUN Scale loss grad op"
;
#endif
}
}
};
void
ScaleLossGradOpHandle
::
RunImpl
()
{
// Doesn't wait any event
std
::
string
var_name
=
static_cast
<
VarHandle
*>
(
this
->
outputs_
[
0
])
->
name_
;
auto
&
local_scope
=
*
scope_
->
FindVar
(
kLocalExecScopeName
)
->
Get
<
Scope
*>
();
auto
*
tensor
=
local_scope
.
FindVar
(
var_name
)
->
GetMutable
<
LoDTensor
>
();
tensor
->
Resize
(
make_ddim
({
1
}));
float
*
tmp
=
local_scope
.
FindVar
(
var_name
)
->
GetMutable
<
LoDTensor
>
()
->
mutable_data
<
float
>
(
make_ddim
({
1
}),
place_
);
if
(
platform
::
is_cpu_place
(
place_
))
{
*
tmp
=
coeff_
;
}
else
{
#ifdef PADDLE_WITH_CUDA
ScaleLossGradFunctor
func
(
coeff_
,
tensor
,
place_
,
this
,
out_dtype_
,
this
->
dev_ctxes_
.
at
(
place_
));
this
->
RunAndRecordEvent
([
&
]
{
framework
::
VisitDataType
(
out_dtype_
,
func
);
});
#else
ScaleLossGradFunctor
func
(
coeff_
,
tensor
,
place_
,
this
,
out_dtype_
,
nullptr
);
framework
::
VisitDataType
(
out_dtype_
,
func
);
this
->
RunAndRecordEvent
([
&
]
{
auto
stream
=
static_cast
<
platform
::
CUDADeviceContext
*>
(
this
->
dev_ctxes_
.
at
(
place_
))
->
stream
();
memory
::
Copy
(
boost
::
get
<
platform
::
CUDAPlace
>
(
place_
),
tmp
,
platform
::
CPUPlace
(),
&
coeff_
,
sizeof
(
float
),
stream
);
VLOG
(
10
)
<<
place_
<<
"RUN Scale loss grad op"
;
});
#endif
}
}
std
::
string
ScaleLossGradOpHandle
::
Name
()
const
{
return
"Scale LossGrad"
;
}
...
...
paddle/fluid/framework/details/scale_loss_grad_op_handle.h
浏览文件 @
da87f7a6
...
...
@@ -26,8 +26,8 @@ namespace details {
struct
ScaleLossGradOpHandle
:
public
OpHandleBase
{
ScaleLossGradOpHandle
(
ir
::
Node
*
node
,
size_t
num_dev
,
Scope
*
scope
,
platform
::
Place
place
,
platform
::
DeviceContext
*
context
,
p
roto
::
VarType
::
Type
dtype
);
platform
::
Place
place
,
p
latform
::
DeviceContext
*
context
);
~
ScaleLossGradOpHandle
()
final
;
...
...
@@ -40,7 +40,6 @@ struct ScaleLossGradOpHandle : public OpHandleBase {
float
coeff_
;
Scope
*
scope_
;
platform
::
Place
place_
;
proto
::
VarType
::
Type
out_dtype_
;
};
}
// namespace details
...
...
paddle/fluid/operators/elementwise/elementwise_div_op.cu
浏览文件 @
da87f7a6
...
...
@@ -12,23 +12,18 @@ 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/elementwise/elementwise_div_op.h"
#include "paddle/fluid/platform/float16.h"
namespace
ops
=
paddle
::
operators
;
REGISTER_OP_CUDA_KERNEL
(
elementwise_div
,
ops
::
ElementwiseDivKernel
<
paddle
::
platform
::
CUDADeviceContext
,
float
>
,
ops
::
ElementwiseDivKernel
<
paddle
::
platform
::
CUDADeviceContext
,
paddle
::
platform
::
float16
>
,
ops
::
ElementwiseDivKernel
<
paddle
::
platform
::
CUDADeviceContext
,
double
>
,
ops
::
ElementwiseDivKernel
<
paddle
::
platform
::
CUDADeviceContext
,
int
>
,
ops
::
ElementwiseDivKernel
<
paddle
::
platform
::
CUDADeviceContext
,
int64_t
>
);
REGISTER_OP_CUDA_KERNEL
(
elementwise_div_grad
,
ops
::
ElementwiseDivGradKernel
<
paddle
::
platform
::
CUDADeviceContext
,
float
>
,
ops
::
ElementwiseDivGradKernel
<
paddle
::
platform
::
CUDADeviceContext
,
paddle
::
platform
::
float16
>
,
ops
::
ElementwiseDivGradKernel
<
paddle
::
platform
::
CUDADeviceContext
,
double
>
,
ops
::
ElementwiseDivGradKernel
<
paddle
::
platform
::
CUDADeviceContext
,
int
>
,
ops
::
ElementwiseDivGradKernel
<
paddle
::
platform
::
CUDADeviceContext
,
...
...
paddle/fluid/operators/elementwise/elementwise_mul_op.cu
浏览文件 @
da87f7a6
...
...
@@ -12,21 +12,19 @@ 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/elementwise/elementwise_mul_op.h"
#include "paddle/fluid/platform/float16.h"
namespace
ops
=
paddle
::
operators
;
namespace
plat
=
paddle
::
platform
;
REGISTER_OP_CUDA_KERNEL
(
elementwise_mul
,
ops
::
ElementwiseMulKernel
<
plat
::
CUDADeviceContext
,
float
>
,
ops
::
ElementwiseMulKernel
<
p
lat
::
CUDADeviceContext
,
double
>
,
ops
::
ElementwiseMulKernel
<
p
lat
::
CUDADeviceContext
,
int
>
,
ops
::
ElementwiseMulKernel
<
p
lat
::
CUDADeviceContext
,
int64_
t
>
,
ops
::
ElementwiseMulKernel
<
p
lat
::
CUDADeviceContext
,
plat
::
float16
>
);
elementwise_mul
,
ops
::
ElementwiseMulKernel
<
p
addle
::
platform
::
CUDADeviceContext
,
float
>
,
ops
::
ElementwiseMulKernel
<
p
addle
::
platform
::
CUDADeviceContext
,
double
>
,
ops
::
ElementwiseMulKernel
<
p
addle
::
platform
::
CUDADeviceContext
,
in
t
>
,
ops
::
ElementwiseMulKernel
<
p
addle
::
platform
::
CUDADeviceContext
,
int64_t
>
);
REGISTER_OP_CUDA_KERNEL
(
elementwise_mul_grad
,
ops
::
ElementwiseMulGradKernel
<
p
lat
::
CUDADeviceContext
,
float
>
,
ops
::
ElementwiseMulGradKernel
<
p
lat
::
CUDADeviceContext
,
double
>
,
ops
::
ElementwiseMulGradKernel
<
p
lat
::
CUDADeviceContext
,
int
>
,
ops
::
ElementwiseMulGradKernel
<
p
lat
::
CUDADeviceContext
,
int64_t
>
,
ops
::
ElementwiseMulGradKernel
<
plat
::
CUDADeviceContext
,
plat
::
float16
>
);
ops
::
ElementwiseMulGradKernel
<
p
addle
::
platform
::
CUDADeviceContext
,
float
>
,
ops
::
ElementwiseMulGradKernel
<
p
addle
::
platform
::
CUDADeviceContext
,
double
>
,
ops
::
ElementwiseMulGradKernel
<
p
addle
::
platform
::
CUDADeviceContext
,
int
>
,
ops
::
ElementwiseMulGradKernel
<
p
addle
::
platform
::
CUDADeviceContext
,
int64_t
>
);
paddle/fluid/operators/fill_zeros_like_op.cu.cc
浏览文件 @
da87f7a6
...
...
@@ -14,7 +14,6 @@ limitations under the License. */
#include "paddle/fluid/operators/fill_zeros_like_op.h"
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/platform/float16.h"
namespace
ops
=
paddle
::
operators
;
REGISTER_OP_CUDA_KERNEL
(
...
...
@@ -23,6 +22,4 @@ REGISTER_OP_CUDA_KERNEL(
ops
::
FillZerosLikeKernel
<
paddle
::
platform
::
CUDADeviceContext
,
int64_t
>
,
ops
::
FillZerosLikeKernel
<
paddle
::
platform
::
CUDADeviceContext
,
float
>
,
ops
::
FillZerosLikeKernel
<
paddle
::
platform
::
CUDADeviceContext
,
double
>
,
ops
::
FillZerosLikeKernel
<
paddle
::
platform
::
CUDADeviceContext
,
paddle
::
platform
::
float16
>
,
ops
::
FillZerosLikeKernel
<
paddle
::
platform
::
CUDADeviceContext
,
bool
>
);
paddle/fluid/operators/metrics/accuracy_op.cu
浏览文件 @
da87f7a6
...
...
@@ -16,7 +16,6 @@ limitations under the License. */
#include <thrust/reduce.h>
#include "paddle/fluid/operators/metrics/accuracy_op.h"
#include "paddle/fluid/platform/cuda_primitives.h"
#include "paddle/fluid/platform/float16.h"
#include "paddle/fluid/platform/gpu_info.h"
namespace
paddle
{
...
...
@@ -95,7 +94,6 @@ class AccuracyOpCUDAKernel : public framework::OpKernel<T> {
// FIXME(typhoonzero): types of T is for inference data.
// label data is always int64
REGISTER_OP_CUDA_KERNEL
(
accuracy
,
paddle
::
operators
::
AccuracyOpCUDAKernel
<
float
>
,
paddle
::
operators
::
AccuracyOpCUDAKernel
<
double
>
,
paddle
::
operators
::
AccuracyOpCUDAKernel
<
paddle
::
platform
::
float16
>
);
REGISTER_OP_CUDA_KERNEL
(
accuracy
,
paddle
::
operators
::
AccuracyOpCUDAKernel
<
float
>
,
paddle
::
operators
::
AccuracyOpCUDAKernel
<
double
>
);
paddle/fluid/operators/optimizers/momentum_op.cu
浏览文件 @
da87f7a6
...
...
@@ -14,11 +14,8 @@ limitations under the License. */
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/operators/optimizers/momentum_op.h"
#include "paddle/fluid/platform/float16.h"
namespace
ops
=
paddle
::
operators
;
REGISTER_OP_CUDA_KERNEL
(
momentum
,
ops
::
MomentumOpKernel
<
paddle
::
platform
::
CUDADeviceContext
,
float
>
,
ops
::
MomentumOpKernel
<
paddle
::
platform
::
CUDADeviceContext
,
double
>
,
ops
::
MomentumOpKernel
<
paddle
::
platform
::
CUDADeviceContext
,
paddle
::
platform
::
float16
>
);
ops
::
MomentumOpKernel
<
paddle
::
platform
::
CUDADeviceContext
,
double
>
);
paddle/fluid/operators/optimizers/momentum_op.h
浏览文件 @
da87f7a6
...
...
@@ -237,8 +237,7 @@ class SparseMomentumFunctor<T, UseNesterov> {
inline
HOSTDEVICE
void
operator
()(
size_t
i
)
{
auto
row_idx
=
math
::
BinarySearch
<
int64_t
>
(
rows_
,
row_height_
,
i
/
row_numel_
);
T
g
=
row_idx
>=
0
?
g_
[
row_idx
*
row_numel_
+
i
%
row_numel_
]
:
static_cast
<
T
>
(
0
);
T
g
=
row_idx
>=
0
?
g_
[
row_idx
*
row_numel_
+
i
%
row_numel_
]
:
0
;
// put memory access in register
const
T
p
=
p_
[
i
];
const
T
lr
=
lr_
[
0
];
...
...
@@ -283,8 +282,7 @@ class SparseMomentumFunctor<T, NoNesterov> {
inline
HOSTDEVICE
void
operator
()(
size_t
i
)
{
auto
row_idx
=
math
::
BinarySearch
<
int64_t
>
(
rows_
,
row_height_
,
i
/
row_numel_
);
T
g
=
row_idx
>=
0
?
g_
[
row_idx
*
row_numel_
+
i
%
row_numel_
]
:
static_cast
<
T
>
(
0
);
T
g
=
row_idx
>=
0
?
g_
[
row_idx
*
row_numel_
+
i
%
row_numel_
]
:
0
;
// put memory access in register
const
T
p
=
p_
[
i
];
const
T
lr
=
lr_
[
0
];
...
...
paddle/fluid/operators/top_k_op.cu
浏览文件 @
da87f7a6
...
...
@@ -16,7 +16,6 @@ limitations under the License. */
#include "paddle/fluid/operators/top_k_op.h"
#include "paddle/fluid/platform/assert.h"
#include "paddle/fluid/platform/cuda_device_function.h"
#include "paddle/fluid/platform/float16.h"
namespace
paddle
{
namespace
operators
{
...
...
@@ -151,7 +150,7 @@ __device__ __forceinline__ void ThreadGetTopK(Pair<T> topk[], int* beam,
if
(
k
<
MaxLength
-
(
*
beam
))
{
topk
[
k
]
=
topk
[
k
+
*
beam
];
}
else
{
topk
[
k
].
set
(
-
static_cast
<
T
>
(
INFINITY
)
,
-
1
);
topk
[
k
].
set
(
-
INFINITY
,
-
1
);
}
}
if
(
!
(
*
is_empty
))
{
...
...
@@ -161,7 +160,7 @@ __device__ __forceinline__ void ThreadGetTopK(Pair<T> topk[], int* beam,
}
*
max
=
topk
[
MaxLength
-
1
];
if
((
*
max
).
v
==
-
static_cast
<
T
>
(
1
)
)
*
is_empty
=
true
;
if
((
*
max
).
v
==
-
1
)
*
is_empty
=
true
;
*
beam
=
0
;
}
}
...
...
@@ -182,7 +181,7 @@ __device__ __forceinline__ void ThreadGetTopK(Pair<T> topk[], int* beam,
if
(
k
<
MaxLength
-
*
beam
)
{
topk
[
k
]
=
topk
[
k
+
*
beam
];
}
else
{
topk
[
k
].
set
(
-
static_cast
<
T
>
(
INFINITY
)
,
-
1
);
topk
[
k
].
set
(
-
INFINITY
,
-
1
);
}
}
if
(
!
(
*
is_empty
))
{
...
...
@@ -279,7 +278,7 @@ __global__ void KeMatrixTopK(T* output, int output_stride, int64_t* indices,
bool
firststep
=
true
;
for
(
int
j
=
0
;
j
<
MaxLength
;
j
++
)
{
topk
[
j
].
set
(
-
static_cast
<
T
>
(
INFINITY
)
,
-
1
);
topk
[
j
].
set
(
-
INFINITY
,
-
1
);
}
while
(
top_num
)
{
ThreadGetTopK
<
T
,
MaxLength
,
BlockSize
>
(
...
...
@@ -363,7 +362,5 @@ class TopkOpCUDAKernel : public framework::OpKernel<T> {
}
// namespace operators
}
// namespace paddle
REGISTER_OP_CUDA_KERNEL
(
top_k
,
paddle
::
operators
::
TopkOpCUDAKernel
<
float
>
,
paddle
::
operators
::
TopkOpCUDAKernel
<
double
>
,
paddle
::
operators
::
TopkOpCUDAKernel
<
paddle
::
platform
::
float16
>
);
REGISTER_OP_CUDA_KERNEL
(
top_k
,
paddle
::
operators
::
TopkOpCUDAKernel
<
float
>
,
paddle
::
operators
::
TopkOpCUDAKernel
<
double
>
);
paddle/fluid/platform/nccl_helper.h
浏览文件 @
da87f7a6
...
...
@@ -23,7 +23,6 @@
#include "paddle/fluid/framework/data_type.h"
#include "paddle/fluid/platform/dynload/nccl.h"
#include "paddle/fluid/platform/enforce.h"
#include "paddle/fluid/platform/float16.h"
#define NCCL_ID_VARNAME "NCCLID"
...
...
@@ -39,8 +38,6 @@ inline ncclDataType_t ToNCCLDataType(framework::proto::VarType::Type type) {
return
ncclInt
;
}
else
if
(
type
==
framework
::
proto
::
VarType
::
INT64
)
{
return
ncclInt64
;
}
else
if
(
type
==
framework
::
proto
::
VarType
::
FP16
)
{
return
ncclFloat16
;
}
else
{
PADDLE_THROW
(
"Not supported"
);
}
...
...
python/paddle/fluid/data_feeder.py
浏览文件 @
da87f7a6
...
...
@@ -44,8 +44,6 @@ class DataToLoDTensorConverter(object):
self
.
dtype
=
'int64'
elif
dtype
==
core
.
VarDesc
.
VarType
.
FP64
:
self
.
dtype
=
'float64'
elif
dtype
==
core
.
VarDesc
.
VarType
.
FP16
:
self
.
dtype
=
'float16'
elif
dtype
==
core
.
VarDesc
.
VarType
.
INT32
:
self
.
dtype
=
'int32'
elif
dtype
==
core
.
VarDesc
.
VarType
.
UINT8
:
...
...
python/paddle/fluid/initializer.py
浏览文件 @
da87f7a6
...
...
@@ -18,7 +18,6 @@ from . import framework
import
numpy
as
np
import
contextlib
from
.core
import
VarDesc
from
.
import
unique_name
__all__
=
[
'Constant'
,
'Uniform'
,
'Normal'
,
'TruncatedNormal'
,
'Xavier'
,
'Bilinear'
,
...
...
@@ -208,39 +207,16 @@ class UniformInitializer(Initializer):
# Initialization Ops should be prepended and not appended
if
self
.
_seed
==
0
:
self
.
_seed
=
block
.
program
.
random_seed
# to be compatible of fp16 initalizers
if
var
.
dtype
==
VarDesc
.
VarType
.
FP16
:
out_dtype
=
VarDesc
.
VarType
.
FP32
out_var
=
block
.
create_var
(
name
=
unique_name
.
generate
(
"."
.
join
([
'gaussian_random'
,
'tmp'
])),
shape
=
var
.
shape
,
dtype
=
out_dtype
,
type
=
VarDesc
.
VarType
.
LOD_TENSOR
,
persistable
=
False
)
else
:
out_dtype
=
var
.
dtype
out_var
=
var
op
=
block
.
_prepend_op
(
type
=
"uniform_random"
,
outputs
=
{
"Out"
:
out_
var
},
outputs
=
{
"Out"
:
var
},
attrs
=
{
"shape"
:
var
.
shape
,
"dtype"
:
out_dtype
,
"dtype"
:
int
(
var
.
dtype
)
,
"min"
:
self
.
_low
,
"max"
:
self
.
_high
,
"seed"
:
self
.
_seed
})
if
var
.
dtype
==
VarDesc
.
VarType
.
FP16
:
block
.
append_op
(
type
=
"cast"
,
inputs
=
{
"X"
:
out_var
},
outputs
=
{
"Out"
:
var
},
attrs
=
{
"in_dtype"
:
out_var
.
dtype
,
"out_dtype"
:
var
.
dtype
})
var
.
op
=
op
return
op
...
...
@@ -285,39 +261,17 @@ class NormalInitializer(Initializer):
# Initialization Ops should be prepended and not appended
if
self
.
_seed
==
0
:
self
.
_seed
=
block
.
program
.
random_seed
# to be compatible of fp16 initalizers
if
var
.
dtype
==
VarDesc
.
VarType
.
FP16
:
out_dtype
=
VarDesc
.
VarType
.
FP32
out_var
=
block
.
create_var
(
name
=
unique_name
.
generate
(
"."
.
join
([
'gaussian_random'
,
'tmp'
])),
shape
=
var
.
shape
,
dtype
=
out_dtype
,
type
=
VarDesc
.
VarType
.
LOD_TENSOR
,
persistable
=
False
)
else
:
out_dtype
=
var
.
dtype
out_var
=
var
op
=
block
.
_prepend_op
(
type
=
"gaussian_random"
,
outputs
=
{
"Out"
:
out_
var
},
outputs
=
{
"Out"
:
var
},
attrs
=
{
"shape"
:
var
.
shape
,
"dtype"
:
out_dtype
,
"dtype"
:
int
(
var
.
dtype
)
,
"mean"
:
self
.
_mean
,
"std"
:
self
.
_std_dev
,
"seed"
:
self
.
_seed
,
"use_mkldnn"
:
False
})
if
var
.
dtype
==
VarDesc
.
VarType
.
FP16
:
block
.
append_op
(
type
=
"cast"
,
inputs
=
{
"X"
:
out_var
},
outputs
=
{
"Out"
:
var
},
attrs
=
{
"in_dtype"
:
out_var
.
dtype
,
"out_dtype"
:
var
.
dtype
})
var
.
op
=
op
return
op
...
...
python/paddle/fluid/layers/learning_rate_scheduler.py
浏览文件 @
da87f7a6
...
...
@@ -63,18 +63,14 @@ def noam_decay(d_model, warmup_steps):
Returns:
The decayed learning rate.
"""
with
default_main_program
().
_lr_schedule_guard
():
global_step
=
_decay_step_counter
(
1
)
def
_lr_schedule
(
dtype
):
with
default_main_program
().
_lr_schedule_guard
():
global_step
=
_decay_step_counter
(
1
)
a
=
global_step
**-
0.5
b
=
(
warmup_steps
**-
1.5
)
*
global_step
lr_value
=
(
d_model
**-
0.5
)
*
nn
.
elementwise_min
(
a
,
b
)
a
=
global_step
**-
0.5
b
=
(
warmup_steps
**-
1.5
)
*
global_step
lr_value
=
(
d_model
**-
0.5
)
*
nn
.
elementwise_min
(
a
,
b
)
return
lr_value
return
_lr_schedule
return
lr_value
def
exponential_decay
(
learning_rate
,
decay_steps
,
decay_rate
,
staircase
=
False
):
...
...
@@ -113,19 +109,15 @@ def exponential_decay(learning_rate, decay_steps, decay_rate, staircase=False):
sgd_optimizer.minimize(avg_cost)
"""
with
default_main_program
().
_lr_schedule_guard
():
global_step
=
_decay_step_counter
()
def
_lr_schedule
(
dtype
):
with
default_main_program
().
_lr_schedule_guard
():
global_step
=
_decay_step_counter
()
div_res
=
global_step
/
decay_steps
if
staircase
:
div_res
=
ops
.
floor
(
div_res
)
decayed_lr
=
learning_rate
*
(
decay_rate
**
div_res
)
div_res
=
global_step
/
decay_steps
if
staircase
:
div_res
=
ops
.
floor
(
div_res
)
decayed_lr
=
learning_rate
*
(
decay_rate
**
div_res
)
return
decayed_lr
return
_lr_schedule
return
decayed_lr
def
natural_exp_decay
(
learning_rate
,
decay_steps
,
decay_rate
,
staircase
=
False
):
...
...
@@ -146,19 +138,15 @@ def natural_exp_decay(learning_rate, decay_steps, decay_rate, staircase=False):
Returns:
The decayed learning rate
"""
with
default_main_program
().
_lr_schedule_guard
():
global_step
=
_decay_step_counter
()
def
_lr_schedule
(
dtype
):
with
default_main_program
().
_lr_schedule_guard
():
global_step
=
_decay_step_counter
()
div_res
=
global_step
/
decay_steps
if
staircase
:
div_res
=
ops
.
floor
(
div_res
)
decayed_lr
=
learning_rate
*
ops
.
exp
(
-
1
*
decay_rate
*
div_res
)
return
decayed_lr
div_res
=
global_step
/
decay_steps
if
staircase
:
div_res
=
ops
.
floor
(
div_res
)
decayed_lr
=
learning_rate
*
ops
.
exp
(
-
1
*
decay_rate
*
div_res
)
return
_lr_schedule
return
decayed_lr
def
inverse_time_decay
(
learning_rate
,
decay_steps
,
decay_rate
,
staircase
=
False
):
...
...
@@ -196,20 +184,16 @@ def inverse_time_decay(learning_rate, decay_steps, decay_rate, staircase=False):
staircase=True))
sgd_optimizer.minimize(avg_cost)
"""
with
default_main_program
().
_lr_schedule_guard
():
global_step
=
_decay_step_counter
()
def
_lr_schedule
(
dtype
):
with
default_main_program
().
_lr_schedule_guard
()
:
global_step
=
_decay_step_counter
(
)
div_res
=
global_step
/
decay_steps
if
staircase
:
div_res
=
ops
.
floor
(
div_res
)
div_res
=
global_step
/
decay_steps
if
staircase
:
div_res
=
ops
.
floor
(
div_res
)
decayed_lr
=
learning_rate
/
(
1
+
decay_rate
*
div_res
)
decayed_lr
=
learning_rate
/
(
1
+
decay_rate
*
div_res
)
return
decayed_lr
return
_lr_schedule
return
decayed_lr
def
polynomial_decay
(
learning_rate
,
...
...
@@ -240,33 +224,28 @@ def polynomial_decay(learning_rate,
Returns:
Variable: The decayed learning rate
"""
with
default_main_program
().
_lr_schedule_guard
():
global_step
=
_decay_step_counter
()
def
_lr_schedule
(
dtype
,
decay_steps
=
decay_steps
):
with
default_main_program
().
_lr_schedule_guard
():
global_step
=
_decay_step_counter
()
if
cycle
:
div_res
=
ops
.
ceil
(
global_step
/
decay_steps
)
zero_var
=
tensor
.
fill_constant
(
shape
=
[
1
],
dtype
=
dtype
,
value
=
0.0
)
one_var
=
tensor
.
fill_constant
(
shape
=
[
1
],
dtype
=
dtype
,
value
=
1.0
)
with
control_flow
.
Switch
()
as
switch
:
with
switch
.
case
(
global_step
==
zero_var
):
tensor
.
assign
(
input
=
one_var
,
output
=
div_res
)
decay_steps
=
decay_steps
*
div_res
else
:
decay_steps_var
=
tensor
.
fill_constant
(
shape
=
[
1
],
dtype
=
dtype
,
value
=
float
(
decay_steps
))
global_step
=
nn
.
elementwise_min
(
x
=
global_step
,
y
=
decay_steps_var
)
if
cycle
:
div_res
=
ops
.
ceil
(
global_step
/
decay_steps
)
zero_var
=
tensor
.
fill_constant
(
shape
=
[
1
],
dtype
=
'float32'
,
value
=
0.0
)
one_var
=
tensor
.
fill_constant
(
shape
=
[
1
],
dtype
=
'float32'
,
value
=
1.0
)
decayed_lr
=
(
learning_rate
-
end_learning_rate
)
*
\
((
1
-
global_step
/
decay_steps
)
**
power
)
+
end_learning_rate
return
decayed_lr
with
control_flow
.
Switch
()
as
switch
:
with
switch
.
case
(
global_step
==
zero_var
):
tensor
.
assign
(
input
=
one_var
,
output
=
div_res
)
decay_steps
=
decay_steps
*
div_res
else
:
decay_steps_var
=
tensor
.
fill_constant
(
shape
=
[
1
],
dtype
=
'float32'
,
value
=
float
(
decay_steps
))
global_step
=
nn
.
elementwise_min
(
x
=
global_step
,
y
=
decay_steps_var
)
return
_lr_schedule
decayed_lr
=
(
learning_rate
-
end_learning_rate
)
*
\
((
1
-
global_step
/
decay_steps
)
**
power
)
+
end_learning_rate
return
decayed_lr
def
piecewise_decay
(
boundaries
,
values
):
...
...
@@ -294,42 +273,38 @@ def piecewise_decay(boundaries, values):
"""
def
_lr_schedule
(
dtype
):
with
default_main_program
().
_lr_schedule_guard
():
if
len
(
values
)
-
len
(
boundaries
)
!=
1
:
raise
ValueError
(
"len(values) - len(boundaries) should be 1"
)
global_step
=
_decay_step_counter
()
lr
=
tensor
.
create_global_var
(
shape
=
[
1
],
value
=
0.0
,
dtype
=
'float32'
,
persistable
=
True
,
name
=
"learning_rate"
)
with
control_flow
.
Switch
()
as
switch
:
for
i
in
range
(
len
(
boundaries
)):
boundary_val
=
tensor
.
fill_constant
(
shape
=
[
1
],
dtype
=
'float32'
,
value
=
float
(
boundaries
[
i
]),
force_cpu
=
True
)
value_var
=
tensor
.
fill_constant
(
shape
=
[
1
],
dtype
=
'float32'
,
value
=
float
(
values
[
i
]))
with
switch
.
case
(
global_step
<
boundary_val
):
tensor
.
assign
(
value_var
,
lr
)
last_value_var
=
tensor
.
fill_constant
(
with
default_main_program
().
_lr_schedule_guard
():
if
len
(
values
)
-
len
(
boundaries
)
!=
1
:
raise
ValueError
(
"len(values) - len(boundaries) should be 1"
)
global_step
=
_decay_step_counter
()
lr
=
tensor
.
create_global_var
(
shape
=
[
1
],
value
=
0.0
,
dtype
=
'float32'
,
persistable
=
True
,
name
=
"learning_rate"
)
with
control_flow
.
Switch
()
as
switch
:
for
i
in
range
(
len
(
boundaries
)):
boundary_val
=
tensor
.
fill_constant
(
shape
=
[
1
],
dtype
=
'float32'
,
value
=
float
(
values
[
len
(
values
)
-
1
]))
with
switch
.
default
():
tensor
.
assign
(
last_value_var
,
lr
)
return
lr
value
=
float
(
boundaries
[
i
]),
force_cpu
=
True
)
value_var
=
tensor
.
fill_constant
(
shape
=
[
1
],
dtype
=
'float32'
,
value
=
float
(
values
[
i
]))
with
switch
.
case
(
global_step
<
boundary_val
):
tensor
.
assign
(
value_var
,
lr
)
last_value_var
=
tensor
.
fill_constant
(
shape
=
[
1
],
dtype
=
'float32'
,
value
=
float
(
values
[
len
(
values
)
-
1
]))
with
switch
.
default
():
tensor
.
assign
(
last_value_var
,
lr
)
return
_lr_schedule
return
lr
def
append_LARS
(
params_grads
,
learning_rate
,
weight_decay
):
...
...
python/paddle/fluid/layers/nn.py
浏览文件 @
da87f7a6
...
...
@@ -2798,10 +2798,6 @@ def batch_norm(input,
helper
=
LayerHelper
(
'batch_norm'
,
**
locals
())
dtype
=
helper
.
input_dtype
()
# use fp32 for bn parameter
if
dtype
==
core
.
VarDesc
.
VarType
.
FP16
:
dtype
=
core
.
VarDesc
.
VarType
.
FP32
input_shape
=
input
.
shape
if
data_layout
==
'NCHW'
:
channel_num
=
input_shape
[
1
]
...
...
@@ -2836,7 +2832,7 @@ def batch_norm(input,
trainable
=
False
,
do_model_average
=
do_model_average_for_mean_and_var
),
shape
=
param_shape
,
dtype
=
dtype
)
dtype
=
input
.
dtype
)
mean
.
stop_gradient
=
True
variance
=
helper
.
create_parameter
(
...
...
@@ -2846,7 +2842,7 @@ def batch_norm(input,
trainable
=
False
,
do_model_average
=
do_model_average_for_mean_and_var
),
shape
=
param_shape
,
dtype
=
dtype
)
dtype
=
input
.
dtype
)
variance
.
stop_gradient
=
True
# create output
...
...
python/paddle/fluid/optimizer.py
浏览文件 @
da87f7a6
...
...
@@ -50,21 +50,17 @@ class Optimizer(object):
def
__init__
(
self
,
learning_rate
,
regularization
=
None
,
name
=
None
):
if
not
isinstance
(
learning_rate
,
float
)
and
\
not
isinstance
(
learning_rate
,
framework
.
Variable
)
and
\
not
callable
(
learning_rate
):
raise
TypeError
(
"learning rate should be float or Variable or callable(dtype)"
)
not
isinstance
(
learning_rate
,
framework
.
Variable
):
raise
TypeError
(
"learning rate should be float or Variable"
)
self
.
_name
=
name
self
.
regularization
=
regularization
self
.
_learning_rate
=
learning_rate
# the learning rate type should be inferenced from loss
self
.
_dtype
=
None
# each program should have a independent learning rate
# program -> Variable(learning_rate) or:
# program -> callable(return learning_rate Variable)
# program -> Variable(learning_rate)
self
.
_learning_rate_map
=
dict
()
if
isinstance
(
self
.
_learning_rate
,
framework
.
Variable
)
or
\
callable
(
self
.
_learning_rate
):
if
isinstance
(
self
.
_learning_rate
,
framework
.
Variable
):
self
.
_learning_rate_map
[
framework
.
default_main_program
(
)]
=
self
.
_learning_rate
# Dictionary of accumulators. Some optimizer subclasses need to
...
...
@@ -79,11 +75,6 @@ class Optimizer(object):
if
isinstance
(
lr
,
framework
.
Variable
):
return
elif
callable
(
lr
):
dtype
=
'float32'
if
self
.
_dtype
is
None
else
self
.
_dtype
self
.
_learning_rate_map
[
framework
.
default_main_program
()]
=
lr
(
dtype
)
return
else
:
if
not
isinstance
(
self
.
_learning_rate
,
float
):
raise
TypeError
(
...
...
python/paddle/fluid/tests/unittests/op_test.py
浏览文件 @
da87f7a6
...
...
@@ -368,8 +368,6 @@ class OpTest(unittest.TestCase):
place
=
core
.
CUDAPlace
(
0
)
if
core
.
is_float16_supported
(
place
):
return
[
place
]
else
:
return
[]
else
:
return
[]
places
=
[
fluid
.
CPUPlace
()]
...
...
python/paddle/fluid/tests/unittests/test_accuracy_op.py
浏览文件 @
da87f7a6
...
...
@@ -22,10 +22,8 @@ from op_test import OpTest
class
TestAccuracyOp
(
OpTest
):
def
setUp
(
self
):
self
.
op_type
=
"accuracy"
self
.
dtype
=
np
.
float32
self
.
init_dtype
()
n
=
8192
infer
=
np
.
random
.
random
((
n
,
1
)).
astype
(
self
.
dtype
)
infer
=
np
.
random
.
random
((
n
,
1
)).
astype
(
"float32"
)
indices
=
np
.
random
.
randint
(
0
,
2
,
(
n
,
1
))
label
=
np
.
random
.
randint
(
0
,
2
,
(
n
,
1
))
self
.
inputs
=
{
'Out'
:
infer
,
'Indices'
:
indices
,
"Label"
:
label
}
...
...
@@ -36,25 +34,14 @@ class TestAccuracyOp(OpTest):
num_correct
+=
1
break
self
.
outputs
=
{
'Accuracy'
:
np
.
array
([
num_correct
/
float
(
n
)]).
astype
(
self
.
dtype
),
'Accuracy'
:
np
.
array
([
num_correct
/
float
(
n
)]).
astype
(
"float32"
),
'Correct'
:
np
.
array
([
num_correct
]).
astype
(
"int32"
),
'Total'
:
np
.
array
([
n
]).
astype
(
"int32"
)
}
def
init_dtype
(
self
):
pass
def
test_check_output
(
self
):
self
.
check_output
()
class
TestAccuracyOpFp16
(
TestAccuracyOp
):
def
init_dtype
(
self
):
self
.
dtype
=
np
.
float16
def
test_check_output
(
self
):
self
.
check_output
(
atol
=
1e-3
)
if
__name__
==
'__main__'
:
unittest
.
main
()
python/paddle/fluid/tests/unittests/test_elementwise_div_op.py
浏览文件 @
da87f7a6
...
...
@@ -21,16 +21,14 @@ from op_test import OpTest
class
ElementwiseDivOp
(
OpTest
):
def
setUp
(
self
):
self
.
op_type
=
"elementwise_div"
self
.
dtype
=
np
.
float32
self
.
init_dtype
()
""" Warning
CPU gradient check error!
'X': np.random.random((32,84)).astype("float32"),
'Y': np.random.random((32,84)).astype("float32")
"""
self
.
inputs
=
{
'X'
:
np
.
random
.
uniform
(
0.1
,
1
,
[
13
,
17
]).
astype
(
self
.
dtype
),
'Y'
:
np
.
random
.
uniform
(
0.1
,
1
,
[
13
,
17
]).
astype
(
self
.
dtype
)
'X'
:
np
.
random
.
uniform
(
0.1
,
1
,
[
13
,
17
]).
astype
(
"float32"
),
'Y'
:
np
.
random
.
uniform
(
0.1
,
1
,
[
13
,
17
]).
astype
(
"float32"
)
}
self
.
outputs
=
{
'Out'
:
np
.
divide
(
self
.
inputs
[
'X'
],
self
.
inputs
[
'Y'
])}
...
...
@@ -48,9 +46,6 @@ class ElementwiseDivOp(OpTest):
self
.
check_grad
(
[
'X'
],
'Out'
,
max_relative_error
=
0.05
,
no_grad_set
=
set
(
'Y'
))
def
init_dtype
(
self
):
pass
class
TestElementwiseDivOp_scalar
(
ElementwiseDivOp
):
def
setUp
(
self
):
...
...
@@ -131,21 +126,5 @@ class TestElementwiseDivOp_broadcast_3(ElementwiseDivOp):
}
class
TestElementwiseDivOpFp16
(
ElementwiseDivOp
):
def
init_dtype
(
self
):
self
.
dtype
=
np
.
float16
def
test_check_grad_normal
(
self
):
self
.
check_grad
([
'X'
,
'Y'
],
'Out'
,
max_relative_error
=
1
)
def
test_check_grad_ingore_x
(
self
):
self
.
check_grad
(
[
'Y'
],
'Out'
,
max_relative_error
=
1
,
no_grad_set
=
set
(
"X"
))
def
test_check_grad_ingore_y
(
self
):
self
.
check_grad
(
[
'X'
],
'Out'
,
max_relative_error
=
1
,
no_grad_set
=
set
(
'Y'
))
if
__name__
==
'__main__'
:
unittest
.
main
()
python/paddle/fluid/tests/unittests/test_elementwise_mul_op.py
浏览文件 @
da87f7a6
...
...
@@ -135,10 +135,5 @@ class TestElementwiseMulOp_broadcast_3(ElementwiseMulOp):
}
class
TestElementwiseMulOpFp16
(
ElementwiseMulOp
):
def
init_dtype
(
self
):
self
.
dtype
=
np
.
float16
if
__name__
==
'__main__'
:
unittest
.
main
()
python/paddle/fluid/tests/unittests/test_fill_zeros_like_op.py
浏览文件 @
da87f7a6
...
...
@@ -22,22 +22,12 @@ from op_test import OpTest
class
TestFillZerosLikeOp
(
OpTest
):
def
setUp
(
self
):
self
.
op_type
=
"fill_zeros_like"
self
.
dtype
=
np
.
float32
self
.
init_dtype
()
self
.
inputs
=
{
'X'
:
np
.
random
.
random
((
219
,
232
)).
astype
(
self
.
dtype
)}
self
.
inputs
=
{
'X'
:
np
.
random
.
random
((
219
,
232
)).
astype
(
"float32"
)}
self
.
outputs
=
{
'Out'
:
np
.
zeros_like
(
self
.
inputs
[
"X"
])}
def
init_dtype
(
self
):
pass
def
test_check_output
(
self
):
self
.
check_output
()
class
TestFillZerosLikeOpFp16
(
TestFillZerosLikeOp
):
def
init_dtype
(
self
):
self
.
dtype
=
np
.
float16
if
__name__
==
"__main__"
:
unittest
.
main
()
python/paddle/fluid/tests/unittests/test_learning_rate_scheduler.py
浏览文件 @
da87f7a6
...
...
@@ -97,7 +97,7 @@ class TestLearningRateDecay(unittest.TestCase):
startup_prog
=
fluid
.
Program
()
with
fluid
.
program_guard
(
main_prog
,
startup_prog
):
decayed_lr
=
fluid_decay_fn
(
**
kwargs
)
(
"float32"
)
decayed_lr
=
fluid_decay_fn
(
**
kwargs
)
place
=
fluid
.
CPUPlace
()
exe
=
fluid
.
Executor
(
place
)
...
...
python/paddle/fluid/tests/unittests/test_momentum_op.py
浏览文件 @
da87f7a6
...
...
@@ -24,13 +24,11 @@ from op_test import OpTest
class
TestMomentumOp1
(
OpTest
):
def
setUp
(
self
):
self
.
op_type
=
"momentum"
self
.
dtype
=
np
.
float32
self
.
init_dtype
()
param
=
np
.
random
.
random
((
123
,
321
)).
astype
(
self
.
dtype
)
grad
=
np
.
random
.
random
((
123
,
321
)).
astype
(
self
.
dtype
)
velocity
=
np
.
zeros
((
123
,
321
)).
astype
(
self
.
dtype
)
learning_rate
=
np
.
array
([
0.001
]).
astype
(
self
.
dtype
)
param
=
np
.
random
.
random
((
123
,
321
)).
astype
(
"float32"
)
grad
=
np
.
random
.
random
((
123
,
321
)).
astype
(
"float32"
)
velocity
=
np
.
zeros
((
123
,
321
)).
astype
(
"float32"
)
learning_rate
=
np
.
array
([
0.001
]).
astype
(
"float32"
)
mu
=
0.0001
use_nesterov
=
False
...
...
@@ -52,21 +50,10 @@ class TestMomentumOp1(OpTest):
self
.
outputs
=
{
'ParamOut'
:
param_out
,
'VelocityOut'
:
velocity_out
}
def
init_dtype
(
self
):
pass
def
test_check_output
(
self
):
self
.
check_output
()
class
TestMomentumOpFp16
(
TestMomentumOp1
):
def
init_dtype
(
self
):
self
.
dtype
=
np
.
float16
def
test_check_output
(
self
):
self
.
check_output
(
atol
=
1e-3
)
class
TestMomentumOp2
(
OpTest
):
'''Test Momentum with default values for attributes
'''
...
...
python/paddle/fluid/tests/unittests/test_top_k_op.py
浏览文件 @
da87f7a6
...
...
@@ -23,11 +23,8 @@ class TestTopkOp(OpTest):
def
setUp
(
self
):
self
.
set_args
()
self
.
op_type
=
"top_k"
self
.
dtype
=
np
.
float32
self
.
init_dtype
()
k
=
self
.
top_k
input
=
np
.
random
.
random
((
self
.
row
,
k
)).
astype
(
self
.
dtype
)
input
=
np
.
random
.
random
((
self
.
row
,
k
)).
astype
(
"float32"
)
output
=
np
.
ndarray
((
self
.
row
,
k
))
indices
=
np
.
ndarray
((
self
.
row
,
k
)).
astype
(
"int64"
)
...
...
@@ -41,9 +38,6 @@ class TestTopkOp(OpTest):
self
.
outputs
=
{
'Out'
:
output
,
'Indices'
:
indices
}
def
init_dtype
(
self
):
pass
def
set_args
(
self
):
self
.
row
=
32
self
.
top_k
=
1
...
...
@@ -52,11 +46,6 @@ class TestTopkOp(OpTest):
self
.
check_output
()
class
TestTopkOpFp16
(
TestTopkOp
):
def
init_dtype
(
self
):
self
.
dtype
=
np
.
float16
class
TestTopkOp3d
(
OpTest
):
def
setUp
(
self
):
self
.
op_type
=
"top_k"
...
...
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