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体验新版 GitCode,发现更多精彩内容 >>
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7743cdf2
编写于
9月 01, 2021
作者:
B
baoachun
提交者:
GitHub
9月 01, 2021
浏览文件
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电子邮件补丁
差异文件
add strided_slice_grad op for npu (#35204)
* add strided_slice_grad op for npu
上级
5fa7d9ce
变更
2
显示空白变更内容
内联
并排
Showing
2 changed file
with
265 addition
and
36 deletion
+265
-36
paddle/fluid/operators/strided_slice_op_npu.cc
paddle/fluid/operators/strided_slice_op_npu.cc
+196
-6
python/paddle/fluid/tests/unittests/npu/test_strided_slice_op_npu.py
...le/fluid/tests/unittests/npu/test_strided_slice_op_npu.py
+69
-30
未找到文件。
paddle/fluid/operators/strided_slice_op_npu.cc
100755 → 100644
浏览文件 @
7743cdf2
...
...
@@ -226,14 +226,204 @@ class StridedSliceNPUKernel : public framework::OpKernel<T> {
}
};
template
<
typename
DeviceContext
,
typename
T
>
class
StridedSliceGradNPUKernel
:
public
framework
::
OpKernel
<
T
>
{
public:
void
Compute
(
const
framework
::
ExecutionContext
&
ctx
)
const
override
{
const
Variable
*
input_var
=
ctx
.
InputVar
(
"Input"
);
bool
is_tensor_array
=
input_var
->
IsType
<
LoDTensorArray
>
();
PADDLE_ENFORCE_EQ
(
is_tensor_array
,
false
,
platform
::
errors
::
InvalidArgument
(
"Tensor array as input is not supported."
));
int
rank
=
ctx
.
Input
<
framework
::
Tensor
>
(
"Input"
)
->
dims
().
size
();
switch
(
rank
)
{
case
1
:
StridedSliceGradCompute
<
1
>
(
ctx
);
break
;
case
2
:
StridedSliceGradCompute
<
2
>
(
ctx
);
break
;
case
3
:
StridedSliceGradCompute
<
3
>
(
ctx
);
break
;
case
4
:
StridedSliceGradCompute
<
4
>
(
ctx
);
break
;
case
5
:
StridedSliceGradCompute
<
5
>
(
ctx
);
break
;
case
6
:
StridedSliceGradCompute
<
6
>
(
ctx
);
break
;
default:
PADDLE_THROW
(
platform
::
errors
::
InvalidArgument
(
"The rank of input is supported up to 6."
));
break
;
}
}
private:
template
<
size_t
D
>
void
StridedSliceGradCompute
(
const
framework
::
ExecutionContext
&
ctx
)
const
{
auto
place
=
ctx
.
GetPlace
();
auto
&
dev_ctx
=
ctx
.
template
device_context
<
paddle
::
platform
::
NPUDeviceContext
>();
auto
*
input
=
ctx
.
Input
<
framework
::
Tensor
>
(
"Input"
);
auto
input_dims
=
input
->
dims
();
auto
*
dout
=
ctx
.
Input
<
framework
::
Tensor
>
(
framework
::
GradVarName
(
"Out"
));
auto
*
dx
=
ctx
.
Output
<
Tensor
>
(
framework
::
GradVarName
(
"Input"
));
dx
->
mutable_data
<
T
>
(
input_dims
,
place
);
auto
starts_int
=
ctx
.
Attr
<
std
::
vector
<
int
>>
(
"starts"
);
auto
ends_int
=
ctx
.
Attr
<
std
::
vector
<
int
>>
(
"ends"
);
auto
strides_int
=
ctx
.
Attr
<
std
::
vector
<
int
>>
(
"strides"
);
std
::
vector
<
int64_t
>
starts
(
starts_int
.
begin
(),
starts_int
.
end
());
std
::
vector
<
int64_t
>
ends
(
ends_int
.
begin
(),
ends_int
.
end
());
std
::
vector
<
int64_t
>
strides
(
strides_int
.
begin
(),
strides_int
.
end
());
auto
axes
=
ctx
.
Attr
<
std
::
vector
<
int
>>
(
"axes"
);
auto
infer_flags
=
ctx
.
Attr
<
std
::
vector
<
int
>>
(
"infer_flags"
);
auto
decrease_axis
=
ctx
.
Attr
<
std
::
vector
<
int
>>
(
"decrease_axis"
);
auto
list_new_ends_tensor
=
ctx
.
MultiInput
<
framework
::
Tensor
>
(
"EndsTensorList"
);
auto
list_new_starts_tensor
=
ctx
.
MultiInput
<
framework
::
Tensor
>
(
"StartsTensorList"
);
auto
list_new_strides_tensor
=
ctx
.
MultiInput
<
framework
::
Tensor
>
(
"StridesTensorList"
);
if
(
list_new_starts_tensor
.
size
()
>
0
)
{
starts
=
GetDataFromTensorList
<
int64_t
>
(
list_new_starts_tensor
);
}
else
if
(
ctx
.
HasInput
(
"StartsTensor"
))
{
auto
*
starts_tensor
=
ctx
.
Input
<
framework
::
Tensor
>
(
"StartsTensor"
);
starts
=
GetDataFromTensor
<
int64_t
>
(
starts_tensor
);
}
if
(
list_new_ends_tensor
.
size
()
>
0
)
{
ends
=
GetDataFromTensorList
<
int64_t
>
(
list_new_ends_tensor
);
}
else
if
(
ctx
.
HasInput
(
"EndsTensor"
))
{
auto
*
ends_tensor
=
ctx
.
Input
<
framework
::
Tensor
>
(
"EndsTensor"
);
ends
=
GetDataFromTensor
<
int64_t
>
(
ends_tensor
);
}
if
(
list_new_strides_tensor
.
size
()
>
0
)
{
strides
=
GetDataFromTensorList
<
int64_t
>
(
list_new_strides_tensor
);
}
else
if
(
ctx
.
HasInput
(
"StridesTensor"
))
{
auto
*
strides_tensor
=
ctx
.
Input
<
framework
::
Tensor
>
(
"StridesTensor"
);
strides
=
GetDataFromTensor
<
int64_t
>
(
strides_tensor
);
}
std
::
vector
<
int64_t
>
out_dims_vector
(
input_dims
.
size
(),
-
1
);
StridedSliceOutDims
(
starts
,
ends
,
strides
,
axes
,
infer_flags
,
input_dims
,
decrease_axis
,
out_dims_vector
.
data
(),
axes
.
size
(),
false
);
std
::
vector
<
int
>
reverse_vector
(
starts
.
size
(),
0
);
StridedSliceFunctor
(
starts
.
data
(),
ends
.
data
(),
strides
.
data
(),
axes
.
data
(),
reverse_vector
.
data
(),
input_dims
,
infer_flags
,
decrease_axis
,
starts
.
size
());
std
::
vector
<
int64_t
>
starts_indices_vector
(
D
,
0
);
std
::
vector
<
int64_t
>
ends_indices_vector
(
out_dims_vector
.
begin
(),
out_dims_vector
.
end
());
std
::
vector
<
int64_t
>
strides_indices_vector
(
D
,
1
);
for
(
size_t
axis
=
0
;
axis
<
axes
.
size
();
axis
++
)
{
int
axis_index
=
axes
[
axis
];
starts_indices_vector
[
axis_index
]
=
starts
[
axis
];
ends_indices_vector
[
axis_index
]
=
ends
[
axis
];
strides_indices_vector
[
axis_index
]
=
strides
[
axis
];
}
Tensor
starts_indices_tensor
;
Tensor
ends_indices_tensor
;
Tensor
strides_indices_tensor
;
starts_indices_tensor
.
mutable_data
<
int64_t
>
({
D
},
place
);
ends_indices_tensor
.
mutable_data
<
int64_t
>
({
D
},
place
);
strides_indices_tensor
.
mutable_data
<
int64_t
>
({
D
},
place
);
TensorFromVector
(
starts_indices_vector
,
dev_ctx
,
&
starts_indices_tensor
);
TensorFromVector
(
ends_indices_vector
,
dev_ctx
,
&
ends_indices_tensor
);
TensorFromVector
(
strides_indices_vector
,
dev_ctx
,
&
strides_indices_tensor
);
std
::
vector
<
int64_t
>
input_dims_vector
;
for
(
int
i
=
0
;
i
<
input_dims
.
size
();
i
++
)
{
input_dims_vector
.
push_back
(
input_dims
[
i
]);
}
Tensor
input_dims_tensor
;
TensorFromVector
(
input_dims_vector
,
dev_ctx
,
&
input_dims_tensor
);
bool
need_reverse
=
false
;
for
(
size_t
axis
=
0
;
axis
<
axes
.
size
();
axis
++
)
{
if
(
reverse_vector
[
axis
]
==
1
)
{
need_reverse
=
true
;
break
;
}
}
auto
stream
=
dev_ctx
.
stream
();
framework
::
NPUAttributeMap
attr_input
=
{{
"begin_mask"
,
0
},
{
"end_mask"
,
0
},
{
"ellipsis_mask"
,
0
},
{
"new_axis_mask"
,
0
},
{
"shrink_axis_mask"
,
0
}};
if
(
need_reverse
)
{
Tensor
reverse_axis
;
std
::
vector
<
int
>
reverse_axis_vector
;
for
(
size_t
axis
=
0
;
axis
<
axes
.
size
();
axis
++
)
{
if
(
reverse_vector
[
axis
]
==
1
)
{
reverse_axis_vector
.
push_back
(
axes
[
axis
]);
}
}
reverse_axis
.
mutable_data
<
int
>
(
{
static_cast
<
int
>
(
reverse_axis_vector
.
size
())},
place
);
TensorFromVector
(
reverse_axis_vector
,
dev_ctx
,
&
reverse_axis
);
Tensor
dout_tmp
;
dout_tmp
.
mutable_data
<
T
>
(
dout
->
dims
(),
place
);
const
auto
&
runner_reverse
=
NpuOpRunner
(
"ReverseV2"
,
{
*
dout
,
reverse_axis
},
{
dout_tmp
});
runner_reverse
.
Run
(
stream
);
const
auto
&
runner
=
NpuOpRunner
(
"StridedSliceGrad"
,
{
input_dims_tensor
,
starts_indices_tensor
,
ends_indices_tensor
,
strides_indices_tensor
,
dout_tmp
},
{
*
dx
},
attr_input
);
runner
.
Run
(
stream
);
}
else
{
const
auto
&
runner
=
NpuOpRunner
(
"StridedSliceGrad"
,
{
input_dims_tensor
,
starts_indices_tensor
,
ends_indices_tensor
,
strides_indices_tensor
,
*
dout
},
{
*
dx
},
attr_input
);
runner
.
Run
(
stream
);
}
}
};
}
// namespace operators
}
// namespace paddle
namespace
ops
=
paddle
::
operators
;
namespace
plat
=
paddle
::
platform
;
REGISTER_OP_NPU_KERNEL
(
strided_slice
,
ops
::
StridedSliceNPUKernel
<
plat
::
NPUDeviceContext
,
bool
>
,
ops
::
StridedSliceNPUKernel
<
plat
::
NPUDeviceContext
,
int
>
,
ops
::
StridedSliceNPUKernel
<
plat
::
NPUDeviceContext
,
int64_t
>
,
ops
::
StridedSliceNPUKernel
<
plat
::
NPUDeviceContext
,
float
>
,
ops
::
StridedSliceNPUKernel
<
plat
::
NPUDeviceContext
,
double
>
);
REGISTER_OP_NPU_KERNEL
(
strided_slice
,
ops
::
StridedSlice
NPUKernel
<
paddle
::
platform
::
NPUDeviceContext
,
bool
>
,
ops
::
StridedSlice
NPUKernel
<
paddle
::
platform
::
NPUDeviceContext
,
in
t
>
,
ops
::
StridedSlice
NPUKernel
<
paddle
::
platform
::
NPUDeviceContext
,
int64_t
>
,
ops
::
StridedSlice
NPUKernel
<
paddle
::
platform
::
NPUDeviceContext
,
floa
t
>
,
ops
::
StridedSlice
NPUKernel
<
paddle
::
platform
::
NPUDeviceContext
,
double
>
);
strided_slice
_grad
,
ops
::
StridedSlice
GradNPUKernel
<
plat
::
NPUDeviceContext
,
plat
::
float16
>
,
ops
::
StridedSlice
GradNPUKernel
<
plat
::
NPUDeviceContext
,
floa
t
>
,
ops
::
StridedSlice
GradNPUKernel
<
plat
::
NPUDeviceContext
,
double
>
,
ops
::
StridedSlice
GradNPUKernel
<
plat
::
NPUDeviceContext
,
in
t
>
,
ops
::
StridedSlice
GradNPUKernel
<
plat
::
NPUDeviceContext
,
int64_t
>
);
python/paddle/fluid/tests/unittests/npu/test_strided_slice_op_npu.py
浏览文件 @
7743cdf2
...
...
@@ -56,11 +56,11 @@ def strided_slice_native_forward(input, axes, starts, ends, strides):
return
result
@
skip_check_grad_ci
(
reason
=
'''forward only, it doesn't need to call check_grad.'''
)
class
TestStridedSliceOp
(
OpTest
):
def
setUp
(
self
):
self
.
initTestCase
()
self
.
set_npu
()
self
.
place
=
paddle
.
NPUPlace
(
0
)
self
.
op_type
=
'strided_slice'
self
.
output
=
strided_slice_native_forward
(
self
.
input
,
self
.
axes
,
self
.
starts
,
self
.
ends
,
self
.
strides
)
...
...
@@ -75,12 +75,17 @@ class TestStridedSliceOp(OpTest):
'infer_flags'
:
self
.
infer_flags
}
def
set_npu
(
self
):
self
.
__class__
.
use_npu
=
True
def
test_check_output
(
self
):
place
=
paddle
.
NPUPlace
(
0
)
self
.
check_output_with_place
(
place
)
self
.
check_output_with_place
(
self
.
place
)
def
test_check_grad
(
self
):
self
.
check_grad_with_place
(
self
.
place
,
[
'Input'
],
'Out'
)
def
initTestCase
(
self
):
self
.
input
=
np
.
random
.
rand
(
10
)
self
.
input
=
np
.
random
.
rand
(
10
0
)
self
.
axes
=
[
0
]
self
.
starts
=
[
2
]
self
.
ends
=
[
7
]
...
...
@@ -283,12 +288,12 @@ class TestStridedSliceOpBool6D(TestStridedSliceOpBool):
self
.
infer_flags
=
[
1
,
1
,
1
,
1
,
1
]
@
skip_check_grad_ci
(
reason
=
'''forward only, it doesn't need to call check_grad.'''
)
class
TestStridedSliceOp_starts_ListTensor
(
OpTest
):
def
setUp
(
self
):
self
.
place
=
paddle
.
NPUPlace
(
0
)
self
.
op_type
=
"strided_slice"
self
.
config
()
self
.
set_npu
()
starts_tensor
=
[]
for
index
,
ele
in
enumerate
(
self
.
starts
):
...
...
@@ -305,6 +310,9 @@ class TestStridedSliceOp_starts_ListTensor(OpTest):
'infer_flags'
:
self
.
infer_flags
}
def
set_npu
(
self
):
self
.
__class__
.
use_npu
=
True
def
config
(
self
):
self
.
input
=
np
.
random
.
random
([
3
,
4
,
5
,
6
]).
astype
(
"float64"
)
self
.
starts
=
[
1
,
0
,
2
]
...
...
@@ -318,16 +326,18 @@ class TestStridedSliceOp_starts_ListTensor(OpTest):
self
.
starts_infer
=
[
1
,
10
,
2
]
def
test_check_output
(
self
):
place
=
paddle
.
NPUPlace
(
0
)
self
.
check_output_with_place
(
place
)
self
.
check_output_with_place
(
self
.
place
)
def
test_check_grad_normal
(
self
):
self
.
check_grad_with_place
(
self
.
place
,
[
'Input'
],
'Out'
)
@
skip_check_grad_ci
(
reason
=
'''forward only, it doesn't need to call check_grad.'''
)
class
TestStridedSliceOp_ends_ListTensor
(
OpTest
):
def
setUp
(
self
):
self
.
place
=
paddle
.
NPUPlace
(
0
)
self
.
op_type
=
"strided_slice"
self
.
config
()
self
.
set_npu
()
ends_tensor
=
[]
for
index
,
ele
in
enumerate
(
self
.
ends
):
...
...
@@ -344,6 +354,9 @@ class TestStridedSliceOp_ends_ListTensor(OpTest):
'infer_flags'
:
self
.
infer_flags
}
def
set_npu
(
self
):
self
.
__class__
.
use_npu
=
True
def
config
(
self
):
self
.
input
=
np
.
random
.
random
([
3
,
4
,
5
,
6
]).
astype
(
"float64"
)
self
.
starts
=
[
1
,
0
,
0
]
...
...
@@ -357,16 +370,19 @@ class TestStridedSliceOp_ends_ListTensor(OpTest):
self
.
ends_infer
=
[
3
,
1
,
4
]
def
test_check_output
(
self
):
place
=
paddle
.
NPUPlace
(
0
)
self
.
check_output_with_place
(
place
)
self
.
check_output_with_place
(
self
.
place
)
def
test_check_grad_normal
(
self
):
self
.
check_grad_with_place
(
self
.
place
,
[
'Input'
],
'Out'
)
@
skip_check_grad_ci
(
reason
=
'''forward only, it doesn't need to call check_grad.'''
)
class
TestStridedSliceOp_starts_Tensor
(
OpTest
):
def
setUp
(
self
):
self
.
place
=
paddle
.
NPUPlace
(
0
)
self
.
op_type
=
"strided_slice"
self
.
config
()
self
.
set_npu
()
self
.
inputs
=
{
'Input'
:
self
.
input
,
"StartsTensor"
:
np
.
array
(
...
...
@@ -381,6 +397,9 @@ class TestStridedSliceOp_starts_Tensor(OpTest):
'infer_flags'
:
self
.
infer_flags
,
}
def
set_npu
(
self
):
self
.
__class__
.
use_npu
=
True
def
config
(
self
):
self
.
input
=
np
.
random
.
random
([
3
,
4
,
5
,
6
]).
astype
(
"float64"
)
self
.
starts
=
[
1
,
0
,
2
]
...
...
@@ -392,16 +411,19 @@ class TestStridedSliceOp_starts_Tensor(OpTest):
self
.
input
,
self
.
axes
,
self
.
starts
,
self
.
ends
,
self
.
strides
)
def
test_check_output
(
self
):
place
=
paddle
.
NPUPlace
(
0
)
self
.
check_output_with_place
(
place
)
self
.
check_output_with_place
(
self
.
place
)
def
test_check_grad_normal
(
self
):
self
.
check_grad_with_place
(
self
.
place
,
[
'Input'
],
'Out'
)
@
skip_check_grad_ci
(
reason
=
'''forward only, it doesn't need to call check_grad.'''
)
class
TestStridedSliceOp_ends_Tensor
(
OpTest
):
def
setUp
(
self
):
self
.
place
=
paddle
.
NPUPlace
(
0
)
self
.
op_type
=
"strided_slice"
self
.
config
()
self
.
set_npu
()
self
.
inputs
=
{
'Input'
:
self
.
input
,
"EndsTensor"
:
np
.
array
(
...
...
@@ -416,6 +438,9 @@ class TestStridedSliceOp_ends_Tensor(OpTest):
'infer_flags'
:
self
.
infer_flags
,
}
def
set_npu
(
self
):
self
.
__class__
.
use_npu
=
True
def
config
(
self
):
self
.
input
=
np
.
random
.
random
([
3
,
4
,
5
,
6
]).
astype
(
"float64"
)
self
.
starts
=
[
1
,
0
,
2
]
...
...
@@ -427,20 +452,23 @@ class TestStridedSliceOp_ends_Tensor(OpTest):
self
.
input
,
self
.
axes
,
self
.
starts
,
self
.
ends
,
self
.
strides
)
def
test_check_output
(
self
):
place
=
paddle
.
NPUPlace
(
0
)
self
.
check_output_with_place
(
place
)
self
.
check_output_with_place
(
self
.
place
)
def
test_check_grad_normal
(
self
):
self
.
check_grad_with_place
(
self
.
place
,
[
'Input'
],
'Out'
)
@
skip_check_grad_ci
(
reason
=
'''forward only, it doesn't need to call check_grad.'''
)
class
TestStridedSliceOp_listTensor_Tensor
(
OpTest
):
def
setUp
(
self
):
self
.
place
=
paddle
.
NPUPlace
(
0
)
self
.
op_type
=
"strided_slice"
self
.
set_npu
()
self
.
config
()
ends_tensor
=
[]
for
index
,
ele
in
enumerate
(
self
.
ends
):
ends_tensor
.
append
((
"x"
+
str
(
index
),
np
.
ones
(
(
1
)).
astype
(
'int32'
)
*
ele
))
self
.
op_type
=
"strided_slice"
self
.
inputs
=
{
'Input'
:
self
.
input
,
...
...
@@ -457,6 +485,9 @@ class TestStridedSliceOp_listTensor_Tensor(OpTest):
'infer_flags'
:
self
.
infer_flags
,
}
def
set_npu
(
self
):
self
.
__class__
.
use_npu
=
True
def
config
(
self
):
self
.
input
=
np
.
random
.
random
([
3
,
4
,
5
,
6
]).
astype
(
"float64"
)
self
.
starts
=
[
1
,
0
,
2
]
...
...
@@ -468,16 +499,19 @@ class TestStridedSliceOp_listTensor_Tensor(OpTest):
self
.
input
,
self
.
axes
,
self
.
starts
,
self
.
ends
,
self
.
strides
)
def
test_check_output
(
self
):
place
=
paddle
.
NPUPlace
(
0
)
self
.
check_output_with_place
(
place
)
self
.
check_output_with_place
(
self
.
place
)
def
test_check_grad_normal
(
self
):
self
.
check_grad_with_place
(
self
.
place
,
[
'Input'
],
'Out'
)
@
skip_check_grad_ci
(
reason
=
'''forward only, it doesn't need to call check_grad.'''
)
class
TestStridedSliceOp_strides_Tensor
(
OpTest
):
def
setUp
(
self
):
self
.
place
=
paddle
.
NPUPlace
(
0
)
self
.
op_type
=
"strided_slice"
self
.
set_npu
()
self
.
config
()
self
.
inputs
=
{
'Input'
:
self
.
input
,
"StridesTensor"
:
np
.
array
(
...
...
@@ -492,6 +526,9 @@ class TestStridedSliceOp_strides_Tensor(OpTest):
'infer_flags'
:
self
.
infer_flags
,
}
def
set_npu
(
self
):
self
.
__class__
.
use_npu
=
True
def
config
(
self
):
self
.
input
=
np
.
random
.
random
([
3
,
4
,
5
,
6
]).
astype
(
"float64"
)
self
.
starts
=
[
1
,
-
1
,
2
]
...
...
@@ -503,8 +540,10 @@ class TestStridedSliceOp_strides_Tensor(OpTest):
self
.
input
,
self
.
axes
,
self
.
starts
,
self
.
ends
,
self
.
strides
)
def
test_check_output
(
self
):
place
=
paddle
.
NPUPlace
(
0
)
self
.
check_output_with_place
(
place
)
self
.
check_output_with_place
(
self
.
place
)
def
test_check_grad_normal
(
self
):
self
.
check_grad_with_place
(
self
.
place
,
[
'Input'
],
'Out'
)
# Test python API
...
...
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