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体验新版 GitCode,发现更多精彩内容 >>
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d3167da8
编写于
7月 25, 2020
作者:
W
wilfChen
浏览文件
操作
浏览文件
下载
电子邮件补丁
差异文件
gpu support stridedslice
上级
0fac402a
变更
12
隐藏空白更改
内联
并排
Showing
12 changed file
with
864 addition
and
187 deletion
+864
-187
mindspore/ccsrc/backend/kernel_compiler/gpu/arrays/slice_gpu_kernel.cc
...rc/backend/kernel_compiler/gpu/arrays/slice_gpu_kernel.cc
+0
-6
mindspore/ccsrc/backend/kernel_compiler/gpu/arrays/slice_gpu_kernel.h
...src/backend/kernel_compiler/gpu/arrays/slice_gpu_kernel.h
+3
-8
mindspore/ccsrc/backend/kernel_compiler/gpu/arrays/slice_grad_gpu_kernel.cc
...ckend/kernel_compiler/gpu/arrays/slice_grad_gpu_kernel.cc
+0
-6
mindspore/ccsrc/backend/kernel_compiler/gpu/arrays/slice_grad_gpu_kernel.h
...ackend/kernel_compiler/gpu/arrays/slice_grad_gpu_kernel.h
+3
-8
mindspore/ccsrc/backend/kernel_compiler/gpu/arrays/strided_slice_gpu_kernel.cc
...nd/kernel_compiler/gpu/arrays/strided_slice_gpu_kernel.cc
+28
-0
mindspore/ccsrc/backend/kernel_compiler/gpu/arrays/strided_slice_gpu_kernel.h
...end/kernel_compiler/gpu/arrays/strided_slice_gpu_kernel.h
+190
-0
mindspore/ccsrc/backend/kernel_compiler/gpu/arrays/strided_slice_grad_gpu_kernel.cc
...rnel_compiler/gpu/arrays/strided_slice_grad_gpu_kernel.cc
+28
-0
mindspore/ccsrc/backend/kernel_compiler/gpu/arrays/strided_slice_grad_gpu_kernel.h
...ernel_compiler/gpu/arrays/strided_slice_grad_gpu_kernel.h
+191
-0
mindspore/ccsrc/backend/kernel_compiler/gpu/cuda_impl/slice_impl.cu
...ccsrc/backend/kernel_compiler/gpu/cuda_impl/slice_impl.cu
+99
-111
mindspore/ccsrc/backend/kernel_compiler/gpu/cuda_impl/slice_impl.cuh
...csrc/backend/kernel_compiler/gpu/cuda_impl/slice_impl.cuh
+11
-14
tests/st/ops/gpu/test_stridedslice_grad_op.py
tests/st/ops/gpu/test_stridedslice_grad_op.py
+244
-17
tests/st/ops/gpu/test_stridedslice_op.py
tests/st/ops/gpu/test_stridedslice_op.py
+67
-17
未找到文件。
mindspore/ccsrc/backend/kernel_compiler/gpu/arrays/slice_gpu_kernel.cc
浏览文件 @
d3167da8
...
...
@@ -24,11 +24,5 @@ MS_REG_GPU_KERNEL_ONE(Slice, KernelAttr().AddInputAttr(kNumberTypeInt32).AddOutp
SliceGpuFwdKernel
,
int
)
MS_REG_GPU_KERNEL_ONE
(
Slice
,
KernelAttr
().
AddInputAttr
(
kNumberTypeFloat16
).
AddOutputAttr
(
kNumberTypeFloat16
),
SliceGpuFwdKernel
,
half
)
MS_REG_GPU_KERNEL_ONE
(
StridedSlice
,
KernelAttr
().
AddInputAttr
(
kNumberTypeFloat32
).
AddOutputAttr
(
kNumberTypeFloat32
),
SliceGpuFwdKernel
,
float
)
MS_REG_GPU_KERNEL_ONE
(
StridedSlice
,
KernelAttr
().
AddInputAttr
(
kNumberTypeFloat16
).
AddOutputAttr
(
kNumberTypeFloat16
),
SliceGpuFwdKernel
,
half
)
MS_REG_GPU_KERNEL_ONE
(
StridedSlice
,
KernelAttr
().
AddInputAttr
(
kNumberTypeInt32
).
AddOutputAttr
(
kNumberTypeInt32
),
SliceGpuFwdKernel
,
int
)
}
// namespace kernel
}
// namespace mindspore
mindspore/ccsrc/backend/kernel_compiler/gpu/arrays/slice_gpu_kernel.h
浏览文件 @
d3167da8
...
...
@@ -41,14 +41,9 @@ class SliceGpuFwdKernel : public GpuKernel {
}
T
*
input
=
GetDeviceAddress
<
T
>
(
inputs
,
0
);
T
*
output
=
GetDeviceAddress
<
T
>
(
outputs
,
0
);
if
(
is_strided_slice_
)
{
CalStridedSlice
(
output_size_
/
sizeof
(
T
),
input
,
input_shape_
,
begin_
,
size_
,
strides_
,
output
,
reinterpret_cast
<
cudaStream_t
>
(
stream_ptr
));
}
else
{
Slice4DKernel
(
begin_
[
0
],
begin_
[
1
],
begin_
[
2
],
begin_
[
3
],
size_
[
0
],
size_
[
1
],
size_
[
2
],
size_
[
3
],
input_shape_
[
0
],
input_shape_
[
1
],
input_shape_
[
2
],
input_shape_
[
3
],
input
,
output
,
reinterpret_cast
<
cudaStream_t
>
(
stream_ptr
));
}
Slice4DKernel
(
begin_
[
0
],
begin_
[
1
],
begin_
[
2
],
begin_
[
3
],
size_
[
0
],
size_
[
1
],
size_
[
2
],
size_
[
3
],
input_shape_
[
0
],
input_shape_
[
1
],
input_shape_
[
2
],
input_shape_
[
3
],
input
,
output
,
reinterpret_cast
<
cudaStream_t
>
(
stream_ptr
));
return
true
;
}
bool
Init
(
const
CNodePtr
&
kernel_node
)
override
{
...
...
mindspore/ccsrc/backend/kernel_compiler/gpu/arrays/slice_grad_gpu_kernel.cc
浏览文件 @
d3167da8
...
...
@@ -29,11 +29,5 @@ MS_REG_GPU_KERNEL_ONE(
SliceGrad
,
KernelAttr
().
AddInputAttr
(
kNumberTypeFloat16
).
AddInputAttr
(
kNumberTypeFloat16
).
AddOutputAttr
(
kNumberTypeFloat16
),
SliceGradGpuKernel
,
half
)
MS_REG_GPU_KERNEL_ONE
(
StridedSliceGrad
,
KernelAttr
().
AddInputAttr
(
kNumberTypeFloat32
).
AddOutputAttr
(
kNumberTypeFloat32
),
SliceGradGpuKernel
,
float
)
MS_REG_GPU_KERNEL_ONE
(
StridedSliceGrad
,
KernelAttr
().
AddInputAttr
(
kNumberTypeInt32
).
AddOutputAttr
(
kNumberTypeInt32
),
SliceGradGpuKernel
,
int
)
MS_REG_GPU_KERNEL_ONE
(
StridedSliceGrad
,
KernelAttr
().
AddInputAttr
(
kNumberTypeFloat16
).
AddOutputAttr
(
kNumberTypeFloat16
),
SliceGradGpuKernel
,
half
)
}
// namespace kernel
}
// namespace mindspore
mindspore/ccsrc/backend/kernel_compiler/gpu/arrays/slice_grad_gpu_kernel.h
浏览文件 @
d3167da8
...
...
@@ -38,13 +38,8 @@ class SliceGradGpuKernel : public GpuKernel {
T
*
dy
=
GetDeviceAddress
<
T
>
(
inputs
,
0
);
T
*
dx
=
GetDeviceAddress
<
T
>
(
outputs
,
0
);
FillDeviceArray
(
outputs
[
0
]
->
size
/
sizeof
(
T
),
dx
,
0.
f
,
reinterpret_cast
<
cudaStream_t
>
(
stream_ptr
));
if
(
is_strided_slice_
)
{
CalStridedSliceGrad
(
output_size_
/
sizeof
(
T
),
dy
,
input_shape_
,
begin_
,
size_
,
strides_
,
dx
,
reinterpret_cast
<
cudaStream_t
>
(
stream_ptr
));
}
else
{
CalSliceGrad
(
output_size_
/
sizeof
(
T
),
dy
,
input_shape_
,
begin_
,
size_
,
dx
,
reinterpret_cast
<
cudaStream_t
>
(
stream_ptr
));
}
CalSliceGrad
(
output_size_
/
sizeof
(
T
),
dy
,
input_shape_
,
begin_
,
size_
,
dx
,
reinterpret_cast
<
cudaStream_t
>
(
stream_ptr
));
return
true
;
}
...
...
@@ -140,7 +135,7 @@ class SliceGradGpuKernel : public GpuKernel {
size_t
input_size_
;
size_t
output_size_
;
size_t
workspace_size_
;
};
};
// namespace kernel
}
// namespace kernel
}
// namespace mindspore
...
...
mindspore/ccsrc/backend/kernel_compiler/gpu/arrays/strided_slice_gpu_kernel.cc
0 → 100644
浏览文件 @
d3167da8
/**
* Copyright 2020 Huawei Technologies Co., Ltd
*
* 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 "backend/kernel_compiler/gpu/arrays/strided_slice_gpu_kernel.h"
namespace
mindspore
{
namespace
kernel
{
MS_REG_GPU_KERNEL_ONE
(
StridedSlice
,
KernelAttr
().
AddInputAttr
(
kNumberTypeFloat32
).
AddOutputAttr
(
kNumberTypeFloat32
),
StridedSliceGpuKernel
,
float
)
MS_REG_GPU_KERNEL_ONE
(
StridedSlice
,
KernelAttr
().
AddInputAttr
(
kNumberTypeFloat16
).
AddOutputAttr
(
kNumberTypeFloat16
),
StridedSliceGpuKernel
,
half
)
MS_REG_GPU_KERNEL_ONE
(
StridedSlice
,
KernelAttr
().
AddInputAttr
(
kNumberTypeInt32
).
AddOutputAttr
(
kNumberTypeInt32
),
StridedSliceGpuKernel
,
int
)
}
// namespace kernel
}
// namespace mindspore
mindspore/ccsrc/backend/kernel_compiler/gpu/arrays/strided_slice_gpu_kernel.h
0 → 100644
浏览文件 @
d3167da8
/**
* Copyright 2020 Huawei Technologies Co., Ltd
*
* 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.
*/
#ifndef MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_GPU_STRIDED_SLICE_GPU_KERNEL_H
#define MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_GPU_STRIDED_SLICE_GPU_KERNEL_H
#include <vector>
#include <bitset>
#include <algorithm>
#include "backend/kernel_compiler/gpu/gpu_kernel.h"
#include "backend/kernel_compiler/gpu/gpu_kernel_factory.h"
#include "backend/kernel_compiler/gpu/cuda_impl/slice_impl.cuh"
namespace
mindspore
{
namespace
kernel
{
constexpr
int
MAX_DIMS
=
4
;
template
<
typename
T
>
class
StridedSliceGpuKernel
:
public
GpuKernel
{
public:
StridedSliceGpuKernel
()
:
null_output_
(
false
)
{}
~
StridedSliceGpuKernel
()
override
=
default
;
const
std
::
vector
<
size_t
>
&
GetInputSizeList
()
const
override
{
return
input_size_list_
;
}
const
std
::
vector
<
size_t
>
&
GetOutputSizeList
()
const
override
{
return
output_size_list_
;
}
const
std
::
vector
<
size_t
>
&
GetWorkspaceSizeList
()
const
override
{
return
workspace_size_list_
;
}
bool
Launch
(
const
std
::
vector
<
AddressPtr
>
&
inputs
,
const
std
::
vector
<
AddressPtr
>
&
,
const
std
::
vector
<
AddressPtr
>
&
outputs
,
void
*
stream_ptr
)
override
{
if
(
null_output_
)
{
return
true
;
}
T
*
input
=
GetDeviceAddress
<
T
>
(
inputs
,
0
);
T
*
output
=
GetDeviceAddress
<
T
>
(
outputs
,
0
);
StridedSlice
(
input_shape_
,
begin_
,
strides_
,
output_shape_
,
input
,
output
,
reinterpret_cast
<
cudaStream_t
>
(
stream_ptr
));
return
true
;
}
bool
Init
(
const
CNodePtr
&
kernel_node
)
override
{
input_shape_
=
AnfAlgo
::
GetPrevNodeOutputInferShape
(
kernel_node
,
0
);
if
(
input_shape_
.
size
()
>
MAX_DIMS
)
{
MS_LOG
(
ERROR
)
<<
"StridedSlice support support dims less than "
<<
input_shape_
.
size
();
return
false
;
}
FillEmptyDims
(
kernel_node
);
ParseMasks
(
kernel_node
);
FillOutputDim
();
null_output_
=
IsNullOutput
();
InitSizeLists
();
return
true
;
}
protected:
void
InitSizeLists
()
override
{
input_size_list_
.
push_back
(
input_shape_
[
0
]
*
input_shape_
[
1
]
*
input_shape_
[
2
]
*
input_shape_
[
3
]
*
sizeof
(
T
));
output_size_list_
.
push_back
(
output_shape_
[
0
]
*
output_shape_
[
1
]
*
output_shape_
[
2
]
*
output_shape_
[
3
]
*
sizeof
(
T
));
}
private:
void
FillEmptyDims
(
const
CNodePtr
&
kernel_node
)
{
begin_
=
GetAttr
<
std
::
vector
<
int
>>
(
kernel_node
,
"begin"
);
end_
=
GetAttr
<
std
::
vector
<
int
>>
(
kernel_node
,
"end"
);
strides_
=
GetAttr
<
std
::
vector
<
int
>>
(
kernel_node
,
"strides"
);
for
(
size_t
i
=
0
;
i
<
MAX_DIMS
;
i
++
)
{
if
(
i
<
begin_
.
size
())
{
begin_
[
i
]
=
std
::
min
(
begin_
[
i
]
<
0
?
SizeToInt
(
begin_
[
i
]
+
input_shape_
[
i
])
:
begin_
[
i
],
SizeToInt
(
input_shape_
[
i
]
-
1
));
}
else
{
begin_
.
push_back
(
0
);
}
if
(
i
<
end_
.
size
())
{
end_
[
i
]
=
std
::
max
(
end_
[
i
]
<
0
?
end_
[
i
]
+
SizeToInt
(
input_shape_
[
i
])
:
end_
[
i
],
-
1
);
}
else
{
end_
.
push_back
(
i
<
input_shape_
.
size
()
?
input_shape_
[
i
]
:
1
);
}
if
(
i
>=
strides_
.
size
())
{
strides_
.
push_back
(
1
);
}
if
(
i
>=
input_shape_
.
size
())
{
input_shape_
.
push_back
(
1
);
}
}
}
void
ParseMasks
(
const
CNodePtr
&
kernel_node
)
{
auto
begin_mask_int
=
GetAttr
<
int
>
(
kernel_node
,
"begin_mask"
);
auto
begin_mask
=
Dec2Bin
(
begin_mask_int
);
for
(
size_t
i
=
0
;
i
<
begin_mask
.
size
();
i
++
)
{
if
(
begin_mask
[
i
])
{
begin_
[
i
]
=
0
;
}
}
auto
end_mask_int
=
GetAttr
<
int
>
(
kernel_node
,
"end_mask"
);
auto
end_mask
=
Dec2Bin
(
end_mask_int
);
for
(
size_t
j
=
0
;
j
<
end_mask
.
size
();
j
++
)
{
if
(
end_mask
[
j
])
{
end_
[
j
]
=
input_shape_
[
j
];
}
}
auto
ellipsis_mask_int
=
GetAttr
<
int
>
(
kernel_node
,
"ellipsis_mask"
);
auto
ellipsis_mask
=
Dec2Bin
(
ellipsis_mask_int
);
for
(
size_t
k
=
0
;
k
<
ellipsis_mask
.
size
();
k
++
)
{
if
(
ellipsis_mask
[
k
])
{
begin_
[
k
]
=
0
;
end_
[
k
]
=
input_shape_
[
k
];
strides_
[
k
]
=
1
;
}
}
auto
shrink_axis_mask_str
=
GetAttr
<
int
>
(
kernel_node
,
"shrink_axis_mask"
);
auto
shrink_axis_mask
=
Dec2Bin
(
shrink_axis_mask_str
);
for
(
size_t
l
=
0
;
l
<
shrink_axis_mask
.
size
();
l
++
)
{
if
(
shrink_axis_mask
[
l
])
{
end_
[
l
]
=
end_
[
l
]
>
begin_
[
l
]
?
begin_
[
l
]
+
1
:
begin_
[
l
]
-
1
;
strides_
[
l
]
=
end_
[
l
]
>
begin_
[
l
]
?
1
:
-
1
;
}
}
}
std
::
vector
<
bool
>
Dec2Bin
(
const
int
&
mask
)
{
auto
mask_str
=
std
::
bitset
<
MAX_DIMS
>
(
mask
).
to_string
();
int
dim_idx
=
0
;
std
::
vector
<
bool
>
result
=
{
false
,
false
,
false
,
false
};
for
(
int
i
=
mask_str
.
size
()
-
1
;
i
>=
0
;
i
--
)
{
if
(
mask_str
[
i
]
==
'1'
)
{
result
[
dim_idx
]
=
true
;
}
dim_idx
++
;
}
return
result
;
}
void
FillOutputDim
()
{
for
(
int
i
=
0
;
i
<
MAX_DIMS
;
i
++
)
{
if
(
begin_
[
i
]
<=
end_
[
i
]
&&
strides_
[
i
]
>
0
)
{
output_shape_
.
push_back
((
end_
[
i
]
-
1
-
begin_
[
i
])
/
strides_
[
i
]
+
1
);
}
else
if
(
begin_
[
i
]
>
end_
[
i
]
&&
strides_
[
i
]
<
0
)
{
output_shape_
.
push_back
((
end_
[
i
]
-
begin_
[
i
]
+
1
)
/
strides_
[
i
]
+
1
);
}
else
{
output_shape_
.
push_back
(
0
);
}
}
}
bool
IsNullOutput
()
{
for
(
int
i
=
0
;
i
<
MAX_DIMS
;
i
++
)
{
if
(
begin_
[
i
]
>=
end_
[
i
]
&&
strides_
[
i
]
>
0
)
{
return
true
;
}
if
(
begin_
[
i
]
<
end_
[
i
]
&&
strides_
[
i
]
<
0
)
{
return
true
;
}
}
return
false
;
}
std
::
vector
<
int
>
begin_
;
std
::
vector
<
int
>
end_
;
std
::
vector
<
int
>
strides_
;
std
::
vector
<
size_t
>
input_shape_
;
std
::
vector
<
int
>
output_shape_
;
int
null_output_
;
std
::
vector
<
size_t
>
input_size_list_
;
std
::
vector
<
size_t
>
output_size_list_
;
std
::
vector
<
size_t
>
workspace_size_list_
;
};
}
// namespace kernel
}
// namespace mindspore
#endif // MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_GPU_STRIDED_SLICE_GPU_KERNEL_H
mindspore/ccsrc/backend/kernel_compiler/gpu/arrays/strided_slice_grad_gpu_kernel.cc
0 → 100644
浏览文件 @
d3167da8
/**
* Copyright 2020 Huawei Technologies Co., Ltd
*
* 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 "backend/kernel_compiler/gpu/arrays/strided_slice_grad_gpu_kernel.h"
namespace
mindspore
{
namespace
kernel
{
MS_REG_GPU_KERNEL_ONE
(
StridedSliceGrad
,
KernelAttr
().
AddInputAttr
(
kNumberTypeFloat32
).
AddOutputAttr
(
kNumberTypeFloat32
),
StridedSliceGradGpuKernel
,
float
)
MS_REG_GPU_KERNEL_ONE
(
StridedSliceGrad
,
KernelAttr
().
AddInputAttr
(
kNumberTypeFloat16
).
AddOutputAttr
(
kNumberTypeFloat16
),
StridedSliceGradGpuKernel
,
half
)
MS_REG_GPU_KERNEL_ONE
(
StridedSliceGrad
,
KernelAttr
().
AddInputAttr
(
kNumberTypeInt32
).
AddOutputAttr
(
kNumberTypeInt32
),
StridedSliceGradGpuKernel
,
int
)
}
// namespace kernel
}
// namespace mindspore
mindspore/ccsrc/backend/kernel_compiler/gpu/arrays/strided_slice_grad_gpu_kernel.h
0 → 100644
浏览文件 @
d3167da8
/**
* Copyright 2020 Huawei Technologies Co., Ltd
*
* 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.
*/
#ifndef MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_GPU_STRIDED_SLICE_GRAD_GPU_KERNEL_H
#define MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_GPU_STRIDED_SLICE_GRAD_GPU_KERNEL_H
#include <vector>
#include <bitset>
#include <algorithm>
#include "backend/kernel_compiler/gpu/gpu_kernel.h"
#include "backend/kernel_compiler/gpu/gpu_kernel_factory.h"
#include "backend/kernel_compiler/gpu/cuda_impl/slice_impl.cuh"
namespace
mindspore
{
namespace
kernel
{
constexpr
int
MAX_DIMS
=
4
;
template
<
typename
T
>
class
StridedSliceGradGpuKernel
:
public
GpuKernel
{
public:
StridedSliceGradGpuKernel
()
:
null_output_
(
false
)
{}
~
StridedSliceGradGpuKernel
()
override
=
default
;
const
std
::
vector
<
size_t
>
&
GetInputSizeList
()
const
override
{
return
input_size_list_
;
}
const
std
::
vector
<
size_t
>
&
GetOutputSizeList
()
const
override
{
return
output_size_list_
;
}
const
std
::
vector
<
size_t
>
&
GetWorkspaceSizeList
()
const
override
{
return
workspace_size_list_
;
}
bool
Launch
(
const
std
::
vector
<
AddressPtr
>
&
inputs
,
const
std
::
vector
<
AddressPtr
>
&
,
const
std
::
vector
<
AddressPtr
>
&
outputs
,
void
*
stream_ptr
)
override
{
T
*
dy
=
GetDeviceAddress
<
T
>
(
inputs
,
0
);
T
*
dx
=
GetDeviceAddress
<
T
>
(
outputs
,
0
);
FillDeviceArray
(
outputs
[
0
]
->
size
/
sizeof
(
T
),
dx
,
0.
f
,
reinterpret_cast
<
cudaStream_t
>
(
stream_ptr
));
if
(
null_output_
)
{
return
true
;
}
StridedSliceGrad
(
output_shape_
,
begin_
,
strides_
,
input_shape_
,
dy
,
dx
,
reinterpret_cast
<
cudaStream_t
>
(
stream_ptr
));
return
true
;
}
bool
Init
(
const
CNodePtr
&
kernel_node
)
override
{
input_shape_
=
GetAttr
<
std
::
vector
<
int
>>
(
kernel_node
,
"shapex"
);
if
(
input_shape_
.
size
()
>
MAX_DIMS
)
{
MS_LOG
(
ERROR
)
<<
"StridedSliceGrad support support dims less than "
<<
input_shape_
.
size
();
return
false
;
}
FillEmptyDims
(
kernel_node
);
ParseMasks
(
kernel_node
);
FillOutputDim
();
null_output_
=
IsNullOutput
();
InitSizeLists
();
return
true
;
}
protected:
void
InitSizeLists
()
override
{
input_size_list_
.
push_back
(
output_shape_
[
0
]
*
output_shape_
[
1
]
*
output_shape_
[
2
]
*
output_shape_
[
3
]
*
sizeof
(
T
));
output_size_list_
.
push_back
(
input_shape_
[
0
]
*
input_shape_
[
1
]
*
input_shape_
[
2
]
*
input_shape_
[
3
]
*
sizeof
(
T
));
}
private:
void
FillEmptyDims
(
const
CNodePtr
&
kernel_node
)
{
begin_
=
GetAttr
<
std
::
vector
<
int
>>
(
kernel_node
,
"begin"
);
end_
=
GetAttr
<
std
::
vector
<
int
>>
(
kernel_node
,
"end"
);
strides_
=
GetAttr
<
std
::
vector
<
int
>>
(
kernel_node
,
"strides"
);
for
(
size_t
i
=
0
;
i
<
MAX_DIMS
;
i
++
)
{
if
(
i
<
begin_
.
size
())
{
begin_
[
i
]
=
std
::
min
(
begin_
[
i
]
<
0
?
SizeToInt
(
begin_
[
i
]
+
input_shape_
[
i
])
:
begin_
[
i
],
SizeToInt
(
input_shape_
[
i
]
-
1
));
}
else
{
begin_
.
push_back
(
0
);
}
if
(
i
<
end_
.
size
())
{
end_
[
i
]
=
std
::
max
(
end_
[
i
]
<
0
?
end_
[
i
]
+
SizeToInt
(
input_shape_
[
i
])
:
end_
[
i
],
-
1
);
}
else
{
end_
.
push_back
(
i
<
input_shape_
.
size
()
?
input_shape_
[
i
]
:
1
);
}
if
(
i
>=
strides_
.
size
())
{
strides_
.
push_back
(
1
);
}
if
(
i
>=
input_shape_
.
size
())
{
input_shape_
.
push_back
(
1
);
}
}
}
void
ParseMasks
(
const
CNodePtr
&
kernel_node
)
{
auto
begin_mask_int
=
GetAttr
<
int
>
(
kernel_node
,
"begin_mask"
);
auto
begin_mask
=
Dec2Bin
(
begin_mask_int
);
for
(
size_t
i
=
0
;
i
<
begin_mask
.
size
();
i
++
)
{
if
(
begin_mask
[
i
])
{
begin_
[
i
]
=
0
;
}
}
auto
end_mask_int
=
GetAttr
<
int
>
(
kernel_node
,
"end_mask"
);
auto
end_mask
=
Dec2Bin
(
end_mask_int
);
for
(
size_t
j
=
0
;
j
<
end_mask
.
size
();
j
++
)
{
if
(
end_mask
[
j
])
{
end_
[
j
]
=
input_shape_
[
j
];
}
}
auto
ellipsis_mask_int
=
GetAttr
<
int
>
(
kernel_node
,
"ellipsis_mask"
);
auto
ellipsis_mask
=
Dec2Bin
(
ellipsis_mask_int
);
for
(
size_t
k
=
0
;
k
<
ellipsis_mask
.
size
();
k
++
)
{
if
(
ellipsis_mask
[
k
])
{
begin_
[
k
]
=
0
;
end_
[
k
]
=
input_shape_
[
k
];
strides_
[
k
]
=
1
;
}
}
auto
shrink_axis_mask_str
=
GetAttr
<
int
>
(
kernel_node
,
"shrink_axis_mask"
);
auto
shrink_axis_mask
=
Dec2Bin
(
shrink_axis_mask_str
);
for
(
size_t
l
=
0
;
l
<
shrink_axis_mask
.
size
();
l
++
)
{
if
(
shrink_axis_mask
[
l
])
{
end_
[
l
]
=
end_
[
l
]
>
begin_
[
l
]
?
begin_
[
l
]
+
1
:
begin_
[
l
]
-
1
;
strides_
[
l
]
=
end_
[
l
]
>
begin_
[
l
]
?
1
:
-
1
;
}
}
}
std
::
vector
<
bool
>
Dec2Bin
(
const
int
&
mask
)
{
auto
mask_str
=
std
::
bitset
<
MAX_DIMS
>
(
mask
).
to_string
();
int
dim_idx
=
0
;
std
::
vector
<
bool
>
result
=
{
false
,
false
,
false
,
false
};
for
(
int
i
=
mask_str
.
size
()
-
1
;
i
>=
0
;
i
--
)
{
if
(
mask_str
[
i
]
==
'1'
)
{
result
[
dim_idx
]
=
true
;
}
dim_idx
++
;
}
return
result
;
}
void
FillOutputDim
()
{
for
(
int
i
=
0
;
i
<
MAX_DIMS
;
i
++
)
{
if
(
begin_
[
i
]
<=
end_
[
i
]
&&
strides_
[
i
]
>
0
)
{
output_shape_
.
push_back
((
end_
[
i
]
-
1
-
begin_
[
i
])
/
strides_
[
i
]
+
1
);
}
else
if
(
begin_
[
i
]
>
end_
[
i
]
&&
strides_
[
i
]
<
0
)
{
output_shape_
.
push_back
((
end_
[
i
]
-
begin_
[
i
]
+
1
)
/
strides_
[
i
]
+
1
);
}
else
{
output_shape_
.
push_back
(
0
);
}
}
}
bool
IsNullOutput
()
{
for
(
int
i
=
0
;
i
<
MAX_DIMS
;
i
++
)
{
if
(
begin_
[
i
]
>=
end_
[
i
]
&&
strides_
[
i
]
>
0
)
{
return
true
;
}
if
(
begin_
[
i
]
<
end_
[
i
]
&&
strides_
[
i
]
<
0
)
{
return
true
;
}
}
return
false
;
}
std
::
vector
<
int
>
begin_
;
std
::
vector
<
int
>
end_
;
std
::
vector
<
int
>
strides_
;
std
::
vector
<
int
>
input_shape_
;
std
::
vector
<
int
>
output_shape_
;
int
null_output_
;
std
::
vector
<
size_t
>
input_size_list_
;
std
::
vector
<
size_t
>
output_size_list_
;
std
::
vector
<
size_t
>
workspace_size_list_
;
};
}
// namespace kernel
}
// namespace mindspore
#endif // MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_GPU_STRIDED_SLICE_GRAD_GPU_KERNEL_H
mindspore/ccsrc/backend/kernel_compiler/gpu/cuda_impl/slice_impl.cu
100755 → 100644
浏览文件 @
d3167da8
...
...
@@ -21,48 +21,29 @@
#include "backend/kernel_compiler/gpu/cuda_impl/slice_impl.cuh"
template
<
typename
T
>
__global__
void
Slice4D
(
const
int
s1
,
const
int
s2
,
const
int
s3
,
const
int
s4
,
const
int
l1
,
const
int
l2
,
const
int
l3
,
const
int
l4
,
const
int
d1
,
const
int
d2
,
const
int
d3
,
const
int
d4
,
const
T
*
input
,
T
*
output
)
{
__global__
void
Slice4D
(
const
int
s1
,
const
int
s2
,
const
int
s3
,
const
int
s4
,
const
int
l1
,
const
int
l2
,
const
int
l3
,
const
int
l4
,
const
int
d1
,
const
int
d2
,
const
int
d3
,
const
int
d4
,
const
T
*
input
,
T
*
output
)
{
for
(
size_t
pos
=
blockIdx
.
x
*
blockDim
.
x
+
threadIdx
.
x
;
pos
<
(
l1
*
l2
*
l3
*
l4
);
pos
+=
blockDim
.
x
*
gridDim
.
x
)
{
int
i
=
pos
/
(
l2
*
l3
*
l4
)
%
l1
;
int
j
=
pos
/
(
l3
*
l4
)
%
l2
;
int
k
=
pos
/
l4
%
l3
;
int
o
=
pos
%
l4
;
int
offset
=
(
i
+
s1
)
*
(
d2
*
d3
*
d4
)
+
(
j
+
s2
)
*
(
d3
*
d4
)
+
(
k
+
s3
)
*
d4
+
(
o
+
s4
);
int
offset
=
(
i
+
s1
)
*
(
d2
*
d3
*
d4
)
+
(
j
+
s2
)
*
(
d3
*
d4
)
+
(
k
+
s3
)
*
d4
+
(
o
+
s4
);
output
[
pos
]
=
input
[
offset
];
}
}
template
<
typename
T
>
__global__
void
SliceGrad
(
const
T
*
dy
,
int
p
,
int
start
,
int
length
,
T
*
output
)
{
__global__
void
SliceGrad
(
const
T
*
dy
,
int
p
,
int
start
,
int
length
,
T
*
output
)
{
for
(
size_t
pos
=
blockIdx
.
x
*
blockDim
.
x
+
threadIdx
.
x
;
pos
<
(
length
);
pos
+=
blockDim
.
x
*
gridDim
.
x
)
{
output
[
start
+
pos
]
=
dy
[
p
+
pos
];
}
return
;
}
template
<
typename
T
>
__global__
void
StridedSlice
(
const
T
*
input
,
int
p
,
int
start
,
int
begin
,
int
stride
,
int
ended
,
T
*
output
)
{
for
(
size_t
pos
=
blockIdx
.
x
*
blockDim
.
x
+
threadIdx
.
x
;
pos
<
std
::
ceil
(
static_cast
<
float
>
(
ended
-
begin
)
/
stride
);
pos
+=
blockDim
.
x
*
gridDim
.
x
)
{
output
[
p
+
pos
]
=
input
[
start
+
pos
*
stride
];
}
return
;
}
template
<
typename
T
>
__global__
void
StridedSliceGrad
(
const
T
*
dy
,
int
p
,
int
start
,
int
begin
,
int
stride
,
int
ended
,
T
*
dx
)
{
for
(
size_t
pos
=
blockIdx
.
x
*
blockDim
.
x
+
threadIdx
.
x
;
pos
<
std
::
ceil
(
static_cast
<
float
>
(
ended
-
begin
)
/
stride
);
pos
+=
blockDim
.
x
*
gridDim
.
x
)
{
dx
[
start
+
pos
*
stride
]
=
dy
[
p
+
pos
];
}
return
;
}
template
<
typename
T
>
__global__
void
FillArray
(
T
*
addr
,
const
size_t
len
,
const
float
value
)
{
__global__
void
FillArray
(
T
*
addr
,
const
size_t
len
,
const
float
value
)
{
T
value_
=
static_cast
<
T
>
(
value
);
for
(
size_t
pos
=
blockIdx
.
x
*
blockDim
.
x
+
threadIdx
.
x
;
pos
<
len
;
pos
+=
blockDim
.
x
*
gridDim
.
x
)
{
addr
[
pos
]
=
value_
;
...
...
@@ -70,21 +51,20 @@ __global__ void FillArray(T* addr, const size_t len, const float value) {
return
;
}
template
<
typename
T
>
void
FillDeviceArray
(
const
size_t
input_size
,
T
*
addr
,
const
float
value
,
cudaStream_t
cuda_stream
)
{
void
FillDeviceArray
(
const
size_t
input_size
,
T
*
addr
,
const
float
value
,
cudaStream_t
cuda_stream
)
{
FillArray
<<<
GET_BLOCKS
(
input_size
),
GET_THREADS
,
0
,
cuda_stream
>>>
(
addr
,
input_size
,
value
);
return
;
}
template
<
typename
T
>
void
Slice4DKernel
(
const
int
s1
,
const
int
s2
,
const
int
s3
,
const
int
s4
,
const
int
l1
,
const
int
l2
,
const
int
l3
,
const
int
l4
,
const
int
d1
,
const
int
d2
,
const
int
d3
,
const
int
d4
,
const
T
*
input
,
T
*
output
,
cudaStream_t
stream
)
{
Slice4D
<<<
GET_BLOCKS
(
l1
*
l2
*
l3
*
l4
),
GET_THREADS
,
0
,
stream
>>>
(
s1
,
s2
,
s3
,
s4
,
l1
,
l2
,
l3
,
l4
,
d1
,
d2
,
d3
,
d4
,
input
,
output
);
void
Slice4DKernel
(
const
int
s1
,
const
int
s2
,
const
int
s3
,
const
int
s4
,
const
int
l1
,
const
int
l2
,
const
int
l3
,
const
int
l4
,
const
int
d1
,
const
int
d2
,
const
int
d3
,
const
int
d4
,
const
T
*
input
,
T
*
output
,
cudaStream_t
stream
)
{
Slice4D
<<<
GET_BLOCKS
(
l1
*
l2
*
l3
*
l4
),
GET_THREADS
,
0
,
stream
>>>
(
s1
,
s2
,
s3
,
s4
,
l1
,
l2
,
l3
,
l4
,
d1
,
d2
,
d3
,
d4
,
input
,
output
);
}
template
<
typename
T
>
void
CalSliceGrad
(
const
size_t
input_size
,
const
T
*
dy
,
const
std
::
vector
<
int
>
in_shape
,
const
std
::
vector
<
int
>
begin
,
const
std
::
vector
<
int
>
size
,
T
*
output
,
cudaStream_t
cuda_stream
)
{
void
CalSliceGrad
(
const
size_t
input_size
,
const
T
*
dy
,
const
std
::
vector
<
int
>
in_shape
,
const
std
::
vector
<
int
>
begin
,
const
std
::
vector
<
int
>
size
,
T
*
output
,
cudaStream_t
cuda_stream
)
{
int
block
=
in_shape
[
1
]
*
in_shape
[
2
]
*
in_shape
[
3
];
int
map
=
in_shape
[
2
]
*
in_shape
[
3
];
int
w
=
in_shape
[
3
];
...
...
@@ -100,92 +80,100 @@ void CalSliceGrad(const size_t input_size, const T* dy, const std::vector<int> i
}
}
}
template
<
typename
T
>
void
CalStridedSlice
(
const
size_t
input_size
,
const
T
*
input
,
const
std
::
vector
<
int
>
in_shape
,
const
std
::
vector
<
int
>
begin
,
const
std
::
vector
<
int
>
end
,
const
std
::
vector
<
int
>
strides
,
T
*
output
,
cudaStream_t
cuda_stream
)
{
int
block
=
in_shape
[
1
]
*
in_shape
[
2
]
*
in_shape
[
3
];
int
map
=
in_shape
[
2
]
*
in_shape
[
3
];
int
w
=
in_shape
[
3
];
int
ended
=
end
[
3
];
int
p
=
0
;
int
start
=
0
;
for
(
int
i
=
begin
[
0
];
i
<
((
end
[
0
]
>
begin
[
0
])
?
end
[
0
]
:
(
2
*
begin
[
0
]
-
end
[
0
]));
i
+=
std
::
abs
(
strides
[
0
]))
{
for
(
int
j
=
begin
[
1
];
j
<
((
end
[
1
]
>
begin
[
1
])
?
end
[
1
]
:
(
2
*
begin
[
1
]
-
end
[
1
]));
j
+=
std
::
abs
(
strides
[
1
]))
{
for
(
int
k
=
begin
[
2
];
k
<
((
end
[
2
]
>
begin
[
2
])
?
end
[
2
]
:
(
2
*
begin
[
2
]
-
end
[
2
]));
k
+=
std
::
abs
(
strides
[
2
]))
{
start
=
(
strides
[
0
]
>
0
?
i
:
2
*
begin
[
0
]
-
i
)
*
block
+
(
strides
[
1
]
>
0
?
j
:
2
*
begin
[
1
]
-
j
)
*
map
+
(
strides
[
2
]
>
0
?
k
:
2
*
begin
[
2
]
-
k
)
*
w
+
begin
[
3
];
StridedSlice
<<<
GET_BLOCKS
(
input_size
),
GET_THREADS
,
0
,
cuda_stream
>>>
(
input
,
p
,
start
,
begin
[
3
],
strides
[
3
],
ended
,
output
);
p
=
p
+
std
::
ceil
(
static_cast
<
float
>
(
end
[
3
]
-
begin
[
3
])
/
strides
[
3
]);
}
}
__global__
void
StridedSliceKernel
(
const
int
b0
,
const
int
b1
,
const
int
b2
,
const
int
b3
,
const
int
s0
,
const
int
s1
,
const
int
s2
,
const
int
s3
,
const
int
i0
,
const
int
i1
,
const
int
i2
,
const
int
i3
,
const
int
o0
,
const
int
o1
,
const
int
o2
,
const
int
o3
,
const
T
*
input_addr
,
T
*
output_addr
)
{
int
output_num
=
o0
*
o1
*
o2
*
o3
;
for
(
size_t
pos
=
blockIdx
.
x
*
blockDim
.
x
+
threadIdx
.
x
;
pos
<
output_num
;
pos
+=
blockDim
.
x
*
gridDim
.
x
)
{
int
i
=
pos
/
(
o1
*
o2
*
o3
)
%
o0
;
int
j
=
pos
/
(
o2
*
o3
)
%
o1
;
int
k
=
pos
/
o3
%
o2
;
int
l
=
pos
%
o3
;
int
input_idx
=
(
i
*
s0
+
b0
)
*
i1
*
i2
*
i3
+
(
j
*
s1
+
b1
)
*
i2
*
i3
+
(
k
*
s2
+
b2
)
*
i3
+
(
l
*
s3
+
b3
);
output_addr
[
pos
]
=
input_addr
[
input_idx
];
}
}
template
<
typename
T
>
void
CalStridedSliceGrad
(
const
size_t
input_size
,
const
T
*
dy
,
const
std
::
vector
<
int
>
in_shape
,
const
std
::
vector
<
int
>
begin
,
const
std
::
vector
<
int
>
end
,
const
std
::
vector
<
int
>
strides
,
T
*
dx
,
cudaStream_t
cuda_stream
)
{
int
block
=
in_shape
[
1
]
*
in_shape
[
2
]
*
in_shape
[
3
];
int
map
=
in_shape
[
2
]
*
in_shape
[
3
];
int
w
=
in_shape
[
3
];
int
ended
=
end
[
3
];
int
p
=
0
;
int
start
=
0
;
for
(
int
i
=
begin
[
0
];
i
<
((
end
[
0
]
>
begin
[
0
])
?
end
[
0
]
:
(
2
*
begin
[
0
]
-
end
[
0
]
+
1
));
i
+=
std
::
abs
(
strides
[
0
]))
{
for
(
int
j
=
begin
[
1
];
j
<
((
end
[
1
]
>
begin
[
1
])
?
end
[
1
]
:
(
2
*
begin
[
1
]
-
end
[
1
]
+
1
));
j
+=
std
::
abs
(
strides
[
1
]))
{
for
(
int
k
=
begin
[
2
];
k
<
((
end
[
2
]
>
begin
[
2
])
?
end
[
2
]
:
(
2
*
begin
[
2
]
-
end
[
2
]
+
1
));
k
+=
std
::
abs
(
strides
[
2
]))
{
start
=
(
strides
[
0
]
>
0
?
i
:
2
*
begin
[
0
]
-
i
)
*
block
+
(
strides
[
1
]
>
0
?
j
:
2
*
begin
[
1
]
-
j
)
*
map
+
(
strides
[
2
]
>
0
?
k
:
2
*
begin
[
2
]
-
k
)
*
w
+
begin
[
3
];
StridedSliceGrad
<<<
GET_BLOCKS
(
input_size
),
GET_THREADS
,
0
,
cuda_stream
>>>
(
dy
,
p
,
start
,
begin
[
3
],
strides
[
3
],
ended
,
dx
);
p
=
p
+
std
::
ceil
(
static_cast
<
float
>
(
end
[
3
]
-
begin
[
3
])
/
strides
[
3
]);
}
}
void
StridedSlice
(
const
std
::
vector
<
size_t
>
&
input_shape
,
const
std
::
vector
<
int
>
&
begin
,
const
std
::
vector
<
int
>
&
strides
,
const
std
::
vector
<
int
>
&
output_shape
,
const
T
*
input
,
T
*
output
,
cudaStream_t
cuda_stream
)
{
int
size
=
output_shape
[
0
]
*
output_shape
[
1
]
*
output_shape
[
2
]
*
output_shape
[
3
];
StridedSliceKernel
<<<
GET_BLOCKS
(
size
),
GET_THREADS
,
0
,
cuda_stream
>>>
(
begin
[
0
],
begin
[
1
],
begin
[
2
],
begin
[
3
],
strides
[
0
],
strides
[
1
],
strides
[
2
],
strides
[
3
],
input_shape
[
0
],
input_shape
[
1
],
input_shape
[
2
],
input_shape
[
3
],
output_shape
[
0
],
output_shape
[
1
],
output_shape
[
2
],
output_shape
[
3
],
input
,
output
);
}
template
<
typename
T
>
__global__
void
StridedSliceGradKernel
(
const
int
b0
,
const
int
b1
,
const
int
b2
,
const
int
b3
,
const
int
s0
,
const
int
s1
,
const
int
s2
,
const
int
s3
,
const
int
i0
,
const
int
i1
,
const
int
i2
,
const
int
i3
,
const
int
o0
,
const
int
o1
,
const
int
o2
,
const
int
o3
,
const
T
*
dy
,
T
*
dx
)
{
int
output_num
=
o0
*
o1
*
o2
*
o3
;
for
(
size_t
pos
=
blockIdx
.
x
*
blockDim
.
x
+
threadIdx
.
x
;
pos
<
output_num
;
pos
+=
blockDim
.
x
*
gridDim
.
x
)
{
int
i
=
pos
/
(
o1
*
o2
*
o3
)
%
o0
;
int
j
=
pos
/
(
o2
*
o3
)
%
o1
;
int
k
=
pos
/
o3
%
o2
;
int
l
=
pos
%
o3
;
int
input_idx
=
(
i
*
s0
+
b0
)
*
i1
*
i2
*
i3
+
(
j
*
s1
+
b1
)
*
i2
*
i3
+
(
k
*
s2
+
b2
)
*
i3
+
(
l
*
s3
+
b3
);
dx
[
input_idx
]
=
dy
[
pos
];
}
return
;
}
template
<
typename
T
>
void
StridedSliceGrad
(
const
std
::
vector
<
int
>
&
dy_shape
,
const
std
::
vector
<
int
>
&
begin
,
const
std
::
vector
<
int
>
&
strides
,
const
std
::
vector
<
int
>
&
dx_shape
,
const
T
*
dy
,
T
*
dx
,
cudaStream_t
cuda_stream
)
{
int
size
=
dy_shape
[
0
]
*
dy_shape
[
1
]
*
dy_shape
[
2
]
*
dy_shape
[
3
];
StridedSliceGradKernel
<<<
GET_BLOCKS
(
size
),
GET_THREADS
,
0
,
cuda_stream
>>>
(
begin
[
0
],
begin
[
1
],
begin
[
2
],
begin
[
3
],
strides
[
0
],
strides
[
1
],
strides
[
2
],
strides
[
3
],
dx_shape
[
0
],
dx_shape
[
1
],
dx_shape
[
2
],
dx_shape
[
3
],
dy_shape
[
0
],
dy_shape
[
1
],
dy_shape
[
2
],
dy_shape
[
3
],
dy
,
dx
);
}
template
void
FillDeviceArray
<
float
>(
const
size_t
input_size
,
float
*
addr
,
const
float
value
,
cudaStream_t
cuda_stream
);
template
void
Slice4DKernel
(
const
int
s1
,
const
int
s2
,
const
int
s3
,
const
int
s4
,
const
int
l1
,
const
int
l2
,
const
int
l3
,
const
int
l4
,
const
int
d1
,
const
int
d2
,
const
int
d3
,
const
int
d4
,
template
void
FillDeviceArray
<
float
>(
const
size_t
input_size
,
float
*
addr
,
const
float
value
,
cudaStream_t
cuda_stream
);
template
void
Slice4DKernel
(
const
int
s1
,
const
int
s2
,
const
int
s3
,
const
int
s4
,
const
int
l1
,
const
int
l2
,
const
int
l3
,
const
int
l4
,
const
int
d1
,
const
int
d2
,
const
int
d3
,
const
int
d4
,
const
float
*
input
,
float
*
output
,
cudaStream_t
stream
);
template
void
CalSliceGrad
<
float
>(
const
size_t
input_size
,
const
float
*
dy
,
const
std
::
vector
<
int
>
in_shape
,
const
std
::
vector
<
int
>
begin
,
const
std
::
vector
<
int
>
size
,
float
*
output
,
template
void
CalSliceGrad
<
float
>(
const
size_t
input_size
,
const
float
*
dy
,
const
std
::
vector
<
int
>
in_shape
,
const
std
::
vector
<
int
>
begin
,
const
std
::
vector
<
int
>
size
,
float
*
output
,
cudaStream_t
cuda_stream
);
template
void
CalStridedSlice
<
float
>(
const
size_t
input_size
,
const
float
*
input
,
const
std
::
vector
<
int
>
in_shape
,
const
std
::
vector
<
int
>
begin
,
const
std
::
vector
<
int
>
end
,
const
std
::
vector
<
int
>
strides
,
float
*
output
,
cudaStream_t
cuda_stream
);
template
void
CalStridedSliceGrad
<
float
>(
const
size_t
input_size
,
const
float
*
dy
,
const
std
::
vector
<
int
>
in_shape
,
const
std
::
vector
<
int
>
begin
,
const
std
::
vector
<
int
>
end
,
const
std
::
vector
<
int
>
strides
,
float
*
dx
,
cudaStream_t
cuda_stream
);
template
void
FillDeviceArray
<
half
>(
const
size_t
input_size
,
half
*
addr
,
const
float
value
,
cudaStream_t
cuda_stream
);
template
void
Slice4DKernel
(
const
int
s1
,
const
int
s2
,
const
int
s3
,
const
int
s4
,
const
int
l1
,
const
int
l2
,
const
int
l3
,
const
int
l4
,
const
int
d1
,
const
int
d2
,
const
int
d3
,
const
int
d4
,
template
void
FillDeviceArray
<
half
>(
const
size_t
input_size
,
half
*
addr
,
const
float
value
,
cudaStream_t
cuda_stream
);
template
void
Slice4DKernel
(
const
int
s1
,
const
int
s2
,
const
int
s3
,
const
int
s4
,
const
int
l1
,
const
int
l2
,
const
int
l3
,
const
int
l4
,
const
int
d1
,
const
int
d2
,
const
int
d3
,
const
int
d4
,
const
half
*
input
,
half
*
output
,
cudaStream_t
stream
);
template
void
CalSliceGrad
<
half
>(
const
size_t
input_size
,
const
half
*
dy
,
const
std
::
vector
<
int
>
in_shape
,
const
std
::
vector
<
int
>
begin
,
const
std
::
vector
<
int
>
size
,
half
*
output
,
template
void
CalSliceGrad
<
half
>(
const
size_t
input_size
,
const
half
*
dy
,
const
std
::
vector
<
int
>
in_shape
,
const
std
::
vector
<
int
>
begin
,
const
std
::
vector
<
int
>
size
,
half
*
output
,
cudaStream_t
cuda_stream
);
template
void
CalStridedSlice
<
half
>(
const
size_t
input_size
,
const
half
*
input
,
const
std
::
vector
<
int
>
in_shape
,
const
std
::
vector
<
int
>
begin
,
const
std
::
vector
<
int
>
end
,
const
std
::
vector
<
int
>
strides
,
half
*
output
,
cudaStream_t
cuda_stream
);
template
void
CalStridedSliceGrad
<
half
>(
const
size_t
input_size
,
const
half
*
dy
,
const
std
::
vector
<
int
>
in_shape
,
const
std
::
vector
<
int
>
begin
,
const
std
::
vector
<
int
>
end
,
const
std
::
vector
<
int
>
strides
,
half
*
dx
,
cudaStream_t
cuda_stream
);
template
void
FillDeviceArray
<
int
>(
const
size_t
input_size
,
int
*
addr
,
const
float
value
,
cudaStream_t
cuda_stream
);
template
void
Slice4DKernel
(
const
int
s1
,
const
int
s2
,
const
int
s3
,
const
int
s4
,
const
int
l1
,
const
int
l2
,
const
int
l3
,
const
int
l4
,
const
int
d1
,
const
int
d2
,
const
int
d3
,
const
int
d4
,
template
void
FillDeviceArray
<
int
>(
const
size_t
input_size
,
int
*
addr
,
const
float
value
,
cudaStream_t
cuda_stream
);
template
void
Slice4DKernel
(
const
int
s1
,
const
int
s2
,
const
int
s3
,
const
int
s4
,
const
int
l1
,
const
int
l2
,
const
int
l3
,
const
int
l4
,
const
int
d1
,
const
int
d2
,
const
int
d3
,
const
int
d4
,
const
int
*
input
,
int
*
output
,
cudaStream_t
stream
);
template
void
CalSliceGrad
<
int
>(
const
size_t
input_size
,
const
int
*
dy
,
const
std
::
vector
<
int
>
in_shape
,
const
std
::
vector
<
int
>
begin
,
const
std
::
vector
<
int
>
size
,
int
*
output
,
template
void
CalSliceGrad
<
int
>(
const
size_t
input_size
,
const
int
*
dy
,
const
std
::
vector
<
int
>
in_shape
,
const
std
::
vector
<
int
>
begin
,
const
std
::
vector
<
int
>
size
,
int
*
output
,
cudaStream_t
cuda_stream
);
template
void
CalStridedSlice
<
int
>(
const
size_t
input_size
,
const
int
*
input
,
const
std
::
vector
<
int
>
in_shape
,
const
std
::
vector
<
int
>
begin
,
const
std
::
vector
<
int
>
end
,
const
std
::
vector
<
int
>
strides
,
int
*
output
,
cudaStream_t
cuda_stream
);
template
void
CalStridedSliceGrad
<
int
>(
const
size_t
input_size
,
const
int
*
dy
,
const
std
::
vector
<
int
>
in_shape
,
const
std
::
vector
<
int
>
begin
,
const
std
::
vector
<
int
>
end
,
const
std
::
vector
<
int
>
strides
,
int
*
dx
,
cudaStream_t
cuda_stream
);
template
void
StridedSlice
(
const
std
::
vector
<
size_t
>
&
input_shape
,
const
std
::
vector
<
int
>
&
begin
,
const
std
::
vector
<
int
>
&
strides
,
const
std
::
vector
<
int
>
&
output_shape
,
const
float
*
input
,
float
*
output
,
cudaStream_t
cuda_stream
);
template
void
StridedSlice
(
const
std
::
vector
<
size_t
>
&
input_shape
,
const
std
::
vector
<
int
>
&
begin
,
const
std
::
vector
<
int
>
&
strides
,
const
std
::
vector
<
int
>
&
output_shape
,
const
half
*
input
,
half
*
output
,
cudaStream_t
cuda_stream
);
template
void
StridedSlice
(
const
std
::
vector
<
size_t
>
&
input_shape
,
const
std
::
vector
<
int
>
&
begin
,
const
std
::
vector
<
int
>
&
strides
,
const
std
::
vector
<
int
>
&
output_shape
,
const
int
*
input
,
int
*
output
,
cudaStream_t
cuda_stream
);
template
void
StridedSliceGrad
(
const
std
::
vector
<
int
>
&
dy_shape
,
const
std
::
vector
<
int
>
&
begin
,
const
std
::
vector
<
int
>
&
strides
,
const
std
::
vector
<
int
>
&
dx_shape
,
const
float
*
dy
,
float
*
dx
,
cudaStream_t
cuda_stream
);
template
void
StridedSliceGrad
(
const
std
::
vector
<
int
>
&
dy_shape
,
const
std
::
vector
<
int
>
&
begin
,
const
std
::
vector
<
int
>
&
strides
,
const
std
::
vector
<
int
>
&
dx_shape
,
const
half
*
dy
,
half
*
dx
,
cudaStream_t
cuda_stream
);
template
void
StridedSliceGrad
(
const
std
::
vector
<
int
>
&
dy_shape
,
const
std
::
vector
<
int
>
&
begin
,
const
std
::
vector
<
int
>
&
strides
,
const
std
::
vector
<
int
>
&
dx_shape
,
const
int
*
dy
,
int
*
dx
,
cudaStream_t
cuda_stream
);
mindspore/ccsrc/backend/kernel_compiler/gpu/cuda_impl/slice_impl.cuh
100755 → 100644
浏览文件 @
d3167da8
...
...
@@ -21,23 +21,20 @@
#include <vector>
#include "runtime/device/gpu/cuda_common.h"
template
<
typename
T
>
void
Slice4DKernel
(
const
int
s1
,
const
int
s2
,
const
int
s3
,
const
int
s4
,
const
int
l1
,
const
int
l2
,
const
int
l3
,
const
int
l4
,
const
int
d1
,
const
int
d2
,
const
int
d3
,
const
int
d4
,
const
T
*
input
,
T
*
output
,
cudaStream_t
stream
);
void
Slice4DKernel
(
const
int
s1
,
const
int
s2
,
const
int
s3
,
const
int
s4
,
const
int
l1
,
const
int
l2
,
const
int
l3
,
const
int
l4
,
const
int
d1
,
const
int
d2
,
const
int
d3
,
const
int
d4
,
const
T
*
input
,
T
*
output
,
cudaStream_t
stream
);
template
<
typename
T
>
void
CalSliceGrad
(
const
size_t
input_size
,
const
T
*
input
,
const
std
::
vector
<
int
>
in_shape
,
const
std
::
vector
<
int
>
begin
,
const
std
::
vector
<
int
>
size
,
T
*
output
,
cudaStream_t
cuda_stream
);
void
CalSliceGrad
(
const
size_t
input_size
,
const
T
*
input
,
const
std
::
vector
<
int
>
in_shape
,
const
std
::
vector
<
int
>
begin
,
const
std
::
vector
<
int
>
size
,
T
*
output
,
cudaStream_t
cuda_stream
);
template
<
typename
T
>
void
CalStridedSlice
(
const
size_t
input_size
,
const
T
*
input
,
const
std
::
vector
<
int
>
in_shape
,
const
std
::
vector
<
int
>
begin
,
const
std
::
vector
<
int
>
end
,
const
std
::
vector
<
int
>
strides
,
T
*
output
,
cudaStream_t
cuda_stream
);
void
StridedSlice
(
const
std
::
vector
<
size_t
>
&
input_shape
,
const
std
::
vector
<
int
>
&
begin
,
const
std
::
vector
<
int
>
&
strides
,
const
std
::
vector
<
int
>
&
output_shape
,
const
T
*
input
,
T
*
output
,
cudaStream_t
cuda_stream
);
template
<
typename
T
>
void
CalStridedSliceGrad
(
const
size_t
input_size
,
const
T
*
dy
,
const
std
::
vector
<
int
>
in_shape
,
const
std
::
vector
<
int
>
begin
,
const
std
::
vector
<
int
>
end
,
const
std
::
vector
<
int
>
strides
,
T
*
dx
,
cudaStream_t
cuda_stream
);
void
StridedSliceGrad
(
const
std
::
vector
<
int
>
&
dy_shape
,
const
std
::
vector
<
int
>
&
begin
,
const
std
::
vector
<
int
>
&
strides
,
const
std
::
vector
<
int
>
&
dx_shape
,
const
T
*
dy
,
T
*
dx
,
cudaStream_t
cuda_stream
);
template
<
typename
T
>
void
FillDeviceArray
(
const
size_t
input_size
,
T
*
addr
,
const
float
value
,
cudaStream_t
cuda_stream
);
void
FillDeviceArray
(
const
size_t
input_size
,
T
*
addr
,
const
float
value
,
cudaStream_t
cuda_stream
);
#endif // MINDSPORE_CCSRC_KERNEL_GPU_CUDA_IMPL_SLICEIMPL_H_
tests/st/ops/gpu/test_stridedslice_grad_op.py
浏览文件 @
d3167da8
...
...
@@ -19,31 +19,258 @@ import pytest
import
mindspore.context
as
context
import
mindspore.nn
as
nn
from
mindspore
import
Tensor
from
mindspore.common.api
import
ms_function
from
mindspore.ops
import
operations
as
P
from
mindspore.ops
.operations
import
_grad_ops
as
G
from
mindspore.ops
import
composite
as
C
context
.
set_context
(
mode
=
context
.
GRAPH_MODE
,
device_target
=
'GPU'
)
class
StridedSliceGrad
(
nn
.
Cell
):
def
__init__
(
self
):
super
(
StridedSliceGrad
,
self
).
__init__
()
self
.
ssg
=
G
.
StridedSliceGrad
()
self
.
shape
=
P
.
Shape
()
class
StridedSliceNet
(
nn
.
Cell
):
def
__init__
(
self
,
begin
,
end
,
stride
,
begin_mask
=
0
,
end_mask
=
0
,
ellipsis_mask
=
0
):
super
(
StridedSliceNet
,
self
).
__init__
()
self
.
begin
=
begin
self
.
end
=
end
self
.
strides
=
stride
self
.
slice
=
P
.
StridedSlice
(
begin_mask
,
end_mask
,
ellipsis_mask
)
@
ms_function
def
construct
(
self
,
dy
,
x
):
return
self
.
ssg
(
dy
,
self
.
shape
(
x
),
(
2
,
0
,
0
),
(
3
,
2
,
3
),
(
1
,
1
,
1
))
def
construct
(
self
,
x
):
return
self
.
slice
(
x
,
self
.
begin
,
self
.
end
,
self
.
strides
)
class
GradData
(
nn
.
Cell
):
def
__init__
(
self
,
network
):
super
(
GradData
,
self
).
__init__
()
self
.
grad
=
C
.
GradOperation
(
name
=
"get_all"
,
get_all
=
True
,
sens_param
=
False
)
self
.
network
=
network
def
construct
(
self
,
x
):
return
self
.
grad
(
self
.
network
)(
x
)
@
pytest
.
mark
.
level0
@
pytest
.
mark
.
platform_x86_gpu_training
@
pytest
.
mark
.
env_onecard
def
test_slice
():
x
=
Tensor
(
np
.
array
([[[
1
,
1
,
1
],
[
2
,
2
,
2
]],
[[
3
,
3
,
3
],
[
4
,
4
,
4
]],
[[
5
,
5
,
5
],
[
6
,
7
,
8
]]]).
astype
(
np
.
float32
))
dy
=
Tensor
(
np
.
array
([[[
5.
,
1.
,
5.
],
[
6.
,
1.
,
8.
]]]).
astype
(
np
.
float32
))
ssg
=
StridedSliceGrad
()
output
=
ssg
(
dy
,
x
)
expect
=
[[[
0
,
0
,
0
],
[
0
,
0
,
0
]],
[[
0
,
0
,
0
],
[
0
,
0
,
0
]],
[[
5
,
1
,
5
],
[
6
,
1
,
8
]]]
assert
(
output
.
asnumpy
()
==
expect
).
all
()
def
test_strided_slice_grad
():
x
=
Tensor
(
np
.
arange
(
0
,
2
*
3
*
4
*
5
).
reshape
(
2
,
3
,
4
,
5
).
astype
(
np
.
float32
))
net
=
StridedSliceNet
((
1
,
0
,
0
,
2
),
(
2
,
2
,
2
,
4
),
(
1
,
1
,
1
,
1
))
dx
=
GradData
(
net
)(
x
)
expect
=
np
.
array
([[[[
0.
,
0.
,
0.
,
0.
,
0.
],
[
0.
,
0.
,
0.
,
0.
,
0.
],
[
0.
,
0.
,
0.
,
0.
,
0.
],
[
0.
,
0.
,
0.
,
0.
,
0.
]],
[[
0.
,
0.
,
0.
,
0.
,
0.
],
[
0.
,
0.
,
0.
,
0.
,
0.
],
[
0.
,
0.
,
0.
,
0.
,
0.
],
[
0.
,
0.
,
0.
,
0.
,
0.
]],
[[
0.
,
0.
,
0.
,
0.
,
0.
],
[
0.
,
0.
,
0.
,
0.
,
0.
],
[
0.
,
0.
,
0.
,
0.
,
0.
],
[
0.
,
0.
,
0.
,
0.
,
0.
]]],
[[[
0.
,
0.
,
1.
,
1.
,
0.
],
[
0.
,
0.
,
1.
,
1.
,
0.
],
[
0.
,
0.
,
0.
,
0.
,
0.
],
[
0.
,
0.
,
0.
,
0.
,
0.
]],
[[
0.
,
0.
,
1.
,
1.
,
0.
],
[
0.
,
0.
,
1.
,
1.
,
0.
],
[
0.
,
0.
,
0.
,
0.
,
0.
],
[
0.
,
0.
,
0.
,
0.
,
0.
]],
[[
0.
,
0.
,
0.
,
0.
,
0.
],
[
0.
,
0.
,
0.
,
0.
,
0.
],
[
0.
,
0.
,
0.
,
0.
,
0.
],
[
0.
,
0.
,
0.
,
0.
,
0.
]]]])
assert
np
.
allclose
(
dx
[
0
].
asnumpy
(),
expect
)
net
=
StridedSliceNet
((
1
,
0
,
0
,
5
),
(
2
,
2
,
2
,
1
),
(
1
,
1
,
1
,
-
2
))
dx
=
GradData
(
net
)(
x
)
expect
=
np
.
array
([[[[
0.
,
0.
,
0.
,
0.
,
0.
],
[
0.
,
0.
,
0.
,
0.
,
0.
],
[
0.
,
0.
,
0.
,
0.
,
0.
],
[
0.
,
0.
,
0.
,
0.
,
0.
]],
[[
0.
,
0.
,
0.
,
0.
,
0.
],
[
0.
,
0.
,
0.
,
0.
,
0.
],
[
0.
,
0.
,
0.
,
0.
,
0.
],
[
0.
,
0.
,
0.
,
0.
,
0.
]],
[[
0.
,
0.
,
0.
,
0.
,
0.
],
[
0.
,
0.
,
0.
,
0.
,
0.
],
[
0.
,
0.
,
0.
,
0.
,
0.
],
[
0.
,
0.
,
0.
,
0.
,
0.
]]],
[[[
0.
,
0.
,
1.
,
0.
,
1.
],
[
0.
,
0.
,
1.
,
0.
,
1.
],
[
0.
,
0.
,
0.
,
0.
,
0.
],
[
0.
,
0.
,
0.
,
0.
,
0.
]],
[[
0.
,
0.
,
1.
,
0.
,
1.
],
[
0.
,
0.
,
1.
,
0.
,
1.
],
[
0.
,
0.
,
0.
,
0.
,
0.
],
[
0.
,
0.
,
0.
,
0.
,
0.
]],
[[
0.
,
0.
,
0.
,
0.
,
0.
],
[
0.
,
0.
,
0.
,
0.
,
0.
],
[
0.
,
0.
,
0.
,
0.
,
0.
],
[
0.
,
0.
,
0.
,
0.
,
0.
]]]])
assert
np
.
allclose
(
dx
[
0
].
asnumpy
(),
expect
)
net
=
StridedSliceNet
((
1
,
0
,
0
,
-
1
),
(
2
,
2
,
2
,
1
),
(
1
,
1
,
1
,
-
1
))
dx
=
GradData
(
net
)(
x
)
expect
=
np
.
array
([[[[
0.
,
0.
,
0.
,
0.
,
0.
],
[
0.
,
0.
,
0.
,
0.
,
0.
],
[
0.
,
0.
,
0.
,
0.
,
0.
],
[
0.
,
0.
,
0.
,
0.
,
0.
]],
[[
0.
,
0.
,
0.
,
0.
,
0.
],
[
0.
,
0.
,
0.
,
0.
,
0.
],
[
0.
,
0.
,
0.
,
0.
,
0.
],
[
0.
,
0.
,
0.
,
0.
,
0.
]],
[[
0.
,
0.
,
0.
,
0.
,
0.
],
[
0.
,
0.
,
0.
,
0.
,
0.
],
[
0.
,
0.
,
0.
,
0.
,
0.
],
[
0.
,
0.
,
0.
,
0.
,
0.
]]],
[[[
0.
,
0.
,
1.
,
1.
,
1.
],
[
0.
,
0.
,
1.
,
1.
,
1.
],
[
0.
,
0.
,
0.
,
0.
,
0.
],
[
0.
,
0.
,
0.
,
0.
,
0.
]],
[[
0.
,
0.
,
1.
,
1.
,
1.
],
[
0.
,
0.
,
1.
,
1.
,
1.
],
[
0.
,
0.
,
0.
,
0.
,
0.
],
[
0.
,
0.
,
0.
,
0.
,
0.
]],
[[
0.
,
0.
,
0.
,
0.
,
0.
],
[
0.
,
0.
,
0.
,
0.
,
0.
],
[
0.
,
0.
,
0.
,
0.
,
0.
],
[
0.
,
0.
,
0.
,
0.
,
0.
]]]])
assert
np
.
allclose
(
dx
[
0
].
asnumpy
(),
expect
)
# ME infer fault
# y = GradData()(x, (1, 0, -1, -2), (2, 2, 0, -5), (1, 1, -1, -2))
# expect = np.array([[[[0., 0., 0., 0., 0.],
# [0., 0., 0., 0., 0.],
# [0., 0., 0., 0., 0.],
# [0., 0., 0., 0., 0.]],
# [[0., 0., 0., 0., 0.],
# [0., 0., 0., 0., 0.],
# [0., 0., 0., 0., 0.],
# [0., 0., 0., 0., 0.]],
# [[0., 0., 0., 0., 0.],
# [0., 0., 0., 0., 0.],
# [0., 0., 0., 0., 0.],
# [0., 0., 0., 0., 0.]]],
# [[[0., 0., 0., 0., 0.],
# [0., 1., 0., 1., 0.],
# [0., 1., 0., 1., 0.],
# [0., 1., 0., 1., 0.]],
# [[0., 0., 0., 0., 0.],
# [0., 1., 0., 1., 0.],
# [0., 1., 0., 1., 0.],
# [0., 1., 0., 1., 0.]],begin_mask=0b1000, end_mask=0b0010, ellipsis_mask=0b0100
# [[0., 0., 0., 0., 0.],
# [0., 0., 0., 0., 0.],
# [0., 0., 0., 0., 0.],
# [0., 0., 0., 0., 0.]]]])
# assert np.allclose(y.asnumpy(), expect)
# y = Grad(begin_mask=0b1000, end_mask=0b0010)(x, (1, 0, 0, 2), (2, 2, 2, 4), (1, 1, 1, 1))
# expect = np.array([[[[0., 0., 0., 0., 0.],
# [0., 0., 0., 0., 0.],
# [0., 0., 0., 0., 0.],
# [0., 0., 0., 0., 0.]],
# [[0., 0., 0., 0., 0.],
# [0., 0., 0., 0., 0.],
# [0., 0., 0., 0., 0.],
# [0., 0., 0., 0., 0.]],
# [[0., 0., 0., 0., 0.],
# [0., 0., 0., 0., 0.],
# [0., 0., 0., 0., 0.],
# [0., 0., 0., 0., 0.]]],
# [[[0., 0., 1., 1., 0.],
# [0., 0., 1., 1., 0.],
# [0., 0., 0., 0., 0.],
# [0., 0., 0., 0., 0.]],
# [[0., 0., 1., 1., 0.],
# [0., 0., 1., 1., 0.],
# [0., 0., 0., 0., 0.],
# [0., 0., 0., 0., 0.]],
# [[0., 0., 0., 0., 0.],
# [0., 0., 0., 0., 0.],
# [0., 0., 0., 0., 0.],
# [0., 0., 0., 0., 0.]]]])
# assert np.allclose(y.asnumpy(), expect)
net
=
StridedSliceNet
((
1
,
0
,
0
,
2
),
(
2
,
2
,
2
,
4
),
(
1
,
1
,
1
,
1
),
begin_mask
=
0b1000
,
end_mask
=
0b0010
,
ellipsis_mask
=
0b0100
)
dx
=
GradData
(
net
)(
x
)
expect
=
np
.
array
([[[[
0.
,
0.
,
0.
,
0.
,
0.
],
[
0.
,
0.
,
0.
,
0.
,
0.
],
[
0.
,
0.
,
0.
,
0.
,
0.
],
[
0.
,
0.
,
0.
,
0.
,
0.
]],
[[
0.
,
0.
,
0.
,
0.
,
0.
],
[
0.
,
0.
,
0.
,
0.
,
0.
],
[
0.
,
0.
,
0.
,
0.
,
0.
],
[
0.
,
0.
,
0.
,
0.
,
0.
]],
[[
0.
,
0.
,
0.
,
0.
,
0.
],
[
0.
,
0.
,
0.
,
0.
,
0.
],
[
0.
,
0.
,
0.
,
0.
,
0.
],
[
0.
,
0.
,
0.
,
0.
,
0.
]]],
[[[
1.
,
1.
,
1.
,
1.
,
0.
],
[
1.
,
1.
,
1.
,
1.
,
0.
],
[
1.
,
1.
,
1.
,
1.
,
0.
],
[
1.
,
1.
,
1.
,
1.
,
0.
]],
[[
1.
,
1.
,
1.
,
1.
,
0.
],
[
1.
,
1.
,
1.
,
1.
,
0.
],
[
1.
,
1.
,
1.
,
1.
,
0.
],
[
1.
,
1.
,
1.
,
1.
,
0.
]],
[[
1.
,
1.
,
1.
,
1.
,
0.
],
[
1.
,
1.
,
1.
,
1.
,
0.
],
[
1.
,
1.
,
1.
,
1.
,
0.
],
[
1.
,
1.
,
1.
,
1.
,
0.
]]]])
assert
np
.
allclose
(
dx
[
0
].
asnumpy
(),
expect
)
x
=
Tensor
(
np
.
arange
(
0
,
3
*
4
*
5
).
reshape
(
3
,
4
,
5
).
astype
(
np
.
float32
))
net
=
StridedSliceNet
((
1
,
0
,
0
),
(
2
,
-
3
,
3
),
(
1
,
1
,
3
))
dx
=
GradData
(
net
)(
x
)
expect
=
np
.
array
([[[
0.
,
0.
,
0.
,
0.
,
0.
],
[
0.
,
0.
,
0.
,
0.
,
0.
],
[
0.
,
0.
,
0.
,
0.
,
0.
],
[
0.
,
0.
,
0.
,
0.
,
0.
]],
[[
1.
,
0.
,
0.
,
0.
,
0.
],
[
0.
,
0.
,
0.
,
0.
,
0.
],
[
0.
,
0.
,
0.
,
0.
,
0.
],
[
0.
,
0.
,
0.
,
0.
,
0.
]],
[[
0.
,
0.
,
0.
,
0.
,
0.
],
[
0.
,
0.
,
0.
,
0.
,
0.
],
[
0.
,
0.
,
0.
,
0.
,
0.
],
[
0.
,
0.
,
0.
,
0.
,
0.
]]])
assert
np
.
allclose
(
dx
[
0
].
asnumpy
(),
expect
)
tests/st/ops/gpu/test_stridedslice_op.py
浏览文件 @
d3167da8
...
...
@@ -17,29 +17,79 @@ import numpy as np
import
pytest
import
mindspore.context
as
context
import
mindspore.nn
as
nn
from
mindspore
import
Tensor
from
mindspore.ops
import
operations
as
P
context
.
set_context
(
mode
=
context
.
GRAPH_MODE
,
device_target
=
'GPU'
)
class
StridedSlice
(
nn
.
Cell
):
def
__init__
(
self
):
super
(
StridedSlice
,
self
).
__init__
()
self
.
stridedslice
=
P
.
StridedSlice
()
def
construct
(
self
,
x
):
return
self
.
stridedslice
(
x
,
(
2
,
0
,
0
),
(
3
,
2
,
3
),
(
1
,
1
,
1
))
@
pytest
.
mark
.
level0
@
pytest
.
mark
.
platform_x86_gpu_training
@
pytest
.
mark
.
env_onecard
def
test_slice
():
x
=
Tensor
(
np
.
array
([[[
1
,
1
,
1
],
[
2
,
2
,
2
]],
[[
3
,
3
,
3
],
[
4
,
4
,
4
]],
[[
5
,
5
,
5
],
[
6
,
7
,
8
]]]).
astype
(
np
.
int32
))
stridedslice
=
StridedSlice
()
output
=
stridedslice
(
x
)
expect
=
[[[
5.
,
5.
,
5.
],
[
6.
,
7.
,
8.
]]]
assert
(
output
.
asnumpy
()
==
expect
).
all
()
def
test_stridedslice
():
x
=
Tensor
(
np
.
arange
(
0
,
2
*
3
*
4
*
5
).
reshape
(
2
,
3
,
4
,
5
).
astype
(
np
.
float32
))
y
=
P
.
StridedSlice
()(
x
,
(
1
,
0
,
0
,
2
),
(
2
,
2
,
2
,
4
),
(
1
,
1
,
1
,
1
))
expect
=
np
.
array
([[[[
62
,
63
],
[
67
,
68
]],
[[
82
,
83
],
[
87
,
88
]]]])
assert
np
.
allclose
(
y
.
asnumpy
(),
expect
)
y
=
P
.
StridedSlice
()(
x
,
(
1
,
0
,
0
,
5
),
(
2
,
2
,
2
,
1
),
(
1
,
1
,
1
,
-
2
))
expect
=
np
.
array
([[[[
64
,
62
],
[
69
,
67
]],
[[
84
,
82
],
[
89
,
87
]]]])
assert
np
.
allclose
(
y
.
asnumpy
(),
expect
)
y
=
P
.
StridedSlice
()(
x
,
(
1
,
0
,
0
,
-
1
),
(
2
,
2
,
2
,
1
),
(
1
,
1
,
1
,
-
1
))
expect
=
np
.
array
([[[[
64
,
63
,
62
],
[
69
,
68
,
67
]],
[[
84
,
83
,
82
],
[
89
,
88
,
87
]]]])
assert
np
.
allclose
(
y
.
asnumpy
(),
expect
)
# ME infer fault
# y = P.StridedSlice()(x, (1, 0, -1, -2), (2, 2, 0, -5), (1, 1, -1, -2))
# expect = np.array([[[[78, 76],
# [73, 71],
# [68, 66]],
# [[98, 96],
# [93, 91],
# [88, 86]]]])
# assert np.allclose(y.asnumpy(), expect)
# y = P.StridedSlice(begin_mask=0b1000, end_mask=0b0010)(x, (1, 0, 0, 2), (2, 2, 2, 4), (1, 1, 1, 1))
# expect = np.array([[[[ 62, 63],
# [ 67, 68]],
# [[ 82, 83],
# [ 87, 88]],
# [[102, 103],
# [107, 108]]]])
# assert np.allclose(y.asnumpy(), expect)
op
=
P
.
StridedSlice
(
begin_mask
=
0b1000
,
end_mask
=
0b0010
,
ellipsis_mask
=
0b0100
)
y
=
op
(
x
,
(
1
,
0
,
0
,
2
),
(
2
,
2
,
2
,
4
),
(
1
,
1
,
1
,
1
))
expect
=
np
.
array
([[[[
60
,
61
,
62
,
63
],
[
65
,
66
,
67
,
68
],
[
70
,
71
,
72
,
73
],
[
75
,
76
,
77
,
78
]],
[[
80
,
81
,
82
,
83
],
[
85
,
86
,
87
,
88
],
[
90
,
91
,
92
,
93
],
[
95
,
96
,
97
,
98
]],
[[
100
,
101
,
102
,
103
],
[
105
,
106
,
107
,
108
],
[
110
,
111
,
112
,
113
],
[
115
,
116
,
117
,
118
]]]])
assert
np
.
allclose
(
y
.
asnumpy
(),
expect
)
x
=
Tensor
(
np
.
arange
(
0
,
3
*
4
*
5
).
reshape
(
3
,
4
,
5
).
astype
(
np
.
float32
))
y
=
P
.
StridedSlice
()(
x
,
(
1
,
0
,
0
),
(
2
,
-
3
,
3
),
(
1
,
1
,
3
))
expect
=
np
.
array
([[[
20
]]])
assert
np
.
allclose
(
y
.
asnumpy
(),
expect
)
x_np
=
np
.
arange
(
0
,
4
*
5
).
reshape
(
4
,
5
).
astype
(
np
.
float32
)
y
=
Tensor
(
x_np
)[:,
::
-
1
]
expect
=
x_np
[:,
::
-
1
]
assert
np
.
allclose
(
y
.
asnumpy
(),
expect
)
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