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545247d7
编写于
3月 04, 2019
作者:
Z
Zhen Wang
浏览文件
操作
浏览文件
下载
电子邮件补丁
差异文件
add channel wise quantize op.
上级
187cffd0
变更
4
隐藏空白更改
内联
并排
Showing
4 changed file
with
121 addition
and
0 deletion
+121
-0
paddle/fluid/operators/fake_quantize_op.cc
paddle/fluid/operators/fake_quantize_op.cc
+62
-0
paddle/fluid/operators/fake_quantize_op.cu
paddle/fluid/operators/fake_quantize_op.cu
+2
-0
paddle/fluid/operators/fake_quantize_op.h
paddle/fluid/operators/fake_quantize_op.h
+33
-0
python/paddle/fluid/tests/unittests/test_fake_quantize_op.py
python/paddle/fluid/tests/unittests/test_fake_quantize_op.py
+24
-0
未找到文件。
paddle/fluid/operators/fake_quantize_op.cc
浏览文件 @
545247d7
...
...
@@ -134,6 +134,61 @@ $$Out = round(X/scale * range)$$
}
};
class
FakeChannelWiseQuantizeAbsMaxOp
:
public
framework
::
OperatorWithKernel
{
public:
using
framework
::
OperatorWithKernel
::
OperatorWithKernel
;
void
InferShape
(
framework
::
InferShapeContext
*
ctx
)
const
override
{
PADDLE_ENFORCE
(
ctx
->
HasInput
(
"X"
),
"Input(X) of FakeChannelWiseQuantizeOp should not be null."
);
PADDLE_ENFORCE
(
ctx
->
HasOutput
(
"Out"
),
"Output(Out) of FakeChannelWiseQuantizeOp should not be null."
);
PADDLE_ENFORCE
(
ctx
->
HasOutput
(
"OutScales"
),
"Output(Scales) of FakeChannelWiseQuantizeOp should not be null."
);
ctx
->
SetOutputDim
(
"Out"
,
ctx
->
GetInputDim
(
"X"
));
ctx
->
SetOutputDim
(
"OutScales"
,
{
ctx
->
GetInputDim
(
"X"
)[
0
]});
ctx
->
ShareLoD
(
"X"
,
/*->*/
"Out"
);
}
protected:
framework
::
OpKernelType
GetExpectedKernelType
(
const
framework
::
ExecutionContext
&
ctx
)
const
override
{
return
framework
::
OpKernelType
(
ctx
.
Input
<
framework
::
LoDTensor
>
(
"X"
)
->
type
(),
ctx
.
GetPlace
());
}
};
class
FakeChannelWiseQuantizeAbsMaxOpMaker
:
public
framework
::
OpProtoAndCheckerMaker
{
public:
void
Make
()
override
{
AddInput
(
"X"
,
"(Tensor) Input is float data type."
);
AddOutput
(
"Out"
,
"(Tensor) Output of quantized low level tensor, "
"but also saved as float data type."
);
AddOutput
(
"OutScales"
,
"(Tensor) Current channel wise scale"
);
AddAttr
<
int
>
(
"bit_length"
,
"(int, default 8)"
)
.
SetDefault
(
8
)
.
AddCustomChecker
([](
const
int
&
bit_length
)
{
PADDLE_ENFORCE
(
bit_length
>=
1
&&
bit_length
<=
16
,
"'bit_length' should be between 1 and 16."
);
});
AddComment
(
R"DOC(
The scale of FakeChannelWiseQuantize operator is a vector.
In detail, each channel of the input X has a scale value.
$$scale_c = max(abs(X_c))$$
$$range = 2^{bit_length - 1} - 1$$
$$Out_c = round(X_c / scale_c * range)$$
In above three formulas, the range value of c is as follow:
$$0 \leq c \leq \ the\ channel\ number\ of\ X$$
)DOC"
);
}
};
class
FakeQuantizeRangeAbsMaxOp
:
public
framework
::
OperatorWithKernel
{
public:
FakeQuantizeRangeAbsMaxOp
(
const
std
::
string
&
type
,
...
...
@@ -218,3 +273,10 @@ REGISTER_OPERATOR(fake_quantize_range_abs_max, ops::FakeQuantizeRangeAbsMaxOp,
paddle
::
framework
::
EmptyGradOpMaker
);
REGISTER_OP_CPU_KERNEL
(
fake_quantize_range_abs_max
,
ops
::
FakeQuantizeRangeAbsMaxKernel
<
CPU
,
float
>
);
REGISTER_OPERATOR
(
fake_channel_wise_quantize_abs_max
,
ops
::
FakeChannelWiseQuantizeAbsMaxOp
,
ops
::
FakeChannelWiseQuantizeAbsMaxOpMaker
,
paddle
::
framework
::
EmptyGradOpMaker
);
REGISTER_OP_CPU_KERNEL
(
fake_channel_wise_quantize_abs_max
,
ops
::
FakeChannelWiseQuantizeAbsMaxKernel
<
CPU
,
float
>
);
paddle/fluid/operators/fake_quantize_op.cu
浏览文件 @
545247d7
...
...
@@ -174,5 +174,7 @@ namespace ops = paddle::operators;
using
CUDA
=
paddle
::
platform
::
CUDADeviceContext
;
REGISTER_OP_CUDA_KERNEL
(
fake_quantize_abs_max
,
ops
::
FakeQuantizeAbsMaxKernel
<
CUDA
,
float
>
);
REGISTER_OP_CUDA_KERNEL
(
fake_channel_wise_quantize_abs_max
,
ops
::
FakeChannelWiseQuantizeAbsMaxKernel
<
CUDA
,
float
>
);
REGISTER_OP_CUDA_KERNEL
(
fake_quantize_range_abs_max
,
ops
::
FakeQuantizeRangeAbsMaxKernel
<
CUDA
,
float
>
);
paddle/fluid/operators/fake_quantize_op.h
浏览文件 @
545247d7
...
...
@@ -63,6 +63,39 @@ class FakeQuantizeAbsMaxKernel : public framework::OpKernel<T> {
}
};
template
<
typename
DeviceContext
,
typename
T
>
class
FakeChannelWiseQuantizeAbsMaxKernel
:
public
framework
::
OpKernel
<
T
>
{
public:
void
Compute
(
const
framework
::
ExecutionContext
&
context
)
const
override
{
auto
*
in
=
context
.
Input
<
framework
::
Tensor
>
(
"X"
);
auto
*
out
=
context
.
Output
<
framework
::
Tensor
>
(
"Out"
);
auto
*
out_scales
=
context
.
Output
<
framework
::
Tensor
>
(
"OutScales"
);
T
*
out_scales_data
=
out_scales
->
mutable_data
<
T
>
(
context
.
GetPlace
());
out
->
mutable_data
<
T
>
(
context
.
GetPlace
());
int
bit_length
=
context
.
Attr
<
int
>
(
"bit_length"
);
int
bin_cnt
=
std
::
pow
(
2
,
bit_length
-
1
)
-
1
;
auto
&
dev_ctx
=
context
.
template
device_context
<
DeviceContext
>();
auto
find_abs_max
=
FindAbsMaxFunctor
<
DeviceContext
,
T
>
();
for
(
int64_t
i
=
0
;
i
<
in
->
dims
()[
0
];
i
++
)
{
framework
::
Tensor
one_channel
=
in
->
Slice
(
i
,
i
+
1
);
const
T
*
one_channel_data
=
one_channel
.
data
<
T
>
();
find_abs_max
(
dev_ctx
,
one_channel_data
,
one_channel
.
numel
(),
&
out_scales_data
[
i
]);
}
auto
clip_quant
=
ClipAndFakeQuantFunctor
<
DeviceContext
,
T
>
();
for
(
int64_t
i
=
0
;
i
<
in
->
dims
()[
0
];
i
++
)
{
framework
::
Tensor
one_channel_in
=
in
->
Slice
(
i
,
i
+
1
);
framework
::
Tensor
one_channel_out
=
out
->
Slice
(
i
,
i
+
1
);
framework
::
Tensor
one_channel_scale
=
out_scales
->
Slice
(
i
,
i
+
1
);
clip_quant
(
dev_ctx
,
one_channel_in
,
one_channel_scale
,
bin_cnt
,
&
one_channel_out
);
}
}
};
template
<
typename
DeviceContext
,
typename
T
>
class
FakeQuantizeRangeAbsMaxKernel
:
public
framework
::
OpKernel
<
T
>
{
public:
...
...
python/paddle/fluid/tests/unittests/test_fake_quantize_op.py
浏览文件 @
545247d7
...
...
@@ -35,6 +35,30 @@ class TestFakeQuantizeOp(OpTest):
self
.
check_output
()
class
TestFakeChannelWiseQuantizeOp
(
OpTest
):
def
setUp
(
self
):
self
.
op_type
=
"fake_channel_wise_quantize_abs_max"
self
.
attrs
=
{
'bit_length'
:
8
}
self
.
inputs
=
{
'X'
:
np
.
random
.
random
((
4
,
3
,
64
,
64
)).
astype
(
"float32"
),
}
scales
=
[]
for
i
in
range
(
self
.
inputs
[
'X'
].
shape
[
0
]):
scales
.
append
(
np
.
max
(
np
.
abs
(
self
.
inputs
[
'X'
][
i
])).
astype
(
"float32"
))
outputs
=
self
.
inputs
[
'X'
].
copy
()
for
i
,
scale
in
enumerate
(
scales
):
outputs
[
i
]
=
np
.
round
(
outputs
[
i
]
/
scale
*
(
(
1
<<
(
self
.
attrs
[
'bit_length'
]
-
1
))
-
1
))
self
.
outputs
=
{
'Out'
:
outputs
,
'OutScales'
:
np
.
array
(
scales
).
astype
(
"float32"
),
}
def
test_check_output
(
self
):
self
.
check_output
()
class
TestFakeQuantizeRangeAbsMaxOp
(
OpTest
):
def
setUp
(
self
):
self
.
op_type
=
"fake_quantize_range_abs_max"
...
...
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