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体验新版 GitCode,发现更多精彩内容 >>
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966447e3
编写于
10月 01, 2020
作者:
W
Wojciech Uss
提交者:
GitHub
10月 01, 2020
浏览文件
操作
浏览文件
下载
电子邮件补丁
差异文件
Added support for quantization of fusion_gru (#27518)
上级
0cd4907e
变更
11
显示空白变更内容
内联
并排
Showing
11 changed file
with
268 addition
and
47 deletion
+268
-47
cmake/external/mkldnn.cmake
cmake/external/mkldnn.cmake
+1
-1
paddle/fluid/framework/ir/graph_pattern_detector.cc
paddle/fluid/framework/ir/graph_pattern_detector.cc
+20
-3
paddle/fluid/framework/ir/graph_pattern_detector.h
paddle/fluid/framework/ir/graph_pattern_detector.h
+15
-0
paddle/fluid/framework/ir/mkldnn/cpu_quantize_pass.cc
paddle/fluid/framework/ir/mkldnn/cpu_quantize_pass.cc
+71
-8
paddle/fluid/framework/ir/mkldnn/cpu_quantize_pass.h
paddle/fluid/framework/ir/mkldnn/cpu_quantize_pass.h
+7
-12
paddle/fluid/framework/ir/mkldnn/cpu_quantize_pass_tester.cc
paddle/fluid/framework/ir/mkldnn/cpu_quantize_pass_tester.cc
+81
-0
paddle/fluid/framework/ir/mkldnn/cpu_quantize_squash_pass.cc
paddle/fluid/framework/ir/mkldnn/cpu_quantize_squash_pass.cc
+8
-2
paddle/fluid/operators/fused/mkldnn/fusion_gru_mkldnn_op.cc
paddle/fluid/operators/fused/mkldnn/fusion_gru_mkldnn_op.cc
+2
-2
python/paddle/fluid/contrib/slim/quantization/quant2_int8_mkldnn_pass.py
...luid/contrib/slim/quantization/quant2_int8_mkldnn_pass.py
+36
-0
python/paddle/fluid/contrib/slim/tests/CMakeLists.txt
python/paddle/fluid/contrib/slim/tests/CMakeLists.txt
+15
-11
python/paddle/fluid/tests/unittests/mkldnn/test_fusion_gru_int8_mkldnn_op.py
.../tests/unittests/mkldnn/test_fusion_gru_int8_mkldnn_op.py
+12
-8
未找到文件。
cmake/external/mkldnn.cmake
浏览文件 @
966447e3
...
...
@@ -20,7 +20,7 @@ SET(MKLDNN_SOURCE_DIR ${THIRD_PARTY_PATH}/mkldnn/src/extern_mkldnn)
SET
(
MKLDNN_INSTALL_DIR
${
THIRD_PARTY_PATH
}
/install/mkldnn
)
SET
(
MKLDNN_INC_DIR
"
${
MKLDNN_INSTALL_DIR
}
/include"
CACHE PATH
"mkldnn include directory."
FORCE
)
SET
(
MKLDNN_REPOSITORY https://github.com/oneapi-src/oneDNN.git
)
SET
(
MKLDNN_TAG
64a48f9565aa72f6359917b3406328075a409939
)
SET
(
MKLDNN_TAG
361725600224f41b7347a1c6bee9b04d1e6c14d7
)
# Introduce variables:
# * CMAKE_INSTALL_LIBDIR
...
...
paddle/fluid/framework/ir/graph_pattern_detector.cc
浏览文件 @
966447e3
...
...
@@ -1882,9 +1882,9 @@ PDNode *patterns::MultipleQuantize::operator()() {
PDNode
*
patterns
::
QuantizePlacement
::
operator
()(
const
std
::
unordered_set
<
std
::
string
>
&
quantize_enabled_op_types
)
{
std
::
unordered_set
<
std
::
string
>
supported_op_types
=
std
::
unordered_set
<
std
::
string
>
(
{
"concat"
,
"conv2d"
,
"elementwise_add"
,
"fc"
,
"matmul"
,
"pool2d"
,
"prior_box
"
,
"relu"
,
"reshape2"
,
"transpose2
"
});
std
::
unordered_set
<
std
::
string
>
(
{
"concat"
,
"conv2d"
,
"elementwise_add"
,
"fc"
,
"matmul"
,
"pool2d
"
,
"prior_box"
,
"relu"
,
"reshape2"
,
"transpose2"
,
"fusion_gru
"
});
if
(
!
quantize_enabled_op_types
.
empty
())
{
supported_op_types
=
quantize_enabled_op_types
;
}
...
...
@@ -2280,6 +2280,23 @@ PDNode *patterns::MatmulTransposeReshapePattern::operator()() {
return
reshape_out
;
}
PDNode
*
patterns
::
FusionGru
::
operator
()()
{
auto
op
=
pattern
->
NewNode
(
op_repr
())
->
assert_is_op
(
"fusion_gru"
);
auto
x
=
pattern
->
NewNode
(
x_repr
())
->
AsInput
()
->
assert_is_op_input
(
"fusion_gru"
,
"X"
);
auto
weight_h
=
pattern
->
NewNode
(
weight_h_repr
())
->
AsInput
()
->
assert_is_op_input
(
"fusion_gru"
,
"WeightH"
);
auto
weight_x
=
pattern
->
NewNode
(
weight_x_repr
())
->
AsInput
()
->
assert_is_op_input
(
"fusion_gru"
,
"WeightX"
);
auto
out
=
pattern
->
NewNode
(
out_repr
())
->
AsOutput
()
->
assert_is_op_output
(
"fusion_gru"
,
"Hidden"
);
op
->
LinksFrom
({
x
,
weight_h
,
weight_x
}).
LinksTo
({
out
});
return
out
;
}
}
// namespace ir
}
// namespace framework
}
// namespace paddle
paddle/fluid/framework/ir/graph_pattern_detector.h
浏览文件 @
966447e3
...
...
@@ -1312,6 +1312,21 @@ struct MatmulTransposeReshapePattern : public PatternBase {
PATTERN_DECL_NODE
(
reshape_out_xshape
);
};
// fusion_gru op
// Forward pass for fusion_gru.
// fusion_gru out is a result of the operator.
struct
FusionGru
:
public
PatternBase
{
FusionGru
(
PDPattern
*
pattern
,
const
std
::
string
&
name_scope
)
:
PatternBase
(
pattern
,
name_scope
,
"fusion_gru"
)
{}
PDNode
*
operator
()();
PATTERN_DECL_NODE
(
op
);
PATTERN_DECL_NODE
(
x
);
PATTERN_DECL_NODE
(
weight_h
);
PATTERN_DECL_NODE
(
weight_x
);
PATTERN_DECL_NODE
(
out
);
};
}
// namespace patterns
// Link two ir::Nodes from each other.
...
...
paddle/fluid/framework/ir/mkldnn/cpu_quantize_pass.cc
浏览文件 @
966447e3
...
...
@@ -63,8 +63,9 @@ enum { U8_MAX = 255, S8_MAX = 127 };
void
CPUQuantizePass
::
QuantizeInput
(
Graph
*
g
,
Node
*
op
,
Node
*
input
,
std
::
string
input_name
,
double
scale_to_one
,
bool
is_unsigned
,
std
::
string
scale_attr_name
)
const
{
bool
is_input_unsigned
,
std
::
string
scale_attr_name
,
float
shift
,
std
::
string
shift_attr_name
)
const
{
auto
inputs
=
op
->
Op
()
->
InputNames
();
bool
name_found
=
std
::
find
(
inputs
.
begin
(),
inputs
.
end
(),
input_name
)
!=
inputs
.
end
();
...
...
@@ -72,7 +73,7 @@ void CPUQuantizePass::QuantizeInput(Graph* g, Node* op, Node* input,
platform
::
errors
::
InvalidArgument
(
"Var(%s) isn't the input of the %s operator."
,
input_name
,
op
->
Op
()
->
Type
()));
unsigned
max
=
is_unsigned
?
U8_MAX
:
S8_MAX
;
unsigned
max
=
is_
input_
unsigned
?
U8_MAX
:
S8_MAX
;
float
scale
=
scale_to_one
*
max
;
// Create quantize output variable
...
...
@@ -86,7 +87,8 @@ void CPUQuantizePass::QuantizeInput(Graph* g, Node* op, Node* input,
q_desc
.
SetOutput
(
"Output"
,
std
::
vector
<
std
::
string
>
({
quantize_out_node
->
Name
()}));
q_desc
.
SetAttr
(
"Scale"
,
scale
);
q_desc
.
SetAttr
(
"is_negative_input"
,
!
is_unsigned
);
q_desc
.
SetAttr
(
"Shift"
,
shift
);
q_desc
.
SetAttr
(
"is_negative_input"
,
!
is_input_unsigned
);
q_desc
.
SetAttr
(
"output_format"
,
Has
(
"data_layout"
)
?
Get
<
std
::
string
>
(
"data_layout"
)
:
"NHWC"
);
...
...
@@ -103,11 +105,13 @@ void CPUQuantizePass::QuantizeInput(Graph* g, Node* op, Node* input,
IR_NODE_LINK_TO
(
quantize_out_node
,
op
);
if
(
!
scale_attr_name
.
empty
())
op
->
Op
()
->
SetAttr
(
scale_attr_name
,
scale
);
if
(
!
shift_attr_name
.
empty
())
op
->
Op
()
->
SetAttr
(
shift_attr_name
,
shift
);
}
void
CPUQuantizePass
::
QuantizeInputs
(
Graph
*
g
,
Node
*
op
,
std
::
string
input_name
,
bool
are_unsigned
,
std
::
string
scale_attr_name
)
const
{
bool
are_inputs_unsigned
,
std
::
string
scale_attr_name
,
float
shift
,
std
::
string
shift_attr_name
)
const
{
auto
inputs
=
op
->
inputs
;
auto
output
=
op
->
outputs
[
0
];
PADDLE_ENFORCE_GE
(
inputs
.
size
(),
1
,
...
...
@@ -127,7 +131,7 @@ void CPUQuantizePass::QuantizeInputs(Graph* g, Node* op, std::string input_name,
std
::
vector
<
std
::
string
>
quantize_out_node_names
(
inputs
.
size
());
double
scale_out
=
GetScaleValueForNode
(
output
);
unsigned
max
=
are_unsigned
?
U8_MAX
:
S8_MAX
;
unsigned
max
=
are_
inputs_
unsigned
?
U8_MAX
:
S8_MAX
;
float
scale
=
scale_out
*
max
;
for
(
size_t
i
=
0
;
i
<
inputs
.
size
();
i
++
)
{
...
...
@@ -137,10 +141,11 @@ void CPUQuantizePass::QuantizeInputs(Graph* g, Node* op, std::string input_name,
quantize_out_node_names
[
i
]
=
quantize_out_nodes
[
i
]
->
Name
();
q_desc
.
SetAttr
(
"Scale"
,
scale
);
q_desc
.
SetAttr
(
"Shift"
,
shift
);
q_desc
.
SetInput
(
"Input"
,
std
::
vector
<
std
::
string
>
({
inputs
[
i
]
->
Name
()}));
q_desc
.
SetOutput
(
"Output"
,
std
::
vector
<
std
::
string
>
({
quantize_out_node_names
[
i
]}));
q_desc
.
SetAttr
(
"is_negative_input"
,
!
are_unsigned
);
q_desc
.
SetAttr
(
"is_negative_input"
,
!
are_
inputs_
unsigned
);
auto
quantize_op
=
g
->
CreateOpNode
(
&
q_desc
);
// OpDesc will be copied.
// link quantize op
...
...
@@ -154,6 +159,7 @@ void CPUQuantizePass::QuantizeInputs(Graph* g, Node* op, std::string input_name,
op
->
Op
()
->
SetInput
(
input_name
,
quantize_out_node_names
);
if
(
!
scale_attr_name
.
empty
())
op
->
Op
()
->
SetAttr
(
scale_attr_name
,
scale
);
if
(
!
shift_attr_name
.
empty
())
op
->
Op
()
->
SetAttr
(
shift_attr_name
,
shift
);
}
void
CPUQuantizePass
::
DequantizeOutput
(
Graph
*
g
,
Node
*
op
,
Node
*
output
,
...
...
@@ -782,6 +788,62 @@ void CPUQuantizePass::QuantizeElementwiseAdd(Graph* graph) const {
quantize_elementwise_add_count
);
}
void
CPUQuantizePass
::
QuantizeFusionGru
(
Graph
*
graph
)
const
{
GraphPatternDetector
gpd
;
patterns
::
FusionGru
pattern
{
gpd
.
mutable_pattern
(),
name_scope_
};
pattern
();
int
quantize_count
=
0
;
auto
handler
=
[
&
](
const
GraphPatternDetector
::
subgraph_t
&
subgraph
,
Graph
*
g
)
{
VLOG
(
4
)
<<
"Quantize fusion_gru op"
;
GET_IR_NODE_FROM_SUBGRAPH
(
op
,
op
,
pattern
);
// skip if should not be quantized
if
(
!
platform
::
HasOpINT8DataType
(
op
->
Op
()))
{
LogQuantizationDisabled
(
op
);
return
;
}
GET_IR_NODE_FROM_SUBGRAPH
(
x
,
x
,
pattern
);
GET_IR_NODE_FROM_SUBGRAPH
(
weight_h
,
weight_h
,
pattern
);
GET_IR_NODE_FROM_SUBGRAPH
(
weight_x
,
weight_x
,
pattern
);
GET_IR_NODE_FROM_SUBGRAPH
(
out
,
out
,
pattern
);
if
(
!
AreScalesPresentForNodes
(
op
,
{
x
,
weight_h
,
weight_x
}))
{
LogCannotQuantizeOp
(
op
);
return
;
}
bool
is_x_unsigned
{
false
};
auto
input_x_scale
=
GetScaleValueForNode
(
x
,
&
is_x_unsigned
);
double
input_x_shift
{
128.
};
if
(
is_x_unsigned
)
input_x_shift
=
0.
;
QuantizeInput
(
g
,
op
,
x
,
"X"
,
input_x_scale
,
is_x_unsigned
,
"Scale_data"
,
input_x_shift
,
"Shift_data"
);
auto
weight_scale_tensor
=
GetScaleTensorForNode
(
weight_x
);
EigenVectorArrayMap
eigen_tensor
{
weight_scale_tensor
.
data
<
double
>
(),
weight_scale_tensor
.
numel
(),
1
};
eigen_tensor
*=
static_cast
<
double
>
(
S8_MAX
);
std
::
vector
<
float
>
scale_weights
{
weight_scale_tensor
.
data
<
double
>
(),
weight_scale_tensor
.
data
<
double
>
()
+
weight_scale_tensor
.
numel
()};
op
->
Op
()
->
SetAttr
(
"Scale_weights"
,
scale_weights
);
// return fp32 data
op
->
Op
()
->
SetAttr
(
"force_fp32_output"
,
true
);
++
quantize_count
;
};
gpd
(
graph
,
handler
);
AddStatis
(
quantize_count
);
PrettyLogDetail
(
"--- quantized %d fusion_gru ops"
,
quantize_count
);
}
void
CPUQuantizePass
::
ApplyImpl
(
ir
::
Graph
*
graph
)
const
{
VLOG
(
3
)
<<
"Quantizing the graph."
;
PADDLE_ENFORCE_NOT_NULL
(
...
...
@@ -801,6 +863,7 @@ void CPUQuantizePass::ApplyImpl(ir::Graph* graph) const {
QuantizeReshape
(
graph
);
QuantizeMatmul
(
graph
);
QuantizeElementwiseAdd
(
graph
);
QuantizeFusionGru
(
graph
);
}
}
// namespace ir
...
...
paddle/fluid/framework/ir/mkldnn/cpu_quantize_pass.h
浏览文件 @
966447e3
...
...
@@ -49,31 +49,26 @@ class CPUQuantizePass : public FusePassBase {
void
ApplyImpl
(
ir
::
Graph
*
graph
)
const
override
;
void
QuantizeConv
(
Graph
*
graph
,
bool
with_residual_data
=
false
)
const
;
void
QuantizeFc
(
Graph
*
graph
)
const
;
void
QuantizePool
(
Graph
*
graph
)
const
;
void
QuantizeConcat
(
Graph
*
graph
)
const
;
void
QuantizePriorBox
(
Graph
*
graph
)
const
;
void
QuantizeTranspose
(
Graph
*
graph
)
const
;
void
QuantizeReshape
(
Graph
*
graph
)
const
;
void
QuantizeMatmul
(
Graph
*
graph
)
const
;
void
QuantizeElementwiseAdd
(
Graph
*
graph
)
const
;
void
QuantizeFusionGru
(
Graph
*
graph
)
const
;
void
QuantizeInput
(
Graph
*
g
,
Node
*
op
,
Node
*
input
,
std
::
string
input_name
,
double
scale_to_one
,
bool
is_unsigned
,
std
::
string
scale_attr_name
=
""
)
const
;
double
scale_to_one
,
bool
is_input_unsigned
,
std
::
string
scale_attr_name
=
""
,
float
shift
=
0.0
,
std
::
string
shift_attr_name
=
""
)
const
;
// quantize all inputs of given name with the same (minimum) scale
void
QuantizeInputs
(
Graph
*
g
,
Node
*
op
,
std
::
string
input_name
,
bool
are_unsigned
,
std
::
string
scale_attr_name
=
""
)
const
;
bool
are_inputs_unsigned
,
std
::
string
scale_attr_name
=
""
,
float
shift
=
0.0
,
std
::
string
shift_attr_name
=
""
)
const
;
void
DequantizeOutput
(
Graph
*
g
,
Node
*
op
,
Node
*
output
,
std
::
string
output_name
,
double
scale_to_one
,
...
...
paddle/fluid/framework/ir/mkldnn/cpu_quantize_pass_tester.cc
浏览文件 @
966447e3
...
...
@@ -91,6 +91,16 @@ void SetOp(ProgramDesc* prog, const std::string& type, const std::string& name,
op
->
SetAttr
(
"Scale_x"
,
1.0
f
);
op
->
SetAttr
(
"Scale_y"
,
1.0
f
);
op
->
SetAttr
(
"Scale_out"
,
1.0
f
);
}
else
if
(
type
==
"fusion_gru"
)
{
op
->
SetInput
(
"X"
,
{
inputs
[
0
]});
op
->
SetInput
(
"Bias"
,
{
inputs
[
1
]});
op
->
SetInput
(
"WeightX"
,
{
inputs
[
2
]});
op
->
SetInput
(
"WeightH"
,
{
inputs
[
3
]});
op
->
SetOutput
(
"Hidden"
,
{
outputs
[
0
]});
op
->
SetAttr
(
"mkldnn_data_type"
,
mkldnn_data_type
);
op
->
SetAttr
(
"Scale_data"
,
1.0
f
);
op
->
SetAttr
(
"Shift_data"
,
0.0
f
);
op
->
SetAttr
(
"Weight_scale"
,
std
::
vector
<
float
>
{
1.0
f
});
}
}
...
...
@@ -389,6 +399,77 @@ TEST(CpuQuantizePass, transpose) {
quant_count
,
dequant_count
,
added_nodes_count
,
2.0
f
*
127
);
}
static
const
std
::
initializer_list
<
std
::
string
>
variable_names_fusion_gru
=
{
"x"
,
"wx"
,
"wh"
,
"b"
,
"h"
};
// x->Fusion_gru->h
ProgramDesc
BuildProgramDescFusionGru
()
{
ProgramDesc
prog
;
for
(
auto
&
v
:
variable_names_transpose
)
{
auto
*
var
=
prog
.
MutableBlock
(
0
)
->
Var
(
v
);
if
(
v
.
find
(
"wx"
)
==
0
||
v
.
find
(
"wh"
)
||
v
.
find
(
"b"
))
{
var
->
SetPersistable
(
true
);
}
}
SetOp
(
&
prog
,
"fusion_gru"
,
"Fusion_gru"
,
{
"x"
,
"wx"
,
"wh"
,
"b"
},
{
"h"
},
true
,
"int8"
);
return
prog
;
}
void
MainTestFusionGru
(
const
ProgramDesc
&
prog
,
int
gru_count
,
int
quant_count
,
int
dequant_count
,
int
added_nodes_count
,
float
scale
,
float
shift
)
{
std
::
unique_ptr
<
ir
::
Graph
>
graph
(
new
ir
::
Graph
(
prog
));
int
original_nodes_num
,
current_nodes_num
;
PreparePass
(
&
graph
,
prog
,
variable_names_fusion_gru
,
&
original_nodes_num
,
&
current_nodes_num
);
int
quantize_nodes_count
=
0
;
int
dequantize_nodes_count
=
0
;
int
gru_nodes_count
=
0
;
for
(
auto
*
node
:
graph
->
Nodes
())
{
if
(
node
->
IsOp
())
{
auto
*
op
=
node
->
Op
();
if
(
op
->
Type
()
==
"fusion_gru"
)
{
gru_nodes_count
++
;
auto
op_name
=
BOOST_GET_CONST
(
std
::
string
,
op
->
GetAttr
(
"name"
));
EXPECT_EQ
(
BOOST_GET_CONST
(
float
,
op
->
GetAttr
(
"Scale_data"
)),
scale
)
<<
"Scale_data for node '"
+
op_name
+
"'."
;
EXPECT_EQ
(
BOOST_GET_CONST
(
float
,
op
->
GetAttr
(
"Shift_data"
)),
shift
)
<<
"Shift_data for node '"
+
op_name
+
"'."
;
EXPECT_EQ
(
BOOST_GET_CONST
(
std
::
vector
<
float
>
,
op
->
GetAttr
(
"Scale_weights"
))[
0
],
scale
)
<<
"Scale_weights for node '"
+
op_name
+
"'."
;
EXPECT_EQ
(
BOOST_GET_CONST
(
bool
,
op
->
GetAttr
(
"force_fp32_output"
)),
true
)
<<
"force_fp32_output for node '"
+
op_name
+
"'."
;
}
else
if
(
op
->
Type
()
==
"quantize"
)
{
quantize_nodes_count
++
;
}
else
if
(
op
->
Type
()
==
"dequantize"
)
{
dequantize_nodes_count
++
;
}
}
}
EXPECT_EQ
(
gru_nodes_count
,
gru_count
);
EXPECT_EQ
(
quantize_nodes_count
,
quant_count
);
EXPECT_EQ
(
dequantize_nodes_count
,
dequant_count
);
EXPECT_EQ
(
original_nodes_num
+
added_nodes_count
,
current_nodes_num
);
}
TEST
(
CpuQuantizePass
,
fusion_gru
)
{
// x->Fusion_gru->h
int
gru_count
=
1
;
int
quant_count
=
1
;
int
dequant_count
=
0
;
// 1 Quant + 1 IN + 0 DeQuant + 0 OUT
int
added_nodes_count
=
1
+
1
+
0
+
0
;
MainTestFusionGru
(
BuildProgramDescFusionGru
(),
gru_count
,
quant_count
,
dequant_count
,
added_nodes_count
,
2.
*
127
,
128.
);
}
static
const
std
::
initializer_list
<
std
::
string
>
variable_names_reshape
=
{
"a"
,
"w1"
,
"b"
,
"c"
,
"d"
,
"e"
,
"f"
};
...
...
paddle/fluid/framework/ir/mkldnn/cpu_quantize_squash_pass.cc
浏览文件 @
966447e3
...
...
@@ -76,6 +76,8 @@ void CPUQuantizeSquashPass::DequantQuantSquash(
BOOST_GET_CONST
(
float
,
dequant_op
->
Op
()
->
GetAttr
(
"Scale"
));
float
quant_scale
=
BOOST_GET_CONST
(
float
,
quant_op
->
Op
()
->
GetAttr
(
"Scale"
));
float
dequant_shift
=
dequant_op
->
Op
()
->
GetAttrIfExists
<
float
>
(
"Shift"
);
float
quant_shift
=
quant_op
->
Op
()
->
GetAttrIfExists
<
float
>
(
"Shift"
);
PADDLE_ENFORCE_NE
(
nodes_keep_counter
->
find
(
dequant_out
),
nodes_keep_counter
->
end
(),
platform
::
errors
::
NotFound
(
"The dequant output node is not found."
));
...
...
@@ -83,7 +85,7 @@ void CPUQuantizeSquashPass::DequantQuantSquash(
// check if dequantize op should be kept or removed, decrease the counter
bool
keep_dequant
=
(
*
nodes_keep_counter
)[
dequant_out
]
--
>
1
;
if
(
dequant_scale
==
quant_scale
)
{
if
(
dequant_scale
==
quant_scale
&&
dequant_shift
==
quant_shift
)
{
// squash dequantize-quantize to nothing
auto
quant_out_var_name
=
quant_out
->
Name
();
auto
next_op_inputs
=
next_op_desc
->
InputNames
();
...
...
@@ -110,7 +112,9 @@ void CPUQuantizeSquashPass::DequantQuantSquash(
desc
.
SetInput
(
"Input"
,
std
::
vector
<
std
::
string
>
({
dequant_in
->
Name
()}));
desc
.
SetOutput
(
"Output"
,
std
::
vector
<
std
::
string
>
({
quant_out
->
Name
()}));
desc
.
SetAttr
(
"Scale_in"
,
dequant_scale
);
desc
.
SetAttr
(
"Shift_in"
,
dequant_shift
);
desc
.
SetAttr
(
"Scale_out"
,
quant_scale
);
desc
.
SetAttr
(
"Shift_out"
,
quant_shift
);
auto
requant_op
=
g
->
CreateOpNode
(
&
desc
);
...
...
@@ -293,6 +297,7 @@ void CPUQuantizeSquashPass::MultipleQuantizeSquash(Graph* graph) const {
}));
auto
*
first_quant_out
=
first_quant_op
->
outputs
[
0
];
float
scale
=
first_quant_op
->
Op
()
->
GetAttrIfExists
<
float
>
(
"Scale"
);
float
shift
=
first_quant_op
->
Op
()
->
GetAttrIfExists
<
float
>
(
"Shift"
);
PADDLE_ENFORCE_NE
(
scale
,
0
,
platform
::
errors
::
InvalidArgument
(
...
...
@@ -302,7 +307,8 @@ void CPUQuantizeSquashPass::MultipleQuantizeSquash(Graph* graph) const {
auto
quant_op
=
prev_out
->
outputs
[
iter
];
if
(
quant_op
->
IsOp
()
&&
quant_op
->
Op
()
->
Type
()
==
"quantize"
&&
quant_op
->
id
()
!=
first_quant_op
->
id
()
&&
quant_op
->
Op
()
->
GetAttrIfExists
<
float
>
(
"Scale"
)
==
scale
)
{
quant_op
->
Op
()
->
GetAttrIfExists
<
float
>
(
"Scale"
)
==
scale
&&
quant_op
->
Op
()
->
GetAttrIfExists
<
float
>
(
"Shift"
)
==
shift
)
{
auto
quant_out
=
quant_op
->
outputs
[
0
];
auto
last_op
=
quant_out
->
outputs
[
0
];
...
...
paddle/fluid/operators/fused/mkldnn/fusion_gru_mkldnn_op.cc
浏览文件 @
966447e3
...
...
@@ -95,7 +95,7 @@ class GRUMKLDNNHandler : public platform::MKLDNNHandlerT<T, dnnl::gru_forward> {
// Create memory descriptors
auto
input_md
=
MKLDNNMemDesc
({
Ti
,
N
,
IC
},
MKLDNNGetDataType
<
T
>
(),
MKLDNNMemoryFormat
::
any
);
MKLDNNMemoryFormat
::
ntc
);
auto
weight_x_md
=
MKLDNNMemDesc
({
L
,
D
,
IC
,
G
,
OC
},
weights_dt
,
MKLDNNMemoryFormat
::
any
);
auto
weight_h_md
=
...
...
@@ -103,7 +103,7 @@ class GRUMKLDNNHandler : public platform::MKLDNNHandlerT<T, dnnl::gru_forward> {
auto
bias_md
=
MKLDNNMemDesc
({
L
,
D
,
G
,
OC
},
MKLDNNGetDataType
<
float
>
(),
MKLDNNMemoryFormat
::
ldgo
);
auto
hidden_md
=
MKLDNNMemDesc
({
Ti
,
N
,
OC
},
MKLDNNGetDataType
<
T_out
>
(),
MKLDNNMemoryFormat
::
any
);
MKLDNNMemoryFormat
::
ntc
);
auto
h0_md
=
MKLDNNMemDesc
({
L
,
D
,
N
,
OC
},
MKLDNNGetDataType
<
T
>
(),
MKLDNNMemoryFormat
::
ldnc
);
...
...
python/paddle/fluid/contrib/slim/quantization/quant2_int8_mkldnn_pass.py
浏览文件 @
966447e3
...
...
@@ -66,6 +66,7 @@ class Quant2Int8MkldnnPass(object):
self
.
_fc_ops
=
[
'fc'
]
self
.
_relu_ops
=
[
'relu'
,
'relu6'
]
self
.
_matmul_ops
=
[
'matmul'
]
self
.
_gru_ops
=
[
'fusion_gru'
]
self
.
_weight_scales
=
{}
# Collect the Input and Output sclaes from Fake quant models
self
.
_var_quant_scales
=
{}
...
...
@@ -449,8 +450,43 @@ class Quant2Int8MkldnnPass(object):
self
.
_var_quant_scales
[
weight_var_name
]
=
(
use_unsigned_int
,
lod_tensor
)
def
_compute_gru_weight_scales
(
wx_name
,
wh_name
):
for
op
in
graph
.
all_op_nodes
():
if
op
.
op
().
type
()
in
self
.
_gru_ops
:
wx_var_name
=
op
.
input
(
wx_name
)[
0
]
wh_var_name
=
op
.
input
(
wh_name
)[
0
]
wx
=
np
.
array
(
self
.
_load_param
(
self
.
_scope
,
wx_var_name
))
wh
=
np
.
array
(
self
.
_load_param
(
self
.
_scope
,
wh_var_name
))
OC
=
wh
.
shape
[
0
]
scale_ur
=
1.0
/
np
.
max
(
np
.
abs
(
np
.
concatenate
(
[
wx
[:,
:
2
*
OC
],
wh
.
flatten
()[:
2
*
OC
*
OC
]
.
reshape
(
OC
,
2
*
OC
)
],
axis
=
0
)),
axis
=
0
)
scale_o
=
1.0
/
np
.
max
(
np
.
abs
(
np
.
concatenate
(
[
wx
[:,
2
*
OC
:],
wh
.
flatten
()[
2
*
OC
*
OC
:]
.
reshape
(
OC
,
OC
)
],
axis
=
0
)),
axis
=
0
)
gru_weights_scale
=
np
.
concatenate
(
[
scale_ur
,
scale_o
]).
astype
(
'float'
)
lod_tensor
=
self
.
_convert_scale2tensor
(
gru_weights_scale
)
use_unsigned_int
=
False
self
.
_var_quant_scales
[
wx_var_name
]
=
(
use_unsigned_int
,
lod_tensor
)
_compute_var_scales
(
self
.
_conv_ops
,
"Filter"
,
axis
=
1
)
_compute_var_scales
(
self
.
_fc_ops
,
"W"
,
axis
=
0
)
_compute_var_scales
(
self
.
_gru_ops
,
"WeightH"
,
axis
=
0
)
_compute_gru_weight_scales
(
"WeightX"
,
"WeightH"
)
return
graph
def
_find_avg_pooling_ids
(
self
,
graph
):
...
...
python/paddle/fluid/contrib/slim/tests/CMakeLists.txt
浏览文件 @
966447e3
...
...
@@ -98,18 +98,16 @@ function(download_quant_model install_dir data_file)
endif
()
endfunction
()
function
(
save_quant_ic_model_test target quant_model_dir
fp32_model_save_path
int8_model_save_path
)
function
(
save_quant_ic_model_test target quant_model_dir int8_model_save_path
)
py_test
(
${
target
}
SRCS
${
CMAKE_CURRENT_SOURCE_DIR
}
/save_quant_model.py
ARGS --quant_model_path
${
quant_model_dir
}
--fp32_model_save_path
${
fp32_model_save_path
}
--int8_model_save_path
${
int8_model_save_path
}
--debug
)
endfunction
()
function
(
save_quant_nlp_model_test target quant_model_dir
fp32_model_save_path
int8_model_save_path ops_to_quantize
)
function
(
save_quant_nlp_model_test target quant_model_dir int8_model_save_path ops_to_quantize
)
py_test
(
${
target
}
SRCS
${
CMAKE_CURRENT_SOURCE_DIR
}
/save_quant_model.py
ARGS --quant_model_path
${
quant_model_dir
}
--fp32_model_save_path
${
fp32_model_save_path
}
--int8_model_save_path
${
int8_model_save_path
}
--ops_to_quantize
${
ops_to_quantize
}
)
endfunction
()
...
...
@@ -227,8 +225,6 @@ if(LINUX AND WITH_MKLDNN)
set
(
NLP_LABLES_PATH
"
${
NLP_DATA_DIR
}
/Ernie_dataset/label.xnli.dev"
)
download_quant_data
(
${
NLP_DATA_DIR
}
${
NLP_DATA_ARCHIVE
}
)
set
(
QUANT2_NLP_OPS_TO_QUANTIZE
"fc,reshape2,transpose2,matmul,elementwise_add"
)
# Quant2 Ernie
set
(
QUANT2_ERNIE_MODEL_ARCHIVE
"ernie_qat.tar.gz"
)
set
(
QUANT2_ERNIE_MODEL_DIR
"
${
QUANT_INSTALL_DIR
}
/Ernie_quant2"
)
...
...
@@ -236,17 +232,25 @@ if(LINUX AND WITH_MKLDNN)
set
(
FP32_ERNIE_MODEL_ARCHIVE
"ernie_fp32_model.tar.gz"
)
set
(
FP32_ERNIE_MODEL_DIR
"
${
QUANT_INSTALL_DIR
}
/Ernie_float"
)
download_quant_fp32_model
(
${
FP32_ERNIE_MODEL_DIR
}
${
FP32_ERNIE_MODEL_ARCHIVE
}
)
inference_quant2_int8_nlp_test
(
test_quant2_int8_ernie_mkldnn
${
QUANT2_ERNIE_MODEL_DIR
}
/Ernie_qat/float
${
FP32_ERNIE_MODEL_DIR
}
/ernie_fp32_model
${
NLP_DATA_PATH
}
${
NLP_LABLES_PATH
}
${
QUANT2_NLP_OPS_TO_QUANTIZE
}
)
set
(
QUANT2_ERNIE_OPS_TO_QUANTIZE
"fc,reshape2,transpose2,matmul,elementwise_add"
)
inference_quant2_int8_nlp_test
(
test_quant2_int8_ernie_mkldnn
${
QUANT2_ERNIE_MODEL_DIR
}
/Ernie_qat/float
${
FP32_ERNIE_MODEL_DIR
}
/ernie_fp32_model
${
NLP_DATA_PATH
}
${
NLP_LABLES_PATH
}
${
QUANT2_ERNIE_OPS_TO_QUANTIZE
}
)
# Quant2 GRU
set
(
QUANT2_GRU_MODEL_ARCHIVE
"GRU_quant_acc.tar.gz"
)
set
(
QUANT2_GRU_MODEL_DIR
"
${
QUANT_INSTALL_DIR
}
/GRU_quant2"
)
download_quant_model
(
${
QUANT2_GRU_MODEL_DIR
}
${
QUANT2_GRU_MODEL_ARCHIVE
}
)
set
(
QUANT2_GRU_OPS_TO_QUANTIZE
"fusion_gru"
)
### Save FP32 model or INT8 model from Quant model
set
(
QUANT2_INT8_RESNET50_SAVE_PATH
"
${
QUANT_INSTALL_DIR
}
/ResNet50_quant2_int8"
)
set
(
QUANT2_FP32_RESNET50_SAVE_PATH
"
${
QUANT_INSTALL_DIR
}
/ResNet50_quant2_fp32"
)
save_quant_ic_model_test
(
save_quant2_model_resnet50
${
QUANT2_RESNET50_MODEL_DIR
}
/ResNet50_qat_perf/float
${
QUANT2_FP32_RESNET50_SAVE_PATH
}
${
QUANT2_INT8_RESNET50_SAVE_PATH
}
)
save_quant_ic_model_test
(
save_quant2_model_resnet50
${
QUANT2_RESNET50_MODEL_DIR
}
/ResNet50_qat_perf/float
${
QUANT2_INT8_RESNET50_SAVE_PATH
}
)
set
(
QUANT2_INT8_ERNIE_SAVE_PATH
"
${
QUANT_INSTALL_DIR
}
/Ernie_quant2_int8"
)
set
(
QUANT2_FP32_ERNIE_SAVE_PATH
"
${
QUANT_INSTALL_DIR
}
/Ernie_quant2_fp32"
)
save_quant_nlp_model_test
(
save_quant2_model_ernie
${
QUANT2_ERNIE_MODEL_DIR
}
/Ernie_qat/float
${
QUANT2_FP32_ERNIE_SAVE_PATH
}
${
QUANT2_INT8_ERNIE_SAVE_PATH
}
${
QUANT2_NLP_OPS_TO_QUANTIZE
}
)
save_quant_nlp_model_test
(
save_quant2_model_ernie
${
QUANT2_ERNIE_MODEL_DIR
}
/Ernie_qat/float
${
QUANT2_INT8_ERNIE_SAVE_PATH
}
${
QUANT2_ERNIE_OPS_TO_QUANTIZE
}
)
set
(
QUANT2_INT8_GRU_SAVE_PATH
"
${
QUANT_INSTALL_DIR
}
/GRU_quant2_int8"
)
save_quant_nlp_model_test
(
save_quant2_model_gru
${
QUANT2_GRU_MODEL_DIR
}
/GRU_quant_acc
${
QUANT2_INT8_GRU_SAVE_PATH
}
${
QUANT2_GRU_OPS_TO_QUANTIZE
}
)
# Convert Quant2 model to dot and pdf files
set
(
QUANT2_INT8_ERNIE_DOT_SAVE_PATH
"
${
QUANT_INSTALL_DIR
}
/Ernie_quant2_int8_dot_file"
)
...
...
python/paddle/fluid/tests/unittests/mkldnn/test_fusion_gru_int8_mkldnn_op.py
浏览文件 @
966447e3
...
...
@@ -45,9 +45,10 @@ class TestFusionGRUINT8MKLDNNOp(OpTest):
# Input data
x_f32
=
np
.
random
.
rand
(
T
,
self
.
IC
).
astype
(
'float32'
)
*
2
-
1
scale_data
=
63
shift_data
=
64
x_u8
=
(
x_f32
*
scale_data
+
shift_data
).
astype
(
np
.
uint8
)
scale_data
=
63.0
shift_data
=
64.0
x_u8
=
np
.
rint
(
x_f32
*
scale_data
+
shift_data
).
astype
(
np
.
uint8
)
# x_u8 = (x_f32 * scale_data + shift_data).astype(np.uint8)
# WeightX/WeightH data
wx
=
np
.
random
.
rand
(
self
.
IC
,
3
*
self
.
OC
).
astype
(
'float32'
)
*
2
-
1
...
...
@@ -58,7 +59,8 @@ class TestFusionGRUINT8MKLDNNOp(OpTest):
# WeightX data shape in PP: [IC, 3 * OC]
# WeightH data shape in PP: [OC, 2 * OC] + [OC, OC]
# Scales shape in oneDNN: [3, OC]
scale_ur
=
63
/
np
.
max
(
np
.
abs
(
s8_max
=
127.0
scale_ur
=
s8_max
/
np
.
max
(
np
.
abs
(
np
.
concatenate
(
[
wx
[:,
:
2
*
self
.
OC
],
wh
.
flatten
()[:
2
*
self
.
OC
*
self
.
OC
]
...
...
@@ -66,7 +68,7 @@ class TestFusionGRUINT8MKLDNNOp(OpTest):
],
axis
=
0
)),
axis
=
0
)
scale_o
=
63
/
np
.
max
(
np
.
abs
(
scale_o
=
s8_max
/
np
.
max
(
np
.
abs
(
np
.
concatenate
(
[
wx
[:,
2
*
self
.
OC
:],
wh
.
flatten
()[
2
*
self
.
OC
*
self
.
OC
:]
...
...
@@ -102,7 +104,9 @@ class TestFusionGRUINT8MKLDNNOp(OpTest):
self
.
outputs
=
{
'Hidden'
:
(
hidden_f32
,
self
.
lod
)}
else
:
self
.
error_margin
=
1
hidden_u8
=
(
hidden_f32
*
scale_data
+
shift_data
).
astype
(
np
.
uint8
)
hidden_u8
=
np
.
rint
(
hidden_f32
*
scale_data
+
shift_data
).
astype
(
np
.
uint8
)
# hidden_u8 = (hidden_f32 * scale_data + shift_data).astype(np.uint8)
self
.
outputs
=
{
'Hidden'
:
(
hidden_u8
,
self
.
lod
)}
self
.
attrs
=
{
...
...
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