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体验新版 GitCode,发现更多精彩内容 >>
提交
a25be53c
编写于
7月 09, 2019
作者:
B
bingyanghuang
提交者:
Tao Luo
7月 09, 2019
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电子邮件补丁
差异文件
QAT int8 MKL-DNN transformation pass with MUL (#18322)
上级
667f88f9
变更
2
隐藏空白更改
内联
并排
Showing
2 changed file
with
83 addition
and
54 deletion
+83
-54
python/paddle/fluid/contrib/slim/quantization/quantization_mkldnn_pass.py
...uid/contrib/slim/quantization/quantization_mkldnn_pass.py
+75
-48
python/paddle/fluid/contrib/slim/tests/test_quantization_mkldnn_pass.py
...fluid/contrib/slim/tests/test_quantization_mkldnn_pass.py
+8
-6
未找到文件。
python/paddle/fluid/contrib/slim/quantization/quantization_mkldnn_pass.py
浏览文件 @
a25be53c
...
...
@@ -27,9 +27,9 @@ class TransformForMkldnnPass(object):
1. Convert int8 range weights with float32 data type, which are generated by
the QuantizationFreezePass, to float32 range weights with float32 data type
by using the corresponding scales. This conversion is because MKL-DNN INT8
conv2d kernel
now only supports float32 weights input, will do weights
quantization inside the conv2d
kernel.
2. Create the new conv2d op with the converted weights and link its output
conv2d kernel
and mul kernel now only support float32 weights input, hence
weights quantization will happen inside the conv2d and mul INT8
kernel.
2. Create the new conv2d o
r mul o
p with the converted weights and link its output
to fake_dequantize_abs_max op's output and set conv2d's attribute "force_fp32
_output" as true
3. Transform fake_quantize_xx op to quantize op
...
...
@@ -73,13 +73,8 @@ class TransformForMkldnnPass(object):
self
.
InScale
=
{}
self
.
max_range
=
{}
self
.
conv_
new_output
=
{}
self
.
new_output
=
{}
self
.
s8_max
=
127
# Temporary code for keeping the mul op as fake quantization
#TODO Intel: Remove the following code when mul int8 mkldnn
# kernel enabled
self
.
mul_input_id
=
[]
self
.
mul_output_id
=
[]
def
apply
(
self
,
graph
):
"""
...
...
@@ -97,7 +92,7 @@ class TransformForMkldnnPass(object):
persistable_vars
=
[
p
.
name
()
for
p
in
graph
.
all_persistable_nodes
()]
# Collect the InScales and max_range to calculate the new scales for MKL-DNN
# INT8 conv2d
# INT8 conv2d
and mul
for
op_node
in
ops
:
if
op_node
.
name
()
in
self
.
dequantize_type
:
input_name
=
op_node
.
input
(
"X"
)[
0
]
...
...
@@ -105,20 +100,14 @@ class TransformForMkldnnPass(object):
self
.
InScale
[
input_name
]
=
self
.
_load_param
(
self
.
_scope
,
scale_name
)[
0
]
self
.
max_range
[
input_name
]
=
op_node
.
op
().
attr
(
"max_range"
)
self
.
conv_new_output
[
input_name
]
=
op_node
.
output
(
"Out"
)[
0
]
# Temporary graph transform on keeping the mul op
# TODO Intel: Remove following code
elif
op_node
.
name
()
in
[
'mul'
]:
input_node
=
graph
.
_find_node_by_name
(
op_node
.
inputs
,
op_node
.
input
(
'X'
)[
0
])
output_node
=
graph
.
_find_node_by_name
(
op_node
.
outputs
,
op_node
.
output
(
'Out'
)[
0
])
self
.
mul_input_id
.
append
(
input_node
.
id
())
self
.
mul_output_id
.
append
(
output_node
.
id
())
self
.
new_output
[
input_name
]
=
op_node
.
output
(
"Out"
)[
0
]
for
op_node
in
ops
:
if
op_node
.
name
()
in
self
.
_conv_ops
:
self
.
_transform_to_conv_mkldnn
(
graph
,
op_node
)
if
op_node
.
name
()
in
self
.
_quantizable_ops
:
if
op_node
.
name
()
in
self
.
_conv_ops
:
self
.
_transform_to_conv_mkldnn
(
graph
,
op_node
)
else
:
self
.
_transform_to_mul_mkldnn
(
graph
,
op_node
)
elif
op_node
.
name
()
in
self
.
quantize_type
:
self
.
_transform_to_quantize_mkldnn
(
graph
,
op_node
)
elif
op_node
.
name
()
in
self
.
dequantize_type
:
...
...
@@ -132,7 +121,7 @@ class TransformForMkldnnPass(object):
# Convert int8 range weights to fp32 range weights
weight
=
self
.
_load_param
(
self
.
_scope
,
weight_name
)
w_fp32
=
np
.
divide
(
np
.
multiply
(
weight
,
127
),
self
.
max_range
[
output_name
])
np
.
multiply
(
weight
,
self
.
s8_max
),
self
.
max_range
[
output_name
])
w_fp32
=
w_fp32
.
reshape
(
weight
.
shape
)
self
.
_restore_var
(
weight_name
,
w_fp32
)
input_var_node
=
graph
.
_find_node_by_name
(
op_node
.
inputs
,
...
...
@@ -140,8 +129,8 @@ class TransformForMkldnnPass(object):
weight_var_node
=
graph
.
_find_node_by_name
(
op_node
.
inputs
,
weight_name
)
# Set fake_dequantize_abs_max's output as new output of conv2d
output_var_node
=
graph
.
_find_node_by_name
(
graph
.
all_var_nodes
(),
self
.
conv_
new_output
[
output_name
])
output_var_node
=
graph
.
_find_node_by_name
(
graph
.
all_var_nodes
(),
self
.
new_output
[
output_name
])
attrs
=
{
name
:
op_node
.
op
().
attr
(
name
)
for
name
in
op_node
.
op
().
attr_names
()
...
...
@@ -157,7 +146,7 @@ class TransformForMkldnnPass(object):
# Based on the QAT's scales to calculate the scales of MKL-DNN INT8 conv2d
scale_in
=
self
.
s8_max
/
self
.
InScale
[
output_name
]
scale_w
=
[]
scale_w
.
append
(
self
.
max_range
[
output_name
]
/
self
.
s8_max
)
scale_w
=
[
self
.
max_range
[
output_name
]
/
self
.
s8_max
]
conv_op_node
.
set_attr
(
"Scale_weights"
,
scale_w
)
conv_op_node
.
set_attr
(
"Scale_in"
,
scale_in
)
...
...
@@ -169,6 +158,50 @@ class TransformForMkldnnPass(object):
graph
.
link_to
(
conv_op_node
,
output_var_node
)
graph
.
safe_remove_nodes
(
op_node
)
def
_transform_to_mul_mkldnn
(
self
,
graph
,
op_node
):
# For MKL-DNN INT8 mul, input Y should be the weights
weight_name
=
op_node
.
input
(
"Y"
)[
0
]
output_name
=
op_node
.
output
(
"Out"
)[
0
]
# Convert int8 range weights to fp32 range weights
weight
=
self
.
_load_param
(
self
.
_scope
,
weight_name
)
w_fp32
=
np
.
divide
(
np
.
multiply
(
weight
,
self
.
s8_max
),
self
.
max_range
[
output_name
])
w_fp32
=
w_fp32
.
reshape
(
weight
.
shape
)
self
.
_restore_var
(
weight_name
,
w_fp32
)
input_var_node
=
graph
.
_find_node_by_name
(
op_node
.
inputs
,
op_node
.
input
(
"X"
)[
0
])
weight_var_node
=
graph
.
_find_node_by_name
(
op_node
.
inputs
,
weight_name
)
# Set fake_dequantize_abs_max's output as new output of mul
output_var_node
=
graph
.
_find_node_by_name
(
graph
.
all_var_nodes
(),
self
.
new_output
[
output_name
])
attrs
=
{
name
:
op_node
.
op
().
attr
(
name
)
for
name
in
op_node
.
op
().
attr_names
()
}
mul_op_node
=
graph
.
create_op_node
(
op_type
=
'mul'
,
attrs
=
attrs
,
inputs
=
{
'X'
:
input_var_node
,
'Y'
:
weight_var_node
},
outputs
=
{
'Out'
:
output_var_node
})
# Based on the QAT's scales to calculate MKL-DNN INT8 mul's scales
scale_in
=
self
.
s8_max
/
self
.
InScale
[
output_name
]
scale_w
=
[]
scale_w
=
[
self
.
max_range
[
output_name
]
/
self
.
s8_max
]
mul_op_node
.
set_attr
(
"scale_y"
,
scale_w
)
mul_op_node
.
set_attr
(
"scale_x"
,
scale_in
)
mul_op_node
.
set_attr
(
"scale_out"
,
1.0
)
mul_op_node
.
set_attr
(
"use_mkldnn"
,
1
)
mul_op_node
.
set_attr
(
"force_fp32_output"
,
1
)
graph
.
link_to
(
input_var_node
,
mul_op_node
)
graph
.
link_to
(
weight_var_node
,
mul_op_node
)
graph
.
link_to
(
mul_op_node
,
output_var_node
)
graph
.
safe_remove_nodes
(
op_node
)
def
_transform_to_quantize_mkldnn
(
self
,
graph
,
op_node
):
"""
Transform fake_quantize_xx op to quantize mkldnn op in the graph.
...
...
@@ -177,32 +210,26 @@ class TransformForMkldnnPass(object):
op_node
.
input
(
"X"
)[
0
])
output_var_node
=
graph
.
_find_node_by_name
(
op_node
.
outputs
,
op_node
.
output
(
"Out"
)[
0
])
if
output_var_node
.
id
()
in
self
.
mul_input_id
:
return
else
:
scale_in
=
self
.
s8_max
/
self
.
_load_param
(
self
.
_scope
,
op_node
.
input
(
"InScale"
)[
0
])[
0
]
quant_op_node
=
graph
.
create_op_node
(
op_type
=
'quantize'
,
attrs
=
{
'data_format'
:
'MKLDNNLAYOUT'
,
'use_mkldnn'
:
1
,
'Scale'
:
scale_in
,
'is_negative_input'
:
1
},
inputs
=
{
'Input'
:
input_var_node
},
outputs
=
{
'Output'
:
output_var_node
})
graph
.
link_to
(
input_var_node
,
quant_op_node
)
graph
.
link_to
(
quant_op_node
,
output_var_node
)
graph
.
safe_remove_nodes
(
op_node
)
scale_in
=
self
.
s8_max
/
self
.
_load_param
(
self
.
_scope
,
op_node
.
input
(
"InScale"
)[
0
])[
0
]
quant_op_node
=
graph
.
create_op_node
(
op_type
=
'quantize'
,
attrs
=
{
'data_format'
:
'MKLDNNLAYOUT'
,
'use_mkldnn'
:
1
,
'Scale'
:
scale_in
,
'is_negative_input'
:
1
},
inputs
=
{
'Input'
:
input_var_node
},
outputs
=
{
'Output'
:
output_var_node
})
graph
.
link_to
(
input_var_node
,
quant_op_node
)
graph
.
link_to
(
quant_op_node
,
output_var_node
)
graph
.
safe_remove_nodes
(
op_node
)
def
_remove_fake_dequantize_op
(
self
,
graph
,
op_node
):
input_var_node
=
graph
.
_find_node_by_name
(
op_node
.
inputs
,
op_node
.
input
(
"X"
)[
0
])
if
input_var_node
.
id
()
in
self
.
mul_output_id
:
return
else
:
graph
.
safe_remove_nodes
(
op_node
)
graph
.
safe_remove_nodes
(
op_node
)
def
_load_param
(
self
,
scope
,
param_name
):
return
np
.
array
(
scope
.
find_var
(
param_name
).
get_tensor
())
...
...
python/paddle/fluid/contrib/slim/tests/test_quantization_mkldnn_pass.py
浏览文件 @
a25be53c
...
...
@@ -55,9 +55,7 @@ class TestMKLDNNTransformBasedFreezePass(unittest.TestCase):
self
.
quantizable_op_and_inputs
=
{
'conv2d'
:
[
'Input'
,
'Filter'
],
'depthwise_conv2d'
:
[
'Input'
,
'Filter'
],
# Mul int8 op is under internal test
# TODO Update this when mul op is merged
#'mul': ['X', 'Y']
'mul'
:
[
'X'
,
'Y'
]
}
def
check_program
(
self
,
program
):
...
...
@@ -162,11 +160,15 @@ class TestMKLDNNTransformBasedFreezePass(unittest.TestCase):
activation_quant_type
+
'_'
+
weight_quant_type
,
marked_nodes
)
mkldnn_program
=
test_graph
.
to_program
()
w_mkldnn
=
np
.
array
(
scope
.
find_var
(
'conv2d_1.w_0'
).
get_tensor
())
# Check the transformation weights of conv2d and mul
conv_w_mkldnn
=
np
.
array
(
scope
.
find_var
(
'conv2d_1.w_0'
).
get_tensor
())
mul_w_mkldnn
=
np
.
array
(
scope
.
find_var
(
'fc_0.w_0'
).
get_tensor
())
# Check if weights are still integer
self
.
assertFalse
(
self
.
isinteger
(
np
.
sum
(
w_mkldnn
)))
self
.
assertFalse
(
self
.
isinteger
(
np
.
sum
(
conv_w_mkldnn
)))
self
.
assertFalse
(
self
.
isinteger
(
np
.
sum
(
mul_w_mkldnn
)))
# Check if the conv2d output
is righ
tly linked to fake_dequantize's
# Check if the conv2d output
and mul output are correc
tly linked to fake_dequantize's
# output
self
.
check_program
(
mkldnn_program
)
if
not
for_ci
:
...
...
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