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84a55138
编写于
3月 24, 2021
作者:
C
cc
提交者:
GitHub
3月 24, 2021
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差异文件
[dygraph qat] Refine saving output scale to infer program (#31784)
* Refine saving output scale to infer program
上级
68497e7b
变更
3
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Showing
3 changed file
with
166 addition
and
120 deletion
+166
-120
python/paddle/fluid/contrib/slim/quantization/imperative/qat.py
.../paddle/fluid/contrib/slim/quantization/imperative/qat.py
+123
-106
python/paddle/fluid/contrib/slim/quantization/imperative/utils.py
...addle/fluid/contrib/slim/quantization/imperative/utils.py
+28
-6
python/paddle/fluid/contrib/slim/tests/test_imperative_out_scale.py
...dle/fluid/contrib/slim/tests/test_imperative_out_scale.py
+15
-8
未找到文件。
python/paddle/fluid/contrib/slim/quantization/imperative/qat.py
浏览文件 @
84a55138
...
...
@@ -251,8 +251,8 @@ class ImperativeQuantizeInputs(object):
super
(
ImperativeQuantizeInputs
,
self
).
__init__
()
self
.
_quantizable_layer_type
=
tuple
(
utils
.
supported_quan
t_layers_map
[
layer
]
if
layer
in
utils
.
supported_quan
t_layers_map
else
layer
utils
.
quant_inpu
t_layers_map
[
layer
]
if
layer
in
utils
.
quant_inpu
t_layers_map
else
layer
for
layer
in
quantizable_layer_type
)
for
layer
in
self
.
_quantizable_layer_type
:
assert
not
isinstance
(
layer
,
str
),
\
...
...
@@ -324,12 +324,11 @@ class ImperativeQuantizeInputs(object):
target
=
name
[
last_idx
:
idx
]
quant_layer
=
self
.
_get_quantized_layer
(
layer
)
setattr
(
quant_layer
,
"layer_name"
,
layer
.
full_name
())
setattr
(
obj
,
target
,
quant_layer
)
def
_get_quantized_layer
(
self
,
layer
):
quant_layer_name
=
None
for
key
,
value
in
utils
.
supported_quan
t_layers_map
.
items
():
for
key
,
value
in
utils
.
quant_inpu
t_layers_map
.
items
():
if
isinstance
(
layer
,
value
):
quant_layer_name
=
'Quantized'
+
key
break
...
...
@@ -372,6 +371,9 @@ class ImperativeCalcOutputScale(object):
"""
assert
isinstance
(
model
,
dygraph
.
Layer
),
\
"The model must be the instance of dygraph.Layer."
# Calculate the target ops's output scale, and don't consider
# the skip_quant attr
for
_
,
layer
in
model
.
named_sublayers
():
if
self
.
_is_target_layer
(
layer
):
self
.
_init_scale_params
(
layer
)
...
...
@@ -411,24 +413,21 @@ class ImperativeCalcOutputScale(object):
assert
isinstance
(
layer
,
dygraph
.
Layer
),
\
"The model must be the instance of dygraph.Layer."
# remove handles and collect output scales
self
.
_gather_output_scale
(
layer
)
with
dygraph
.
guard
():
layer
.
eval
()
for
handle
in
self
.
_register_hook_handle_list
:
handle
.
remove
()
for
_
,
sub_layer
in
layer
.
named_sublayers
():
if
self
.
_is_target_layer
(
sub_layer
):
if
hasattr
(
sub_layer
,
"layer_name"
):
layer_name
=
sub_layer
.
layer_name
else
:
layer_name
=
sub_layer
.
full_name
()
if
hasattr
(
sub_layer
,
"_quant_out_scale"
):
self
.
_out_scale_dict
[
layer_name
]
=
float
(
sub_layer
.
_quant_out_scale
)
# save the quantized model that doesn't have output scales
paddle
.
jit
.
save
(
layer
=
layer
,
path
=
path
,
input_spec
=
input_spec
,
**
config
)
if
len
(
self
.
_out_scale_dict
)
==
0
:
warnings
.
warn
(
"Warning: No Layer of the model while to be "
\
"saved contains the out_threshold attribute, so the "
\
"generated inference model would not contain the "
\
"out_threshold."
)
return
# load static model
is_dynamic_mode
=
False
if
paddle
.
in_dynamic_mode
():
...
...
@@ -443,79 +442,26 @@ class ImperativeCalcOutputScale(object):
basename
=
os
.
path
.
basename
(
path
)
model_filename
=
basename
+
INFER_MODEL_SUFFIX
params_filename
=
basename
+
INFER_PARAMS_SUFFIX
[
inference_program
,
feed_target_names
,
fetch_targets
]
=
(
[
infer_program
,
feed_target_names
,
fetch_targets
]
=
(
load_inference_model
(
dirname
=
dirname
,
executor
=
exe
,
model_filename
=
model_filename
,
params_filename
=
params_filename
))
# TODO(jc): analyse whether the dygraph model has
# several blocks before applying qat
assert
infer_program
.
num_blocks
==
1
,
\
"Quantization aware training (QAT) requires the program "
\
"only has a block for now. When the model has if-else or "
\
"while, the program will have several blocks."
# set output scales to the static model
check_behind_op
=
False
op_count
=
0
ops_list
=
[
key
for
key
,
_
in
self
.
_out_scale_dict
.
items
()]
if
len
(
ops_list
)
==
0
:
warnings
.
warn
(
"Warning: No Layer of the model while to be saved contains "
"the out_threshold attribute, so the generated inference "
"model would not contain the out_threshold."
)
else
:
# Because the Layer in dygraph may correspond to multiple ops
# in static program after being saved. To ensure correctness,
# the outscale collected for output of dygraph Layer can only
# be set to the last op in the corresponding ops in static program.
#
# We can judge the execution order of the ops which corresponding
# to dygraph Layer by check_behind_op
forward_op
=
None
for
block
in
inference_program
.
blocks
:
for
op
in
block
.
ops
:
if
op
.
type
in
utils
.
op_real_in_out_name
:
if
op_count
>
len
(
ops_list
):
warnings
.
warn
(
"The number of Layer which has "
"out_threshold attribute should be bigger than "
"the op in inference model"
)
break
if
check_behind_op
:
check_behind_op
=
False
if
op
.
type
==
"elementwise_add"
:
if
self
.
_is_op_matched
(
ops_list
[
op_count
],
op
,
block
):
op
.
_set_attr
(
"out_threshold"
,
self
.
_out_scale_dict
[
ops_list
[
op_count
]])
op_count
+=
1
forward_op
=
None
continue
else
:
if
forward_op
is
None
:
raise
ValueError
(
"forward_op should not be None"
)
if
self
.
_is_op_matched
(
ops_list
[
op_count
],
forward_op
,
block
):
forward_op
.
_set_attr
(
"out_threshold"
,
self
.
_out_scale_dict
[
ops_list
[
op_count
]])
op_count
+=
1
forward_op
=
None
if
op
.
type
in
[
"conv2d"
,
"depthwise_conv2d"
,
"matmul"
]:
check_behind_op
=
True
forward_op
=
op
continue
if
op_count
>=
len
(
ops_list
):
warnings
.
warn
(
"The number of Layer which has out_threshold attribute should be bigger than the op in inference model"
)
break
if
self
.
_is_op_matched
(
ops_list
[
op_count
],
op
,
block
):
op
.
_set_attr
(
"out_threshold"
,
self
.
_out_scale_dict
[
ops_list
[
op_count
]])
op_count
+=
1
self
.
_save_output_scale
(
infer_program
)
self
.
_set_skip_quant_attr
(
inference_program
)
# process skip quant
self
.
_set_skip_quant_attr
(
infer_program
)
# save the final quantized model that has output scales
save_inference_model
(
...
...
@@ -523,16 +469,75 @@ class ImperativeCalcOutputScale(object):
feeded_var_names
=
feed_target_names
,
target_vars
=
fetch_targets
,
executor
=
exe
,
main_program
=
infer
ence
_program
.
clone
(),
main_program
=
infer_program
.
clone
(),
model_filename
=
model_filename
,
params_filename
=
params_filename
)
if
is_dynamic_mode
:
paddle
.
disable_static
()
def
_gather_output_scale
(
self
,
layer
):
"""
Gather all output scales to self._out_scale_dict
"""
with
dygraph
.
guard
():
layer
.
eval
()
for
_
,
sub_layer
in
layer
.
named_sublayers
():
if
self
.
_is_target_layer
(
sub_layer
):
layer_name
=
sub_layer
.
full_name
()
if
hasattr
(
sub_layer
,
"_quant_out_scale"
):
self
.
_out_scale_dict
[
layer_name
]
=
float
(
sub_layer
.
_quant_out_scale
)
def
_save_output_scale
(
self
,
infer_program
):
"""
Save all output scales to the corresponding ops in static
inference program.
Because the Layer in dygraph may correspond to multiple ops
in static program after being saved. To ensure correctness,
the outscale collected for output of dygraph Layer can only
be set to the last op in the corresponding ops in static program.
"""
assert
infer_program
.
num_blocks
==
1
,
\
"The inference program should only have a block."
global_block
=
infer_program
.
global_block
()
target_ops
=
global_block
.
ops
scale_idx
=
0
op_idx
=
0
attr_name
=
"out_threshold"
for
scale_name
,
scale_value
in
self
.
_out_scale_dict
.
items
():
while
True
:
if
op_idx
>=
len
(
target_ops
):
break
op
=
target_ops
[
op_idx
]
if
not
self
.
_is_scale_op_matched
(
scale_name
,
op
,
global_block
):
op_idx
+=
1
else
:
if
op
.
type
in
utils
.
weight_op_types
\
and
op_idx
+
1
<
len
(
target_ops
)
\
and
target_ops
[
op_idx
+
1
].
type
==
"elementwise_add"
:
target_ops
[
op_idx
+
1
].
_set_attr
(
attr_name
,
scale_value
)
op_idx
+=
2
else
:
op
.
_set_attr
(
attr_name
,
scale_value
)
op_idx
+=
1
scale_idx
+=
1
break
if
scale_idx
!=
len
(
self
.
_out_scale_dict
):
_logger
.
warning
(
"Warning: the model have %s output scales, "
\
"but it only saves %s output scales."
\
%
(
len
(
self
.
_out_scale_dict
),
scale_idx
))
def
_is_target_layer
(
self
,
layer
):
return
isinstance
(
layer
,
utils
.
out_scale_layers_list
)
\
or
'quantized_'
in
layer
.
full_name
()
return
isinstance
(
layer
,
tuple
(
utils
.
quant_output_layers_map
.
values
()))
\
or
(
'quantized_'
in
layer
.
full_name
()
and
\
'quantized_noweight'
not
in
layer
.
full_name
())
def
_init_scale_params
(
self
,
layer
,
name
=
None
):
"""
...
...
@@ -570,27 +575,39 @@ class ImperativeCalcOutputScale(object):
layer
.
_quant_out_accum
=
_create_param
(
layer
,
name
,
"accum"
,
dtype
)
layer
.
_quant_out_accum
.
stop_gradient
=
True
# Judge whether the op in program matches the Layer in dynamic model
def
_is_op_matched
(
self
,
layer_name
,
op
,
block
):
def
_is_scale_op_matched
(
self
,
scale_name
,
op
,
block
):
"""
Based on the op name and attrs to judge whether the op in
program matches the scale_name. We must know the corresponding
name between dgraph and static model.
"""
fp_type
=
[
core
.
VarDesc
.
VarType
.
FP64
,
core
.
VarDesc
.
VarType
.
FP32
]
if
op
.
type
in
quantization_pass
.
_op_real_in_out_name
.
keys
():
output_var_names
=
quantization_pass
.
_get_op_output_var_names
(
op
)
for
output_var_name
in
output_var_names
:
output_var_tensor
=
block
.
var
(
output_var_name
)
if
output_var_tensor
.
dtype
not
in
[
core
.
VarDesc
.
VarType
.
FP64
,
core
.
VarDesc
.
VarType
.
FP32
]:
if
output_var_tensor
.
dtype
not
in
fp_type
:
return
False
# Because the naming styles of static and dynamic graph are different,
# in order to avoid mistakes, we unify the name here.
op_type
=
output_var_names
[
0
].
split
(
"."
)[
0
]
op_type
=
op_type
.
rsplit
(
"_"
,
1
)[
0
]
if
op_type
==
'depthwise_conv2d'
:
op_type
=
'conv2d'
if
'prelu'
in
op_type
:
op_type
=
op_type
.
replace
(
'prelu'
,
'p_re_lu'
)
if
'relu'
in
op_type
:
op_type
=
op_type
.
replace
(
'relu'
,
're_lu'
)
return
op_type
in
layer_name
# corresponding_map: [name, op_types, function]
# Note that, the items have priority in corresponding_map
corresponding_map
=
[
[
'conv2d_tranpose'
,
[
'conv2d_transpose'
,
\
'depthwise_conv2d_transpose'
],
None
],
[
'conv2d'
,
[
'conv2d'
,
'depthwise_conv2d'
],
None
],
[
'linear'
,
[
'matmul'
],
None
],
[
're_lu6'
,
[
'relu6'
],
None
],
[
'p_re_lu'
,
[
'prelu'
],
None
],
[
'leaky_re_lu'
,
[
'leaky_relu'
],
None
],
[
're_lu'
,
[
'relu'
],
None
],
]
for
item
in
corresponding_map
:
if
item
[
0
]
in
scale_name
:
return
(
op
.
type
in
item
[
1
])
and
\
(
len
(
item
)
==
2
or
item
[
2
]
is
None
or
item
[
2
](
op
))
return
op
.
type
in
scale_name
def
_set_skip_quant_attr
(
self
,
program
):
block
=
program
.
global_block
()
...
...
python/paddle/fluid/contrib/slim/quantization/imperative/utils.py
浏览文件 @
84a55138
...
...
@@ -30,7 +30,7 @@ op_real_in_out_name = {
"swish"
:
[[
"X"
],
[
"Out"
]],
}
supported_quan
t_layers_map
=
{
quant_inpu
t_layers_map
=
{
'Conv2D'
:
paddle
.
nn
.
Conv2D
,
'Linear'
:
paddle
.
nn
.
Linear
,
'AdaptiveAvgPool2D'
:
paddle
.
nn
.
AdaptiveAvgPool2D
,
...
...
@@ -58,8 +58,30 @@ fake_quantize_dequantize_types = [
"fake_quantize_dequantize_moving_average_abs_max"
]
out_scale_layers_list
=
(
paddle
.
nn
.
Conv2D
,
paddle
.
nn
.
Linear
,
paddle
.
nn
.
MaxPool2D
,
paddle
.
nn
.
BatchNorm
,
paddle
.
nn
.
BatchNorm2D
,
paddle
.
nn
.
SyncBatchNorm
,
paddle
.
nn
.
LeakyReLU
,
paddle
.
nn
.
PReLU
,
paddle
.
nn
.
ReLU
,
paddle
.
nn
.
ReLU6
,
paddle
.
nn
.
Sigmoid
,
paddle
.
nn
.
Softmax
,
paddle
.
nn
.
Tanh
,
paddle
.
nn
.
Swish
)
quant_output_layers_map
=
{
'Conv2D'
:
paddle
.
nn
.
Conv2D
,
'Conv2DTranspose'
:
paddle
.
nn
.
Conv2DTranspose
,
'Linear'
:
paddle
.
nn
.
Linear
,
'AdaptiveAvgPool2D'
:
paddle
.
nn
.
AdaptiveAvgPool2D
,
'AdaptiveMaxPool2D'
:
paddle
.
nn
.
AdaptiveMaxPool2D
,
'AvgPool2D'
:
paddle
.
nn
.
AvgPool2D
,
'MaxPool2D'
:
paddle
.
nn
.
MaxPool2D
,
'BatchNorm'
:
paddle
.
nn
.
BatchNorm
,
'BatchNorm2D'
:
paddle
.
nn
.
BatchNorm2D
,
'SyncBatchNorm'
:
paddle
.
nn
.
SyncBatchNorm
,
'ELU'
:
paddle
.
nn
.
ELU
,
'GELU'
:
paddle
.
nn
.
GELU
,
'LeakyReLU'
:
paddle
.
nn
.
LeakyReLU
,
'PReLU'
:
paddle
.
nn
.
PReLU
,
'ReLU'
:
paddle
.
nn
.
ReLU
,
'ReLU6'
:
paddle
.
nn
.
ReLU6
,
'Sigmoid'
:
paddle
.
nn
.
Sigmoid
,
'Softmax'
:
paddle
.
nn
.
Softmax
,
'Tanh'
:
paddle
.
nn
.
Tanh
,
'Swish'
:
paddle
.
nn
.
Swish
,
}
weight_op_types
=
[
"conv2d"
,
"depthwise_conv2d"
,
"matmul"
,
"conv2d_transpose"
,
"depthwise_conv2d_transpose"
]
python/paddle/fluid/contrib/slim/tests/test_imperative_out_scale.py
浏览文件 @
84a55138
...
...
@@ -33,7 +33,6 @@ from paddle.fluid.dygraph.container import Sequential
from
paddle.fluid.dygraph.io
import
INFER_MODEL_SUFFIX
,
INFER_PARAMS_SUFFIX
from
paddle.nn.layer
import
ReLU
,
LeakyReLU
,
Sigmoid
,
Softmax
,
PReLU
from
paddle.nn
import
Linear
,
Conv2D
,
Softmax
,
BatchNorm2D
,
MaxPool2D
from
paddle.fluid.dygraph.nn
import
Pool2D
from
paddle.fluid.log_helper
import
get_logger
from
paddle.fluid.dygraph
import
nn
...
...
@@ -131,8 +130,8 @@ class ImperativeLenet(fluid.dygraph.Layer):
bias_attr
=
False
),
BatchNorm2D
(
6
),
ReLU
(),
Pool2D
(
pool_size
=
2
,
pool_type
=
'max'
,
pool_
stride
=
2
),
Max
Pool2D
(
kernel_size
=
2
,
stride
=
2
),
Conv2D
(
in_channels
=
6
,
out_channels
=
16
,
...
...
@@ -357,7 +356,6 @@ class TestImperativeOutSclae(unittest.TestCase):
"diff({}) at {}, dynamic loss = {}, static loss = {}"
.
format
(
diff
,
i
,
loss_d
,
loss_s
))
break
self
.
assertTrue
(
np
.
allclose
(
np
.
array
(
dynamic_loss_rec
),
...
...
@@ -398,10 +396,15 @@ class TestImperativeOutSclae(unittest.TestCase):
if
dynamic_ops
[
i
].
has_attr
(
"out_threshold"
):
op_count
+=
1
self
.
assertTrue
(
dynamic_ops
[
i
].
type
==
static_ops
[
i
].
type
)
if
dynamic_ops
[
i
].
attr
(
"out_threshold"
)
!=
static_ops
[
i
].
attr
(
"out_threshold"
):
_logger
.
info
(
dynamic_ops
[
i
].
attr
(
"out_threshold"
))
_logger
.
info
(
static_ops
[
i
].
attr
(
"out_threshold"
))
self
.
assertTrue
(
dynamic_ops
[
i
].
attr
(
"out_threshold"
)
==
static_ops
[
i
].
attr
(
"out_threshold"
))
self
.
assertTrue
(
op_count
==
13
)
_logger
.
info
(
"op_cout: {}"
.
format
(
op_count
))
self
.
assertTrue
(
op_count
==
14
)
class
TestSaveQuanztizedModelFromCheckPoint
(
unittest
.
TestCase
):
...
...
@@ -470,7 +473,9 @@ class TestSaveQuanztizedModelFromCheckPoint(unittest.TestCase):
self
.
assertTrue
(
dynamic_ops
[
i
].
type
==
static_ops
[
i
].
type
)
self
.
assertTrue
(
dynamic_ops
[
i
].
attr
(
"out_threshold"
)
==
static_ops
[
i
].
attr
(
"out_threshold"
))
self
.
assertTrue
(
op_count
==
13
)
_logger
.
info
(
"op_cout: {}"
.
format
(
op_count
))
self
.
assertTrue
(
op_count
==
14
)
class
TestSaveQuantizedModel_Warning
(
unittest
.
TestCase
):
...
...
@@ -490,8 +495,10 @@ class TestSaveQuantizedModel_Warning(unittest.TestCase):
shape
=
[
None
,
1
,
28
,
28
],
dtype
=
'float32'
)
])
warning_message
=
"Warning: No Layer of the model while to be saved contains the out_threshold attribute, "
\
"so the generated inference model would not contain the out_threshold."
warning_message
=
"Warning: No Layer of the model while to be "
\
"saved contains the out_threshold attribute, so the "
\
"generated inference model would not contain the "
\
"out_threshold."
num
=
get_vaild_warning_num
(
warning_message
,
w
)
assert
num
==
1
...
...
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