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a038747c
编写于
9月 26, 2022
作者:
G
Guanghua Yu
提交者:
GitHub
9月 26, 2022
浏览文件
操作
浏览文件
下载
电子邮件补丁
差异文件
update quantization new format (#46305)
上级
7fb20b46
变更
5
隐藏空白更改
内联
并排
Showing
5 changed file
with
202 addition
and
86 deletion
+202
-86
python/paddle/fluid/contrib/slim/quantization/imperative/qat.py
.../paddle/fluid/contrib/slim/quantization/imperative/qat.py
+22
-16
python/paddle/fluid/contrib/slim/quantization/post_training_quantization.py
...d/contrib/slim/quantization/post_training_quantization.py
+6
-9
python/paddle/fluid/contrib/slim/quantization/quantization_pass.py
...ddle/fluid/contrib/slim/quantization/quantization_pass.py
+166
-12
python/paddle/fluid/contrib/slim/quantization/utils.py
python/paddle/fluid/contrib/slim/quantization/utils.py
+8
-1
python/paddle/fluid/tests/unittests/mkldnn/test_onnx_format_quantization_mobilenetv1.py
...tests/mkldnn/test_onnx_format_quantization_mobilenetv1.py
+0
-48
未找到文件。
python/paddle/fluid/contrib/slim/quantization/imperative/qat.py
浏览文件 @
a038747c
...
@@ -223,7 +223,8 @@ class ImperativeQuantAware(object):
...
@@ -223,7 +223,8 @@ class ImperativeQuantAware(object):
self
.
_quantize_inputs
=
ImperativeQuantizeInputs
(
**
kwargs
)
self
.
_quantize_inputs
=
ImperativeQuantizeInputs
(
**
kwargs
)
self
.
_quantize_outputs
=
ImperativeQuantizeOutputs
(
moving_rate
)
self
.
_quantize_outputs
=
ImperativeQuantizeOutputs
(
moving_rate
,
activation_bits
)
def
quantize
(
self
,
model
):
def
quantize
(
self
,
model
):
"""
"""
...
@@ -412,16 +413,18 @@ class ImperativeQuantizeOutputs(object):
...
@@ -412,16 +413,18 @@ class ImperativeQuantizeOutputs(object):
Calculate the output scales for target layers.
Calculate the output scales for target layers.
"""
"""
def
__init__
(
self
,
moving_rate
=
0.9
):
def
__init__
(
self
,
moving_rate
=
0.9
,
activation_bits
=
8
):
"""
"""
The constructor for ImperativeQuantizeOutputs.
The constructor for ImperativeQuantizeOutputs.
Args:
Args:
moving_rate(float): The decay coefficient of moving average.
moving_rate(float): The decay coefficient of moving average.
The default value is 0.9.
The default value is 0.9.
activation_bits(int, optional): quantization bit number for activation. Default is 8.
"""
"""
super
(
ImperativeQuantizeOutputs
,
self
).
__init__
()
super
(
ImperativeQuantizeOutputs
,
self
).
__init__
()
self
.
_moving_rate
=
moving_rate
self
.
_moving_rate
=
moving_rate
self
.
_activation_bits
=
activation_bits
def
apply
(
self
,
model
):
def
apply
(
self
,
model
):
"""
"""
...
@@ -478,7 +481,7 @@ class ImperativeQuantizeOutputs(object):
...
@@ -478,7 +481,7 @@ class ImperativeQuantizeOutputs(object):
the saved model. Default None.
the saved model. Default None.
onnx_format (bool, optional): Whether to export the quantized model
onnx_format (bool, optional): Whether to export the quantized model
with format of ONNX. Default is False.
with format of ONNX. Default is False.
**config
s
(dict, optional): Other save configuration options for
**config (dict, optional): Other save configuration options for
compatibility. We do not recommend using these configurations,
compatibility. We do not recommend using these configurations,
they may be removed in the future. If not necessary, DO NOT use
they may be removed in the future. If not necessary, DO NOT use
them. Default None.
them. Default None.
...
@@ -518,27 +521,30 @@ class ImperativeQuantizeOutputs(object):
...
@@ -518,27 +521,30 @@ class ImperativeQuantizeOutputs(object):
model_filename
=
model_filename
,
model_filename
=
model_filename
,
params_filename
=
params_filename
))
params_filename
=
params_filename
))
self
.
_gather_scales
(
infer_program
,
scope
,
fetch_targets
)
if
not
onnx_format
:
self
.
_gather_scales
(
infer_program
,
scope
,
fetch_targets
)
# Remove `moving_average_abs_max_scale` node in sub graphs.
# Remove `moving_average_abs_max_scale` node in sub graphs.
graph
=
IrGraph
(
core
.
Graph
(
infer_program
.
desc
),
for_test
=
False
)
graph
=
IrGraph
(
core
.
Graph
(
infer_program
.
desc
),
for_test
=
False
)
for
sub_graph
in
graph
.
all_sub_graphs
():
for
sub_graph
in
graph
.
all_sub_graphs
():
for
_op
in
sub_graph
.
all_op_nodes
():
for
_op
in
sub_graph
.
all_op_nodes
():
if
_op
.
name
()
==
"moving_average_abs_max_scale"
:
if
_op
.
name
()
==
"moving_average_abs_max_scale"
:
sub_graph
.
safe_remove_nodes
(
_op
)
sub_graph
.
safe_remove_nodes
(
_op
)
sub_graph
.
resolve_hazard
()
sub_graph
.
resolve_hazard
()
infer_program
=
graph
.
to_program
()
infer_program
=
graph
.
to_program
()
self
.
_set_skip_quant_attr
(
infer_program
)
self
.
_set_skip_quant_attr
(
infer_program
)
clip_extra
=
False
clip_extra
=
False
if
onnx_format
:
else
:
graph
=
IrGraph
(
core
.
Graph
(
infer_program
.
desc
),
for_test
=
False
)
graph
=
IrGraph
(
core
.
Graph
(
infer_program
.
desc
),
for_test
=
False
)
transform_pass
=
ReplaceFakeQuantDequantPass
(
scope
,
place
)
transform_pass
=
ReplaceFakeQuantDequantPass
(
scope
,
place
,
quant_bits
=
self
.
_activation_bits
)
transform_pass
.
apply
(
graph
)
transform_pass
.
apply
(
graph
)
quant_weight_pass
=
QuantWeightPass
(
scope
,
place
)
quant_weight_pass
=
QuantWeightPass
(
scope
,
place
)
quant_weight_pass
.
apply
(
graph
)
quant_weight_pass
.
apply
(
graph
)
infer_program
=
graph
.
to_program
()
infer_program
=
graph
.
to_program
()
clip_extra
=
True
clip_extra
=
True
...
...
python/paddle/fluid/contrib/slim/quantization/post_training_quantization.py
浏览文件 @
a038747c
...
@@ -344,7 +344,7 @@ class PostTrainingQuantization(object):
...
@@ -344,7 +344,7 @@ class PostTrainingQuantization(object):
self
.
_fetch_list
=
None
self
.
_fetch_list
=
None
self
.
_data_loader
=
data_loader
self
.
_data_loader
=
data_loader
self
.
_out_scale_op_list
=
utils
.
_out_scale_op_list
self
.
_out_scale_op_list
=
utils
.
QUANT_SUPPORTED_OP_TYPE_LIST
self
.
_quantized_weight_var_name
=
set
()
self
.
_quantized_weight_var_name
=
set
()
self
.
_quantized_act_var_name
=
set
()
self
.
_quantized_act_var_name
=
set
()
self
.
_weight_op_pairs
=
{}
self
.
_weight_op_pairs
=
{}
...
@@ -843,9 +843,6 @@ class PostTrainingQuantization(object):
...
@@ -843,9 +843,6 @@ class PostTrainingQuantization(object):
hist
,
_
=
np
.
histogram
(
var_tensor_abs
,
bins
=
bins
)
hist
,
_
=
np
.
histogram
(
var_tensor_abs
,
bins
=
bins
)
self
.
_sampling_act_histogram
[
var_name
][
0
]
+=
hist
self
.
_sampling_act_histogram
[
var_name
][
0
]
+=
hist
def
l2_loss
(
self
,
gt
,
pred
):
return
((
gt
-
pred
)
**
2
).
mean
()
def
_sample_ptf
(
self
):
def
_sample_ptf
(
self
):
"""
"""
The following code are modified from:
The following code are modified from:
...
@@ -885,10 +882,10 @@ class PostTrainingQuantization(object):
...
@@ -885,10 +882,10 @@ class PostTrainingQuantization(object):
q_max
)
*
scale4
q_max
)
*
scale4
quant_dequant_var_scale8
=
np
.
clip
(
np
.
round
(
var_tensor
/
scale8
),
0
,
quant_dequant_var_scale8
=
np
.
clip
(
np
.
round
(
var_tensor
/
scale8
),
0
,
q_max
)
*
scale8
q_max
)
*
scale8
score1
=
self
.
l2_loss
(
var_tensor
,
quant_dequant_var_scale1
)
score1
=
utils
.
l2_loss
(
var_tensor
,
quant_dequant_var_scale1
)
score2
=
self
.
l2_loss
(
var_tensor
,
quant_dequant_var_scale2
)
score2
=
utils
.
l2_loss
(
var_tensor
,
quant_dequant_var_scale2
)
score4
=
self
.
l2_loss
(
var_tensor
,
quant_dequant_var_scale4
)
score4
=
utils
.
l2_loss
(
var_tensor
,
quant_dequant_var_scale4
)
score8
=
self
.
l2_loss
(
var_tensor
,
quant_dequant_var_scale8
)
score8
=
utils
.
l2_loss
(
var_tensor
,
quant_dequant_var_scale8
)
score
=
[
score1
,
score2
,
score4
,
score8
]
score
=
[
score1
,
score2
,
score4
,
score8
]
mask
=
2
**
score
.
index
(
min
(
score
))
mask
=
2
**
score
.
index
(
min
(
score
))
scale
=
scale1
*
mask
scale
=
scale1
*
mask
...
@@ -1035,7 +1032,7 @@ class PostTrainingQuantization(object):
...
@@ -1035,7 +1032,7 @@ class PostTrainingQuantization(object):
scope
=
self
.
_scope
,
scope
=
self
.
_scope
,
place
=
self
.
_place
,
place
=
self
.
_place
,
quantizable_op_type
=
minor_quantizable_op_types
,
quantizable_op_type
=
minor_quantizable_op_types
,
is_full_quantized
=
self
.
_is_full_quantiz
e
)
is_full_quantized
=
Tru
e
)
for
sub_graph
in
graph
.
all_sub_graphs
():
for
sub_graph
in
graph
.
all_sub_graphs
():
sub_graph
.
_for_test
=
True
sub_graph
.
_for_test
=
True
...
...
python/paddle/fluid/contrib/slim/quantization/quantization_pass.py
浏览文件 @
a038747c
...
@@ -44,6 +44,7 @@ __all__ = [
...
@@ -44,6 +44,7 @@ __all__ = [
'AddQuantDequantPassV2'
,
'AddQuantDequantPassV2'
,
'ReplaceFakeQuantDequantPass'
,
'ReplaceFakeQuantDequantPass'
,
'QuantWeightPass'
,
'QuantWeightPass'
,
'AddQuantDequantForInferencePass'
,
]
]
_fake_quant_op_list
=
[
_fake_quant_op_list
=
[
...
@@ -1437,7 +1438,7 @@ class OutScaleForTrainingPass(object):
...
@@ -1437,7 +1438,7 @@ class OutScaleForTrainingPass(object):
self
.
_place
=
_get_paddle_place
(
place
)
self
.
_place
=
_get_paddle_place
(
place
)
self
.
_moving_rate
=
moving_rate
self
.
_moving_rate
=
moving_rate
self
.
_is_test
=
is_test
self
.
_is_test
=
is_test
self
.
_teller_set
=
utils
.
_out_scale_op_list
self
.
_teller_set
=
utils
.
QUANT_SUPPORTED_OP_TYPE_LIST
self
.
_scale_dict
=
scale_dict
self
.
_scale_dict
=
scale_dict
def
apply
(
self
,
graph
):
def
apply
(
self
,
graph
):
...
@@ -1567,7 +1568,7 @@ class OutScaleForInferencePass(object):
...
@@ -1567,7 +1568,7 @@ class OutScaleForInferencePass(object):
scope(fluid.Scope): The scope is used to initialize these new parameters.
scope(fluid.Scope): The scope is used to initialize these new parameters.
"""
"""
self
.
_scope
=
scope
self
.
_scope
=
scope
self
.
_teller_set
=
utils
.
_out_scale_op_list
self
.
_teller_set
=
utils
.
QUANT_SUPPORTED_OP_TYPE_LIST
def
apply
(
self
,
graph
):
def
apply
(
self
,
graph
):
"""
"""
...
@@ -1852,6 +1853,7 @@ class InsertQuantizeLinear(object):
...
@@ -1852,6 +1853,7 @@ class InsertQuantizeLinear(object):
channel_wise(bool, optional): Whether quantization with per channel or not. Default is False.
channel_wise(bool, optional): Whether quantization with per channel or not. Default is False.
moving_rate(float): the rate for 'moving average' method.
moving_rate(float): the rate for 'moving average' method.
is_test(bool, optional): Whether quantization with training or not. Default is True.
is_test(bool, optional): Whether quantization with training or not. Default is True.
scale_dict(dict, optional): calibration ranges of tensors output.
"""
"""
def
__init__
(
self
,
def
__init__
(
self
,
...
@@ -1861,7 +1863,8 @@ class InsertQuantizeLinear(object):
...
@@ -1861,7 +1863,8 @@ class InsertQuantizeLinear(object):
quant_axis
=-
1
,
quant_axis
=-
1
,
channel_wise
=
False
,
channel_wise
=
False
,
moving_rate
=
0.9
,
moving_rate
=
0.9
,
is_test
=
True
):
is_test
=
True
,
scale_dict
=
None
):
self
.
_place
=
place
self
.
_place
=
place
self
.
_scope
=
scope
self
.
_scope
=
scope
self
.
quant_bits
=
quant_bits
self
.
quant_bits
=
quant_bits
...
@@ -1869,6 +1872,7 @@ class InsertQuantizeLinear(object):
...
@@ -1869,6 +1872,7 @@ class InsertQuantizeLinear(object):
self
.
channel_wise
=
channel_wise
self
.
channel_wise
=
channel_wise
self
.
_is_test
=
is_test
self
.
_is_test
=
is_test
self
.
_moving_rate
=
moving_rate
self
.
_moving_rate
=
moving_rate
self
.
_scale_dict
=
scale_dict
def
insert_quant_op
(
self
,
graph
,
var_node
,
var_name
=
None
):
def
insert_quant_op
(
self
,
graph
,
var_node
,
var_name
=
None
):
assert
var_node
.
is_var
(),
'{} is not a var'
.
format
(
var_node
.
name
())
assert
var_node
.
is_var
(),
'{} is not a var'
.
format
(
var_node
.
name
())
...
@@ -1880,16 +1884,24 @@ class InsertQuantizeLinear(object):
...
@@ -1880,16 +1884,24 @@ class InsertQuantizeLinear(object):
var_dtype
=
var_node
.
dtype
())
var_dtype
=
var_node
.
dtype
())
data_type
=
'float64'
if
var_node
.
dtype
(
data_type
=
'float64'
if
var_node
.
dtype
(
)
==
core
.
VarDesc
.
VarType
.
FP64
else
'float32'
)
==
core
.
VarDesc
.
VarType
.
FP64
else
'float32'
scale_name
=
self
.
_quantized_scale_name
(
var_name
)
if
self
.
channel_wise
:
if
self
.
channel_wise
:
scale_var_shape
=
var_node
.
shape
()[
self
.
quant_axis
]
scale_var_shape
=
var_node
.
shape
()[
self
.
quant_axis
]
scale_var_type
=
core
.
VarDesc
.
VarType
.
LOD_TENSOR
scale_var_type
=
core
.
VarDesc
.
VarType
.
LOD_TENSOR
init_scale_value
=
np
.
zeros
(
scale_var_shape
,
dtype
=
data_type
)
init_scale_value
=
np
.
ones
(
scale_var_shape
,
dtype
=
data_type
)
*
_SCALE_DEFAULT_VALUE
else
:
else
:
scale_var_shape
=
1
scale_var_shape
=
1
scale_var_type
=
var_node
.
type
()
scale_var_type
=
var_node
.
type
()
init_scale_value
=
np
.
array
([
_SCALE_DEFAULT_VALUE
],
dtype
=
data_type
)
init_scale_value
=
np
.
array
([
_SCALE_DEFAULT_VALUE
],
dtype
=
data_type
)
if
self
.
_scale_dict
is
not
None
and
var_node
.
name
(
)
in
self
.
_scale_dict
.
keys
():
init_scale_value
=
np
.
array
([
self
.
_scale_dict
[
var_node
.
name
()]],
dtype
=
data_type
)
scale_var_node
=
graph
.
create_persistable_node
(
scale_var_node
=
graph
.
create_persistable_node
(
name
=
s
elf
.
_quantized_scale_name
(
var_name
)
,
name
=
s
cale_name
,
var_type
=
scale_var_type
,
var_type
=
scale_var_type
,
shape
=
[
scale_var_shape
],
shape
=
[
scale_var_shape
],
var_dtype
=
var_node
.
dtype
())
var_dtype
=
var_node
.
dtype
())
...
@@ -2346,7 +2358,8 @@ class AddQuantDequantPassV2(object):
...
@@ -2346,7 +2358,8 @@ class AddQuantDequantPassV2(object):
skip_pattern
=
[
"skip_quant"
],
skip_pattern
=
[
"skip_quant"
],
quantizable_op_type
=
[
"elementwise_add"
,
"pool2d"
],
quantizable_op_type
=
[
"elementwise_add"
,
"pool2d"
],
is_full_quantized
=
False
,
is_full_quantized
=
False
,
is_test
=
None
):
is_test
=
None
,
scale_dict
=
None
):
"""
"""
Args:
Args:
scope(paddle.Scope): The scope is used to initialize these new parameters.
scope(paddle.Scope): The scope is used to initialize these new parameters.
...
@@ -2366,6 +2379,7 @@ class AddQuantDequantPassV2(object):
...
@@ -2366,6 +2379,7 @@ class AddQuantDequantPassV2(object):
quantization to all supported quantizable op type. If set is_full_quantized
quantization to all supported quantizable op type. If set is_full_quantized
as False, only apply quantization to the op type according to the input
as False, only apply quantization to the op type according to the input
quantizable_op_type.
quantizable_op_type.
scale_dict(dict, optional): calibration ranges of tensors output.
Examples:
Examples:
.. code-block:: python
.. code-block:: python
...
@@ -2388,6 +2402,7 @@ class AddQuantDequantPassV2(object):
...
@@ -2388,6 +2402,7 @@ class AddQuantDequantPassV2(object):
self
.
_quant_bits
=
quant_bits
self
.
_quant_bits
=
quant_bits
self
.
_is_test
=
is_test
self
.
_is_test
=
is_test
self
.
_skip_pattern
=
skip_pattern
self
.
_skip_pattern
=
skip_pattern
self
.
_scale_dict
=
scale_dict
if
is_full_quantized
:
if
is_full_quantized
:
self
.
_quantizable_op_type
=
utils
.
_act_supported_quantizable_op_type
self
.
_quantizable_op_type
=
utils
.
_act_supported_quantizable_op_type
...
@@ -2444,8 +2459,6 @@ class AddQuantDequantPassV2(object):
...
@@ -2444,8 +2459,6 @@ class AddQuantDequantPassV2(object):
if
is_skip
or
is_quantized
:
if
is_skip
or
is_quantized
:
continue
continue
op_node
.
op
().
_set_attr
(
"quantization_type"
,
"qat_without_weight"
)
arg_names
=
utils
.
_get_op_input_var_names
(
op_node
)
arg_names
=
utils
.
_get_op_input_var_names
(
op_node
)
for
arg_name
in
arg_names
:
for
arg_name
in
arg_names
:
in_node
=
graph
.
_find_node_by_name
(
in_node
=
graph
.
_find_node_by_name
(
...
@@ -2462,7 +2475,8 @@ class AddQuantDequantPassV2(object):
...
@@ -2462,7 +2475,8 @@ class AddQuantDequantPassV2(object):
quant_axis
=-
1
,
quant_axis
=-
1
,
channel_wise
=
False
,
channel_wise
=
False
,
moving_rate
=
self
.
_moving_rate
,
moving_rate
=
self
.
_moving_rate
,
is_test
=
self
.
_is_test
)
is_test
=
self
.
_is_test
,
scale_dict
=
self
.
_scale_dict
)
quant_var_node
,
scale_var_node
=
insert_quant_pass
.
insert_quant_op
(
quant_var_node
,
scale_var_node
=
insert_quant_pass
.
insert_quant_op
(
graph
,
in_node
)
graph
,
in_node
)
dequant_var_node
=
insert_quant_pass
.
insert_dequant_op
(
dequant_var_node
=
insert_quant_pass
.
insert_dequant_op
(
...
@@ -2491,13 +2505,14 @@ class ReplaceFakeQuantDequantPass(object):
...
@@ -2491,13 +2505,14 @@ class ReplaceFakeQuantDequantPass(object):
replace quant-dequant ops with quantize_linear and dequantize_linear ops.
replace quant-dequant ops with quantize_linear and dequantize_linear ops.
"""
"""
def
__init__
(
self
,
scope
,
place
):
def
__init__
(
self
,
scope
,
place
,
quant_bits
=
8
):
r
"""
r
"""
Args:
Args:
scope(paddle.Scope): The scope is used to initialize these new parameters.
scope(paddle.Scope): The scope is used to initialize these new parameters.
place(paddle.CPUPlace|paddle.CUDAPlace|str): place is used to initialize new
place(paddle.CPUPlace|paddle.CUDAPlace|str): place is used to initialize new
parameters described above. If ``place`` is string, it can be It can be ``cpu``
parameters described above. If ``place`` is string, it can be It can be ``cpu``
or ``gpu:x``, where ``x`` is the index of the GPUs.
or ``gpu:x``, where ``x`` is the index of the GPUs.
quant_bits(int, optional): quantization bit number for activation. Default is 8.
Examples:
Examples:
.. code-block:: python
.. code-block:: python
...
@@ -2516,6 +2531,7 @@ class ReplaceFakeQuantDequantPass(object):
...
@@ -2516,6 +2531,7 @@ class ReplaceFakeQuantDequantPass(object):
"""
"""
self
.
_place
=
_get_paddle_place
(
place
)
self
.
_place
=
_get_paddle_place
(
place
)
self
.
_scope
=
scope
self
.
_scope
=
scope
self
.
_quant_bits
=
quant_bits
assert
self
.
_scope
!=
None
,
"scope must not be None."
assert
self
.
_scope
!=
None
,
"scope must not be None."
assert
self
.
_place
!=
None
,
"place must not be None."
assert
self
.
_place
!=
None
,
"place must not be None."
...
@@ -2525,7 +2541,8 @@ class ReplaceFakeQuantDequantPass(object):
...
@@ -2525,7 +2541,8 @@ class ReplaceFakeQuantDequantPass(object):
fake_quant_dequant_ops
=
[]
fake_quant_dequant_ops
=
[]
for
op
in
graph
.
all_op_nodes
():
for
op
in
graph
.
all_op_nodes
():
if
op
.
name
()
in
_fake_quant_dequant_op_list
:
if
op
.
name
()
in
_fake_quant_dequant_op_list
or
op
.
name
(
)
==
"moving_average_abs_max_scale"
:
fake_quant_dequant_ops
.
append
(
op
)
fake_quant_dequant_ops
.
append
(
op
)
for
_op
in
fake_quant_dequant_ops
:
for
_op
in
fake_quant_dequant_ops
:
...
@@ -2544,7 +2561,7 @@ class ReplaceFakeQuantDequantPass(object):
...
@@ -2544,7 +2561,7 @@ class ReplaceFakeQuantDequantPass(object):
quant_axis
=
op
.
op
().
attr
(
"quant_axis"
)
if
op
.
op
().
has_attr
(
quant_axis
=
op
.
op
().
attr
(
"quant_axis"
)
if
op
.
op
().
has_attr
(
"quant_axis"
)
else
-
1
"quant_axis"
)
else
-
1
bit_length
=
op
.
op
().
attr
(
"bit_length"
)
if
op
.
op
().
has_attr
(
bit_length
=
op
.
op
().
attr
(
"bit_length"
)
if
op
.
op
().
has_attr
(
"bit_length"
)
else
8
"bit_length"
)
else
self
.
_quant_bits
zero_point_node
=
None
zero_point_node
=
None
quanted_node
=
x_node
quanted_node
=
x_node
...
@@ -2733,3 +2750,140 @@ class QuantWeightPass(object):
...
@@ -2733,3 +2750,140 @@ class QuantWeightPass(object):
def
_restore_var
(
self
,
name
,
array
):
def
_restore_var
(
self
,
name
,
array
):
tensor
=
self
.
_scope
.
find_var
(
name
).
get_tensor
()
tensor
=
self
.
_scope
.
find_var
(
name
).
get_tensor
()
tensor
.
set
(
array
,
self
.
_place
)
tensor
.
set
(
array
,
self
.
_place
)
class
AddQuantDequantForInferencePass
(
object
):
"""
When export quant model, it will traverse to find the output of each op, and then insert the quant/dequant op after it.
"""
def
__init__
(
self
,
scope
,
place
,
quant_bits
=
8
):
"""
Args:
scope(fluid.Scope): The scope is used to initialize these new parameters.
place(paddle.CPUPlace|paddle.CUDAPlace|str): place is used to restore the weight tensors.
If it's string, it can be ``cpu``, and ``gpu:x``, where ``x`` is the index of the GPUs.
quant_bits(int, optional): quantization bit number for weight. Default is 8.
"""
self
.
_scope
=
scope
self
.
_place
=
place
self
.
_quant_bits
=
quant_bits
self
.
_teller_set
=
utils
.
QUANT_SUPPORTED_OP_TYPE_LIST
def
apply
(
self
,
graph
):
"""
Args:
graph(IrGraph): the target graph.
"""
assert
isinstance
(
graph
,
IrGraph
),
'graph must be the instance of IrGraph.'
dequant_node_map
=
{}
dequantized_vars_map
=
collections
.
OrderedDict
()
for
op_node
in
graph
.
all_op_nodes
():
if
op_node
.
name
()
in
self
.
_teller_set
:
var_names
=
utils
.
_get_op_output_var_names
(
op_node
)
for
var_name
in
var_names
:
out_node
=
graph
.
_find_node_by_name
(
op_node
.
outputs
,
var_name
)
if
out_node
.
dtype
()
not
in
\
[
core
.
VarDesc
.
VarType
.
FP64
,
core
.
VarDesc
.
VarType
.
FP32
]:
continue
if
var_name
in
dequantized_vars_map
:
dequant_var_node
=
dequantized_vars_map
[
var_name
]
else
:
dequant_var_node
=
self
.
_insert_quant_dequant_op
(
graph
,
out_node
)
dequantized_vars_map
[
var_name
]
=
dequant_var_node
dequant_node_map
[
var_name
]
=
dequant_var_node
# remove unuse node and link act quant/dequant linear to op node
for
op_node
in
graph
.
all_op_nodes
():
if
op_node
.
name
()
==
'moving_average_abs_max_scale'
:
graph
.
safe_remove_nodes
(
op_node
)
else
:
var_names
=
utils
.
_get_op_input_var_names
(
op_node
)
for
var_name
in
var_names
:
if
var_name
in
dequant_node_map
:
in_node
=
graph
.
_find_node_by_name
(
op_node
.
inputs
,
var_name
)
graph
.
update_input_link
(
in_node
,
dequant_node_map
[
var_name
],
op_node
)
return
graph
def
_scale_name
(
self
,
var_name
):
"""
Return the scale name for the var named `var_name`.
"""
return
"%s@scale"
%
(
var_name
)
def
_insert_quant_dequant_op
(
self
,
graph
,
var_node
):
assert
var_node
.
is_var
(),
'{} is not a var'
.
format
(
var_node
.
name
())
var_name
=
var_node
.
name
()
quant_axis
=
-
1
quant_var_node
=
graph
.
create_var_node
(
name
=
"{}.quantized"
.
format
(
var_name
),
var_type
=
var_node
.
type
(),
shape
=
var_node
.
shape
(),
var_dtype
=
var_node
.
dtype
())
scale_var_node
=
graph
.
_find_node_by_name
(
graph
.
all_persistable_nodes
(),
self
.
_scale_name
(
var_name
))
try
:
zero_point_node
=
graph
.
_find_node_by_name
(
graph
.
all_persistable_nodes
(),
"{}@zero_point"
.
format
(
quant_var_node
.
name
()))
except
:
zero_point_node
=
graph
.
create_persistable_node
(
name
=
"{}@zero_point"
.
format
(
quant_var_node
.
name
()),
var_type
=
core
.
VarDesc
.
VarType
.
LOD_TENSOR
,
shape
=
scale_var_node
.
shape
(),
var_dtype
=
core
.
VarDesc
.
VarType
.
INT32
)
_init_var_node
(
zero_point_node
,
np
.
zeros
(
scale_var_node
.
shape
(),
dtype
=
"int32"
),
self
.
_scope
,
self
.
_place
)
inputs
=
{
"X"
:
var_node
,
"Scale"
:
scale_var_node
}
if
zero_point_node
is
not
None
:
inputs
[
"ZeroPoint"
]
=
zero_point_node
attrs
=
{
"quant_axis"
:
quant_axis
,
"bit_length"
:
self
.
_quant_bits
}
attrs
[
"op_role"
]
=
core
.
op_proto_and_checker_maker
.
OpRole
.
Forward
outputs
=
{
"Y"
:
quant_var_node
}
quant_op_node
=
graph
.
create_op_node
(
op_type
=
"quantize_linear"
,
attrs
=
attrs
,
inputs
=
inputs
,
outputs
=
outputs
)
graph
.
link_to
(
var_node
,
quant_op_node
)
graph
.
link_to
(
scale_var_node
,
quant_op_node
)
if
zero_point_node
is
not
None
:
graph
.
link_to
(
zero_point_node
,
quant_op_node
)
graph
.
link_to
(
quant_op_node
,
quant_var_node
)
# add dequant_linear node
dequant_var_node
=
graph
.
create_var_node
(
name
=
"{}.dequantized"
.
format
(
quant_var_node
.
name
()),
var_type
=
quant_var_node
.
type
(),
shape
=
quant_var_node
.
shape
(),
var_dtype
=
quant_var_node
.
dtype
())
inputs
=
{
"X"
:
quant_var_node
,
"Scale"
:
scale_var_node
}
if
zero_point_node
is
not
None
:
inputs
[
"ZeroPoint"
]
=
zero_point_node
attrs
=
{
"quant_axis"
:
-
1
,
"bit_length"
:
self
.
_quant_bits
}
attrs
[
"op_role"
]
=
core
.
op_proto_and_checker_maker
.
OpRole
.
Forward
dequant_op_node
=
graph
.
create_op_node
(
op_type
=
"dequantize_linear"
,
attrs
=
attrs
,
inputs
=
inputs
,
outputs
=
{
"Y"
:
dequant_var_node
})
graph
.
link_to
(
quant_var_node
,
dequant_op_node
)
graph
.
link_to
(
scale_var_node
,
dequant_op_node
)
if
zero_point_node
is
not
None
:
graph
.
link_to
(
zero_point_node
,
dequant_op_node
)
graph
.
link_to
(
dequant_op_node
,
dequant_var_node
)
return
dequant_var_node
python/paddle/fluid/contrib/slim/quantization/utils.py
浏览文件 @
a038747c
...
@@ -38,6 +38,7 @@ _act_supported_quantizable_op_type = [
...
@@ -38,6 +38,7 @@ _act_supported_quantizable_op_type = [
"mean"
,
"mean"
,
"not_equal"
,
"not_equal"
,
"reshape"
,
"reshape"
,
"reshape2"
,
"dropout"
,
"dropout"
,
"bilinear_interp"
,
"bilinear_interp"
,
"nearest_interp"
,
"nearest_interp"
,
...
@@ -112,10 +113,12 @@ _act_supported_quantizable_op_type = [
...
@@ -112,10 +113,12 @@ _act_supported_quantizable_op_type = [
"scale"
,
"scale"
,
]
]
_out_scale_op_list
=
list
(
QUANT_SUPPORTED_OP_TYPE_LIST
=
list
(
set
(
_weight_supported_quantizable_op_type
+
set
(
_weight_supported_quantizable_op_type
+
_act_supported_quantizable_op_type
))
_act_supported_quantizable_op_type
))
_out_scale_op_list
=
QUANT_SUPPORTED_OP_TYPE_LIST
_channelwise_quant_axis1_ops
=
[
_channelwise_quant_axis1_ops
=
[
'conv2d_transpose'
,
'mul'
,
'matmul'
,
'matmul_v2'
'conv2d_transpose'
,
'mul'
,
'matmul'
,
'matmul_v2'
]
]
...
@@ -430,6 +433,10 @@ def calculate_quant_cos_error(orig_tensor, qdq_tensor):
...
@@ -430,6 +433,10 @@ def calculate_quant_cos_error(orig_tensor, qdq_tensor):
return
cos_sim
return
cos_sim
def
l2_loss
(
gt
,
pred
):
return
((
gt
-
pred
)
**
2
).
mean
()
class
tqdm
(
object
):
class
tqdm
(
object
):
def
__init__
(
self
,
total
,
bar_format
=
'Loading|{bar}'
,
ncols
=
80
):
def
__init__
(
self
,
total
,
bar_format
=
'Loading|{bar}'
,
ncols
=
80
):
...
...
python/paddle/fluid/tests/unittests/mkldnn/test_onnx_format_quantization_mobilenetv1.py
浏览文件 @
a038747c
...
@@ -292,24 +292,6 @@ class TestPostTrainingQuantization(unittest.TestCase):
...
@@ -292,24 +292,6 @@ class TestPostTrainingQuantization(unittest.TestCase):
is_use_cache_file
=
is_use_cache_file
)
is_use_cache_file
=
is_use_cache_file
)
ptq
.
quantize
()
ptq
.
quantize
()
ptq
.
save_quantized_model
(
self
.
int8_model
)
ptq
.
save_quantized_model
(
self
.
int8_model
)
if
onnx_format
:
try
:
collect_dict
=
ptq
.
_calibration_scales
save_quant_table_path
=
os
.
path
.
join
(
self
.
int8_model
,
'calibration_table.txt'
)
with
open
(
save_quant_table_path
,
'w'
)
as
txt_file
:
for
tensor_name
in
collect_dict
.
keys
():
write_line
=
'{} {}'
.
format
(
tensor_name
,
collect_dict
[
tensor_name
][
'scale'
])
+
'
\n
'
txt_file
.
write
(
write_line
)
print
(
"Quantization clip ranges of tensors is save in: {}"
.
format
(
save_quant_table_path
))
except
:
print
(
"Unable to generate `calibration_table.txt`, please update PaddlePaddle >= 2.3.3"
)
def
run_test
(
self
,
def
run_test
(
self
,
model
,
model
,
...
@@ -429,36 +411,6 @@ class TestMKLDNNInt8ForMobilenetv1Avg(TestPostTrainingQuantization):
...
@@ -429,36 +411,6 @@ class TestMKLDNNInt8ForMobilenetv1Avg(TestPostTrainingQuantization):
onnx_format
=
False
)
onnx_format
=
False
)
class
TestMKLDNNInt8ForMobilenetv1AbsMaxONNXFormat
(
TestPostTrainingQuantization
):
def
test_onnx_format_abs_max_mobilenetv1
(
self
):
model
=
"MobileNet-V1"
algo
=
"abs_max"
round_type
=
"round"
data_urls
=
[
'http://paddle-inference-dist.bj.bcebos.com/int8/mobilenetv1_int8_model.tar.gz'
]
data_md5s
=
[
'13892b0716d26443a8cdea15b3c6438b'
]
quantizable_op_type
=
[
"conv2d"
,
"depthwise_conv2d"
,
"mul"
]
is_full_quantize
=
False
is_use_cache_file
=
False
is_optimize_model
=
False
# The accuracy diff of post-training quantization (abs_max) maybe bigger
diff_threshold
=
0
self
.
run_test
(
model
,
algo
,
round_type
,
data_urls
,
data_md5s
,
quantizable_op_type
,
is_full_quantize
,
is_use_cache_file
,
is_optimize_model
,
diff_threshold
,
onnx_format
=
True
)
class
TestMKLDNNInt8ForMobilenetv1AbsMax
(
TestPostTrainingQuantization
):
class
TestMKLDNNInt8ForMobilenetv1AbsMax
(
TestPostTrainingQuantization
):
def
test_abs_max_mobilenetv1
(
self
):
def
test_abs_max_mobilenetv1
(
self
):
...
...
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