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8967a66a
编写于
8月 18, 2021
作者:
X
XGZhang
提交者:
GitHub
8月 18, 2021
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差异文件
support quantization of conv2d_transpose (#34547)
上级
4d88cdb8
变更
6
隐藏空白更改
内联
并排
Showing
6 changed file
with
225 addition
and
32 deletion
+225
-32
python/paddle/fluid/contrib/slim/quantization/imperative/qat.py
.../paddle/fluid/contrib/slim/quantization/imperative/qat.py
+76
-25
python/paddle/fluid/contrib/slim/quantization/imperative/utils.py
...addle/fluid/contrib/slim/quantization/imperative/utils.py
+14
-5
python/paddle/fluid/contrib/slim/tests/test_imperative_qat.py
...on/paddle/fluid/contrib/slim/tests/test_imperative_qat.py
+8
-2
python/paddle/fluid/contrib/slim/tests/test_imperative_qat_user_defined.py
...id/contrib/slim/tests/test_imperative_qat_user_defined.py
+19
-0
python/paddle/nn/quant/quant_layers.py
python/paddle/nn/quant/quant_layers.py
+107
-0
tools/sampcd_processor.py
tools/sampcd_processor.py
+1
-0
未找到文件。
python/paddle/fluid/contrib/slim/quantization/imperative/qat.py
浏览文件 @
8967a66a
...
...
@@ -42,17 +42,18 @@ class ImperativeQuantAware(object):
Applying quantization aware training (QAT) to the dgraph model.
"""
def
__init__
(
self
,
quantizable_layer_type
=
[
'Conv2D'
,
'Linear'
],
weight_quantize_type
=
'abs_max'
,
activation_quantize_type
=
'moving_average_abs_max'
,
weight_bits
=
8
,
activation_bits
=
8
,
moving_rate
=
0.9
,
weight_preprocess_layer
=
None
,
act_preprocess_layer
=
None
,
weight_quantize_layer
=
None
,
act_quantize_layer
=
None
):
def
__init__
(
self
,
quantizable_layer_type
=
[
'Conv2D'
,
'Linear'
,
'Conv2DTranspose'
],
weight_quantize_type
=
'abs_max'
,
activation_quantize_type
=
'moving_average_abs_max'
,
weight_bits
=
8
,
activation_bits
=
8
,
moving_rate
=
0.9
,
weight_preprocess_layer
=
None
,
act_preprocess_layer
=
None
,
weight_quantize_layer
=
None
,
act_quantize_layer
=
None
):
"""
The constructor for ImperativeQuantAware.
...
...
@@ -212,9 +213,44 @@ class ImperativeQuantAware(object):
the out_scale value of outputs would be calculated.
Args:
model(
fluid.dygraph
.Layer): the model to be quantized.
model(
paddle.nn
.Layer): the model to be quantized.
Returns:
None
Examples:
.. code-block:: python
import paddle
from paddle.fluid.contrib.slim.quantization
\
import ImperativeQuantAware
class ImperativeModel(paddle.nn.Layer):
def __init__(self):
super(ImperativeModel, self).__init__()
# self.linear_0 would skip the quantization.
self.linear_0 = paddle.nn.Linear(784, 400)
self.linear_0.skip_quant = True
# self.linear_1 would not skip the quantization.
self.linear_1 = paddle.nn.Linear(400, 10)
self.linear_1.skip_quant = False
def forward(self, inputs):
x = self.linear_0(inputs)
x = self.linear_1(inputs)
return x
model = ImperativeModel()
imperative_qat = ImperativeQuantAware(
weight_quantize_type='abs_max',
activation_quantize_type='moving_average_abs_max')
# Add the fake quant logical.
# The original model will be rewrite.
#
# There is only one Layer(self.linear1) would be added the
# fake quant logical.
imperative_qat.quantize(model)
"""
assert
isinstance
(
model
,
dygraph
.
Layer
),
\
"The model must be the instance of dygraph.Layer."
...
...
@@ -232,17 +268,18 @@ class ImperativeQuantizeInputs(object):
logic both for activation inputs and weight inputs.
"""
def
__init__
(
self
,
quantizable_layer_type
=
[
'Conv2D'
,
'Linear'
],
weight_quantize_type
=
'abs_max'
,
activation_quantize_type
=
'moving_average_abs_max'
,
weight_bits
=
8
,
activation_bits
=
8
,
moving_rate
=
0.9
,
weight_preprocess_layer
=
None
,
act_preprocess_layer
=
None
,
weight_quantize_layer
=
None
,
act_quantize_layer
=
None
):
def
__init__
(
self
,
quantizable_layer_type
=
[
'Conv2D'
,
'Linear'
,
'Conv2DTranspose'
],
weight_quantize_type
=
'abs_max'
,
activation_quantize_type
=
'moving_average_abs_max'
,
weight_bits
=
8
,
activation_bits
=
8
,
moving_rate
=
0.9
,
weight_preprocess_layer
=
None
,
act_preprocess_layer
=
None
,
weight_quantize_layer
=
None
,
act_quantize_layer
=
None
):
"""
The constructor for ImperativeQuantizeInputs.
...
...
@@ -303,6 +340,18 @@ class ImperativeQuantizeInputs(object):
}
def
apply
(
self
,
model
):
"""
Quantize the weights and activations to calculate for specific
layers.
Args:
model(paddle.nn.Layer): The target model which would
calculate the input quantization scale.
Returns:
None
"""
assert
isinstance
(
model
,
dygraph
.
Layer
),
\
"The model must be the instance of dygraph.Layer."
...
...
@@ -354,7 +403,7 @@ class ImperativeQuantizeOutputs(object):
output scales for specific layers in the dygraph model.
Args:
model(
fluid.dygraph
.Layer): The target model which would be
model(
paddle.nn
.Layer): The target model which would be
calculate the output quantization scale.
Returns:
...
...
@@ -544,7 +593,9 @@ class ImperativeQuantizeOutputs(object):
1. the type of input op should be conv2d, depthwise_conv2d or matmul
2. the previous ops of the input op are not fake_quantize_dequantize ops
"""
target_op_types
=
[
"conv2d"
,
"depthwise_conv2d"
,
"matmul"
]
target_op_types
=
[
"conv2d"
,
"depthwise_conv2d"
,
"matmul"
,
"conv2d_transpose"
]
if
in_op
.
type
not
in
target_op_types
:
return
False
...
...
python/paddle/fluid/contrib/slim/quantization/imperative/utils.py
浏览文件 @
8967a66a
...
...
@@ -24,6 +24,7 @@ from ..quantization_pass import _get_output_name_index
from
..quantization_pass
import
_get_input_name_index
layer_name_map
=
{
'Conv2DTranspose'
:
paddle
.
nn
.
Conv2DTranspose
,
'Conv2D'
:
paddle
.
nn
.
Conv2D
,
'Linear'
:
paddle
.
nn
.
Linear
,
'AdaptiveAvgPool2D'
:
paddle
.
nn
.
AdaptiveAvgPool2D
,
...
...
@@ -46,8 +47,9 @@ layer_name_map = {
}
# Apply fake quant for the inputs of these layers
# TODO (jc): support paddle.nn.Conv2DTranspose
fake_quant_input_layers
=
[
paddle
.
nn
.
Conv2D
,
paddle
.
nn
.
Linear
]
fake_quant_input_layers
=
[
paddle
.
nn
.
Conv2D
,
paddle
.
nn
.
Linear
,
paddle
.
nn
.
Conv2DTranspose
]
# Apply fake quant for the output of these layers
# TODO(jc): fix the problem of adding duplicate fake_quant ops
...
...
@@ -65,7 +67,8 @@ fake_quant_leaf_layers = [
]
fake_quant_wrap_layers
=
[
quant_layers
.
QuantizedConv2D
,
quant_layers
.
QuantizedLinear
quant_layers
.
QuantizedConv2D
,
quant_layers
.
QuantizedLinear
,
quant_layers
.
QuantizedConv2DTranspose
]
# The weight format of these layers is Cin * Cout * H * W
...
...
@@ -84,9 +87,9 @@ fake_quantize_dequantize_op_types = [
def
load_variable_data
(
scope
,
var_name
):
'''
"""
Load variable value from scope
'''
"""
var_node
=
scope
.
find_var
(
var_name
)
assert
var_node
is
not
None
,
\
"Can not find "
+
var_name
+
" in the scope."
...
...
@@ -120,6 +123,12 @@ def find_parent_layer_and_sub_name(model, name):
the sub_name of the layer.
For example, if name is 'block_1/convbn_1/conv_1', the parent layer is
'block_1/convbn_1' and the sub_name is `conv_1`.
Args:
model(paddle.nn.Layer): the model to be quantized.
name(string): the name of a layer
Returns:
parent_layer, subname
"""
assert
isinstance
(
model
,
paddle
.
nn
.
Layer
),
\
"The model must be the instance of paddle.nn.Layer."
...
...
python/paddle/fluid/contrib/slim/tests/test_imperative_qat.py
浏览文件 @
8967a66a
...
...
@@ -28,10 +28,10 @@ from paddle.fluid import core
from
paddle.fluid.optimizer
import
AdamOptimizer
from
paddle.fluid.contrib.slim.quantization
import
ImperativeQuantAware
from
paddle.fluid.dygraph.container
import
Sequential
from
paddle.nn
import
Linear
,
Conv2D
,
Softmax
from
paddle.nn
import
Linear
,
Conv2D
,
Softmax
,
Conv2DTranspose
from
paddle.fluid.log_helper
import
get_logger
from
paddle.fluid.dygraph.io
import
INFER_MODEL_SUFFIX
,
INFER_PARAMS_SUFFIX
from
paddle.nn.quant.quant_layers
import
QuantizedConv2D
from
paddle.nn.quant.quant_layers
import
QuantizedConv2D
,
QuantizedConv2DTranspose
from
imperative_test_utils
import
fix_model_dict
,
ImperativeLenet
...
...
@@ -75,6 +75,12 @@ class TestImperativeQat(unittest.TestCase):
data
=
np
.
random
.
uniform
(
-
1
,
1
,
[
10
,
3
,
32
,
32
]).
astype
(
'float32'
)
quant_conv1
(
fluid
.
dygraph
.
to_variable
(
data
))
conv_transpose
=
Conv2DTranspose
(
4
,
6
,
(
3
,
3
))
quant_conv_transpose
=
QuantizedConv2DTranspose
(
conv_transpose
)
x_var
=
paddle
.
uniform
(
(
2
,
4
,
8
,
8
),
dtype
=
'float32'
,
min
=-
1.0
,
max
=
1.0
)
quant_conv_transpose
(
x_var
)
seed
=
1
np
.
random
.
seed
(
seed
)
fluid
.
default_main_program
().
random_seed
=
seed
...
...
python/paddle/fluid/contrib/slim/tests/test_imperative_qat_user_defined.py
浏览文件 @
8967a66a
...
...
@@ -28,6 +28,7 @@ from paddle.nn import Sequential
from
paddle.fluid.dygraph
import
Conv2D
from
paddle.fluid.dygraph
import
Pool2D
from
paddle.fluid.dygraph
import
Linear
from
paddle.nn.quant.quant_layers
import
QuantizedConv2DTranspose
from
paddle.fluid.log_helper
import
get_logger
os
.
environ
[
"CPU_NUM"
]
=
"1"
...
...
@@ -100,6 +101,19 @@ class CustomQAT(nn.Layer):
return
x
class
ModelForConv2dT
(
nn
.
Layer
):
def
__init__
(
self
,
num_classes
=
10
):
super
(
ModelForConv2dT
,
self
).
__init__
()
self
.
features
=
nn
.
Conv2DTranspose
(
4
,
6
,
(
3
,
3
))
self
.
fc
=
Linear
(
input_dim
=
600
,
output_dim
=
num_classes
)
def
forward
(
self
,
inputs
):
x
=
self
.
features
(
inputs
)
x
=
paddle
.
flatten
(
x
,
1
)
x
=
self
.
fc
(
x
)
return
x
class
ImperativeLenet
(
paddle
.
nn
.
Layer
):
def
__init__
(
self
,
num_classes
=
10
,
classifier_activation
=
'softmax'
):
super
(
ImperativeLenet
,
self
).
__init__
()
...
...
@@ -168,6 +182,11 @@ class TestUserDefinedActPreprocess(unittest.TestCase):
imperative_qat
.
quantize
(
lenet
)
adam
=
Adam
(
learning_rate
=
0.001
,
parameters
=
lenet
.
parameters
())
dynamic_loss_rec
=
[]
#for CI coverage
conv_transpose
=
ModelForConv2dT
()
imperative_qat
.
quantize
(
conv_transpose
)
x_var
=
paddle
.
uniform
((
2
,
4
,
8
,
8
),
dtype
=
'float32'
,
min
=-
1.
,
max
=
1.
)
conv_transpose
(
x_var
)
def
train
(
model
):
adam
=
Adam
(
learning_rate
=
0.001
,
parameters
=
model
.
parameters
())
...
...
python/paddle/nn/quant/quant_layers.py
浏览文件 @
8967a66a
...
...
@@ -31,6 +31,7 @@ __all__ = [
'FakeQuantMovingAverageAbsMax'
,
'FakeQuantChannelWiseAbsMax'
,
'QuantizedConv2D'
,
'QuantizedConv2DTranspose'
,
'QuantizedLinear'
,
'MovingAverageAbsMaxScale'
,
'MAOutputScaleLayer'
,
...
...
@@ -481,6 +482,112 @@ class QuantizedConv2D(layers.Layer):
data_format
=
self
.
_data_format
)
class
QuantizedConv2DTranspose
(
layers
.
Layer
):
"""
The computational logic of QuantizedConv2DTranspose is the same with Conv2DTranspose.
The only difference is that its inputs are all fake quantized.
Examples:
.. code-block:: python
import paddle
import paddle.nn as nn
from paddle.nn.quant.quant_layers import QuantizedConv2DTranspose
x_var = paddle.uniform((2, 4, 8, 8), dtype='float32', min=-1., max=1.)
conv = nn.Conv2DTranspose(4, 6, (3, 3))
conv_quantized = QuantizedConv2DTranspose(conv)
y_quantized = conv_quantized(x_var)
y_var = conv(x_var)
y_quantized_np = y_quantized.numpy()
y_np = y_var.numpy()
print(y_np.shape, y_quantized_np.shape)
# (2, 6, 10, 10), (2, 6, 10, 10)
"""
def
__init__
(
self
,
layer
,
weight_bits
=
8
,
activation_bits
=
8
,
moving_rate
=
0.9
,
weight_quantize_type
=
'abs_max'
,
activation_quantize_type
=
'abs_max'
,
weight_pre_layer
=
None
,
act_pre_layer
=
None
,
weight_quant_layer
=
None
,
act_quant_layer
=
None
):
r
"""
Constructor.
The arguments are the same as ImperativeQuantAware.
"""
super
(
QuantizedConv2DTranspose
,
self
).
__init__
()
# For Conv2DTranspose
self
.
_groups
=
getattr
(
layer
,
'_groups'
)
self
.
_stride
=
getattr
(
layer
,
'_stride'
)
self
.
_padding
=
getattr
(
layer
,
'_padding'
)
self
.
_output_padding
=
getattr
(
layer
,
'output_padding'
)
self
.
_dilation
=
getattr
(
layer
,
'_dilation'
)
self
.
_data_format
=
getattr
(
layer
,
'_data_format'
)
self
.
weight
=
getattr
(
layer
,
'weight'
)
self
.
bias
=
getattr
(
layer
,
'bias'
)
# For FakeQuant
self
.
_conv2d_transpose_quant_axis
=
1
if
weight_quant_layer
is
not
None
:
self
.
_fake_quant_weight
=
weight_quant_layer
()
else
:
self
.
_fake_quant_weight
=
_get_fake_quant_type
(
weight_quantize_type
,
name
=
self
.
weight
.
name
,
moving_rate
=
moving_rate
,
quant_bits
=
weight_bits
,
dtype
=
self
.
_dtype
,
quant_on_weight
=
True
,
channel_num
=
self
.
weight
.
shape
[
self
.
_conv2d_transpose_quant_axis
],
quant_axis
=
self
.
_conv2d_transpose_quant_axis
)
if
act_quant_layer
is
not
None
:
self
.
_fake_quant_input
=
act_quant_layer
()
else
:
self
.
_fake_quant_input
=
_get_fake_quant_type
(
activation_quantize_type
,
name
=
layer
.
full_name
(),
moving_rate
=
moving_rate
,
quant_bits
=
activation_bits
,
dtype
=
self
.
_dtype
,
quant_on_weight
=
False
)
self
.
_act_preprocess
=
act_pre_layer
(
)
if
act_pre_layer
is
not
None
else
None
self
.
_weight_preprocess
=
weight_pre_layer
(
)
if
weight_pre_layer
is
not
None
else
None
def
forward
(
self
,
input
,
output_size
=
None
):
if
self
.
_act_preprocess
is
not
None
:
input
=
self
.
_act_preprocess
(
input
)
quant_input
=
self
.
_fake_quant_input
(
input
)
weight
=
self
.
weight
if
self
.
_weight_preprocess
is
not
None
:
weight
=
self
.
_weight_preprocess
(
self
.
weight
)
quant_weight
=
self
.
_fake_quant_weight
(
weight
)
if
output_size
is
None
:
output_padding
=
self
.
_output_padding
else
:
output_padding
=
0
return
F
.
conv2d_transpose
(
quant_input
,
quant_weight
,
bias
=
self
.
bias
,
padding
=
self
.
_padding
,
output_padding
=
output_padding
,
stride
=
self
.
_stride
,
dilation
=
self
.
_dilation
,
groups
=
self
.
_groups
,
output_size
=
output_size
,
data_format
=
self
.
_data_format
)
class
QuantizedLinear
(
layers
.
Layer
):
"""
The computational logic of QuantizedLinear is the same with Linear.
...
...
tools/sampcd_processor.py
浏览文件 @
8967a66a
...
...
@@ -440,6 +440,7 @@ def get_filenames(full_test=False):
'''
global
whl_error
import
paddle
import
paddle.fluid.contrib.slim.quantization
whl_error
=
[]
if
full_test
:
get_full_api_from_pr_spec
()
...
...
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