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体验新版 GitCode,发现更多精彩内容 >>
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提交
f7f5044b
编写于
3月 28, 2019
作者:
Z
Zhen Wang
提交者:
GitHub
3月 28, 2019
浏览文件
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差异文件
Merge pull request #16489 from wzzju/fix_slim_quant_bugs
Clean codes and fix some bugs.
上级
69cb9792
46e1bb06
变更
5
隐藏空白更改
内联
并排
Showing
5 changed file
with
93 addition
and
108 deletion
+93
-108
python/paddle/fluid/contrib/slim/quantization/quantization_pass.py
...ddle/fluid/contrib/slim/quantization/quantization_pass.py
+65
-55
python/paddle/fluid/contrib/slim/quantization/quantization_strategy.py
.../fluid/contrib/slim/quantization/quantization_strategy.py
+11
-5
python/paddle/fluid/contrib/slim/tests/quantization/compress.yaml
...addle/fluid/contrib/slim/tests/quantization/compress.yaml
+2
-0
python/paddle/fluid/contrib/slim/tests/test_quantization_pass.py
...paddle/fluid/contrib/slim/tests/test_quantization_pass.py
+0
-3
python/paddle/fluid/framework.py
python/paddle/fluid/framework.py
+15
-45
未找到文件。
python/paddle/fluid/contrib/slim/quantization/quantization_pass.py
浏览文件 @
f7f5044b
...
...
@@ -26,6 +26,17 @@ __all__ = [
]
def
_init_var_node
(
var_node
,
value
,
scope
,
place
):
assert
isinstance
(
value
,
np
.
ndarray
),
'The type of value should be numpy array.'
assert
scope
is
not
None
,
\
'The scope cannot be set None.'
assert
place
is
not
None
,
\
'The place cannot be set None.'
tensor
=
scope
.
var
(
var_node
.
name
()).
get_tensor
()
tensor
.
set
(
value
,
place
)
class
QuantizationTransformPass
(
object
):
def
__init__
(
self
,
scope
=
None
,
...
...
@@ -88,14 +99,14 @@ class QuantizationTransformPass(object):
assert
activation_quantize_type
!=
'channel_wise_abs_max'
,
"The activation quantization type does not support 'channel_wise_abs_max'."
if
activation_quantize_type
not
in
quant_type
:
raise
ValueError
(
"Unknown activation_quantize_type : '%s'. It can only be "
,
"'abs_max' or 'range_abs_max' or 'moving_average_abs_max'."
,
str
(
activation_quantize_type
))
"Unknown activation_quantize_type : '%s'. It can only be "
"'abs_max' or 'range_abs_max' or 'moving_average_abs_max'."
%
(
str
(
activation_quantize_type
)
))
if
weight_quantize_type
not
in
quant_type
:
raise
ValueError
(
"Unknown weight_quantize_type: '%s'. It can only be "
,
"'abs_max' or 'channel_wise_abs_max' or 'range_abs_max' or 'moving_average_abs_max'."
,
str
(
weight_quantize_type
))
"Unknown weight_quantize_type: '%s'. It can only be "
"'abs_max' or 'channel_wise_abs_max' or 'range_abs_max' or 'moving_average_abs_max'."
%
(
str
(
weight_quantize_type
)
))
self
.
_activation_quantize_type
=
activation_quantize_type
self
.
_weight_quantize_type
=
weight_quantize_type
...
...
@@ -121,8 +132,6 @@ class QuantizationTransformPass(object):
"""
assert
isinstance
(
graph
,
IrGraph
),
'graph must be the instance of IrGraph.'
#sequential_execution = core.get_pass('sequential_execution_pass')
#sequential_execution.apply(graph.graph)
self
.
_is_test
=
graph
.
is_test
()
# marked the variable which has been dequantized.
dequantized_vars
=
collections
.
OrderedDict
()
...
...
@@ -203,9 +212,12 @@ class QuantizationTransformPass(object):
var_type
=
core
.
VarDesc
.
VarType
.
LOD_TENSOR
,
shape
=
[
1
],
var_dtype
=
core
.
VarDesc
.
VarType
.
INT64
)
self
.
_init_var_node
(
global_step_in
,
np
.
zeros
(
[
1
],
dtype
=
'int64'
))
_init_var_node
(
global_step_in
,
np
.
zeros
(
[
1
],
dtype
=
'int64'
),
self
.
_scope
,
self
.
_place
)
global_step_out
=
graph
.
create_var_node_from_desc
(
global_step_in
.
var
())
# The attribute of `op_role` is needed by ParallelExecutor.
...
...
@@ -284,7 +296,12 @@ class QuantizationTransformPass(object):
var_dtype
=
var_node
.
dtype
())
data_type
=
'float64'
if
var_node
.
dtype
(
)
==
core
.
VarDesc
.
VarType
.
FP64
else
'float32'
self
.
_init_var_node
(
scale_in_node
,
np
.
array
([
0.001
],
dtype
=
data_type
))
_init_var_node
(
scale_in_node
,
np
.
array
(
[
0.001
],
dtype
=
data_type
),
self
.
_scope
,
self
.
_place
)
scale_out_node
=
graph
.
create_var_node_from_desc
(
scale_in_node
.
var
())
inputs
=
{
'X'
:
var_node
,
'InScale'
:
scale_in_node
}
...
...
@@ -299,9 +316,13 @@ class QuantizationTransformPass(object):
var_dtype
=
var_node
.
dtype
())
data_type
=
'float64'
if
var_node
.
dtype
(
)
==
core
.
VarDesc
.
VarType
.
FP64
else
'float32'
self
.
_init_var_node
(
scales_node
,
np
.
zeros
(
[
self
.
_window_size
],
dtype
=
data_type
))
_init_var_node
(
scales_node
,
np
.
zeros
(
[
self
.
_window_size
],
dtype
=
data_type
),
self
.
_scope
,
self
.
_place
)
inputs
[
'Iter'
]
=
self
.
_global_step
outputs
[
'OutScales'
]
=
scales_node
attrs
=
{
...
...
@@ -343,7 +364,12 @@ class QuantizationTransformPass(object):
var_dtype
=
var_node
.
dtype
())
data_type
=
'float64'
if
var_node
.
dtype
(
)
==
core
.
VarDesc
.
VarType
.
FP64
else
'float32'
self
.
_init_var_node
(
scale_in_node
,
np
.
array
([
0.001
],
dtype
=
data_type
))
_init_var_node
(
scale_in_node
,
np
.
array
(
[
0.001
],
dtype
=
data_type
),
self
.
_scope
,
self
.
_place
)
scale_out_node
=
graph
.
create_var_node_from_desc
(
scale_in_node
.
var
())
ins
=
{
'X'
:
var_node
,
'InScale'
:
scale_in_node
}
...
...
@@ -356,13 +382,23 @@ class QuantizationTransformPass(object):
shape
=
[
1
])
data_type
=
'float64'
if
var_node
.
dtype
(
)
==
core
.
VarDesc
.
VarType
.
FP64
else
'float32'
self
.
_init_var_node
(
scale_in_node
,
np
.
ones
([
1
],
dtype
=
data_type
))
_init_var_node
(
scale_in_node
,
np
.
ones
(
[
1
],
dtype
=
data_type
),
self
.
_scope
,
self
.
_place
)
accum_in_node
=
graph
.
create_persistable_node
(
name
=
unique_name
.
generate
(
'accum'
),
var_type
=
core
.
VarDesc
.
VarType
.
LOD_TENSOR
,
var_dtype
=
var_node
.
dtype
(),
shape
=
[
1
])
self
.
_init_var_node
(
accum_in_node
,
np
.
ones
([
1
],
dtype
=
data_type
))
_init_var_node
(
accum_in_node
,
np
.
ones
(
[
1
],
dtype
=
data_type
),
self
.
_scope
,
self
.
_place
)
state_out_node
=
graph
.
create_var_node_from_desc
(
state_in_node
.
var
(
))
accum_out_node
=
graph
.
create_var_node_from_desc
(
accum_in_node
.
var
(
...
...
@@ -482,16 +518,6 @@ class QuantizationTransformPass(object):
graph
.
link_to
(
dequant_op_node
,
dequant_var_node
)
return
dequant_var_node
def
_init_var_node
(
self
,
var_node
,
value
):
assert
isinstance
(
value
,
np
.
ndarray
),
'The type of value should be numpy array.'
assert
self
.
_scope
is
not
None
,
\
'The scope cannot be set None when activation_quantize_type equals to range_abs_max.'
assert
self
.
_place
is
not
None
,
\
'The place cannot be set None when activation_quantize_type equals to range_abs_max.'
tensor
=
self
.
_scope
.
var
(
var_node
.
name
()).
get_tensor
()
tensor
.
set
(
value
,
self
.
_place
)
def
_quantized_var_name
(
self
,
var_name
):
"""
Return quantized variable name for the input `var_name`.
...
...
@@ -594,8 +620,8 @@ class QuantizationFreezePass(object):
self
.
_weight_bits
)
self
.
_restore_var
(
input_arg_name
,
quantized_param_v
)
else
:
scale_v
=
self
.
_to_node
(
op_node
.
outputs
,
op_node
.
output
(
'OutScale'
)[
0
])
scale_v
=
graph
.
_find_node_by_name
(
op_node
.
outputs
,
op_node
.
output
(
'OutScale'
)[
0
])
self
.
_var_scale_map
[
input_arg_name
]
=
scale_v
ops
=
graph
.
all_op_nodes
()
...
...
@@ -627,8 +653,8 @@ class QuantizationFreezePass(object):
return
graph
def
_remove_fake_quant_and_dequant_op
(
self
,
graph
,
op_node
):
k
=
self
.
_to_nod
e
(
op_node
.
outputs
,
op_node
.
output
(
'Out'
)[
0
])
v
=
self
.
_to_nod
e
(
op_node
.
inputs
,
op_node
.
input
(
'X'
)[
0
])
k
=
graph
.
_find_node_by_nam
e
(
op_node
.
outputs
,
op_node
.
output
(
'Out'
)[
0
])
v
=
graph
.
_find_node_by_nam
e
(
op_node
.
inputs
,
op_node
.
input
(
'X'
)[
0
])
if
v
.
node
not
in
self
.
_op_input_rename_map
:
self
.
_op_input_rename_map
[
k
.
node
]
=
v
else
:
...
...
@@ -663,8 +689,8 @@ class QuantizationFreezePass(object):
raise
ValueError
(
"Only support one output, but op %s has"
" more than one output."
%
(
op_node
.
name
()))
output_var_node
=
self
.
_to_node
(
op_node
.
outputs
,
op_node
.
output_arg_names
()[
0
])
output_var_node
=
graph
.
_find_node_by_name
(
op_node
.
outputs
,
op_node
.
output_arg_names
()[
0
])
weight_scale_node
=
graph
.
create_persistable_node
(
name
=
unique_name
.
generate
(
'channel_scale'
),
var_type
=
core
.
VarDesc
.
VarType
.
LOD_TENSOR
,
...
...
@@ -672,7 +698,9 @@ class QuantizationFreezePass(object):
var_dtype
=
output_var_node
.
dtype
())
data_type
=
'float64'
if
output_var_node
.
dtype
(
)
==
core
.
VarDesc
.
VarType
.
FP64
else
'float32'
self
.
_init_var_node
(
weight_scale_node
,
channel_scale
.
astype
(
data_type
))
_init_var_node
(
weight_scale_node
,
channel_scale
.
astype
(
data_type
),
self
.
_scope
,
self
.
_place
)
dequant_var_node
=
graph
.
create_var_node
(
name
=
self
.
_dequantized_var_name
(
output_var_node
.
name
()),
var_type
=
output_var_node
.
type
(),
...
...
@@ -724,8 +752,8 @@ class QuantizationFreezePass(object):
raise
ValueError
(
"Only support one output, but op %s has"
" more than one output."
%
(
op_node
.
name
()))
output_var_node
=
self
.
_to_node
(
op_node
.
outputs
,
op_node
.
output_arg_names
()[
0
])
output_var_node
=
graph
.
_find_node_by_name
(
op_node
.
outputs
,
op_node
.
output_arg_names
()[
0
])
dequant_var_node
=
graph
.
create_var_node
(
name
=
self
.
_dequantized_var_name
(
output_var_node
.
name
()),
var_type
=
output_var_node
.
type
(),
...
...
@@ -746,24 +774,6 @@ class QuantizationFreezePass(object):
self
.
_op_output_rename_map
[
output_var_node
.
node
]
=
dequant_var_node
return
dequant_var_node
def
_init_var_node
(
self
,
var_node
,
value
):
assert
isinstance
(
value
,
np
.
ndarray
),
'The type of value should be numpy array.'
assert
self
.
_scope
is
not
None
,
\
'The scope cannot be set None when activation_quantize_type equals to range_abs_max.'
assert
self
.
_place
is
not
None
,
\
'The place cannot be set None when activation_quantize_type equals to range_abs_max.'
tensor
=
self
.
_scope
.
var
(
var_node
.
name
()).
get_tensor
()
tensor
.
set
(
value
,
self
.
_place
)
def
_to_node
(
self
,
nodes
,
node_name
):
target_node
=
None
for
n
in
nodes
:
if
n
.
name
()
==
node_name
:
target_node
=
n
assert
target_node
is
not
None
,
"Cannot find the target node in the giving set."
return
target_node
def
_load_var
(
self
,
name
):
return
np
.
array
(
self
.
_scope
.
find_var
(
name
).
get_tensor
())
...
...
python/paddle/fluid/contrib/slim/quantization/quantization_strategy.py
浏览文件 @
f7f5044b
...
...
@@ -45,13 +45,14 @@ class QuantizationStrategy(Strategy):
activation_bits
=
8
,
weight_bits
=
8
,
activation_quantize_type
=
'abs_max'
,
weight_quantize_type
=
'abs_max'
,
save_in_nodes
=
None
,
save_out_nodes
=
None
):
"""
Args:
start_epoch(int): The 'on_epoch_begin' function will be called in start_epoch. default: 0
end_epoch(int): The 'on_epoch_end' function will be called in end_epoch. default: 0
float_model_save_path(str): The path to save model with float weights.
float_model_save_path(str): The path to save model with float weights.
None means it doesn't save float model. defalut: None.
mobile_model_save_path(str): The path to save model for paddle-mobile execution.
None means it doesn't save mobile model. defalut: None.
...
...
@@ -66,9 +67,11 @@ class QuantizationStrategy(Strategy):
dynamically each step in both training and testing period. If use
'range_abs_max', a static quantization scale will be calculated
during training and used in inference.
save_in_nodes(list<str>): A list of variable names used to prune graph
weight_quantize_type (str): quantization type for weights, support 'abs_max' and 'channel_wise_abs_max'.
The 'range_abs_max' usually is not used for weight, since weights are fixed once the model is well trained.
save_in_nodes(list<str>): A list of variable names used to prune graph
for saving inference model.
save_out_nodes(list<str>): A list of variable names used to prune graph
save_out_nodes(list<str>): A list of variable names used to prune graph
for saving inference model.
"""
...
...
@@ -81,6 +84,7 @@ class QuantizationStrategy(Strategy):
self
.
activation_bits
=
activation_bits
self
.
weight_bits
=
weight_bits
self
.
activation_quantize_type
=
activation_quantize_type
self
.
weight_quantize_type
=
weight_quantize_type
self
.
save_out_nodes
=
save_out_nodes
self
.
save_in_nodes
=
save_in_nodes
...
...
@@ -100,7 +104,8 @@ class QuantizationStrategy(Strategy):
place
=
context
.
place
,
weight_bits
=
self
.
weight_bits
,
activation_bits
=
self
.
activation_bits
,
activation_quantize_type
=
self
.
activation_quantize_type
)
activation_quantize_type
=
self
.
activation_quantize_type
,
weight_quantize_type
=
self
.
weight_quantize_type
)
transform_pass
.
apply
(
train_ir_graph
)
transform_pass
.
apply
(
test_ir_graph
)
...
...
@@ -134,7 +139,8 @@ class QuantizationStrategy(Strategy):
scope
=
context
.
scope
,
place
=
context
.
place
,
weight_bits
=
self
.
weight_bits
,
activation_bits
=
self
.
activation_bits
)
activation_bits
=
self
.
activation_bits
,
weight_quantize_type
=
self
.
weight_quantize_type
)
freeze_pass
.
apply
(
test_ir_graph
)
# for other strategies
...
...
python/paddle/fluid/contrib/slim/tests/quantization/compress.yaml
浏览文件 @
f7f5044b
...
...
@@ -35,6 +35,8 @@ strategies:
start_epoch
:
0
end_epoch
:
0
float_model_save_path
:
'
./output/float'
mobile_model_save_path
:
'
./output/mobile'
int8_model_save_path
:
'
./output/int8'
weight_bits
:
8
activation_bits
:
8
weight_quantize_type
:
'
abs_max'
...
...
python/paddle/fluid/contrib/slim/tests/test_quantization_pass.py
浏览文件 @
f7f5044b
...
...
@@ -256,8 +256,6 @@ class TestQuantizationFreezePass(unittest.TestCase):
place
=
place
,
activation_quantize_type
=
activation_quant_type
,
weight_quantize_type
=
weight_quant_type
)
#transform_pass = QuantizationTransformPass(
# scope=scope, place=place, activation_quantize_type=activation_quant_type)
transform_pass
.
apply
(
main_graph
)
transform_pass
.
apply
(
test_graph
)
dev_name
=
'_gpu_'
if
use_cuda
else
'_cpu_'
...
...
@@ -315,7 +313,6 @@ class TestQuantizationFreezePass(unittest.TestCase):
# Freeze graph for inference, but the weight of fc/conv is still float type.
freeze_pass
=
QuantizationFreezePass
(
scope
=
scope
,
place
=
place
,
weight_quantize_type
=
weight_quant_type
)
#freeze_pass = QuantizationFreezePass(scope=scope, place=place)
freeze_pass
.
apply
(
test_graph
)
if
not
for_ci
:
marked_nodes
=
set
()
...
...
python/paddle/fluid/framework.py
浏览文件 @
f7f5044b
...
...
@@ -2347,40 +2347,6 @@ class IrGraph(object):
"""
return
{
IrOpNode
(
node
)
for
node
in
self
.
graph
.
nodes
()
if
node
.
is_op
()}
def
_find_var_node
(
self
,
key
):
"""
Get a variable node by the `key` from this graph. The key
can be a node name or a node id.
WARNS:
There are some nodes may have the same name. So, be
cautious about using this method when you find the
target var node by its name.
Args:
key(str|int): The str type denotes that the target variable node's name.
And the int type denotes that the target variable node's id.
Raises:
ValueError: If this graph doesn't have a variable with the giving name or id.
Returns:
IrVarNode: the variable node with the giving name or id.
"""
target_var_node
=
None
var_nodes
=
self
.
all_var_nodes
()
if
isinstance
(
key
,
six
.
string_types
):
for
var_node
in
var_nodes
:
if
var_node
.
name
()
==
key
:
target_var_node
=
var_node
elif
isinstance
(
key
,
int
):
for
var_node
in
var_nodes
:
if
var_node
.
id
()
==
key
:
target_var_node
=
var_node
if
target_var_node
is
None
:
raise
ValueError
(
"var_node %s not in this graph"
%
key
)
return
target_var_node
def
create_persistable_node
(
self
,
name
,
var_type
,
shape
,
var_dtype
):
"""
Create a persistable variable node in the graph. In IrGraph,
...
...
@@ -2525,14 +2491,6 @@ class IrGraph(object):
core
.
graph_safe_remove_nodes
(
self
.
graph
,
original_nodes
)
def
resolve_hazard
(
self
):
def
_to_node
(
nodes
,
node_name
):
target_node
=
None
for
n
in
nodes
:
if
n
.
name
()
==
node_name
:
target_node
=
n
assert
target_node
is
not
None
,
"Cannot find the target node in the giving set."
return
target_node
ordered_nodes
=
core
.
topology_sort
(
self
.
graph
)
var_nodes
=
dict
()
for
node
in
ordered_nodes
:
...
...
@@ -2540,16 +2498,17 @@ class IrGraph(object):
for
each_var_name
in
node
.
op
().
input_arg_names
():
if
each_var_name
not
in
var_nodes
:
var_nodes
[
each_var_name
]
=
[
_to_nod
e
(
node
.
inputs
,
each_var_name
)
self
.
_find_node_by_nam
e
(
node
.
inputs
,
each_var_name
)
]
for
each_var_name
in
node
.
op
().
output_arg_names
():
if
each_var_name
not
in
var_nodes
:
var_nodes
[
each_var_name
]
=
[
_to_nod
e
(
node
.
outputs
,
each_var_name
)
self
.
_find_node_by_nam
e
(
node
.
outputs
,
each_var_name
)
]
else
:
var_nodes
[
each_var_name
].
append
(
_to_node
(
node
.
outputs
,
each_var_name
))
self
.
_find_node_by_name
(
node
.
outputs
,
each_var_name
))
self
.
graph
.
resolve_hazard
(
var_nodes
)
def
has_circle
(
self
):
...
...
@@ -2662,6 +2621,17 @@ class IrGraph(object):
program
=
Program
.
_construct_from_desc
(
desc
)
return
program
def
_find_node_by_name
(
self
,
nodes
,
node_name
):
"""
Find a node in the giving nodes set by the name.
"""
target_node
=
None
for
n
in
nodes
:
if
n
.
name
()
==
node_name
:
target_node
=
n
assert
target_node
is
not
None
,
"Cannot find the target node in the giving set."
return
target_node
def
_update_desc_attr
(
self
,
desc
,
name
,
val
):
"""
Update the value of desc's attribute by attribute's name.
...
...
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