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体验新版 GitCode,发现更多精彩内容 >>
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69a8316b
编写于
12月 29, 2020
作者:
S
SunAhong1993
提交者:
GitHub
12月 29, 2020
浏览文件
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差异文件
Merge pull request #17 from PaddlePaddle/develop
add
上级
55d5eb24
a94afe1a
变更
16
隐藏空白更改
内联
并排
Showing
16 changed file
with
827 addition
and
103 deletion
+827
-103
x2paddle/core/program.py
x2paddle/core/program.py
+9
-3
x2paddle/decoder/onnx_decoder.py
x2paddle/decoder/onnx_decoder.py
+20
-5
x2paddle/op_mapper/dygraph/onnx2paddle/opset9/opset.py
x2paddle/op_mapper/dygraph/onnx2paddle/opset9/opset.py
+31
-10
x2paddle/op_mapper/dygraph/pytorch2paddle/aten.py
x2paddle/op_mapper/dygraph/pytorch2paddle/aten.py
+99
-16
x2paddle/op_mapper/dygraph/pytorch2paddle/prim2code.py
x2paddle/op_mapper/dygraph/pytorch2paddle/prim2code.py
+97
-39
x2paddle/op_mapper/dygraph/pytorch2paddle/pytorch_op_mapper.py
...dle/op_mapper/dygraph/pytorch2paddle/pytorch_op_mapper.py
+1
-1
x2paddle/op_mapper/static/onnx2paddle/opset9/opset.py
x2paddle/op_mapper/static/onnx2paddle/opset9/opset.py
+28
-8
x2paddle/optimizer/code_optimizer/__init__.py
x2paddle/optimizer/code_optimizer/__init__.py
+2
-1
x2paddle/optimizer/code_optimizer/layer_code_generator.py
x2paddle/optimizer/code_optimizer/layer_code_generator.py
+26
-11
x2paddle/optimizer/code_optimizer/module_graph.py
x2paddle/optimizer/code_optimizer/module_graph.py
+373
-0
x2paddle/optimizer/code_optimizer/subgraphs_union.py
x2paddle/optimizer/code_optimizer/subgraphs_union.py
+6
-3
x2paddle/optimizer/fusion/dygraph/__init__.py
x2paddle/optimizer/fusion/dygraph/__init__.py
+2
-0
x2paddle/optimizer/fusion/dygraph/if_fuse_pass.py
x2paddle/optimizer/fusion/dygraph/if_fuse_pass.py
+33
-0
x2paddle/optimizer/fusion/dygraph/if_fuser.py
x2paddle/optimizer/fusion/dygraph/if_fuser.py
+58
-0
x2paddle/optimizer/optimizer.py
x2paddle/optimizer/optimizer.py
+2
-1
x2paddle/optimizer/pattern_matcher.py
x2paddle/optimizer/pattern_matcher.py
+40
-5
未找到文件。
x2paddle/core/program.py
浏览文件 @
69a8316b
...
...
@@ -279,8 +279,14 @@ class PaddleGraph(object):
hierarchical_tree
.
save_source_files
(
save_dir
)
self
.
dump_dygraph_parameter
(
save_dir
)
else
:
self
.
gen_dygraph_code
(
save_dir
)
self
.
dump_dygraph_parameter
(
save_dir
)
if
self
.
source_type
==
"pytorch"
:
from
x2paddle.optimizer.code_optimizer
import
ModuleGraph
module_graph
=
ModuleGraph
(
self
)
module_graph
.
save_source_files
(
save_dir
)
self
.
dump_dygraph_parameter
(
save_dir
)
else
:
self
.
gen_dygraph_code
(
save_dir
)
self
.
dump_dygraph_parameter
(
save_dir
)
# 动转静
code_path
=
osp
.
join
(
osp
.
abspath
(
save_dir
),
"x2paddle_code.py"
)
print
(
"Exporting inference model from python code ('{}')...
\n
"
.
format
(
code_path
))
...
...
@@ -621,7 +627,7 @@ class PaddleGraph(object):
layer
.
outputs
[
0
])],
indent
=
indent
))
else
:
self
.
forward_func
.
extend
(
gen_codes
([
line
],
indent
=
indent
))
if
indent
==
2
:
if
indent
==
2
and
code_dir
is
not
None
:
gen_main_code
(
code_dir
)
write_code
(
code_dir
)
else
:
...
...
x2paddle/decoder/onnx_decoder.py
浏览文件 @
69a8316b
...
...
@@ -205,7 +205,7 @@ class ONNXGraph(Graph):
shape
=
raw_input
(
"Shape of Input(e.g. -1,3,224,224), enter 'N' to skip: "
)
except
:
except
NameError
:
shape
=
input
(
"Shape of Input(e.g. -1,3,224,224), enter 'N' to skip: "
)
...
...
@@ -302,7 +302,18 @@ class ONNXGraph(Graph):
if
opt
==
in_node
:
self
.
connect
(
nd
.
name
,
layer_name
)
flag
=
1
node
.
which_child
[
nd
.
name
]
=
idx
if
nd
.
name
in
node
.
which_child
:
for
n_i
,
n_ipt
in
enumerate
(
node
.
inputs
):
if
first_i
==
n_i
:
continue
if
n_ipt
==
nd
.
name
:
new_nd_name
=
"{}/{}"
.
format
(
nd
.
name
,
n_i
)
if
new_nd_name
not
in
node
.
which_child
:
node
.
which_child
[
new_nd_name
]
=
idx
break
else
:
first_i
=
node
.
inputs
.
index
(
nd
.
name
)
node
.
which_child
[
nd
.
name
]
=
idx
self
.
node_map
[
nd
.
name
].
index
=
0
break
if
flag
==
1
:
...
...
@@ -318,11 +329,15 @@ class ONNXGraph(Graph):
if
len
(
node
.
which_child
)
==
0
:
ipt_node
=
super
(
ONNXGraph
,
self
).
get_node
(
node
.
inputs
[
idx
],
copy
)
return
ipt_node
else
:
ipt_node
=
super
(
ONNXGraph
,
self
).
get_node
(
node
.
inputs
[
idx
],
copy
)
if
ipt_node
.
layer_name
in
node
.
which_child
:
ipt_node
.
index
=
node
.
which_child
[
ipt_node
.
layer_name
]
new_ipt_name
=
"{}/{}"
.
format
(
ipt_node
.
layer_name
,
idx
)
if
new_ipt_name
in
node
.
which_child
:
ipt_node
.
index
=
node
.
which_child
[
new_ipt_name
]
else
:
if
ipt_node
.
layer_name
in
node
.
which_child
:
ipt_node
.
index
=
node
.
which_child
[
ipt_node
.
layer_name
]
return
ipt_node
...
...
x2paddle/op_mapper/dygraph/onnx2paddle/opset9/opset.py
浏览文件 @
69a8316b
...
...
@@ -250,15 +250,22 @@ class OpSet9():
def
_interpolate
(
self
,
node
):
val_x
=
self
.
graph
.
get_input_node
(
node
,
idx
=
0
,
copy
=
True
)
inputs
=
{
'x'
:
val_x
.
name
}
attrs
=
dict
()
if
node
.
layer_type
==
'Resize'
:
if
len
(
node
.
layer
.
input
)
==
2
:
# opset 10
val_scales
=
self
.
graph
.
get_input_node
(
node
,
idx
=
1
,
copy
=
True
)
inputs
[
'scale_factor'
]
=
val_scales
.
name
# TODO(syf): paddle.nn.functional.interpolate will support the length
# which is the same as the rank of input.
# inputs['scale_factor'] = val_scales.name
attrs
[
'scale_factor'
]
=
self
.
weights
[
val_scales
.
name
].
tolist
()[
2
:]
elif
len
(
node
.
layer
.
input
)
==
3
:
# opset 11
val_scales
=
self
.
graph
.
get_input_node
(
node
,
idx
=
2
,
copy
=
True
)
inputs
[
'scale_factor'
]
=
val_scales
.
name
# TODO(syf): paddle.nn.functional.interpolate will support the length
# which is the same as the rank of input.
# inputs['scale_factor'] = val_scales.name
attrs
[
'scale_factor'
]
=
self
.
weights
[
val_scales
.
name
].
tolist
()[
2
:]
elif
len
(
node
.
layer
.
input
)
==
4
:
# opset 11
val_sizes
=
self
.
graph
.
get_input_node
(
node
,
idx
=
3
,
copy
=
True
)
...
...
@@ -281,7 +288,7 @@ class OpSet9():
ipt
=
inputs
.
pop
(
"x"
)
inputs
[
"input"
]
=
ipt
mode
=
node
.
get_attr
(
'mode'
,
'nearest'
)
attrs
=
{
"align_corners"
:
False
}
attrs
.
update
({
"align_corners"
:
False
})
self
.
paddle_graph
.
add_layer
(
kernel
=
"fluid.layers.resize_nearest"
,
inputs
=
inputs
,
...
...
@@ -290,12 +297,12 @@ class OpSet9():
return
elif
node
.
layer_type
==
'Upsample'
:
val_scales
=
self
.
graph
.
get_input_node
(
node
,
idx
=
1
,
copy
=
True
)
inputs
[
'scale'
]
=
val_scales
inputs
[
'scale
_factor
'
]
=
val_scales
mode
=
node
.
get_attr
(
'mode'
,
'nearest'
)
attrs
=
{
"align_corners"
:
False
,
"mode"
:
string
(
mode
),
"align_mode"
:
1
}
attrs
.
update
(
{
"align_corners"
:
False
,
"mode"
:
string
(
mode
),
"align_mode"
:
1
})
self
.
paddle_graph
.
add_layer
(
kernel
=
"paddle.nn.functional.interpolate"
,
inputs
=
inputs
,
...
...
@@ -926,16 +933,17 @@ class OpSet9():
'max'
:
max_value
,
'min'
:
min_value
,
}
self
.
paddle_graph
.
add_layer
(
'paddle.clip'
,
inputs
=
{
"x"
:
val_x
.
name
},
outputs
=
[
node
.
name
],
**
layer_attrs
)
else
:
max_ipt
=
self
.
graph
.
get_input_node
(
node
,
idx
=
1
,
copy
=
True
)
min_ipt
=
self
.
graph
.
get_input_node
(
node
,
idx
=
2
,
copy
=
True
)
max_value
=
_const_weight_or_none
(
max_ipt
)
min_ipt
=
self
.
graph
.
get_input_node
(
node
,
idx
=
1
,
copy
=
True
)
max_ipt
=
self
.
graph
.
get_input_node
(
node
,
idx
=
2
,
copy
=
True
)
min_value
=
_const_weight_or_none
(
min_ipt
)
max_value
=
_const_weight_or_none
(
max_ipt
)
if
max_value
.
shape
==
(
1
,
):
max_value
=
max_value
[
0
]
if
min_value
.
shape
==
(
1
,
):
...
...
@@ -1637,3 +1645,16 @@ class OpSet9():
inputs
=
inputs_dict
,
outputs
=
[
node
.
name
],
**
layer_attrs
)
@
print_mapping_info
def
ArgMax
(
self
,
node
):
val_x
=
self
.
graph
.
get_input_node
(
node
,
idx
=
0
,
copy
=
True
)
axis
=
node
.
get_attr
(
'axis'
)
keepdims
=
False
if
node
.
get_attr
(
'keepdims'
)
==
0
else
True
layer_attrs
=
{
'axis'
:
axis
,
'keepdim'
:
keepdims
}
self
.
paddle_graph
.
add_layer
(
'paddle.argmax'
,
inputs
=
{
"x"
:
val_x
.
name
},
outputs
=
[
node
.
name
],
**
layer_attrs
)
x2paddle/op_mapper/dygraph/pytorch2paddle/aten.py
浏览文件 @
69a8316b
...
...
@@ -663,6 +663,37 @@ def aten_batch_norm(mapper, graph, node):
return
current_inputs
,
current_outputs
def
aten_bmm
(
mapper
,
graph
,
node
):
""" 构造矩阵相乘的PaddleLayer。
TorchScript示例:
%x.222 : Tensor = aten::bmm(%32, %7)
参数含义:
%x.222 (Tensor): 输出,矩阵相乘后的结果。
%i.12 (list): 输入1。
%7 (int): 输入2。
"""
scope_name
=
mapper
.
normalize_scope_name
(
node
)
output_name
=
mapper
.
_get_outputs_name
(
node
)[
0
]
layer_outputs
=
[
output_name
]
layer_inputs
=
{}
inputs_name
,
inputs_node
=
mapper
.
_get_inputs_name
(
node
)
# 获取当前节点输出的list
current_outputs
=
[
output_name
]
# 处理输入0,即%i.12
mapper
.
_check_input
(
graph
,
inputs_node
[
0
],
inputs_name
[
0
],
current_outputs
,
scope_name
)
layer_inputs
[
"x"
]
=
inputs_name
[
0
]
# 处理输入1,即%288
mapper
.
_check_input
(
graph
,
inputs_node
[
1
],
inputs_name
[
1
],
current_outputs
,
scope_name
,
add_dim
=
True
)
layer_inputs
[
"y"
]
=
inputs_name
[
1
]
# 获取当前节点输入的list
current_inputs
=
list
(
layer_inputs
.
values
())
graph
.
add_layer
(
"paddle.bmm"
,
inputs
=
layer_inputs
,
outputs
=
layer_outputs
,
scope_name
=
scope_name
)
return
current_inputs
,
current_outputs
def
aten_cat
(
mapper
,
graph
,
node
):
""" 构造连接Tensor的PaddleLayer。
...
...
@@ -885,7 +916,7 @@ def aten_constant_pad_nd(mapper, graph, node):
outputs
=
[
inputs_name
[
0
]
+
"_if"
,
output_name
],
scope_name
=
scope_name
)
if_layer
=
graph
.
layers
[
list
(
graph
.
layers
.
keys
())[
-
1
]]
block
=
PaddleGraph
(
parent_layer
=
if_layer
,
graph_type
=
"dygraph"
)
block
=
PaddleGraph
(
source_type
=
"pytorch"
,
parent_layer
=
if_layer
,
graph_type
=
"dygraph"
)
block
.
add_layer
(
"prim.sub"
,
inputs
=
{
"y"
:
inputs_name
[
0
]
+
"_len"
},
...
...
@@ -916,7 +947,7 @@ def aten_constant_pad_nd(mapper, graph, node):
outputs
=
[
output_name
],
scope_name
=
scope_name
)
if_layer
.
add_block
(
block
)
block
=
PaddleGraph
(
parent_layer
=
if_layer
,
graph_type
=
"dygraph"
)
block
=
PaddleGraph
(
source_type
=
"pytorch"
,
parent_layer
=
if_layer
,
graph_type
=
"dygraph"
)
layer_inputs
[
"input"
]
=
inputs_name
[
0
]
block
.
add_layer
(
kernel
,
inputs
=
layer_inputs
,
outputs
=
layer_outputs
,
scope_name
=
scope_name
,
**
layer_attrs
)
...
...
@@ -1525,6 +1556,32 @@ def aten_eq(mapper, graph, node):
return
current_inputs
,
current_outputs
def
aten_erf
(
mapper
,
graph
,
node
):
""" 构造逐元素计算 Erf 激活函数的PaddleLayer。
TorchScript示例:
%94 : Tensor = aten::erf(%sinusoid_inp.1)
参数含义:
%94 (Tensor): 输出,erf之后的结果。
%sinusoid_inp.1 (Tensor): 需要进行erf的Tensor。
"""
scope_name
=
mapper
.
normalize_scope_name
(
node
)
output_name
=
mapper
.
_get_outputs_name
(
node
)[
0
]
layer_outputs
=
[
output_name
]
layer_inputs
=
{}
inputs_name
,
inputs_node
=
mapper
.
_get_inputs_name
(
node
)
# 获取当前节点输出的list
current_outputs
=
[
output_name
]
# 处理输入0,即%sinusoid_inp.1
mapper
.
_check_input
(
graph
,
inputs_node
[
0
],
inputs_name
[
0
],
current_outputs
,
scope_name
)
layer_inputs
[
"x"
]
=
inputs_name
[
0
]
# 获取当前节点输入、输出的list
current_inputs
=
list
(
layer_inputs
.
values
())
graph
.
add_layer
(
"paddle.erf"
,
inputs
=
layer_inputs
,
outputs
=
layer_outputs
,
scope_name
=
scope_name
)
return
current_inputs
,
current_outputs
def
aten_exp
(
mapper
,
graph
,
node
):
""" 构造以自然数e为底指数运算的PaddleLayer。
...
...
@@ -1639,7 +1696,7 @@ def aten_expand_as(mapper, graph, node):
outputs
=
[
inputs_name
[
0
]
+
"_if1"
],
scope_name
=
scope_name
)
if_layer
=
graph
.
layers
[
list
(
graph
.
layers
.
keys
())[
-
1
]]
block
=
PaddleGraph
(
parent_layer
=
if_layer
,
graph_type
=
"dygraph"
)
block
=
PaddleGraph
(
source_type
=
"pytorch"
,
parent_layer
=
if_layer
,
graph_type
=
"dygraph"
)
block
.
add_layer
(
"prim.type"
,
inputs
=
{
"input"
:
inputs_name
[
1
]},
...
...
@@ -1652,7 +1709,7 @@ def aten_expand_as(mapper, graph, node):
scope_name
=
scope_name
,
dtype
=
inputs_name
[
1
]
+
"_type"
)
if_layer
.
add_block
(
block
)
block
=
PaddleGraph
(
parent_layer
=
if_layer
,
graph_type
=
"dygraph"
)
block
=
PaddleGraph
(
source_type
=
"pytorch"
,
parent_layer
=
if_layer
,
graph_type
=
"dygraph"
)
if_layer
.
add_block
(
block
)
if_layer
.
inputs
[
"input-0"
]
=
inputs_name
[
0
]
if_layer
.
inputs
[
"input-1"
]
=
inputs_name
[
1
]
...
...
@@ -1663,7 +1720,7 @@ def aten_expand_as(mapper, graph, node):
outputs
=
[
inputs_name
[
0
]
+
"_if2"
],
scope_name
=
scope_name
)
if_layer
=
graph
.
layers
[
list
(
graph
.
layers
.
keys
())[
-
1
]]
block
=
PaddleGraph
(
parent_layer
=
if_layer
,
graph_type
=
"dygraph"
)
block
=
PaddleGraph
(
source_type
=
"pytorch"
,
parent_layer
=
if_layer
,
graph_type
=
"dygraph"
)
block
.
add_layer
(
"fluid.layers.cast"
,
inputs
=
{
"x"
:
layer_outputs
[
0
]},
...
...
@@ -1671,7 +1728,7 @@ def aten_expand_as(mapper, graph, node):
scope_name
=
scope_name
,
dtype
=
string
(
"bool"
))
if_layer
.
add_block
(
block
)
block
=
PaddleGraph
(
if_layer
,
graph_type
=
"dygraph"
)
block
=
PaddleGraph
(
source_type
=
"pytorch"
,
parent_layer
=
if_layer
,
graph_type
=
"dygraph"
)
if_layer
.
add_block
(
block
)
if_layer
.
inputs
[
"input-0"
]
=
layer_outputs
[
0
]
# TODO(syf): check expand_as
...
...
@@ -1868,10 +1925,10 @@ def aten_floor(mapper, graph, node):
outputs
=
[
inputs_name
[
0
]
+
"_if"
],
scope_name
=
scope_name
)
if_layer
=
graph
.
layers
[
list
(
graph
.
layers
.
keys
())[
-
1
]]
block
=
PaddleGraph
(
parent_layer
=
if_layer
,
graph_type
=
"dygraph"
)
block
=
PaddleGraph
(
source_type
=
"pytorch"
,
parent_layer
=
if_layer
,
graph_type
=
"dygraph"
)
block
.
add_layer
(
"paddle.floor"
,
inputs
=
copy
.
deepcopy
(
layer_inputs
),
outputs
=
copy
.
deepcopy
(
layer_outputs
),
scope_name
=
scope_name
)
if_layer
.
add_block
(
block
)
block
=
PaddleGraph
(
parent_layer
=
if_layer
,
graph_type
=
"dygraph"
)
block
=
PaddleGraph
(
source_type
=
"pytorch"
,
parent_layer
=
if_layer
,
graph_type
=
"dygraph"
)
block
.
add_layer
(
"prim.floor"
,
inputs
=
copy
.
deepcopy
(
layer_inputs
),
outputs
=
copy
.
deepcopy
(
layer_outputs
),
scope_name
=
scope_name
)
if_layer
.
add_block
(
block
)
if_layer
.
inputs
[
"input-0"
]
=
inputs_name
[
0
]
...
...
@@ -2569,14 +2626,14 @@ def aten_masked_fill_(mapper, graph, node):
outputs
=
[
inputs_name
[
2
]
+
"_if"
],
scope_name
=
scope_name
)
if_layer
=
graph
.
layers
[
list
(
graph
.
layers
.
keys
())[
-
1
]]
block
=
PaddleGraph
(
parent_layer
=
if_layer
,
graph_type
=
"dygraph"
)
block
=
PaddleGraph
(
source_type
=
"pytorch"
,
parent_layer
=
if_layer
,
graph_type
=
"dygraph"
)
block
.
add_layer
(
"prim.equal"
,
inputs
=
{
"input"
:
inputs_name
[
1
]
+
"_mask"
},
outputs
=
[
inputs_name
[
2
]
+
"_1"
],
scope_name
=
scope_name
)
if_layer
.
add_block
(
block
)
block
=
PaddleGraph
(
parent_layer
=
if_layer
,
graph_type
=
"dygraph"
)
block
=
PaddleGraph
(
source_type
=
"pytorch"
,
parent_layer
=
if_layer
,
graph_type
=
"dygraph"
)
block
.
add_layer
(
"prim.mul"
,
inputs
=
{
"x"
:
inputs_name
[
1
]
+
"_mask"
,
...
...
@@ -2677,14 +2734,14 @@ def aten_masked_fill(mapper, graph, node):
outputs
=
[
inputs_name
[
2
]
+
"_if"
],
scope_name
=
scope_name
)
if_layer
=
graph
.
layers
[
list
(
graph
.
layers
.
keys
())[
-
1
]]
block
=
PaddleGraph
(
parent_layer
=
if_layer
,
graph_type
=
"dygraph"
)
block
=
PaddleGraph
(
source_type
=
"pytorch"
,
parent_layer
=
if_layer
,
graph_type
=
"dygraph"
)
block
.
add_layer
(
"prim.equal"
,
inputs
=
{
"input"
:
inputs_name
[
1
]
+
"_mask"
},
outputs
=
[
inputs_name
[
2
]
+
"_1"
],
scope_name
=
scope_name
)
if_layer
.
add_block
(
block
)
block
=
PaddleGraph
(
parent_layer
=
if_layer
,
graph_type
=
"dygraph"
)
block
=
PaddleGraph
(
source_type
=
"pytorch"
,
parent_layer
=
if_layer
,
graph_type
=
"dygraph"
)
block
.
add_layer
(
"prim.mul"
,
inputs
=
{
"x"
:
inputs_name
[
1
]
+
"_mask"
,
...
...
@@ -3986,16 +4043,18 @@ def aten_sub(mapper, graph, node):
""" 构造数值相减的PaddleLayer。
TorchScript示例:
%840 : int = aten::sub(%839, %836)
%840 : int = aten::sub(%839, %836
, %3
)
参数含义:
%840 (-): 相减结果。
%839 (-): 输入数值 x。
%836 (-): 输入数值 y。
%3 (-): alpha。
"""
scope_name
=
mapper
.
normalize_scope_name
(
node
)
output_name
=
mapper
.
_get_outputs_name
(
node
)[
0
]
layer_outputs
=
[
output_name
]
layer_inputs
=
{}
layer_attrs
=
{}
inputs_name
,
inputs_node
=
mapper
.
_get_inputs_name
(
node
)
# 获取当前节点输出的list
current_outputs
=
[
output_name
]
...
...
@@ -4006,13 +4065,37 @@ def aten_sub(mapper, graph, node):
mapper
.
_check_input
(
graph
,
inputs_node
[
1
],
inputs_name
[
1
],
current_outputs
,
scope_name
,
add_dim
=
True
)
layer_inputs
[
"y"
]
=
inputs_name
[
1
]
# 处理输入2,即%3
if
len
(
inputs_node
)
>
2
:
if
inputs_name
[
2
]
in
mapper
.
attrs
:
layer_attrs
[
"alpha"
]
=
mapper
.
attrs
[
inputs_name
[
2
]]
else
:
mapper
.
_check_input
(
graph
,
inputs_node
[
2
],
inputs_name
[
2
],
current_outputs
,
scope_name
)
layer_inputs
[
"alpha"
]
=
inputs_name
[
2
]
current_inputs
.
append
(
inputs_name
[
2
])
else
:
layer_attrs
[
"alpha"
]
=
1.0
# 获取当前节点输入的list
current_inputs
=
list
(
layer_inputs
.
values
())
graph
.
add_layer
(
"prim.sub"
,
inputs
=
layer_inputs
,
outputs
=
layer_outputs
,
scope_name
=
scope_name
)
graph
.
add_layer
(
"prim.sub"
,
inputs
=
layer_inputs
,
outputs
=
layer_outputs
,
scope_name
=
scope_name
,
**
layer_attrs
)
return
current_inputs
,
current_outputs
def
aten_sub_
(
mapper
,
graph
,
node
):
""" 构造数值相减的PaddleLayer。
TorchScript示例:
%840 : int = aten::sub_(%839, %836, %3)
参数含义:
%840 (-): 相减结果。
%839 (-): 输入数值 x。
%836 (-): 输入数值 y。
%3 (-): alpha。
"""
return
aten_sub
(
mapper
,
graph
,
node
)
def
aten_t
(
mapper
,
graph
,
node
):
""" 构造矩阵转置的PaddleLayer。
...
...
@@ -4366,14 +4449,14 @@ def aten_upsample_bilinear2d(mapper, graph, node):
outputs
=
[
inputs_name
[
0
]
+
"_if1"
],
scope_name
=
scope_name
)
if_layer
=
graph
.
layers
[
list
(
graph
.
layers
.
keys
())[
-
1
]]
block
=
PaddleGraph
(
parent_layer
=
if_layer
,
graph_type
=
"dygraph"
)
block
=
PaddleGraph
(
source_type
=
"pytorch"
,
parent_layer
=
if_layer
,
graph_type
=
"dygraph"
)
block
.
add_layer
(
"prim.var2list"
,
inputs
=
{
"input"
:
inputs_name
[
1
]},
outputs
=
[
inputs_name
[
1
]],
scope_name
=
scope_name
)
if_layer
.
add_block
(
block
)
block
=
PaddleGraph
(
parent_layer
=
if_layer
,
graph_type
=
"dygraph"
)
block
=
PaddleGraph
(
source_type
=
"pytorch"
,
parent_layer
=
if_layer
,
graph_type
=
"dygraph"
)
if_layer
.
add_block
(
block
)
if_layer
.
inputs
[
"input-0"
]
=
inputs_name
[
1
]
# 处理输入2,即%5421
...
...
x2paddle/op_mapper/dygraph/pytorch2paddle/prim2code.py
浏览文件 @
69a8316b
...
...
@@ -67,9 +67,11 @@ def prim_add_(layer, indent=1, init_func=[], forward_func=[], layer_id=None, dif
forward_func
.
extend
(
gen_codes
([
line
],
indent
=
indent
))
def
prim_and
(
layer
,
indent
=
1
,
init_func
=
[],
forward_func
=
[],
layer_id
=
None
,
different_attrs
=
None
):
def
prim_and
(
layer
,
indent
=
1
,
init_func
=
[],
forward_func
=
[],
layer_id
=
None
,
different_attrs
=
None
,
is_return_line
=
False
):
line
=
"{} = {} and {}"
.
format
(
layer
.
outputs
[
0
],
get_value
(
layer
,
"x"
,
different_attrs
),
get_value
(
layer
,
"y"
,
different_attrs
))
if
is_return_line
:
return
line
.
split
(
" = "
)[
1
]
forward_func
.
extend
(
gen_codes
([
line
],
indent
=
indent
))
...
...
@@ -91,16 +93,23 @@ def prim_assert(layer, indent=1, init_func=[], forward_func=[], layer_id=None, d
s
+=
"{} == {} or "
.
format
(
get_value
(
layer
,
"key"
),
v
)
if
len
(
s
)
>
0
:
s
=
s
[:
-
4
]
lc
=
locals
()
exec
(
"assert_result = {}"
.
format
(
s
))
assert_result
=
lc
[
'assert_result'
]
line
=
"assert {},
\'
The {} must be {}!
\'
"
.
format
(
s
,
get_value
(
layer
,
"key"
),
get_value
(
layer
,
"value"
))
else
:
line
=
"assert {} == {},
\'
The {} must be {}!
\'
"
.
format
(
get_value
(
layer
,
"key"
),
get_value
(
layer
,
"value"
),
get_value
(
layer
,
"key"
),
get_value
(
layer
,
"value"
))
s
=
"{} == {}"
.
format
(
get_value
(
layer
,
"key"
),
get_value
(
layer
,
"value"
))
lc
=
locals
()
exec
(
"assert_result = {}"
.
format
(
s
))
assert_result
=
lc
[
'assert_result'
]
line
=
"assert {},
\'
The {} must be {}!
\'
"
.
format
(
s
,
get_value
(
layer
,
"key"
),
get_value
(
layer
,
"value"
))
else
:
raise
Exception
(
"Not implement yet!"
)
forward_func
.
extend
(
gen_codes
([
line
],
indent
=
indent
))
if
not
assert_result
:
forward_func
.
extend
(
gen_codes
([
line
],
indent
=
indent
))
def
prim_check_dim
(
layer
,
indent
=
1
,
init_func
=
[],
forward_func
=
[],
layer_id
=
None
,
different_attrs
=
None
):
...
...
@@ -119,10 +128,12 @@ def prim_constant(layer, indent=1, init_func=[], forward_func=[], layer_id=None,
forward_func
.
extend
(
gen_codes
([
line
],
indent
=
indent
))
def
prim_contain
(
layer
,
indent
=
1
,
init_func
=
[],
forward_func
=
[],
layer_id
=
None
,
different_attrs
=
None
):
def
prim_contain
(
layer
,
indent
=
1
,
init_func
=
[],
forward_func
=
[],
layer_id
=
None
,
different_attrs
=
None
,
is_return_line
=
False
):
line
=
"{} = {} in {}"
.
format
(
layer
.
outputs
[
0
],
get_value
(
layer
,
"element"
,
different_attrs
),
get_value
(
layer
,
"input"
,
different_attrs
))
if
is_return_line
:
return
line
.
split
(
" = "
)[
1
]
forward_func
.
extend
(
gen_codes
([
line
],
indent
=
indent
))
...
...
@@ -156,10 +167,12 @@ def prim_div(layer, indent=1, init_func=[], forward_func=[], layer_id=None, diff
forward_func
.
extend
(
gen_codes
([
line
],
indent
=
indent
))
def
prim_eq
(
layer
,
indent
=
1
,
init_func
=
[],
forward_func
=
[],
layer_id
=
None
,
different_attrs
=
None
):
def
prim_eq
(
layer
,
indent
=
1
,
init_func
=
[],
forward_func
=
[],
layer_id
=
None
,
different_attrs
=
None
,
is_return_line
=
False
):
line
=
"{} = {} == {}"
.
format
(
layer
.
outputs
[
0
],
get_value
(
layer
,
"x"
,
different_attrs
),
get_value
(
layer
,
"y"
,
different_attrs
))
if
is_return_line
:
return
line
.
split
(
" = "
)[
1
]
forward_func
.
extend
(
gen_codes
([
line
],
indent
=
indent
))
...
...
@@ -198,33 +211,56 @@ def prim_getitem(layer, indent=1, init_func=[], forward_func=[], layer_id=None,
forward_func
.
extend
(
gen_codes
([
line
],
indent
=
indent
))
def
prim_gt
(
layer
,
indent
=
1
,
init_func
=
[],
forward_func
=
[],
layer_id
=
None
,
different_attrs
=
None
):
def
prim_gt
(
layer
,
indent
=
1
,
init_func
=
[],
forward_func
=
[],
layer_id
=
None
,
different_attrs
=
None
,
is_return_line
=
False
):
line
=
"{} = {} > {}"
.
format
(
layer
.
outputs
[
0
],
get_value
(
layer
,
"x"
,
different_attrs
),
get_value
(
layer
,
"y"
,
different_attrs
))
if
is_return_line
:
return
line
.
split
(
" = "
)[
1
]
forward_func
.
extend
(
gen_codes
([
line
],
indent
=
indent
))
def
prim_if
(
layer
,
indent
=
1
,
init_func
=
[],
forward_func
=
[],
layer_id
=
None
,
different_attrs
=
None
):
line
=
"if {} :"
.
format
(
get_value
(
layer
,
"input"
,
different_attrs
))
forward_func
.
extend
(
gen_codes
([
line
],
indent
=
indent
))
block
=
layer
.
blocks
[
0
]
if
len
(
block
.
layers
)
==
0
:
line
=
"pass"
forward_func
.
extend
(
gen_codes
([
line
],
indent
=
indent
+
1
))
else
:
b_init_lines
,
b_forward_lines
=
block
.
gen_dygraph_code
(
indent
=
indent
+
1
)
init_func
.
extend
(
b_init_lines
)
forward_func
.
extend
(
b_forward_lines
)
block
=
layer
.
blocks
[
1
]
if
len
(
block
.
layers
)
>
0
:
b_init_lines
,
b_forward_lines
=
block
.
gen_dygraph_code
(
indent
=
indent
+
1
)
if
len
(
b_forward_lines
)
!=
0
:
line
=
"else:"
forward_func
.
extend
(
gen_codes
([
line
],
indent
=
indent
))
init_func
.
extend
(
b_init_lines
)
forward_func
.
extend
(
b_forward_lines
)
try
:
exec_s
=
None
for
line
in
forward_func
:
s
=
line
.
replace
(
" "
,
""
)
if
s
.
startswith
(
"{} = "
.
format
(
get_value
(
layer
,
"input"
,
different_attrs
))):
exec_s
=
s
.
split
(
" = "
)[
1
]
lc
=
locals
()
if
exec_s
is
not
None
:
exec
(
"if_result = {}"
.
format
(
exec_s
))
else
:
exec
(
"if_result = {}"
.
format
(
get_value
(
layer
,
"input"
,
different_attrs
)))
if_result
=
lc
[
'if_result'
]
if
if_result
:
block
=
layer
.
blocks
[
0
]
else
:
block
=
layer
.
blocks
[
1
]
if
len
(
block
.
layers
)
>
0
:
b_init_lines
,
b_forward_lines
=
block
.
gen_dygraph_code
(
indent
=
indent
)
init_func
.
extend
(
b_init_lines
)
forward_func
.
extend
(
b_forward_lines
)
except
:
line
=
"if {} :"
.
format
(
get_value
(
layer
,
"input"
,
different_attrs
))
forward_func
.
extend
(
gen_codes
([
line
],
indent
=
indent
))
block
=
layer
.
blocks
[
0
]
if
len
(
block
.
layers
)
==
0
:
line
=
"pass"
forward_func
.
extend
(
gen_codes
([
line
],
indent
=
indent
+
1
))
else
:
b_init_lines
,
b_forward_lines
=
block
.
gen_dygraph_code
(
indent
=
indent
+
1
)
init_func
.
extend
(
b_init_lines
)
forward_func
.
extend
(
b_forward_lines
)
block
=
layer
.
blocks
[
1
]
if
len
(
block
.
layers
)
>
0
:
b_init_lines
,
b_forward_lines
=
block
.
gen_dygraph_code
(
indent
=
indent
+
1
)
if
len
(
b_forward_lines
)
!=
0
:
line
=
"else:"
forward_func
.
extend
(
gen_codes
([
line
],
indent
=
indent
))
init_func
.
extend
(
b_init_lines
)
forward_func
.
extend
(
b_forward_lines
)
def
prim_int
(
layer
,
indent
=
1
,
init_func
=
[],
forward_func
=
[],
layer_id
=
None
,
different_attrs
=
None
):
...
...
@@ -232,31 +268,39 @@ def prim_int(layer, indent=1, init_func=[], forward_func=[], layer_id=None, diff
forward_func
.
extend
(
gen_codes
([
line
],
indent
=
indent
))
def
prim_is
(
layer
,
indent
=
1
,
init_func
=
[],
forward_func
=
[],
layer_id
=
None
,
different_attrs
=
None
):
def
prim_is
(
layer
,
indent
=
1
,
init_func
=
[],
forward_func
=
[],
layer_id
=
None
,
different_attrs
=
None
,
is_return_line
=
False
):
line
=
"{} = {} is {}"
.
format
(
layer
.
outputs
[
0
],
get_value
(
layer
,
"x"
,
different_attrs
),
get_value
(
layer
,
"y"
,
different_attrs
))
if
is_return_line
:
return
line
.
split
(
" = "
)[
1
]
forward_func
.
extend
(
gen_codes
([
line
],
indent
=
indent
))
def
prim_isinstance
(
layer
,
indent
=
1
,
init_func
=
[],
forward_func
=
[],
layer_id
=
None
,
different_attrs
=
None
):
def
prim_isinstance
(
layer
,
indent
=
1
,
init_func
=
[],
forward_func
=
[],
layer_id
=
None
,
different_attrs
=
None
,
is_return_line
=
False
):
line
=
"{} = isinstance({}, {})"
.
format
(
layer
.
outputs
[
0
],
get_value
(
layer
,
"input"
,
different_attrs
),
layer
.
attrs
[
"cls"
])
if
is_return_line
:
return
line
.
split
(
" = "
)[
1
]
forward_func
.
extend
(
gen_codes
([
line
],
indent
=
indent
))
def
prim_isnot
(
layer
,
indent
=
1
,
init_func
=
[],
forward_func
=
[],
layer_id
=
None
,
different_attrs
=
None
):
def
prim_isnot
(
layer
,
indent
=
1
,
init_func
=
[],
forward_func
=
[],
layer_id
=
None
,
different_attrs
=
None
,
is_return_line
=
False
):
line
=
"{} = {} is not {}"
.
format
(
layer
.
outputs
[
0
],
get_value
(
layer
,
"x"
,
different_attrs
),
get_value
(
layer
,
"y"
,
different_attrs
))
if
is_return_line
:
return
line
.
split
(
" = "
)[
1
]
forward_func
.
extend
(
gen_codes
([
line
],
indent
=
indent
))
def
prim_le
(
layer
,
indent
=
1
,
init_func
=
[],
forward_func
=
[],
layer_id
=
None
,
different_attrs
=
None
):
def
prim_le
(
layer
,
indent
=
1
,
init_func
=
[],
forward_func
=
[],
layer_id
=
None
,
different_attrs
=
None
,
is_return_line
=
False
):
line
=
"{} = {} <= {}"
.
format
(
layer
.
outputs
[
0
],
get_value
(
layer
,
"x"
,
different_attrs
),
get_value
(
layer
,
"y"
,
different_attrs
))
if
is_return_line
:
return
line
.
split
(
" = "
)[
1
]
forward_func
.
extend
(
gen_codes
([
line
],
indent
=
indent
))
...
...
@@ -273,10 +317,12 @@ def prim_len2list(layer, indent=1, init_func=[], forward_func=[], layer_id=None,
forward_func
.
extend
(
gen_codes
(
lines
,
indent
=
indent
))
def
prim_lt
(
layer
,
indent
=
1
,
init_func
=
[],
forward_func
=
[],
layer_id
=
None
,
different_attrs
=
None
):
def
prim_lt
(
layer
,
indent
=
1
,
init_func
=
[],
forward_func
=
[],
layer_id
=
None
,
different_attrs
=
None
,
is_return_line
=
False
):
line
=
"{} = {} < {}"
.
format
(
layer
.
outputs
[
0
],
get_value
(
layer
,
"x"
,
different_attrs
),
get_value
(
layer
,
"y"
,
different_attrs
))
if
is_return_line
:
return
line
.
split
(
" = "
)[
1
]
forward_func
.
extend
(
gen_codes
([
line
],
indent
=
indent
))
...
...
@@ -317,10 +363,12 @@ def prim_mul(layer, indent=1, init_func=[], forward_func=[], layer_id=None, diff
forward_func
.
extend
(
gen_codes
([
line
],
indent
=
indent
))
def
prim_ne
(
layer
,
indent
=
1
,
init_func
=
[],
forward_func
=
[],
layer_id
=
None
,
different_attrs
=
None
):
def
prim_ne
(
layer
,
indent
=
1
,
init_func
=
[],
forward_func
=
[],
layer_id
=
None
,
different_attrs
=
None
,
is_return_line
=
False
):
line
=
"{} = {} != {}"
.
format
(
layer
.
outputs
[
0
],
get_value
(
layer
,
"x"
,
different_attrs
),
get_value
(
layer
,
"y"
,
different_attrs
))
if
is_return_line
:
return
line
.
split
(
" = "
)[
1
]
forward_func
.
extend
(
gen_codes
([
line
],
indent
=
indent
))
...
...
@@ -329,15 +377,19 @@ def prim_neg(layer, indent=1, init_func=[], forward_func=[], layer_id=None, diff
forward_func
.
extend
(
gen_codes
([
line
],
indent
=
indent
))
def
prim_not
(
layer
,
indent
=
1
,
init_func
=
[],
forward_func
=
[],
layer_id
=
None
,
different_attrs
=
None
):
def
prim_not
(
layer
,
indent
=
1
,
init_func
=
[],
forward_func
=
[],
layer_id
=
None
,
different_attrs
=
None
,
is_return_line
=
False
):
line
=
"{} = not {}"
.
format
(
layer
.
outputs
[
0
],
get_value
(
layer
,
"input"
,
different_attrs
))
if
is_return_line
:
return
line
.
split
(
" = "
)[
1
]
forward_func
.
extend
(
gen_codes
([
line
],
indent
=
indent
))
def
prim_or
(
layer
,
indent
=
1
,
init_func
=
[],
forward_func
=
[],
layer_id
=
None
,
different_attrs
=
None
):
def
prim_or
(
layer
,
indent
=
1
,
init_func
=
[],
forward_func
=
[],
layer_id
=
None
,
different_attrs
=
None
,
is_return_line
=
False
):
line
=
"{} = {} or {}"
.
format
(
layer
.
outputs
[
0
],
get_value
(
layer
,
"x"
,
different_attrs
),
get_value
(
layer
,
"y"
,
different_attrs
))
if
is_return_line
:
return
line
.
split
(
" = "
)[
1
]
forward_func
.
extend
(
gen_codes
([
line
],
indent
=
indent
))
...
...
@@ -419,9 +471,15 @@ def prim_str(layer, indent=1, init_func=[], forward_func=[], layer_id=None, diff
def
prim_sub
(
layer
,
indent
=
1
,
init_func
=
[],
forward_func
=
[],
layer_id
=
None
,
different_attrs
=
None
):
line
=
"{} = {} - {}"
.
format
(
layer
.
outputs
[
0
],
get_value
(
layer
,
"x"
,
different_attrs
),
get_value
(
layer
,
"y"
,
different_attrs
))
if
int
(
get_value
(
layer
,
"alpha"
,
different_attrs
))
==
1
:
line
=
"{} = {} - {}"
.
format
(
layer
.
outputs
[
0
],
get_value
(
layer
,
"x"
,
different_attrs
),
get_value
(
layer
,
"y"
,
different_attrs
))
else
:
line
=
"{} = {} - {} * {}"
.
format
(
layer
.
outputs
[
0
],
get_value
(
layer
,
"x"
,
different_attrs
),
get_value
(
layer
,
"alpha"
,
different_attrs
),
get_value
(
layer
,
"y"
,
different_attrs
))
forward_func
.
extend
(
gen_codes
([
line
],
indent
=
indent
))
...
...
x2paddle/op_mapper/dygraph/pytorch2paddle/pytorch_op_mapper.py
浏览文件 @
69a8316b
...
...
@@ -84,7 +84,7 @@ class PyTorchOpMapper(OpMapper):
current_node_outputs
.
extend
(
outputs
)
# 初始化
graph
=
PaddleGraph
(
parent_layer
=
parent_layer
,
graph_type
=
"dygraph"
)
graph
=
PaddleGraph
(
source_type
=
"pytorch"
,
parent_layer
=
parent_layer
,
graph_type
=
"dygraph"
)
if
"TopLevelTracedModule"
in
str
(
type
(
self
.
script
)):
graph
.
set_script
(
self
.
script
)
current_node_outputs
=
[]
...
...
x2paddle/op_mapper/static/onnx2paddle/opset9/opset.py
浏览文件 @
69a8316b
...
...
@@ -240,15 +240,22 @@ class OpSet9():
def
_interpolate
(
self
,
node
):
val_x
=
self
.
graph
.
get_input_node
(
node
,
idx
=
0
,
copy
=
True
)
inputs
=
{
'x'
:
val_x
.
name
}
attrs
=
dict
()
if
node
.
layer_type
==
'Resize'
:
if
len
(
node
.
layer
.
input
)
==
2
:
# opset 10
val_scales
=
self
.
graph
.
get_input_node
(
node
,
idx
=
1
,
copy
=
True
)
inputs
[
'scale_factor'
]
=
val_scales
.
name
# TODO(syf): paddle.nn.functional.interpolate will support the length
# which is the same as the rank of input.
# inputs['scale_factor'] = val_scales.name
attrs
[
'scale_factor'
]
=
self
.
params
[
val_scales
.
name
].
tolist
()[
2
:]
elif
len
(
node
.
layer
.
input
)
==
3
:
# opset 11
val_scales
=
self
.
graph
.
get_input_node
(
node
,
idx
=
2
,
copy
=
True
)
inputs
[
'scale_factor'
]
=
val_scales
.
name
# TODO(syf): paddle.nn.functional.interpolate will support the length
# which is the same as the rank of input.
# inputs['scale_factor'] = val_scales.name
attrs
[
'scale_factor'
]
=
self
.
params
[
val_scales
.
name
].
tolist
()[
2
:]
elif
len
(
node
.
layer
.
input
)
==
4
:
# opset 11
val_sizes
=
self
.
graph
.
get_input_node
(
node
,
idx
=
3
,
copy
=
True
)
...
...
@@ -271,7 +278,7 @@ class OpSet9():
ipt
=
inputs
.
pop
(
"x"
)
inputs
[
"input"
]
=
ipt
mode
=
node
.
get_attr
(
'mode'
,
'nearest'
)
attrs
=
{
"align_corners"
:
False
}
attrs
.
update
({
"align_corners"
:
False
})
self
.
paddle_graph
.
add_layer
(
kernel
=
"fluid.layers.resize_nearest"
,
inputs
=
inputs
,
...
...
@@ -283,9 +290,9 @@ class OpSet9():
inputs
[
'scale'
]
=
val_scales
mode
=
node
.
get_attr
(
'mode'
,
'nearest'
)
attrs
=
{
"align_corners"
:
False
,
attrs
.
update
(
{
"align_corners"
:
False
,
"mode"
:
string
(
mode
),
"align_mode"
:
1
}
"align_mode"
:
1
}
)
self
.
paddle_graph
.
add_layer
(
kernel
=
"paddle.nn.functional.interpolate"
,
inputs
=
inputs
,
...
...
@@ -917,10 +924,10 @@ class OpSet9():
outputs
=
[
node
.
name
],
**
layer_attrs
)
else
:
max_ipt
=
self
.
graph
.
get_input_node
(
node
,
idx
=
1
,
copy
=
True
)
min_ipt
=
self
.
graph
.
get_input_node
(
node
,
idx
=
2
,
copy
=
True
)
max_value
=
_const_weight_or_none
(
max_ipt
)
min_ipt
=
self
.
graph
.
get_input_node
(
node
,
idx
=
1
,
copy
=
True
)
max_ipt
=
self
.
graph
.
get_input_node
(
node
,
idx
=
2
,
copy
=
True
)
min_value
=
_const_weight_or_none
(
min_ipt
)
max_value
=
_const_weight_or_none
(
max_ipt
)
if
max_value
.
shape
==
(
1
,
):
max_value
=
max_value
[
0
]
if
min_value
.
shape
==
(
1
,
):
...
...
@@ -1576,4 +1583,17 @@ class OpSet9():
kernel
=
paddle_op
,
inputs
=
layer_inputs
,
outputs
=
[
node
.
name
],
**
layer_attrs
)
@
print_mapping_info
def
ArgMax
(
self
,
node
):
val_x
=
self
.
graph
.
get_input_node
(
node
,
idx
=
0
,
copy
=
True
)
axis
=
node
.
get_attr
(
'axis'
)
keepdims
=
False
if
node
.
get_attr
(
'keepdims'
)
==
0
else
True
layer_attrs
=
{
'axis'
:
axis
,
'keepdim'
:
keepdims
}
self
.
paddle_graph
.
add_layer
(
'paddle.argmax'
,
inputs
=
{
"x"
:
val_x
.
name
},
outputs
=
[
node
.
name
],
**
layer_attrs
)
\ No newline at end of file
x2paddle/optimizer/code_optimizer/__init__.py
浏览文件 @
69a8316b
...
...
@@ -13,4 +13,5 @@
# limitations under the License.
from
x2paddle.optimizer.code_optimizer.hierachical_tree
import
HierarchicalTree
\ No newline at end of file
from
x2paddle.optimizer.code_optimizer.hierachical_tree
import
HierarchicalTree
from
x2paddle.optimizer.code_optimizer.module_graph
import
ModuleGraph
\ No newline at end of file
x2paddle/optimizer/code_optimizer/layer_code_generator.py
浏览文件 @
69a8316b
...
...
@@ -38,7 +38,7 @@ NN_KERNEL_NAME = {"paddle.nn.BatchNorm": "bn",
"paddle.nn.LeakyReLU"
:
"leakly_relu"
}
NN_KERNEL_WITH_PARAMS
=
list
(
NN_KERNEL_NAME
.
keys
())[:
6
]
def
rename_layers
(
layers
,
param_tree
=
None
):
def
rename_layers
(
layers
,
param_tree
=
None
,
is_rename_module
=
False
):
""" 对子模块的输入输出等进行重命名。
"""
layers_cp
=
copy
.
deepcopy
(
layers
)
...
...
@@ -84,17 +84,23 @@ def rename_layers(layers, param_tree=None):
layer
.
outputs
[
0
]
=
new_name
nn_count_dict
[
layer
.
kernel
]
+=
1
elif
i
==
0
and
layer
.
kernel
==
"module"
:
old_name
=
layer
.
outputs
[
0
].
split
(
"/"
)[
0
]
if
old_name
not
in
nn_count_dict
:
nn_count_dict
[
old_name
]
=
0
if
is_rename_module
:
if
param_tree
is
not
None
:
param_node
=
param_tree
.
get_node
(
layer
.
outputs
[
0
])
nn_param_nodes
.
append
(
param_node
)
param_node
.
new_name
=
layer
.
outputs
[
0
]
else
:
nn_count_dict
[
old_name
]
+=
1
new_name
=
old_name
+
str
(
nn_count_dict
[
old_name
])
if
param_tree
is
not
None
:
param_node
=
param_tree
.
get_node
(
layer
.
outputs
[
0
])
nn_param_nodes
.
append
(
param_node
)
param_node
.
new_name
=
new_name
layer
.
outputs
[
0
]
=
new_name
old_name
=
layer
.
outputs
[
0
].
split
(
"/"
)[
0
]
if
old_name
not
in
nn_count_dict
:
nn_count_dict
[
old_name
]
=
0
else
:
nn_count_dict
[
old_name
]
+=
1
new_name
=
old_name
+
str
(
nn_count_dict
[
old_name
])
if
param_tree
is
not
None
:
param_node
=
param_tree
.
get_node
(
layer
.
outputs
[
0
])
nn_param_nodes
.
append
(
param_node
)
param_node
.
new_name
=
new_name
layer
.
outputs
[
0
]
=
new_name
else
:
old_name
=
layer
.
outputs
[
i
]
new_name
=
"x{}"
.
format
(
count
)
...
...
@@ -196,6 +202,15 @@ def gen_layer_code(graph, sub_layers, sub_layers_name, different_attrs=list()):
outputs
.
append
(
output_name
)
else
:
outputs
.
append
(
output_name
)
if
layer
.
kernel
==
"prim.dict"
:
is_set_item
=
True
for
out_layer_id
in
graph
.
edges_out
[
layer_id
]:
out_layer
=
sub_layers
[
out_layer_id
]
if
out_layer
.
kernel
!=
"prim.set_item"
:
is_set_item
=
False
break
if
is_set_item
:
outputs
.
append
(
layer
.
outputs
[
0
])
no_output_count
=
0
for
i
,
(
layer_id
,
layer
)
in
enumerate
(
sub_layers
.
items
()):
if
(
"paddle.nn"
in
layer
.
kernel
and
"functional"
not
in
layer
.
kernel
)
or
\
...
...
x2paddle/optimizer/code_optimizer/module_graph.py
0 → 100644
浏览文件 @
69a8316b
# -*- coding:UTF-8 -*-
# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License"
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import
copy
import
os
import
os.path
as
osp
from
x2paddle.core.program
import
PaddleLayer
from
x2paddle.optimizer.code_optimizer.subgraphs_union
import
construct_attrs_table
,
get_inputs_outputs
from
x2paddle.optimizer.code_optimizer.layer_code_generator
import
gen_layer_code
,
rename_layers
from
x2paddle.optimizer.code_optimizer.parameter_tree
import
PamareterNode
,
PamareterTree
NoModuleStart
=
[
"paddle.nn.ReLU"
]
class
Apriori
(
object
):
""" 使用Apriori算法挖掘频繁子图
1. 构建频繁1项集
2. 挖掘频繁k项集
3. 最终k项集和节点数满足最少节点数的子图组成集合GS
Args:
min_support (int): 子图出现次数的最小值。
"""
def
__init__
(
self
,
min_support
):
self
.
min_support
=
min_support
def
is_match
(
self
,
item
,
sublayers
):
for
i
in
range
(
len
(
item
)):
if
len
(
sublayers
)
<=
i
or
item
[
i
]
!=
sublayers
[
i
].
kernel
:
return
False
return
True
def
create_C1
(
self
):
# 构建候选1-项集
C1
=
list
()
for
layer_id
,
layer
in
self
.
layers
.
items
():
if
layer
.
kernel
==
"paddle.to_tensor"
or
\
layer
.
kernel
==
"prim.if"
or
\
layer
.
kernel
==
"prim.loop"
:
#or \
# layer.kernel == "prim.list" or \
# layer.kernel == "prim.tuple" or \
# layer.kernel == "prim.dict_construct":
continue
if
self
.
pd_graph
.
edges_in
.
get
(
layer_id
,
0
)
==
0
and
\
self
.
pd_graph
.
edges_out
.
get
(
layer_id
,
0
)
==
0
:
continue
if
[
layer
.
kernel
]
not
in
C1
:
C1
.
append
([
layer
.
kernel
])
return
C1
def
create_Ck
(
self
,
Lk_last
,
C1
):
# 构建候选k-项集
Ck
=
list
()
for
item
in
Lk_last
:
for
item_one
in
C1
:
new_item
=
copy
.
deepcopy
(
item
)
new_item
.
append
(
item_one
[
0
])
if
new_item
[
0
]
in
NoModuleStart
:
continue
Ck
.
append
(
new_item
)
return
Ck
def
generate_Lk_by_Ck
(
self
,
Ck
):
# 生成频繁k-项集
Lk
=
list
()
for
item
in
Ck
:
count
=
0
for
i
in
range
(
len
(
self
.
layers
)):
sublayers
=
list
(
self
.
layers
.
values
())[
i
:]
if
self
.
is_match
(
item
,
sublayers
):
count
+=
1
if
count
>=
self
.
min_support
:
Lk
.
append
(
item
)
return
Lk
def
run
(
self
,
graph
):
self
.
pd_graph
=
graph
self
.
layers
=
graph
.
layers
itemset
=
list
()
C1
=
self
.
create_C1
()
L1
=
self
.
generate_Lk_by_Ck
(
C1
)
Lk
=
L1
while
len
(
Lk
)
>
0
:
Ck
=
self
.
create_Ck
(
Lk
,
C1
)
Lk
=
self
.
generate_Lk_by_Ck
(
Ck
)
itemset
.
extend
(
Lk
)
return
itemset
class
DP
(
object
):
""" 使用动动态规划找到使代码最短的组合方式。
"""
def
__init__
(
self
,
combination_itemset
):
self
.
combination_itemset
=
combination_itemset
def
get_combination_id
(
self
,
combination
,
layers
):
combination_id
=
list
()
for
layer_obj
in
combination
:
if
len
(
layer_obj
)
>
1
:
kernel_itemset
=
list
()
for
layer_id
in
layer_obj
:
kernel_itemset
.
append
(
layers
[
layer_id
].
kernel
)
id
=
self
.
combination_itemset
.
index
(
kernel_itemset
)
combination_id
.
append
(
id
)
else
:
combination_id
.
append
(
-
1
)
return
combination_id
def
run
(
self
,
graph
):
layers
=
graph
.
layers
layer_combination_list
=
list
()
for
i
,
(
layer_id
,
layer
)
in
enumerate
(
layers
.
items
()):
if
i
==
0
:
layer_combination_list
.
append
([[
layer_id
]])
continue
current_itemset
=
[
layer_id
]
kernel_itemset
=
[
layer
.
kernel
]
candidate_itemset
=
list
()
min_count
=
len
(
layers
)
prefix_ids
=
list
(
range
(
i
))
prefix_ids
.
reverse
()
for
j
in
prefix_ids
:
current_layer_id
=
list
(
layers
.
keys
())[
j
]
current_layer
=
list
(
layers
.
values
())[
j
]
current_itemset
.
insert
(
0
,
current_layer_id
)
kernel_itemset
.
insert
(
0
,
current_layer
.
kernel
)
if
kernel_itemset
in
self
.
combination_itemset
:
current_count
=
len
(
layer_combination_list
[
j
-
1
])
all_count
=
current_count
+
1
if
all_count
<
min_count
:
min_count
=
all_count
candidate_itemset
=
copy
.
deepcopy
(
current_itemset
)
if
j
-
1
<
0
:
last_itemset
=
list
()
else
:
last_itemset
=
copy
.
deepcopy
(
layer_combination_list
[
j
-
1
])
else
:
if
j
==
prefix_ids
[
0
]:
min_count
=
len
(
layer_combination_list
[
j
])
+
1
current_itemset
.
pop
(
0
)
candidate_itemset
=
copy
.
deepcopy
(
current_itemset
)
last_itemset
=
copy
.
deepcopy
(
layer_combination_list
[
j
])
break
last_itemset
.
append
(
candidate_itemset
)
layer_combination_list
.
append
(
last_itemset
)
final_combination
=
layer_combination_list
[
-
1
]
combination_id
=
self
.
get_combination_id
(
final_combination
,
layers
)
return
final_combination
,
combination_id
class
ModuleGraph
(
object
):
""" 更新PaddleGraph,生成代码。
"""
def
__init__
(
self
,
graph
):
self
.
pd_graph
=
graph
self
.
global_layers
=
graph
.
get_global_layers
()
self
.
codes
=
list
()
self
.
param_tree
=
PamareterTree
()
def
get_updation_information
(
self
):
aprior
=
Apriori
(
3
)
combination_itemset
=
aprior
.
run
(
self
.
pd_graph
)
dp
=
DP
(
combination_itemset
)
combination
,
combination_id
=
dp
.
run
(
self
.
pd_graph
)
return
combination
,
combination_id
def
analyze_attrs_table
(
self
,
attrs_table
):
""" 分析属性表格,哪些属性取值不一致。
"""
diff_attrs_column
=
list
()
for
column
in
list
(
attrs_table
.
columns
):
elements
=
list
(
attrs_table
.
get
(
column
))
base
=
elements
[
0
]
for
element
in
elements
[
1
:]:
if
isinstance
(
base
,
str
)
and
"'"
not
in
base
:
break
if
element
!=
base
:
diff_attrs_column
.
append
(
column
)
break
return
diff_attrs_column
def
analyze_graph
(
self
,
sub_layers_list
):
def
is_same
(
sub_layers1
,
sub_layers2
,
id1
,
id2
):
inputs1
,
outputs1
=
ipt_opt_list
[
id1
]
inputs2
,
outputs2
=
ipt_opt_list
[
id2
]
if
len
(
inputs1
)
!=
len
(
inputs2
)
or
len
(
outputs1
)
!=
len
(
outputs2
):
return
False
layer_id_list1
=
list
(
sub_layers1
.
keys
())
layer_id_list2
=
list
(
sub_layers2
.
keys
())
for
i
,
layer_id1
in
enumerate
(
layer_id_list1
):
layer_id2
=
layer_id_list2
[
i
]
if
len
(
self
.
pd_graph
.
edges_in
[
layer_id1
])
!=
len
(
self
.
pd_graph
.
edges_in
[
layer_id2
]):
return
False
for
j
,
ipt_layer_id1
in
enumerate
(
self
.
pd_graph
.
edges_in
[
layer_id1
]):
ipt_layer_id2
=
self
.
pd_graph
.
edges_in
[
layer_id2
][
j
]
if
(
ipt_layer_id1
in
layer_id_list1
)
^
(
ipt_layer_id2
in
layer_id_list2
):
return
False
if
(
layer_id1
in
self
.
pd_graph
.
edges_out
)
^
(
layer_id2
in
self
.
pd_graph
.
edges_out
):
return
False
if
(
layer_id1
in
self
.
pd_graph
.
edges_out
)
and
(
layer_id2
in
self
.
pd_graph
.
edges_out
):
if
(
len
(
self
.
pd_graph
.
edges_out
[
layer_id1
])
>
1
and
len
(
self
.
pd_graph
.
edges_out
[
layer_id2
])
==
1
)
or
\
(
len
(
self
.
pd_graph
.
edges_out
[
layer_id1
])
==
1
and
len
(
self
.
pd_graph
.
edges_out
[
layer_id2
])
>
1
):
return
False
for
j
,
opt_layer_id1
in
enumerate
(
self
.
pd_graph
.
edges_out
[
layer_id1
]):
if
len
(
self
.
pd_graph
.
edges_out
[
layer_id1
])
==
1
and
len
(
self
.
pd_graph
.
edges_out
[
layer_id2
])
==
1
:
opt_layer_id2
=
self
.
pd_graph
.
edges_out
[
layer_id2
][
j
]
if
(
opt_layer_id1
in
layer_id_list1
)
^
(
opt_layer_id2
in
layer_id_list2
):
return
False
return
True
sub_layers_list_list
=
list
()
id_list
=
list
()
ipt_opt_list
=
list
()
sub_layers_list_list
.
append
([
sub_layers_list
[
0
]])
id_list
.
append
(
0
)
for
i
,
sub_layer
in
enumerate
(
sub_layers_list
):
ipt_opt_list
.
append
(
get_inputs_outputs
(
self
.
pd_graph
,
sub_layer
))
if
i
==
0
:
continue
id_list_cp
=
copy
.
deepcopy
(
id_list
)
for
j
,
index
in
enumerate
(
id_list_cp
):
if
is_same
(
sub_layers_list
[
index
],
sub_layer
,
index
,
i
):
sub_layers_list_list
[
j
].
append
(
sub_layer
)
break
if
j
==
len
(
id_list_cp
)
-
1
:
sub_layers_list_list
.
append
(
list
())
sub_layers_list_list
[
j
+
1
].
append
(
sub_layer
)
id_list
.
append
(
i
)
return
sub_layers_list_list
def
merge_node
(
self
,
sub_layers_list
,
attrs_table
,
module_name
):
sub_layers
=
sub_layers_list
[
0
]
diff_attrs_column
=
self
.
analyze_attrs_table
(
attrs_table
)
sub_layers
,
_
,
_
=
rename_layers
(
sub_layers
)
code_str
=
gen_layer_code
(
self
.
pd_graph
,
sub_layers
,
module_name
,
different_attrs
=
diff_attrs_column
)
self
.
codes
.
append
(
code_str
)
for
index
,
sub_layers
in
enumerate
(
sub_layers_list
):
inputs
,
outputs
=
get_inputs_outputs
(
self
.
pd_graph
,
sub_layers
)
inputs_dict
=
dict
()
for
i
,
input
in
enumerate
(
inputs
):
inputs_dict
[
"input_{}"
.
format
(
i
)]
=
input
mn
=
module_name
.
lower
()
outputs
=
[
"{}_{}"
.
format
(
mn
,
index
)]
+
outputs
node_name
=
"{}_{}"
.
format
(
module_name
,
index
)
diff_attrs
=
dict
()
for
column
in
diff_attrs_column
:
diff_attrs
[
column
]
=
attrs_table
.
get
(
column
).
loc
[
node_name
]
new_layer
=
PaddleLayer
(
id
=
list
(
sub_layers
.
keys
())[
-
1
],
kernel
=
"module"
,
inputs
=
inputs_dict
,
outputs
=
outputs
,
module
=
module_name
,
**
diff_attrs
)
_
,
nn_param_nodes
,
_
=
rename_layers
(
sub_layers
,
self
.
param_tree
)
param_node
=
PamareterNode
(
old_name
=
outputs
[
0
])
for
node
in
nn_param_nodes
:
param_node
.
add_child
(
node
)
self
.
param_tree
.
add_node
(
param_node
)
for
i
,
(
layer_id
,
layer
)
in
enumerate
(
sub_layers
.
items
()):
if
i
==
len
(
sub_layers
)
-
1
:
self
.
pd_graph
.
layers
[
layer_id
]
=
new_layer
else
:
if
len
(
layer_id
.
split
(
"."
))
>
1
:
continue
self
.
pd_graph
.
layers
.
pop
(
layer_id
)
self
.
pd_graph
.
build
()
def
convert_subgraph_to_layer
(
self
,
combination
,
combination_id
):
combination_id_set
=
set
(
combination_id
)
for
s
in
list
(
combination_id_set
):
if
s
==
-
1
:
continue
module_name
=
"Block{}"
.
format
(
s
)
sub_layers_list
=
list
()
for
i
,
c
in
enumerate
(
combination
):
if
len
(
c
)
>
1
and
combination_id
[
i
]
==
s
:
sub_layers
=
dict
()
for
layer_id
in
c
:
sub_layers
[
layer_id
]
=
self
.
global_layers
[
layer_id
]
sub_layers_list
.
append
(
sub_layers
)
sub_layers_list_list
=
self
.
analyze_graph
(
sub_layers_list
)
for
i
,
sub_layers_list
in
enumerate
(
sub_layers_list_list
):
if
i
==
0
:
real_module_name
=
module_name
else
:
real_module_name
=
module_name
+
"__{}"
.
format
(
i
)
if
len
(
sub_layers_list
)
>
1
:
attrs_table
=
construct_attrs_table
(
sub_layers_list
,
module_name
=
real_module_name
)
self
.
merge_node
(
sub_layers_list
,
attrs_table
,
real_module_name
)
layers
,
nn_param_nodes
,
_
=
rename_layers
(
self
.
pd_graph
.
layers
,
self
.
param_tree
,
is_rename_module
=
True
)
code_str
=
gen_layer_code
(
self
.
pd_graph
,
layers
,
self
.
pd_graph
.
name
)
self
.
codes
.
append
(
code_str
)
param_node
=
PamareterNode
(
old_name
=
"Module"
)
for
node
in
nn_param_nodes
:
param_node
.
add_child
(
node
)
self
.
param_tree
.
add_node
(
param_node
)
def
update_parameters
(
self
):
""" 更新参数。
"""
self
.
param_tree
.
traverse
()
full_old_name_list
=
copy
.
deepcopy
(
list
(
self
.
pd_graph
.
parameters
.
keys
()))
for
old_name
,
new_name
in
self
.
param_tree
.
old2new
.
items
():
for
full_old_name
in
full_old_name_list
:
if
full_old_name
.
startswith
(
"{}."
.
format
(
old_name
)):
full_new_name
=
full_old_name
.
replace
(
"{}."
.
format
(
old_name
),
"{}."
.
format
(
new_name
))
params
=
self
.
pd_graph
.
parameters
.
pop
(
full_old_name
)
self
.
pd_graph
.
parameters
[
full_new_name
]
=
params
if
full_old_name
==
old_name
:
full_new_name
=
full_old_name
.
replace
(
old_name
,
new_name
)
params
=
self
.
pd_graph
.
parameters
.
pop
(
full_old_name
)
self
.
pd_graph
.
parameters
[
full_new_name
]
=
params
def
save_source_files
(
self
,
save_dir
):
def
gen_main_code
():
input_data_name
=
', '
.
join
(
self
.
pd_graph
.
inputs
)
run_func_list
=
list
()
run_func_list
.
append
(
"def main({}):"
.
format
(
input_data_name
))
run_func_list
.
append
(
" # 共{}个输入"
.
format
(
len
(
self
.
pd_graph
.
inputs_info
)))
for
k
,
v
in
self
.
pd_graph
.
inputs_info
.
items
():
run_func_list
.
append
(
" # {}: 形状为{},类型为{}。"
.
format
(
k
,
v
[
0
],
v
[
1
]))
run_func_list
.
extend
(
[
" paddle.disable_static()"
,
" params = paddle.load('{}/model.pdparams')"
.
format
(
osp
.
abspath
(
save_dir
)),
" model = {}()"
.
format
(
self
.
pd_graph
.
name
),
" model.set_dict(params)"
,
" model.eval()"
,
" out = model({})"
.
format
(
input_data_name
),
" return out"
])
return
"
\n
"
.
join
(
run_func_list
)
combination
,
combination_id
=
self
.
get_updation_information
()
self
.
convert_subgraph_to_layer
(
combination
,
combination_id
)
self
.
update_parameters
()
import_list
=
[
"import paddle"
,
"import paddle.fluid as fluid"
,
"from paddle.fluid.initializer import Constant"
,
"from paddle.fluid.param_attr import ParamAttr"
,
"import math"
,
"from x2paddle.op_mapper.dygraph.pytorch2paddle "
+
\
"import pytorch_custom_layer as x2paddle_nn"
"
\n
"
,]
import_str
=
"
\n
"
.
join
(
import_list
)
if
not
osp
.
exists
(
save_dir
):
os
.
makedirs
(
save_dir
)
f
=
open
(
osp
.
join
(
save_dir
,
'x2paddle_code.py'
),
'w'
)
f
.
write
(
import_str
)
for
code
in
self
.
codes
:
f
.
write
(
code
)
f
.
write
(
"
\n
"
)
run_func
=
gen_main_code
()
f
.
write
(
run_func
)
f
.
close
()
\ No newline at end of file
x2paddle/optimizer/code_optimizer/subgraphs_union.py
浏览文件 @
69a8316b
...
...
@@ -19,7 +19,7 @@ import pandas as pd
from
x2paddle.optimizer.code_optimizer.layer_code_generator
import
rename_layers
def
construct_attrs_table
(
sub_layers_list
,
node_name2sub_layers
):
def
construct_attrs_table
(
sub_layers_list
,
node_name2sub_layers
=
None
,
module_name
=
None
):
""" 构造不同属性的表格。
"""
def
get_node_name
(
sub_layers
):
...
...
@@ -32,9 +32,12 @@ def construct_attrs_table(sub_layers_list, node_name2sub_layers):
_
,
_
,
new_names
=
rename_layers
(
sub_layers
)
table
=
list
()
node_names
=
list
()
for
sub_layers
in
sub_layers_list
:
for
i
,
sub_layers
in
enumerate
(
sub_layers_list
)
:
attrs
=
dict
()
node_names
.
append
(
get_node_name
(
sub_layers
))
if
node_name2sub_layers
is
not
None
:
node_names
.
append
(
get_node_name
(
sub_layers
))
else
:
node_names
.
append
(
"{}_{}"
.
format
(
module_name
,
i
))
for
i
,
(
layer_id
,
layer
)
in
enumerate
(
sub_layers
.
items
()):
for
k
,
v
in
layer
.
attrs
.
items
():
attrs
[
new_names
[
i
]
+
"_{}"
.
format
(
k
)]
=
v
...
...
x2paddle/optimizer/fusion/dygraph/__init__.py
浏览文件 @
69a8316b
...
...
@@ -26,6 +26,8 @@ from .dropout_fuser import DygraphDropoutFuser
from
.dropout_fuse_pass
import
DygraphDropoutFusePass
from
.fc_fuser
import
DygraphFcFuser
from
.fc_fuse_pass
import
DygraphFcFusePass
from
.if_fuser
import
DygraphIfFuser
from
.if_fuse_pass
import
DygraphIfFusePass
from
.interpolate_bilinear_fuser
import
DygraphInterpolateBilinearFuser
from
.interpolate_bilinear_fuse_pass
import
DygraphInterpolateBilinearFusePass
from
.prelu_fuser
import
DygraphPReLUFuser
...
...
x2paddle/optimizer/fusion/dygraph/if_fuse_pass.py
0 → 100644
浏览文件 @
69a8316b
# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License"
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from
x2paddle.optimizer.pass_
import
Pass
from
x2paddle.optimizer.fusion.dygraph
import
DygraphIfFuser
from
x2paddle.optimizer.pass_manager
import
pass_register
@
pass_register
class
DygraphIfFusePass
(
Pass
):
name
=
"dygraph_if_fuse_pass"
def
__init__
(
self
):
Pass
.
__init__
(
self
)
def
apply
(
self
,
graph
):
fuser
=
DygraphIfFuser
()
fuser
.
operate
(
graph
,
match_kind
=
"op"
)
# 用于注册
if_fuse_pass
=
DygraphIfFuser
()
x2paddle/optimizer/fusion/dygraph/if_fuser.py
0 → 100644
浏览文件 @
69a8316b
# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License"
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import
numpy
as
np
from
x2paddle.optimizer.pattern_matcher
import
FuseBase
from
x2paddle.core.program
import
PaddleGraph
,
PaddleLayer
from
x2paddle.core.util
import
*
class
DygraphIfFuser
(
FuseBase
):
def
__init__
(
self
):
super
(
DygraphIfFuser
,
self
).
__init__
(
graph_type
=
"dygraph"
)
def
build_pattern
(
self
):
""" 描述需要替换的if图结构。
if层模式python实现代码示例:
x81 = 'relu' in {'layer4': 'out', 'layer3': 'aux'}
if x81 :
...
"""
self
.
pattern
.
add_layer
(
"prim.if"
,
inputs
=
{
"input"
:
"if-input-0"
},
outputs
=
[
"x0"
])
self
.
pattern
.
build
(
inputs
=
{
"input-0"
:
"if-input-0"
})
def
insert_new_layer
(
self
,
graph
,
parameters
,
matches
):
layer_id
=
list
(
matches
.
keys
())[
0
]
layer
=
list
(
matches
.
values
())[
0
]
if
"input"
not
in
layer
.
inputs
:
matches
.
pop
(
layer_id
)
return
for
id
in
graph
.
edges_in
[
layer_id
]:
input_layer
=
graph
.
layers
[
id
]
if
input_layer
.
outputs
==
[
layer
.
inputs
[
"input"
]]:
if
input_layer
.
kernel
==
"prim.if"
:
matches
.
pop
(
layer_id
)
return
input_id
=
id
break
func_name
=
input_layer
.
kernel
.
replace
(
"."
,
"_"
)
from
x2paddle.op_mapper.dygraph.pytorch2paddle
import
prim2code
func
=
getattr
(
prim2code
,
func_name
)
line
=
func
(
input_layer
,
is_return_line
=
True
)
layer
.
attrs
[
"input"
]
=
line
layer
.
inputs
.
pop
(
"input"
)
matches
.
pop
(
layer_id
)
if
len
(
input_layer
.
outputs
)
==
1
:
matches
[
input_id
]
=
input_layer
\ No newline at end of file
x2paddle/optimizer/optimizer.py
浏览文件 @
69a8316b
...
...
@@ -31,7 +31,8 @@ class GraphOptimizer(object):
"dygraph_fc_fuse_pass"
,
"dygraph_adaptive_pool2d_fuse_pass"
,
"dygraph_reshape_fuse_pass"
,
"dygraph_dropout_fuse_pass"
"dygraph_dropout_fuse_pass"
,
"dygraph_if_fuse_pass"
]
elif
source_frame
==
"caffe"
:
if
paddle_type
==
"dygraph"
:
...
...
x2paddle/optimizer/pattern_matcher.py
浏览文件 @
69a8316b
...
...
@@ -28,6 +28,8 @@ class PatternMatcher(object):
self
.
detect_patterns_by_topo
(
graph
)
elif
match_kind
==
"edge"
:
self
.
detect_patterns_by_edge
(
graph
)
elif
match_kind
==
"op"
:
self
.
detect_patterns_by_op
(
graph
)
self
.
remove_overlapped_match
()
return
self
.
matches
...
...
@@ -228,6 +230,42 @@ class PatternMatcher(object):
for
j
,
block
in
enumerate
(
layer
.
blocks
):
if
len
(
block
.
layers
)
>
0
:
self
.
detect_patterns_by_edge
(
layer
.
blocks
[
j
])
def
detect_patterns_by_op
(
self
,
graph
):
""" 当只匹配op时使用此方式。
"""
def
get_subgraph
(
pattern
,
graph
,
start_index
):
pattern_id2layers
=
pattern
.
get_global_layers
()
pattern_ids
=
list
(
pattern_id2layers
.
keys
())
pattern_layer_id
=
pattern_ids
[
0
]
subgraph_id2layers
=
dict
()
layer_id
=
list
(
graph
.
layers
.
keys
())[
start_index
]
graph_layers
=
graph
.
layers
def
update
(
layer_id
,
pattern_layer_id
):
layer
=
graph_layers
[
layer_id
]
pattern_layer
=
pattern_id2layers
[
pattern_layer_id
]
if
layer
.
kernel
!=
pattern_layer
.
kernel
:
return
False
subgraph_id2layers
[
layer_id
]
=
layer
while
len
(
subgraph_id2layers
)
!=
len
(
pattern_id2layers
):
out
=
update
(
layer_id
,
pattern_layer_id
)
if
out
==
False
:
return
False
else
:
if
len
(
subgraph_id2layers
)
==
len
(
pattern_id2layers
):
return
subgraph_id2layers
else
:
return
False
for
i
,
(
layer_id
,
layer
)
in
enumerate
(
graph
.
layers
.
items
()):
match_info
=
get_subgraph
(
self
.
pattern
,
graph
,
i
)
if
match_info
:
self
.
matches
.
append
(
match_info
)
for
j
,
block
in
enumerate
(
layer
.
blocks
):
if
len
(
block
.
layers
)
>
0
:
self
.
detect_patterns_by_op
(
layer
.
blocks
[
j
])
def
remove_overlapped_match
(
self
):
""" 如果2个子图有重叠,只取前一个子图。
...
...
@@ -297,14 +335,11 @@ class FuseBase(object):
""" 删除不需要的中间layer及其对应参数。
"""
for
match
in
self
.
matches
:
if
len
(
match
)
==
0
:
continue
first_layer_id
=
list
(
match
.
keys
())[
0
]
subgraph
=
get_subgraph
(
""
,
first_layer_id
,
graph
)
for
layer_id
,
layer
in
match
.
items
():
if
layer
.
kernel
==
"fluid.dygraph.base.to_variable"
and
\
layer
.
attrs
[
"value"
].
startswith
(
"params["
):
param_name
=
layer
.
attrs
[
"value"
][
8
:
-
2
]
if
param_name
in
graph
.
parameters
:
graph
.
parameters
.
pop
(
param_name
)
if
layer_id
in
subgraph
.
layers
:
# layer_id可能是属于子图的,此时删除父layer,即删除整个子图
subgraph
.
layers
.
pop
(
layer_id
)
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