def __init__(self, decoder):
self.support_op_sets = [9, ]
self.default_op_set = 9
self.graph = decoder.graph
self.paddle_graph = PaddleGraph(parent_layer=None, source_type="onnx")
self.paddle_graph.outputs = self.graph.output_nodes
self.opset = self.create_opset(decoder)
if not self.op_checker():
raise Exception("Model is not supported yet.")
print("Total nodes: {}".format(
sum([
isinstance(node, ONNXGraphNode)
for name, node in self.graph.node_map.items()
])))
print("Nodes converting ...")
for i, node_name in enumerate(self.graph.topo_sort):
sys.stderr.write("\rConverting node {} ... ".format(i + 1))
node = self.graph.get_node(node_name)
op = node.layer_type
if hasattr(self.opset, op):
func = getattr(self.opset, op)
func(node)
elif op in self.opset.directly_map_ops:
self.opset.directly_map(node)
elif op in self.opset.elementwise_ops:
self.opset.elementwise_map(node)
print("\nNodes converted.")
self.paddle_graph.set_name(self.graph.graph_name)
self.paddle_graph.set_parameters(self.opset.weights)
self.paddle_graph.set_inputs_info(self.opset.inputs_info)
def build_pattern(self):
""" 描述需要替换的batchnorm2d图结构。
batchnorm2d层模式python实现代码示例:
模式一：
bn_conv1 = self.batchnorm0(conv1)
scale_conv1_cparam1 = self.scale_conv1_cparam1
scale_conv1_mul = paddle.multiply(x=bn_conv1, y=scale_conv1_cparam1, axis=1)
scale_conv1_cparam2 = self.scale_conv1_cparam2
scale_conv1 = paddle.add(x=scale_conv1_mul, y=scale_conv1_cparam2, axis=1)
模式二：
bn_conv1 = self.batchnorm0(conv1)
scale_conv1_cparam1 = self.scale_conv1_cparam1
scale_conv1_mul = paddle.multiply(x=bn_conv1, y=scale_conv1_cparam1, axis=1)
scale_conv1_cparam2 = self.scale_conv1_cparam2
scale_conv1_cparam2 = paddle.reshape(x=scale_conv1_cparam2, shape=[32, 1, 1])
scale_conv1 = paddle.add(x=scale_conv1_mul, y=scale_conv1_cparam2, axis=1)
"""
def gen_name(id):
return "x" + str(id)
pattern = PaddleGraph()
pattern.add_layer(
"paddle.nn.BatchNorm2D",
inputs={"input": "bn-input-0"},
outputs=[gen_name(0)])
pattern.add_layer(
"self.create_parameter", inputs={}, outputs=[gen_name(1)])
inputs_dict = {}
inputs_dict['x'] = gen_name(0)
inputs_dict['y'] = gen_name(1)
pattern.add_layer(
"paddle.multiply", inputs=inputs_dict, outputs=[gen_name(2)])
pattern.add_layer(
"self.create_parameter", inputs={}, outputs=[gen_name(3)])
inputs_dict = {}
inputs_dict['x'] = gen_name(2)
inputs_dict['y'] = gen_name(3)
pattern.add_layer(
"paddle.add", inputs=inputs_dict, outputs=[gen_name(4)])
pattern.build(inputs={"input-0": "bn-input-0"})
self.patterns.append(pattern)
pattern = PaddleGraph()
pattern.add_layer(
"paddle.nn.BatchNorm2D",
inputs={"input": "bn-input-0"},
outputs=[gen_name(0)])
pattern.add_layer(
"self.create_parameter", inputs={}, outputs=[gen_name(1)])
inputs_dict = {}
inputs_dict['x'] = gen_name(0)
inputs_dict['y'] = gen_name(1)
pattern.add_layer(
"paddle.multiply", inputs=inputs_dict, outputs=[gen_name(2)])
pattern.add_layer(
"self.create_parameter", inputs={}, outputs=[gen_name(3)])
pattern.add_layer(
"paddle.reshape", inputs={"x": gen_name(3)}, outputs=[gen_name(3)])
inputs_dict = {}
inputs_dict['x'] = gen_name(2)
inputs_dict['y'] = gen_name(3)
pattern.add_layer(
"paddle.add", inputs=inputs_dict, outputs=[gen_name(4)])
pattern.build(inputs={"input-0": "bn-input-0"})
self.patterns.append(pattern)
def build_pattern(self):
""" 描述需要替换的fc图结构。
fc层模式python实现代码示例:
模式一：
encoder_layer_8_attention_self_key_weight = self.encoder_layer_8_attention_self_key_weight
x748 = paddle.transpose(x=encoder_layer_8_attention_self_key_weight, perm=[1, 0])
x749 = paddle.matmul(x=x732, y=x748)
encoder_layer_8_attention_self_key_bias = self.encoder_layer_8_attention_self_key_bias
x750 = x749 + 1 * encoder_layer_8_attention_self_key_bias
模式二：
x13 = self.x13
x14 = paddle.transpose(x=x13, perm=[1, 0])
x15 = self.x15
x16 = paddle.addmm(input=x15, x=x12, y=x14, beta=1, alpha=1)
"""
def gen_name(id):
return "x" + str(id)
pattern = PaddleGraph()
pattern.add_layer("self.create_parameter",
inputs={},
outputs=[gen_name(0)])
pattern.add_layer("paddle.transpose",
inputs={"x": gen_name(0)},
outputs=[gen_name(1)],
perm=[1, 0])
pattern.add_layer("paddle.matmul",
inputs={
"x": "fc-input-0",
"y": gen_name(1)
},
outputs=[gen_name(2)])
pattern.add_layer("self.create_parameter",
inputs={},
outputs=[gen_name(3)])
pattern.add_layer("prim.add_",
inputs={
"x": gen_name(2),
"y": gen_name(3)
},
outputs=[gen_name(4)],
alpha=1)
pattern.build(inputs={"input-0": "fc-input-0"})
self.patterns.append(pattern)
pattern = PaddleGraph()
pattern.add_layer("self.create_parameter",
inputs={},
outputs=[gen_name(0)])
pattern.add_layer("paddle.transpose",
inputs={"x": gen_name(0)},
outputs=[gen_name(1)],
perm=[1, 0])
pattern.add_layer("self.create_parameter",
inputs={},
outputs=[gen_name(2)])
pattern.add_layer("paddle.addmm",
inputs={
"input": gen_name(2),
"x": "fc-input-0",
"y": gen_name(1)
},
outputs=[gen_name(4)],
alpha=1,
beta=1)
pattern.build(inputs={"input-0": "fc-input-0"})
self.patterns.append(pattern)
def build_pattern(self):
""" 描述需要替换的fc图结构。
fc层模式python实现代码示例:
x133 = x128.shape
x133 = len(x133)
x134 = x133 == 2
if x134 :
classifier_6_weight = self.classifier_6_weight
x136 = paddle.transpose(x=classifier_6_weight, perm=[1, 0])
classifier_6_bias = self.classifier_6_bias
x137 = paddle.addmm(input=classifier_6_bias, x=x128, y=x136, beta=1, alpha=1)
x135 = x137
else:
classifier_6_weight = self.classifier_6_weight
x138 = paddle.transpose(x=classifier_6_weight, perm=[1, 0])
x139 = paddle.matmul(x=x128, y=x138)
classifier_6_bias = self.classifier_6_bias
x140 = x139 + 1 * classifier_6_bias
x135 = x140
"""
def gen_name(id):
return "x" + str(id)
self.pattern.add_layer(
"prim.shape", inputs={'input': "fc-input-0"},
outputs=[gen_name(2)])
self.pattern.add_layer(
"prim.len", inputs={'input': gen_name(2)}, outputs=[gen_name(2)])
self.pattern.add_layer(
"prim.eq",
inputs={"eq0": gen_name(2)},
outputs=[gen_name(3)],
eq1=2)
self.pattern.add_layer("prim.if", {'input': gen_name(3)}, [gen_name(4)])
self.pattern.outputs.append(gen_name(4))
if_layer1 = self.pattern.layers[list(self.pattern.layers.keys())[-1]]
pattern_block0 = PaddleGraph(parent_layer=if_layer1)
pattern_block0.add_layer(
"self.create_parameter", inputs={}, outputs=[gen_name(5)])
pattern_block0.add_layer(
"paddle.transpose",
inputs={"x": gen_name(5)},
outputs=[gen_name(6)],
perm=[1, 0])
pattern_block0.add_layer(
"self.create_parameter", inputs={}, outputs=[gen_name(7)])
pattern_block0.add_layer(
"paddle.addmm",
inputs={"input": gen_name(7),
"x": "fc-input-0",
"y": gen_name(6)},
outputs=[gen_name(8)],
beta=1,
alpha=1)
if_layer1.inputs["input-0"] = "fc-input-0"
self.pattern.inputs.append("fc-input-0")
pattern_block0.add_layer(
"prim.equal", inputs={'input': gen_name(8)}, outputs=[gen_name(4)])
if_layer1.add_block(pattern_block0)
pattern_block1 = PaddleGraph(parent_layer=if_layer1)
pattern_block1.add_layer(
"self.create_parameter", inputs={}, outputs=[gen_name(5)])
pattern_block1.add_layer(
"paddle.transpose",
inputs={"x": gen_name(5)},
outputs=[gen_name(6)],
perm=[1, 0])
pattern_block1.add_layer(
"paddle.matmul",
inputs={"x": "fc-input-0",
"y": gen_name(6)},
outputs=[gen_name(9)])
if_layer1.inputs["input-1"] = "fc-input-0"
pattern_block1.add_layer(
"self.create_parameter", inputs={}, outputs=[gen_name(12)])
pattern_block1.add_layer(
"prim.add_",
inputs={"x": gen_name(9),
"y": gen_name(12)},
outputs=[gen_name(13)],
alpha=1)
pattern_block1.add_layer(
"prim.equal", inputs={'input': gen_name(13)},
outputs=[gen_name(4)])
if_layer1.add_block(pattern_block1)
self.pattern.build(inputs={"input-0": "fc-input-0"})
def build_pattern(self):
""" 描述需要替换的conv2d+add图结构。
conv2d+add层模式python实现代码示例:
模式一：
MobilenetV1_Logits_Conv2d_1c_1x1_biases = self.MobilenetV1_Logits_Conv2d_1c_1x1_biases
conv2d_transpose_14 = paddle.transpose(x=MobilenetV1_Logits_AvgPool_1a_AvgPool, perm=[0, 3, 1, 2])
MobilenetV1_Logits_Conv2d_1c_1x1_Conv2D = self.conv27(conv2d_transpose_14)
MobilenetV1_Logits_Conv2d_1c_1x1_Conv2D = paddle.transpose(x=MobilenetV1_Logits_Conv2d_1c_1x1_Conv2D, perm=[0, 2, 3, 1])
MobilenetV1_Logits_Conv2d_1c_1x1_BiasAdd = paddle.add(x=MobilenetV1_Logits_Conv2d_1c_1x1_Conv2D, y=MobilenetV1_Logits_Conv2d_1c_1x1_biases)
模式二：
MobilenetV1_Logits_Conv2d_1c_1x1_biases = self.MobilenetV1_Logits_Conv2d_1c_1x1_biases
MobilenetV1_Logits_Conv2d_1c_1x1_Conv2D = self.conv27(conv2d_transpose_14)
MobilenetV1_Logits_Conv2d_1c_1x1_BiasAdd = paddle.add(x=MobilenetV1_Logits_Conv2d_1c_1x1_Conv2D, y=MobilenetV1_Logits_Conv2d_1c_1x1_biases)
"""
def gen_name(id):
return "x" + str(id)
pattern = PaddleGraph()
pattern.add_layer("self.create_parameter",
inputs={},
outputs=[gen_name(0)])
pattern.add_layer(kernel="paddle.transpose",
inputs={"x": "conv-input-0"},
outputs=[gen_name(1)],
perm=[0, 3, 1, 2])
pattern.add_layer(kernel="paddle.nn.Conv2D",
inputs={"input": gen_name(1)},
outputs=[gen_name(2)])
pattern.add_layer(kernel="paddle.transpose",
inputs={"x": gen_name(2)},
outputs=[gen_name(2)],
perm=[0, 2, 3, 1])
pattern.add_layer(kernel="paddle.add",
inputs={
"x": gen_name(2),
"y": gen_name(0)
},
outputs=[gen_name(3)])
pattern.build(inputs={
"input-0": "conv-input-0",
})
self.patterns.append(pattern)
pattern = PaddleGraph()
pattern.add_layer("self.create_parameter",
inputs={},
outputs=[gen_name(0)])
pattern.add_layer(kernel="paddle.nn.Conv2D",
inputs={"input": "conv-input-0"},
outputs=[gen_name(1)])
pattern.add_layer(kernel="paddle.add",
inputs={
"x": gen_name(1),
"y": gen_name(0)
},
outputs=[gen_name(2)])
pattern.build(inputs={
"input-0": "conv-input-0",
})
self.patterns.append(pattern)
def build_pattern(self):
""" 描述需要替换的双线性插值图结构。
interpolate_bilinear层模式python实现代码示例:
x2195 = x2181.shape
x2195 = len(x2195)
x2196 = x2195 - 2
x2197 = []
for _x2199 in range(x2196):
x2197.append(None)
x2200 = (x2181, x8, None, None)
...
x2267 = x2266 == 3
if x2267 :
raise RaiseException('Exception')
x2268 = None
else:
x2270 = x2181.shape
x2270 = len(x2270)
x2271 = x2270 == 4
if x2271 :
x2274 = x2197[0]
x2275 = x2197[1]
x2233_isinstance = isinstance(x2233, paddle.fluid.Variable)
if x2233_isinstance :
x2233 = x2233.numpy().tolist()
x2276 = paddle.nn.functional.interpolate(x=x2181, size=x2233, scale_factor=x2274, align_corners=False, align_mode=0, mode='bilinear')
x2272 = x2276
else:
x2277 = x2181.shape
x2277 = len(x2277)
x2278 = x2277 == 5
if x2278 :
raise RaiseException('Exception')
else:
raise RaiseException('Exception')
x2272 = None
x2268 = x2272
"""
def gen_name(id):
return "x" + str(id)
pattern = PaddleGraph()
pattern.add_layer("prim.shape",
inputs={"input": "interpolate-input-0"},
outputs=[gen_name(9)])
pattern.add_layer("prim.len",
inputs={"input": gen_name(9)},
outputs=[gen_name(9)])
pattern.add_layer("prim.sub",
inputs={"x": gen_name(9)},
outputs=[gen_name(10)],
y=2)
pattern.add_layer("prim.list", inputs={}, outputs=[gen_name(11)])
pattern.add_layer("prim.loop",
inputs={"input": gen_name(10)},
outputs=[gen_name(12.1),
gen_name(12.2)])
loop_layer = pattern.layers[list(pattern.layers.keys())[-1]]
pattern_block = PaddleGraph(loop_layer)
pattern_block.add_layer("prim.append",
inputs={"list": gen_name(11)},
outputs=[],
element=None)
loop_layer.inputs["input-0"] = gen_name(11)
loop_layer.add_block(pattern_block)
pattern.add_layer("prim.tuple",
inputs={
"input0": "interpolate-input-0",
"input1": "interpolate-input-4",
},
outputs=[gen_name(12)],
input2=None,
input3=None)
pattern.add_layer("prim.eq",
inputs={"x": "interpolate-input-2"},
outputs=[gen_name(10.1)],
y=3)
pattern.add_layer("prim.if",
inputs={"input": gen_name(10.1)},
outputs=[gen_name(14)])
if_layer1 = pattern.layers[list(pattern.layers.keys())[-1]]
pattern_block = PaddleGraph(parent_layer=if_layer1)
pattern_block.add_layer("prim.exception",
inputs={},
outputs=[gen_name(15)],
input="Exception")
pattern_block.add_layer("prim.equal",
inputs={},
outputs=[gen_name(14)],
input=None)
if_layer1.add_block(pattern_block)
pattern_block = PaddleGraph(parent_layer=if_layer1)
pattern_block.add_layer("prim.shape",
inputs={"input": "interpolate-input-0"},
outputs=[gen_name(18)])
pattern_block.add_layer("prim.len",
inputs={"input": gen_name(18)},
outputs=[gen_name(18)])
pattern_block.add_layer("prim.eq",
inputs={"x": gen_name(18)},
outputs=[gen_name(19)],
y=4)
pattern_block.add_layer("prim.if",
inputs={"input": gen_name(19)},
outputs=[gen_name(20)])
if_layer2 = pattern_block.layers[list(pattern_block.layers.keys())[-1]]
pattern_block_block = PaddleGraph(parent_layer=if_layer2)
pattern_block_block.add_layer("prim.getitem",
inputs={"list": gen_name(11)},
outputs=[gen_name(21)],
element=0)
pattern_block_block.add_layer("prim.getitem",
inputs={"list": gen_name(11)},
outputs=[gen_name(22)],
element=1)
pattern_block_block.add_layer(
"prim.isinstance",
inputs={"input": "interpolate-input-3"},
outputs=["interpolate-input-0_isinstance"],
cls="paddle.fluid.Variable")
pattern_block_block.add_layer(
"prim.if", {"input": "interpolate-input-0_isinstance"},
outputs=["interpolate-input-0_if1"])
if_layer_isinstance = pattern_block_block.layers[list(
pattern_block_block.layers.keys())[-1]]
pattern_block_block_block = PaddleGraph(if_layer_isinstance)
pattern_block_block_block.add_layer(
"prim.var2list",
inputs={"input": "interpolate-input-3"},
outputs=["interpolate-input-3"])
if_layer_isinstance.add_block(pattern_block_block_block)
pattern_block_block_block = PaddleGraph(if_layer_isinstance)
if_layer_isinstance.add_block(pattern_block_block_block)
if_layer_isinstance.inputs["input-0"] = "interpolate-input-3"
pattern_block_block.add_layer("paddle.nn.functional.interpolate",
inputs={
"input": "interpolate-input-0",
"size": "interpolate-input-3",
},
outputs=[gen_name(23)])
pattern_block_block.add_layer("prim.equal",
inputs={"input": gen_name(23)},
outputs=[gen_name(20)])
if_layer2.add_block(pattern_block_block)
pattern_block_block = PaddleGraph(if_layer2)
pattern_block_block.add_layer("prim.shape",
inputs={"input": "interpolate-input-0"},
outputs=[gen_name(24)])
pattern_block_block.add_layer("prim.len",
inputs={"input": gen_name(24)},
outputs=[gen_name(24)])
pattern_block_block.add_layer("prim.eq",
inputs={"x": gen_name(24)},
outputs=[gen_name(25)],
y=5)
pattern_block_block.add_layer("prim.if",
inputs={"input": gen_name(25)},
outputs=[gen_name(26)])
if_layer3 = pattern_block_block.layers[list(
pattern_block_block.layers.keys())[-1]]
pattern_block_block_block = PaddleGraph(parent_layer=if_layer3)
pattern_block_block_block.add_layer("prim.exception",
inputs={},
outputs=[gen_name(27)],
input="Exception")
if_layer3.add_block(pattern_block_block_block)
pattern_block_block_block = PaddleGraph(parent_layer=if_layer3)
pattern_block_block_block.add_layer("prim.exception",
inputs={},
outputs=[gen_name(28)],
input="Exception")
if_layer3.add_block(pattern_block_block_block)
pattern_block_block.add_layer("prim.equal",
inputs={},
outputs=[gen_name(20)],
input=None)
if_layer2.add_block(pattern_block_block)
if_layer2.inputs.update({
"input-0": "interpolate-input-0",
"input-1": "interpolate-input-3",
"input-2": "interpolate-input-3",
"input-3": gen_name(11),
"input-5": gen_name(11),
})
pattern_block.add_layer("prim.equal",
inputs={"input": gen_name(20)},
outputs=[gen_name(14)])
if_layer1.add_block(pattern_block)
if_layer1.inputs.update({
'input-2': 'interpolate-input-0',
'input-4': gen_name(11),
'input-6': gen_name(11),
'input-8': 'interpolate-input-0',
'input-9': 'interpolate-input-3',
'input-10': 'interpolate-input-0'
})
pattern.build(
inputs={
"input-0": "interpolate-input-0",
"input-1": "interpolate-input-1",
"input-2": "interpolate-input-2",
"input-3": "interpolate-input-3",
"input-4": "interpolate-input-4"
})
self.patterns.append(pattern)
def build_pattern(self):
""" 描述需要替换的adaptive pool2d图结构。
adaptive pool2d层模式python实现代码示例:
模式一：
x68 = prim.shape(input=x60)
x69 = len(x68)
x70 = x69 <= 2
if x70 :
raise RaiseException('Exception')
x73 = []
x74 = x68[-2: 2147483647: 1]
x75 = len(x74)
x76 = [2, x75]
x77 = min(x76)
for _x79 in range(x77):
x80 = [6, 6][_x79]
x73.append(x80)
x81 = paddle.nn.functional.adaptive_avg_pool2d(input=x60, pool_size=x73, pool_type='avg')
模式二：
x64 = x60.shape
x65 = len(x64)
x66 = x65 > 2
if x66 :
pass
else:
raise RaiseException('AssertionError: ')
x69 = self.pool2d3(x60)
"""
def gen_name(id):
return "x" + str(id)
# 模式一：
pattern = PaddleGraph()
pattern.add_layer("prim.shape",
inputs={'input': "pool-input-0"},
outputs=[gen_name(1)])
pattern.add_layer("prim.len",
inputs={"input": gen_name(1)},
outputs=[gen_name(6)])
pattern.add_layer("prim.le",
inputs={"x": gen_name(6)},
outputs=[gen_name(8)],
y=2)
pattern.add_layer("prim.if", {'input': gen_name(8)}, [gen_name(9)])
if_layer = pattern.layers[list(pattern.layers.keys())[-1]]
pattern_block0 = PaddleGraph(parent_layer=if_layer)
pattern_block0.add_layer("prim.exception",
inputs={},
outputs=[gen_name(9)],
input="Exception")
if_layer.add_block(pattern_block0)
pattern_block1 = PaddleGraph(parent_layer=if_layer)
if_layer.add_block(pattern_block1)
pattern.add_layer("prim.list", inputs={}, outputs=[gen_name(10)])
pattern.add_layer("prim.slice",
inputs={
"input": gen_name(1),
},
outputs=[gen_name(12)])
pattern.add_layer("prim.len",
inputs={"input": gen_name(12)},
outputs=[gen_name(14)])
pattern.add_layer("prim.list",
inputs={"input1": gen_name(14)},
outputs=[gen_name(15)])
pattern.add_layer("prim.min",
inputs={"input": gen_name(15)},
outputs=[gen_name(16)])
pattern.add_layer("prim.loop", {'input': gen_name(16)},
[gen_name(17), gen_name(18)])
loop_layer = pattern.layers[list(pattern.layers.keys())[-1]]
pattern_block = PaddleGraph(loop_layer)
pattern_block.add_layer("prim.getitem",
inputs={"index": gen_name(18)},
outputs=[gen_name(19)])
pattern_block.add_layer("prim.append",
inputs={
"list": gen_name(10),
"index": gen_name(19)
},
outputs=[gen_name(20)])
loop_layer.inputs["input-0"] = gen_name(10)
loop_layer.add_block(pattern_block)
pool_attrs = {'pool_type': string("avg")}
pattern.add_layer("paddle.nn.functional.adaptive_avg_pool2d",
inputs={
'input': "pool-input-0",
"pool_size": gen_name(10)
},
outputs=[gen_name(21)],
**pool_attrs)
pattern.build(inputs={
"input-0": "pool-input-0",
})
self.patterns.append(pattern)
# 模式二：
pattern = PaddleGraph()
pattern.add_layer("prim.shape",
inputs={'input': "pool-input-0"},
outputs=[gen_name(0)])
pattern.add_layer("prim.len",
inputs={"input": gen_name(0)},
outputs=[gen_name(1)])
pattern.add_layer("prim.gt",
inputs={"x": gen_name(1)},
outputs=[gen_name(2)],
y=2)
pattern.add_layer("prim.if", {'input': gen_name(2)}, [gen_name(3)])
if_layer = pattern.layers[list(pattern.layers.keys())[-1]]
pattern_block0 = PaddleGraph(parent_layer=if_layer)
if_layer.add_block(pattern_block0)
pattern_block1 = PaddleGraph(parent_layer=if_layer)
pattern_block1.add_layer("prim.exception",
inputs={},
outputs=[gen_name(4)],
input="Exception")
if_layer.add_block(pattern_block1)
pattern.add_layer("paddle.nn.AdaptiveAvgPool2D",
inputs={"input": "pool-input-0"},
outputs=["pool1", gen_name(5)])
pattern.build(inputs={
"input-0": "pool-input-0",
"input-1": "pool-input-0",
})
self.patterns.append(pattern)
def build_pattern(self):
""" 描述需要替换的batchnorm2d图结构。
batchnorm2d层模式python实现代码示例:
x2011 = x2009.shape
x2011 = len(x2011)
x2012 = x2011 != 4
if x2012 :
raise RaiseException('Exception')
if False :
x2026 = x2009.shape
x2027 = x2026[0]
x2028 = len(x2026)
x2029 = x2028 - 2
x2032 = x2027
for _x2031 in range(x2029):
x2033 = _x2031 + 2
x2034 = x2026[x2033]
x2035 = x2032 * x2034
x2030 = x2035
x2036 = x2030 == 1
if x2036 :
raise RaiseException('Exception')
x2039 = self.batchnorm50(x2009)
"""
def gen_name(id):
return "x" + str(id)
self.pattern.add_layer("prim.shape",
inputs={'input': "bn-input-0"},
outputs=[gen_name(0)])
self.pattern.add_layer("prim.len",
inputs={'input': gen_name(0)},
outputs=[gen_name(0)])
self.pattern.add_layer("prim.ne",
inputs={"x": gen_name(0)},
outputs=[gen_name(1)],
y=4)
self.pattern.add_layer("prim.if", {'input': gen_name(1)},
[gen_name(2)])
if_layer1 = self.pattern.layers[list(self.pattern.layers.keys())[-1]]
pattern_block0 = PaddleGraph(parent_layer=if_layer1)
pattern_block0.add_layer("prim.exception",
inputs={},
outputs=[gen_name(3)],
input="Exception")
if_layer1.add_block(pattern_block0)
pattern_block1 = PaddleGraph(parent_layer=if_layer1)
if_layer1.add_block(pattern_block1)
self.pattern.add_layer("prim.if", {}, [gen_name(4)], input=False)
if_layer2 = self.pattern.layers[list(self.pattern.layers.keys())[-1]]
pattern_block0 = PaddleGraph(parent_layer=if_layer2)
pattern_block0.add_layer("prim.shape",
inputs={'input': "bn-input-0"},
outputs=[gen_name(5)])
pattern_block0.add_layer("prim.getitem",
inputs={"list": gen_name(5)},
outputs=[gen_name(6)],
index=0)
pattern_block0.add_layer("prim.len",
inputs={"input": gen_name(5)},
outputs=[gen_name(7)])
pattern_block0.add_layer("prim.sub",
inputs={"x": gen_name(7)},
outputs=[gen_name(8)],
y=2)
pattern_block0.add_layer("prim.equal",
inputs={"input": gen_name(6)},
outputs=[gen_name(9)])
pattern_block0.add_layer("prim.loop",
inputs={"input": gen_name(8)},
outputs=[gen_name(8.1),
gen_name(10)])
loop_layer = pattern_block0.layers[list(
pattern_block0.layers.keys())[-1]]
pattern_block0_block0 = PaddleGraph(parent_layer=loop_layer)
pattern_block0_block0.add_layer("prim.add",
inputs={"x": gen_name(10)},
outputs=[gen_name(11)],
y=2)
pattern_block0_block0.add_layer("prim.getitem",
inputs={
"list": gen_name(5),
"index": gen_name(11)
},
outputs=[gen_name(12)])
pattern_block0_block0.add_layer("prim.mul",
inputs={
"x": gen_name(9),
"y": gen_name(12)
},
outputs=[gen_name(13)])
pattern_block0_block0.add_layer("prim.equal",
inputs={"input": gen_name(13)},
outputs=[gen_name(8.1)])
loop_layer.inputs["input-1"] = gen_name(5)
loop_layer.inputs["input-2"] = gen_name(9)
loop_layer.add_block(pattern_block0_block0)
pattern_block0.add_layer("prim.eq",
inputs={"x": gen_name(8.1)},
outputs=[gen_name(14)],
y=1)
pattern_block0.add_layer("prim.if",
inputs={"input": gen_name(14)},
outputs=[gen_name(15)])
if_layer21 = pattern_block0.layers[list(
pattern_block0.layers.keys())[-1]]
pattern_block0_block0 = PaddleGraph(parent_layer=if_layer21)
pattern_block0_block0.add_layer("prim.exception",
inputs={},
outputs=[gen_name(15)],
input="Exception")
if_layer21.add_block(pattern_block0_block0)
pattern_block0_block1 = PaddleGraph(parent_layer=if_layer21)
if_layer21.add_block(pattern_block0_block1)
if_layer2.add_block(pattern_block0)
pattern_block1 = PaddleGraph(parent_layer=if_layer2)
if_layer2.add_block(pattern_block1)
if_layer2.inputs["input-0"] = "bn-input-0"
self.pattern.add_layer("paddle.nn.BatchNorm",
inputs={"input": "bn-input-0"},
outputs=[gen_name(16),
gen_name(17)],
is_test=True)
self.pattern.build(inputs={"input-0": "bn-input-0"})
def __init__(self):
self.pattern = PaddleGraph()
self.patterns = list()
def build_pattern(self):
""" 描述需要替换的batchnorm图结构。
batchnorm层模式python实现代码示例:
"""
def gen_name(id):
return "x" + str(id)
pattern = PaddleGraph()
pattern.add_layer("self.create_parameter",
inputs={},
outputs=[gen_name(0)])
pattern.add_layer("paddle.full",
inputs={},
outputs=[gen_name(1)],
shape=[1])
pattern.add_layer("paddle.add",
inputs={
"x": gen_name(0),
"y": gen_name(1)
},
outputs=[gen_name(2)])
pattern.add_layer("paddle.rsqrt",
inputs={"x": gen_name(2)},
outputs=[gen_name(3)])
pattern.add_layer("self.create_parameter",
inputs={},
outputs=[gen_name(4)])
pattern.add_layer("paddle.multiply",
inputs={
"x": gen_name(3),
"y": gen_name(4)
},
outputs=[gen_name(5)])
pattern.add_layer("self.create_parameter",
inputs={},
outputs=[gen_name(6)])
pattern.add_layer("paddle.multiply",
inputs={
"x": gen_name(6),
"y": gen_name(5)
},
outputs=[gen_name(7)])
pattern.add_layer("self.create_parameter",
inputs={},
outputs=[gen_name(8)])
pattern.add_layer("paddle.subtract",
inputs={
"x": gen_name(8),
"y": gen_name(7)
},
outputs=[gen_name(9)])
pattern.add_layer("paddle.multiply",
inputs={
"x": "bn-input-0",
"y": gen_name(5)
},
outputs=[gen_name(10)])
pattern.add_layer("paddle.add",
inputs={
"x": gen_name(10),
"y": gen_name(9)
},
outputs=[gen_name(11)])
pattern.build(inputs={
"input-0": "bn-input-0",
})
self.patterns.append(pattern)
pattern = PaddleGraph()
pattern.add_layer("self.create_parameter",
inputs={},
outputs=[gen_name(0)])
pattern.add_layer("paddle.full",
inputs={},
outputs=[gen_name(1)],
shape=[1])
pattern.add_layer("paddle.add",
inputs={
"x": gen_name(0),
"y": gen_name(1)
},
outputs=[gen_name(2)])
pattern.add_layer("paddle.rsqrt",
inputs={"x": gen_name(2)},
outputs=[gen_name(3)])
pattern.add_layer("self.create_parameter",
inputs={},
outputs=[gen_name(4)])
pattern.add_layer("paddle.multiply",
inputs={
"x": gen_name(3),
"y": gen_name(4)
},
outputs=[gen_name(5)])
pattern.add_layer("paddle.multiply",
inputs={
"x": "bn-input-0",
"y": gen_name(5)
},
outputs=[gen_name(10)])
pattern.add_layer("self.create_parameter",
inputs={},
outputs=[gen_name(6)])
pattern.add_layer("paddle.multiply",
inputs={
"x": gen_name(6),
"y": gen_name(5)
},
outputs=[gen_name(7)])
pattern.add_layer("self.create_parameter",
inputs={},
outputs=[gen_name(8)])
pattern.add_layer("paddle.subtract",
inputs={
"x": gen_name(8),
"y": gen_name(7)
},
outputs=[gen_name(9)])
pattern.add_layer("paddle.add",
inputs={
"x": gen_name(10),
"y": gen_name(9)
},
outputs=[gen_name(11)])
pattern.build(inputs={
"input-0": "bn-input-0",
})
self.patterns.append(pattern)
def traverse(self, script_graph, parent_layer=None):
# 用于获取graph的输入
def _update_graph_inputs(kind, inputs, outputs):
# extend只能放更新graph_inputs之前的情况：
# 1. loop的输出i也是输入；i是输入的原因是：子图中为父图得到的。
# 2. 在_check_input中需要使用to_variable。
# extend只能放更新graph_inputs之后的情况：
# 使用了append。
if kind != "aten::append":
current_node_outputs.extend(outputs)
for name in inputs:
if name not in current_node_outputs:
graph_inputs.append(name)
if kind == "aten::append":
current_node_outputs.extend(outputs)
# 初始化
graph = PaddleGraph(source_type="pytorch", parent_layer=parent_layer)
if "TopLevelTracedModule" in str(type(self.script)):
graph.set_script(self.script)
current_node_outputs = []
graph_inputs = []
# 转换输入节点
if isinstance(script_graph, torch._C.Graph):
input_ct = 0
for i, ivalue in enumerate(script_graph.inputs()):
node = ivalue.node()
if str(ivalue.type()) not in ["Tensor", "Dict[str, Tensor]"]:
graph.set_name(str(ivalue.type()).split(".")[-1])
continue
inputs, outputs = self.data(graph, node, ivalue.unique(),
input_ct)
input_ct += 1
# 转换中间节点
for node in script_graph.nodes():
kind = node.kind()
func_name = kind.replace('::', '_')
if hasattr(prim, func_name):
func = getattr(prim, func_name)
inputs, outputs = func(self, graph, node)
_update_graph_inputs(kind, inputs, outputs)
elif hasattr(aten, func_name):
func = getattr(aten, func_name)
inputs, outputs = func(self, graph, node)
_update_graph_inputs(kind, inputs, outputs)
# 转换输出节点
if hasattr(script_graph, 'returnNode'):
for i, ivalue in enumerate(script_graph.returnNode().inputs()):
if parent_layer.kernel == "prim.loop" and i == 0:
continue
node = ivalue.node()
script_unique_id = ivalue.unique()
inputs, outputs = self.equal(graph,
node,
uid=script_unique_id,
parent_layer=parent_layer,
index=i)
_update_graph_inputs("equal", inputs, outputs)
# 设置graph的参数和输出节点
if isinstance(script_graph, torch._C.Graph):
graph.set_parameters(self.paddle_params)
if hasattr(script_graph, 'return_node'):
inputs_name, inputs_node = self._get_inputs_name(
script_graph.return_node())
graph.outputs = inputs_name
# 更新split参数
for layer in graph.layers.values():
if layer.kernel == "paddle.split" and "num_or_sections" in layer.attrs \
and not isinstance(layer.attrs["num_or_sections"], int) and len(set(layer.attrs["num_or_sections"])) == 1:
layer.attrs["num_or_sections"] = self.split_len[
layer.outputs[0]]
return graph, graph_inputs
class ONNXOpMapper():
def __init__(self, decoder):
self.support_op_sets = [9, ]
self.default_op_set = 9
self.graph = decoder.graph
self.paddle_graph = PaddleGraph(parent_layer=None, source_type="onnx")
self.paddle_graph.outputs = self.graph.output_nodes
self.opset = self.create_opset(decoder)
if not self.op_checker():
raise Exception("Model is not supported yet.")
print("Total nodes: {}".format(
sum([
isinstance(node, ONNXGraphNode)
for name, node in self.graph.node_map.items()
])))
print("Nodes converting ...")
for i, node_name in enumerate(self.graph.topo_sort):
sys.stderr.write("\rConverting node {} ... ".format(i + 1))
node = self.graph.get_node(node_name)
op = node.layer_type
if hasattr(self.opset, op):
func = getattr(self.opset, op)
func(node)
elif op in self.opset.directly_map_ops:
self.opset.directly_map(node)
elif op in self.opset.elementwise_ops:
self.opset.elementwise_map(node)
print("\nNodes converted.")
self.paddle_graph.set_name(self.graph.graph_name)
self.paddle_graph.set_parameters(self.opset.weights)
self.paddle_graph.set_inputs_info(self.opset.inputs_info)
def op_checker(self):
unsupported_ops = set()
for node_name in self.graph.topo_sort:
node = self.graph.get_node(node_name)
op = node.layer_type
if not hasattr(self.opset, op) and \
op not in self.opset.directly_map_ops and \
op not in self.opset.elementwise_ops:
unsupported_ops.add(op)
if len(unsupported_ops) == 0:
return True
else:
if len(unsupported_ops) > 0:
print("\n========= {} OPs are not supported yet ===========".
format(len(unsupported_ops)))
for op in unsupported_ops:
print("========== {} ============".format(op))
return False
def create_opset(self, decoder):
run_op_set = self.default_op_set
opset = ''
if decoder.op_set in self.support_op_sets:
opset = 'OpSet' + str(decoder.op_set)
elif decoder.op_set < self.default_op_set:
opset = 'OpSet' + str(self.default_op_set)
else:
for op_set in self.support_op_sets:
if decoder.op_set > op_set:
run_op_set = op_set
else:
break
opset = 'OpSet' + str(run_op_set)
print(
'Now, onnx2paddle support convert onnx model opset_verison {},'
'opset_verison of your onnx model is {}, automatically treated as op_set: {}.'
.format(self.support_op_sets, decoder.op_set, run_op_set))
return eval(opset)(decoder, self.paddle_graph)