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X2Paddle
提交 5bcd803c 编写于 作者: S SunAhong1993
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x2paddle/core/convert_prim.py
浏览文件 @ 5bcd803c
......@@ -73,7 +73,7 @@ def convert_prim(layer, indent=1, init_func=[], forward_func=[]):
elif layer.kernel == "prim.min":
line = "{} = min({})".format(layer.outputs[0],
list(layer.inputs.values())[0])
elif layer.kernel == "prim.add":
elif layer.kernel == "prim.add_":
line = "{} = {} + {} * {}".format(layer.outputs[0],
list(layer.inputs.values())[0],
layer.attrs["alpha"],
......@@ -124,11 +124,33 @@ def convert_prim(layer, indent=1, init_func=[], forward_func=[]):
if list(layer.inputs.values())[1] is None:
item1 = str(layer.attrs[list(layer.inputs.keys())[1]])
line = "{} = {} < {}".format(layer.outputs[0], item0, item1)
elif layer.kernel == "prim.ne":
item0 = list(layer.inputs.values())[0]
item1 = list(layer.inputs.values())[1]
line = "{} = {} < {}".format(layer.outputs[0], item0, item1)
elif layer.kernel == "prim.slice":
attrs_str = ""
for k, v in layer.attrs.items():
attrs_str += "{}:".format(v)
attrs_str = attrs_str[:-1]
inputs_str = ""
for v in list(layer.inputs.values())[1:]:
inputs_str += "{}:".format(v)
inputs_str = inputs_str[:-1]
line = "{} = {}[{}]".format(layer.outputs[0],
list(layer.inputs.values())[0], attrs_str)
list(layer.inputs.values())[0], inputs_str)
elif layer.kernel == "prim.add":
line = "{} = {} + {}".format(layer.outputs[0],
list(layer.inputs.values())[0],
list(layer.inputs.values())[1])
elif layer.kernel == "prim.sub":
line = "{} = {} - {}".format(layer.outputs[0],
list(layer.inputs.values())[0],
list(layer.inputs.values())[1])
elif layer.kernel == "prim.mul":
line = "{} = {} * {}".format(layer.outputs[0],
list(layer.inputs.values())[0],
list(layer.inputs.values())[1])
elif layer.kernel == "prim.neg":
line = "{} = -{}".format(layer.outputs[0],
list(layer.inputs.values())[0])
else:
print(layer.kernel)
line = ""
forward_func.extend(gen_codes([line], indent=indent))
x2paddle/core/program.py
浏览文件 @ 5bcd803c
......@@ -297,6 +297,7 @@ class PaddleGraph(object):
for output_name in layer.outputs:
if not output_name.startswith("x"):
continue
print(layer.kernel)
self.outputs.append(output_name)
self.outputs = list(set(self.outputs))
......
x2paddle/op_mapper/pytorch2paddle/aten.py
浏览文件 @ 5bcd803c
......@@ -30,16 +30,19 @@ def aten_adaptive_avg_pool2d(mapper, graph, node):
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]
# 处理输入0，即%x.3
mapper._check_input(graph, inputs_node[0], inputs_name[0], layer_outputs)
mapper._check_input(graph, inputs_node[0], inputs_name[0], current_outputs)
layer_inputs["input"] = inputs_name[0]
# 获取当前节点输入、输出的list
# 获取当前节点输入的list
current_inputs = list(layer_inputs.values())
current_outputs = layer_outputs
# 处理输入1，即%_output_size.1
if inputs_name[1] in mapper.attrs:
layer_attrs["pool_size"] = mapper.attrs[inputs_name[1]]
else:
mapper._check_input(graph, inputs_node[1], inputs_name[1],
current_outputs)
layer_attrs["pool_size"] = inputs_name[1]
current_inputs.append(inputs_name[1])
layer_attrs["pool_type"] = string("avg")
......@@ -70,29 +73,34 @@ def aten_addmm(mapper, graph, node):
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]
# 处理输入0，即%150
mapper._check_input(
graph, inputs_node[0], inputs_name[0], layer_outputs, add_dim=True)
graph, inputs_node[0], inputs_name[0], current_outputs, add_dim=True)
layer_inputs["input"] = inputs_name[0]
# 处理输入1，即%input.3
mapper._check_input(graph, inputs_node[1], inputs_name[1], layer_outputs)
mapper._check_input(graph, inputs_node[1], inputs_name[1], current_outputs)
layer_inputs["x"] = inputs_name[1]
# 处理输入2，即%156
mapper._check_input(graph, inputs_node[2], inputs_name[2], layer_outputs)
mapper._check_input(graph, inputs_node[2], inputs_name[2], current_outputs)
layer_inputs["y"] = inputs_name[2]
# 获取当前节点输入、输出的list
# 获取当前节点输入的list
current_inputs = list(layer_inputs.values())
current_outputs = layer_outputs
# 处理输入3，即%152
if inputs_name[3] in mapper.attrs:
layer_attrs["beta"] = mapper.attrs[inputs_name[3]]
else:
mapper._check_input(graph, inputs_node[3], inputs_name[3],
current_outputs)
layer_attrs["beta"] = inputs_name[3]
current_inputs.append(inputs_name[3])
# 处理输入4，即%151
if inputs_name[4] in mapper.attrs:
layer_attrs["alpha"] = mapper.attrs[inputs_name[4]]
else:
mapper._check_input(graph, inputs_node[4], inputs_name[4],
current_outputs)
layer_attrs["alpha"] = inputs_name[4]
current_inputs.append(inputs_name[4])
......@@ -104,11 +112,41 @@ def aten_addmm(mapper, graph, node):
return current_inputs, current_outputs
def aten_add(mapper, graph, node):
""" 构造数值相加的PaddleLayer，该节点实现out = x + y。
TorchScript示例:
%296 : int = aten::add(%i.12, %288)
参数含义:
%296 (-): 相加结果。
%i.12 (-): 输入数值 x。
%288 (-): 输入数值 y。
"""
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)
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, 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("prim.add", inputs=layer_inputs, outputs=layer_outputs)
return current_inputs, current_outputs
def aten_add_(mapper, graph, node):
""" 构造add的PaddleLayer，该节点实现out = x + alpha * y。
""" 构造数值相加的PaddleLayer，该节点实现out = x + alpha * y。
TorchScript示例:
%output.5 : Tensor = aten::add_(%output.2, %150, %151)
%137 : Tensor = aten::add(%136, %130, %130)
参数含义:
%output.5 (Tensor): add结果Tensor。
%output.2 (Tensor): 输入Tensor x。
......@@ -120,25 +158,28 @@ def aten_add_(mapper, graph, node):
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]
# 处理输入0，即%output.2
mapper._check_input(graph, inputs_node[0], inputs_name[0], layer_outputs)
mapper._check_input(graph, inputs_node[0], inputs_name[0], current_outputs)
layer_inputs["x"] = inputs_name[0]
# 处理输入1，即%150
mapper._check_input(
graph, inputs_node[1], inputs_name[1], layer_outputs, add_dim=True)
graph, inputs_node[1], inputs_name[1], current_outputs, add_dim=True)
layer_inputs["y"] = inputs_name[1]
# 获取当前节点输入、输出的list
# 获取当前节点输入的list
current_inputs = list(layer_inputs.values())
current_outputs = layer_outputs
# 处理输入2，即%151
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)
layer_attrs["alpha"] = inputs_name[2]
current_inputs.append(inputs_name[2])
graph.add_layer(
"prim.add", inputs=layer_inputs, outputs=layer_outputs, **layer_attrs)
"prim.add_", inputs=layer_inputs, outputs=layer_outputs, **layer_attrs)
return current_inputs, current_outputs
......@@ -156,20 +197,87 @@ def aten_append(mapper, graph, node):
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_output_size.1
mapper._check_input(graph, inputs_node[0], inputs_name[0], layer_outputs)
mapper._check_input(graph, inputs_node[0], inputs_name[0], current_outputs)
layer_inputs["list"] = inputs_name[0]
# 处理输入1，即v.1
mapper._check_input(graph, inputs_node[1], inputs_name[1], layer_outputs)
mapper._check_input(graph, inputs_node[1], inputs_name[1], current_outputs)
layer_inputs["element"] = inputs_name[1]
# 获取当前节点输入、输出的list
# 获取当前节点输入的list
current_inputs = list(layer_inputs.values())
current_outputs = layer_outputs
graph.add_layer("prim.append", inputs=layer_inputs, outputs=layer_outputs)
return current_inputs, current_outputs
def aten_batch_norm(mapper, graph, node):
""" 构造BatchNorm的PaddleLayer。
TorchScript示例:
%input.81 : Tensor = aten::batch_norm(%input.80, %778, %779, %776, %777, %780,
%exponential_average_factor.23, %766, %781)
参数含义:
%input.81 (Tensor): 输出，批处理后的结果。
%input.80 (Tensor): 需要进行批处理的特征层。
%778 (Tensor): weights。
%779 (Tensor): bias。
%776 (Tensor): 全局均值。
%777 (Tensor): 全局方差。
%780 (bool): 是否训练。
%exponential_average_factor.23 (float): 用于计算均值和方差的比例。
%766 (float): 为了数值稳定加在分母上的值。
%781 (bool): 是否启用cudnn。
"""
if "batchnorm" in mapper.dygraph_name_id:
mapper.dygraph_name_id["batchnorm"] += 1
else:
mapper.dygraph_name_id["batchnorm"] = 0
batchnorm_name = "batchnorm" + str(mapper.dygraph_name_id["batchnorm"])
output_name = mapper._get_outputs_name(node)[0]
layer_outputs = [batchnorm_name, output_name]
layer_inputs = {}
layer_attrs = {}
layer_attrs["is_test"] = True
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%input.80
mapper._check_input(graph, inputs_node[0], inputs_name[0], current_outputs)
layer_inputs["input"] = inputs_name[0]
# 获取当前节点输入、输出的list
current_inputs = list(layer_inputs.values())
# 处理输入1，即%778
weights = mapper.pytorch_params[inputs_name[1]]
mapper.paddle_params[batchnorm_name + ".weight"] = weights
layer_attrs['num_channels'] = weights.shape[0]
# 处理输入2，即%779
if inputs_name[2] in mapper.pytorch_params:
bias = mapper.pytorch_params[inputs_name[2]]
if bias is not None:
mapper.paddle_params[batchnorm_name + ".bias"] = bias
else:
mapper.paddle_params[batchnorm_name + ".bias"] = False
# 处理输入3，即%776
mean = mapper.pytorch_params[inputs_name[3]]
mapper.paddle_params[batchnorm_name + "._mean"] = mean
# 处理输入4，即%777
var = mapper.pytorch_params[inputs_name[4]]
mapper.paddle_params[batchnorm_name + "._variance"] = var
# 处理输入6，即%exponential_average_factor.23
layer_attrs["momentum"] = mapper.attrs[inputs_name[6]]
# 处理输入7，即%766
layer_attrs["epsilon"] = mapper.attrs[inputs_name[7]]
graph.add_layer(
"fluid.dygraph.BatchNorm",
inputs=layer_inputs,
outputs=layer_outputs,
**layer_attrs)
return current_inputs, current_outputs
def aten_conv2d(mapper, graph, node):
""" 构造conv2d的PaddleLayer。
......@@ -195,12 +303,13 @@ def aten_conv2d(mapper, graph, node):
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]
# 处理输入0，即%input.8
mapper._check_input(graph, inputs_node[0], inputs_name[0], layer_outputs)
mapper._check_input(graph, inputs_node[0], inputs_name[0], current_outputs)
layer_inputs["input"] = inputs_name[0]
# 获取当前节点输入、输出的list
# 获取当前节点输入的list
current_inputs = list(layer_inputs.values())
current_outputs = layer_outputs[1:]
# 处理输入1，即%25
weights = mapper.pytorch_params[inputs_name[1]]
mapper.paddle_params[conv2d_name + ".weight"] = weights
......@@ -209,9 +318,12 @@ def aten_conv2d(mapper, graph, node):
# 处理输入2，即%27
if inputs_name[2] in mapper.pytorch_params:
bias = mapper.pytorch_params[inputs_name[2]]
mapper.paddle_params[conv2d_name + ".bias"] = bias
if bias is not None:
mapper.paddle_params[conv2d_name + ".bias"] = bias
else:
layer_attrs["bias_attr"] = False
else:
mapper.paddle_params[conv2d_name + ".bias"] = False
layer_attrs["bias_attr"] = False
# 处理输入3，即%28
layer_attrs["stride"] = mapper.attrs[inputs_name[3]]
# 处理输入4，即%29
......@@ -244,12 +356,13 @@ def aten_dim(mapper, graph, node):
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%input.8
mapper._check_input(graph, inputs_node[0], inputs_name[0], layer_outputs)
mapper._check_input(graph, inputs_node[0], inputs_name[0], current_outputs)
layer_inputs["input"] = inputs_name[0]
# 获取当前节点输入、输出的list
# 获取当前节点输入的list
current_inputs = list(layer_inputs.values())
current_outputs = layer_outputs
graph.add_layer("prim.shape", inputs=layer_inputs, outputs=layer_outputs)
graph.add_layer(
......@@ -276,12 +389,13 @@ def aten_dropout(mapper, graph, node):
layer_outputs = [dropout_name, 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%119
mapper._check_input(graph, inputs_node[0], inputs_name[0], layer_outputs)
mapper._check_input(graph, inputs_node[0], inputs_name[0], current_outputs)
layer_inputs["input"] = inputs_name[0]
# 获取当前节点输入、输出的list
current_inputs = list(layer_inputs.values())
current_outputs = layer_outputs[1:]
graph.add_layer(
"fluid.dygraph.Dropout",
......@@ -305,15 +419,16 @@ def aten_eq(mapper, graph, node):
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%124
mapper._check_input(graph, inputs_node[0], inputs_name[0], layer_outputs)
layer_inputs["eq0"] = inputs_name[0]
mapper._check_input(graph, inputs_node[0], inputs_name[0], current_outputs)
layer_inputs["x"] = inputs_name[0]
# 处理输入1，即%123
mapper._check_input(graph, inputs_node[1], inputs_name[1], layer_outputs)
layer_inputs["eq1"] = inputs_name[1]
# 获取当前节点输入、输出的list
mapper._check_input(graph, inputs_node[1], inputs_name[1], current_outputs)
layer_inputs["y"] = inputs_name[1]
# 获取当前节点输入的list
current_inputs = list(layer_inputs.values())
current_outputs = layer_outputs
graph.add_layer("prim.eq", inputs=layer_inputs, outputs=layer_outputs)
return current_inputs, current_outputs
......@@ -336,6 +451,8 @@ def aten_flatten(mapper, graph, node):
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1，即%4
graph.add_layer(
"prim.assert",
......@@ -353,11 +470,10 @@ def aten_flatten(mapper, graph, node):
key=mapper.attrs[inputs_name[2]],
value=-1)
# 处理输入0，即%x
mapper._check_input(graph, inputs_node[0], inputs_name[0], layer_outputs)
mapper._check_input(graph, inputs_node[0], inputs_name[0], current_outputs)
layer_inputs["x"] = inputs_name[0]
# 获取当前节点输入、输出的list
# 获取当前节点输入的list
current_inputs = list(layer_inputs.values())
current_outputs = layer_outputs
graph.add_layer(
"fluid.layers.flatten",
......@@ -381,20 +497,68 @@ def aten___getitem__(mapper, graph, node):
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%72
mapper._check_input(graph, inputs_node[0], inputs_name[0], layer_outputs)
mapper._check_input(graph, inputs_node[0], inputs_name[0], current_outputs)
layer_inputs["list"] = inputs_name[0]
# 处理输入1，即%88
mapper._check_input(graph, inputs_node[1], inputs_name[1], layer_outputs)
mapper._check_input(graph, inputs_node[1], inputs_name[1], current_outputs)
layer_inputs["index"] = inputs_name[1]
# 获取当前节点输入、输出的list
# 获取当前节点输入的list
current_inputs = list(layer_inputs.values())
current_outputs = layer_outputs
graph.add_layer("prim.getitem", inputs=layer_inputs, outputs=layer_outputs)
return current_inputs, current_outputs
def aten_hardtanh_(mapper, graph, node):
""" 构造hardtanh激活的PaddleLayer。
TorchScript示例:
%result.9 : Tensor = aten::hardtanh_(%input.20, %67, %66)
参数含义:
%result.9 (Tensor): 输出，hardtanh激活后的Tensor。
%input.20 (Tensor): 需要hardtanh激活的Tensor。
%67 (float): hardtanh激活的最小阈值。
%66 (float): hardtanh激活的最大阈值。
"""
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1，即%67
graph.add_layer(
"prim.assert",
inputs={},
outputs=[inputs_name[1]],
type='eq',
key=mapper.attrs[inputs_name[1]],
value=0.0)
# 处理输入2，即%66
graph.add_layer(
"prim.assert",
inputs={},
outputs=[inputs_name[2]],
type='eq',
key=mapper.attrs[inputs_name[2]],
value=6.0)
# 处理输入0，即%input.20
mapper._check_input(graph, inputs_node[0], inputs_name[0], current_outputs)
layer_inputs["x"] = inputs_name[0]
# 获取当前节点输入的list
current_inputs = list(layer_inputs.values())
graph.add_layer(
'fluid.layers.relu6',
inputs=layer_inputs,
outputs=layer_outputs,
threshold=6.0)
return current_inputs, current_outputs
def aten_le(mapper, graph, node):
""" 构造对比大小的PaddleLayer。
......@@ -409,15 +573,16 @@ def aten_le(mapper, graph, node):
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%78
mapper._check_input(graph, inputs_node[0], inputs_name[0], layer_outputs)
mapper._check_input(graph, inputs_node[0], inputs_name[0], current_outputs)
layer_inputs["input0"] = inputs_name[0]
# 处理输入1，即%79
mapper._check_input(graph, inputs_node[1], inputs_name[1], layer_outputs)
mapper._check_input(graph, inputs_node[1], inputs_name[1], current_outputs)
layer_inputs["input1"] = inputs_name[1]
# 获取当前节点输入、输出的list
# 获取当前节点输入的list
current_inputs = list(layer_inputs.values())
current_outputs = layer_outputs
graph.add_layer("prim.le", inputs=layer_inputs, outputs=layer_outputs)
return current_inputs, current_outputs
......@@ -436,12 +601,13 @@ def aten_len(mapper, graph, node):
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%72
mapper._check_input(graph, inputs_node[0], inputs_name[0], layer_outputs)
mapper._check_input(graph, inputs_node[0], inputs_name[0], current_outputs)
layer_inputs["input"] = inputs_name[0]
# 获取当前节点输入、输出的list
# 获取当前节点输入的list
current_inputs = list(layer_inputs.values())
current_outputs = layer_outputs
graph.add_layer("prim.len", inputs=layer_inputs, outputs=layer_outputs)
return current_inputs, current_outputs
......@@ -471,12 +637,13 @@ def aten_max_pool2d(mapper, graph, node):
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]
# 处理输入0，即%result.11
mapper._check_input(graph, inputs_node[0], inputs_name[0], layer_outputs)
mapper._check_input(graph, inputs_node[0], inputs_name[0], current_outputs)
layer_inputs["input"] = inputs_name[0]
# 获取当前节点输入、输出的list
# 获取当前节点输入的list
current_inputs = list(layer_inputs.values())
current_outputs = layer_outputs[1:]
# 处理输入1，即%20
layer_attrs["pool_size"] = mapper.attrs[inputs_name[1]]
# 处理输入2，即%23
......@@ -517,21 +684,106 @@ def aten_matmul(mapper, graph, node):
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%101
mapper._check_input(graph, inputs_node[0], inputs_name[0], layer_outputs)
mapper._check_input(graph, inputs_node[0], inputs_name[0], current_outputs)
layer_inputs["x"] = inputs_name[0]
# 处理输入1，即%102
mapper._check_input(graph, inputs_node[1], inputs_name[1], layer_outputs)
mapper._check_input(graph, inputs_node[1], inputs_name[1], current_outputs)
layer_inputs["y"] = inputs_name[1]
# 获取当前节点输入、输出的list
# 获取当前节点输入的list
current_inputs = list(layer_inputs.values())
current_outputs = layer_outputs
graph.add_layer(
"fluid.layers.matmul", inputs=layer_inputs, outputs=layer_outputs)
return current_inputs, current_outputs
def aten_mul(mapper, graph, node):
""" 构造数值相乘的PaddleLayer。
TorchScript示例:
%size_prods.39 : int = aten::mul(%size_prods.38, %114)
参数含义:
%size_prods.39 (Tensor): 输出，相乘后的结果。
%size_prods.38 (-): 数值1。
%114 (-): 数值2。
"""
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%size_prods.38
mapper._check_input(graph, inputs_node[0], inputs_name[0], current_outputs)
layer_inputs["x"] = inputs_name[0]
# 处理输入1，即%114
mapper._check_input(graph, inputs_node[1], inputs_name[1], current_outputs)
layer_inputs["y"] = inputs_name[1]
# 获取当前节点输入的list
current_inputs = list(layer_inputs.values())
current_outputs = layer_outputs
graph.add_layer("prim.mul", inputs=layer_inputs, outputs=layer_outputs)
return current_inputs, current_outputs
def aten_ne(mapper, graph, node):
""" 构造判断数值是否不相等的PaddleLayer。
TorchScript示例:
%134 : bool = aten::ne(%133, %132)
参数含义:
%134 (bool): 对比后结果。
%133 (-): 需对比的输入1。
%132 (-): 需对比的输入2。
"""
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%124
mapper._check_input(graph, inputs_node[0], inputs_name[0], current_outputs)
layer_inputs["x"] = inputs_name[0]
# 处理输入1，即%123
mapper._check_input(graph, inputs_node[1], inputs_name[1], current_outputs)
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("prim.ne", inputs=layer_inputs, outputs=layer_outputs)
return current_inputs, current_outputs
def aten_neg(mapper, graph, node):
""" 构造对数值取负的PaddleLayer。
TorchScript示例:
%909 : int = aten::neg(%908)
参数含义:
%909 (int): 取负后结果。
%908 (int): 需取负的输入。
"""
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%124
mapper._check_input(graph, inputs_node[0], inputs_name[0], current_outputs)
layer_inputs["input"] = inputs_name[0]
# 获取当前节点输入的list
current_inputs = list(layer_inputs.values())
graph.add_layer("prim.neg", inputs=layer_inputs, outputs=layer_outputs)
return current_inputs, current_outputs
def aten_relu_(mapper, graph, node):
""" 构造ReLU激活的PaddleLayer。
......@@ -547,12 +799,13 @@ def aten_relu_(mapper, graph, node):
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%result.5
mapper._check_input(graph, inputs_node[0], inputs_name[0], layer_outputs)
mapper._check_input(graph, inputs_node[0], inputs_name[0], current_outputs)
layer_inputs["x"] = inputs_name[0]
# 获取当前节点输入、输出的list
# 获取当前节点输入的list
current_inputs = list(layer_inputs.values())
current_outputs = layer_outputs
graph.add_layer(
"fluid.layers.relu", inputs=layer_inputs, outputs=layer_outputs)
......@@ -574,12 +827,13 @@ def aten_relu6(mapper, graph, node):
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%result.5
mapper._check_input(graph, inputs_node[0], inputs_name[0], layer_outputs)
mapper._check_input(graph, inputs_node[0], inputs_name[0], current_outputs)
layer_inputs["x"] = inputs_name[0]
# 获取当前节点输入、输出的list
current_inputs = list(layer_inputs.values())
current_outputs = layer_outputs
graph.add_layer(
"fluid.layers.relu6",
......@@ -589,6 +843,37 @@ def aten_relu6(mapper, graph, node):
return current_inputs, current_outputs
def aten_reshape(mapper, graph, node):
""" 构造调整大小的PaddleLayer。
TorchScript示例:
%x.6 : Tensor = aten::reshape(%4700, %4703)
参数含义:
%x.6 (Tensor): 输出，reshape后的Tensor。
%4700 (Tensor): 需要reshape的Tensor。
%4703 (list): 形状大小组成的list。
"""
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]
# 处理输入0，即%4700
mapper._check_input(graph, inputs_node[0], inputs_name[0], current_outputs)
layer_inputs["x"] = inputs_name[0]
# 处理输入1，即%4703
mapper._check_input(graph, inputs_node[1], inputs_name[1], current_outputs)
layer_inputs["shape"] = inputs_name[1]
# 获取当前节点输入、输出的list
current_inputs = list(layer_inputs.values())
graph.add_layer(
"fluid.layers.reshape", inputs=layer_inputs, outputs=layer_outputs)
return current_inputs, current_outputs
def aten_size(mapper, graph, node):
""" 构造获取shape的PaddleLayer。
......@@ -602,12 +887,13 @@ def aten_size(mapper, graph, node):
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%x.12
mapper._check_input(graph, inputs_node[0], inputs_name[0], layer_outputs)
mapper._check_input(graph, inputs_node[0], inputs_name[0], current_outputs)
layer_inputs["input"] = inputs_name[0]
# 获取当前节点输入、输出的list
# 获取当前节点输入的list
current_inputs = list(layer_inputs.values())
current_outputs = layer_outputs
graph.add_layer("prim.shape", inputs=layer_inputs, outputs=layer_outputs)
return current_inputs, current_outputs
......@@ -628,35 +914,55 @@ def aten_slice(mapper, graph, 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]
# 处理输入0，即%73
mapper._check_input(graph, inputs_node[0], inputs_name[0], layer_outputs)
mapper._check_input(graph, inputs_node[0], inputs_name[0], current_outputs)
layer_inputs["input"] = inputs_name[0]
# 获取当前节点输入、输出的list
current_inputs = list(layer_inputs.values())
current_outputs = layer_outputs
# 处理输入1，即%82
if inputs_name[1] in mapper.attrs:
layer_attrs["start"] = mapper.attrs[inputs_name[1]]
else:
layer_attrs["start"] = inputs_name[1]
current_inputs.append(inputs_name[1])
mapper._check_input(graph, inputs_node[1], inputs_name[1], current_outputs)
layer_inputs["start"] = inputs_name[1]
# 处理输入2，即%75
if inputs_name[2] in mapper.attrs:
layer_attrs["end"] = mapper.attrs[inputs_name[2]]
else:
layer_attrs["end"] = inputs_name[2]
current_inputs.append(inputs_name[2])
mapper._check_input(graph, inputs_node[2], inputs_name[2], current_outputs)
layer_inputs["end"] = inputs_name[2]
# 处理输入3，即%77
if inputs_name[3] in mapper.attrs:
layer_attrs["step"] = mapper.attrs[inputs_name[3]]
else:
layer_attrs["step"] = inputs_name[3]
current_inputs.append(inputs_name[3])
mapper._check_input(graph, inputs_node[3], inputs_name[3], current_outputs)
layer_inputs["step"] = inputs_name[3]
# 获取当前节点输入的list
current_inputs = list(layer_inputs.values())
graph.add_layer(
"prim.slice", inputs=layer_inputs, outputs=layer_outputs, **layer_attrs)
graph.add_layer("prim.slice", inputs=layer_inputs, outputs=current_outputs)
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)
参数含义:
%840 (-): 相减结果。
%839 (-): 输入数值 x。
%836 (-): 输入数值 y。
"""
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%839
mapper._check_input(graph, inputs_node[0], inputs_name[0], current_outputs)
layer_inputs["x"] = inputs_name[0]
# 处理输入1，即%836
mapper._check_input(
graph, inputs_node[1], inputs_name[1], current_outputs, 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("prim.sub", inputs=layer_inputs, outputs=layer_outputs)
return current_inputs, current_outputs
......@@ -664,7 +970,7 @@ def aten_t(mapper, graph, node):
""" 构造矩阵转置的PaddleLayer。
TorchScript示例:
%109 : Tensor = aten::t(%102)
%840 : int = aten::sub(%839, %836)
参数含义:
%109 (Tensor): 输出，转置后的矩阵。
%102 (Tensor): 需要转置的Tensor。
......@@ -673,12 +979,13 @@ def aten_t(mapper, graph, node):
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%x.12
mapper._check_input(graph, inputs_node[0], inputs_name[0], layer_outputs)
mapper._check_input(graph, inputs_node[0], inputs_name[0], current_outputs)
layer_inputs["x"] = inputs_name[0]
# 获取当前节点输入、输出的list
# 获取当前节点输入的list
current_inputs = list(layer_inputs.values())
current_outputs = layer_outputs
graph.add_layer(
"fluid.layers.transpose",
......
x2paddle/op_mapper/pytorch2paddle/prim.py
浏览文件 @ 5bcd803c
......@@ -53,14 +53,18 @@ def prim_GetAttr(mapper, graph, node):
node = input_node
except Exception:
break
part_script = mapper.script
for field_name in field_name_list:
if hasattr(part_script, field_name):
param = getattr(part_script, field_name)
if isinstance(param, torch.Tensor):
param = param.detach().numpy()
mapper.pytorch_params[output_name] = param
part_script = param
if ".".join(field_name_list) in mapper.pytorch_params:
mapper.pytorch_params[output_name] = mapper.pytorch_params[".".join(
field_name_list)]
else:
part_script = mapper.script
for field_name in field_name_list:
if hasattr(part_script, field_name):
param = getattr(part_script, field_name)
if isinstance(param, torch.Tensor):
param = param.detach().numpy()
mapper.pytorch_params[output_name] = param
part_script = param
return [], [output_name]
......@@ -78,12 +82,13 @@ def prim_ListConstruct(mapper, graph, node):
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]
# 处理每个输入
for i, input_name in enumerate(inputs_name):
layer_inputs["input{}".format(i)] = input_name
# 获取当前节点输入、输出的list
# 获取当前节点输入的list
current_inputs = list(layer_inputs.values())
current_outputs = layer_outputs
graph.add_layer("prim.list", inputs=layer_inputs, outputs=layer_outputs)
return current_inputs, current_outputs
......@@ -101,12 +106,13 @@ def prim_RaiseException(mapper, graph, node):
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%76
mapper._check_input(graph, inputs_node[0], inputs_name[0], layer_outputs)
mapper._check_input(graph, inputs_node[0], inputs_name[0], current_outputs)
layer_inputs["input"] = inputs_name[0]
# 获取当前节点输入、输出的list
# 获取当前节点输入的list
current_inputs = list(layer_inputs.values())
current_outputs = layer_outputs
graph.add_layer(
"prim.exception", inputs=layer_inputs, outputs=layer_outputs)
......@@ -134,7 +140,10 @@ def prim_Loop(mapper, graph, node):
block = list(node.blocks())[0]
loop_outputs = node_outputs
for i, block_input_ivalue in enumerate(block.inputs()):
block_input_node_name = 'x' + str(mapper.output_index)
if i == 0:
block_input_node_name = '_x' + str(mapper.output_index)
else:
block_input_node_name = 'x' + str(mapper.output_index)
unique_id = block_input_ivalue.unique()
if unique_id not in mapper.outputs_info:
mapper.outputs_info[unique_id] = block_input_node_name
......@@ -226,12 +235,65 @@ def prim_min(mapper, graph, node):
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%86
mapper._check_input(graph, inputs_node[0], inputs_name[0], layer_outputs)
mapper._check_input(graph, inputs_node[0], inputs_name[0], current_outputs)
layer_inputs["input"] = inputs_name[0]
# 获取当前节点输入、输出的list
# 获取当前节点输入的list
current_inputs = list(layer_inputs.values())
current_outputs = layer_outputs
graph.add_layer("prim.min", inputs=layer_inputs, outputs=layer_outputs)
return current_inputs, current_outputs
def prim_SetAttr(mapper, graph, node):
""" 设置attribute信息。
TorchScript示例:
= prim::SetAttr[name="num_batches_tracked"](%260, %277)
参数含义:
%260 (-): 属性名前缀。
%277 (-): 需要设置的值。
"""
output_name = mapper._get_outputs_name(node)[0]
field_name_list = []
tmp_node = node
while True:
input_node = list(tmp_node.inputs())[0].node()
try:
field_name_list.insert(0, input_node.s('name'))
tmp_node = input_node
except Exception:
break
field_name_list.append(node.s('name'))
inputs_name, inputs_node = mapper._get_inputs_name(node)
param = {"Tensor": inputs_name[1]}
mapper.pytorch_params[".".join(field_name_list)] = param
return [], [output_name]
def prim_shape(mapper, graph, node):
""" 构造获取shape的PaddleLayer。
TorchScript示例:
%4701 : int[] = prim::shape(%result.1)
参数含义:
%4701 (list): 输出，shape信息。
%result.1 (Tensor): 需要获取shape的值。
"""
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%input.8
mapper._check_input(graph, inputs_node[0], inputs_name[0], current_outputs)
layer_inputs["input"] = inputs_name[0]
# 获取当前节点输入的list
current_inputs = list(layer_inputs.values())
graph.add_layer("prim.shape", inputs=layer_inputs, outputs=layer_outputs)
return current_inputs, current_outputs
x2paddle/op_mapper/pytorch2paddle/pytorch_op_mapper.py
浏览文件 @ 5bcd803c
......@@ -32,8 +32,28 @@ class PyTorchOpMapper(OpMapper):
self.output_index = 0
self.dygraph_name_id = {} # 动态图__init__输出名字中的id，key为kernel类型，value为id
# 转换
self.check_op(decoder.graph)
self.graph, _ = self.traverse(decoder.graph)
def check_op(self, script_graph):
def _update_op_list(graph):
for node in graph.nodes():
op_list.append(node.kind())
for block in node.blocks():
_update_op_list(block)
op_list = list()
_update_op_list(script_graph)
op_list = list(set(op_list))
unsupported_op_list = []
for op in op_list:
func_name = op.replace('::', '_')
if not (hasattr(prim, func_name) or hasattr(aten, func_name)):
unsupported_op_list.append(op)
if len(unsupported_op_list) > 0:
raise Exception("The kind {} in model is not supported yet.".format(
unsupported_op_list))
def traverse(self, script_graph, parent_layer=None):
# 用于获取graph的输入
def _update_graph_inputs(inputs, outputs):
......@@ -65,9 +85,7 @@ class PyTorchOpMapper(OpMapper):
func = getattr(aten, func_name)
inputs, outputs = func(self, graph, node)
_update_graph_inputs(inputs, outputs)
else:
raise Exception("The kind {} in model is not supported yet.".
format(node.kind()))
# 转换输出节点
if hasattr(script_graph, 'returnNode'):
for i, ivalue in enumerate(script_graph.returnNode().inputs()):
......@@ -97,9 +115,9 @@ class PyTorchOpMapper(OpMapper):
self.outputs_info[script_unique_id] = output_name
self.output_index += 1
outputs_name.append(output_name)
# if节点没有输出的情况
# if或loop节点没有输出的情况
if len(list(node.outputs())) == 0:
output_name = 'x' + str(self.output_index)
output_name = '_x' + str(self.output_index)
self.output_index += 1
outputs_name.append(output_name)
return outputs_name
......@@ -122,11 +140,19 @@ class PyTorchOpMapper(OpMapper):
outputs=[output_name],
value="params[{}]".format(string(output_name)))
else:
graph.add_layer(
"prim.constant",
inputs={},
outputs=[output_name],
value=string(param) if isinstance(param, str) else param)
if isinstance(param, dict) and "Tensor" in param:
graph.add_layer(
"prim.constant",
inputs={},
outputs=[output_name],
value=param["Tensor"])
else:
graph.add_layer(
"prim.constant",
inputs={},
outputs=[output_name],
value=string(param)
if isinstance(param, str) else param)
node_outputs.append(output_name)
def _get_inputs_name(self, node):
......@@ -135,9 +161,9 @@ class PyTorchOpMapper(OpMapper):
for script_input_ivalue in node.inputs():
script_input_node = script_input_ivalue.node()
script_input_unique_id = script_input_ivalue.unique()
input_node_name = self.outputs_info[script_input_unique_id]
input_name = self.outputs_info[script_input_unique_id]
inputs_node.append(script_input_node)
inputs_name.append(input_node_name)
inputs_name.append(input_name)
return inputs_name, inputs_node
def data(self, graph, node, uid):
......
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