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Support Marian model #786

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May 7, 2022
Merged

Support Marian model #786

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5f30dc7
Support Marian model
wjj19950828 May 6, 2022
5a15630
resolve conflict
wjj19950828 May 7, 2022
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2 changes: 1 addition & 1 deletion docs/inference_model_convertor/op_list.md
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Original file line number Diff line number Diff line change
Expand Up @@ -115,7 +115,7 @@ Aten:
| 121 | aten::repeat\_interleave | 122 | aten::maxpool1d | 123 | aten::frobenius\_norm | 124 | aten::format |
| 125 | aten::complex | 126 | aten::real | 127 | aten::imag | 128 | aten::fft\_rfftn |
| 129 | aten::fft\_irfftn | 130 | aten::hardsigmoid | 131 | aten::hardswish | 132 | aten::linear |
| 133 | aten::rsqrt | 134 | aten::replication\_pad1d | | | | |
| 133 | aten::rsqrt | 134 | aten::replication\_pad1d | 135 | aten::full | | |

Prim:
| 序号 | OP | 序号 | OP | 序号 | OP | 序号 | OP |
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168 changes: 84 additions & 84 deletions x2paddle/op_mapper/pytorch2paddle/aten.py
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Expand Up @@ -2416,6 +2416,53 @@ def aten_format(mapper, graph, node):
return current_inputs, current_outputs


def aten_full(mapper, graph, node):
"""
TorchScript Code:
%159 : Tensor = aten::full(%775, %50, %49, %56, %48, %53)
Parameter meaning:
%159 (Tensor): Output Tensor
%775 (Tensor): size
%50 (int/float/bool): fill_value
%49 (int): dtype
%56 (int): layout
%48 (int): device
%53 (bool): requires_grad
"""
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)
# output list
current_outputs = [output_name]
mapper._check_input(graph, inputs_node[0], inputs_name[0], current_outputs,
scope_name)
layer_inputs["shape"] = inputs_name[0]
# input list
current_inputs = list(layer_inputs.values())

if inputs_name[1] in mapper.attrs:
layer_attrs["fill_value"] = mapper.attrs[inputs_name[1]]
else:
mapper._check_input(graph, inputs_node[1], inputs_name[1],
current_outputs, scope_name)
layer_inputs["fill_value"] = inputs_name[1]
current_inputs.append(inputs_name[1])
# dtype
if mapper.attrs[inputs_name[2]] is not None:
layer_attrs["dtype"] = dtype_dict[mapper.attrs[inputs_name[2]]]

graph.add_layer(
"paddle.full",
inputs=layer_inputs,
outputs=layer_outputs,
scope_name=scope_name,
**layer_attrs)
return current_inputs, current_outputs


def aten_full_like(mapper, graph, node):
""" 构造创建一个与输入具有相同的形状并且数据类型固定的Tensor的PaddleLayer。
TorchScript示例:
Expand Down Expand Up @@ -3487,109 +3534,62 @@ def aten_lt(mapper, graph, node):


def aten_masked_fill(mapper, graph, node):
""" 构造填充mask的PaddleLayer。
TorchScript示例:
"""
TorchScript Code:
%input.4 : Tensor = aten::masked_fill(%scores.2, %mask.2, %46)
参数含义:
%input.4 (Tensor): 输出，填充后的结果。
%scores.2 (Tensor): 需要填充的Tensor。
%mask.2 (Tensor): bool型的Tensor，哪些位置需要填充。
%46 (-): 填充的值。
Parameter meaning:
%input.4 (Tensor): Output Tensor
%scores.2 (Tensor): Input Tensor
%mask.2 (Tensor): bool mask
%46 (-): fill value
"""
scope_name = mapper.normalize_scope_name(node)
output_name = mapper._get_outputs_name(node)[0]
layer_outputs = [output_name]
inputs_name, inputs_node = mapper._get_inputs_name(node)
# 获取当前节点输入的list
layer_full_inputs = {}
layer_full_attrs = {}
layer_where_inputs = {}
current_inputs = []
# 获取当前节点输出的list
current_outputs = [output_name]
# 处理输入0，即%input.4
# input list
mapper._check_input(graph, inputs_node[0], inputs_name[0], current_outputs,
scope_name)
current_inputs.append(inputs_name[0])
# paddle.full
graph.add_layer(
"prim.type",
"prim.shape",
inputs={"input": inputs_name[0]},
outputs=[inputs_name[0] + "_type"],
outputs=[inputs_name[0] + "_shape"],
scope_name=scope_name)
# 处理输入1，即%scores.2
mapper._check_input(graph, inputs_node[1], inputs_name[1], current_outputs,
scope_name)
current_inputs.append(inputs_name[1])
graph.add_layer(
"paddle.logical_not",
inputs={"x": inputs_name[1]},
outputs=[inputs_name[1] + "_not"],
scope_name=scope_name)
graph.add_layer(
"paddle.cast",
inputs={"x": inputs_name[1]},
outputs=[inputs_name[1] + "_mask"],
scope_name=scope_name,
dtype=inputs_name[0] + "_type")
graph.add_layer(
"paddle.cast",
inputs={"x": inputs_name[1] + "_not"},
outputs=[inputs_name[1] + "_not_mask"],
scope_name=scope_name,
dtype=inputs_name[0] + "_type")
layer_full_inputs["shape"] = inputs_name[0] + "_shape"
if inputs_name[2] in mapper.attrs:
layer_full_attrs["fill_value"] = mapper.attrs[inputs_name[2]]
else:
mapper._check_input(graph, inputs_node[2], inputs_name[2],
current_outputs, scope_name)
layer_full_inputs["fill_value"] = inputs_name[2]
current_inputs.append(inputs_name[2])

graph.add_layer(
"paddle.multiply",
inputs={"x": inputs_name[0],
"y": inputs_name[1] + "_not_mask"},
outputs=[inputs_name[0] + "_not_mask"],
"prim.type",
inputs={"input": inputs_name[0]},
outputs=[inputs_name[0] + "_type"],
scope_name=scope_name)
# 处理输入2，即%46
mapper._check_input(graph, inputs_node[2], inputs_name[2], current_outputs,
scope_name)
layer_full_attrs["dtype"] = inputs_name[0] + "_type"
graph.add_layer(
"prim.eq",
inputs={"x": inputs_name[2]},
outputs=[inputs_name[2] + "_cond1"],
"paddle.full",
inputs=layer_full_inputs,
outputs=[inputs_name[0] + "_full"],
scope_name=scope_name,
y="-float('inf')")
**layer_full_attrs)
# paddle.where
layer_where_inputs["condition"] = inputs_name[1]
layer_where_inputs["x"] = inputs_name[0] + "_full"
layer_where_inputs["y"] = inputs_name[0]
graph.add_layer(
"prim.eq",
inputs={"x": inputs_name[2]},
outputs=[inputs_name[2] + "_cond2"],
scope_name=scope_name,
y="float('inf')")
graph.add_layer(
"prim.or",
inputs={
"x": inputs_name[2] + "_cond1",
"y": inputs_name[2] + "_cond2"
},
outputs=[inputs_name[2] + "_cond"],
scope_name=scope_name)
graph.add_layer(
"prim.if", {'input': inputs_name[2] + "_cond"},
outputs=[inputs_name[2] + "_if"],
scope_name=scope_name)
if_layer = graph.layers[list(graph.layers.keys())[-1]]
block = PaddleGraph(source_type="pytorch", parent_layer=if_layer)
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(source_type="pytorch", parent_layer=if_layer)
block.add_layer(
"prim.mul",
inputs={"x": inputs_name[1] + "_mask",
"y": inputs_name[2]},
outputs=[inputs_name[2] + "_1"],
scope_name=scope_name)
if_layer.add_block(block)
if_layer.inputs["input-0"] = inputs_name[1] + "_mask"
if_layer.inputs["input-1"] = inputs_name[2]
if_layer.outputs.append(inputs_name[2] + "_1")
graph.add_layer(
"paddle.add",
inputs={"x": inputs_name[2] + "_1",
"y": inputs_name[0] + "_not_mask"},
"paddle.where",
inputs=layer_where_inputs,
outputs=layer_outputs,
scope_name=scope_name)
return current_inputs, current_outputs
Expand Down