resolve conflict

docs/inference_model_convertor/op_list.md

@@ -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 | 135 | aten::full | 136 | aten::argmax |
+| 133 | aten::rsqrt | 134 | aten::replication\_pad1d | 135 | aten::full | 136 | aten::group\_norm |
 | 137 | aten::argmax | 138 | aten::copy |  |  |  |  |
 
 Prim:

x2paddle/op_mapper/pytorch2paddle/aten.py

@@ -2732,6 +2732,59 @@ def aten_gt(mapper, graph, node):
     return current_inputs, current_outputs
 
 
+def aten_group_norm(mapper, graph, node):
+    """
+    TorchScript Code:
+        %input.81 : Tensor = aten::group_norm(%input.2, %25, %60, %59, %26, %30)
+        Parameter meaning:
+        %input.81 (Tensor): Output Tensor
+        %input.2 (Tensor): Input Tensor
+        %25 (Tensor): num_groups
+        %60 (Tensor): weight
+        %59 (Tensor): bias
+        %26 (Tensor): eps
+        %30 (bool): enabled cudnn
+    """
+    scope_name = mapper.normalize_scope_name(node)
+    op_name = name_generator("groupnorm", mapper.nn_name2id)
+    output_name = mapper._get_outputs_name(node)[0]
+    layer_outputs = [op_name, output_name]
+    layer_inputs = {}
+    layer_attrs = {}
+    inputs_name, inputs_node = mapper._get_inputs_name(node)
+    # output list
+    current_outputs = [output_name]
+    # process Input Tensor
+    mapper._check_input(graph, inputs_node[0], inputs_name[0], current_outputs,
+                        scope_name)
+    layer_inputs["input"] = inputs_name[0]
+    # input list
+    current_inputs = list(layer_inputs.values())
+    # process num_groups
+    layer_attrs['num_groups'] = mapper.attrs[inputs_name[1]]
+    # process weight
+    weights = mapper.pytorch_params[inputs_name[2]]
+    mapper.paddle_params[op_name + ".weight"] = weights
+    layer_attrs['num_channels'] = weights.shape[0]
+    # process bias
+    if inputs_name[2] in mapper.pytorch_params:
+        bias = mapper.pytorch_params[inputs_name[3]]
+        if bias is not None:
+            mapper.paddle_params[op_name + ".bias"] = bias
+    else:
+        mapper.paddle_params[op_name + ".bias"] = False
+    # process eps
+    layer_attrs["epsilon"] = mapper.attrs[inputs_name[4]]
+
+    graph.add_layer(
+        "paddle.nn.GroupNorm",
+        inputs=layer_inputs,
+        outputs=layer_outputs,
+        scope_name=scope_name,
+        **layer_attrs)
+    return current_inputs, current_outputs
+
+
 def aten_gru(mapper, graph, node):
     """ 构造门控循环单元网络（GRU）的PaddleLayer。
     TorchScript示例:
@@ -2931,45 +2984,38 @@ def aten_hardswish(mapper, graph, node):
 
 
 def aten_index(mapper, graph, node):
-    """ 构造选择元素的PaddleLayer。
-    TorchScript示例:
+    """
+    TorchScript Code:
         %1681 : Float = aten::index(%1653, %1680)
-        参数含义:
-        %1681 (Tensor): 输出，选择后的Tensor。
-        %1653 (Tensor): 需要选择的Tensor。
-        %1680 (int): 选择的索引。
+        Parameter meaning:
+        %1681 (Tensor): Output Tensor
+        %1653 (Tensor): Input Tensor
+        %1680 (int): Index
     """
     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
+    # output list
     current_outputs = [output_name]
-    # 处理输入0，即%1653
+    # process Input Tensor
     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%1680
+    # process Index
     mapper._check_input(graph, inputs_node[1], inputs_name[1], current_outputs,
                         scope_name)
     layer_inputs["index"] = inputs_name[1]
-    # 获取当前节点输入的list
+
     current_inputs = list(layer_inputs.values())
 
     graph.add_layer(
         "prim.getitem",
-        inputs={"list": layer_inputs["index"]},
-        outputs=[layer_inputs["index"]],
-        scope_name=scope_name,
-        index=0)
-    graph.add_layer(
-        "paddle.index_select",
-        inputs=layer_inputs,
+        inputs={"list": layer_inputs["x"]},
         outputs=layer_outputs,
         scope_name=scope_name,
-        **layer_attrs)
+        index=layer_inputs["index"])
     return current_inputs, current_outputs