Handling the case of underslashes

x2paddle/op_mapper/pytorch2paddle/aten.py

@@ -263,41 +263,6 @@ def aten_addmm(mapper, graph, node):
 
 
 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。
-    """
-    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)
-    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,
-        scope_name=scope_name)
-    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 + alpha * y。
     TorchScript示例:
         %137 : Tensor = aten::add(%136, %130, %130)
@@ -325,6 +290,7 @@ def aten_add_(mapper, graph, node):
     layer_inputs["y"] = inputs_name[1]
     # 获取当前节点输入的list
     current_inputs = list(layer_inputs.values())
+    if len(inputs_name) > 2:
         # 处理输入2，即%151
         if inputs_name[2] in mapper.attrs:
             layer_attrs["alpha"] = mapper.attrs[inputs_name[2]]
@@ -340,6 +306,13 @@ def aten_add_(mapper, graph, node):
             outputs=layer_outputs,
             scope_name=scope_name,
             **layer_attrs)
+    else:
+        graph.add_layer(
+            "prim.add",
+            inputs=layer_inputs,
+            outputs=layer_outputs,
+            scope_name=scope_name,
+            **layer_attrs)
     return current_inputs, current_outputs
 
 
@@ -1634,41 +1607,6 @@ def aten_dim(mapper, graph, node):
     return current_inputs, current_outputs
 
 
-def aten_div_(mapper, graph, node):
-    """ 构造除法的PaddleLayer。
-    TorchScript示例:
-        %bx_bw0.3 : Tensor = aten::div_(%bx_bw.3, %2678)
-        参数含义:
-        %bx_bw0.3 (-): 除后的结果。
-        %bx_bw.3 (-): 被除数。
-        %2678 (int): 除数。
-    """
-    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%124
-    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%123
-    mapper._check_input(graph, inputs_node[1], inputs_name[1], current_outputs,
-                        scope_name)
-    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.div",
-        inputs=layer_inputs,
-        outputs=layer_outputs,
-        scope_name=scope_name)
-    return current_inputs, current_outputs
-
-
 def aten_div(mapper, graph, node):
     """ 构造除法的PaddleLayer。
     TorchScript示例:
@@ -1737,39 +1675,6 @@ def aten_dropout(mapper, graph, node):
     return current_inputs, current_outputs
 
 
-def aten_dropout_(mapper, graph, node):
-    """ 构造Dropout的PaddleLayer。
-    TorchScript示例:
-        %119 : Tensor = aten::dropout_(%result.3, %117, %118)
-        参数含义:
-        %119 (Tensor): Dropout后的Tensor。
-        %result.3 (Tensor): 输入Tensor。
-        %118 (bool): 是否是训练阶段。
-    """
-    scope_name = mapper.normalize_scope_name(node)
-    op_name = name_generator("dropout", mapper.nn_name2id)
-    output_name = mapper._get_outputs_name(node)[0]
-    layer_outputs = [op_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], current_outputs,
-                        scope_name)
-    layer_inputs["input"] = 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.nn.Dropout",
-        inputs=layer_inputs,
-        outputs=layer_outputs,
-        scope_name=scope_name,
-        p=0.0)
-    return current_inputs, current_outputs
-
-
 def aten_embedding(mapper, graph, node):
     """ 构造embedding的PaddleLayer。
     TorchScript示例:
@@ -2607,10 +2512,10 @@ def aten_gru(mapper, graph, node):
     return current_inputs, current_outputs
 
 
-def aten_hardtanh_(mapper, graph, node):
+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)
+        %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。
@@ -2990,42 +2895,6 @@ def aten_le(mapper, graph, node):
     return current_inputs, current_outputs
 
 
-def aten_leaky_relu_(mapper, graph, node):
-    """ 构造leaky relu激活的PaddleLayer。
-    TorchScript示例:
-        %input.117 : Tensor = aten::leaky_relu_(%input.114, %1570)
-        参数含义:
-        %input.117 (Tensor): 输出，leaky relu后的结果。
-        %input.114 (Tensor): 需要leaky relu的Tensor。
-        %1570 (float): 输入中的元素小于0时的斜率。
-    """
-    scope_name = mapper.normalize_scope_name(node)
-    op_name = name_generator("leakly_relu", 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)
-    # 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], 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())
-    # 处理输入1，即%1570
-    layer_attrs["negative_slope"] = mapper.attrs[inputs_name[1]]
-
-    graph.add_layer(
-        "paddle.nn.LeakyReLU",
-        inputs=layer_inputs,
-        outputs=layer_outputs,
-        scope_name=scope_name,
-        **layer_attrs)
-    return current_inputs, current_outputs
-
-
 def aten_leaky_relu(mapper, graph, node):
     """ 构造leaky relu激活的PaddleLayer。
     TorchScript示例:
@@ -3293,115 +3162,6 @@ def aten_lt(mapper, graph, node):
     return current_inputs, current_outputs
 
 
-def aten_masked_fill_(mapper, graph, node):
-    """ 构造填充mask的PaddleLayer。
-    TorchScript示例:
-        %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。
-    """
-    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
-    current_inputs = []
-    # 获取当前节点输出的list
-    current_outputs = [output_name]
-    # 处理输入0，即%input.4
-    mapper._check_input(graph, inputs_node[0], inputs_name[0], current_outputs,
-                        scope_name)
-    current_inputs.append(inputs_name[0])
-    graph.add_layer(
-        "prim.type",
-        inputs={"input": inputs_name[0]},
-        outputs=[inputs_name[0] + "_type"],
-        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")
-    graph.add_layer(
-        "paddle.multiply",
-        inputs={"x": inputs_name[0],
-                "y": inputs_name[1] + "_not_mask"},
-        outputs=[inputs_name[0] + "_not_mask"],
-        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)
-    graph.add_layer(
-        "prim.eq",
-        inputs={"x": inputs_name[2]},
-        outputs=[inputs_name[2] + "_cond1"],
-        scope_name=scope_name,
-        y="-float('inf')")
-    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"},
-        outputs=layer_outputs,
-        scope_name=scope_name)
-    return current_inputs, current_outputs
-
-
 def aten_masked_fill(mapper, graph, node):
     """ 构造填充mask的PaddleLayer。
     TorchScript示例:
@@ -3799,42 +3559,6 @@ def aten_mul(mapper, graph, node):
     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。
-    """
-    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%size_prods.38
-    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%114
-    mapper._check_input(graph, inputs_node[1], inputs_name[1], current_outputs,
-                        scope_name)
-    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,
-        scope_name=scope_name)
-    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:
@@ -4304,38 +4028,6 @@ def aten_relu(mapper, graph, node):
     return current_inputs, current_outputs
 
 
-def aten_relu_(mapper, graph, node):
-    """ 构造ReLU激活的PaddleLayer。
-    TorchScript示例:
-        %result.3 : Tensor = aten::relu_(%input.5)
-        参数含义:
-        %result.3 (Tensor): 输出，ReLU后的结果。
-        %result.5 (Tensor): 需要ReLU的Tensor。
-    注意: inplace这个参数在paddle中未实现
-    """
-    scope_name = mapper.normalize_scope_name(node)
-    op_name = name_generator("relu", mapper.nn_name2id)
-    output_name = mapper._get_outputs_name(node)[0]
-    layer_outputs = [op_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%result.5
-    mapper._check_input(graph, inputs_node[0], inputs_name[0], current_outputs,
-                        scope_name)
-    layer_inputs["x"] = inputs_name[0]
-    # 获取当前节点输入的list
-    current_inputs = list(layer_inputs.values())
-
-    graph.add_layer(
-        "paddle.nn.ReLU",
-        inputs=layer_inputs,
-        outputs=layer_outputs,
-        scope_name=scope_name)
-    return current_inputs, current_outputs
-
-
 def aten_relu6(mapper, graph, node):
     """ 构造ReLU6激活的PaddleLayer。
     TorchScript示例:
@@ -4748,38 +4440,6 @@ def aten_silu(mapper, graph, node):
     return current_inputs, current_outputs
 
 
-def aten_silu_(mapper, graph, node):
-    """ 构造Silu激活的PaddleLayer。
-    TorchScript示例:
-        %result.3 : Tensor = aten::silu_(%input.5)
-        参数含义:
-        %result.3 (Tensor): 输出，Silu后的结果。
-        %input.5 (Tensor): 需要Silu的Tensor。
-    注意: inplace这个参数在paddle中未实现
-    """
-    scope_name = mapper.normalize_scope_name(node)
-    op_name = name_generator("silu", mapper.nn_name2id)
-    output_name = mapper._get_outputs_name(node)[0]
-    layer_outputs = [op_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%input.5
-    mapper._check_input(graph, inputs_node[0], inputs_name[0], current_outputs,
-                        scope_name)
-    layer_inputs["x"] = inputs_name[0]
-    # 获取当前节点输入的list
-    current_inputs = list(layer_inputs.values())
-
-    graph.add_layer(
-        "paddle.nn.Silu",
-        inputs=layer_inputs,
-        outputs=layer_outputs,
-        scope_name=scope_name)
-    return current_inputs, current_outputs
-
-
 def aten_sin(mapper, graph, node):
     """ 构造数学计算sin的PaddleLayer。
     TorchScript示例:

x2paddle/op_mapper/pytorch2paddle/pytorch_op_mapper.py

@@ -55,6 +55,10 @@ class PyTorchOpMapper():
         unsupported_ops = []
         for op in op_list:
             func_name = op.replace('::', '_')
+            # Processing suffix is "_" situation, eg: aten_relu_ to aten_relu
+            # avoid aten::__isnot__ situation
+            if func_name[-1] == "_" and func_name[-2] != "_":
+                func_name = func_name[:-1]
             if not (hasattr(prim, func_name) or hasattr(aten, func_name)):
                 unsupported_ops.append(op)
         if len(unsupported_ops) == 0:
@@ -104,6 +108,10 @@ class PyTorchOpMapper():
         for node in script_graph.nodes():
             kind = node.kind()
             func_name = kind.replace('::', '_')
+            # Processing suffix is "_" situation, eg: aten_relu_ to aten_relu
+            # avoid aten::__isnot__ situation
+            if func_name[-1] == "_" and func_name[-2] != "_":
+                func_name = func_name[:-1]
             if hasattr(prim, func_name):
                 func = getattr(prim, func_name)
                 inputs, outputs = func(self, graph, node)