fix the bug

x2paddle/core/program.py

@@ -447,6 +447,9 @@ class PaddleGraph(object):
             if self.source_type == "caffe":
                 custom_import = "from x2paddle.op_mapper.dygraph.caffe2paddle " + \
                                  "import caffe_custom_layer as x2paddle_nn"
+            elif self.source_type == "pytorch":
+                custom_import = "from x2paddle.op_mapper.dygraph.pytorch2paddle " + \
+                                 "import pytorch_custom_layer as x2paddle_nn"
             else:
                 custom_import = ""
             self.head = gen_codes(
@@ -455,6 +458,7 @@ class PaddleGraph(object):
                     "from paddle.fluid.param_attr import ParamAttr",
                     "import paddle",
                     "import paddle.fluid as fluid",
+                    "import math",
                     custom_import,
                     "",
                     "class {}(paddle.nn.Layer):".format(self.name),
@@ -590,7 +594,10 @@ class PaddleGraph(object):
                     if isinstance(v, list):
                         line += "{}=[{}], ".format(k, ", ".join(v))
                     else:
-                        line += "{}={}, ".format(k, v)
+                        if k == "args":
+                            line += v
+                        else:
+                            line += "{}={}, ".format(k, v)
                 for k, v in layer.attrs.items():
                     line += "{}={}, ".format(k, v)
                 line = line.strip(", ")

x2paddle/decoder/pytorch_decoder.py

@@ -21,14 +21,14 @@ import numpy as np
 class Decoder(object):
      def _optimize_graph(self, graph):
         torch._C._jit_pass_constant_propagation(graph)
-        torch._C._jit_pass_dce(graph)
-        torch._C._jit_pass_lint(graph)
-        torch._C._jit_pass_peephole(graph)
-        torch._C._jit_pass_lint(graph)
-        torch._C._jit_pass_dce(graph)
-        torch._C._jit_pass_lint(graph)
-        torch._C._jit_pass_canonicalize(graph)
-        torch._C._jit_pass_lint(graph)
+#         torch._C._jit_pass_dce(graph)
+#         torch._C._jit_pass_lint(graph)
+#         torch._C._jit_pass_peephole(graph)
+#         torch._C._jit_pass_lint(graph)
+#         torch._C._jit_pass_dce(graph)
+#         torch._C._jit_pass_lint(graph)
+#         torch._C._jit_pass_canonicalize(graph)
+#         torch._C._jit_pass_lint(graph)
         torch._C._jit_pass_constant_propagation(graph)
         return graph       
 

x2paddle/op_mapper/dygraph/pytorch2paddle/aten.py

@@ -752,6 +752,56 @@ def aten_chunk(mapper, graph, node):
     return current_inputs, current_outputs
 
 
+def aten_clamp(mapper, graph, node):
+    """ 构造元素剪裁的PaddleLayer。
+
+    TorchScript示例:
+        %56 : Tensor = aten::clamp(%input.1, %46, %48, %49)
+        参数含义:
+        %56 (Tensor): 输出，累加后的结果。
+        %input.1 (Tensor): 输入，需要剪裁的Tensor。
+        %46 (float/Tensor): 最小值。
+        %48 (float/Tensor): 最大值。
+    """
+    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
+    current_outputs = [output_name]
+    # 处理输入0，即%input.1
+    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%46
+    if inputs_name[1] in mapper.attrs:
+        layer_attrs["min"] = mapper.attrs[inputs_name[1]]
+    else:
+        mapper._check_input(graph, inputs_node[1], inputs_name[1],
+                            current_outputs, scope_name)
+        layer_inputs["min"] = inputs_name[1]
+        current_inputs.append(inputs_name[1])
+    # 处理输入2，即%48，代表dtype
+    if inputs_name[2] in mapper.attrs:
+        layer_attrs["max"] = mapper.attrs[inputs_name[2]]
+    else:
+        mapper._check_input(graph, inputs_node[2], inputs_name[2],
+                            current_outputs, scope_name)
+        layer_inputs["max"] = inputs_name[2]
+        current_inputs.append(inputs_name[2])
+
+    graph.add_layer(
+        "paddle.clip",
+        inputs=layer_inputs,
+        outputs=layer_outputs,
+        scope_name=scope_name,
+        **layer_attrs)
+    return current_inputs, current_outputs
+
+
 def aten___contains__(mapper, graph, node):
     """ 构造in的PaddleLayer。
 
@@ -810,7 +860,7 @@ def aten_constant_pad_nd(mapper, graph, node):
     # 处理输入1，即%4876
     layer_attrs["padding"] = mapper.attrs[inputs_name[1]]
     # 处理输入2，即%42
-    layer_attrs["pad_value"] = mapper.attrs[inputs_name[2]]
+    layer_attrs["value"] = mapper.attrs[inputs_name[2]]
 
     graph.add_layer(
         "prim.shape",
@@ -856,7 +906,7 @@ def aten_constant_pad_nd(mapper, graph, node):
         block.add_layer(
             kernel,
             inputs={"input": inputs_name[0] + "_var"},
-            outputs=layer_outputs,
+            outputs=copy.deepcopy(layer_outputs),
             scope_name=scope_name,
             **layer_attrs)
         block.add_layer(
@@ -1517,76 +1567,88 @@ def aten_expand(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%1875
     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%1888
-    mapper._check_input(graph, inputs_node[1], inputs_name[1], current_outputs, scope_name)
-
-    graph.add_layer(
-        "prim.type",
-        inputs={"input": inputs_name[0]},
-        outputs=[inputs_name[0] + "_type"],
-        scope_name=scope_name)
-    graph.add_layer(
-        "prim.str",
-        inputs={"input": inputs_name[0] + "_type"},
-        outputs=[inputs_name[0] + "_type"],
-        scope_name=scope_name)
-    graph.add_layer(
-        "prim.eq",
-        inputs={"x": inputs_name[0] + "_type"},
-        outputs=[inputs_name[0] + "_cond"],
-        scope_name=scope_name,
-        y=string("VarType.BOOL"))
+    current_inputs = list(layer_inputs.values())
+    # 处理输入1，即%51
+    if inputs_name[1] in mapper.attrs:
+        layer_attrs["shape"] = mapper.attrs[inputs_name[1]]
+    else:
+        mapper._check_input(graph, inputs_node[1], inputs_name[1],
+                            current_outputs, scope_name)
+        layer_inputs["shape"] = inputs_name[1]
+        current_inputs.append(inputs_name[1])
     graph.add_layer(
-        "prim.if", {'input': inputs_name[0] + "_cond"},
-        outputs=[inputs_name[0] + "_if1", inputs_name[1] + "_var"],
-        scope_name=scope_name)
-    if_layer = graph.layers[list(graph.layers.keys())[-1]]
-    block = PaddleGraph(parent_layer=if_layer, graph_type="dygraph")
-    block.add_layer(
-        "paddle.cast",
-        inputs={"x": inputs_name[0]},
-        outputs=[inputs_name[0]],
-        scope_name=scope_name,
-        dtype=string("int64"))
-    block.add_layer(
-        "self.create_parameter",
-        inputs={"shape": inputs_name[1]},
-        outputs=[inputs_name[1] + "_var"],
+        "paddle.expand", 
+        inputs=layer_inputs, 
+        outputs=layer_outputs, 
         scope_name=scope_name,
-        dtype=string("int64"),
-        default_initializer="paddle.nn.initializer.Constant(value=0.0)")
-    if_layer.add_block(block)
-    block = PaddleGraph(parent_layer=if_layer, graph_type="dygraph")
-    block.add_layer(
-        "prim.type",
-        inputs={"input": inputs_name[0]},
-        outputs=[inputs_name[0] + "_type"],
-        scope_name=scope_name)
-    block.add_layer(
-        "self.create_parameter",
-        inputs={"shape": inputs_name[1]},
-        outputs=[inputs_name[1] + "_var"],
-        scope_name=scope_name,
-        dtype=inputs_name[0] + "_type",
-        default_initializer="paddle.nn.initializer.Constant(value=0.0)")
-    if_layer.add_block(block)
-    if_layer.inputs["input-0"] = inputs_name[0]
-    if_layer.inputs["input-1"] = inputs_name[1]
+        **layer_attrs)
 
-    layer_inputs["y"] = inputs_name[1] + "_var"
-    current_outputs.append(inputs_name[1] + "_var")
-    # 获取当前节点输入的list
-    current_inputs = list(layer_inputs.values())
-    current_inputs.append(inputs_name[1])
+#     graph.add_layer(
+#         "prim.type",
+#         inputs={"input": inputs_name[0]},
+#         outputs=[inputs_name[0] + "_type"],
+#         scope_name=scope_name)
+#     graph.add_layer(
+#         "prim.str",
+#         inputs={"input": inputs_name[0] + "_type"},
+#         outputs=[inputs_name[0] + "_type"],
+#         scope_name=scope_name)
+#     graph.add_layer(
+#         "prim.eq",
+#         inputs={"x": inputs_name[0] + "_type"},
+#         outputs=[inputs_name[0] + "_cond"],
+#         scope_name=scope_name,
+#         y=string("VarType.BOOL"))
+#     graph.add_layer(
+#         "prim.if", {'input': inputs_name[0] + "_cond"},
+#         outputs=[inputs_name[0] + "_if1", inputs_name[1] + "_var"],
+#         scope_name=scope_name)
+#     if_layer = graph.layers[list(graph.layers.keys())[-1]]
+#     block = PaddleGraph(parent_layer=if_layer, graph_type="dygraph")
+#     block.add_layer(
+#         "paddle.cast",
+#         inputs={"x": inputs_name[0]},
+#         outputs=[inputs_name[0]],
+#         scope_name=scope_name,
+#         dtype=string("int64"))
+#     block.add_layer(
+#         "paddle.zeros",
+#         inputs={"shape": inputs_name[1]},
+#         outputs=[inputs_name[1] + "_var"],
+#         scope_name=scope_name,
+#         dtype=string("int64"))
+#     if_layer.add_block(block)
+#     block = PaddleGraph(parent_layer=if_layer, graph_type="dygraph")
+#     block.add_layer(
+#         "prim.type",
+#         inputs={"input": inputs_name[0]},
+#         outputs=[inputs_name[0] + "_type"],
+#         scope_name=scope_name)
+#     block.add_layer(
+#         "paddle.zeros",
+#         inputs={"shape": inputs_name[1]},
+#         outputs=[inputs_name[1] + "_var"],
+#         scope_name=scope_name,
+#         dtype=inputs_name[0] + "_type")
+#     if_layer.add_block(block)
+#     if_layer.inputs["input-0"] = inputs_name[0]
+#     if_layer.inputs["input-1"] = inputs_name[1]
 
-    graph.add_layer(
-        "paddle.expand_as", inputs=layer_inputs, outputs=layer_outputs, scope_name=scope_name)
+#     layer_inputs["y"] = inputs_name[1] + "_var"
+#     current_outputs.append(inputs_name[1] + "_var")
+#     # 获取当前节点输入的list
+#     current_inputs = list(layer_inputs.values())
+#     current_inputs.append(inputs_name[1])
+
+#     graph.add_layer(
+#         "paddle.expand_as", inputs=layer_inputs, outputs=layer_outputs, scope_name=scope_name)
     return current_inputs, current_outputs
 
 
@@ -1841,11 +1903,39 @@ def aten_floor(mapper, graph, node):
     current_outputs = [output_name]
     # 处理输入0，即%scale.18
     mapper._check_input(graph, inputs_node[0], inputs_name[0], current_outputs, scope_name)
-    layer_inputs["input"] = inputs_name[0]
+    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("prim.floor", inputs=layer_inputs, outputs=layer_outputs, scope_name=scope_name)
+    graph.add_layer(
+        "prim.type", 
+        {'input': inputs_name[0]},
+        outputs=[inputs_name[0] + "_type"],
+        scope_name=scope_name)
+    graph.add_layer(
+        "prim.str", 
+        {'input': inputs_name[0] + "_type"},
+        outputs=[inputs_name[0] + "_type"],
+        scope_name=scope_name)
+    graph.add_layer(
+        "prim.startswith", 
+        {'input': inputs_name[0] + "_type"},
+        outputs=[inputs_name[0] + "_cond"],
+        scope_name=scope_name,
+        start_str=string("VarType")) 
+    graph.add_layer(
+        "prim.if", 
+        {'input': inputs_name[0] + "_cond"},
+        outputs=[inputs_name[0] + "_if"],
+        scope_name=scope_name)
+    if_layer = graph.layers[list(graph.layers.keys())[-1]]
+    block = PaddleGraph(parent_layer=if_layer, graph_type="dygraph")
+    block.add_layer("paddle.floor", inputs=copy.deepcopy(layer_inputs), outputs=copy.deepcopy(layer_outputs), scope_name=scope_name)
+    if_layer.add_block(block)
+    block = PaddleGraph(parent_layer=if_layer, graph_type="dygraph")
+    block.add_layer("prim.floor", inputs=copy.deepcopy(layer_inputs), outputs=copy.deepcopy(layer_outputs), scope_name=scope_name)
+    if_layer.add_block(block)
+    if_layer.inputs["input-0"] = inputs_name[0]
+    if_layer.outputs.append(output_name)
     return current_inputs, current_outputs
 
 
@@ -1957,6 +2047,46 @@ def aten_full_like(mapper, graph, node):
     return current_inputs, current_outputs
 
 
+def aten_gather(mapper, graph, node):
+    """ 构造gather激活的PaddleLayer。
+
+    TorchScript示例:
+        %result.3 : Tensor = aten::gather(%input.5, %18, %19, %20, %21)
+        参数含义:
+        %result.3 (Tensor): 输出，gather后的结果。
+        %result.5 (Tensor): 需要gather的Tensor。
+        %18 (int): 需要gather的维度。
+        %19 (Tensor): 需要gather的索引。
+    """
+    scope_name = mapper.normalize_scope_name(node)
+    op_name = name_generator("gather", 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]
+    # 处理输入1，即%18
+    layer_attrs["dim"] = mapper.attrs[inputs_name[1]]
+    # 处理输入2，即%19
+    mapper._check_input(graph, inputs_node[2], inputs_name[2], current_outputs, scope_name)
+    layer_inputs["index"] = inputs_name[2]
+    # 获取当前节点输入的list
+    current_inputs = list(layer_inputs.values())
+    
+    graph.add_layer(
+        "custom_layer:Gather", 
+        inputs=layer_inputs, 
+        outputs=layer_outputs, 
+        scope_name=scope_name,
+        **layer_attrs)
+    return current_inputs, current_outputs
+
+
 def aten_gelu(mapper, graph, node):
     """ 构造GeLU激活的PaddleLayer。
 
@@ -2855,6 +2985,33 @@ def aten_mean(mapper, graph, node):
     return current_inputs, current_outputs
 
 
+def aten_meshgrid(mapper, graph, node):
+    """ 构造对每个张量做扩充操作的PaddleLayer。
+
+    TorchScript示例:
+        %out.39 : int = aten::mshgrid(%input.1)
+        参数含义:
+        %out.39 (Tensor): 输出，扩充后的结果。
+        %input.1 (Tensor): 输入。
+    """
+    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%input.1
+    mapper._check_input(graph, inputs_node[0], inputs_name[0], current_outputs, scope_name)
+    layer_inputs["args"] = inputs_name[0]
+    # 获取当前节点输入的list
+    current_inputs = layer_inputs.values()
+    current_outputs = layer_outputs
+
+    graph.add_layer("paddle.meshgrid", inputs=layer_inputs, outputs=layer_outputs, scope_name=scope_name)
+    return current_inputs, current_outputs
+
+
 def aten_mul(mapper, graph, node):
     """ 构造数值相乘的PaddleLayer。
 

x2paddle/op_mapper/dygraph/pytorch2paddle/prim2code.py

@@ -180,7 +180,7 @@ def prim_float(layer, indent=1, init_func=[], forward_func=[], layer_id=None, di
 
 def prim_floor(layer, indent=1, init_func=[], forward_func=[], layer_id=None, different_attrs=None):
     line = "{} = math.floor({})".format(layer.outputs[0],
-                                        get_value(layer, "input", different_attrs))
+                                        get_value(layer, "x", different_attrs))
     forward_func.extend(gen_codes([line], indent=indent))
 
 
@@ -404,6 +404,13 @@ def prim_slice(layer, indent=1, init_func=[], forward_func=[], layer_id=None, di
                                         get_value(layer, "end", different_attrs),
                                         get_value(layer, "step", different_attrs))
     forward_func.extend(gen_codes([line], indent=indent))
+    
+    
+def prim_startswith(layer, indent=1, init_func=[], forward_func=[], layer_id=None, different_attrs=None):
+    line = "{} = {}.startswith({})".format(layer.outputs[0],
+                                           get_value(layer, "input", different_attrs),
+                                           get_value(layer, "start_str", different_attrs))
+    forward_func.extend(gen_codes([line], indent=indent))
 
 
 def prim_str(layer, indent=1, init_func=[], forward_func=[], layer_id=None, different_attrs=None):
@@ -451,3 +458,4 @@ def prim_warnings(layer, indent=1, init_func=[], forward_func=[], layer_id=None,
         get_value(layer, "input", different_attrs), layer.attrs["stacklevel"])
     lines.append(line)
     forward_func.extend(gen_codes(lines, indent=indent))
+    

x2paddle/op_mapper/dygraph/pytorch2paddle/pytorch_custom_layer/__init__.py

+# Copyright (c) 2020  PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License"
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+
+from .gather import Gather
\ No newline at end of file

x2paddle/op_mapper/dygraph/pytorch2paddle/pytorch_custom_layer/gather.py

+# Copyright (c) 2020  PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License"
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import paddle
+import paddle.fluid as fluid
+from itertools import product
+import numpy as np
+
+class Gather(object):
+    def __init__(self, dim):
+        self.dim = dim
+        
+    def __call__(self, x, index):
+        out_list = list()
+        dims = list()
+        index_shape = index.shape
+        x_type = x.numpy().dtype
+        for s in index_shape:
+            dims.append(list(range(s))) 
+        for id in product(*dims):
+            id = list(id)
+            id_tensor = paddle.to_tensor(np.array(id).astype('int32'))
+            dim_id = paddle.gather_nd(index, id_tensor).numpy()
+            id[self.dim] = dim_id
+            id_tensor = paddle.to_tensor(np.array(id).astype('int32'))
+            data = paddle.gather_nd(x, id_tensor).numpy()
+            out_list.append(data)
+        out = paddle.to_tensor(np.array(out_list).astype(x_type))
+        out = paddle.reshape(out, index_shape)
+        return out

x2paddle/optimizer/code_optimizer/hierachical_tree.py

@@ -201,7 +201,6 @@ class HierarchicalTree(Tree):
         code_str = gen_layer_code(self.pd_graph, sub_layers, module_name, 
                                                      different_attrs=diff_attrs_column)
         
-#         print(code_str)
         self.codes.append(code_str)
         for sub_layers in sub_layers_list:
             inputs, outputs = get_inputs_outputs(self.pd_graph, sub_layers)
@@ -371,7 +370,12 @@ class HierarchicalTree(Tree):
         self.update_parameters()
         import_list = ["import paddle",
                        "import paddle.fluid as fluid",
-                       "",]
+                       "from paddle.fluid.initializer import Constant",
+                       "from paddle.fluid.param_attr import ParamAttr",
+                       "imort math",
+                       "from x2paddle.op_mapper.dygraph.pytorch2paddle " + \
+                                 "import pytorch_custom_layer as x2paddle_nn"
+                       "\n",]
         import_str = "\n".join(import_list)
         if not osp.exists(save_dir):
             os.makedirs(save_dir)

x2paddle/optimizer/code_optimizer/layer_code_generator.py

@@ -29,9 +29,9 @@ NN_KERNEL_NAME = {"paddle.nn.BatchNorm": "bn",
                   "paddle.nn.Tanh": "tanh",
                   "paddle.nn.AvgPool2D": "pool",
                   "paddle.nn.MaxPool2D": "pool",
-                  "paddle.nn.Pad1d": "pad",
-                  "paddle.nn.Pad2d": "pad",
-                  "paddle.nn.Pad3d": "pad",
+                  "paddle.nn.Pad1D": "pad",
+                  "paddle.nn.Pad2D": "pad",
+                  "paddle.nn.Pad3D": "pad",
                   "paddle.nn.Dropout": "dropout",
                   "paddle.nn.GELU": "gelu",
                   "paddle.nn.Hardtanh": "tanh",
@@ -175,9 +175,11 @@ def gen_layer_code(graph, sub_layers, sub_layers_name, different_attrs=list()):
                 if layer.kernel.startswith("paddle.nn") and index == 0:
                     continue
                 if not output_name.startswith("x") or output_name in outputs \
-                        or layer.kernel == "prim.assert" or \
-                        layer.kernel == "prim.if" or layer.kernel == "prim.loop":
+                        or layer.kernel == "prim.assert":
                     continue
+                elif layer.kernel == "prim.if" or layer.kernel == "prim.loop":
+                    if index != 0:
+                        outputs.append(output_name)
                 elif output_name not in outputs:
                     outputs.append(output_name)
             continue
@@ -187,15 +189,22 @@ def gen_layer_code(graph, sub_layers, sub_layers_name, different_attrs=list()):
                     if layer.kernel.startswith("paddle.nn") and index == 0 and "functional" not in layer.kernel:
                         continue
                     if not output_name.startswith("x") or output_name in outputs \
-                            or layer.kernel == "prim.assert" or \
-                            layer.kernel == "prim.if" or layer.kernel == "prim.loop":
+                            or layer.kernel == "prim.assert":
                         continue
+                    elif layer.kernel == "prim.if" or layer.kernel == "prim.loop":
+                        if index != 0:
+                            outputs.append(output_name)
                     else:
                         outputs.append(output_name)
     no_output_count = 0
     for i, (layer_id, layer) in enumerate(sub_layers.items()):
-        if ("paddle.nn" in layer.kernel and "functional" not in layer.kernel):
-            line = "self.{} = {}(".format(layer.outputs[0], layer.kernel)
+        if ("paddle.nn" in layer.kernel and "functional" not in layer.kernel) or \
+                layer.kernel.startswith("custom_layer"):
+            line = "self.{}".format(layer.outputs[0])
+            if layer.kernel.startswith("custom_layer"):
+                line += "= x2paddle_nn.{}(".format(layer.kernel.split(":")[-1])
+            else:
+                line += " = {}(".format(layer.kernel)
             for k, v in layer.attrs.items():
                 key_name = "{}_{}".format(layer.outputs[0], k)
                 if key_name in different_attrs:
@@ -289,7 +298,10 @@ def gen_layer_code(graph, sub_layers, sub_layers_name, different_attrs=list()):
                 else:
                     if v not in cur_outputs and v not in inputs:
                         inputs.append(v)
-                    line += "{}={}, ".format(k, v)
+                    if k == "args":
+                        line += v
+                    else:
+                        line += "{}={}, ".format(k, v)
             for k, v in layer.attrs.items():
                 key_name = "{}_{}".format(layer.outputs[0], k)
                 if key_name in different_attrs:

x2paddle/optimizer/code_optimizer/subgraphs_union.py

@@ -50,21 +50,25 @@ def get_inputs_outputs(pd_graph, layers):
     for layer_id, layer in layers.items():
         # 获取输出节点名字
         if layer_id not in pd_graph.edges_out:
-            for output_name in layer.outputs:
+            for index, output_name in enumerate(layer.outputs):
                 if not output_name.startswith("x") or output_name in outputs \
-                        or layer.kernel == "prim.assert" or \
-                        layer.kernel == "prim.if" or layer.kernel == "prim.loop":
+                        or layer.kernel == "prim.assert":
                     continue
+                elif layer.kernel == "prim.if" or layer.kernel == "prim.loop":
+                        if index != 0:
+                            outputs.append(output_name)
                 elif output_name not in outputs:
                     outputs.append(output_name)
         else:
             for out_layer_id in pd_graph.edges_out[layer_id]:
                 if out_layer_id not in layer_ids:
-                    for output_name in layer.outputs:
+                    for index, output_name in enumerate(layer.outputs):
                         if not output_name.startswith("x") or output_name in outputs \
-                                or layer.kernel == "prim.assert" or \
-                                layer.kernel == "prim.if" or layer.kernel == "prim.loop":
+                                or layer.kernel == "prim.assert":
                             continue
+                        elif layer.kernel == "prim.if" or layer.kernel == "prim.loop":
+                            if index != 0:
+                                outputs.append(output_name)
                         else:
                             outputs.append(output_name)
         # 获取输入节点名字

x2paddle/optimizer/optimizer.py

@@ -21,7 +21,8 @@ class GraphOptimizer(object):
     def __init__(self, source_frame, paddle_type="dygraph", jit_type="trace"):
         if source_frame == "pytorch":
             if jit_type == "trace":
-                self.passes = ["trace_fc_fuse_pass"]
+                self.passes = ["dygraph_constant_fuse_pass", 
+                               "trace_fc_fuse_pass"]
             else:
                 self.passes = [
                     "dygraph_constant_fuse_pass",