Merge pull request #435 from SunAhong1993/paddle-2.0

add pytorch2paddle

README.md

@@ -16,6 +16,7 @@ paddlepaddle >= 1.8.0
 tensorflow ： tensorflow == 1.14.0  
 caffe ： 无  
 onnx ： onnx >= 1.6.0
+pytorch：torch >=1.5.0 (script方式暂不支持1.7.0)
 
 ## 安装
 ### 安装方式一（推荐）
@@ -45,11 +46,10 @@ x2paddle --framework=caffe --prototxt=deploy.prototxt --weight=deploy.caffemodel
 x2paddle --framework=onnx --model=onnx_model.onnx --save_dir=pd_model
 ```
 
-### Paddle2ONNX
-```
-# 注意：paddle_infer_model_dir下需包含__model__和__params__两个文件
-x2paddle --framework=paddle2onnx --model=paddle_infer_model_dir --save_dir=onnx_model
-```
+### PyTorch
+> PyTorch不支持命令行使用方式，详见[PyTorch2Paddle](pytorch2paddle.md)
+
+
 ### 参数选项
 | 参数 | |
 |----------|--------------|

pytorch2paddle.md

+# PyTorch2Paddle
+
+PyTorch2Paddle支持trace和script两种方式的转换，均是PyTorch动态图到Paddle动态图的转换，转换后的Paddle动态图运用动转静可转换为静态图模型。trace方式生成的代码可读性较强，较为接近原版PyTorch代码的组织结构；script方式不需要知道输入数据的类型和大小即可转换，使用上较为方便，但目前PyTorch支持的script代码方式有所限制，所以支持转换的代码也有所限制。用户可根据自身需求，选择转换方式。
+
+## 环境依赖
+
+python == 2.7 | python >= 3.5  
+paddlepaddle >= 1.8.0  
+pytorch：torch >=1.5.0 (script方式暂不支持1.7.0)
+
+**使用trace方式需安装以下依赖** 
+pandas
+treelib 
+
+## 使用方式
+
+```
+from x2paddle.convert import pytorch2paddle
+pytorch2paddle(module=torch_module, 
+               save_dir="./pd_model", 
+               jit_type="trace", 
+               input_examples=[torch_input])
+# module (torch.nn.Module): PyTorch的Module。
+# save_dir (str): 转换后模型的保存路径。
+# jit_type (str): 转换方式。默认为"trace"。
+# input_examples (list[torch.tensor]): torch.nn.Module的输入示例，list的长度必须与输入的长度一致。默认为None。
+```
+
+**注意：** 当jit_type为"trace"时，input_examples不可为None，转换后自动进行动转静；
+          当jit_type为"script"时"，input_examples不为None时，才可以进行动转静。
+
+## 使用示例
+
+```
+import torch
+import numpy as np
+from torchvision.models import AlexNet
+from torchvision.models.utils import load_state_dict_from_url
+# 构建输入
+input_data = np.random.rand(1, 3, 224, 224).astype("float32")
+# 获取PyTorch Module
+torch_module = AlexNet()
+torch_state_dict = load_state_dict_from_url('https://download.pytorch.org/models/alexnet-owt-4df8aa71.pth')
+torch_module.load_state_dict(torch_state_dict)
+# 设置为eval模式
+torch_module.eval()
+# 进行转换
+from x2paddle.convert import pytorch2paddle
+pytorch2paddle(torch_model, 
+               save_dir="pd_model_trace", 
+               jit_type="trace", 
+               input_examples=[torch.tensor(input_data)])
+```
\ No newline at end of file

x2paddle/convert.py

@@ -209,7 +209,7 @@ def onnx2paddle(model_path, save_dir, paddle_type, params_merge=False):
         mapper.save_inference_model(save_dir, params_merge)
 
 
-def pytorch2paddle(model_path, save_dir, jit_type, input_files):
+def pytorch2paddle(module, save_dir, jit_type="trace", input_examples=None):
     # check pytorch installation and version
     try:
         import torch
@@ -225,21 +225,22 @@ def pytorch2paddle(model_path, save_dir, jit_type, input_files):
         )
         return
     print("Now translating model from pytorch to paddle.")
-
+    
     from x2paddle.decoder.pytorch_decoder import ScriptDecoder, TraceDecoder
-    from x2paddle.op_mapper.pytorch2paddle import pytorch_op_mapper
+    from x2paddle.op_mapper.dygraph.pytorch2paddle.pytorch_op_mapper import PyTorchOpMapper
+
     if jit_type == "trace":
-        model = TraceDecoder(model_path, input_files)
+        model = TraceDecoder(module, input_examples)
     else:
-        model = ScriptDecoder(model_path)
-    mapper = pytorch_op_mapper.PyTorchOpMapper(model)
-    mapper.graph.build()
+        model = ScriptDecoder(module, input_examples)
+    mapper = PyTorchOpMapper(model)
+    mapper.paddle_graph.build()
     print("Model optimizing ...")
-    from x2paddle.optimizer.pytorch_optimizer.optimizer import GraphOptimizer
-    graph_opt = GraphOptimizer()
-    graph_opt.optimize(mapper.graph)
+    from x2paddle.optimizer.optimizer import GraphOptimizer
+    graph_opt = GraphOptimizer(source_frame="pytorch", paddle_type="dygraph", jit_type=jit_type)
+    graph_opt.optimize(mapper.paddle_graph)
     print("Model optimized.")
-    mapper.graph.gen_model(save_dir, jit_type, input_files)
+    mapper.paddle_graph.gen_model(save_dir, jit_type=jit_type)
 
 
 def paddle2onnx(model_path, save_dir, opset_version=10):
@@ -323,10 +324,6 @@ def main():
         if args.params_merge:
             params_merge = True
         onnx2paddle(args.model, args.save_dir, args.paddle_type, params_merge)
-    elif args.framework == "pytorch":
-        assert args.model is not None, "--model should be defined while translating pytorch model"
-        pytorch2paddle(args.model, args.save_dir, args.jit_type, args.input_files)
-
     elif args.framework == "paddle2onnx":
         assert args.model is not None, "--model should be defined while translating paddle model to onnx"
         paddle2onnx(args.model, args.save_dir, opset_version=args.onnx_opset)

x2paddle/core/program.py

@@ -281,13 +281,14 @@ class PaddleGraph(object):
         else:
             self.gen_dygraph_code(save_dir)
             self.dump_dygraph_parameter(save_dir)
-        input_shapes = list()
-        input_types = list()
-        for input_name in self.inputs:
-            input_shapes.append(self.inputs_info[input_name][0])
-            input_types.append(self.inputs_info[input_name][1])
-        # 如果input_files非空，则导出推理模型；其值类似[[None, 3, 224, 224]]
-        self.dygraph2static(save_dir, input_shapes, input_types)
+        # 动转静
+        if len(self.inputs_info) > 0:
+            input_shapes = list()
+            input_types = list()
+            for input_name in self.inputs:
+                input_shapes.append(self.inputs_info[input_name][0])
+                input_types.append(self.inputs_info[input_name][1])
+            self.dygraph2static(save_dir, input_shapes, input_types)
 
     def gen_static_code(self, code_dir):
         def write_code(f, code_list, indent=0):
@@ -424,9 +425,7 @@ class PaddleGraph(object):
             if self.edges_out.get(layer_id, 0) == 0:
                 
                 for i, output_name in enumerate(layer.outputs):
-                    if ("paddle.nn" in layer.kernel and "functional" not in layer.kernel) or \
-                            (layer.kernel == "paddle.to_tensor" and layer.attrs["data"].startswith("params["))or \
-                            "paddle.fluid.dygraph" in layer.kernel:
+                    if ("paddle.nn" in layer.kernel and "functional" not in layer.kernel):
                         if i == 0:
                             continue
                     if output_name not in self.outputs:
@@ -512,6 +511,8 @@ class PaddleGraph(object):
             return_code = "return {}".format(", ".join(self.outputs))
             self.forward_func.extend(gen_codes([return_code], indent=2))
             for code_line in self.forward_func:
+                if "assert [1, 1] == 1 or [1, 1] == [1, 1], 'The [1, 1] must be [1, [1, 1]]!'" in code_line:
+                    continue
                 f.write(code_line)
             for code_line in self.run_func:
                 f.write(code_line)
@@ -525,7 +526,6 @@ class PaddleGraph(object):
         for layer_id, layer in self.layers.items():
             if ("paddle.nn" in layer.kernel and "functional" not in layer.kernel
                 ) or layer.kernel == "paddle.to_tensor" or \
-               "paddle.fluid.dygraph" in layer.kernel or \
                 layer.kernel.startswith("custom_layer"):
                 line = "{}".format(
                     layer.outputs[0]
@@ -566,7 +566,7 @@ class PaddleGraph(object):
                 self.forward_func.extend(gen_codes([line], indent=indent))                
             elif "prim" in layer.kernel:
                 func_name = layer.kernel.replace(".", "_")
-                from x2paddle.op_mapper.dygraph import prim2code
+                from x2paddle.op_mapper.dygraph.pytorch2paddle import prim2code
                 if hasattr(prim2code, func_name):
                     func = getattr(prim2code, func_name)
                     func(
@@ -594,7 +594,7 @@ class PaddleGraph(object):
                 line = line.strip(", ")
                 line += ")"
                 if layer.kernel == "self.create_parameter":
-                    self.init_func.extend(gen_codes(["self." + line], indent=indent))
+                    self.init_func.extend(gen_codes(["self." + line], indent=2))
                     self.forward_func.extend(gen_codes(["{} = self.{}".format(layer.outputs[0], 
                                                                               layer.outputs[0])], indent=indent))
                 else:
@@ -614,7 +614,6 @@ class PaddleGraph(object):
         from paddle.fluid.dygraph.jit import declarative
         sepc_list = list()
         for i, name in enumerate(self.inputs):
-            input_shapes[i][0] = -1
             sepc_list.append(
                 paddle.static.InputSpec(
                     shape=input_shapes[i], name=name, dtype=input_types[i]))
@@ -631,4 +630,11 @@ class PaddleGraph(object):
             model.set_dict(restore)
         model.eval()
         static_model = paddle.jit.to_static(model, input_spec=sepc_list)
-        paddle.jit.save(static_model, osp.join(save_dir, "inference_model/model"))
+        try:
\ No newline at end of file
+            paddle.jit.save(static_model, osp.join(save_dir, "inference_model/model"))
+        except ValueError as e:
+            if str(e) == "'target_vars' should be a list of Variable.":
+                print("[DyGraph2StaticGraph Error] Can not convert the dygraph to static! The output of PyTorch mustbe Variable or a list of Variable.")
+            else:
+                print(e)
+                exit(0)

x2paddle/decoder/pytorch_decoder.py

@@ -41,9 +41,10 @@ class ScriptDecoder(Decoder):
             script_path (str): ScriptModule保存路径。
             model_path (str): PyTorchModule保存路径。
     """
-    def __init__(self, script_path=None):
-        self.script = torch.jit.load(script_path)
+    def __init__(self, module, input_examples=None):
+        self.script = torch.jit.script(module)
         self.graph = self._optimize_graph(self.script.inlined_graph)
+        self.input_examples = input_examples
             
 class TraceDecoder(Decoder):
     """ PyTorchModule后使用trace方式转换为ScriptModule。
@@ -53,14 +54,16 @@ class TraceDecoder(Decoder):
             input_files (list): 输入网络的numpy，每个numpy保存成.npy文件, 
                                 文件路径存储在input_files中。
     """
-    def __init__(self, model_path, input_files=list()):
-        # TODO(syf): 传入pytorch的Module(即import)，否则出错
-        model = torch.load(model_path)
-        model.eval()
-        input_list = list()
-        for npy_file in input_files:
-            input_list.append(torch.tensor(np.load(npy_file)))
-        self.script = torch.jit.trace(model, input_list, strict=False)
+    def __init__(self, module, input_examples):
+        try:
+            self.script = torch.jit.trace(module, input_examples)
+        except RuntimeError as e:
+            if "strict" in str(e):
+                self.script = torch.jit.trace(module, input_examples, strict=False)
+            else:
+                print(e)
+                exit(0)
         self.graph = self._optimize_graph(self.script.inlined_graph)
-#         print(self.graph)
-#         print(getattr(getattr(self.script.decoder.block, "5").layer, "2"))
+        self.input_examples = input_examples
+        
+            

x2paddle/op_mapper/dygraph/onnx2paddle/opset9/opset.py

@@ -1180,7 +1180,7 @@ class OpSet9():
                     scale=beta)
                 add_inputs = {"x": val_mm, "y": var_beta}
                 self.paddle_graph.add_layer(
-                    "paddle.addd",
+                    "paddle.add",
                     inputs=add_inputs,
                     outputs=[node.layer_name])
 

x2paddle/op_mapper/dygraph/pytorch2paddle/prim.py

@@ -25,6 +25,7 @@ def prim_Constant(mapper, graph, node):
         参数含义:
         %2 (常量类型由赋值类型定义，该示例中为int型): 常量赋值结果输出。
     """
+    scope_name = mapper.normalize_scope_name(node)
     output_name = mapper._get_outputs_name(node)[0]
     output = list(node.outputs())[0]
     value = output.toIValue()
@@ -32,7 +33,10 @@ def prim_Constant(mapper, graph, node):
     if isinstance(value, str):
         value = string(value)
     if str(output_type) == "Tensor":
+        tensor_value = value
         value = "{}".format(value)
+        if "tensor" in value:
+            mapper.pytorch_params[output_name] = tensor_value.cpu().detach().numpy()
 
     if "inf" in str(value):
         t = str(type(value)).split("'")[1]
@@ -45,7 +49,7 @@ def prim_Constant(mapper, graph, node):
         value = int(math.pow(2, 31) - 1)
     mapper.attrs[output_name] = value
     graph.add_layer(
-        "prim.constant", inputs={}, outputs=[output_name], value=value)
+        "prim.constant", inputs={}, outputs=[output_name], scope_name=scope_name, value=value)
     return [], [output_name]
 
 
@@ -60,6 +64,7 @@ def prim_data(mapper, graph, node):
 
     【注意】Paddle中无此用法，所以此处翻译成赋值。
     """
+    scope_name = mapper.normalize_scope_name(node)
     output_name = mapper._get_outputs_name(node)[0]
     layer_outputs = [output_name]
     layer_inputs = {}
@@ -68,15 +73,53 @@ def prim_data(mapper, graph, node):
     # 获取当前节点输出的list
     current_outputs = [output_name]
     # 处理输入0，即%4336
-    mapper._check_input(graph, inputs_node[0], inputs_name[0], current_outputs)
+    mapper._check_input(graph, inputs_node[0], inputs_name[0], current_outputs, scope_name)
     layer_inputs["input"] = inputs_name[0]
     # 获取当前节点输入的list
     current_inputs = list(layer_inputs.values())
 
-    graph.add_layer("prim.equal", inputs=layer_inputs, outputs=layer_outputs)
+    graph.add_layer("prim.equal", inputs=layer_inputs, outputs=layer_outputs, scope_name=scope_name)
     return current_inputs, current_outputs
 
 
+def prim_DictConstruct(mapper, graph, node):
+    """ 构建dict。
+    
+    TorchScript示例:
+        %32 : Dict(str, Tensor) = prim::DictConstruct(%30, %23, %31, %29)
+        参数含义:
+        %32 (dict): 组成的字典。
+        %30 (str): key。
+        %23 (-): value。
+        %31 (str): key。
+        %29 (-): value。
+    """
+    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]
+    # 处理每个输入
+    for i, input_name in enumerate(inputs_name):
+        if i%2 == 0:
+            layer_attrs["key{}".format(int(i/2))] = mapper.attrs[input_name]
+        else:
+            layer_inputs["value{}".format(int(i/2))] = input_name
+    # 获取当前节点输入的list
+    current_inputs = list(layer_inputs.values())
+
+    graph.add_layer("prim.dict_construct", 
+                    inputs=layer_inputs, 
+                    outputs=layer_outputs, 
+                    scope_name=scope_name,
+                    **layer_attrs)
+    return current_inputs, current_outputs
+
+
+
 def prim_GetAttr(mapper, graph, node):
     """ 获取attribute信息。
 
@@ -86,6 +129,7 @@ def prim_GetAttr(mapper, graph, node):
         %7 (Tensor): 输入Tensor。
         %27 (Tensor): 输入Tensor。
     """
+    scope_name = mapper.normalize_scope_name(node)
     current_node = node
     field_name_list = [node.s('name')]
     while True:
@@ -102,7 +146,7 @@ def prim_GetAttr(mapper, graph, node):
         if hasattr(part_script, field_name):
             param = getattr(part_script, field_name)
             if isinstance(param, torch.Tensor):
-                param = param.detach().numpy()
+                param = param.cpu().detach().numpy()
                 if len(param.shape) == 0:
                     param = np.reshape(param, 1)
                 if str(param.dtype) == "uint8":
@@ -129,14 +173,15 @@ def prim_If(mapper, graph, node):
         %107 (bool): if判断条件。
         %input.5 (Tensor): if控制流的输出，与%output.4对应。
     """
+    scope_name = mapper.normalize_scope_name(node)
     outputs_name = mapper._get_outputs_name(node)
     node_outputs = outputs_name.copy()
     current_outputs = outputs_name.copy()
     input_node = list(node.inputs())[0].node()
     script_input_unique_id = list(node.inputs())[0].unique()
     input_node_name = mapper.outputs_info[script_input_unique_id]
-    mapper._check_input(graph, input_node, input_node_name, current_outputs)
-    graph.add_layer("prim.if", {'input': input_node_name}, node_outputs)
+    mapper._check_input(graph, input_node, input_node_name, current_outputs, scope_name)
+    graph.add_layer("prim.if", inputs={'input': input_node_name}, outputs=node_outputs, scope_name=scope_name)
     current_layer = list(graph.layers.values())[-1]
     block0 = list(node.blocks())[0]
     block0_graph, graph_inputs0 = mapper.traverse(block0, current_layer)
@@ -163,6 +208,7 @@ def prim_ListConstruct(mapper, graph, node):
         %84 (int/其他): list第一个元素信息。
         %85 (int/其他): list第二个元素信息。
     """
+    scope_name = mapper.normalize_scope_name(node)
     output_name = mapper._get_outputs_name(node)[0]
     layer_outputs = [output_name]
     layer_inputs = {}
@@ -175,7 +221,7 @@ def prim_ListConstruct(mapper, graph, node):
     # 获取当前节点输入的list
     current_inputs = list(layer_inputs.values())
 
-    graph.add_layer("prim.list", inputs=layer_inputs, outputs=layer_outputs)
+    graph.add_layer("prim.list", inputs=layer_inputs, outputs=layer_outputs, scope_name=scope_name)
     return current_inputs, current_outputs
 
 
@@ -189,6 +235,7 @@ def prim_ListUnpack(mapper, graph, node):
         %x2.4 (Tensor): 输出，list的第二个元素。
         %4354 (list): 列表。
     """
+    scope_name = mapper.normalize_scope_name(node)
     outputs_name = mapper._get_outputs_name(node)
     layer_outputs = outputs_name.copy()
     layer_inputs = {}
@@ -196,13 +243,13 @@ def prim_ListUnpack(mapper, graph, node):
     # 获取当前节点输出的list
     current_outputs = layer_outputs.copy()
     # 处理输入0，即%4354
-    mapper._check_input(graph, inputs_node[0], inputs_name[0], current_outputs)
+    mapper._check_input(graph, inputs_node[0], inputs_name[0], current_outputs, scope_name)
     layer_inputs["input"] = inputs_name[0]
     # 获取当前节点输入的list
     current_inputs = list(layer_inputs.values())
 
     graph.add_layer(
-        "prim.list_unpack", inputs=layer_inputs, outputs=layer_outputs)
+        "prim.list_unpack", inputs=layer_inputs, outputs=layer_outputs, scope_name=scope_name)
     mapper.split_len[list(layer_inputs.values())[0]] = len(layer_outputs)
     return current_inputs, current_outputs
 
@@ -223,6 +270,7 @@ def prim_Loop(mapper, graph, node):
        %x.3 (Tensor): 循环中修改的Tensor。
        %x (Tensor): loop循环的输出，与%x.5对应。
     """
+    scope_name = mapper.normalize_scope_name(node)
     node_outputs = mapper._get_outputs_name(node)
     loop_inputs = {}
     block = list(node.blocks())[0]
@@ -242,7 +290,7 @@ def prim_Loop(mapper, graph, node):
             loop_input_node_name = mapper.outputs_info[
                 script_loop_input_unique_id]
             mapper._check_input(graph, loop_input_node, loop_input_node_name,
-                                node_outputs)
+                                node_outputs, scope_name)
             loop_inputs['input'] = loop_input_node_name
             loop_outputs.append(block_input_node_name)
             node_outputs.append(block_input_node_name)
@@ -252,14 +300,15 @@ def prim_Loop(mapper, graph, node):
             loop_input_node_name = mapper.outputs_info[
                 script_loop_input_unique_id]
             mapper._check_input(graph, loop_input_node, loop_input_node_name,
-                                node_outputs)
+                                node_outputs, scope_name)
             graph.add_layer(
                 "prim.equal",
                 inputs={'input': loop_input_node_name},
-                outputs=[block_input_node_name])
+                outputs=[block_input_node_name],
+                scope_name=scope_name)
             node_outputs.append(block_input_node_name)
 
-    graph.add_layer("prim.loop", inputs=loop_inputs, outputs=loop_outputs)
+    graph.add_layer("prim.loop", inputs=loop_inputs, outputs=loop_outputs, scope_name=scope_name)
     current_layer = list(graph.layers.values())[-1]
     block_graph, graph_inputs = mapper.traverse(block, current_layer)
     for i, input_name in enumerate(graph_inputs):
@@ -279,6 +328,7 @@ def prim_min(mapper, graph, node):
         %86 (list): 输入。
         %87 (int): 输出。
     """
+    scope_name = mapper.normalize_scope_name(node)
     output_name = mapper._get_outputs_name(node)[0]
     layer_outputs = [output_name]
     layer_inputs = {}
@@ -286,12 +336,12 @@ def prim_min(mapper, graph, 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], current_outputs)
+    mapper._check_input(graph, inputs_node[0], inputs_name[0], current_outputs, scope_name)
     layer_inputs["input"] = inputs_name[0]
     # 获取当前节点输入的list
     current_inputs = list(layer_inputs.values())
 
-    graph.add_layer("prim.min", inputs=layer_inputs, outputs=layer_outputs)
+    graph.add_layer("prim.min", inputs=layer_inputs, outputs=layer_outputs, scope_name=scope_name)
     return current_inputs, current_outputs
 
 
@@ -304,6 +354,7 @@ def prim_NumToTensor(mapper, graph, node):
         %other.2 (Tensor): 输出。
         %1736 (-): 输入。
     """
+    scope_name = mapper.normalize_scope_name(node)
     output_name = mapper._get_outputs_name(node)[0]
     layer_outputs = [output_name]
     layer_inputs = {}
@@ -312,25 +363,26 @@ def prim_NumToTensor(mapper, graph, 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], current_outputs)
-    if inputs_node[0].kind() == "aten::size":
+    mapper._check_input(graph, inputs_node[0], inputs_name[0], current_outputs, scope_name)
+    inputs_inputs_name, inputs_inputs_node = mapper._get_inputs_name(inputs_node[0])
+    if inputs_node[0].kind() == "aten::size" and len(inputs_inputs_name) > 1:
         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_equal", inputs=layer_inputs, outputs=layer_outputs)
+            "prim_equal", inputs=layer_inputs, outputs=layer_outputs, scope_name=scope_name)
     else:
-        layer_inputs["value"] = inputs_name[0]
+        layer_inputs["fill_value"] = inputs_name[0]
         # 获取当前节点输入的list
         current_inputs = list(layer_inputs.values())
         input_type = list(node.inputs())[0].type()
         layer_attrs["dtype"] = input_type
-        layer_attrs["persistable"] = True
         layer_attrs["shape"] = [1]
         graph.add_layer(
-            "fluid.layers.create_global_var",
+            "paddle.full",
             inputs=layer_inputs,
             outputs=layer_outputs,
+            scope_name=scope_name,
             **layer_attrs)
     return current_inputs, current_outputs
 
@@ -343,6 +395,7 @@ def prim_RaiseException(mapper, graph, node):
         参数含义:
         %76 (str): 异常信息。
     """
+    scope_name = mapper.normalize_scope_name(node)
     output_name = mapper._get_outputs_name(node)[0]
     layer_outputs = [output_name]
     layer_inputs = {}
@@ -350,13 +403,13 @@ def prim_RaiseException(mapper, graph, 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], current_outputs)
+    mapper._check_input(graph, inputs_node[0], inputs_name[0], current_outputs, scope_name)
     layer_inputs["input"] = inputs_name[0]
     # 获取当前节点输入的list
     current_inputs = list(layer_inputs.values())
 
     graph.add_layer(
-        "prim.exception", inputs=layer_inputs, outputs=layer_outputs)
+        "prim.exception", inputs=layer_inputs, outputs=layer_outputs, scope_name=scope_name)
     return current_inputs, current_outputs
 
 
@@ -369,6 +422,7 @@ def prim_requires_grad(mapper, graph, node):
         %356 (bool): 输出，当前Tensor是否计算梯度。
         %tensor.31 (Tensor): 输入的Tensor。
     """
+    scope_name = mapper.normalize_scope_name(node)
     output_name = mapper._get_outputs_name(node)[0]
     layer_outputs = [output_name]
     layer_inputs = {}
@@ -376,13 +430,13 @@ def prim_requires_grad(mapper, graph, 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], current_outputs)
+    mapper._check_input(graph, inputs_node[0], inputs_name[0], current_outputs, scope_name)
     layer_inputs["input"] = inputs_name[0]
     # 获取当前节点输入的list
     current_inputs = list(layer_inputs.values())
 
     graph.add_layer(
-        "prim.requires_grad", inputs=layer_inputs, outputs=layer_outputs)
+        "prim.requires_grad", inputs=layer_inputs, outputs=layer_outputs, scope_name=scope_name)
     return current_inputs, current_outputs
 
 
@@ -395,6 +449,7 @@ def prim_SetAttr(mapper, graph, node):
         %260 (-): 属性名前缀。
         %277 (-): 需要设置的值。
     """
+    scope_name = mapper.normalize_scope_name(node)
     output_name = mapper._get_outputs_name(node)[0]
     field_name_list = []
     tmp_node = node
@@ -416,7 +471,8 @@ def prim_SetAttr(mapper, graph, node):
     graph.add_layer(
         "prim.set_attr",
         inputs={"input": inputs_name[1]},
-        outputs=["self." + ".".join(field_name_list).replace(".", "_")])
+        outputs=["self." + ".".join(field_name_list).replace(".", "_")],
+        scope_name=scope_name)
     return [], [output_name]
 
 
@@ -429,6 +485,7 @@ def prim_shape(mapper, graph, node):
         %4701 (list): 输出，shape信息。
         %result.1 (Tensor): 需要获取shape的值。
     """
+    scope_name = mapper.normalize_scope_name(node)
     output_name = mapper._get_outputs_name(node)[0]
     layer_outputs = [output_name]
     layer_inputs = {}
@@ -436,13 +493,13 @@ def prim_shape(mapper, graph, 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)
+    mapper._check_input(graph, inputs_node[0], inputs_name[0], current_outputs, scope_name)
     layer_inputs["input"] = inputs_name[0]
     # 获取当前节点输入的list
     current_inputs = list(layer_inputs.values())
 
     graph.add_layer(
-        "fluid.layers.shape", inputs=layer_inputs, outputs=layer_outputs)
+        "paddle.shape", inputs=layer_inputs, outputs=layer_outputs, scope_name=scope_name)
     return current_inputs, current_outputs
 
 
@@ -456,6 +513,7 @@ def prim_TupleConstruct(mapper, graph, node):
         %x.46 (Tensor/其他): tuple第一个元素信息。
         %aux (Tensor/其他): tuple第二个元素信息。
     """
+    scope_name = mapper.normalize_scope_name(node)
     output_name = mapper._get_outputs_name(node)[0]
     layer_outputs = [output_name]
     layer_inputs = {}
@@ -468,7 +526,7 @@ def prim_TupleConstruct(mapper, graph, node):
     # 获取当前节点输入的list
     current_inputs = list(layer_inputs.values())
 
-    graph.add_layer("prim.tuple", inputs=layer_inputs, outputs=layer_outputs)
+    graph.add_layer("prim.tuple", inputs=layer_inputs, outputs=layer_outputs, scope_name=scope_name)
     return current_inputs, current_outputs
 
 
@@ -482,6 +540,7 @@ def prim_TupleUnpack(mapper, graph, node):
         %aux.3 (Tensor/其他): 输出，tuple第二个元素信息。
         %4492 (tuple): 需要获取元素的tuple。
     """
+    scope_name = mapper.normalize_scope_name(node)
     outputs_name = mapper._get_outputs_name(node)
     layer_outputs = outputs_name
     layer_inputs = {}
@@ -493,7 +552,7 @@ def prim_TupleUnpack(mapper, graph, node):
     current_inputs = list(layer_inputs.values())
 
     graph.add_layer(
-        "prim.tuple_unpack", inputs=layer_inputs, outputs=layer_outputs)
+        "prim.tuple_unpack", inputs=layer_inputs, outputs=layer_outputs, scope_name=scope_name)
     return current_inputs, current_outputs
 
 
@@ -508,6 +567,7 @@ def prim_unchecked_cast(mapper, graph, node):
 
     【注意】Paddle中无此用法，所以此处翻译成赋值。
     """
+    scope_name = mapper.normalize_scope_name(node)
     output_name = mapper._get_outputs_name(node)[0]
     layer_outputs = [output_name]
     layer_inputs = {}
@@ -516,12 +576,12 @@ def prim_unchecked_cast(mapper, graph, node):
     # 获取当前节点输出的list
     current_outputs = [output_name]
     # 处理输入0，即%size.63
-    mapper._check_input(graph, inputs_node[0], inputs_name[0], current_outputs)
+    mapper._check_input(graph, inputs_node[0], inputs_name[0], current_outputs, scope_name)
     layer_inputs["input"] = inputs_name[0]
     # 获取当前节点输入的list
     current_inputs = list(layer_inputs.values())
 
-    graph.add_layer("prim.equal", inputs=layer_inputs, outputs=layer_outputs)
+    graph.add_layer("prim.equal", inputs=layer_inputs, outputs=layer_outputs, scope_name=scope_name)
     return current_inputs, current_outputs
 
 
@@ -533,9 +593,10 @@ def prim_Uninitialized(mapper, graph, node):
         参数含义:
         %345 (bool): 输出，为赋值的bool。
     """
+    scope_name = mapper.normalize_scope_name(node)
     output_name = mapper._get_outputs_name(node)[0]
     output = list(node.outputs())[0]
     mapper.attrs[output_name] = None
     graph.add_layer(
-        "prim.constant", inputs={}, outputs=[output_name], value=None)
+        "prim.constant", inputs={}, outputs=[output_name], scope_name=scope_name, value=None)
     return [], [output_name]

x2paddle/op_mapper/dygraph/pytorch2paddle/pytorch_op_mapper.py

@@ -17,24 +17,29 @@ import numpy as np
 from x2paddle.core.op_mapper import OpMapper
 from x2paddle.core.util import *
 from x2paddle.core.program import PaddleGraph
-from x2paddle.op_mapper.pytorch2paddle import prim
-from x2paddle.op_mapper.pytorch2paddle import aten
+from x2paddle.op_mapper.dygraph.pytorch2paddle import prim
+from x2paddle.op_mapper.dygraph.pytorch2paddle import aten
 
 
 class PyTorchOpMapper(OpMapper):
     def __init__(self, decoder):
         super(PyTorchOpMapper, self).__init__()
         self.script = decoder.script
+        self.input_examples = decoder.input_examples
         self.paddle_params = dict()
         self.outputs_info = {}  # key为output unique id，value为当前节点的输出名字
         self.pytorch_params = {}  # key为节点名，value为参数
         self.attrs = {}  # key为节点名，value为属性值
         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
+        self.nn_name2id = {}  # 动态图__init__输出名字中的id，key为kernel类型，value为id
         self.split_len = {}  # split的长度
+        self.scope_name_list = list()
+        self.scope_name2id = dict()
+        self.inputs_info = dict()
         # 转换
         self.check_op(decoder.graph)
-        self.graph, _ = self.traverse(decoder.graph)
+        self.paddle_graph, _ = self.traverse(decoder.graph)
+        self.paddle_graph.set_inputs_info(self.inputs_info)
 
     def check_op(self, script_graph):
         def _update_op_list(graph):
@@ -72,17 +77,21 @@ class PyTorchOpMapper(OpMapper):
                 current_node_outputs.extend(outputs)
 
         # 初始化
-        graph = PaddleGraph(parent_layer, graph_type="dygraph")
+        graph = PaddleGraph(parent_layer=parent_layer, graph_type="dygraph")
+        if "TopLevelTracedModule" in str(type(self.script)):
+            graph.set_script(self.script)
         current_node_outputs = []
         graph_inputs = []
         # 转换输入节点
         if isinstance(script_graph, torch._C.Graph):
+            input_ct = 0 
             for i, ivalue in enumerate(script_graph.inputs()):
                 node = ivalue.node()
-                if str(ivalue.type()) != "Tensor":
+                if str(ivalue.type()) not in ["Tensor", "Dict[str, Tensor]"]:
                     graph.set_name(str(ivalue.type()).split(".")[-1])
                     continue
-                inputs, outputs = self.data(graph, node, ivalue.unique())
+                inputs, outputs = self.data(graph, node, ivalue.unique(), input_ct)
+                input_ct += 1
         # 转换中间节点
         for node in script_graph.nodes():
             kind = node.kind()
@@ -120,7 +129,7 @@ class PyTorchOpMapper(OpMapper):
                 graph.outputs = inputs_name
         # 更新split参数
         for layer in graph.layers.values():
-            if layer.kernel == "fluid.layers.split" and "num_or_sections" in layer.attrs:
+            if layer.kernel == "paddle.split" and "num_or_sections" in layer.attrs:
                 layer.attrs["num_or_sections"] = self.split_len[layer.outputs[
                     0]]
         return graph, graph_inputs
@@ -151,6 +160,7 @@ class PyTorchOpMapper(OpMapper):
                      node,
                      output_name,
                      node_outputs,
+                     scope_name,
                      add_dim=False):
         if node.kind() == "prim::GetAttr":
             param = self.pytorch_params[output_name]
@@ -159,10 +169,13 @@ class PyTorchOpMapper(OpMapper):
                     param = param[np.newaxis, :]
                 self.paddle_params[output_name] = param
                 graph.add_layer(
-                    "fluid.dygraph.base.to_variable",
+                    "self.create_parameter",
                     inputs={},
                     outputs=[output_name],
-                    value="params[{}]".format(string(output_name)))
+                    scope_name=scope_name,
+                    dtype=string(str(param.dtype)),
+                    shape = param.shape,
+                    default_initializer="paddle.nn.initializer.Constant(value=0.0)")
             else:
                 if isinstance(param, dict) and "Tensor" in param and \
                 "parent_layer_id" in param:
@@ -183,11 +196,13 @@ class PyTorchOpMapper(OpMapper):
                                             param = param[np.newaxis, :]
                                         self.paddle_params[output_name] = param
                                         graph.add_layer(
-                                            "fluid.dygraph.base.to_variable",
+                                            "self.create_parameter",
                                             inputs={},
                                             outputs=[output_name],
-                                            value="params[{}]".format(
-                                                string(output_name)))
+                                            scope_name=scope_name,
+                                            dtype=string(str(param.dtype)),
+                                            shape = param.shape,
+                                          default_initializer="paddle.nn.initializer.Constant(value=0.0)")
                                         node_outputs.append(output_name)
                                         return
                     # 若if-else外，则可直接引用if-else中的赋值结果
@@ -195,16 +210,30 @@ class PyTorchOpMapper(OpMapper):
                         "prim.constant",
                         inputs={},
                         outputs=[output_name],
+                        scope_name=scope_name,
                         value=param["Tensor"])
                 else:
                     graph.add_layer(
                         "prim.constant",
                         inputs={},
                         outputs=[output_name],
+                        scope_name=scope_name,
                         value=string(param)
                         if isinstance(param, str) else param)
             node_outputs.append(output_name)
+        elif node.kind() == "prim::Constant" and output_name in self.pytorch_params:
+            param = self.pytorch_params[output_name]
+            self.paddle_params[output_name] = param
+            graph.add_layer(
+                "self.create_parameter",
+                inputs={},
+                outputs=[output_name],
+                scope_name=scope_name,
+                dtype=string(str(param.dtype)),
+                shape = param.shape,
+                default_initializer="paddle.nn.initializer.Constant(value=0.0)")     
 
+            
     def _get_inputs_name(self, node):
         inputs_name = []
         inputs_node = []
@@ -215,8 +244,10 @@ class PyTorchOpMapper(OpMapper):
             inputs_node.append(script_input_node)
             inputs_name.append(input_name)
         return inputs_name, inputs_node
+    
 
-    def data(self, graph, node, uid):
+    def data(self, graph, node, uid, input_ct):
+        scope_name = self.normalize_scope_name(node)
         for output_ivalue in node.outputs():
             script_unique_id = output_ivalue.unique()
             if script_unique_id in self.outputs_info or script_unique_id != uid:
@@ -226,13 +257,18 @@ class PyTorchOpMapper(OpMapper):
             self.output_index += 1
         output_name = self.outputs_info[uid]
         graph.add_layer(
-            "fluid.dygraph.base.to_variable",
+            "paddle.to_tensor",
             inputs={},
             outputs=[node_name],
-            value=output_name)
+            scope_name=scope_name,
+            data=output_name)
+        if self.input_examples is not None:
+            input_np = self.input_examples[input_ct].detach().numpy()
+            self.inputs_info[output_name] = [list(input_np.shape), str(input_np.dtype)]
         return [], [output_name]
 
     def equal(self, graph, node, uid=None, parent_layer=None, index=None):
+        scope_name = self.normalize_scope_name(node)
         if parent_layer is not None and index is not None:
             # block的输出
             input_node_name = self.outputs_info[uid]
@@ -241,9 +277,61 @@ class PyTorchOpMapper(OpMapper):
                 control_output_id = index - 1
             output_node_name = parent_layer.outputs[control_output_id]
             current_outputs = [output_node_name]
-            self._check_input(graph, node, input_node_name, current_outputs)
+            self._check_input(graph, node, input_node_name, current_outputs, scope_name)
             graph.add_layer(
                 "prim.equal",
                 inputs={'input': input_node_name},
-                outputs=[output_node_name])
+                outputs=[output_node_name],
+                scope_name=scope_name)
             return [input_node_name], current_outputs
+
+    def normalize_scope_name(self, node):
+        """ 对scope的名字进行标准化。
+        """
+        scope_name = node.scopeName()
+        if scope_name == "":
+            return scope_name
+        scope_name_part = scope_name.split("/")
+        for index in range(len(scope_name_part) - 1):
+            if scope_name_part[index] in scope_name_part[index + 1]:
+                continue
+            last_name_segments = scope_name_part[index].split(".")
+            name_segments = scope_name_part[index + 1].split(".")
+            for j, name in enumerate(last_name_segments):
+                name_segments[j] = name
+            scope_name_part[index + 1] = ".".join(name_segments)
+        last_name = scope_name_part[-1]
+        name_segments = last_name.split(".")
+        for i, ns in enumerate(name_segments):
+            if i not in self.scope_name2id:
+                self.scope_name2id[i] = dict()
+            if ns not in self.scope_name2id[i]:
+                self.scope_name2id[i][ns] = 0
+        real_scope_name = "/".join(name_segments[1:])
+        real_father_scope_name = "/".join(name_segments[1:-1])
+        
+        for i, ns in enumerate(name_segments):
+            if i == 0:
+                continue
+            if self.scope_name2id[i][ns] != 0:
+                name_segments[i] = name_segments[i] + \
+                "__{}".format(self.scope_name2id[i][ns])
+            prefix_scope_name = "/".join(name_segments[1 :i + 1])
+            is_found = False
+            for j in range(len(self.scope_name_list)):
+                last_scope_name = self.scope_name_list[-1-j]
+                if last_scope_name.startswith(prefix_scope_name + "/") \
+                        or last_scope_name == prefix_scope_name:
+                    if j != 0: # and i != len(name_segments) - 1:                        
+                        is_found = True
+                        origin_name_segment_i = name_segments[i].split("__")[0]
+                        self.scope_name2id[i][origin_name_segment_i] += 1
+                        name_segments[i] = origin_name_segment_i + \
+                            "__" + str(self.scope_name2id[i][origin_name_segment_i])
+                    break
+            if is_found:
+                break
+        real_scope_name = "/".join(name_segments[1:])
+        self.scope_name_list.append(real_scope_name)
+        return real_scope_name
+                
\ No newline at end of file

x2paddle/optimizer/code_optimizer/__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 x2paddle.optimizer.code_optimizer.hierachical_tree import HierarchicalTree
\ No newline at end of file

x2paddle/optimizer/code_optimizer/parameter_tree.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.
+
+
+class PamareterNode(object):
+    def __init__(self, old_name=None, new_name=None):
+        self.old_name = old_name
+        self.new_name = new_name
+        self.childs = list()
+
+    def add_child(self, child):
+        self.childs.append(child)
+        
+    def has_child(self):
+        if len(self.childs) == 0:
+            return False
+        else:
+            return True
+       
+        
+class PamareterTree(object):
+    def __init__(self):
+        self.nodes = list()
+        self.old2new = dict()
+        
+    def add_node(self, node):
+        self.nodes.append(node)
+    
+    def traverse(self):
+        tmp = list()
+        def recurs(node, prefix_name):
+            for child in node.childs:
+                child_prefix_name = prefix_name + "." + child.new_name
+                if child.has_child():
+                    recurs(child, child_prefix_name)
+                else:
+                    self.old2new[child.old_name] = child_prefix_name[1:]
+        recurs(self.nodes[-1], "")
+
+        
+    def get_node(self, old_name):
+        for node in self.nodes:
+            if node.old_name == old_name:
+                return node
\ No newline at end of file

x2paddle/optimizer/fusion/dygraph/__init__.py

@@ -32,3 +32,5 @@ from .reshape_fuser import DygraphReshapeFuser
 from .reshape_fuse_pass import DygraphReshapeFusePass
 from .tf_batchnorm_fuser import DygraphTFBatchNormFuser
 from .tf_batchnorm_fuse_pass import DygraphTFBatchNormFusePass
+from .trace_fc_fuser import TraceFcFuser
+from .trace_fc_fuse_pass import TraceFcFusePass

x2paddle/optimizer/fusion/dygraph/trace_fc_fuse_pass.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 x2paddle.optimizer.pass_ import Pass
+from x2paddle.optimizer.fusion.dygraph import TraceFcFuser
+from x2paddle.optimizer.pass_manager import pass_register
+
+
+@pass_register
+class TraceFcFusePass(Pass):
+    name = "trace_fc_fuse_pass"
+
+    def __init__(self):
+        Pass.__init__(self)
+
+    def apply(self, graph):
+        fuser = TraceFcFuser()
+        fuser.operate(graph, match_kind="topo")
+
+
+# 用于注册
+trace_fc_fuse_pass = TraceFcFusePass()
\ No newline at end of file

x2paddle/optimizer/optimizer.py

@@ -18,14 +18,20 @@ from x2paddle.optimizer.fusion.static import *
 from x2paddle.optimizer.elimination.dygraph import *
 
 class GraphOptimizer(object):
-    def __init__(self, source_frame, paddle_type="dygraph"):
+    def __init__(self, source_frame, paddle_type="dygraph", jit_type="trace"):
         if source_frame == "pytorch":
-            self.passes = [
-                "dygraph_constant_fuse_pass", "dygraph_batchnorm2d_fuse_pass",
-                "dygraph_interpolate_bilinear_fuse_pass", "dygraph_fc_fuse_pass",
-                "dygraph_adaptive_pool2d_fuse_pass", "dygraph_reshape_fuse_pass",
-                "dygraph_dropout_fuse_pass"
-            ]
+            if jit_type == "trace":
+                self.passes = ["trace_fc_fuse_pass"]
+            else:
+                self.passes = [
+                    "dygraph_constant_fuse_pass", 
+                    "dygraph_batchnorm2d_fuse_pass",
+                    "dygraph_interpolate_bilinear_fuse_pass", 
+                    "dygraph_fc_fuse_pass",
+                    "dygraph_adaptive_pool2d_fuse_pass", 
+                    "dygraph_reshape_fuse_pass",
+                    "dygraph_dropout_fuse_pass"
+                ]
         elif source_frame == "caffe":
             if paddle_type == "dygraph":
                 self.passes = ["dygraph_bn_scale_fuse_pass"]
@@ -38,8 +44,7 @@ class GraphOptimizer(object):
                 "transpose_eliminate_pass"
             ]
         else:
-            # TODO
-            pass
+            self.passes = []
 
     def optimize(self, graph):
         for pass_name in self.passes: