# 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 torch 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 class PyTorchOpMapper(OpMapper): def __init__(self, decoder): super(PyTorchOpMapper, self).__init__() self.script = decoder.script 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.split_len = {} # split的长度 # 转换 self.check_op(decoder.graph) self.graph, _ = self.traverse(decoder.graph) def check_op(self, script_graph): def _update_op_list(graph): for node in graph.nodes(): op_list.append(node.kind()) for block in node.blocks(): _update_op_list(block) op_list = list() _update_op_list(script_graph) op_list = list(set(op_list)) unsupported_op_list = [] for op in op_list: func_name = op.replace('::', '_') if not (hasattr(prim, func_name) or hasattr(aten, func_name)): unsupported_op_list.append(op) if len(unsupported_op_list) > 0: raise Exception("The kind {} in model is not supported yet.".format( unsupported_op_list)) def traverse(self, script_graph, parent_layer=None): # 用于获取graph的输入 def _update_graph_inputs(kind, inputs, outputs): # extend只能放更新graph_inputs之前的情况： # 1. loop的输出i也是输入；i是输入的原因是：子图中为父图得到的。 # 2. 在_check_input中需要使用to_variable。 # extend只能放更新graph_inputs之后的情况： # 使用了append。 if kind != "aten::append": current_node_outputs.extend(outputs) for name in inputs: if name not in current_node_outputs: graph_inputs.append(name) if kind == "aten::append": current_node_outputs.extend(outputs) # 初始化 graph = PaddleGraph(parent_layer, graph_type="dygraph") current_node_outputs = [] graph_inputs = [] # 转换输入节点 if isinstance(script_graph, torch._C.Graph): for i, ivalue in enumerate(script_graph.inputs()): node = ivalue.node() if str(ivalue.type()) != "Tensor": graph.set_name(str(ivalue.type()).split(".")[-1]) continue inputs, outputs = self.data(graph, node, ivalue.unique()) # 转换中间节点 for node in script_graph.nodes(): kind = node.kind() func_name = kind.replace('::', '_') if hasattr(prim, func_name): func = getattr(prim, func_name) inputs, outputs = func(self, graph, node) _update_graph_inputs(kind, inputs, outputs) elif hasattr(aten, func_name): func = getattr(aten, func_name) inputs, outputs = func(self, graph, node) _update_graph_inputs(kind, inputs, outputs) # 转换输出节点 if hasattr(script_graph, 'returnNode'): for i, ivalue in enumerate(script_graph.returnNode().inputs()): if parent_layer.kernel == "prim.loop" and i == 0: continue node = ivalue.node() script_unique_id = ivalue.unique() inputs, outputs = self.equal( graph, node, uid=script_unique_id, parent_layer=parent_layer, index=i) _update_graph_inputs("equal", inputs, outputs) # 设置graph的参数和输出节点 if isinstance(script_graph, torch._C.Graph): graph.set_parameters(self.paddle_params) if hasattr(script_graph, 'return_node'): inputs_name, inputs_node = self._get_inputs_name( script_graph.return_node()) 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: layer.attrs["num_or_sections"] = self.split_len[layer.outputs[ 0]] return graph, graph_inputs def _get_outputs_name(self, node, attr_name=None): outputs_name = [] for output_ivalue in node.outputs(): script_unique_id = output_ivalue.unique() if attr_name is None: output_name = 'x' + str(self.output_index) if script_unique_id in self.outputs_info: output_name = self.outputs_info[script_unique_id] else: output_name = attr_name.replace(".", "_") self.outputs_info[script_unique_id] = output_name self.output_index += 1 outputs_name.append(output_name) # if或loop节点没有输出的情况 if len(list(node.outputs())) == 0: output_name = '_x' + str(self.output_index) self.output_index += 1 outputs_name.append(output_name) return outputs_name def _check_input(self, graph, node, output_name, node_outputs, add_dim=False): if node.kind() == "prim::GetAttr": param = self.pytorch_params[output_name] if isinstance(param, np.ndarray): if add_dim: param = param[np.newaxis, :] self.paddle_params[output_name] = param graph.add_layer( "fluid.dygraph.base.to_variable", inputs={}, outputs=[output_name], value="params[{}]".format(string(output_name))) else: if isinstance(param, dict) and "Tensor" in param and \ "parent_layer_id" in param: if graph.parent_layer is not None: # 当某个param被2个控制流（if-else）赋值时，else不可以引用if中的赋值结果 id1 = param["parent_layer_id"] id2 = graph.parent_layer.id id1_part = id1.split(".") id2_part = id2.split(".") if len(id1_part) >= len(id2_part): for i in range(len(id1_part)): if id1_part[i] == id2_part[i]: continue else: if id1_part[i] == "0" and id2_part[ i] == "1": if add_dim: param = param[np.newaxis, :] self.paddle_params[output_name] = param graph.add_layer( "fluid.dygraph.base.to_variable", inputs={}, outputs=[output_name], value="params[{}]".format( string(output_name))) node_outputs.append(output_name) return # 若if-else外，则可直接引用if-else中的赋值结果 graph.add_layer( "prim.constant", inputs={}, outputs=[output_name], value=param["Tensor"]) else: graph.add_layer( "prim.constant", inputs={}, outputs=[output_name], value=string(param) if isinstance(param, str) else param) node_outputs.append(output_name) def _get_inputs_name(self, node): inputs_name = [] inputs_node = [] for script_input_ivalue in node.inputs(): script_input_node = script_input_ivalue.node() script_input_unique_id = script_input_ivalue.unique() input_name = self.outputs_info[script_input_unique_id] inputs_node.append(script_input_node) inputs_name.append(input_name) return inputs_name, inputs_node def data(self, graph, node, uid): 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: continue node_name = 'x' + str(self.output_index) self.outputs_info[script_unique_id] = node_name self.output_index += 1 output_name = self.outputs_info[uid] graph.add_layer( "fluid.dygraph.base.to_variable", inputs={}, outputs=[node_name], value=output_name) return [], [output_name] def equal(self, graph, node, uid=None, parent_layer=None, index=None): if parent_layer is not None and index is not None: # block的输出 input_node_name = self.outputs_info[uid] control_output_id = index if parent_layer.kernel == "prim.loop": 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) graph.add_layer( "prim.equal", inputs={'input': input_node_name}, outputs=[output_node_name]) return [input_node_name], current_outputs