# -*- coding:UTF-8 -*- # 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.util import * def prim_Constant(mapper, graph, node): """ 构造constant的PaddleLayer，该节点实现常量赋值。 TorchScript示例: %2 : int = prim::Constant[value=-1]() 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() output_type = output.type() 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] if str(value).startswith("-"): value = "-{}({})".format(t, string(str(value)[1:])) else: value = "{}({})".format(t, string(str(value))) if "9223372036854775807" in str(value): import math value = int(math.pow(2, 31) - 1) mapper.attrs[output_name] = value graph.add_layer( "prim.constant", inputs={}, outputs=[output_name], scope_name=scope_name, value=value) return [], [output_name] def prim_data(mapper, graph, node): """ 构造Tensor的PaddleLayer。 TorchScript示例: %4336 : Tensor = prim::data(%out.6) 参数含义: %4336 (Tensor): 输出Tensor。 %out.6 (Tensor): 原始Tensor。 【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 = {} 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%4336 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, 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。 TorchScript示例: %27 : Tensor? = prim::GetAttr[name="bias"](%7) 参数含义: %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: input_node = list(node.inputs())[0].node() try: field_name_list.insert(0, input_node.s('name')) node = input_node except Exception: break attr_name = ".".join(field_name_list) output_name = mapper._get_outputs_name(current_node, attr_name)[0] part_script = mapper.script for field_name in field_name_list: if hasattr(part_script, field_name): param = getattr(part_script, field_name) if isinstance(param, torch.Tensor): param = param.cpu().detach().numpy() if len(param.shape) == 0: param = np.reshape(param, 1) if str(param.dtype) == "uint8": param = param.astype("int32") mapper.pytorch_params[output_name] = param part_script = param return [], [output_name] def prim_If(mapper, graph, node): """ 构造if控制流的PaddleLayer。 TorchScript示例: %input.5 : Tensor = prim::If(%107) block0(): %109 : Tensor = aten::t(%102) %ret.2 : Tensor = aten::addmm(%103, %101, %109, %104, %104) -> (%ret.2) block1(): %111 : Tensor = aten::t(%102) ... -> (%output.4) 参数含义: %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, 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) len0 = 0 for i, input_name in enumerate(graph_inputs0): current_layer.inputs['input-{}'.format(i)] = input_name len0 = i current_layer.add_block(block0_graph) block1 = list(node.blocks())[1] block1_graph, graph_inputs1 = mapper.traverse(block1, current_layer) for i, input_name in enumerate(graph_inputs1): current_layer.inputs['input-{}'.format(len0 + 1 + i)] = input_name current_layer.add_block(block1_graph) return list(current_layer.inputs.values()), current_outputs def prim_ListConstruct(mapper, graph, node): """ 构造list的PaddleLayer。 TorchScript示例: %86 : int[] = prim::ListConstruct(%84, %85) 参数含义: %86 (list): list节点输出。 %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 = {} 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): layer_inputs["input{}".format(i)] = input_name # 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, scope_name=scope_name) return current_inputs, current_outputs def prim_ListUnpack(mapper, graph, node): """ 构造获取list中元素的PaddleLayer。 TorchScript示例: %x1.4 : Tensor, %x2.4 : Tensor = prim::ListUnpack(%4354) 参数含义: %x1.4 (Tensor): 输出，list的第一个元素。 %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 = {} inputs_name, inputs_node = mapper._get_inputs_name(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, 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, scope_name=scope_name) mapper.split_len[list(layer_inputs.values())[0]] = len(layer_outputs) return current_inputs, current_outputs def prim_Loop(mapper, graph, node): """ 构造loop循环的PaddleLayer。 TorchScript示例: %x : Tensor = prim::Loop(%4, %3, %x.3) block0(%i : int, %x.12 : Tensor): %72 : int[] = prim::Constant[value=[6, 6]]() ... %x.5 : Tensor = aten::adaptive_avg_pool2d(%x.12, %_output_size.1) -> (%3, %x.5) 参数含义: %4 (int): 循环次数。 %3 (bool): 是否进入退出。 %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] loop_outputs = node_outputs.copy() for i, block_input_ivalue in enumerate(block.inputs()): if i == 0: block_input_node_name = '_x' + str(mapper.output_index) else: block_input_node_name = 'x' + str(mapper.output_index) unique_id = block_input_ivalue.unique() if unique_id not in mapper.outputs_info: mapper.outputs_info[unique_id] = block_input_node_name mapper.output_index += 1 if i == 0: loop_input_node = list(node.inputs())[0].node() script_loop_input_unique_id = list(node.inputs())[0].unique() 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, scope_name) loop_inputs['input'] = loop_input_node_name loop_outputs.append(block_input_node_name) node_outputs.append(block_input_node_name) else: loop_input_node = list(node.inputs())[i + 1].node() script_loop_input_unique_id = list(node.inputs())[i + 1].unique() 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, scope_name) graph.add_layer( "prim.equal", inputs={'input': loop_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, 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): if input_name == loop_outputs[1]: continue current_layer.inputs['input-{}'.format(i)] = input_name current_layer.add_block(block_graph) return list(current_layer.inputs.values()), node_outputs def prim_min(mapper, graph, node): """ 构造min的PaddleLayer。 TorchScript示例: %87 : int = prim::min(%86) 参数含义: %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 = {} 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%86 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, scope_name=scope_name) return current_inputs, current_outputs def prim_NumToTensor(mapper, graph, node): """ 构造转为Tensor的PaddleLayer。 TorchScript示例: %other.2 : Tensor = prim::NumToTensor(%1736) 参数含义: %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 = {} 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%86 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, scope_name=scope_name) else: 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["shape"] = [1] graph.add_layer( "paddle.full", inputs=layer_inputs, outputs=layer_outputs, scope_name=scope_name, **layer_attrs) return current_inputs, current_outputs def prim_RaiseException(mapper, graph, node): """ 构造抛出异常的PaddleLayer。 TorchScript示例: = prim::RaiseException(%76) 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 = {} 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%76 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, scope_name=scope_name) return current_inputs, current_outputs def prim_requires_grad(mapper, graph, node): """ 构造是否计算梯度的PaddleLayer。 TorchScript示例: %356 : bool = prim::requires_grad(%tensor.31) 参数含义: %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 = {} 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%86 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, scope_name=scope_name) return current_inputs, current_outputs def prim_SetAttr(mapper, graph, node): """ 设置attribute信息。 TorchScript示例: = prim::SetAttr[name="num_batches_tracked"](%260, %277) 参数含义: %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 while True: input_node = list(tmp_node.inputs())[0].node() try: field_name_list.insert(0, input_node.s('name')) tmp_node = input_node except Exception: break field_name_list.append(node.s('name')) inputs_name, inputs_node = mapper._get_inputs_name(node) param = { "Tensor": "self." + ".".join(field_name_list).replace(".", "_"), "parent_layer_id": graph.parent_layer.id } mapper.pytorch_params[".".join(field_name_list)] = param graph.add_layer( "prim.set_attr", inputs={"input": inputs_name[1]}, outputs=["self." + ".".join(field_name_list).replace(".", "_")], scope_name=scope_name) return [], [output_name] def prim_shape(mapper, graph, node): """ 构造获取shape的PaddleLayer。 TorchScript示例: %4701 : int[] = prim::shape(%result.1) 参数含义: %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 = {} 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.8 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( "paddle.shape", inputs=layer_inputs, outputs=layer_outputs, scope_name=scope_name) return current_inputs, current_outputs def prim_TupleConstruct(mapper, graph, node): """ 构造tuple的PaddleLayer。 TorchScript示例: %4492 : (Tensor, Tensor?) = prim::TupleConstruct(%x.46, %aux) 参数含义: %4492 (tuple): 输出，tuple。 %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 = {} 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): layer_inputs["input{}".format(i)] = input_name # 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, scope_name=scope_name) return current_inputs, current_outputs def prim_TupleUnpack(mapper, graph, node): """ 构造获取tuple元素的PaddleLayer。 TorchScript示例: %x.223 : Tensor, %aux.3 : Tensor? = prim::TupleUnpack(%4492) 参数含义: %x.223 (Tensor/其他): 输出，tuple第一个元素信息。 %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 = {} inputs_name, inputs_node = mapper._get_inputs_name(node) # 获取当前节点输出的list current_outputs = outputs_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.tuple_unpack", inputs=layer_inputs, outputs=layer_outputs, scope_name=scope_name) return current_inputs, current_outputs def prim_unchecked_cast(mapper, graph, node): """ 构造确认类型的PaddleLayer。 TorchScript示例: %size.64 : int[] = prim::unchecked_cast(%size.63) 参数含义: %size.64 (-): 输出。 %size.63 (-): 输入。 【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 = {} 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%size.63 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, scope_name=scope_name) return current_inputs, current_outputs def prim_Uninitialized(mapper, graph, node): """ 构造表示编译器永远不会使用的值的PaddleLayer，该节点转换为None。 TorchScript示例: %345 : bool = prim::Uninitialized() 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], scope_name=scope_name, value=None) return [], [output_name]