From fee5405a70b1c6db294f9c20ff4b38e1d98ce7c0 Mon Sep 17 00:00:00 2001 From: WJJ1995 Date: Thu, 2 Dec 2021 14:37:20 +0800 Subject: [PATCH] Support YOLOX and Adapt to PyTorch 1.7.0 (#696) * add SiLU op * fixed bugs * support yolox model * fix code style * Handling the case of underslashes * add aten::format and remove to_tensor * deal with comments * fixed for CI * update rm to_tensor --- docs/inference_model_convertor/op_list.md | 4 +- x2paddle/convert.py | 9 +- x2paddle/core/program.py | 6 +- x2paddle/op_mapper/pytorch2paddle/aten.py | 424 ++++-------------- .../op_mapper/pytorch2paddle/prim2code.py | 29 +- .../pytorch2paddle/pytorch_op_mapper.py | 8 + .../layer_code_generator.py | 8 +- 7 files changed, 143 insertions(+), 345 deletions(-) diff --git a/docs/inference_model_convertor/op_list.md b/docs/inference_model_convertor/op_list.md index 579ed9f..ea3520a 100644 --- a/docs/inference_model_convertor/op_list.md +++ b/docs/inference_model_convertor/op_list.md @@ -1,5 +1,5 @@ # X2Paddle支持OP列表 -> 目前X2Paddle支持90+的TensorFlow OP，30+的Caffe Layer，80+的ONNX OP，110+的PyTorch Aten，10+的PyTorch Prim覆盖了大部分CV分类模型常用的操作。我们在如下列表中给出了目前X2Paddle支持的全部OP。 +> 目前X2Paddle支持90+的TensorFlow OP，30+的Caffe Layer，80+的ONNX OP，120+的PyTorch Aten，10+的PyTorch Prim覆盖了大部分CV分类模型常用的操作。我们在如下列表中给出了目前X2Paddle支持的全部OP。 **注：** 目前，部分OP暂未支持，如您在转换过程中出现OP不支持的情况，可自行添加或反馈给我们。欢迎通过[ISSUE反馈](https://github.com/PaddlePaddle/X2Paddle/issues/new)的方式告知我们(模型名，代码实现或模型获取方式)，我们会及时跟进：） @@ -109,7 +109,7 @@ Aten: | 105 | aten::where | 106 | aten::zeros |107|aten::zeros\_like|108|aten::bmm| | 109 | aten::sub\_ | 110 | aten:erf |111|aten::lstm|112|aten::gather| | 113 | aten::upsample\_nearest2d | 114 | aten::split\_with\_sizes | 115 | aten::sum | 116 | aten::instance_norm | -| 117 | aten::bitwise_not | 118 | aten::bitwise_xor | 119 | aten::bitwise_and | | | +| 117 | aten::bitwise_not | 118 | aten::bitwise_xor | 119 | aten::bitwise_and | 120 | aten::silu | Prim: | 序号 | OP | 序号 | OP | 序号 | OP | 序号 | OP | diff --git a/x2paddle/convert.py b/x2paddle/convert.py index 2f1319e..62737b5 100644 --- a/x2paddle/convert.py +++ b/x2paddle/convert.py @@ -88,6 +88,11 @@ def arg_parser(): type=_text_type, default=None, help="pretrain model file of pytorch model") + parser.add_argument( + "--enable_code_optim", + "-co", + default=True, + help="Turn on code optimization") parser.add_argument( "--to_lite", "-tl", default=False, help="convert to Paddle-Lite format") parser.add_argument( @@ -222,6 +227,7 @@ def pytorch2paddle(module, save_dir, jit_type="trace", input_examples=None, + enable_code_optim=True, convert_to_lite=False, lite_valid_places="arm", lite_model_type="naive_buffer"): @@ -262,7 +268,8 @@ def pytorch2paddle(module, graph_opt = GraphOptimizer(source_frame="pytorch", jit_type=jit_type) graph_opt.optimize(mapper.paddle_graph) logging.info("Model optimized.") - mapper.paddle_graph.gen_model(save_dir, jit_type=jit_type) + mapper.paddle_graph.gen_model( + save_dir, jit_type=jit_type, enable_code_optim=enable_code_optim) if convert_to_lite: convert2lite(save_dir, lite_valid_places, lite_model_type) diff --git a/x2paddle/core/program.py b/x2paddle/core/program.py index 09d0270..1841c7c 100644 --- a/x2paddle/core/program.py +++ b/x2paddle/core/program.py @@ -237,11 +237,11 @@ class PaddleGraph(object): return update(self.layers) - def gen_model(self, save_dir, jit_type=None): + def gen_model(self, save_dir, jit_type=None, enable_code_optim=True): if not osp.exists(save_dir): os.makedirs(save_dir) if jit_type == "trace": - if not self.has_unpack: + if not self.has_unpack and enable_code_optim: from x2paddle.optimizer.pytorch_code_optimizer import HierarchicalTree hierarchical_tree = HierarchicalTree(self) for layer_id, layer in self.layers.items(): @@ -252,7 +252,7 @@ class PaddleGraph(object): self.gen_code(save_dir) self.dump_parameter(save_dir) else: - if self.source_type == "pytorch": + if self.source_type == "pytorch" and enable_code_optim: from x2paddle.optimizer.pytorch_code_optimizer import ModuleGraph module_graph = ModuleGraph(self) module_graph.save_source_files(save_dir) diff --git a/x2paddle/op_mapper/pytorch2paddle/aten.py b/x2paddle/op_mapper/pytorch2paddle/aten.py index d641f94..28bef45 100755 --- a/x2paddle/op_mapper/pytorch2paddle/aten.py +++ b/x2paddle/op_mapper/pytorch2paddle/aten.py @@ -263,41 +263,6 @@ def aten_addmm(mapper, graph, node): def aten_add(mapper, graph, node): - """ 构造数值相加的PaddleLayer，该节点实现out = x + y。 - TorchScript示例: - %296 : int = aten::add(%i.12, %288) - 参数含义: - %296 (-): 相加结果。 - %i.12 (-): 输入数值 x。 - %288 (-): 输入数值 y。 - """ - scope_name = mapper.normalize_scope_name(node) - output_name = mapper._get_outputs_name(node)[0] - layer_outputs = [output_name] - layer_inputs = {} - inputs_name, inputs_node = mapper._get_inputs_name(node) - # 获取当前节点输出的list - current_outputs = [output_name] - # 处理输入0，即%i.12 - mapper._check_input(graph, inputs_node[0], inputs_name[0], current_outputs, - scope_name) - layer_inputs["x"] = inputs_name[0] - # 处理输入1，即%288 - mapper._check_input(graph, inputs_node[1], inputs_name[1], current_outputs, - scope_name) - layer_inputs["y"] = inputs_name[1] - # 获取当前节点输入的list - current_inputs = list(layer_inputs.values()) - - graph.add_layer( - "prim.add", - inputs=layer_inputs, - outputs=layer_outputs, - scope_name=scope_name) - return current_inputs, current_outputs - - -def aten_add_(mapper, graph, node): """ 构造数值相加的PaddleLayer，该节点实现out = x + alpha * y。 TorchScript示例: %137 : Tensor = aten::add(%136, %130, %130) @@ -325,21 +290,29 @@ def aten_add_(mapper, graph, node): layer_inputs["y"] = inputs_name[1] # 获取当前节点输入的list current_inputs = list(layer_inputs.values()) - # 处理输入2，即%151 - if inputs_name[2] in mapper.attrs: - layer_attrs["alpha"] = mapper.attrs[inputs_name[2]] - else: - mapper._check_input(graph, inputs_node[2], inputs_name[2], - current_outputs, scope_name) - layer_inputs["alpha"] = inputs_name[2] - current_inputs.append(inputs_name[2]) + if len(inputs_name) > 2: + # 处理输入2，即%151 + if inputs_name[2] in mapper.attrs: + layer_attrs["alpha"] = mapper.attrs[inputs_name[2]] + else: + mapper._check_input(graph, inputs_node[2], inputs_name[2], + current_outputs, scope_name) + layer_inputs["alpha"] = inputs_name[2] + current_inputs.append(inputs_name[2]) - graph.add_layer( - "prim.add_", - inputs=layer_inputs, - outputs=layer_outputs, - scope_name=scope_name, - **layer_attrs) + graph.add_layer( + "prim.add_", + inputs=layer_inputs, + outputs=layer_outputs, + scope_name=scope_name, + **layer_attrs) + else: + graph.add_layer( + "prim.add", + inputs=layer_inputs, + outputs=layer_outputs, + scope_name=scope_name, + **layer_attrs) return current_inputs, current_outputs @@ -1634,41 +1607,6 @@ def aten_dim(mapper, graph, node): return current_inputs, current_outputs -def aten_div_(mapper, graph, node): - """ 构造除法的PaddleLayer。 - TorchScript示例: - %bx_bw0.3 : Tensor = aten::div_(%bx_bw.3, %2678) - 参数含义: - %bx_bw0.3 (-): 除后的结果。 - %bx_bw.3 (-): 被除数。 - %2678 (int): 除数。 - """ - scope_name = mapper.normalize_scope_name(node) - output_name = mapper._get_outputs_name(node)[0] - layer_outputs = [output_name] - layer_inputs = {} - inputs_name, inputs_node = mapper._get_inputs_name(node) - # 获取当前节点输出的list - current_outputs = [output_name] - # 处理输入0，即%124 - mapper._check_input(graph, inputs_node[0], inputs_name[0], current_outputs, - scope_name) - layer_inputs["x"] = inputs_name[0] - # 处理输入1，即%123 - mapper._check_input(graph, inputs_node[1], inputs_name[1], current_outputs, - scope_name) - layer_inputs["y"] = inputs_name[1] - # 获取当前节点输入的list - current_inputs = list(layer_inputs.values()) - - graph.add_layer( - "prim.div", - inputs=layer_inputs, - outputs=layer_outputs, - scope_name=scope_name) - return current_inputs, current_outputs - - def aten_div(mapper, graph, node): """ 构造除法的PaddleLayer。 TorchScript示例: @@ -1737,39 +1675,6 @@ def aten_dropout(mapper, graph, node): return current_inputs, current_outputs -def aten_dropout_(mapper, graph, node): - """ 构造Dropout的PaddleLayer。 - TorchScript示例: - %119 : Tensor = aten::dropout_(%result.3, %117, %118) - 参数含义: - %119 (Tensor): Dropout后的Tensor。 - %result.3 (Tensor): 输入Tensor。 - %118 (bool): 是否是训练阶段。 - """ - scope_name = mapper.normalize_scope_name(node) - op_name = name_generator("dropout", mapper.nn_name2id) - output_name = mapper._get_outputs_name(node)[0] - layer_outputs = [op_name, output_name] - layer_inputs = {} - inputs_name, inputs_node = mapper._get_inputs_name(node) - # 获取当前节点输出的list - current_outputs = [output_name] - # 处理输入0，即%119 - mapper._check_input(graph, inputs_node[0], inputs_name[0], current_outputs, - scope_name) - layer_inputs["input"] = inputs_name[0] - # 获取当前节点输入、输出的list - current_inputs = list(layer_inputs.values()) - - graph.add_layer( - "paddle.nn.Dropout", - inputs=layer_inputs, - outputs=layer_outputs, - scope_name=scope_name, - p=0.0) - return current_inputs, current_outputs - - def aten_embedding(mapper, graph, node): """ 构造embedding的PaddleLayer。 TorchScript示例: @@ -2326,6 +2231,38 @@ def aten_floor_divide(mapper, graph, node): return current_inputs, current_outputs +def aten_format(mapper, graph, node): + """ 构造取浮点型的PaddleLayer。 + TorchScript示例: + %628 : str = aten::format(%8, %627) + 参数含义: + %628 (str): 输出,为一个字符串 + %8 (str): 输入字符串 + %627 (-): format后的参数 + """ + scope_name = mapper.normalize_scope_name(node) + output_name = mapper._get_outputs_name(node)[0] + layer_outputs = [output_name] + layer_inputs = {} + inputs_name, inputs_node = mapper._get_inputs_name(node) + # 获取当前节点输出的list + current_outputs = [output_name] + # 处理输入 + for i in range(len(inputs_node)): + mapper._check_input(graph, inputs_node[i], inputs_name[i], + current_outputs, scope_name) + layer_inputs["input" + str(i)] = inputs_name[i] + # 获取当前节点输入的list + current_inputs = list(layer_inputs.values()) + + graph.add_layer( + "prim.format", + inputs=layer_inputs, + outputs=layer_outputs, + scope_name=scope_name) + return current_inputs, current_outputs + + def aten_full_like(mapper, graph, node): """ 构造创建一个与输入具有相同的形状并且数据类型固定的Tensor的PaddleLayer。 TorchScript示例: @@ -2607,10 +2544,10 @@ def aten_gru(mapper, graph, node): return current_inputs, current_outputs -def aten_hardtanh_(mapper, graph, node): +def aten_hardtanh(mapper, graph, node): """ 构造hardtanh激活的PaddleLayer。 TorchScript示例: - %result.9 : Tensor = aten::hardtanh_(%input.20, %67, %66) + %result.9 : Tensor = aten::hardtanh(%input.20, %67, %66) 参数含义: %result.9 (Tensor): 输出，hardtanh激活后的Tensor。 %input.20 (Tensor): 需要hardtanh激活的Tensor。 @@ -2990,42 +2927,6 @@ def aten_le(mapper, graph, node): return current_inputs, current_outputs -def aten_leaky_relu_(mapper, graph, node): - """ 构造leaky relu激活的PaddleLayer。 - TorchScript示例: - %input.117 : Tensor = aten::leaky_relu_(%input.114, %1570) - 参数含义: - %input.117 (Tensor): 输出，leaky relu后的结果。 - %input.114 (Tensor): 需要leaky relu的Tensor。 - %1570 (float): 输入中的元素小于0时的斜率。 - """ - scope_name = mapper.normalize_scope_name(node) - op_name = name_generator("leakly_relu", mapper.nn_name2id) - output_name = mapper._get_outputs_name(node)[0] - layer_outputs = [op_name, output_name] - layer_inputs = {} - layer_attrs = {} - inputs_name, inputs_node = mapper._get_inputs_name(node) - # 获取当前节点输出的list - current_outputs = [output_name] - # 处理输入0，即%result.5 - mapper._check_input(graph, inputs_node[0], inputs_name[0], current_outputs, - scope_name) - layer_inputs["x"] = inputs_name[0] - # 获取当前节点输入、输出的list - current_inputs = list(layer_inputs.values()) - # 处理输入1，即%1570 - layer_attrs["negative_slope"] = mapper.attrs[inputs_name[1]] - - graph.add_layer( - "paddle.nn.LeakyReLU", - inputs=layer_inputs, - outputs=layer_outputs, - scope_name=scope_name, - **layer_attrs) - return current_inputs, current_outputs - - def aten_leaky_relu(mapper, graph, node): """ 构造leaky relu激活的PaddleLayer。 TorchScript示例: @@ -3293,115 +3194,6 @@ def aten_lt(mapper, graph, node): return current_inputs, current_outputs -def aten_masked_fill_(mapper, graph, node): - """ 构造填充mask的PaddleLayer。 - TorchScript示例: - %input.4 : Tensor = aten::masked_fill_(%scores.2, %mask.2, %46) - 参数含义: - %input.4 (Tensor): 输出，填充后的结果。 - %scores.2 (Tensor): 需要填充的Tensor。 - %mask.2 (Tensor): bool型的Tensor，哪些位置需要填充。 - %46 (-): 填充的值。 - """ - scope_name = mapper.normalize_scope_name(node) - output_name = mapper._get_outputs_name(node)[0] - layer_outputs = [output_name] - inputs_name, inputs_node = mapper._get_inputs_name(node) - # 获取当前节点输入的list - current_inputs = [] - # 获取当前节点输出的list - current_outputs = [output_name] - # 处理输入0，即%input.4 - mapper._check_input(graph, inputs_node[0], inputs_name[0], current_outputs, - scope_name) - current_inputs.append(inputs_name[0]) - graph.add_layer( - "prim.type", - inputs={"input": inputs_name[0]}, - outputs=[inputs_name[0] + "_type"], - scope_name=scope_name) - # 处理输入1，即%scores.2 - mapper._check_input(graph, inputs_node[1], inputs_name[1], current_outputs, - scope_name) - current_inputs.append(inputs_name[1]) - graph.add_layer( - "paddle.logical_not", - inputs={"x": inputs_name[1]}, - outputs=[inputs_name[1] + "_not"], - scope_name=scope_name) - graph.add_layer( - "paddle.cast", - inputs={"x": inputs_name[1]}, - outputs=[inputs_name[1] + "_mask"], - scope_name=scope_name, - dtype=inputs_name[0] + "_type") - graph.add_layer( - "paddle.cast", - inputs={"x": inputs_name[1] + "_not"}, - outputs=[inputs_name[1] + "_not_mask"], - scope_name=scope_name, - dtype=inputs_name[0] + "_type") - graph.add_layer( - "paddle.multiply", - inputs={"x": inputs_name[0], - "y": inputs_name[1] + "_not_mask"}, - outputs=[inputs_name[0] + "_not_mask"], - scope_name=scope_name) - # 处理输入2，即%46 - mapper._check_input(graph, inputs_node[2], inputs_name[2], current_outputs, - scope_name) - graph.add_layer( - "prim.eq", - inputs={"x": inputs_name[2]}, - outputs=[inputs_name[2] + "_cond1"], - scope_name=scope_name, - y="-float('inf')") - graph.add_layer( - "prim.eq", - inputs={"x": inputs_name[2]}, - outputs=[inputs_name[2] + "_cond2"], - scope_name=scope_name, - y="float('inf')") - graph.add_layer( - "prim.or", - inputs={ - "x": inputs_name[2] + "_cond1", - "y": inputs_name[2] + "_cond2" - }, - outputs=[inputs_name[2] + "_cond"], - scope_name=scope_name) - graph.add_layer( - "prim.if", {'input': inputs_name[2] + "_cond"}, - outputs=[inputs_name[2] + "_if"], - scope_name=scope_name) - if_layer = graph.layers[list(graph.layers.keys())[-1]] - block = PaddleGraph(source_type="pytorch", parent_layer=if_layer) - block.add_layer( - "prim.equal", - inputs={"input": inputs_name[1] + "_mask"}, - outputs=[inputs_name[2] + "_1"], - scope_name=scope_name) - if_layer.add_block(block) - block = PaddleGraph(source_type="pytorch", parent_layer=if_layer) - block.add_layer( - "prim.mul", - inputs={"x": inputs_name[1] + "_mask", - "y": inputs_name[2]}, - outputs=[inputs_name[2] + "_1"], - scope_name=scope_name) - if_layer.add_block(block) - if_layer.inputs["input-0"] = inputs_name[1] + "_mask" - if_layer.inputs["input-1"] = inputs_name[2] - if_layer.outputs.append(inputs_name[2] + "_1") - graph.add_layer( - "paddle.add", - inputs={"x": inputs_name[2] + "_1", - "y": inputs_name[0] + "_not_mask"}, - outputs=layer_outputs, - scope_name=scope_name) - return current_inputs, current_outputs - - def aten_masked_fill(mapper, graph, node): """ 构造填充mask的PaddleLayer。 TorchScript示例: @@ -3799,42 +3591,6 @@ def aten_mul(mapper, graph, node): return current_inputs, current_outputs -def aten_mul_(mapper, graph, node): - """ 构造数值相乘的PaddleLayer。 - TorchScript示例: - %size_prods.39 : int = aten::mul_(%size_prods.38, %114) - 参数含义: - %size_prods.39 (Tensor): 输出，相乘后的结果。 - %size_prods.38 (-): 数值1。 - %114 (-): 数值2。 - """ - scope_name = mapper.normalize_scope_name(node) - output_name = mapper._get_outputs_name(node)[0] - layer_outputs = [output_name] - layer_inputs = {} - inputs_name, inputs_node = mapper._get_inputs_name(node) - # 获取当前节点输出的list - current_outputs = [output_name] - # 处理输入0，即%size_prods.38 - mapper._check_input(graph, inputs_node[0], inputs_name[0], current_outputs, - scope_name) - layer_inputs["x"] = inputs_name[0] - # 处理输入1，即%114 - mapper._check_input(graph, inputs_node[1], inputs_name[1], current_outputs, - scope_name) - layer_inputs["y"] = inputs_name[1] - # 获取当前节点输入的list - current_inputs = list(layer_inputs.values()) - current_outputs = layer_outputs - - graph.add_layer( - "prim.mul", - inputs=layer_inputs, - outputs=layer_outputs, - scope_name=scope_name) - return current_inputs, current_outputs - - def aten_ne(mapper, graph, node): """ 构造判断数值是否不相等的PaddleLayer。 TorchScript示例: @@ -4304,38 +4060,6 @@ def aten_relu(mapper, graph, node): return current_inputs, current_outputs -def aten_relu_(mapper, graph, node): - """ 构造ReLU激活的PaddleLayer。 - TorchScript示例: - %result.3 : Tensor = aten::relu_(%input.5) - 参数含义: - %result.3 (Tensor): 输出，ReLU后的结果。 - %result.5 (Tensor): 需要ReLU的Tensor。 - 注意: inplace这个参数在paddle中未实现 - """ - scope_name = mapper.normalize_scope_name(node) - op_name = name_generator("relu", mapper.nn_name2id) - output_name = mapper._get_outputs_name(node)[0] - layer_outputs = [op_name, output_name] - layer_inputs = {} - inputs_name, inputs_node = mapper._get_inputs_name(node) - # 获取当前节点输出的list - current_outputs = [output_name] - # 处理输入0，即%result.5 - mapper._check_input(graph, inputs_node[0], inputs_name[0], current_outputs, - scope_name) - layer_inputs["x"] = inputs_name[0] - # 获取当前节点输入的list - current_inputs = list(layer_inputs.values()) - - graph.add_layer( - "paddle.nn.ReLU", - inputs=layer_inputs, - outputs=layer_outputs, - scope_name=scope_name) - return current_inputs, current_outputs - - def aten_relu6(mapper, graph, node): """ 构造ReLU6激活的PaddleLayer。 TorchScript示例: @@ -4716,6 +4440,38 @@ def aten_sigmoid(mapper, graph, node): return current_inputs, current_outputs +def aten_silu(mapper, graph, node): + """ 构造Silu激活的PaddleLayer。 + TorchScript示例: + %result.3 : Tensor = aten::silu(%input.5) + 参数含义: + %result.3 (Tensor): 输出，Silu后的结果。 + %input.5 (Tensor): 需要Silu的Tensor。 + 注意: inplace这个参数在paddle中未实现 + """ + scope_name = mapper.normalize_scope_name(node) + op_name = name_generator("silu", mapper.nn_name2id) + output_name = mapper._get_outputs_name(node)[0] + layer_outputs = [op_name, output_name] + layer_inputs = {} + inputs_name, inputs_node = mapper._get_inputs_name(node) + # 获取当前节点输出的list + current_outputs = [output_name] + # 处理输入0，即%input.5 + mapper._check_input(graph, inputs_node[0], inputs_name[0], current_outputs, + scope_name) + layer_inputs["x"] = inputs_name[0] + # 获取当前节点输入的list + current_inputs = list(layer_inputs.values()) + + graph.add_layer( + "paddle.nn.Silu", + inputs=layer_inputs, + outputs=layer_outputs, + scope_name=scope_name) + return current_inputs, current_outputs + + def aten_sin(mapper, graph, node): """ 构造数学计算sin的PaddleLayer。 TorchScript示例: @@ -5700,9 +5456,9 @@ def aten_upsample_nearest2d(mapper, graph, node): if_layer.add_block(block) if_layer.inputs["input-0"] = inputs_name[1] if "size" in layer_attrs and layer_attrs["size"] is None: - mapper._check_input(graph, inputs_node[3], inputs_name[3], + mapper._check_input(graph, inputs_node[2], inputs_name[2], current_outputs, scope_name) - layer_inputs["scale_factor"] = inputs_name[3] + layer_inputs["scale_factor"] = inputs_name[2] layer_attrs["align_mode"] = 0 layer_attrs["mode"] = string("nearest") graph.add_layer( diff --git a/x2paddle/op_mapper/pytorch2paddle/prim2code.py b/x2paddle/op_mapper/pytorch2paddle/prim2code.py index ef31eee..17ad504 100644 --- a/x2paddle/op_mapper/pytorch2paddle/prim2code.py +++ b/x2paddle/op_mapper/pytorch2paddle/prim2code.py @@ -304,6 +304,27 @@ def prim_floordiv(layer, forward_func.extend(gen_codes([line], indent=indent)) +def prim_format(layer, + indent=1, + init_func=[], + forward_func=[], + layer_id=None, + different_attrs=None): + line = "" + if len(layer.inputs) == 3: + line = "{} = {}.format({}, {})".format( + layer.outputs[0], + get_value(layer, "input0", different_attrs), + get_value(layer, "input1", different_attrs), + get_value(layer, "input2", different_attrs)) + elif len(layer.inputs) == 2: + line = "{} = {}.format({})".format( + layer.outputs[0], + get_value(layer, "input0", different_attrs), + get_value(layer, "input1", different_attrs)) + forward_func.extend(gen_codes([line], indent=indent)) + + def prim_getitem(layer, indent=1, init_func=[], @@ -609,8 +630,8 @@ def prim_or(layer, if is_return_line: return line.split(" = ")[1] forward_func.extend(gen_codes([line], indent=indent)) - - + + def prim_remainder(layer, indent=1, init_func=[], @@ -619,8 +640,8 @@ def prim_remainder(layer, different_attrs=None, is_return_line=False): line = "{} = {} % {}".format(layer.outputs[0], - get_value(layer, "x", different_attrs), - get_value(layer, "y", different_attrs)) + get_value(layer, "x", different_attrs), + get_value(layer, "y", different_attrs)) if is_return_line: return line.split(" = ")[1] forward_func.extend(gen_codes([line], indent=indent)) diff --git a/x2paddle/op_mapper/pytorch2paddle/pytorch_op_mapper.py b/x2paddle/op_mapper/pytorch2paddle/pytorch_op_mapper.py index 5783598..562512a 100644 --- a/x2paddle/op_mapper/pytorch2paddle/pytorch_op_mapper.py +++ b/x2paddle/op_mapper/pytorch2paddle/pytorch_op_mapper.py @@ -55,6 +55,10 @@ class PyTorchOpMapper(): unsupported_ops = [] for op in op_list: func_name = op.replace('::', '_') + # Processing suffix is "_" situation, eg: aten_relu_ to aten_relu + # avoid aten::__isnot__ situation + if func_name[-1] == "_" and func_name[-2] != "_": + func_name = func_name[:-1] if not (hasattr(prim, func_name) or hasattr(aten, func_name)): unsupported_ops.append(op) if len(unsupported_ops) == 0: @@ -104,6 +108,10 @@ class PyTorchOpMapper(): for node in script_graph.nodes(): kind = node.kind() func_name = kind.replace('::', '_') + # Processing suffix is "_" situation, eg: aten_relu_ to aten_relu + # avoid aten::__isnot__ situation + if func_name[-1] == "_" and func_name[-2] != "_": + func_name = func_name[:-1] if hasattr(prim, func_name): func = getattr(prim, func_name) inputs, outputs = func(self, graph, node) diff --git a/x2paddle/optimizer/pytorch_code_optimizer/layer_code_generator.py b/x2paddle/optimizer/pytorch_code_optimizer/layer_code_generator.py index ff61a82..7ee9559 100644 --- a/x2paddle/optimizer/pytorch_code_optimizer/layer_code_generator.py +++ b/x2paddle/optimizer/pytorch_code_optimizer/layer_code_generator.py @@ -396,8 +396,14 @@ def gen_layer_code(graph, sub_layers, sub_layers_name, different_attrs=dict()): head, init_func_head, forward_func_head = gen_head(inputs, different_attrs) output_data_name = ", ".join(outputs) + # remove to_tensor op + forward_func_new = list() + for line in forward_func: + if "paddle.to_tensor" in line: + continue + forward_func_new.append(line) code_list = head + init_func_head + init_func + \ - forward_func_head + forward_func + \ + forward_func_head + forward_func_new + \ gen_codes(["return {}".format(output_data_name)], indent=2) code_str = "".join(code_list) return code_str -- GitLab