From 5dc86f9da0ad214c2d5631329f8b6ad72b7e1a97 Mon Sep 17 00:00:00 2001 From: Jian9ww <1500082371@qq.com> Date: Sat, 1 Aug 2026 18:02:30 +0800 Subject: [PATCH] =?UTF-8?q?[Fix]=20aten=5Favg=5Fpool1d=20=E8=B6=8A?= =?UTF-8?q?=E7=95=8C=E8=AE=BF=E9=97=AE=20avg=5Fpool1d=20=E4=B8=8D=E5=AD=98?= =?UTF-8?q?=E5=9C=A8=E7=9A=84=20divisor=5Foverride?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit aten::avg_pool1d 仅 6 个输入，无 divisor_override。原实现由 aten_avg_pool2d 复制而来，遗留了对第 7 个输入 inputs_name[6] 的 prim.assert，导致任何 AvgPool1d 转换必现 IndexError。删除该 divisor_override 处理，并修正 docstring 与算子命名。 补充 test_autoscan/torch 下的 avg_pool1d autoscan 回归测试（此前 torch 侧无任何 pooling 测试）。 --- .../torch/test_auto_scan_avg_pool1d.py | 82 +++++++++++++++++++ x2paddle/op_mapper/pytorch2paddle/aten.py | 16 +--- 2 files changed, 85 insertions(+), 13 deletions(-) create mode 100644 test_autoscan/torch/test_auto_scan_avg_pool1d.py diff --git a/test_autoscan/torch/test_auto_scan_avg_pool1d.py b/test_autoscan/torch/test_auto_scan_avg_pool1d.py new file mode 100644 index 000000000..912fe9e9d --- /dev/null +++ b/test_autoscan/torch/test_auto_scan_avg_pool1d.py @@ -0,0 +1,82 @@ +# Copyright (c) 2022 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 auto_scan_test import OPConvertAutoScanTest, BaseNet +from hypothesis import reproduce_failure +import hypothesis.strategies as st +import torch +import numpy as np +import unittest + + +class Net(BaseNet): + """ + simple Net + """ + + def forward(self, inputs): + """ + forward + """ + x = torch.nn.functional.avg_pool1d( + inputs, + kernel_size=self.config["kernel_size"], + stride=self.config["stride"], + padding=self.config["padding"], + ceil_mode=self.config["ceil_mode"], + count_include_pad=self.config["count_include_pad"]) + return x + + +class TestAvgPool1dConvert(OPConvertAutoScanTest): + """ + Torch API: torch.nn.functional.avg_pool1d + """ + + def sample_convert_config(self, draw): + input_shape = draw( + st.lists(st.integers(min_value=16, max_value=32), + min_size=3, + max_size=3)) + + kernel_size = draw(st.integers(min_value=1, max_value=5)) + stride = draw(st.integers(min_value=1, max_value=5)) + padding = draw(st.integers(min_value=0, max_value=kernel_size // 2)) + ceil_mode = draw(st.booleans()) + count_include_pad = draw(st.booleans()) + + config = { + "op_names": ["avg_pool1d"], + "test_data_shapes": [input_shape], + "test_data_types": [['float32']], + "inputs_shape": [[-1, input_shape[1], -1]], + "kernel_size": kernel_size, + "stride": stride, + "padding": padding, + "ceil_mode": ceil_mode, + "count_include_pad": count_include_pad, + "delta": 1e-4, + "rtol": 1e-4, + } + + models = Net(config) + + return (config, models) + + def test(self): + self.run_and_statis(max_examples=30) + + +if __name__ == "__main__": + unittest.main() diff --git a/x2paddle/op_mapper/pytorch2paddle/aten.py b/x2paddle/op_mapper/pytorch2paddle/aten.py index c3526fff4..540766b05 100755 --- a/x2paddle/op_mapper/pytorch2paddle/aten.py +++ b/x2paddle/op_mapper/pytorch2paddle/aten.py @@ -641,9 +641,9 @@ def aten_avg_pool3d(mapper, graph, node): def aten_avg_pool1d(mapper, graph, node): - """ 构造最大池化的PaddleLayer。 + """ 构造平均池化的PaddleLayer。 TorchScript示例: - %branch_pool.2 : Tensor = aten::avg_pool1d(%x.43, %538, %539, %540, %273, %272, %271) + %branch_pool.2 : Tensor = aten::avg_pool1d(%x.43, %538, %539, %540, %273, %272) 参数含义: %branch_pool.2 (Tensor): 输出，池化后的结果。 %x.43 (Tensor): 需要池化的Tensor。 @@ -652,10 +652,9 @@ def aten_avg_pool1d(mapper, graph, node): %540 (list): 填充大小。 %273 (bool): 是否用ceil函数计算输出高度和宽度。 %272 (bool): 是否在平均池化模式不忽略填充值，False为忽略。 - %271 (int): 如果指定，它将用作除数，否则将使用池化区域的大小。 """ scope_name = mapper.normalize_scope_name(node) - op_name = name_generator("pool2d", mapper.nn_name2id) + op_name = name_generator("pool1d", mapper.nn_name2id) output_name = mapper._get_outputs_name(node)[0] layer_outputs = [op_name, output_name] layer_inputs = {} @@ -679,15 +678,6 @@ def aten_avg_pool1d(mapper, graph, node): layer_attrs["ceil_mode"] = mapper.attrs[inputs_name[4]] # 处理输入5，即%272 layer_attrs["exclusive"] = not mapper.attrs[inputs_name[5]] - # 处理输入6，即%271 - graph.add_layer("prim.assert", - inputs={}, - outputs=[inputs_name[6] + "_assert"], - scope_name=scope_name if scope_name == "" else scope_name + - "_assert", - type="eq", - key=mapper.attrs[inputs_name[6]], - value=None) graph.add_layer(kernel="paddle.nn.AvgPool1D", inputs=layer_inputs,