UnsupportedOperatorError: aten::scatter_reduce when include_self=False #126660
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module: onnx
Related to torch.onnx
onnx-triaged
triaged by ONNX team
triaged
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Comments
|
MLP is a fc layer, which is import torch
import torch.nn as nn
import torch.nn.functional as F
import onnx
import onnxruntime as ort
# MLP
class MLP(nn.Module):
def __init__(
self,
in_channel,
out_channel,
hidden=64,
bias=True,
activation="relu",
norm="layer",
):
super(MLP, self).__init__()
# define the activation function
if activation == "relu":
act_layer = nn.ReLU
elif activation == "relu6":
act_layer = nn.ReLU6
elif activation == "leaky":
act_layer = nn.LeakyReLU
elif activation == "prelu":
act_layer = nn.PReLU
else:
raise NotImplementedError
# define the normalization function
if norm == "layer":
norm_layer = nn.LayerNorm
elif norm == "batch":
norm_layer = nn.BatchNorm1d
else:
raise NotImplementedError
# insert the layers
self.linear1 = nn.Linear(in_channel, hidden, bias=bias)
self.linear1.apply(self._init_weights)
self.linear2 = nn.Linear(hidden, out_channel, bias=bias)
self.linear2.apply(self._init_weights)
self.norm1 = norm_layer(hidden)
self.norm2 = norm_layer(out_channel)
self.act1 = act_layer(inplace=True)
self.act2 = act_layer(inplace=True)
self.shortcut = None
if in_channel != out_channel:
self.shortcut = nn.Sequential(
nn.Linear(in_channel, out_channel, bias=bias),
norm_layer(out_channel)
)
# self.layers = nn.Sequential(
# self.linear1,
# self.norm1,
# self.act1,
# self.linear2,
# self.norm2,
# )
@staticmethod
def _init_weights(m):
if isinstance(m, nn.Linear):
torch.nn.init.xavier_uniform_(m.weight)
m.bias.data.fill_(0.01)
def forward(self, x):
# print("\nMLP")
out = self.linear1(x)
out = self.norm1(out)
out = self.act1(out)
out = self.linear2(out)
out = self.norm2(out)
# print("\nx {} \nout {}".format(x.shape, out.shape))
# out = self.layers(x)
# print(self.shortcut)
if self.shortcut:
out += self.shortcut(x)
else:
out += x
# print("\nx {} \nout {}".format(x.shape, out.shape))
return self.act2(out)```
it can be directly export as onnx |
|
scatter_reduce should be supported after/include opset_version=16: pytorch/torch/onnx/symbolic_opset16.py Line 120 in b40fb2d What do you get when you export it with 16? Please try a newer version of PyTorch. It looks like you are using torch==2.0.0, which is too old. opset 11 is indeed not supporting scatter_reduce. |
thanks for reply, opset 16 doesn't work either. I will update my torch version to the newest version and try again. |
another error occured |
uninstall torch-sparse and reinstall torch-sparse solves the first two UserWarning, the following TraceWarning I will try to figure out. A higher version pytorch truely supports operator aten::scatter_reduce, but there is still an error. |
|
Hi @LTsommer, Unfortunately, include_self=False is not supported in ONNX spec: onnx/onnx#5100. You would have to modify the model code to get around with that. |
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馃悰 Describe the bug
Hello, experts,
recently I am trying to export .pt model to .oonx model by function torch.onnx.export, but an error occured: "torch.onnx.errors.UnsupportedOperatorError: Exporting the operator 'aten::scatter_reduce' to ONNX opset version 11 is not supported.". I have tried different opset version from 11 - 18, it still doesn't work. Actually aten::scatter_reduce is not explicit used in the code, so I guess it is called in another function.
the code is as follows:
The error imfomation is as follows:
Traceback (most recent call last):
File "
/Desktop/Studien/VN/core/model/layers/subgraph.py", line 141, in/opt/anaconda3/envs/torch/lib/python3.9/site-packages/torch/onnx/utils.py", line 506, in exporttorch.onnx.export(
File "
_export(
File "
/opt/anaconda3/envs/torch/lib/python3.9/site-packages/torch/onnx/utils.py", line 1548, in _export/opt/anaconda3/envs/torch/lib/python3.9/site-packages/torch/onnx/utils.py", line 1117, in _model_to_graphgraph, params_dict, torch_out = _model_to_graph(
File "
graph = _optimize_graph(
File "
/opt/anaconda3/envs/torch/lib/python3.9/site-packages/torch/onnx/utils.py", line 665, in _optimize_graph/opt/anaconda3/envs/torch/lib/python3.9/site-packages/torch/onnx/utils.py", line 1901, in _run_symbolic_functiongraph = _C._jit_pass_onnx(graph, operator_export_type)
File "
raise errors.UnsupportedOperatorError(
torch.onnx.errors.UnsupportedOperatorError: Exporting the operator 'aten::scatter_reduce' to ONNX opset version 11 is not supported. Please feel free to request support or submit a pull request on PyTorch GitHub: https://github.com/pytorch/pytorch/issues.
The packae installed in my virtual env:
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