Error information when I run the, gsimclr.py --DS ENZYMES --lr 0.01 --local --num-gc-layers 3 --aug random4 --seed 0 #29
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Hi @Austinzhenghua, Thanks for your feedback. Does torch_geometric==1.7.2 not work for you? You can take a try version 1.6.0/1.6.1 for this experiment. |
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Hi. can I have your we-chat to ask you some more detailed questions? hua zheng ***@***.*** 签名由 网易邮箱大师 定制 On 06/29/2021 21:38, Yuning You wrote: Hi @Austinzhenghua, Thanks for your feedback. Does torch_geometric==1.7.2 not work for you? You can take a try version 1.6.0/1.6.1 for this experiment. — You are receiving this because you were mentioned. Reply to this email directly, view it on GitHub, or unsubscribe.
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Just for a test, are you capable to run this https://github.com/fanyun-sun/InfoGraph/tree/master/unsupervised which the unsupervised_TU experiment is built on? |
Yes, I can run this algorithm, but it seems it didn't use GPU to train. The error above did cause by the version of torch_geometric. Can you run it in your computrer? Thanks a lot! |
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Traceback (most recent call last): I run it on the CPU get this error. |
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I find the shape of x is different from your algorithm and infograph. the first one is infograph. |
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It works well on my machine. What is the command u use? Please take a look at readme https://github.com/Shen-Lab/GraphCL/tree/master/unsupervised_TU#readme. |
I have the same error. Have you fixed it? |
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Hi @ztk1996, I remember I tested the command and it worked ok in my machine. Would you also share your environment and the command you run? |
Thanks for your reply. Error information when I run "./go.sh 1 AIDS subgraph" on CPU is as follows.
torch: 1.7.0 |
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Please take a try to run with torch-geometric==1.6.0 and on GPU. Since both of you use torch-geometric>=1.7.0 and on CPU, I guess it might be the source of error. |
I try to run with torch_geometric==1.6.0, pytorch==1.7.0 and on GPU. And the error information is as follows.
Besides, when I run with torch_geometric==1.6.0, pytorch==1.7.0 and on CPU. The error information is the same as run with torch_geometric==1.7.2. |
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My impression is that the version of torch_geometric and pytorch should be consistent (https://github.com/rusty1s/pytorch_geometric)? If using torch_geometric==1.6 I would also use pytorch==1.6. Please notify me if this also not works. Thanks. |
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lr: 0.01
num_features: 1
hidden_dim: 32
num_gc_layers: 3
/opt/conda/conda-bld/pytorch_1623448224956/work/aten/src/ATen/native/cuda/Indexing.cu:702: indexSelectLargeIndex: block: [158,0,0], thread: [105,0,0] Assertion
srcIndex < srcSelectDimSizefailed./opt/conda/conda-bld/pytorch_1623448224956/work/aten/src/ATen/native/cuda/Indexing.cu:702: indexSelectLargeIndex: block: [158,0,0], thread: [55,0,0] Assertion
srcIndex < srcSelectDimSizefailed./opt/conda/conda-bld/pytorch_1623448224956/work/aten/src/ATen/native/cuda/Indexing.cu:702: indexSelectLargeIndex: block: [158,0,0], thread: [56,0,0] Assertion
srcIndex < srcSelectDimSizefailed./opt/conda/conda-bld/pytorch_1623448224956/work/aten/src/ATen/native/cuda/Indexing.cu:702: indexSelectLargeIndex: block: [158,0,0], thread: [57,0,0] Assertion
srcIndex < srcSelectDimSizefailed./opt/conda/conda-bld/pytorch_1623448224956/work/aten/src/ATen/native/cuda/Indexing.cu:702: indexSelectLargeIndex: block: [158,0,0], thread: [58,0,0] Assertion
srcIndex < srcSelectDimSizefailed./opt/conda/conda-bld/pytorch_1623448224956/work/aten/src/ATen/native/cuda/Indexing.cu:702: indexSelectLargeIndex: block: [158,0,0], thread: [59,0,0] Assertion
srcIndex < srcSelectDimSizefailed./opt/conda/conda-bld/pytorch_1623448224956/work/aten/src/ATen/native/cuda/Indexing.cu:702: indexSelectLargeIndex: block: [158,0,0], thread: [60,0,0] Assertion
srcIndex < srcSelectDimSizefailed./opt/conda/conda-bld/pytorch_1623448224956/work/aten/src/ATen/native/cuda/Indexing.cu:702: indexSelectLargeIndex: block: [158,0,0], thread: [61,0,0] Assertion
srcIndex < srcSelectDimSizefailed.Traceback (most recent call last):
File "/home/zhenghua/pythoncode/unsupervised_graph_TU/gsimclr.py", line 190, in
emb, y = model.encoder.get_embeddings(dataloader_eval)
File "/home/zhenghua/pythoncode/unsupervised_graph_TU/gin.py", line 76, in get_embeddings
x, _ = self.forward(x, edge_index, batch)
File "/home/zhenghua/pythoncode/unsupervised_graph_TU/gin.py", line 52, in forward
x = F.relu(self.convs[i](x, edge_index))
File "/home/zhenghua/.conda/envs/pytorchgeo/lib/python3.7/site-packages/torch/nn/modules/module.py", line 1051, in call_impl
return forward_call(*input, **kwargs)
File "/home/zhenghua/.conda/envs/pytorchgeo/lib/python3.7/site-packages/torch_geometric/nn/conv/gin_conv.py", line 64, in forward
out = self.propagate(edge_index, x=x, size=size)
File "/home/zhenghua/.conda/envs/pytorchgeo/lib/python3.7/site-packages/torch_geometric/nn/conv/message_passing.py", line 253, in propagate
out = self.aggregate(out, **aggr_kwargs)
File "/home/zhenghua/.conda/envs/pytorchgeo/lib/python3.7/site-packages/torch_geometric/nn/conv/message_passing.py", line 288, in aggregate
reduce=self.aggr)
File "/home/zhenghua/.conda/envs/pytorchgeo/lib/python3.7/site-packages/torch_scatter/scatter.py", line 153, in scatter
return scatter_sum(src, index, dim, out, dim_size)
File "/home/zhenghua/.conda/envs/pytorchgeo/lib/python3.7/site-packages/torch_scatter/scatter.py", line 21, in scatter_sum
return out.scatter_add(dim, index, src)
RuntimeError: CUDA error: device-side assert triggered
CUDA kernel errors might be asynchronously reported at some other API call,so the stacktrace below might be incorrect.
For debugging consider passing CUDA_LAUNCH_BLOCKING=1.
Can anyone help me with what wrong with the algorithm or the enviroment?
the environment as follows:
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