pytorch LTS support (1.8.2) or stable (1.11.1)

Hello!

I was wondering if someone can confirm that this package still runs under pytroch lts or current stable (1.11.1)?

I’m getting a curious error. Note this is for CPU training. Maybe someone can confirm this is only broken under cpu training.

Thank you!

`03:44 $ python ./tasks/adding_task.py -lr 0.0001 -rnn_type lstm -memory_type sam -nlayer 1 -nhlayer 1 -nhid 100 -dropout 0 -mem_slot 1000 -mem_size 32 -read_heads 1 -sparse_reads 4 -batch_size 20 -optim rmsprop -input_size 3 -sequence_max_length 100 Namespace(batch_size=20, check_freq=100, clip=50, cuda=-1, dropout=0.0, input_size=3, iterations=2000, lr=0.0001, mem_size=32, mem_slot=1000, memory_type=‘sam’, nhid=100, nhlayer=1, nlayer=1, optim=‘rmsprop’, read_heads=1, rnn_type=‘lstm’, sequence_max_length=100, sparse_reads=4, summarize_freq=100, temporal_reads=2, visdom=False) Using CPU.

---

SAM(3, 100, num_hidden_layers=1, nr_cells=1000, read_heads=1, cell_size=32) SAM( (lstm_layer_0): LSTM(35, 100, batch_first=True) (rnn_layer_memory_shared): SparseMemory( (interface_weights): Linear(in_features=100, out_features=70, bias=True) ) (output): Linear(in_features=132, out_features=3, bias=True) )

Iteration 0/2000 Falling back to FLANN (CPU). For using faster, GPU based indexes, install FAISS: “conda install faiss-gpu -c pytorch” Traceback (most recent call last): File “./tasks/adding_task.py”, line 222, in <module> loss.backward() File “/home/eziegenbalg/.conda/envs/default/lib/python3.8/site-packages/torch/tensor.py”, line 245, in backward torch.autograd.backward(self, gradient, retain_graph, create_graph, inputs=inputs) File “/home/eziegenbalg/.conda/envs/default/lib/python3.8/site-packages/torch/autograd/init.py”, line 145, in backward Variable._execution_engine.run_backward( RuntimeError: one of the variables needed for gradient computation has been modified by an inplace operation: [torch.FloatTensor [1, 1000]], which is output 0 of AsStridedBackward, is at version 70; expected version 69 instead. Hint: enable anomaly detection to find the operation that failed to compute its gradient, with torch.autograd.set_detect_anomaly(True).

^C (default) ✘-INT ~/pytorch-dnc [master|✚ 2] 03:45 $ `

[ixaxaar]

AFAIK I did not really use the cpu training except while some testing. Anyway, I need to update pytorch support, lemme look at it this weekend.

[Marchetz]

Hi, I continue this issue to ask the same thing. In these days, I was trying to use SDNC and SAM architecture with GPU setting but I have many problems with FAISS and with related libraries and packages. Instead, DNC model works perfectly. I think that I have installed all the necessary package. I would like to know if these two archictetures support the new pytorch version. If everything works, it means that I’m wrong something during the installation process.

Thank you for the repository!!