RuntimeError: There were no tensor arguments to this function (e.g., you passed an empty list of Tensors) ....

[Al-Dailami]

Hello

First, I would like to thank you for sharing the code of your awesome projects. I am trying to run your code and reproduce your experiments. Currently, I'm facing a problem.
Here are the errors and my fixes:

[0]

File ".../eICU-GNN-LSTM/graph_construction/create_bert_graph.py", line 19, in make_graph_bert
distances = torch.cdist(batch, bert, p=2.0, compute_mode='use_mm_for_euclid_dist_if_necessary')

RuntimeError: cdist only supports floating-point dtypes, X1 got: Byte

Fix:
changed dtype from ByteTensor to FloatTensor
File ".../eICU-GNN-LSTM/graph_construction/create_graph.py", line 15
dtype = torch.cuda.sparse.FloatTensor if device.type == 'cuda' else torch.sparse.FloatTensor

eICU-GNN-LSTM/graph_construction/create_graph.py

Line 15 in 5167eea

dtype = torch.cuda.sparse.ByteTensor if device.type == 'cuda' else torch.sparse.ByteTensor

File "/home/sale/eICU-GNN-LSTM/graph_construction/create_graph.py", line 65, in make_graph_penalise
s_pen = 5 * s - total_combined_diags # the 5 is fairly arbitrary but I don't want to penalise not sharing diagnoses too much

RuntimeError: The size of tensor a (89123) must match the size of tensor b (1000) at non-singleton dimension 1

Fix:
File ".../eICU-GNN-LSTM/graph_construction/create_graph.py", line 194

u, v, vals, k = make_graph_penalise(all_diagnoses, scores, debug=False, k=args.k) ############### debug=False Fixes problem

eICU-GNN-LSTM/graph_construction/create_graph.py

Line 194 in 5167eea

u, v, vals, k = make_graph_penalise(all_diagnoses, scores, k=args.k)

[1]

File "../projects/eICU-GNN-LSTM/src/models/pyg_ns.py", line 241, in inference
edge_attn = torch.cat(edge_attn, dim=0) # [no. of edges, n_heads of that layer]

RuntimeError: There were no tensor arguments to this function (e.g., you passed an empty list of Tensors), but no fallback function is registered for schema aten::_cat. This usually means that this function requires a non-empty list of Tensors. Available functions are [CPU, CUDA, QuantizedCPU, BackendSelect, Named, AutogradOther, AutogradCPU, AutogradCUDA, AutogradXLA, AutogradPrivateUse1, AutogradPrivateUse2, AutogradPrivateUse3, Tracer, Autocast, Batched, VmapMode].

Fix:
if i == 1 and get_attn:
edge_index_w_self_loops = torch.cat(edge_index_w_self_loops, dim=1) # [2, n. of edges]
if get_attn:
edge_attn = torch.cat(edge_attn, dim=0) # [no. of edges, n_heads of that layer]
all_edge_attn.append(edge_attn)

eICU-GNN-LSTM/src/models/pyg_ns.py

Line 241 in 5167eea

edge_attn = torch.cat(edge_attn, dim=0) # [no. of edges, n_heads of that layer]

[2]

File "../eICU-GNN-LSTM/train_ns_lstmgnn.py", line 94, in validation_step
out = out[self.dataset.data.val_mask]
TypeError: only integer tensors of a single element can be converted to an index

Fix:
out = out[0][self.dataset.data.val_mask]

eICU-GNN-LSTM/train_ns_lstmgnn.py

Line 94 in 5167eea

out = out[self.dataset.data.val_mask]

[3]

ValueError: Input contains NaN, infinity or a value too large for dtype('float32').

Fix:
In the same file "../eICU-GNN-LSTM/train_ns_lstmgnn.py" line 96 Added the following lines:
out[out != out] = 0
out_lstm[out_lstm != out_lstm] = 0

eICU-GNN-LSTM/train_ns_lstmgnn.py

Line 94 in 5167eea

out = out[self.dataset.data.val_mask]

because when I print those matrices found some NaN values.

After this, the code starts training. BUT with wired training progress (loss always nan) !!!
Print out the output matrices found that it's always NANs !!!

acc: 0.9049
prec0: 0.9049
prec1: nan
rec0: 1.0000
rec1: 0.0000
auroc: 0.5000
auprc: 0.5476
minpse: 0.0951
f1macro: 0.4750
Epoch 1: 92%|█████████████████████████████████████████████████████████████████████████████████▎ | 452/489 [00:35<00:02, 12.78it/s, loss=nan, v_num=83]

I tried to trace the source of the error and the NaNs come after lstm layer this line:

eICU-GNN-LSTM/src/models/lstm.py

Line 39 in 5167eea

out, _ = self.lstm_encoder.forward(seq)

Please correct me if I'm wrong ... Thanks a lot in advance...

Note: I have used the same version of packages listed on the requirements.txt file

[EmmaRocheteau]

Hello! Thanks for the detailed comment. I’ll work through the code and tell you what outputs I should get. I won’t have time this weekend though, so I’ll get back to you after that!

[Al-Dailami]

Thanks for your reply,

One more error, this is the first one I got.

File ".../eICU-GNN-LSTM/src/dataloader/convert.py", line 75, in convert_into_mmap
write_file[n : n+arr_len, :] = arr # write into mmap
ValueError: could not broadcast input array from shape (62385,92) into shape (62385,57)

eICU-GNN-LSTM/src/dataloader/convert.py

Line 55 in 5167eea

csv_to_cols = {'diagnoses': 520, 'diagnoses_1033': 1034, 'labels': 5, 'flat': 58} # including patient column

These numbers are different from the ones I got after running the preprocessing code.
diagnosis= 356, labels=5, and flat=93

Fixed by:
df = pd.read_csv(csv_path)

n_cols = (df.shape[1] -1) if n_cols is None else n_cols

eICU-GNN-LSTM/src/dataloader/convert.py

Line 56 in 5167eea

n_cols = (csv_to_cols[csv_name] -1) if n_cols is None else n_cols

[EmmaRocheteau]

Thank you for sharing! I will be looking into this soon. Apologies for the incredibly long delay in getting back and I appreciate you sharing the solution here