Fix to make Directed graph to Undirected

[aditya0by0]

• Clarification on Directed vs Undirected Graph Construction in _read_data of GraphPropertyReader #6

[aditya0by0]

Tried running the normal GNN model with undirected graph and looks like for undirected graph the edge attributes needs to twice its number to able to be mapped to undirected edge (which is present as directed in both direction in edge_index).
Also need to check the mapping of right edge_attribute to right edge.

https://wandb.ai/chebai/chebai/runs/fu1tcxf4/logs

[rank0]:     return self._call_impl(*args, **kwargs)
[rank0]:   File "/home/staff/a/akhedekar/miniconda3/envs/gnn/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1750, in _call_impl
[rank0]:     return forward_call(*args, **kwargs)
[rank0]:   File "/home/staff/a/akhedekar/miniconda3/envs/gnn/lib/python3.10/site-packages/torch_geometric/nn/conv/res_gated_graph_conv.py", line 128, in forward
[rank0]:     out = self.propagate(edge_index, k=k, q=q, v=v, edge_attr=edge_attr)
[rank0]:   File "/home/staff/a/akhedekar/atmp_dir/torch_geometric.nn.conv.res_gated_graph_conv_ResGatedGraphConv_propagate_x5frmxhf.py", line 231, in propagate
[rank0]:     out = self.message(
[rank0]:   File "/home/staff/a/akhedekar/miniconda3/envs/gnn/lib/python3.10/site-packages/torch_geometric/nn/conv/res_gated_graph_conv.py", line 144, in message
[rank0]:     k_i = self.lin_key(torch.cat([k_i, edge_attr], dim=-1))
[rank0]: RuntimeError: Sizes of tensors must match except in dimension 1. Expected size 2438 but got size 1219 for tensor number 1 in the list.

[aditya0by0]

Training has been started for this change : https://wandb.ai/chebai/chebai/runs/9xjpb6wi?nw=nwuseraditya0by0

Another job started with 2 gpus: https://wandb.ai/chebai/chebai/runs/ejg3ksex?nw=nwuseraditya0by0

[aditya0by0]

@sfluegel05,

The training seems to be quite slow. I'm wondering if all of the following properties were actually used in the original training setup. Could you please share the corresponding Weights & Biases (wandb) link for the original run?

Encoding lengths are: 
[('AtomAromaticity', 1), 
 ('AtomCharge', 13), 
 ('AtomHybridization', 7), 
 ('AtomNumHs', 7), 
 ('AtomType', 119), 
 ('BondAromaticity', 1), 
 ('BondInRing', 1), 
 ('BondType', 5), 
 ('NumAtomBonds', 11), 
 ('RDKit2DNormalized', 200)]

[sfluegel05]

Hi, I can confirm that the properties were used in actual runs, e.g. this one: https://wandb.ai/chebai/chebai/runs/cxmgl4eb (the technical setup is not the same one we use now, making it hard to compare, but I would expect it to get better with our current setup, not worse).

The bottleneck for this model is the creation of the dataset (especially RDKit2DNormalized). But one that is done, I would expect normal-ish behaviour during training.

[aditya0by0]

@sfluegel05, Please find the training below for this fix.
https://wandb.ai/chebai/chebai/runs/7h1icve9?nw=nwuseraditya0by0

Please review and merge.

[sfluegel05]

I'm not sure what the run is telling me. Training seems to be doing fine, but the macro-f1 is a few percent lower compared to https://wandb.ai/chebai/chebai/runs/0oksfx9u

Does that mean that undirected is worse than directed? Or am I missing something here?

[aditya0by0]

I noticed that you have mentioned a particular comment on this run. Is there something different you did with the token limit?

[aditya0by0]

Also, for your run, have you changed the matmul precision?. For my runs, I haven't changed anything particular to precision. So the default precision for my run is "highest".

You are using a CUDA device ('NVIDIA H100 80GB HBM3') that has Tensor Cores. To properly utilize them, you should set `torch.set_float32_matmul_precision('medium' | 'high')` which will trade-off precision for performance. For more details, read https://pytorch.org/docs/stable/generated/torch.set_float32_matmul_precision.html#torch.set_float32_matmul_precision
Initializing distributed: GLOBAL_RANK: 0, MEMBER: 1/1

[sfluegel05]

I noticed that you have mentioned a particular comment on this run. Is there something different you did with the token limit?
The token_limit was only for the Electra model, not for the GNN. So no token limit was applied

Also, for your run, have you changed the matmul precision?. For my runs, I haven't changed anything particular to precision. So the default precision for my run is "highest".

I have been using the default precision for my run as well (32-true)

[aditya0by0]

Some of the code repository of the corresponding research papers which used undirected graph for chemical bonds.
https://github.com/snap-stanford/pretrain-gnns/blob/8b20528a83b8869ce16451305b32c827258d19a3/chem/loader.py#L72-L86
https://github.com/snap-stanford/pretrain-gnns/blob/8b20528a83b8869ce16451305b32c827258d19a3/bio/loader.py#L56-L68
https://github.com/ZangXuan/HiMol/blob/main/data_utils.py#L179-L194

[aditya0by0]

Directed: https://wandb.ai/chebai/chebai/runs/5yhpkxci/overview
Undirected: https://wandb.ai/chebai/chebai/runs/dlt1iug5/overview

Metric	Directed	Undirected
Train Loss (epoch)	0.00087	0.00140
Train Loss (step)	0.00077	0.00218
Train Macro-F1	0.9603	0.9274
Train Micro-F1	0.9903	0.9830
Global Step	62,799	62,799
Val Loss (epoch)	0.02067	0.01783
Val Loss (step)	0.01475	0.00697
Val Macro-F1	0.6810	0.6635
Val Micro-F1	0.9094	0.9067

[aditya0by0]

I repeated the training on the same GPU type after making the training deterministic, and the results are consistent with the earlier observation:
➡️ Directed graphs outperform undirected graphs.

Undirected Graph (Deterministic runs):

• Seed 12345
• Seed 24
• Seed 0

Directed Graph (Deterministic runs):

• Seed 12345
• Seed 24
• Seed 0

[aditya0by0]

I have few hypothesis for why directed graph perform better than undirected for an GNN end to end classification task.

• Two undirected graphs with the same molecular structure ( but with different atom types and node features) are more likely to produce similar graph-level representations after 5 GNN convolution layers, compared to their directed counterparts.
• This is because undirected graphs inherently preserve more structural symmetry, making distinct molecules appear isomorphic to the model, especially in the absence of rich node features. Consequently, undirected graphs increase the likelihood of representation collapse, where different molecules are mapped to similar embeddings.

• Additionally, atoms with the same number and type of neighbors (e.g., carbons in aromatic rings) are more prone to receiving identical embeddings in undirected graphs due to symmetric message passing, particularly over deeper GNN stacks (e.g., 5 layers).
• In contrast, directed graphs break this symmetry, allowing for more diverse and discriminative representations, even when directionality is assigned arbitrarily, meaning now the under directed graph the same atom will have different/less number of neighbors even though of same type. Hence, the amount of aggregation is reduced for same number of convolution layers.

Eg: Aspirin molecule (information flow from left to right due internal logic rdkit atom index numbers

Bonds:
	Bond index: 0, Atoms: (0, 1), Type: SINGLE
	Bond index: 1, Atoms: (1, 2), Type: DOUBLE
	Bond index: 2, Atoms: (1, 3), Type: SINGLE
	Bond index: 3, Atoms: (3, 4), Type: SINGLE
	Bond index: 4, Atoms: (4, 5), Type: AROMATIC
	Bond index: 5, Atoms: (5, 6), Type: AROMATIC
...
	Bond index: 9, Atoms: (9, 10), Type: SINGLE
	Bond index: 10, Atoms: (10, 11), Type: DOUBLE
	Bond index: 11, Atoms: (10, 12), Type: SINGLE
	Bond index: 12, Atoms: (9, 4), Type: AROMATIC