Fixed GatedGCN in float16. Minor cleanups.

[DomInvivo]

Changelogs

• Added an eps parameter to the GatedGCNPyg layer, with a default value of 1e-3 since 1e-6 was causing numerical issues in float16.
• Removed some useless lines of code from the FullGraphMultiTaskNetwork.forward

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Checklist:

• Was this PR discussed in an issue? It is recommended to first discuss a new feature into a GitHub issue before opening a PR.
• Add tests to cover the fixed bug(s) or the new introduced feature(s) (if appropriate).
• Update the API documentation is a new function is added, or an existing one is deleted.
• Write concise and explanatory changelogs above.
• If possible, assign one of the following labels to the PR: feature, fix or test (or ask a maintainer to do it for you).

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discussion related to that PR

[codecov]

Codecov Report

Merging #426 (c7966f0) into main (57399b3) will decrease coverage by 0.03%.
Report is 14 commits behind head on main.
The diff coverage is 72.72%.

@@            Coverage Diff             @@
##             main     #426      +/-   ##
==========================================
- Coverage   66.91%   66.88%   -0.03%     
==========================================
  Files          82       82              
  Lines        7819     7821       +2     
==========================================
- Hits         5232     5231       -1     
- Misses       2587     2590       +3     

Flag	Coverage Δ
unittests	66.88% <72.72%> (-0.03%)	⬇️

Flags with carried forward coverage won't be shown. Click here to find out more.

Components	Coverage Δ
ipu	49.14% <ø> (ø)

[notion-workspace]

Fix the GatedGCN model

[callumm-graphcore]

I don't understand why this was in the code in the first place?

[DomInvivo]

hahaha, My guess is that, when killing DGL in favor for PyG only, we did a ctrl+F to replace all calls to g.get_node_feats("feat") by g["feat"]

[callumm-graphcore]

GatedGCN part looks fine to me - have you tried to train in 16-true with this change?

[DomInvivo]

GatedGCN part looks fine to me - have you tried to train in 16-true with this change?

I don't have access to an IPU to train with 16-true. I can only do mixed on GPU