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Enable AMP Compatibility for GCP Model to Reduce VRAM Usage #102

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Merged
merged 3 commits into from
Apr 27, 2025
Merged

Enable AMP Compatibility for GCP Model to Reduce VRAM Usage #102

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d7bd205
fix: gcp.py support amp
mahdip72 Apr 26, 2025
4f2bfa7
docs: add changelog entry for AMP compatibility fix (#102)
mahdip72 Apr 27, 2025
4b4f3d0
docs: add pull request link
mahdip72 Apr 27, 2025
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1 change: 1 addition & 0 deletions CHANGELOG.md
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Original file line number Diff line number Diff line change
Expand Up @@ -16,6 +16,7 @@
* Add support for handling backward OOMs gracefully [#83](https://github.com/a-r-j/ProteinWorkshop/pull/83)
* Update GCPNet paper link [#85](https://github.com/a-r-j/ProteinWorkshop/pull/85)
* Add ability for `BenchmarkModel` to have its decoder disabled [#101](https://github.com/a-r-j/ProteinWorkshop/pull/101)
* Fix dtype mismatch in `gcp.py` that broke Automatic Mixed Precision (AMP) training [#102](https://github.com/a-r-j/ProteinWorkshop/pull/102)

### Framework

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8 changes: 7 additions & 1 deletion proteinworkshop/models/graph_encoders/layers/gcp.py
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Expand Up @@ -265,14 +265,20 @@ def scalarize(

if node_mask is not None:
edge_mask = node_mask[row] & node_mask[col]
# Initialize destination tensor
local_scalar_rep_i = torch.zeros(
(edge_index.shape[1], 3, 3), device=edge_index.device
)
local_scalar_rep_i[edge_mask] = torch.matmul(
# Calculate the source value (result of matmul, likely Half under AMP)
matmul_result = torch.matmul(
frames[edge_mask], vector_rep_i[edge_mask]
)
# Explicitly cast the source value to the destination's dtype before assignment
local_scalar_rep_i[edge_mask] = matmul_result.to(local_scalar_rep_i.dtype)

local_scalar_rep_i = local_scalar_rep_i.transpose(-1, -2)
else:
# This path might need similar treatment if it causes issues
local_scalar_rep_i = torch.matmul(frames, vector_rep_i).transpose(-1, -2)

# potentially enable E(3)-equivariance and, thereby, chirality-invariance
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