RosettaCommons / RosettaCommons/foundry
RFD3 error with inference_sampler.allow_realignment option
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Description
I'm getting this error in RFdiffusion3 when running motif scaffolding with allow_realignment=True
foundry/utils/alignment.py:60 in weighted_rigid_align │
│ │
│ 57 │ # Computation of the covariance matrix │
│ 58 │ C = torch.einsum("bji,bjk->bik", w_resolved[..., None] * X_gt_resol │
│ 59 │ │
│ ❱ 60 │ U, S, V = torch.linalg.svd(C) │
│ 61 │ │
│ 62 │ R = U @ V │
│ 63 │ B, _, _ = X_L.shape │
RuntimeError: "svd_cuda_gesvdjBatched" not implemented for 'BFloat16'
I'm wondering if this is a bug or a mistake in my command
rfd3 design \
out_dir=test \
inputs=input.json \
dump_trajectories=True \
prevalidate_inputs=True \
inference_sampler.allow_realignment=True \
n_batches=1 \
diffusion_batch_size=1
with this example json file used as input:
{
"test": {
"input": "7v11.pdb",
"ligand": "OQO",
"unindex": "A431,A572-573",
"length": "200",
"select_fixed_atoms": {
"A431": "TIP",
"A572": "BKBN",
"A573": "BKBN"
}
}
}
The error does not occur when doing unconditional generation with allow_realignment=True set or when doing motif scaffolding without the argument.
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Research direction
Start at foundry/utils/alignment.py:60 and reproduce the provided rfd3 command with the example input. Compare the dtype paths for motif scaffolding with allow_realignment=True against unconditional generation and motif scaffolding without the option. Done means the reported BFloat16 SVD error no longer occurs for the supplied motif-scaffolding case.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python, pytorch
- Domain
- machine-learning
- Issue type
- Bug
- Difficulty
- 3/5
- Estimated time
- 1-2 days
- Activity status
- Stale
- Clarity
- Mostly clear
- Newbie friendliness
- 45/100