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perf: reduce GPU memory usage during inference #447

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4 changes: 1 addition & 3 deletions rfdiffusion/inference/utils.py
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Original file line number Diff line number Diff line change
Expand Up @@ -6,7 +6,7 @@
from rfdiffusion.diffusion import get_beta_schedule
from scipy.spatial.transform import Rotation as scipy_R
from rfdiffusion.util import rigid_from_3_points
from rfdiffusion.util_module import ComputeAllAtomCoords

from rfdiffusion import util
import random
import logging
Expand Down Expand Up @@ -152,7 +152,6 @@ def get_next_ca(
noise_scale: scale factor for the noise being added

"""
get_allatom = ComputeAllAtomCoords().to(device=xt.device)
L = len(xt)

# bring to origin after global alignment (when don't have a motif) or replace input motif and bring to origin, and then scale
Expand Down Expand Up @@ -435,7 +434,6 @@ def get_next_pose(
include_motif_sidechains (bool): Provide sidechains of the fixed motif to the model
"""

get_allatom = ComputeAllAtomCoords().to(device=xt.device)
L, n_atom = xt.shape[:2]
assert (xt.shape[1] == 14) or (xt.shape[1] == 27)
assert (px0.shape[1] == 14) or (px0.shape[1] == 27)
Expand Down
11 changes: 7 additions & 4 deletions scripts/run_inference.py
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Expand Up @@ -94,10 +94,13 @@ def main(conf: HydraConfig) -> None:
px0, x_t, seq_t, plddt = sampler.sample_step(
t=t, x_t=x_t, seq_init=seq_t, final_step=sampler.inf_conf.final_step
)
px0_xyz_stack.append(px0)
denoised_xyz_stack.append(x_t)
seq_stack.append(seq_t)
plddt_stack.append(plddt[0]) # remove singleton leading dimension
# Move trajectory data to CPU immediately to free GPU memory
# for the next denoising step. x_t/seq_t are cloned inside
# sample_step, so moving previous outputs to CPU is safe.
px0_xyz_stack.append(px0.cpu())
denoised_xyz_stack.append(x_t.cpu())
seq_stack.append(seq_t.cpu())
plddt_stack.append(plddt[0].cpu()) # remove singleton leading dimension

# Flip order for better visualization in pymol
denoised_xyz_stack = torch.stack(denoised_xyz_stack)
Expand Down