RoseTTAFold-All-Atom/rf2aa/training/recycling.py

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2024-03-05 06:38:17 +00:00
import torch
import torch.nn as nn
import numpy as np
from contextlib import ExitStack
from rf2aa.chemical import ChemicalData as ChemData
def recycle_step_legacy(ddp_model, input, n_cycle, use_amp, nograds=False, force_device=None):
if force_device is not None:
gpu = force_device
else:
gpu = ddp_model.device
xyz_prev, alpha_prev, mask_recycle = \
input["xyz_prev"], input["alpha_prev"], input["mask_recycle"]
output_i = (None, None, xyz_prev, alpha_prev, mask_recycle)
for i_cycle in range(n_cycle):
with ExitStack() as stack:
stack.enter_context(torch.cuda.amp.autocast(enabled=use_amp))
if i_cycle < n_cycle -1 or nograds is True:
stack.enter_context(torch.no_grad())
if force_device is None:
stack.enter_context(ddp_model.no_sync())
return_raw = (i_cycle < n_cycle -1)
use_checkpoint = not nograds and (i_cycle == n_cycle -1)
input_i = add_recycle_inputs(input, output_i, i_cycle, gpu, return_raw=return_raw, use_checkpoint=use_checkpoint)
output_i = ddp_model(**input_i)
return output_i
def run_model_forward_legacy(model, network_input, device="cpu"):
""" run model forward pass, no recycling or ddp with legacy model (for tests)"""
gpu = device
xyz_prev, alpha_prev, mask_recycle = \
network_input["xyz_prev"], network_input["alpha_prev"], network_input["mask_recycle"]
output_i = (None, None, xyz_prev, alpha_prev, mask_recycle)
input_i = add_recycle_inputs(network_input, output_i, 0, gpu, return_raw=False, use_checkpoint=False)
input_i["seq_unmasked"] = input_i["seq_unmasked"].to(gpu)
input_i["sctors"] = input_i["sctors"].to(gpu)
model.eval()
with torch.no_grad():
output_i = model(**input_i)
return output_i
def add_recycle_inputs(network_input, output_i, i_cycle, gpu, return_raw=False, use_checkpoint=False):
input_i = {}
for key in network_input:
if key in ['msa_latent', 'msa_full', 'seq']:
input_i[key] = network_input[key][:,i_cycle].to(gpu, non_blocking=True)
else:
input_i[key] = network_input[key]
L = input_i["msa_latent"].shape[2]
msa_prev, pair_prev, _, alpha, mask_recycle = output_i
xyz_prev = ChemData().INIT_CRDS.reshape(1,1,ChemData().NTOTAL,3).repeat(1,L,1,1).to(gpu, non_blocking=True)
input_i['msa_prev'] = msa_prev
input_i['pair_prev'] = pair_prev
input_i['xyz'] = xyz_prev
input_i['mask_recycle'] = mask_recycle
input_i['sctors'] = alpha
input_i['return_raw'] = return_raw
input_i['use_checkpoint'] = use_checkpoint
input_i.pop('xyz_prev')
input_i.pop('alpha_prev')
return input_i