import torch from icecream import ic import random import numpy as np from kinematics import get_init_xyz import torch.nn as nn from util_module import ComputeAllAtomCoords from util import * from inpainting_util import MSAFeaturize_fixbb, TemplFeaturizeFixbb, lddt_unbin from kinematics import xyz_to_t2d def mask_inputs_RFnar(seq, msa_masked, msa_full, xyz_t, t1d, input_seq_mask=None, input_str_mask=None, input_t1dconf_mask=None, nar= None, t=None, T= None, mask_seq_token=True, mask_seq_random=False, mask_xyz_hole=True, mask_xyz_random=False): """ RFnar inference """ ic(seq.shape) seq = seq[0,:1] ic(seq.shape) msa_masked = msa_masked[0,:1] msa_full = msa_full[0,:1] t1d = t1d[0] xyz_t = xyz_t[0] seq_mask = ~input_seq_mask[0] ic(seq_mask.shape) str_mask = ~input_str_mask[0] #nar = Nonautoregressive() r = (T-t)/T print(f'USING THIS R: {r}') #mask sequence if mask_seq_token: print("MASK SEQ TOKEN") seq_corrupt = seq.clone() seq_corrupt[:,seq_mask] = 21 ic(seq) ic(seq_corrupt) elif mask_seq_random: print("MASK SEQ RANDOM") ic(seq_corrupt) seq=seq_corrupt.repeat(seq.shape[0], 1) ic(seq.shape) seq_corrupt_onehot=torch.nn.functional.one_hot(seq_corrupt,num_classes=22).float()[0] ic(seq_corrupt_onehot.shape) ### msa_masked ### ic(msa_masked.shape) B,N,L,_=msa_masked.shape msa_masked[:,0,:,:22] = seq_corrupt_onehot msa_seq_mask = seq_mask.unsqueeze(0).repeat(N-1, 1) #msa_masked[:,1:,:,:22] = torch.clone(msa_diffused) # index 44/45 is insertion/deletion # index 43 is the masked token NOTE check this # index 42 is the unknown token msa_masked[:,0,:,22:44] = seq_corrupt_onehot #msa_masked[:,1:,:,22:44] = msa_diffused # insertion/deletion stuff msa_masked[:,0,~seq_mask,44:46] = 0 # msa_full # #make msa_full same size as msa_masked ic(msa_full.shape) msa_full = msa_full[:,:msa_masked.shape[1],:,:] msa_full[:,0,:,:22] = seq_corrupt_onehot #msa_full[:,1:,:,:22] = msa_diffused ########### ### t1d ### ########### # NOTE: adjusting t1d last dim (confidence) from sequence mask t1d = torch.cat((t1d, torch.zeros((t1d.shape[0],t1d.shape[1],2)).float()), -1).to(seq.device) t1d[:,:,:21] = seq_corrupt_onehot[...,:21] #t1d[:,:,21] *= input_t1dconf_mask #set diffused conf to 0 and everything else to 1 t1d[:,seq_mask,21] = 0.0 t1d[:,~seq_mask,21] = 1.0 t1d[:,str_mask,23] = 0.0 t1d[:,~str_mask,23] = 1.0 t1d[:1,:,22] = r ################ #mask structure# ################ if mask_xyz_hole: print("MASK XYZ BLACK HOLE") ic(xyz_t.shape) xyz_corrupt, xyz_mask = nar.xyz_mask_0(xyz_t[0], r, seq_mask = seq_mask, seq_xyz_mask_same = True) ic(xyz_corrupt.shape) elif mask_xyz_random: print("MASK XYZ RANDOM") xyz_corrupt, xyz_mask = nar.xyz_mask_random(xyz_t[0], r, seq_mask = seq_mask, seq_xyz_mask_same = True) #only corrupt first template xyz_t[0]=xyz_corrupt assert torch.sum(torch.isnan(xyz_t[:,:,:3,:]))==0 seq_diffused = seq_corrupt_onehot return seq, msa_masked, msa_full, xyz_t, t1d, seq_diffused def mask_inputs(seq, msa_masked, msa_full, xyz_t, t1d, input_seq_mask=None, input_str_mask=None, input_t1dconf_mask=None, diffuser=None, t=None, T=None, RFnar = False): """ JG - adapted slightly for the inference case Parameters: seq (torch.tensor, required): (I,L) integer sequence msa_masked (torch.tensor, required): (I,N_short,L,48) msa_full (torch,.tensor, required): (I,N_long,L,25) xyz_t (torch,tensor): (T,L,27,3) template crds BEFORE they go into get_init_xyz t1d (torch.tensor, required): (I,L,22) this is the t1d before tacking on the chi angles str_mask_1D (torch.tensor, required): Shape (L) rank 1 tensor where structure is masked at False positions seq_mask_1D (torch.tensor, required): Shape (L) rank 1 tensor where seq is masked at False positions t1d_24: is there an extra dimension to input structure confidence? diffuser: diffuser class t: time step NOTE: in the MSA, the order is 20aa, 1x unknown, 1x mask token. We set the masked region to 22 (masked). For the t1d, this has 20aa, 1x unkown, and 1x template conf. Here, we set the masked region to 21 (unknown). This, we think, makes sense, as the template in normal RF training does not perfectly correspond to the MSA. """ if RFnar: return mask_inputs_RFnar(seq, msa_masked, msa_full, xyz_t, t1d, input_seq_mask=input_seq_mask, input_str_mask=input_str_mask, input_t1dconf_mask=input_t1dconf_mask, nar=diffuser, t=t, T=T) assert diffuser != None, 'please choose a diffuser' ########### seq = seq[0,:1] msa_masked = msa_masked[0,:1] msa_full = msa_full[0,:1] t1d = t1d[0] xyz_t = xyz_t[0] seq_mask = input_seq_mask[0] ###################### ###sequence diffusion### ###################### """ #muate some percentage of sequence to have model be able to mutate residues later in denoising trajectory if True: masked_values=input_seq_mask[0].nonzero()[:,0] print(masked_values) mut_p=math.floor(masked_values.shape[0]*.05) print(mut_p) mutate_indices = torch.randperm(len(masked_values))[:mut_p] print(mutate_indices) for i in range(len(mutate_indices)): seq[0,masked_values[mutate_indices[i]]] = torch.randint(0, 21, (1,)) """ str_mask = input_str_mask[0] x_0 = torch.nn.functional.one_hot(seq[0,...],num_classes=22).float()*2-1 #ic(seq_mask) seq_diffused = diffuser.q_sample(x_0,torch.tensor([t-1]),mask=seq_mask) #seq_diffused = torch.clamp(seq_diffused, min=-1, max=1) seq_tmp=torch.argmax(seq_diffused,axis=-1).to(device=seq.device) seq=seq_tmp.repeat(seq.shape[0], 1) ################### ###msa diffusion### ################### ### msa_masked ### #ic(msa_masked.shape) B,N,L,_=msa_masked.shape msa_masked[:,0,:,:22] = seq_diffused x_0_msa = msa_masked[0,1:,:,:22].float()*2-1 msa_seq_mask = seq_mask.unsqueeze(0).repeat(N-1, 1) msa_diffused = diffuser.q_sample(x_0_msa,torch.tensor([t-1]),mask=msa_seq_mask) #msa_diffused = torch.clamp(msa_diffused, min=-1, max=1) msa_masked[:,1:,:,:22] = torch.clone(msa_diffused) # index 44/45 is insertion/deletion # index 43 is the masked token NOTE check this # index 42 is the unknown token msa_masked[:,0,:,22:44] = seq_diffused msa_masked[:,1:,:,22:44] = msa_diffused # insertion/deletion stuff msa_masked[:,0,~seq_mask,44:46] = 0 ### msa_full ### ################ #msa_full[:,0,:,:22] = seq_diffused #make msa_full same size as msa_masked msa_full = msa_full[:,:msa_masked.shape[1],:,:] msa_full[:,0,:,:22] = seq_diffused msa_full[:,1:,:,:22] = msa_diffused ### t1d ### ########### # NOTE: adjusting t1d last dim (confidence) from sequence mask t1d = torch.cat((t1d, torch.zeros((t1d.shape[0],t1d.shape[1],2)).float()), -1).to(seq.device) t1d[:,:,:21] = seq_diffused[...,:21] #t1d[:,:,21] *= input_t1dconf_mask #set diffused conf to 0 and everything else to 1 t1d[:,~seq_mask,21] = 0.0 t1d[:,seq_mask,21] = 1.0 t1d[:1,:,22] = 1-t/diffuser.num_timesteps t1d[:,~str_mask,23] = 0.0 t1d[:,str_mask,23] = 1.0 xyz_t = get_init_xyz(xyz_t[None]) xyz_t = xyz_t[0] xyz_t[:,~seq_mask,3:,:] = float('nan') # Structure masking xyz_t[:,~str_mask,:,:] = float('nan') xyz_t = get_init_xyz(xyz_t[None]) xyz_t = xyz_t[0] assert torch.sum(torch.isnan(xyz_t[:,:,:3,:]))==0 return seq, msa_masked, msa_full, xyz_t, t1d, seq_diffused conversion = 'ARNDCQEGHILKMFPSTWYVX-' def take_step(model, msa, msa_extra, seq, t1d, t2d, idx_pdb, N_cycle, xyz_prev, alpha, xyz_t, alpha_t, params, T, diffuser, seq_diffused, msa_prev, pair_prev, state_prev): """ Single step in the diffusion process """ compute_allatom_coords=ComputeAllAtomCoords().to(seq.device) #ic(msa.shape) B, _, N, L, _ = msa.shape with torch.no_grad(): with torch.cuda.amp.autocast(True): for i_cycle in range(N_cycle-1): msa_prev, pair_prev, xyz_prev, state_prev, alpha = model(msa[:,0], msa_extra[:,0], seq[:,0], xyz_prev, idx_pdb, seq1hot=seq_diffused, t1d=t1d, t2d=t2d, xyz_t=xyz_t, alpha_t=alpha_t, msa_prev=msa_prev, pair_prev=pair_prev, state_prev=state_prev, return_raw=True) logit_s, logit_aa_s, logits_exp, xyz_prev, pred_lddt, msa_prev, pair_prev, state_prev, alpha = model(msa[:,0], msa_extra[:,0], seq[:,0], xyz_prev, idx_pdb, seq1hot=seq_diffused, t1d=t1d, t2d=t2d, xyz_t=xyz_t, alpha_t=alpha_t, msa_prev=msa_prev, pair_prev=pair_prev, state_prev=state_prev, return_infer=True) #ic(logit_aa_s.shape) logit_aa_s_msa = torch.clone(logit_aa_s) logit_aa_s = logit_aa_s.reshape(B,-1,N,L)[:,:,0,:] #ic(logit_aa_s.shape) logit_aa_s = logit_aa_s.reshape(B,-1,L) #ic(logit_aa_s.shape) seq_out = torch.argmax(logit_aa_s, dim=-2) #ic(seq_out.shape) #ic(alpha.shape) pred_lddt_unbinned = lddt_unbin(pred_lddt) _, xyz_prev = compute_allatom_coords(seq_out, xyz_prev, alpha) if N>1: return seq_out, xyz_prev, pred_lddt_unbinned, logit_s, logit_aa_s, logit_aa_s_msa, alpha, msa_prev, pair_prev, state_prev else: return seq_out, xyz_prev, pred_lddt_unbinned, logit_s, logit_aa_s, alpha, msa_prev, pair_prev, state_prev def take_step_nostate(model, msa, msa_extra, seq, t1d, t2d, idx_pdb, N_cycle, xyz_prev, alpha, xyz_t, alpha_t, params, T, diffuser, seq_diffused, msa_prev, pair_prev, state_prev): """ Single step in the diffusion process, with no conditioning on state """ compute_allatom_coords=ComputeAllAtomCoords().to(seq.device) #ic(msa.shape ) msa_prev = None pair_prev = None state_prev = None B, _, N, L, _ = msa.shape with torch.no_grad(): with torch.cuda.amp.autocast(True): for i_cycle in range(N_cycle-1): msa_prev, pair_prev, xyz_prev, state_prev, alpha = model(msa[:,0], msa_extra[:,0], seq[:,0], xyz_prev, idx_pdb, seq1hot=seq_diffused, t1d=t1d, t2d=t2d, xyz_t=xyz_t, alpha_t=alpha_t, msa_prev=msa_prev, pair_prev=pair_prev, state_prev=state_prev, return_raw=True) logit_s, logit_aa_s, logits_exp, xyz_prev, pred_lddt, msa_prev, pair_prev, state_prev, alpha = model(msa[:,0], msa_extra[:,0], seq[:,0], xyz_prev, idx_pdb, seq1hot=seq_diffused, t1d=t1d, t2d=t2d, xyz_t=xyz_t, alpha_t=alpha_t, msa_prev=msa_prev, pair_prev=pair_prev, state_prev=state_prev, return_infer=True) #ic(xyz_prev.shape) #xyz_prev = xyz_prev[-1] #ic(xyz_prev.shape) #ic(logit_aa_s.shape) logit_aa_s_msa = torch.clone(logit_aa_s) logit_aa_s = logit_aa_s.reshape(B,-1,N,L)[:,:,0,:] #ic(logit_aa_s.shape) logit_aa_s = logit_aa_s.reshape(B,-1,L) #ic(logit_aa_s.shape) #ic(t1d.shape) t1d[:,:,:,:21] = logit_aa_s[0,:21,:].permute(1,0) seq_out = torch.argmax(logit_aa_s, dim=-2) #ic(seq_out.shape) #ic(alpha.shape) pred_lddt_unbinned = lddt_unbin(pred_lddt) _, xyz_prev = compute_allatom_coords(seq_out, xyz_prev, alpha) if N>1: return seq_out, xyz_prev, pred_lddt_unbinned, logit_s, logit_aa_s, logit_aa_s_msa, alpha, msa_prev, pair_prev, state_prev else: return seq_out, xyz_prev, pred_lddt_unbinned, logit_s, logit_aa_s, alpha, msa_prev, pair_prev, state_prev