model/utils/.ipynb_checkpoints/diff_utils-checkpoint.py

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(seq, msa_masked, msa_full, xyz_t, t1d, input_seq_mask=None, 
        input_str_mask=None, input_t1dconf_mask=None, diffuser=None, t=None):
    """
    RFnar inference
    """
    seq_mask = ~input_seq_mask
    str_mask = input_str_mask
    nar = Nonautoregressive()
    r = t
    print(f'USING THIS R: {r}')
    
    #mask sequence
    if mask_seq_token:
        print("MASK SEQ TOKEN")
        seq_corrupt[seq_mask] = self.masked_token
    elif mask_seq_random:
        print("MASK SEQ RANDOM")
        
        
    ic(seq_corrupt)
        
    seq=seq_corrupt.repeat(seq.shape[0], 1)
    seq_corrupt_onehot=torch.nn.functional.one_hot(seq_corrupt,num_classes=22).float()
    
    ### 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],1)).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

    #to do add structure confidence metric; need to expand dimensions of chkpt b4
    if t1d_24:
        print("expanding t1d dim to 24")
        t1d = torch.cat((t1d, torch.zeros((t1d.shape[0],t1d.shape[1],1)).float()), -1).to(seq.device)
        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, cos=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, cos=True)
    #only corrupt first template
    xyz_t[0]=xyz_corrupt 
    
    assert torch.sum(torch.isnan(xyz_t[:,:,:3,:]))==0
    
    mask_msa[:,:,~loss_seq_mask] = False
    
    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, 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.
    """
    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###
    ######################
    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 = None #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_nar():

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