added multi-chain permutation to AlphaFoldMultimerLoss

openfold/utils/loss.py

@@ -34,6 +34,8 @@ from openfold.utils.tensor_utils import (
     permute_final_dims,
     batched_gather,
 )
+import random
+from openfold.np import residue_constants as rc
 import logging 
 logger = logging.getLogger(__name__)
 
@@ -1669,6 +1671,226 @@ def chain_center_of_mass_loss(
     loss = masked_mean(loss_mask, losses, dim=(-1, -2))
     return loss
 
+# #
+# below are the functions required for permutations
+# #
+def kabsch_rotation(P, Q):
+    """
+    Using the Kabsch algorithm with two sets of paired point P and Q, centered
+    around the centroid. Each vector set is represented as an NxD
+    matrix, where D is the the dimension of the space.
+    The algorithm works in three steps:
+    - a centroid translation of P and Q (assumed done before this function
+      call)
+    - the computation of a covariance matrix C
+    - computation of the optimal rotation matrix U
+    For more info see http://en.wikipedia.org/wiki/Kabsch_algorithm
+    Parameters
+    ----------
+    P : array
+        (N,D) matrix, where N is points and D is dimension.
+    Q : array
+        (N,D) matrix, where N is points and D is dimension.
+    Returns
+    -------
+    U : matrix
+        Rotation matrix (D,D)
+    """
+
+    # Computation of the covariance matrix
+    P,Q = P.to('cpu'),Q.to('cpu') # move to cpu memory just in case it takes up too much gpu mem 
+    C = P.transpose(-1, -2) @ Q
+
+    # Computation of the optimal rotation matrix
+    # This can be done using singular value decomposition (SVD)
+    # Getting the sign of the det(V)*(W) to decide
+    # whether we need to correct our rotation matrix to ensure a
+    # right-handed coordinate system.
+    # And finally calculating the optimal rotation matrix U
+    # see http://en.wikipedia.org/wiki/Kabsch_algorithm
+    V, _, W = torch.linalg.svd(C)
+    d = (torch.linalg.det(V) * torch.linalg.det(W)) < 0.0
+
+    if d:
+        V[:, -1] = -V[:, -1]
+
+    # Create Rotation matrix U
+    U = V @ W
+    return U
+
+def get_optimal_transform(
+    src_atoms: torch.Tensor,
+    tgt_atoms: torch.Tensor,
+    mask: torch.Tensor = None,
+):
+    assert src_atoms.shape == tgt_atoms.shape, (src_atoms.shape, tgt_atoms.shape)
+    assert src_atoms.shape[-1] == 3
+    if mask is not None:
+        assert mask.dtype == torch.bool
+        assert mask.shape[-1] == src_atoms.shape[-2]
+        if mask.sum() == 0:
+            src_atoms = torch.zeros((1, 3), device=src_atoms.device).float()
+            tgt_atoms = src_atoms
+        else:
+            src_atoms = src_atoms[mask, :]
+            tgt_atoms = tgt_atoms[mask, :]
+    src_center = src_atoms.mean(-2, keepdim=True)
+    tgt_center = tgt_atoms.mean(-2, keepdim=True)
+    r = kabsch_rotation(src_atoms - src_center, tgt_atoms - tgt_center)
+    tgt_center,src_center = tgt_center.to('cpu'),src_center.to('cpu') # load to cpu memory just in case 
+    x = tgt_center - src_center @ r
+    return r, x
+
+
+def compute_rmsd(
+    true_atom_pos: torch.Tensor,
+    pred_atom_pos: torch.Tensor,
+    atom_mask: torch.Tensor = None,
+    eps: float = 1e-6,
+) -> torch.Tensor:
+    # shape check
+    sq_diff = torch.square(true_atom_pos - pred_atom_pos).sum(dim=-1, keepdim=False)
+    if atom_mask is not None:
+        sq_diff = sq_diff[atom_mask]
+    msd = torch.mean(sq_diff)
+    msd = torch.nan_to_num(msd, nan=1e8)
+    return torch.sqrt(msd + eps)
+
+def kabsch_rmsd(
+    true_atom_pos: torch.Tensor,
+    pred_atom_pos: torch.Tensor,
+    atom_mask: torch.Tensor,
+):
+    r, x = get_optimal_transform(
+        true_atom_pos,
+        pred_atom_pos,
+        atom_mask,
+    )
+    aligned_true_atom_pos = true_atom_pos @ r + x
+    return compute_rmsd(aligned_true_atom_pos, pred_atom_pos, atom_mask)
+
+
+def get_least_asym_entity_or_longest_length(batch):
+    """
+    First check how many subunit(s) one sequence has, if there is no tie, e.g. AABBB then select 
+    one of the A as anchor
+
+    If there is a tie, e.g. AABB, first check which sequence is the longer/longest,
+    then choose one of the corresponding subunits as anchor 
+    """
+    unique_entity_ids = torch.unique(batch["entity_id"])
+    entity_asym_count = {}
+    entity_length = {}
+
+    for entity_id in unique_entity_ids:
+        asym_ids = torch.unique(batch["asym_id"][batch["entity_id"] == entity_id])
+        entity_asym_count[int(entity_id)] = len(asym_ids)
+        
+        # Calculate entity length
+        entity_mask = (batch["entity_id"] == entity_id)
+        entity_length[int(entity_id)] = entity_mask.sum().item()
+
+    min_asym_count = min(entity_asym_count.values())
+    least_asym_entities = [entity for entity, count in entity_asym_count.items() if count == min_asym_count]
+
+    # If multiple entities have the least asym_id count, return those with the shortest length
+    if len(least_asym_entities) > 1:
+        max_length = max([entity_length[entity] for entity in least_asym_entities])
+        least_asym_entities = [entity for entity in least_asym_entities if entity_length[entity] == max_length]
+
+    # If still multiple entities, return a random one
+    if len(least_asym_entities) > 1:
+        least_asym_entities = random.choice(least_asym_entities)
+    assert len(least_asym_entities)==1
+    best_pred_asym = torch.unique(batch["asym_id"][batch["entity_id"] == least_asym_entities[0]])
+    return least_asym_entities[0], best_pred_asym
+
+
+def greedy_align(
+    batch,
+    per_asym_residue_index,
+    unique_asym_ids,
+    entity_2_asym_list,
+    pred_ca_pos,
+    pred_ca_mask,
+    true_ca_poses,
+    true_ca_masks,
+):
+    used = [False for _ in range(len(true_ca_poses))]
+    align = []
+    for cur_asym_id in unique_asym_ids:
+        # skip padding
+        if cur_asym_id == 0:
+            continue
+        i = int(cur_asym_id - 1)
+        asym_mask = batch["asym_id"] == cur_asym_id
+        entity_id = batch["entity_id"][asym_mask][0]
+        # don't need to align
+        if (entity_id) == 1:
+            align.append((i, i))
+            assert used[i] == False
+            used[i] = True
+            continue
+        cur_entity_ids = batch["entity_id"][asym_mask][0]
+        best_rmsd = 1e20    
+        best_idx = None
+        cur_asym_list = entity_2_asym_list[int(cur_entity_ids)]
+        cur_residue_index = per_asym_residue_index[int(cur_asym_id)]
+        
+        cur_pred_pos = pred_ca_pos[asym_mask] # only need the first 1 column of asym_mask
+        cur_pred_mask = pred_ca_mask[asym_mask]
+        for next_asym_id in cur_asym_list:
+            if next_asym_id == 0:
+                continue
+            j = int(next_asym_id - 1)
+            if not used[j]:  # possible candidate
+                cropped_pos = true_ca_poses[j]
+                mask = true_ca_masks[j][cur_residue_index]
+                rmsd = compute_rmsd(
+                    cropped_pos, cur_pred_pos, (cur_pred_mask.to('cpu') * mask.to('cpu')).bool()
+                )
+                if rmsd < best_rmsd:
+                    best_rmsd = rmsd
+                    best_idx = j
+        assert best_idx is not None
+        used[best_idx] = True
+        align.append((i, best_idx))
+    print(f"align is {align}")
+    return align
+
+
+def merge_labels(batch, per_asym_residue_index, labels, align):
+    """
+    batch:
+    labels: list of label dicts, each with shape [nk, *]
+    align: list of int, such as [2, None, 0, 1], each entry specify the corresponding label of the asym.
+    """
+    num_res = batch["msa_mask"].shape[-1]
+    outs = {}
+    for k, v in labels[0].items():
+        if k in [
+            "resolution",
+        ]:
+            continue
+        cur_out = {}
+        for i, j in align:
+            label = labels[j][k]
+            # to 1-based
+            cur_residue_index = per_asym_residue_index[i + 1]
+            cur_out[i] = label[cur_residue_index]
+        cur_out = [x[1] for x in sorted(cur_out.items())]
+        new_v = torch.concat(cur_out, dim=0)
+        merged_nres = new_v.shape[0]
+        assert (
+            merged_nres <= num_res
+        ), f"bad merged num res: {merged_nres} > {num_res}. something is wrong."
+        if merged_nres < num_res:  # must pad
+            pad_dim = new_v.shape[1:]
+            pad_v = new_v.new_zeros((num_res - merged_nres, *pad_dim))
+            new_v = torch.concat((new_v, pad_v), dim=0)
+        outs[k] = new_v
+    return outs
+
 
 class AlphaFoldLoss(nn.Module):
     """Aggregation of the various losses described in the supplement"""
@@ -1782,12 +2004,111 @@ class AlphaFoldMultimerLoss(AlphaFoldLoss):
     AlphaFoldLoss
     """
     def __init__(self, config):
-        super(AlphaFoldMultimerLoss, self).__init__()
+        super(AlphaFoldMultimerLoss, self).__init__(config)
         self.config = config
 
+    def multi_chain_perm_align(self,out, batch, labels, shuffle_times=2):
+        """
+        A class method that first permutate chains in ground truth first
+        before calculating the loss.
+
+        Details are described in Section 7.3 in the Supplementary of AlphaFold-Multimer paper:
+        https://www.biorxiv.org/content/10.1101/2021.10.04.463034v2
+        """
+        assert isinstance(labels, list)
+        ca_idx = rc.atom_order["CA"]
+        pred_ca_pos = out["final_atom_positions"][..., ca_idx, :].float()  # [bsz, nres, 3]
+        pred_ca_mask = out["final_atom_mask"][..., ca_idx].float()  # [bsz, nres]
+        true_ca_poses = [
+            l["all_atom_positions"][..., ca_idx, :].float() for l in labels
+        ]  # list([nres, 3])
+        true_ca_masks = [
+            l["all_atom_mask"][..., ca_idx].float() for l in labels
+        ]  # list([nres,])
+
+        unique_asym_ids = torch.unique(batch["asym_id"])
+
+        per_asym_residue_index = {}
+        for cur_asym_id in unique_asym_ids:
+            asym_mask = (batch["asym_id"] == cur_asym_id).bool()
+            per_asym_residue_index[int(cur_asym_id)] = batch["residue_index"][asym_mask]
+
+        anchor_gt_asym, anchor_pred_asym = get_least_asym_entity_or_longest_length(batch)
+        logger.info(f"anchor_gt_asym is chosen to be: {anchor_gt_asym}")
+        anchor_gt_idx = int(anchor_gt_asym) - 1
+
+        best_rmsd = 1e20
+        best_labels = None
+
+        unique_entity_ids = torch.unique(batch["entity_id"])
+        entity_2_asym_list = {}
+        for cur_ent_id in unique_entity_ids:
+            ent_mask = batch["entity_id"] == cur_ent_id
+            cur_asym_id = torch.unique(batch["asym_id"][ent_mask])
+            entity_2_asym_list[int(cur_ent_id)] = cur_asym_id
+        for cur_asym_id in anchor_pred_asym:
+            asym_mask = (batch["asym_id"] == cur_asym_id).bool()
+            anchor_residue_idx = per_asym_residue_index[int(cur_asym_id)]
+
+            anchor_true_pos = true_ca_poses[anchor_gt_idx][anchor_residue_idx] 
+            anchor_pred_pos = pred_ca_pos[asym_mask]
+            anchor_true_mask = true_ca_masks[anchor_gt_idx][anchor_residue_idx]
+            anchor_pred_mask = pred_ca_mask[asym_mask]
+            r, x = get_optimal_transform(
+                anchor_true_pos,
+                anchor_pred_pos,
+                (anchor_true_mask.to('cpu') * anchor_pred_mask.to('cpu')).bool(),
+            )
+        
+            aligned_true_ca_poses = [ca.to('cpu') @ r.to('cpu') + x.to('cpu') for ca in true_ca_poses]  # apply transforms
+            for _ in range(shuffle_times):
+                shuffle_idx = torch.randperm(
+                    unique_asym_ids.shape[0], device=unique_asym_ids.device
+                )
+                shuffled_asym_ids = unique_asym_ids[shuffle_idx]
+                align = greedy_align(
+                    batch,
+                    per_asym_residue_index,
+                    shuffled_asym_ids,
+                    entity_2_asym_list,
+                    pred_ca_pos,
+                    pred_ca_mask,
+                    aligned_true_ca_poses,
+                    true_ca_masks,
+                )
+                merged_labels = merge_labels(
+                    batch,
+                    per_asym_residue_index,
+                    labels,
+                    align,
+                )
+                rmsd = kabsch_rmsd(
+                    merged_labels["all_atom_positions"][..., ca_idx, :].to('cpu') @ r.to('cpu') + x.to('cpu'),
+                    pred_ca_pos,
+                    (pred_ca_mask.to('cpu') * merged_labels["all_atom_mask"][..., ca_idx].to('cpu')).bool(),
+                )
+
+                if rmsd < best_rmsd:
+                    best_rmsd = rmsd
+                    best_labels = merged_labels
+
+        return best_labels
+
     def forward(self,out,batch,_return_breakdown=False):
         """
         Overwrite AlphaFoldLoss forward function so that
         it first compute multi-chain permutation 
+
+        args:
+        out: the output of model.forward()
+        batch: a pair of input features and its corresponding ground truth structure
         """
-        logger.info(f"out is {type(out)} and batch is {type(batch)}")
+        features,labels = batch
\ No newline at end of file
+        # first remove the recycling dimention of input features
+        features = tensor_tree_map(lambda t: t[..., -1], features)
+        # then permutate ground truth chains before calculating the loss
+        permutated_labels = self.multi_chain_perm_align(out,features,labels)
+        logger.info("finished multi-chain permutation")
+        return permutated_labels
+        ## TODO next need to check how the ground truth label is used
+        #  in loss calculation. 
\ No newline at end of file