Get FP16 training working

config.py

@@ -44,7 +44,7 @@ def model_config(name, train=False, low_prec=False):
         raise ValueError("Invalid model name")
 
     if(train):
-        c.globals.model.blocks_per_ckpt = 1
+        c.globals.blocks_per_ckpt = 1
         c.globals.chunk_size = None
 
     if(low_prec):
@@ -137,7 +137,7 @@ config = mlc.ConfigDict({
             },
             "inf": 1e9,
             "eps": eps,#1e-6,
-            "enabled": True,
+            "enabled": False,#True,
             "embed_angles": True,
         },
         "extra_msa": {
@@ -239,7 +239,7 @@ config = mlc.ConfigDict({
             "max_bin": 21.6875, 
             "no_bins": 64, 
             "eps": eps,#1e-6,
-            "weight": 0.,#0.3, 
+            "weight": 0.3, 
         },
         "experimentally_resolved": {
             "eps": eps,#1e-8,
@@ -267,17 +267,17 @@ config = mlc.ConfigDict({
             "cutoff": 15.,
             "no_bins": 50,
             "eps": eps,#1e-10,
-            "weight": 0.,#0.01,
+            "weight": 0.01,
         },
         "masked_msa": {
             "eps": eps,#1e-8,
-            "weight": 0.,#2.0,
+            "weight": 2.0,
         },
         "supervised_chi": {
             "chi_weight": 0.5,
             "angle_norm_weight": 0.01,
             "eps": eps,#1e-6,
-            "weight": 0.,#1.0,
+            "weight": 1.0,
         },
         "violation": {
             "violation_tolerance_factor": 12.0,

openfold/model/model.py

@@ -389,6 +389,7 @@ class AlphaFold(nn.Module):
         m_1_prev, z_prev, x_prev = None, None, None
         
         is_grad_enabled = torch.is_grad_enabled()
+        self._disable_activation_checkpointing()
 
         # Main recycling loop
         for cycle_no in range(self.config.no_cycles):
@@ -400,7 +401,9 @@ class AlphaFold(nn.Module):
             is_final_iter = (cycle_no == (self.config.no_cycles - 1))
             with torch.set_grad_enabled(is_grad_enabled and is_final_iter):
                 # Sidestep AMP bug discussed in pytorch issue #65766
-                if(is_final_iter and torch.is_autocast_enabled()):
+                if(is_final_iter):
+                    self._enable_activation_checkpointing()
+                    if(torch.is_autocast_enabled()):
                         torch.clear_autocast_cache()
                 # Run the next iteration of the model
                 outputs, m_1_prev, z_prev, x_prev = self.iteration(

openfold/model/primitives.py

@@ -257,8 +257,6 @@ class Attention(nn.Module):
                 a = a + b
         a = self.softmax(a)
 
-        #print(torch.any(torch.isnan(a)))
-    
         # [*, H, Q, C_hidden]
         o = torch.matmul(
             a,

openfold/model/structure_module.py

@@ -26,6 +26,10 @@ from openfold.np.residue_constants import (
     restype_atom14_rigid_group_positions,
 )
 from openfold.utils.affine_utils import T, quat_to_rot 
+from openfold.utils.feats import (
+    frames_and_literature_positions_to_atom14_pos,
+    torsion_angles_to_frames,
+)
 from openfold.utils.tensor_utils import (
     dict_multimap, 
     permute_final_dims, 
@@ -305,7 +309,7 @@ class InvariantPointAttention(nn.Module):
         pt_att = pt_att ** 2
 
         # [*, N_res, N_res, H, P_q]
-        pt_att = torch.sum(pt_att, dim=-1)
+        pt_att = sum(torch.unbind(pt_att, dim=-1))
         head_weights = self.softplus(self.head_weights).view(
             *((1,) * len(pt_att.shape[:-2]) + (-1, 1))
         ) 
@@ -358,7 +362,7 @@ class InvariantPointAttention(nn.Module):
         )
 
         # [*, N_res, H * P_v, 3]
-        o_pt = o_pt.view(*o_pt.shape[:-3], -1, 3)
+        o_pt = o_pt.reshape(*o_pt.shape[:-3], -1, 3)
 
         # [*, N_res, H, C_z]
         o_pair = torch.matmul(a.transpose(-2, -3), z)
@@ -409,14 +413,17 @@ class BackboneUpdate(nn.Module):
         quats, trans = params[...,:3], params[...,3:]
 
         # [*]
+        #norm_denom = torch.sqrt(sum(torch.unbind(quats ** 2, dim=-1)) + 1)
         norm_denom = torch.sqrt(torch.sum(quats ** 2, dim=-1) + 1)
 
-        # As many ones as there are dimensions in quats
-        ones = s.new_ones((1,) * len(quats.shape))
+        # [*, 3] 
+        ones = (
+            s.new_ones((1,) * len(quats.shape)).expand(quats.shape[:-1] + (1,))
+        )
 
         # [*, 4]
-        quats = torch.cat((ones.expand(*quats.shape[:-1], 1), quats), dim=-1)
-        quats = quats / norm_denom.unsqueeze(-1)
+        quats = torch.cat([ones, quats], dim=-1)
+        quats = quats / norm_denom[..., None]
 
         # [*, 3, 3]
         rots = quat_to_rot(quats)
@@ -424,105 +431,6 @@ class BackboneUpdate(nn.Module):
         return T(rots, trans)
 
 
-def _torsion_angles_to_frames(t, alpha, f, rrgdf):
-    # [*, N, 8, 4, 4]
-    default_4x4 = rrgdf[f,...]
-    
-    # [*, N, 8] transformations, i.e.
-    #   One [*, N, 8, 3, 3] rotation matrix and
-    #   One [*, N, 8, 3]    translation matrix
-    default_t = T.from_4x4(default_4x4)
-
-    bb_rot = alpha.new_zeros((*((1,) * len(alpha.shape[:-1])), 2))
-    bb_rot[..., 1] = 1
-    
-    # [*, N, 8, 2]
-    alpha = torch.cat(
-        [bb_rot.expand(*alpha.shape[:-2], -1, -1), alpha], 
-        dim=-2
-    )
-
-    # [*, N, 8, 3, 3]
-    # Produces rotation matrices of the form:
-    # [
-    #   [1, 0  , 0  ],
-    #   [0, a_2,-a_1],
-    #   [0, a_1, a_2]
-    # ]
-    # This follows the original code rather than the supplement, which uses
-    # different indices.
-        
-    all_rots = alpha.new_zeros(default_t.rots.shape)
-    all_rots[..., 0, 0] = 1
-    all_rots[..., 1, 1] = alpha[..., 1]
-    all_rots[..., 1, 2] = -alpha[..., 0]
-    all_rots[..., 2, 1:] = alpha
-
-    all_rots = T(all_rots, None)
-
-    all_frames = default_t.compose(all_rots)
-
-    chi2_frame_to_frame = all_frames[..., 5]
-    chi3_frame_to_frame = all_frames[..., 6]
-    chi4_frame_to_frame = all_frames[..., 7]
-
-    chi1_frame_to_bb = all_frames[..., 4]
-    chi2_frame_to_bb = chi1_frame_to_bb.compose(chi2_frame_to_frame)
-    chi3_frame_to_bb = chi2_frame_to_bb.compose(chi3_frame_to_frame)
-    chi4_frame_to_bb = chi3_frame_to_bb.compose(chi4_frame_to_frame)
-
-    all_frames_to_bb = T.concat([
-            all_frames[..., :5],
-            chi2_frame_to_bb.unsqueeze(-1),
-            chi3_frame_to_bb.unsqueeze(-1),
-            chi4_frame_to_bb.unsqueeze(-1),
-        ], dim=-1,
-    )
-
-    all_frames_to_global = t[..., None].compose(all_frames_to_bb)
-
-    return all_frames_to_global
-
-
-def _frames_and_literature_positions_to_atom14_pos(
-    t,
-    f,
-    default_frames,
-    group_idx,
-    atom_mask,
-    lit_positions,
-):
-
-    # [*, N, 14, 4, 4] 
-    default_4x4 = default_frames[f, ...]
-
-    # [*, N, 14]
-    group_mask = group_idx[f, ...]
-
-    # [*, N, 14, 8]
-    group_mask = nn.functional.one_hot(
-        group_mask, num_classes=default_frames.shape[-3],
-    )
- 
-    # [*, N, 14, 8]
-    t_atoms_to_global = t[..., None, :] * group_mask
-
-    # [*, N, 14]
-    t_atoms_to_global = t_atoms_to_global.map_tensor_fn(
-        lambda x: torch.sum(x, dim=-1)
-    )
-
-    # [*, N, 14, 1]
-    atom_mask = atom_mask[f,...].unsqueeze(-1)
-
-    # [*, N, 14, 3]
-    lit_positions = lit_positions[f, ...]
-    pred_positions = t_atoms_to_global.apply(lit_positions)
-    pred_positions = pred_positions * atom_mask
-
-    return pred_positions
-
-
 class StructureModuleTransitionLayer(nn.Module):
     def __init__(self, c):
         super(StructureModuleTransitionLayer, self).__init__()
@@ -664,6 +572,7 @@ class StructureModule(nn.Module):
             self.no_qk_points,
             self.no_v_points,
             inf=self.inf,
+            eps=self.epsilon,
         )
 
         self.ipa_dropout = nn.Dropout(self.dropout_rate)
@@ -791,7 +700,7 @@ class StructureModule(nn.Module):
         # Lazily initialize the residue constants on the correct device
         self._init_residue_constants(alpha.dtype, alpha.device)
         # Separated purely to make testing less annoying
-        return _torsion_angles_to_frames(t, alpha, f, self.default_frames)
+        return torsion_angles_to_frames(t, alpha, f, self.default_frames)
 
     def frames_and_literature_positions_to_atom14_pos(self, 
         t,    # [*, N, 8]
@@ -799,7 +708,7 @@ class StructureModule(nn.Module):
     ):
         # Lazily initialize the residue constants on the correct device
         self._init_residue_constants(t.rots.dtype, t.rots.device)
-        return _frames_and_literature_positions_to_atom14_pos(
+        return frames_and_literature_positions_to_atom14_pos(
             t, 
             f, 
             self.default_frames, 

openfold/utils/affine_utils.py

@@ -188,17 +188,29 @@ class T:
 
     @staticmethod
     def from_3_points(p_neg_x_axis, origin, p_xy_plane, eps=1e-8):
-        e0 = origin - p_neg_x_axis
-        e1 = p_xy_plane - origin
-
-        e0 = e0 / torch.sqrt(torch.sum(e0 ** 2, dim=-1, keepdims=True) + eps)
-        e1 = e1 - e0 * torch.sum(e0 * e1, dim=-1, keepdims=True) 
-        e1 = e1 / torch.sqrt(torch.sum(e1 ** 2, dim=-1, keepdims=True) + eps)
-        e2 = torch.cross(e0, e1)
-
-        rots = torch.stack([e0, e1, e2], dim=-1)
-
-        return T(rots, origin)
+        p_neg_x_axis = torch.unbind(p_neg_x_axis, dim=-1)
+        origin = torch.unbind(origin, dim=-1)
+        p_xy_plane = torch.unbind(p_xy_plane, dim=-1)
+        
+        e0 = [c1 - c2 for c1, c2 in zip(origin, p_neg_x_axis)]
+        e1 = [c1 - c2 for c1, c2 in zip(p_xy_plane, origin)]
+
+        denom = torch.sqrt(sum((c * c for c in e0)) + eps)
+        e0 = [c / denom for c in e0]
+        dot = sum((c1 * c2 for c1, c2 in zip(e0, e1)))
+        e1 = [c1 - c2 * dot for c1, c2 in zip(e1, e0)]
+        denom = torch.sqrt(sum((c * c for c in e1)) + eps)
+        e1 = [c / denom for c in e1]
+        e2 = [
+            e0[1] * e1[2] - e0[2] * e1[1],
+            e0[2] * e1[0] - e0[0] * e1[2],
+            e0[0] * e1[1] - e0[1] * e1[0],
+        ]
+
+        rots = torch.stack([c for tup in zip(e0, e1, e2) for c in tup], dim=-1)
+        rots = rots.reshape(rots.shape[:-1] + (3, 3))
+       
+        return T(rots, torch.stack(origin, dim=-1))
 
     @staticmethod
     def concat(ts, dim):
@@ -294,6 +306,9 @@ class T:
 
         return T(rots, translation)
 
+    def cuda(self):
+        return T(self.rots.cuda(), self.trans.cuda())
+
  
 _quat_elements = ['a', 'b', 'c', 'd']
 _qtr_keys = [l1 + l2 for l1 in _quat_elements for l2 in _quat_elements]
@@ -325,10 +340,11 @@ def quat_to_rot(
     # [*, 4, 4]
     quat = quat[..., None] * quat[..., None, :]
 
+    # [4, 4, 3, 3]
     mat = quat.new_tensor(_qtr_mat)
 
     # [*, 4, 4, 3, 3]
-    shaped_qtr_mat = mat.view((1,) * len(quat.shape[:-2]) + (4, 4, 3, 3))
+    shaped_qtr_mat = mat.view((1,) * len(quat.shape[:-2]) + mat.shape)
     quat = quat[..., None, None] * shaped_qtr_mat
 
     # [*, 3, 3]

openfold/utils/deepspeed.py

@@ -70,11 +70,8 @@ def checkpoint_blocks(
 
     for s in range(0, len(blocks), blocks_per_ckpt):
         e = s + blocks_per_ckpt
-        #print(len(args))
-        #for a in args:
-        #    print(a.requires_grad)
-        args = checkpoint(chunker(s, e), *args)
-        #args = deepspeed.checkpointing.checkpoint(chunker(s, e), *args)
+        #args = checkpoint(chunker(s, e), *args)
+        args = deepspeed.checkpointing.checkpoint(chunker(s, e), *args)
         args = wrap(args)
 
     return args

openfold/utils/feats.py

@@ -173,6 +173,11 @@ def atom37_to_torsion_angles(
     **kwargs,
 ) -> Dict[str, torch.Tensor]:
     """
+        Convert coordinates to torsion angles.
+
+        This function is extremely sensitive to floating point imprecisions
+        and should be run with double precision whenever possible.
+
         Args:
             aatype:
                 [*, N_res] residue indices
@@ -228,10 +233,10 @@ def atom37_to_torsion_angles(
     )
     phi_mask = (
         prev_all_atom_mask[..., 2] *
-        torch.prod(all_atom_mask[..., :3], dim=-1)
+        torch.prod(all_atom_mask[..., :3], dim=-1, dtype=all_atom_mask.dtype)
     )
     psi_mask = (
-        torch.prod(all_atom_mask[..., :3], dim=-1) *
+        torch.prod(all_atom_mask[..., :3], dim=-1, dtype=all_atom_mask.dtype) *
         all_atom_mask[..., 4]
     )
 
@@ -256,7 +261,9 @@ def atom37_to_torsion_angles(
         dim=-1, 
         no_batch_dims=len(atom_indices.shape[:-2])
     )
-    chi_angle_atoms_mask = torch.prod(chi_angle_atoms_mask, dim=-1)
+    chi_angle_atoms_mask = torch.prod(
+        chi_angle_atoms_mask, dim=-1, dtype=chi_angle_atoms_mask.dtype
+    )
     chis_mask = chis_mask * chi_angle_atoms_mask
 
     torsions_atom_pos = torch.cat(
@@ -281,6 +288,7 @@ def atom37_to_torsion_angles(
         torsions_atom_pos[..., 1, :],
         torsions_atom_pos[..., 2, :],
         torsions_atom_pos[..., 0, :],
+        eps=eps,
     )
 
     fourth_atom_rel_pos = torsion_frames.invert().apply(
@@ -290,15 +298,19 @@ def atom37_to_torsion_angles(
     torsion_angles_sin_cos = torch.stack(
         [fourth_atom_rel_pos[..., 2], fourth_atom_rel_pos[..., 1]], dim=-1
     )
+
     denom = torch.sqrt(
         torch.sum(
-            torch.square(torsion_angles_sin_cos), dim=-1, keepdims=True
+            torch.square(torsion_angles_sin_cos), 
+            dim=-1, 
+            dtype=torsion_angles_sin_cos.dtype, 
+            keepdims=True
         ) + eps
     )
     torsion_angles_sin_cos = torsion_angles_sin_cos / denom
 
-    torsion_angles_sin_cos = torsion_angles_sin_cos * torch.tensor(
-        [1., 1., -1., 1., 1., 1., 1.], device=aatype.device,
+    torsion_angles_sin_cos = torsion_angles_sin_cos * all_atom_mask.new_tensor(
+        [1., 1., -1., 1., 1., 1., 1.], 
     )[((None,) * len(torsion_angles_sin_cos.shape[:-2])) + (slice(None), None)]
 
     chi_is_ambiguous = torsion_angles_sin_cos.new_tensor(
@@ -327,6 +339,7 @@ def atom37_to_frames(
     aatype: torch.Tensor,
     all_atom_positions: torch.Tensor,
     all_atom_mask: torch.Tensor,
+    eps: float,
     **kwargs,
 ) -> Dict[str, torch.Tensor]:
     batch_dims = len(aatype.shape[:-1])
@@ -387,6 +400,7 @@ def atom37_to_frames(
         p_neg_x_axis=base_atom_pos[..., 0, :],
         origin=base_atom_pos[..., 1, :],
         p_xy_plane=base_atom_pos[..., 2, :],
+        eps=eps,
     )
 
     group_exists = batched_gather(
@@ -638,3 +652,106 @@ def build_ambiguity_feats(batch: Dict[str, torch.Tensor]) -> None:
         "atom14_alt_gt_positions": atom14_alt_gt_positions,
         "atom14_alt_gt_exists": atom14_alt_gt_exists,
     }
+
+
+def torsion_angles_to_frames(
+    t: T, 
+    alpha: torch.Tensor, 
+    aatype: torch.Tensor, 
+    rrgdf: torch.Tensor,
+):
+    # [*, N, 8, 4, 4]
+    default_4x4 = rrgdf[aatype, ...]
+    
+    # [*, N, 8] transformations, i.e.
+    #   One [*, N, 8, 3, 3] rotation matrix and
+    #   One [*, N, 8, 3]    translation matrix
+    default_t = T.from_4x4(default_4x4)
+
+    bb_rot = alpha.new_zeros((*((1,) * len(alpha.shape[:-1])), 2))
+    bb_rot[..., 1] = 1
+    
+    # [*, N, 8, 2]
+    alpha = torch.cat(
+        [bb_rot.expand(*alpha.shape[:-2], -1, -1), alpha], 
+        dim=-2
+    )
+
+    # [*, N, 8, 3, 3]
+    # Produces rotation matrices of the form:
+    # [
+    #   [1, 0  , 0  ],
+    #   [0, a_2,-a_1],
+    #   [0, a_1, a_2]
+    # ]
+    # This follows the original code rather than the supplement, which uses
+    # different indices.
+        
+    all_rots = alpha.new_zeros(default_t.rots.shape)
+    all_rots[..., 0, 0] = 1
+    all_rots[..., 1, 1] = alpha[..., 1]
+    all_rots[..., 1, 2] = -alpha[..., 0]
+    all_rots[..., 2, 1:] = alpha
+
+    all_rots = T(all_rots, None)
+
+    all_frames = default_t.compose(all_rots)
+
+    chi2_frame_to_frame = all_frames[..., 5]
+    chi3_frame_to_frame = all_frames[..., 6]
+    chi4_frame_to_frame = all_frames[..., 7]
+
+    chi1_frame_to_bb = all_frames[..., 4]
+    chi2_frame_to_bb = chi1_frame_to_bb.compose(chi2_frame_to_frame)
+    chi3_frame_to_bb = chi2_frame_to_bb.compose(chi3_frame_to_frame)
+    chi4_frame_to_bb = chi3_frame_to_bb.compose(chi4_frame_to_frame)
+
+    all_frames_to_bb = T.concat([
+            all_frames[..., :5],
+            chi2_frame_to_bb.unsqueeze(-1),
+            chi3_frame_to_bb.unsqueeze(-1),
+            chi4_frame_to_bb.unsqueeze(-1),
+        ], dim=-1,
+    )
+
+    all_frames_to_global = t[..., None].compose(all_frames_to_bb)
+
+    return all_frames_to_global
+
+
+def frames_and_literature_positions_to_atom14_pos(
+    t: T,
+    aatype: torch.Tensor,
+    default_frames,
+    group_idx,
+    atom_mask,
+    lit_positions,
+):
+    # [*, N, 14, 4, 4] 
+    default_4x4 = default_frames[aatype, ...]
+    
+    # [*, N, 14]
+    group_mask = group_idx[aatype, ...]
+    
+    # [*, N, 14, 8]
+    group_mask = nn.functional.one_hot(
+        group_mask, num_classes=default_frames.shape[-3],
+    )
+
+    # [*, N, 14, 8]
+    t_atoms_to_global = t[..., None, :] * group_mask
+    
+    # [*, N, 14]
+    t_atoms_to_global = t_atoms_to_global.map_tensor_fn(
+        lambda x: torch.sum(x, dim=-1)
+    )
+
+    # [*, N, 14, 1]
+    atom_mask = atom_mask[aatype, ...].unsqueeze(-1)
+
+    # [*, N, 14, 3]
+    lit_positions = lit_positions[aatype, ...]
+    pred_positions = t_atoms_to_global.apply(lit_positions)
+    pred_positions = pred_positions * atom_mask
+
+    return pred_positions

openfold/utils/import_weights.py

@@ -417,7 +417,7 @@ def import_jax_weights_(model, npz_path, version="model_1"):
     if("_ptm" in version):
         translations["predicted_aligned_error_head"] = {
             "logits":
-                LinearParams(model.aux_heads.tm_score.linear)
+                LinearParams(model.aux_heads.tm.linear)
         }
 
     # Flatten keys and insert missing key prefixes

openfold/utils/loss.py

@@ -273,7 +273,6 @@ def supervised_chi_loss(
 
     shifted_mask = (1 - 2 * chi_pi_periodic).unsqueeze(-1)
     true_chi_shifted = shifted_mask * true_chi
-
     sq_chi_error = torch.sum(
         (true_chi - pred_angles)**2, dim=-1
     )
@@ -498,11 +497,11 @@ def tm_loss(
     )
 
     loss = torch.sum(errors * square_mask, dim=-1)
-    scale = 0.1 # hack to help FP16 training along
+    scale = 0.5 # hack to help FP16 training along
     denom = eps + torch.sum(scale * square_mask, dim=(-1, -2))
     loss = loss / denom[..., None]
     loss = torch.sum(loss, dim=-1)
-    loss = loss / scale
+    loss = loss * scale
 
     loss = loss * (
         (resolution >= min_resolution) &
@@ -744,7 +743,7 @@ def between_residue_clash_loss(
   
     # Backbone C--N bond between subsequent residues is no clash.
     c_one_hot = torch.nn.functional.one_hot(
-        residue_index.new_tensor(2.), num_classes=14
+        residue_index.new_tensor(2), num_classes=14
     )
     c_one_hot = c_one_hot.reshape(
         *((1,) * len(residue_index.shape[:-1])), *c_one_hot.shape
@@ -1319,11 +1318,11 @@ def masked_msa_loss(logits, true_msa, bert_mask, eps=1e-8, **kwargs):
     # )
     loss = errors * bert_mask
     loss = torch.sum(loss, dim=-1)
-    scale = 0.1
+    scale = 0.5
     denom = eps + torch.sum(scale * bert_mask, dim=(-1, -2))
     loss = loss / denom[..., None]
     loss = torch.sum(loss, dim=-1)
-    loss = loss / scale
+    loss = loss * scale
 
     return loss
 
@@ -1352,7 +1351,7 @@ class AlphaFoldLoss(nn.Module):
             ))
 
         if("backbone_affine_tensor" not in batch.keys()):
-            batch.update(feats.atom37_to_frames(**batch)) 
+            batch.update(feats.atom37_to_frames(eps=self.config.eps, **batch)) 
             
             # TODO: Verify that this is correct
             batch["backbone_affine_tensor"] = (
@@ -1363,16 +1362,19 @@ class AlphaFoldLoss(nn.Module):
             )
 
         if("chi_angles_sin_cos" not in batch.keys()):
+            with torch.no_grad():
                 batch.update(feats.atom37_to_torsion_angles(
-               **batch,
+                    aatype=batch["aatype"],
+                    all_atom_positions=batch["all_atom_positions"].double(),
+                    all_atom_mask=batch["all_atom_mask"].double(),
                     eps=self.config.eps,
                 ))
 
                 # TODO: Verify that this is correct
                 batch["chi_angles_sin_cos"] = (
                     batch["torsion_angles_sin_cos"][..., 3:, :]
-            )
-            batch["chi_mask"] = batch["torsion_angles_mask"][..., 3:]
+                ).to(batch["all_atom_mask"].dtype)
+                batch["chi_mask"] = batch["torsion_angles_mask"][..., 3:].to(batch["all_atom_mask"].dtype)
 
         loss_fns = {
             "distogram": 

openfold/utils/tensor_utils.py

@@ -25,11 +25,11 @@ def permute_final_dims(tensor: torch.Tensor, inds: List[int]):
     return tensor.permute(first_inds + [zero_index + i for i in inds])
 
 
-def flatten_final_dims(tensor: torch.Tensor, no_dims: int):
-    return tensor.reshape(tensor.shape[:-no_dims] + (-1,))
+def flatten_final_dims(t: torch.Tensor, no_dims: int):
+    return t.reshape(t.shape[:-no_dims] + (-1,))
 
 
-def masked_mean(mask, value, dim, eps=1e-10):
+def masked_mean(mask, value, dim, eps=1e-4):
     mask = mask.expand(*value.shape)
     return torch.sum(mask * value, dim=dim) / (eps + torch.sum(mask, dim=dim))