Fix no-DeepSpeed DDP

openfold/config.py

@@ -64,6 +64,7 @@ c_s = mlc.FieldReference(384, field_type=int)
 blocks_per_ckpt = mlc.FieldReference(None, field_type=int)
 chunk_size = mlc.FieldReference(4, field_type=int)
 aux_distogram_bins = mlc.FieldReference(64, field_type=int)
+tm_enabled = mlc.FieldReference(False, field_type=bool)
 eps = mlc.FieldReference(1e-8, field_type=float)
 templates_enabled = mlc.FieldReference(True, field_type=bool)
 embed_template_torsion_angles = mlc.FieldReference(True, field_type=bool)
@@ -148,7 +149,7 @@ config = mlc.ConfigDict(
                     "same_prob": 0.1,
                     "uniform_prob": 0.1,
                 },
-                "max_extra_msa": 5120,
+                "max_extra_msa": 1024,
                 "max_recycling_iters": 3,
                 "msa_cluster_features": True,
                 "reduce_msa_clusters_by_max_templates": False,
@@ -174,7 +175,6 @@ config = mlc.ConfigDict(
             },
             "supervised": {
                 "clamp_prob": 0.9,
-                "uniform_recycling": True,
                 "supervised_features": [
                     "all_atom_mask",
                     "all_atom_positions",
@@ -194,6 +194,7 @@ config = mlc.ConfigDict(
                 "crop_size": None,
                 "supervised": False,
                 "subsample_recycling": False,
+                "uniform_recycling": False,
             },
             "eval": {
                 "fixed_size": True,
@@ -206,27 +207,29 @@ config = mlc.ConfigDict(
                 "crop_size": None,
                 "supervised": True,
                 "subsample_recycling": False,
+                "uniform_recycling": False,
             },
             "train": {
                 "fixed_size": True,
                 "subsample_templates": True,
                 "masked_msa_replace_fraction": 0.15,
-                "max_msa_clusters": 512,
+                "max_msa_clusters": 128,
                 "max_template_hits": 4,
                 "max_templates": 4,
                 "shuffle_top_k_prefiltered": 20,
                 "crop": True,
-                "crop_size": 384,
+                "crop_size": 256,
                 "supervised": True,
                 "clamp_prob": 0.9,
                 "subsample_recycling": True,
                 "max_distillation_msa_clusters": 1000,
+                "uniform_recycling": True,
             },
             "data_module": {
                 "use_small_bfd": False,
                 "data_loaders": {
                     "batch_size": 1,
-                    "num_workers": 2,
+                    "num_workers": 4,
                 },
             },
         },
@@ -374,7 +377,7 @@ config = mlc.ConfigDict(
                 "tm": {
                     "c_z": c_z,
                     "no_bins": aux_distogram_bins,
-                    "enabled": False,
+                    "enabled": tm_enabled,
                 },
                 "masked_msa": {
                     "c_m": c_m,
@@ -452,6 +455,7 @@ config = mlc.ConfigDict(
                 "max_resolution": 3.0,
                 "eps": eps,  # 1e-8,
                 "weight": 0.0,
+                "enabled": tm_enabled,
             },
             "eps": eps,
         },

openfold/utils/loss.py

@@ -43,8 +43,8 @@ def softmax_cross_entropy(logits, labels):
 
 
 def sigmoid_cross_entropy(logits, labels):
-    log_p = torch.nn.functional.logsigmoid(logits)
-    log_not_p = torch.nn.functional.logsigmoid(-logits)
+    log_p = torch.log(torch.sigmoid(logits))
+    log_not_p = torch.log(torch.sigmoid(-logits))
     loss = -labels * log_p - (1 - labels) * log_not_p
     return loss
 
@@ -1462,7 +1462,7 @@ class AlphaFoldLoss(nn.Module):
         self.config = config
 
     def forward(self, out, batch):
-        if "violation" not in out.keys() and self.config.violation.weight:
+        if "violation" not in out.keys():
             out["violation"] = find_structural_violations(
                 batch,
                 out["sm"]["positions"][-1],
@@ -1509,16 +1509,17 @@ class AlphaFoldLoss(nn.Module):
                 out["violation"],
                 **batch,
             ),
-            "tm": lambda: tm_loss(
+        }
+
+        if(self.config.tm.enabled):
+            loss_fns["tm"] = lambda: tm_loss(
                 logits=out["tm_logits"],
                 **{**batch, **out, **self.config.tm},
-            ),
-        }
+            )
 
         cum_loss = 0.
         for loss_name, loss_fn in loss_fns.items():
             weight = self.config[loss_name].weight
-            if weight:
             loss = loss_fn()
             if(torch.isnan(loss) or torch.isinf(loss)):
                 logging.warning(f"{loss_name} loss is NaN. Skipping...")