Fix chain data cache script bug, improve logging

openfold/utils/loss.py

@@ -1627,6 +1627,8 @@ class AlphaFoldLoss(nn.Module):
         crop_len = batch["aatype"].shape[-1]
         cum_loss = cum_loss * torch.sqrt(min(seq_len, crop_len))
 
+        losses["loss"] = cum_loss.detach().clone()
+
         if(not _return_breakdown):
             return cum_loss
         

scripts/generate_chain_data_cache.py

@@ -39,12 +39,7 @@ def parse_file(
             local_data["seq"] = seq
             local_data["resolution"] = mmcif.header["resolution"]
             
-            cluster_size = chain_cluster_size_dict.get(full_name.upper(), None)
-            if(cluster_size is None):
-                print(file_id)
-                out.pop(full_name)
-                continue
-            else:
+            cluster_size = chain_cluster_size_dict.get(full_name.upper(), -1)
             local_data["cluster_size"] = cluster_size
     elif(ext == ".pdb"):
         with open(os.path.join(args.data_dir, f), "r") as fp:
@@ -112,7 +107,12 @@ if __name__ == "__main__":
     )
     parser.add_argument(
         "--cluster_file", type=str, default=None,
-        help="Path to a cluster file (e.g. PDB40), one cluster per line"
+        help=(
+            "Path to a cluster file (e.g. PDB40), one cluster "
+            "({PROT1_ID}_{CHAIN_ID} {PROT2_ID}_{CHAIN_ID} ...) per line. "
+            "Chains not in this cluster file will NOT be filtered by cluster "
+            "size."
+        )
     )
     parser.add_argument(
         "--no_workers", type=int, default=4,

train_openfold.py

@@ -63,6 +63,36 @@ class OpenFoldWrapper(pl.LightningModule):
     def forward(self, batch):
         return self.model(batch)
 
+    def _log(self, loss, loss_breakdown, train=True):
+        phase = "train" if train else "val"
+        for loss_name, indiv_loss in loss_breakdown.items():
+            self.log(
+                f"{phase}/{loss_name}", 
+                indiv_loss, 
+                on_step=train, on_epoch=(not train), logger=True,
+            )
+
+        if(train):
+            self.log(
+                f"train/loss_epoch", 
+                loss_breakdown["loss"], 
+                on_step=False, on_epoch=True, logger=True,
+            )
+
+        with torch.no_grad():
+            other_metrics = self._compute_validation_metrics(
+                batch, 
+                outputs,
+                superimposition_metrics=(not train)
+            )
+
+        for k,v in other_metrics.items():
+            self.log(
+                f"{phase}/{k}", 
+                v, 
+                on_step=False, on_epoch=True, logger=True
+            )
+
     def training_step(self, batch, batch_idx):
         if(self.ema.device != batch["aatype"].device):
             self.ema.to(batch["aatype"].device)
@@ -79,28 +109,7 @@ class OpenFoldWrapper(pl.LightningModule):
         )
 
         # Log it
-        self.log(
-            "train/loss", 
-            loss, 
-            on_step=True, logger=True,
-        )
-        self.log(
-            "train/loss_epoch", 
-            loss, 
-            on_step=False, on_epoch=True, logger=True,
-        )
-        for loss_name, indiv_loss in loss_breakdown.items():
-            self.log(
-                f"train/{loss_name}", 
-                indiv_loss, 
-                on_step=True, logger=True,
-            )
-
-        with torch.no_grad():
-            other_metrics = self.compute_validation_metrics(batch, outputs) 
-
-        for k,v in other_metrics.items():
-            self.log(f"train/{k}", v, on_step=False, on_epoch=True, logger=True)
+        self._log(loss_breakdown)
 
         return loss
 
@@ -125,22 +134,11 @@ class OpenFoldWrapper(pl.LightningModule):
 
         # Compute loss and other metrics
         batch["use_clamped_fape"] = 0.
-        loss, loss_breakdown = self.loss(
+        _, loss_breakdown = self.loss(
             outputs, batch, _return_breakdown=True
         )
-        self.log("val/loss", loss, on_step=False, on_epoch=True, logger=True)
-        for loss_name, indiv_loss in loss_breakdown.items():
-            self.log(
-                f"val/{loss_name}", 
-                indiv_loss, 
-                on_step=False, on_epoch=True, logger=True,
-            )
 
-        other_metrics = self.compute_validation_metrics(
-            batch, outputs, superimposition_metrics=True,
-        ) 
-        for k,v in other_metrics.items():
-            self.log(f"val/{k}", v, on_step=False, on_epoch=True, logger=True)
+        self._log(loss_breakdown, train=False)
         
     def validation_epoch_end(self, _):
         # Restore the model weights to normal
@@ -440,18 +438,23 @@ if __name__ == "__main__":
     )
     parser.add_argument(
         "--wandb", action="store_true", default=False,
+        help="Whether to log metrics to Weights & Biases"
     )
     parser.add_argument(
         "--experiment_name", type=str, default=None,
+        help="Name of the current experiment. Used for wandb logging"
     )
     parser.add_argument(
         "--wandb_id", type=str, default=None,
+        help="ID of a previous run to be resumed"
     )
     parser.add_argument(
         "--wandb_project", type=str, default=None,
+        help="Name of the wandb project to which this run will belong"
     )
     parser.add_argument(
         "--wandb_entity", type=str, default=None,
+        help="wandb username or team name to which runs are attributed"
     )
     parser.add_argument(
         "--script_modules", type=bool_type, default=False,
@@ -465,16 +468,27 @@ if __name__ == "__main__":
     )
     parser.add_argument(
         "--train_epoch_len", type=int, default=10000,
+        help=(
+            "The virtual length of each training epoch. Stochastic filtering "
+            "of training data means that training datasets have no "
+            "well-defined length. This virtual length affects frequency of "
+            "validation & checkpointing (by default, one of each per epoch)."
         )
-    parser.add_argument(
-        "--_alignment_index_path", type=str, default=None,
     )
     parser.add_argument(
         "--log_lr", action="store_true", default=False,
+        help="Whether to log the actual learning rate"
     )
     parser.add_argument(
         "--config_preset", type=str, default="initial_training",
-        help='Config setting. Choose e.g. "initial_training", "finetuning", "model_1", etc.'
+        help=(
+            'Config setting. Choose e.g. "initial_training", "finetuning", '
+            '"model_1", etc. By default, the actual values in the config are '
+            'used.'
+        )
+    )
+    parser.add_argument(
+        "--_alignment_index_path", type=str, default=None,
     )
     parser = pl.Trainer.add_argparse_args(parser)