Fix TorchScripting bug, add command-line parameter for scripting

openfold/model/primitives.py

@@ -218,6 +218,7 @@ class Attention(nn.Module):
             self.c_hidden * self.no_heads, self.c_q, init="final"
         )
 
+        self.linear_g = None
         if self.gating:
             self.linear_g = Linear(
                 self.c_q, self.c_hidden * self.no_heads, init="gating"
@@ -282,7 +283,7 @@ class Attention(nn.Module):
 
         # [*, Q, H, C_hidden]
         o = o.transpose(-2, -3)
-        if self.gating:
+        if(self.linear_g is not None):
             g = self.sigmoid(self.linear_g(q_x))
             # [*, Q, H, C_hidden]
             g = g.view(g.shape[:-1] + (self.no_heads, -1))

train_openfold.py

@@ -2,7 +2,7 @@ import argparse
 import logging
 import os
 
-#os.environ["CUDA_VISIBLE_DEVICES"] = "5"
+os.environ["CUDA_VISIBLE_DEVICES"] = "4,5"
 #os.environ["MASTER_ADDR"]="10.119.81.14"
 #os.environ["MASTER_PORT"]="42069"
 #os.environ["NODE_RANK"]="0"
@@ -13,7 +13,7 @@ import time
 import numpy as np
 import pytorch_lightning as pl
 from pytorch_lightning.callbacks.model_checkpoint import ModelCheckpoint
-from pytorch_lightning.plugins.training_type import DeepSpeedPlugin
+from pytorch_lightning.plugins.training_type import DeepSpeedPlugin, DDPPlugin
 from pytorch_lightning.plugins.environments import SLURMEnvironment
 import torch
 
@@ -120,6 +120,7 @@ def main(args):
         logging.info("Successfully loaded model weights...")
 
     # TorchScript components of the model
+    if(args.script_modules):
         script_preset_(model_module)
 
     #data_module = DummyDataLoader("batch.pickle")
@@ -172,7 +173,7 @@ def main(args):
             cluster_environment=cluster_environment,
         )
     elif (args.gpus is not None and args.gpus) > 1 or args.num_nodes > 1:
-        strategy = "ddp"
+        strategy = DDPPlugin(find_unused_parameters=False)
     else:
         strategy = None
     
@@ -306,9 +307,13 @@ if __name__ == "__main__":
         help="Whether to load just model weights as opposed to training state"
     )
     parser.add_argument(
-        "--log_performance", action='store_true',
+        "--log_performance", type=bool_type, default=False,
         help="Measure performance"
     )
+    parser.add_argument(
+        "--script_modules", type=bool_type, default=False,
+        help="Whether to TorchScript eligible components of them model"
+    )
     parser = pl.Trainer.add_argparse_args(parser)
    
     # Disable the initial validation pass