Removes OF copy of zero_to_fp32.py favoring deepspeed.util version

scripts/convert_v1_to_v2_weights.py

@@ -22,7 +22,9 @@ import shutil
 import torch
 
 from openfold.utils.import_weights import convert_deprecated_v1_keys
-from zero_to_fp32 import get_optim_files, parse_optim_states, get_model_state_file
+from deepspeed.utils.zero_to_fp32 import (
+    get_optim_files, parse_optim_states, get_model_state_file
+)
 
 
 def convert_v1_to_v2_weights(args):

train_openfold.py

@@ -11,6 +11,7 @@ from pytorch_lightning.loggers import WandbLogger
 from pytorch_lightning.strategies import DeepSpeedStrategy, DDPStrategy
 from pytorch_lightning import seed_everything
 import torch
+from deepspeed.utils import zero_to_fp32 
 
 from openfold.config import model_config
 from openfold.data.data_modules import OpenFoldDataModule, OpenFoldMultimerDataModule
@@ -35,11 +36,6 @@ from openfold.utils.import_weights import (
     import_jax_weights_,
     import_openfold_weights_
 )
-from scripts.zero_to_fp32 import (
-    get_fp32_state_dict_from_zero_checkpoint,
-    get_global_step_from_zero_checkpoint
-)
-
 from openfold.utils.logger import PerformanceLoggingCallback
 
 
@@ -274,6 +270,18 @@ class OpenFoldWrapper(pl.LightningModule):
             self.model, jax_path, version=model_version
         )
 
+def get_model_state_dict_from_ds_checkpoint(checkpoint_dir):
+    latest_path = os.path.join(checkpoint_dir, 'latest')
+    if os.path.isfile(latest_path):
+        with open(latest_path, 'r') as fd:
+            tag = fd.read().strip()
+    else:
+        raise ValueError(f"Unable to find 'latest' file at {latest_path}")
+
+    ds_checkpoint_dir = os.path.join(checkpoint_dir, tag)
+    _DS_CHECKPOINT_VERSION = 2  # based on manual parsing of checkpoint files
+    state_file = zero_to_fp32.get_model_state_file(ds_checkpoint_dir, _DS_CHECKPOINT_VERSION)
+    return torch.load(state_file)
 
 def main(args):
     if (args.seed is not None):
@@ -314,11 +322,10 @@ def main(args):
 
         else:  # Loads a checkpoint to start from a specific time step
             if os.path.isdir(args.resume_from_ckpt):
-                last_global_step = get_global_step_from_zero_checkpoint(
-                    args.resume_from_ckpt)
+                sd = get_model_state_dict_from_ds_checkpoint(args.resume_from_ckpt)
             else:
                 sd = torch.load(args.resume_from_ckpt)
-                last_global_step = int(sd['global_step'])
+            last_global_step = int(sd['global_step'])
             model_module.resume_last_lr_step(last_global_step)
             logging.info("Successfully loaded last lr step...")