Merge weight-loading changes into setup-improvements

openfold/utils/import_weights.py

@@ -14,6 +14,7 @@
 # limitations under the License.
 
 import re
+import logging
 from enum import Enum
 from dataclasses import dataclass
 from functools import partial
@@ -681,15 +682,18 @@ def convert_deprecated_v1_keys(state_dict):
     }
 
     convert_key_re = re.compile("(%s)" % "|".join(map(re.escape, replacements.keys())))
+    template_emb_re = re.compile(r"^((module\.)?(model\.)?)(template(?!_embedder).*)") 
 
     converted_state_dict = {}
     for key, value in state_dict.items():
         # For each match, look-up replacement value in the dictionary
-        new_key = convert_key_re.sub(lambda m: replacements[m.group()], key)
+        new_key = convert_key_re.sub(lambda m: replacements[m.group(1)], key)
 
-        # Add prefix for template modules
-        if new_key.startswith('template'):
-            new_key = f'template_embedder.{new_key}'
+        # Add prefix for template layers 
+        template_match = re.match(template_emb_re, new_key)
+        if template_match:
+            prefix = template_match.group(1)
+            new_key = f'{prefix if prefix else ""}template_embedder.{template_match.group(4)}'
 
         converted_state_dict[new_key] = value
 

scripts/convert_v1_to_v2_weights.py

+# Copyright 2022 AlQuraishi Laboratory
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+# Converts OpenFold .pt checkpoints into AlphaFold .npz ones, which can then be
+# used to run inference using DeepMind's JAX code.
+
+import logging
+import argparse
+import os
+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
+
+
+def convert_v1_to_v2_weights(args):
+    checkpoint_path = args.input_ckpt_path
+    is_dir = os.path.isdir(checkpoint_path)
+    if is_dir:
+        # A DeepSpeed checkpoint
+        logging.info(
+            'Converting deepspeed checkpoint found at {args.input_checkpoint_path}')
+        state_dict_key = 'module'
+        latest_path = os.path.join(checkpoint_path, '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_path, tag)
+        model_output_path = os.path.join(args.output_ckpt_path, tag)
+        optim_files = get_optim_files(ds_checkpoint_dir)
+        zero_stage, _, _ = parse_optim_states(optim_files, ds_checkpoint_dir)
+        model_file = get_model_state_file(ds_checkpoint_dir, zero_stage)
+    else:
+        # A Pytorch Lightning checkpoint
+        logging.info(
+            'Converting pytorch lightning checkpoint found at {args.input_checkpoint_path}')
+        state_dict_key = 'state_dict'
+        model_output_path = args.output_ckpt_path
+        model_file = checkpoint_path
+
+    model_dict = torch.load(model_file, map_location=torch.device('cpu'))
+    model_dict[state_dict_key] = convert_deprecated_v1_keys(
+        model_dict[state_dict_key])
+
+    if 'ema' in model_dict:
+        ema_state_dict = model_dict['ema']['params']
+        model_dict['ema']['params'] = convert_deprecated_v1_keys(
+            ema_state_dict)
+
+    if is_dir:
+        param_shapes = convert_deprecated_v1_keys(
+            model_dict['param_shapes'][0])
+        model_dict['param_shapes'] = [param_shapes]
+
+        shutil.copytree(checkpoint_path, args.output_ckpt_path)
+        out_fname = os.path.join(
+            model_output_path, os.path.basename(model_file))
+
+        for optim_file in optim_files:
+            optim_dict = torch.load(optim_file)
+            new_optim_dict = optim_dict.copy()
+            new_optim_dict['optimizer_state_dict']['param_slice_mappings'][0] = convert_deprecated_v1_keys(
+                optim_dict['optimizer_state_dict']['param_slice_mappings'][0])
+            out_optim_fname = os.path.join(
+                model_output_path, os.path.basename(optim_file))
+            torch.save(new_optim_dict, out_optim_fname)
+    else:
+        out_fname = model_output_path
+
+    torch.save(model_dict, out_fname)
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument("input_ckpt_path", type=str)
+    parser.add_argument("output_ckpt_path", type=str)
+
+    args = parser.parse_args()
+
+    convert_v1_to_v2_weights(args)

train_openfold.py

@@ -39,6 +39,7 @@ from scripts.zero_to_fp32 import (
     get_fp32_state_dict_from_zero_checkpoint,
     get_global_step_from_zero_checkpoint
 )
+from scripts.zero_to_fp32 import get_optim_files, parse_optim_states, get_model_state_file
 
 from openfold.utils.logger import PerformanceLoggingCallback
 
@@ -294,8 +295,13 @@ def main(args):
             sd = get_fp32_state_dict_from_zero_checkpoint(args.resume_from_ckpt)
         else:
             sd = torch.load(args.resume_from_ckpt)
-        sd = {k[len("module."):]:v for k,v in sd.items()}
-        import_openfold_weights_(model=model_module, state_dict=sd)
+        if 'module' in sd:
+            module_sd = {k[len("module."):]:v for k,v in sd['module'].items()}
+            import_openfold_weights_(model=model_module, state_dict=module_sd)
+        elif 'state_dict' in sd:
+            import_openfold_weights_(model=model_module, state_dict=sd['state_dict'])
+        else:
+            import_openfold_weights_(model=model_module, state_dict=sd)
         logging.info("Successfully loaded model weights...")
     if(args.resume_from_jax_params):
         model_module.load_from_jax(args.resume_from_jax_params)