Adjust weight conversion and add a script for converting checkpoints.

scripts/convert_v1_to_v2_weights.py

@@ -0,0 +1,78 @@
+# 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 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 get_latest_checkpoint_dir(checkpoint_dir):
+    # Based on zero_to_fp32.get_fp32_state_dict_from_zero_checkpoint
+    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}")
+
+    return os.path.join(checkpoint_dir, tag)
+
+
+def convert_v1_to_v2_weights(args):
+    # TODO can we use zero_to_fp32.get_fp32_state_dict_from_zero_checkpoint here?
+
+    checkpoint_path = args.input_ckpt_path
+    is_dir = os.path.isdir(checkpoint_path)
+    if is_dir:
+        # A DeepSpeed checkpoint
+        ds_checkpoint_path = get_latest_checkpoint_dir(checkpoint_path)
+        state_dict_key = 'module'
+        optim_files = get_optim_files(ds_checkpoint_path)
+        zero_stage, _, _ = parse_optim_states(optim_files, ds_checkpoint_path)
+        model_file = get_model_state_file(ds_checkpoint_path, zero_stage)
+    else:
+        # A Pytorch Lightning checkpoint
+        state_dict_key = 'state_dict'
+        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:
+        shutil.copytree(checkpoint_path, args.output_ckpt_path)
+        out_fname = os.path.join(args.output_ckpt_path, os.path.basename(model_file))
+    else:
+        out_fname = args.output_ckpt_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)