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# 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. | ||
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import logging | ||
import argparse | ||
import os | ||
import shutil | ||
import torch | ||
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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 | ||
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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}") | ||
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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 | ||
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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]) | ||
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if 'ema' in model_dict: | ||
ema_state_dict = model_dict['ema']['params'] | ||
model_dict['ema']['params'] = convert_deprecated_v1_keys( | ||
ema_state_dict) | ||
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if is_dir: | ||
param_shapes = convert_deprecated_v1_keys( | ||
model_dict['param_shapes'][0]) | ||
model_dict['param_shapes'] = [param_shapes] | ||
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shutil.copytree(checkpoint_path, args.output_ckpt_path) | ||
out_fname = os.path.join( | ||
model_output_path, os.path.basename(model_file)) | ||
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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 | ||
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torch.save(model_dict, out_fname) | ||
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if __name__ == "__main__": | ||
parser = argparse.ArgumentParser() | ||
parser.add_argument("input_ckpt_path", type=str) | ||
parser.add_argument("output_ckpt_path", type=str) | ||
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args = parser.parse_args() | ||
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convert_v1_to_v2_weights(args) |
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