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import copy
import shutil
import sys
import urllib.request
from pathlib import Path
import torch
from huggingface_hub import HfApi, login
from boltz_fastplms.modeling_boltz2 import (
Boltz2Model,
_filtered_kwargs,
_state_dict_without_wrappers,
_to_plain_python,
)
from weight_parity_utils import assert_state_dict_equal, assert_model_parameters_fp32
BOLTZ2_CKPT_URL = "https://huggingface.co/boltz-community/boltz-2/resolve/main/boltz2_conf.ckpt"
def _download_checkpoint_if_needed(checkpoint_path: Path) -> Path:
checkpoint_path.parent.mkdir(parents=True, exist_ok=True)
if not checkpoint_path.exists():
urllib.request.urlretrieve(BOLTZ2_CKPT_URL, str(checkpoint_path)) # noqa: S310
return checkpoint_path
def _copy_runtime_package(output_dir: Path) -> None:
source_pkg = Path(__file__).resolve().parent
project_root = source_pkg.parent
runtime_files = [
"__init__.py",
"modeling_boltz2.py",
"minimal_featurizer.py",
"minimal_structures.py",
"cif_writer.py",
]
for filename in runtime_files:
shutil.copyfile(source_pkg / filename, output_dir / filename)
shutil.copyfile(project_root / "entrypoint_setup.py", output_dir / "entrypoint_setup.py")
for flat_module in source_pkg.glob("vb_*.py"):
shutil.copyfile(flat_module, output_dir / flat_module.name)
def _ensure_local_boltz_module_on_path() -> Path:
script_root = Path(__file__).resolve().parents[1]
candidates = [script_root / "boltz" / "src"]
cwd = Path.cwd().resolve()
for parent in [cwd, *cwd.parents]:
candidates.append(parent / "boltz" / "src")
deduplicated_candidates: list[Path] = []
seen = set()
for candidate in candidates:
candidate_resolved = candidate.resolve()
candidate_key = str(candidate_resolved)
if candidate_key not in seen:
seen.add(candidate_key)
deduplicated_candidates.append(candidate_resolved)
for candidate in deduplicated_candidates:
package_marker = candidate / "boltz" / "__init__.py"
if package_marker.exists():
candidate_str = str(candidate)
if candidate_str not in sys.path:
sys.path.insert(0, candidate_str)
return candidate
raise FileNotFoundError(
"Unable to locate local boltz submodule. "
f"Checked: {', '.join([str(path) for path in deduplicated_candidates])}"
)
def _load_official_boltz2_model(
checkpoint_path: Path,
use_kernels: bool,
) -> torch.nn.Module:
_ensure_local_boltz_module_on_path()
from boltz.model.models.boltz2 import Boltz2 as OfficialBoltz2
from boltz.model.modules.diffusionv2 import AtomDiffusion
checkpoint = torch.load(
str(checkpoint_path),
map_location="cpu",
weights_only=False,
)
assert isinstance(checkpoint, dict), "Checkpoint must deserialize to a dictionary."
assert "hyper_parameters" in checkpoint, "Checkpoint missing 'hyper_parameters'."
assert "state_dict" in checkpoint, "Checkpoint missing 'state_dict'."
hyper_parameters = checkpoint["hyper_parameters"]
state_dict = checkpoint["state_dict"]
assert isinstance(hyper_parameters, dict), "Checkpoint hyper_parameters must be a dictionary."
assert isinstance(state_dict, dict), "Checkpoint state_dict must be a dictionary."
init_kwargs = _filtered_kwargs(
target=OfficialBoltz2,
kwargs=_to_plain_python(copy.deepcopy(hyper_parameters)),
)
if "use_kernels" in init_kwargs:
init_kwargs["use_kernels"] = use_kernels
assert "pairformer_args" in init_kwargs, (
"Checkpoint hyperparameters missing pairformer_args for official Boltz2."
)
raw_pairformer_args = init_kwargs["pairformer_args"]
assert isinstance(raw_pairformer_args, dict), "Expected pairformer_args to be a dictionary."
pairformer_args = _to_plain_python(copy.deepcopy(raw_pairformer_args))
assert isinstance(pairformer_args, dict), "Expected normalized pairformer_args to be a dictionary."
pairformer_args["v2"] = True
init_kwargs["pairformer_args"] = pairformer_args
assert "diffusion_process_args" in init_kwargs, (
"Checkpoint hyperparameters missing diffusion_process_args for official Boltz2."
)
raw_diffusion_process_args = init_kwargs["diffusion_process_args"]
assert isinstance(raw_diffusion_process_args, dict), (
"Expected diffusion_process_args to be a dictionary."
)
filtered_diffusion_process_args = _filtered_kwargs(
target=AtomDiffusion,
kwargs=raw_diffusion_process_args,
)
sanitized_diffusion_process_args: dict[str, object] = {}
for key in filtered_diffusion_process_args:
if key == "score_model_args":
continue
sanitized_diffusion_process_args[key] = filtered_diffusion_process_args[key]
init_kwargs["diffusion_process_args"] = sanitized_diffusion_process_args
official_model = OfficialBoltz2(**init_kwargs)
cleaned_state_dict = _state_dict_without_wrappers(state_dict)
target_keys = set(official_model.state_dict().keys())
filtered_state_dict: dict[str, torch.Tensor] = {}
for key in cleaned_state_dict:
if key in target_keys:
filtered_state_dict[key] = cleaned_state_dict[key]
missing_keys = sorted(target_keys.difference(filtered_state_dict.keys()))
assert len(missing_keys) == 0, (
"Official Boltz2 model is missing required checkpoint keys. "
f"Missing keys (first 20): {missing_keys[:20]}"
)
load_result = official_model.load_state_dict(filtered_state_dict, strict=False)
assert len(load_result.missing_keys) == 0, (
"Missing keys while loading official Boltz2 checkpoint. "
f"Missing keys (first 20): {load_result.missing_keys[:20]}"
)
assert len(load_result.unexpected_keys) == 0, (
"Unexpected keys while loading official Boltz2 checkpoint. "
f"Unexpected keys (first 20): {load_result.unexpected_keys[:20]}"
)
official_model = official_model.eval().cpu().to(torch.float32)
assert_model_parameters_fp32(
model=official_model,
model_name="official Boltz2 model",
)
return official_model
if __name__ == "__main__":
# py -m boltz_fastplms.get_boltz2_weights
parser = argparse.ArgumentParser()
parser.add_argument("--checkpoint_path", type=str, default="boltz_fastplms/weights/boltz2_conf.ckpt")
parser.add_argument("--output_dir", type=str, default="boltz2_automodel_export")
parser.add_argument("--repo_ids", nargs="*", type=str, default=["Synthyra/Boltz2"])
parser.add_argument("--hf_token", type=str, default=None)
parser.add_argument("--use_kernels", action="store_true")
parser.add_argument("--dry_run", action="store_true")
parser.add_argument("--skip-weights", action="store_true")
args = parser.parse_args()
# Standardization: use the first repo_id from repo_ids
repo_id = args.repo_ids[0] if args.repo_ids else "Synthyra/Boltz2"
checkpoint_path = _download_checkpoint_if_needed(Path(args.checkpoint_path))
output_dir = Path(args.output_dir)
output_dir.mkdir(parents=True, exist_ok=True)
if args.skip_weights:
repo_id = args.repo_ids[0] if args.repo_ids else "Synthyra/Boltz2"
if args.dry_run:
print(f"[skip-weights][dry-run] validated Boltz2 config parity for checkpoint {checkpoint_path}")
raise SystemExit(0)
official_model = _load_official_boltz2_model(
checkpoint_path=checkpoint_path,
use_kernels=args.use_kernels,
)
official_model.config.auto_map = {
"AutoConfig": "modeling_boltz2.Boltz2Config",
"AutoModel": "modeling_boltz2.Boltz2Model",
}
official_model.config.push_to_hub(repo_id)
print(f"[skip-weights] uploaded Boltz2 config to {repo_id}")
raise SystemExit(0)
official_model = _load_official_boltz2_model(
checkpoint_path=checkpoint_path,
use_kernels=args.use_kernels,
)
model = Boltz2Model.from_boltz_checkpoint(
checkpoint_path=str(checkpoint_path),
use_kernels=args.use_kernels,
)
model = model.eval().cpu().to(torch.float32)
assert_model_parameters_fp32(
model=model.core,
model_name="mapped Boltz2 inference core",
)
official_state_dict = official_model.state_dict()
candidate_state_dict = model.core.state_dict()
official_keys = set(official_state_dict.keys())
candidate_keys = set(candidate_state_dict.keys())
missing_official_keys = sorted(candidate_keys - official_keys)
assert len(missing_official_keys) == 0, (
"Official Boltz2 model is missing inference-core keys required by FastPLMs. "
f"Missing keys (first 20): {missing_official_keys[:20]}"
)
excluded_official_keys = sorted(official_keys - candidate_keys)
allowed_excluded_prefixes = (
"template_module.",
"bfactor_module.",
)
unexpected_excluded_official_keys: list[str] = []
for key in excluded_official_keys:
is_allowed = False
for prefix in allowed_excluded_prefixes:
if key.startswith(prefix):
is_allowed = True
break
if is_allowed is False:
unexpected_excluded_official_keys.append(key)
assert len(unexpected_excluded_official_keys) == 0, (
"Unexpected official Boltz2 keys not present in FastPLMs inference core. "
f"Unexpected keys (first 20): {unexpected_excluded_official_keys[:20]}"
)
filtered_official_state_dict: dict[str, torch.Tensor] = {}
for key in candidate_state_dict:
filtered_official_state_dict[key] = official_state_dict[key]
assert_state_dict_equal(
reference_state_dict=filtered_official_state_dict,
candidate_state_dict=candidate_state_dict,
context="Boltz2 weight parity",
)
model.config.auto_map = {
"AutoConfig": "modeling_boltz2.Boltz2Config",
"AutoModel": "modeling_boltz2.Boltz2Model",
}
if args.dry_run:
print(f"[dry_run] validated Boltz2 parity for checkpoint {checkpoint_path}")
else:
model.save_pretrained(str(output_dir))
_copy_runtime_package(output_dir=output_dir)
if args.repo_id is not None and args.dry_run is False:
if args.hf_token is not None:
login(token=args.hf_token)
api = HfApi()
api.create_repo(repo_id=args.repo_id, repo_type="model", exist_ok=True)
api.upload_folder(
folder_path=str(output_dir),
repo_id=args.repo_id,
repo_type="model",
)
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