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import copy
import inspect
from collections.abc import Mapping, Sequence
from dataclasses import dataclass
from typing import Any, Dict, Optional
import torch
import torch._dynamo
import torch.nn as nn
from torch import Tensor
from transformers import PreTrainedModel, PretrainedConfig
from transformers.modeling_outputs import ModelOutput
from .cif_writer import write_cif
from .minimal_featurizer import build_boltz2_features
from .minimal_structures import ProteinStructureTemplate
from .vb_const import bond_types as _vb_const_bond_types # noqa: F401
from .vb_layers_attention import AttentionPairBias as _vb_layers_attention_marker # noqa: F401
from .vb_layers_attentionv2 import AttentionPairBias as _vb_layers_attentionv2_marker # noqa: F401
from .vb_layers_confidence_utils import compute_ptms as _vb_layers_confidence_utils_marker # noqa: F401
from .vb_layers_dropout import get_dropout_mask as _vb_layers_dropout_marker # noqa: F401
from .vb_layers_initialize import gating_init_ as _vb_layers_initialize_marker # noqa: F401
from .vb_layers_outer_product_mean import OuterProductMean as _vb_layers_outer_product_mean_marker # noqa: F401
from .vb_layers_pair_averaging import PairWeightedAveraging as _vb_layers_pair_averaging_marker # noqa: F401
from .vb_layers_transition import Transition as _vb_layers_transition_marker # noqa: F401
from .vb_layers_triangular_mult import TriangleMultiplicationIncoming as _vb_layers_triangular_mult_marker # noqa: F401
from .vb_loss_diffusionv2 import weighted_rigid_align as _vb_loss_diffusionv2_marker # noqa: F401
from .vb_modules_transformersv2 import DiffusionTransformer as _vb_modules_transformersv2_marker # noqa: F401
from .vb_modules_utils import LinearNoBias as _vb_modules_utils_marker # noqa: F401
from .vb_potentials_potentials import get_potentials as _vb_potentials_potentials_marker # noqa: F401
from .vb_potentials_schedules import ParameterSchedule as _vb_potentials_schedules_marker # noqa: F401
from .vb_tri_attn_attention import TriangleAttentionStartingNode as _vb_tri_attn_attention_marker # noqa: F401
from .vb_tri_attn_primitives import Attention as _vb_tri_attn_primitives_marker # noqa: F401
from .vb_tri_attn_utils import permute_final_dims as _vb_tri_attn_utils_marker # noqa: F401
from . import vb_const as const
from . import vb_layers_initialize as init
from .vb_layers_pairformer import PairformerModule
from .vb_modules_confidencev2 import ConfidenceModule
from .vb_modules_diffusion_conditioning import DiffusionConditioning
from .vb_modules_diffusionv2 import AtomDiffusion, DiffusionModule
from .vb_modules_encodersv2 import RelativePositionEncoder
from .vb_modules_trunkv2 import (
ContactConditioning,
DistogramModule,
InputEmbedder,
MSAModule,
)
def _default_steering_args() -> dict[str, Any]:
return {
"fk_steering": False,
"num_particles": 3,
"fk_lambda": 4.0,
"fk_resampling_interval": 3,
"physical_guidance_update": False,
"contact_guidance_update": False,
"num_gd_steps": 16,
}
def _boltz2_reference_diffusion_overrides() -> dict[str, Any]:
# Match Boltz2 CLI inference defaults from boltz.main/Boltz2DiffusionParams.
return {
"gamma_0": 0.8,
"gamma_min": 1.0,
"noise_scale": 1.003,
"rho": 7,
"step_scale": 1.5,
"sigma_min": 0.0001,
"sigma_max": 160.0,
"sigma_data": 16.0,
"P_mean": -1.2,
"P_std": 1.5,
"coordinate_augmentation": True,
"alignment_reverse_diff": True,
"synchronize_sigmas": True,
}
def _enforce_pairformer_v2(pairformer_args: Mapping[str, Any], context: str) -> dict[str, Any]:
assert isinstance(pairformer_args, Mapping), (
f"Expected {context} pairformer_args to be a dictionary."
)
out = _to_plain_python(copy.deepcopy(pairformer_args))
if "v2" in out:
assert out["v2"], f"{context} pairformer_args['v2'] must be True for Boltz2."
out["v2"] = True
return out
def _require_key(mapping: dict[str, Any], key: str) -> Any:
assert key in mapping, f"Missing required key '{key}' in checkpoint hyperparameters."
return mapping[key]
def _state_dict_without_wrappers(state_dict: dict[str, Tensor]) -> dict[str, Tensor]:
cleaned: dict[str, Tensor] = {}
for key, value in state_dict.items():
if key.startswith("ema."):
continue
new_key = key
if new_key.startswith("model."):
new_key = new_key[len("model.") :]
if new_key.startswith("module."):
new_key = new_key[len("module.") :]
cleaned[new_key] = value
return cleaned
def _to_cpu_detached(value: Any) -> Any:
if torch.is_tensor(value):
return value.detach().cpu()
if isinstance(value, dict):
out: dict[Any, Any] = {}
for key, nested_value in value.items():
out[key] = _to_cpu_detached(nested_value)
return out
if isinstance(value, list):
return [_to_cpu_detached(item) for item in value]
if isinstance(value, tuple):
return tuple(_to_cpu_detached(item) for item in value)
return value
def _to_plain_python(value: Any) -> Any:
if isinstance(value, Mapping):
out: dict[Any, Any] = {}
for key, nested_value in value.items():
out[key] = _to_plain_python(nested_value)
return out
if isinstance(value, list):
return [_to_plain_python(item) for item in value]
if isinstance(value, tuple):
return [_to_plain_python(item) for item in value]
if isinstance(value, Sequence) and not isinstance(value, (str, bytes)):
return [_to_plain_python(item) for item in value]
return value
def _filtered_kwargs(target: Any, kwargs: dict[str, Any]) -> dict[str, Any]:
signature = inspect.signature(target.__init__)
allowed = set(signature.parameters.keys())
allowed.discard("self")
filtered: dict[str, Any] = {}
for key, value in kwargs.items():
if key in allowed:
filtered[key] = value
return filtered
@dataclass
class Boltz2StructureOutput(ModelOutput):
sample_atom_coords: Optional[torch.Tensor] = None
atom_pad_mask: Optional[torch.Tensor] = None
plddt: Optional[torch.Tensor] = None
confidence_score: Optional[torch.Tensor] = None
complex_plddt: Optional[torch.Tensor] = None
iptm: Optional[torch.Tensor] = None
ptm: Optional[torch.Tensor] = None
sequence: Optional[str] = None
structure_template: Optional[ProteinStructureTemplate] = None
raw_output: Optional[dict[str, torch.Tensor]] = None
class Boltz2Config(PretrainedConfig):
model_type = "boltz2_automodel"
def __init__(
self,
core_kwargs: Optional[dict[str, Any]] = None,
num_bins: int = 64,
default_recycling_steps: int = 3,
default_sampling_steps: int = 200,
default_diffusion_samples: int = 1,
**kwargs,
) -> None:
super().__init__(**kwargs)
if core_kwargs is None:
core_kwargs = {}
self.core_kwargs = core_kwargs
self.num_bins = num_bins
self.default_recycling_steps = default_recycling_steps
self.default_sampling_steps = default_sampling_steps
self.default_diffusion_samples = default_diffusion_samples
@classmethod
def from_hyperparameters(
cls,
hparams: dict[str, Any],
use_kernels: bool = False,
default_recycling_steps: Optional[int] = None,
default_sampling_steps: Optional[int] = None,
default_diffusion_samples: Optional[int] = None,
) -> "Boltz2Config":
assert isinstance(hparams, dict), "Expected checkpoint hyperparameters as a dictionary."
required = [
"atom_s",
"atom_z",
"token_s",
"token_z",
"num_bins",
"embedder_args",
"msa_args",
"pairformer_args",
"score_model_args",
"diffusion_process_args",
]
for key in required:
_require_key(hparams, key)
pairformer_args = _enforce_pairformer_v2(
hparams["pairformer_args"],
context="checkpoint",
)
diffusion_process_args = _to_plain_python(
copy.deepcopy(hparams["diffusion_process_args"])
)
diffusion_overrides = _boltz2_reference_diffusion_overrides()
for key in diffusion_overrides:
diffusion_process_args[key] = diffusion_overrides[key]
core_kwargs: dict[str, Any] = {
"atom_s": hparams["atom_s"],
"atom_z": hparams["atom_z"],
"token_s": hparams["token_s"],
"token_z": hparams["token_z"],
"num_bins": hparams["num_bins"],
"embedder_args": _to_plain_python(copy.deepcopy(hparams["embedder_args"])),
"msa_args": _to_plain_python(copy.deepcopy(hparams["msa_args"])),
"pairformer_args": pairformer_args,
"score_model_args": _to_plain_python(copy.deepcopy(hparams["score_model_args"])),
"diffusion_process_args": diffusion_process_args,
"use_kernels": use_kernels,
}
if "confidence_model_args" in hparams:
confidence_model_args = _to_plain_python(
copy.deepcopy(hparams["confidence_model_args"])
)
if "pairformer_args" in confidence_model_args:
confidence_model_args["pairformer_args"] = _enforce_pairformer_v2(
confidence_model_args["pairformer_args"],
context="confidence",
)
core_kwargs["confidence_model_args"] = confidence_model_args
else:
core_kwargs["confidence_model_args"] = None
if "confidence_prediction" in hparams:
core_kwargs["confidence_prediction"] = hparams["confidence_prediction"]
else:
core_kwargs["confidence_prediction"] = True
if "token_level_confidence" in hparams:
core_kwargs["token_level_confidence"] = hparams["token_level_confidence"]
else:
core_kwargs["token_level_confidence"] = True
if "alpha_pae" in hparams:
core_kwargs["alpha_pae"] = hparams["alpha_pae"]
else:
core_kwargs["alpha_pae"] = 0.0
if "atoms_per_window_queries" in hparams:
core_kwargs["atoms_per_window_queries"] = hparams["atoms_per_window_queries"]
else:
core_kwargs["atoms_per_window_queries"] = 32
if "atoms_per_window_keys" in hparams:
core_kwargs["atoms_per_window_keys"] = hparams["atoms_per_window_keys"]
else:
core_kwargs["atoms_per_window_keys"] = 128
if "atom_feature_dim" in hparams:
core_kwargs["atom_feature_dim"] = hparams["atom_feature_dim"]
else:
core_kwargs["atom_feature_dim"] = 128
if "bond_type_feature" in hparams:
core_kwargs["bond_type_feature"] = hparams["bond_type_feature"]
else:
core_kwargs["bond_type_feature"] = False
if "run_trunk_and_structure" in hparams:
core_kwargs["run_trunk_and_structure"] = hparams["run_trunk_and_structure"]
else:
core_kwargs["run_trunk_and_structure"] = True
if "skip_run_structure" in hparams:
core_kwargs["skip_run_structure"] = hparams["skip_run_structure"]
else:
core_kwargs["skip_run_structure"] = False
if "fix_sym_check" in hparams:
core_kwargs["fix_sym_check"] = hparams["fix_sym_check"]
else:
core_kwargs["fix_sym_check"] = False
if "cyclic_pos_enc" in hparams:
core_kwargs["cyclic_pos_enc"] = hparams["cyclic_pos_enc"]
else:
core_kwargs["cyclic_pos_enc"] = False
if "use_no_atom_char" in hparams:
core_kwargs["use_no_atom_char"] = hparams["use_no_atom_char"]
else:
core_kwargs["use_no_atom_char"] = False
if "use_atom_backbone_feat" in hparams:
core_kwargs["use_atom_backbone_feat"] = hparams["use_atom_backbone_feat"]
else:
core_kwargs["use_atom_backbone_feat"] = False
if "use_residue_feats_atoms" in hparams:
core_kwargs["use_residue_feats_atoms"] = hparams["use_residue_feats_atoms"]
else:
core_kwargs["use_residue_feats_atoms"] = False
if "conditioning_cutoff_min" in hparams:
core_kwargs["conditioning_cutoff_min"] = hparams["conditioning_cutoff_min"]
else:
core_kwargs["conditioning_cutoff_min"] = 4.0
if "conditioning_cutoff_max" in hparams:
core_kwargs["conditioning_cutoff_max"] = hparams["conditioning_cutoff_max"]
else:
core_kwargs["conditioning_cutoff_max"] = 20.0
if "steering_args" in hparams and hparams["steering_args"] is not None:
core_kwargs["steering_args"] = _to_plain_python(
copy.deepcopy(hparams["steering_args"])
)
else:
core_kwargs["steering_args"] = _default_steering_args()
if "validation_args" in hparams:
validation_args = hparams["validation_args"]
assert isinstance(validation_args, Mapping), (
"Expected 'validation_args' in checkpoint hyperparameters to be a mapping."
)
if default_recycling_steps is None and "recycling_steps" in validation_args:
default_recycling_steps = validation_args["recycling_steps"]
if default_sampling_steps is None and "sampling_steps" in validation_args:
default_sampling_steps = validation_args["sampling_steps"]
if default_diffusion_samples is None and "diffusion_samples" in validation_args:
default_diffusion_samples = validation_args["diffusion_samples"]
if default_recycling_steps is None:
default_recycling_steps = 3
if default_sampling_steps is None:
default_sampling_steps = 200
if default_diffusion_samples is None:
default_diffusion_samples = 1
return cls(
core_kwargs=core_kwargs,
num_bins=hparams["num_bins"],
default_recycling_steps=default_recycling_steps,
default_sampling_steps=default_sampling_steps,
default_diffusion_samples=default_diffusion_samples,
)
class Boltz2InferenceCore(nn.Module):
def __init__(
self,
atom_s: int,
atom_z: int,
token_s: int,
token_z: int,
num_bins: int,
embedder_args: dict[str, Any],
msa_args: dict[str, Any],
pairformer_args: dict[str, Any],
score_model_args: dict[str, Any],
diffusion_process_args: dict[str, Any],
confidence_model_args: Optional[dict[str, Any]] = None,
atom_feature_dim: int = 128,
confidence_prediction: bool = True,
token_level_confidence: bool = True,
alpha_pae: float = 0.0,
atoms_per_window_queries: int = 32,
atoms_per_window_keys: int = 128,
run_trunk_and_structure: bool = True,
skip_run_structure: bool = False,
bond_type_feature: bool = False,
fix_sym_check: bool = False,
cyclic_pos_enc: bool = False,
use_no_atom_char: bool = False,
use_atom_backbone_feat: bool = False,
use_residue_feats_atoms: bool = False,
conditioning_cutoff_min: float = 4.0,
conditioning_cutoff_max: float = 20.0,
use_kernels: bool = False,
steering_args: Optional[dict[str, Any]] = None,
) -> None:
super().__init__()
self.use_kernels = use_kernels
self.confidence_prediction = confidence_prediction
self.token_level_confidence = token_level_confidence
self.alpha_pae = alpha_pae
self.run_trunk_and_structure = run_trunk_and_structure
self.skip_run_structure = skip_run_structure
self.bond_type_feature = bond_type_feature
self.steering_args = steering_args if steering_args is not None else _default_steering_args()
assert "v2" in pairformer_args, "Boltz2 requires pairformer_args['v2']."
assert pairformer_args["v2"], "Boltz2 requires pairformer_args['v2']=True."
full_embedder_args = {
"atom_s": atom_s,
"atom_z": atom_z,
"token_s": token_s,
"token_z": token_z,
"atoms_per_window_queries": atoms_per_window_queries,
"atoms_per_window_keys": atoms_per_window_keys,
"atom_feature_dim": atom_feature_dim,
"use_no_atom_char": use_no_atom_char,
"use_atom_backbone_feat": use_atom_backbone_feat,
"use_residue_feats_atoms": use_residue_feats_atoms,
**embedder_args,
}
full_embedder_args = _filtered_kwargs(InputEmbedder, full_embedder_args)
self.input_embedder = InputEmbedder(**full_embedder_args)
self.s_init = nn.Linear(token_s, token_s, bias=False)
self.z_init_1 = nn.Linear(token_s, token_z, bias=False)
self.z_init_2 = nn.Linear(token_s, token_z, bias=False)
self.rel_pos = RelativePositionEncoder(
token_z,
fix_sym_check=fix_sym_check,
cyclic_pos_enc=cyclic_pos_enc,
)
self.token_bonds = nn.Linear(1, token_z, bias=False)
if self.bond_type_feature:
self.token_bonds_type = nn.Embedding(len(const.bond_types) + 1, token_z)
self.contact_conditioning = ContactConditioning(
token_z=token_z,
cutoff_min=conditioning_cutoff_min,
cutoff_max=conditioning_cutoff_max,
)
self.s_norm = nn.LayerNorm(token_s)
self.z_norm = nn.LayerNorm(token_z)
self.s_recycle = nn.Linear(token_s, token_s, bias=False)
self.z_recycle = nn.Linear(token_z, token_z, bias=False)
init.gating_init_(self.s_recycle.weight)
init.gating_init_(self.z_recycle.weight)
torch._dynamo.config.cache_size_limit = 512 # noqa: SLF001
torch._dynamo.config.accumulated_cache_size_limit = 512 # noqa: SLF001
msa_kwargs = _filtered_kwargs(MSAModule, {"token_z": token_z, "token_s": token_s, **msa_args})
self.msa_module = MSAModule(**msa_kwargs)
pairformer_kwargs = _filtered_kwargs(
PairformerModule,
{"token_s": token_s, "token_z": token_z, **pairformer_args},
)
assert "token_s" in pairformer_kwargs and "token_z" in pairformer_kwargs
pairformer_token_s = pairformer_kwargs.pop("token_s")
pairformer_token_z = pairformer_kwargs.pop("token_z")
self.pairformer_module = PairformerModule(
pairformer_token_s,
pairformer_token_z,
**pairformer_kwargs,
)
diffusion_conditioning_kwargs = {
"token_s": token_s,
"token_z": token_z,
"atom_s": atom_s,
"atom_z": atom_z,
"atoms_per_window_queries": atoms_per_window_queries,
"atoms_per_window_keys": atoms_per_window_keys,
"atom_encoder_depth": score_model_args["atom_encoder_depth"],
"atom_encoder_heads": score_model_args["atom_encoder_heads"],
"token_transformer_depth": score_model_args["token_transformer_depth"],
"token_transformer_heads": score_model_args["token_transformer_heads"],
"atom_decoder_depth": score_model_args["atom_decoder_depth"],
"atom_decoder_heads": score_model_args["atom_decoder_heads"],
"atom_feature_dim": atom_feature_dim,
"conditioning_transition_layers": score_model_args["conditioning_transition_layers"],
"use_no_atom_char": use_no_atom_char,
"use_atom_backbone_feat": use_atom_backbone_feat,
"use_residue_feats_atoms": use_residue_feats_atoms,
}
diffusion_conditioning_kwargs = _filtered_kwargs(
DiffusionConditioning,
diffusion_conditioning_kwargs,
)
self.diffusion_conditioning = DiffusionConditioning(**diffusion_conditioning_kwargs)
structure_score_model_args = {
"token_s": token_s,
"atom_s": atom_s,
"atoms_per_window_queries": atoms_per_window_queries,
"atoms_per_window_keys": atoms_per_window_keys,
**score_model_args,
}
structure_score_model_args = _filtered_kwargs(
DiffusionModule,
structure_score_model_args,
)
structure_module_kwargs = {
"score_model_args": structure_score_model_args,
"compile_score": False,
**diffusion_process_args,
}
structure_module_kwargs = _filtered_kwargs(AtomDiffusion, structure_module_kwargs)
self.structure_module = AtomDiffusion(**structure_module_kwargs)
self.distogram_module = DistogramModule(token_z, num_bins)
if self.confidence_prediction:
assert confidence_model_args is not None, (
"confidence_prediction=True requires confidence_model_args in config."
)
confidence_kwargs = {
"token_s": token_s,
"token_z": token_z,
"token_level_confidence": token_level_confidence,
"bond_type_feature": bond_type_feature,
"fix_sym_check": fix_sym_check,
"cyclic_pos_enc": cyclic_pos_enc,
"conditioning_cutoff_min": conditioning_cutoff_min,
"conditioning_cutoff_max": conditioning_cutoff_max,
**confidence_model_args,
}
confidence_kwargs = _filtered_kwargs(ConfidenceModule, confidence_kwargs)
self.confidence_module = ConfidenceModule(**confidence_kwargs)
def forward(
self,
feats: dict[str, Tensor],
recycling_steps: int = 3,
num_sampling_steps: Optional[int] = None,
diffusion_samples: int = 1,
max_parallel_samples: Optional[int] = None,
run_confidence_sequentially: bool = True,
) -> dict[str, Tensor]:
s_inputs = self.input_embedder(feats)
s_init = self.s_init(s_inputs)
z_init = self.z_init_1(s_inputs)[:, :, None] + self.z_init_2(s_inputs)[:, None, :]
relative_position_encoding = self.rel_pos(feats)
z_init = z_init + relative_position_encoding
z_init = z_init + self.token_bonds(feats["token_bonds"].float())
if self.bond_type_feature:
z_init = z_init + self.token_bonds_type(feats["type_bonds"].long())
z_init = z_init + self.contact_conditioning(feats)
s = torch.zeros_like(s_init)
z = torch.zeros_like(z_init)
mask = feats["token_pad_mask"].float()
pair_mask = mask[:, :, None] * mask[:, None, :]
if self.run_trunk_and_structure:
for _ in range(recycling_steps + 1):
s = s_init + self.s_recycle(self.s_norm(s))
z = z_init + self.z_recycle(self.z_norm(z))
z = z + self.msa_module(
z,
s_inputs,
feats,
use_kernels=self.use_kernels,
)
s, z = self.pairformer_module(
s,
z,
mask=mask,
pair_mask=pair_mask,
use_kernels=self.use_kernels,
)
pdistogram = self.distogram_module(z)
output: dict[str, Tensor] = {
"pdistogram": pdistogram,
"s": s,
"z": z,
}
if self.run_trunk_and_structure and (not self.skip_run_structure):
q, c, to_keys, atom_enc_bias, atom_dec_bias, token_trans_bias = (
self.diffusion_conditioning(
s_trunk=s,
z_trunk=z,
relative_position_encoding=relative_position_encoding,
feats=feats,
)
)
diffusion_conditioning = {
"q": q,
"c": c,
"to_keys": to_keys,
"atom_enc_bias": atom_enc_bias,
"atom_dec_bias": atom_dec_bias,
"token_trans_bias": token_trans_bias,
}
with torch.autocast("cuda", enabled=False):
struct_out = self.structure_module.sample(
s_trunk=s.float(),
s_inputs=s_inputs.float(),
feats=feats,
num_sampling_steps=num_sampling_steps,
atom_mask=feats["atom_pad_mask"].float(),
multiplicity=diffusion_samples,
max_parallel_samples=max_parallel_samples,
steering_args=self.steering_args,
diffusion_conditioning=diffusion_conditioning,
)
output.update(struct_out)
if self.confidence_prediction:
if self.skip_run_structure:
x_pred = feats["coords"].repeat_interleave(diffusion_samples, 0)
else:
assert "sample_atom_coords" in output, (
"Structure sampling did not produce sample_atom_coords."
)
x_pred = output["sample_atom_coords"]
output.update(
self.confidence_module(
s_inputs=s_inputs.detach(),
s=s.detach(),
z=z.detach(),
x_pred=x_pred.detach(),
feats=feats,
pred_distogram_logits=output["pdistogram"][:, :, :, 0].detach(),
multiplicity=diffusion_samples,
run_sequentially=run_confidence_sequentially,
use_kernels=self.use_kernels,
)
)
return output
class Boltz2Model(PreTrainedModel):
config_class = Boltz2Config
base_model_prefix = "core"
all_tied_weights_keys = {}
def __init__(self, config: Boltz2Config) -> None:
super().__init__(config)
assert isinstance(config.core_kwargs, dict), "config.core_kwargs must be a dictionary."
self.core = Boltz2InferenceCore(**config.core_kwargs)
def _init_weights(self, module: nn.Module) -> None: # noqa: ARG002
return
def _detied_state_dict(self) -> dict[str, Tensor]:
raw_state = self.state_dict()
seen_ptrs: dict[int, str] = {}
out: dict[str, Tensor] = {}
for key, tensor in raw_state.items():
if torch.is_tensor(tensor):
ptr = tensor.untyped_storage().data_ptr()
if ptr in seen_ptrs:
out[key] = tensor.clone()
else:
seen_ptrs[ptr] = key
out[key] = tensor
else:
out[key] = tensor
return out
def save_pretrained(self, save_directory: str, **kwargs: Any) -> None:
if "safe_serialization" not in kwargs:
kwargs["safe_serialization"] = False
if "state_dict" not in kwargs:
kwargs["state_dict"] = self._detied_state_dict()
super().save_pretrained(save_directory, **kwargs)
@property
def device(self) -> torch.device:
return next(self.parameters()).device
@classmethod
def from_boltz_checkpoint(
cls,
checkpoint_path: str,
map_location: str | torch.device = "cpu",
use_kernels: bool = False,
default_recycling_steps: Optional[int] = None,
default_sampling_steps: Optional[int] = None,
default_diffusion_samples: Optional[int] = None,
) -> "Boltz2Model":
# Boltz Lightning checkpoints include OmegaConf objects and require full unpickling.
checkpoint = torch.load(
checkpoint_path,
map_location=map_location,
weights_only=False,
)
assert isinstance(checkpoint, dict), "Checkpoint must deserialize to a dictionary."
_require_key(checkpoint, "hyper_parameters")
_require_key(checkpoint, "state_dict")
hparams = checkpoint["hyper_parameters"]
assert isinstance(hparams, dict), "Checkpoint hyper_parameters must be a dictionary."
state_dict = checkpoint["state_dict"]
assert isinstance(state_dict, dict), "Checkpoint state_dict must be a dictionary."
config = Boltz2Config.from_hyperparameters(
hparams,
use_kernels=use_kernels,
default_recycling_steps=default_recycling_steps,
default_sampling_steps=default_sampling_steps,
default_diffusion_samples=default_diffusion_samples,
)
model = cls(config)
cleaned = _state_dict_without_wrappers(state_dict)
target_keys = set(model.core.state_dict().keys())
for key in target_keys:
assert ".attention.norm_s." not in key, (
"Boltz2 inference core unexpectedly uses v1 attention parameters. "
"Expected pairformer v2 architecture."
)
filtered: dict[str, Tensor] = {}
for key, value in cleaned.items():
if key in target_keys:
filtered[key] = value
missing = sorted(target_keys.difference(filtered.keys()))
assert len(missing) == 0, (
"Checkpoint is missing required parameters for Boltz2 inference core. "
f"Missing keys (first 20): {missing[:20]}"
)
load_result = model.core.load_state_dict(filtered, strict=False)
loaded_missing = sorted(load_result.missing_keys)
assert len(loaded_missing) == 0, (
"Model has unexpected missing keys after load_state_dict. "
f"Missing keys (first 20): {loaded_missing[:20]}"
)
assert len(load_result.unexpected_keys) == 0
model.eval()
return model
def forward(
self,
feats: dict[str, Tensor],
recycling_steps: Optional[int] = None,
num_sampling_steps: Optional[int] = None,
diffusion_samples: Optional[int] = None,
max_parallel_samples: Optional[int] = None,
run_confidence_sequentially: bool = True,
) -> dict[str, Tensor]:
if recycling_steps is None:
recycling_steps = self.config.default_recycling_steps
if num_sampling_steps is None:
num_sampling_steps = self.config.default_sampling_steps
if diffusion_samples is None:
diffusion_samples = self.config.default_diffusion_samples
return self.core(
feats=feats,
recycling_steps=recycling_steps,
num_sampling_steps=num_sampling_steps,
diffusion_samples=diffusion_samples,
max_parallel_samples=max_parallel_samples,
run_confidence_sequentially=run_confidence_sequentially,
)
def _to_model_device(
self,
feats: dict[str, Tensor],
float_dtype: torch.dtype,
) -> dict[str, Tensor]:
moved: dict[str, Tensor] = {}
for key, value in feats.items():
if torch.is_tensor(value):
if value.is_floating_point():
moved[key] = value.to(device=self.device, dtype=float_dtype)
else:
moved[key] = value.to(device=self.device)
else:
moved[key] = value
return moved
def predict_structure(
self,
amino_acid_sequence: str,
recycling_steps: Optional[int] = None,
num_sampling_steps: Optional[int] = None,
diffusion_samples: Optional[int] = None,
max_parallel_samples: Optional[int] = None,
run_confidence_sequentially: bool = True,
float_dtype: Optional[torch.dtype] = None,
) -> Boltz2StructureOutput:
if float_dtype is None:
float_dtype = torch.float32
feats, template = build_boltz2_features(
amino_acid_sequence=amino_acid_sequence,
num_bins=self.config.num_bins,
atoms_per_window_queries=self.core.input_embedder.atom_encoder.atoms_per_window_queries,
)
feats = self._to_model_device(feats, float_dtype=float_dtype)
with torch.no_grad():
output = self.forward(
feats=feats,
recycling_steps=recycling_steps,
num_sampling_steps=num_sampling_steps,
diffusion_samples=diffusion_samples,
max_parallel_samples=max_parallel_samples,
run_confidence_sequentially=run_confidence_sequentially,
)
sample_atom_coords = output["sample_atom_coords"].detach().cpu()
non_finite_mask = torch.logical_not(torch.isfinite(sample_atom_coords))
assert not torch.any(non_finite_mask), (
"sample_atom_coords contains non-finite values. "
f"Non-finite count: {int(non_finite_mask.sum().item())}"
)
atom_pad_mask = feats["atom_pad_mask"][0].detach().cpu()
plddt = output["plddt"].detach().cpu() if "plddt" in output else None
complex_plddt = output["complex_plddt"].detach().cpu() if "complex_plddt" in output else None
iptm = output["iptm"].detach().cpu() if "iptm" in output else None
ptm = output["ptm"].detach().cpu() if "ptm" in output else None
confidence_score = None
if (complex_plddt is not None) and (iptm is not None) and (ptm is not None):
if torch.allclose(iptm, torch.zeros_like(iptm)):
confidence_score = (4 * complex_plddt + ptm) / 5
else:
confidence_score = (4 * complex_plddt + iptm) / 5
return Boltz2StructureOutput(
sample_atom_coords=sample_atom_coords,
atom_pad_mask=atom_pad_mask,
plddt=plddt,
confidence_score=confidence_score,
complex_plddt=complex_plddt,
iptm=iptm,
ptm=ptm,
sequence=template.sequence,
structure_template=template,
raw_output={key: _to_cpu_detached(val) for key, val in output.items()},
)
def save_as_cif(
self,
structure_output: Boltz2StructureOutput,
output_path: str,
sample_index: int = 0,
) -> str:
assert structure_output.structure_template is not None, (
"structure_output.structure_template is required for CIF export."
)
assert structure_output.sample_atom_coords is not None, (
"structure_output.sample_atom_coords is required for CIF export."
)
assert structure_output.atom_pad_mask is not None, (
"structure_output.atom_pad_mask is required for CIF export."
)
return write_cif(
structure_template=structure_output.structure_template,
atom_coords=structure_output.sample_atom_coords,
atom_mask=structure_output.atom_pad_mask,
output_path=output_path,
plddt=structure_output.plddt,
sample_index=sample_index,
)
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