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#
# 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.
"""Configuration schema for release and experimental ESMFold2 checkpoints."""
from __future__ import annotations
from dataclasses import asdict, dataclass, field
from typing import Any, TypeVar, cast
from transformers.configuration_utils import PretrainedConfig
from fastplms.attention import canonical_checkpoint_attention_backend
_ESMC_ATTENTION_IMPLEMENTATIONS = frozenset({"eager", "flex_attention", "sdpa"})
_ESMC_PRECISIONS = frozenset({"auto", "bf16", "fp32", "fp8"})
def _esmc_backbone_checkpoint_ids() -> tuple[str, str]:
"""Return the manifest-pinned official and FastPLMs ESMC repositories."""
from fastplms.registry import RegistryError, get_model_registry
registry = get_model_registry()
family = registry.families["esmfold2"]
if family.backbone_model is None:
raise RegistryError("families.esmfold2 must declare backbone_model.")
backbone = registry[family.backbone_model]
return backbone.official.repo_id, backbone.fast.repo_id
def normalize_esmc_id(esmc_id: str) -> str:
"""Resolve an official ESMC identifier to its FastPLMs checkpoint mirror."""
official_repo, fast_repo = _esmc_backbone_checkpoint_ids()
return fast_repo if esmc_id == official_repo else esmc_id
def normalize_esmc_attention_implementation(
implementation: str | dict[str, str] | None,
) -> str | None:
"""Validate the ESMC backend and translate the historical ``flex`` name."""
if isinstance(implementation, dict):
if tuple(implementation) != ("",):
raise ValueError(
"ESMFold2 has one ESMC attention backbone; use a string or {'': implementation}."
)
implementation = implementation[""]
canonical = canonical_checkpoint_attention_backend(implementation)
if canonical is not None and canonical not in _ESMC_ATTENTION_IMPLEMENTATIONS:
expected = sorted(_ESMC_ATTENTION_IMPLEMENTATIONS)
raise ValueError(
f"Unsupported ESMFold2 attention implementation {canonical!r}; "
f"expected one of {expected}."
)
return canonical
NestedConfig = TypeVar("NestedConfig")
def _nested_config(value: Any, config_type: type[NestedConfig]) -> NestedConfig:
if isinstance(value, config_type):
return value
return config_type(**value) if isinstance(value, dict) else config_type()
def _coerce_nested_field(
value: NestedConfig | dict[str, Any], config_type: type[NestedConfig]
) -> NestedConfig:
"""Convert serialized nested dictionaries while retaining supplied objects."""
return config_type(**value) if isinstance(value, dict) else value
@dataclass
class AtomAttentionConfig:
"""Sliding-window atom attention and three-dimensional RoPE settings."""
d_atom: int = field(default=128)
d_token: int = field(default=768)
n_blocks: int = field(default=3)
n_heads: int = field(default=4)
swa_window_size: int = field(default=128)
expansion_ratio: int = field(default=2)
spatial_rope_base_frequency: float = field(default=20.0)
n_spatial_rope_pairs_per_axis: int = field(default=2)
n_uid_rope_pairs: int = field(default=10)
uid_rope_base_frequency: float = field(default=10000.0)
@dataclass
class DiffusionModuleConfig:
"""Dimensions and depth of the coordinate diffusion network."""
sigma_data: float = field(default=16.0)
c_atom: int = field(default=128)
c_token: int = field(default=768)
c_z: int = field(default=256)
c_s_inputs: int = field(default=451)
fourier_dim: int = field(default=256)
relpos_r_max: int = field(default=32)
relpos_s_max: int = field(default=2)
atom_num_blocks: int = field(default=3)
atom_num_heads: int = field(default=4)
token_num_blocks: int = field(default=12)
token_num_heads: int = field(default=16)
transition_multiplier: int = field(default=2)
@dataclass
class FoldingTrunkConfig:
"""Iterative pair/single trunk dimensions."""
n_layers: int = field(default=24)
n_heads: int = field(default=8)
dropout: float = field(default=0.0)
@dataclass
class InputsEmbedderConfig:
"""Input feature width and atom encoder settings."""
d_inputs: int = field(default=451)
atom_encoder: AtomAttentionConfig = field(default_factory=AtomAttentionConfig)
def __post_init__(self) -> None:
self.atom_encoder = _coerce_nested_field(self.atom_encoder, AtomAttentionConfig)
@dataclass
class DiffusionStructureHeadConfig:
"""Training and inference schedules for coordinate denoising."""
diffusion_module: DiffusionModuleConfig = field(default_factory=DiffusionModuleConfig)
distogram_bins: int = field(default=128)
train_noise_log_mean: float = field(default=-1.2)
train_noise_log_std: float = field(default=1.5)
gamma_0: float = field(default=0.605)
gamma_min: float = field(default=1.107)
noise_scale: float = field(default=0.0)
step_scale: float = field(default=1.0)
inference_s_max: float = field(default=160.0)
inference_s_min: float = field(default=4e-4)
inference_p: float = field(default=8.0)
inference_num_steps: int = field(default=68)
def __post_init__(self) -> None:
self.diffusion_module = _coerce_nested_field(self.diffusion_module, DiffusionModuleConfig)
@dataclass
class ConfidenceHeadConfig:
"""Confidence-bin definitions and the compact confidence trunk."""
enabled: bool = field(default=True)
num_plddt_bins: int = field(default=50)
num_pde_bins: int = field(default=64)
num_pae_bins: int = field(default=64)
min_dist: float = field(default=2.0)
max_dist: float = field(default=52.0)
distogram_bins: int = field(default=128)
folding_trunk: FoldingTrunkConfig = field(
default_factory=lambda: FoldingTrunkConfig(n_layers=4)
)
def __post_init__(self) -> None:
self.folding_trunk = _coerce_nested_field(self.folding_trunk, FoldingTrunkConfig)
@dataclass
class MSAEncoderConfig:
"""Optional multiple-sequence-alignment encoder settings."""
enabled: bool = field(default=False)
d_msa: int = field(default=128)
d_hidden: int = field(default=32)
n_layers: int = field(default=4)
n_heads_msa: int = field(default=8)
msa_head_width: int = field(default=32)
@dataclass
class LMEncoderConfig:
"""Release-model pair encoder derived from language-model states."""
enabled: bool = field(default=True)
n_layers: int = field(default=4)
lm_dropout: float = field(default=0.25)
per_loop_lm_dropout: bool = field(default=True)
@dataclass
class ParcaeConfig:
"""Release-model diffusion-loop scheduler settings."""
enabled: bool = field(default=True)
poisson_mean: float = field(default=3.0)
min_steps: int = field(default=1)
max_steps: int | None = field(default=6)
coda_n_layers: int = field(default=2)
_SCALAR_DEFAULTS: tuple[tuple[str, Any], ...] = (
("d_single", 384),
("d_pair", 256),
("n_relative_residx_bins", 32),
("n_relative_chain_bins", 2),
("num_loops", 10),
("num_diffusion_samples", 8),
("disable_msa_features", False),
("lm_dropout", 0.0),
("force_lm_dropout_during_inference", False),
("lm_mask_pct", 0.0),
("lm_d_model", 2560),
("lm_num_layers", 80),
)
_NESTED_CONFIGS = (
("inputs", InputsEmbedderConfig),
("folding_trunk", FoldingTrunkConfig),
("structure_head", DiffusionStructureHeadConfig),
("confidence_head", ConfidenceHeadConfig),
("msa_encoder", MSAEncoderConfig),
("parcae", ParcaeConfig),
("lm_encoder", LMEncoderConfig),
)
class ESMFold2Config(PretrainedConfig):
"""Serializable ESMFold2 architecture, runtime, and precision settings."""
model_type = "esmfold2"
has_no_defaults_at_init = True
def __init__(self, **kwargs: Any) -> None:
legacy_backend = normalize_esmc_attention_implementation(kwargs.get("esmc_attn_backend"))
requested_backend = normalize_esmc_attention_implementation(
kwargs.get("attn_implementation")
)
resolved_backend = requested_backend or legacy_backend
kwargs["attn_implementation"] = resolved_backend
super().__init__(**kwargs)
self.type = kwargs.get("type", "release")
if self.type not in {"experimental", "release"}:
raise ValueError(
f"ESMFold2Config.type must be 'release' or 'experimental', got {self.type!r}"
)
for name, default in _SCALAR_DEFAULTS:
setattr(self, name, kwargs.get(name, default))
_official_esmc_repo, default_esmc_repo = _esmc_backbone_checkpoint_ids()
self.esmc_id = normalize_esmc_id(kwargs.get("esmc_id", default_esmc_repo))
self.esmc_attn_backend = resolved_backend
self.esmc_precision = str(kwargs.get("esmc_precision", "auto"))
if self.esmc_precision not in _ESMC_PRECISIONS:
raise ValueError(
"esmc_precision must be 'auto', 'bf16', 'fp32', or 'fp8', "
f"got {self.esmc_precision!r}."
)
for name, config_type in _NESTED_CONFIGS:
setattr(self, name, _nested_config(kwargs.get(name), config_type))
if not isinstance(self.msa_encoder.enabled, bool):
raise TypeError("msa_encoder.enabled must be a boolean.")
declared_msa_conditioning = kwargs.get("msa_conditioning")
if "msa_conditioning" in kwargs and not isinstance(declared_msa_conditioning, bool):
raise TypeError("msa_conditioning must be a boolean when provided.")
self.msa_conditioning = (
self.msa_encoder.enabled
if "msa_conditioning" not in kwargs
else declared_msa_conditioning
)
if self.msa_conditioning != self.msa_encoder.enabled:
raise ValueError(
"msa_conditioning must match msa_encoder.enabled; received "
f"{self.msa_conditioning!r} and {self.msa_encoder.enabled!r}."
)
self.msa_encoder_overwrite = bool(kwargs.get("msa_encoder_overwrite", True))
self.classifier_train_scope = str(kwargs.get("classifier_train_scope", "probe"))
if self.classifier_train_scope not in {"probe", "projection"}:
raise ValueError(
"classifier_train_scope must be 'probe' or 'projection', "
f"got {self.classifier_train_scope!r}."
)
self.classifier_probe_hidden_size = int(
kwargs.get("classifier_probe_hidden_size", 512)
)
self.classifier_probe_num_heads = int(kwargs.get("classifier_probe_num_heads", 4))
self.classifier_probe_dropout = float(kwargs.get("classifier_probe_dropout", 0.1))
self.classifier_hidden_size = int(kwargs.get("classifier_hidden_size", 4096))
self.classifier_dropout = float(kwargs.get("classifier_dropout", 0.2))
self.classifier_pooling_types = list(
kwargs.get("classifier_pooling_types", ["mean"])
)
def to_dict(self) -> dict[str, Any]:
output = cast(dict[str, Any], super().to_dict())
for name, _config_type in _NESTED_CONFIGS:
output[name] = asdict(getattr(self, name))
return output
__all__ = [
"ESMFold2Config",
"LMEncoderConfig",
"MSAEncoderConfig",
"ParcaeConfig",
"normalize_esmc_attention_implementation",
"normalize_esmc_id",
]
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