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import torch
import torch.nn as nn
from esm.models.esm3 import ESM3
from esm.models.esmc import ESMC
from esm.models.function_decoder import FunctionTokenDecoder
from esm.models.vqvae import (
StructureTokenDecoder,
StructureTokenEncoder,
)
from esm.tokenization import (
get_esm3_model_tokenizers,
get_esmc_model_tokenizers,
)
from esm.utils.constants.esm3 import data_root
from esm.utils.constants.models import (
ESM3_FUNCTION_DECODER_V0,
ESM3_OPEN_SMALL,
ESM3_STRUCTURE_DECODER_V0,
ESM3_STRUCTURE_ENCODER_V0,
ESMC_300M,
ESMC_600M,
)
ModelBuilder = Callable[[torch.device | str], nn.Module]
def ESM3_structure_encoder_v0(device: torch.device | str = "cpu"):
with torch.device(device):
model = StructureTokenEncoder(
d_model=1024, n_heads=1, v_heads=128, n_layers=2, d_out=128, n_codes=4096
).eval()
state_dict = torch.load(
data_root("esm3") / "data/weights/esm3_structure_encoder_v0.pth",
map_location=device,
)
model.load_state_dict(state_dict)
return model
def ESM3_structure_decoder_v0(device: torch.device | str = "cpu"):
with torch.device(device):
model = StructureTokenDecoder(d_model=1280, n_heads=20, n_layers=30).eval()
state_dict = torch.load(
data_root("esm3") / "data/weights/esm3_structure_decoder_v0.pth",
map_location=device,
)
model.load_state_dict(state_dict)
return model
def ESM3_function_decoder_v0(device: torch.device | str = "cpu"):
with torch.device(device):
model = FunctionTokenDecoder().eval()
state_dict = torch.load(
data_root("esm3") / "data/weights/esm3_function_decoder_v0.pth",
map_location=device,
)
model.load_state_dict(state_dict)
return model
def ESMC_300M_202412(device: torch.device | str = "cpu", use_flash_attn: bool = True):
with torch.device(device):
model = ESMC(
d_model=960,
n_heads=15,
n_layers=30,
tokenizer=get_esmc_model_tokenizers(),
use_flash_attn=use_flash_attn,
).eval()
state_dict = torch.load(
data_root("esmc-300") / "data/weights/esmc_300m_2024_12_v0.pth",
map_location=device,
)
model.load_state_dict(state_dict)
return model
def ESMC_600M_202412(device: torch.device | str = "cpu", use_flash_attn: bool = True):
with torch.device(device):
model = ESMC(
d_model=1152,
n_heads=18,
n_layers=36,
tokenizer=get_esmc_model_tokenizers(),
use_flash_attn=use_flash_attn,
).eval()
state_dict = torch.load(
data_root("esmc-600") / "data/weights/esmc_600m_2024_12_v0.pth",
map_location=device,
)
model.load_state_dict(state_dict)
return model
def ESM3_sm_open_v0(device: torch.device | str = "cpu"):
with torch.device(device):
model = ESM3(
d_model=1536,
n_heads=24,
v_heads=256,
n_layers=48,
structure_encoder_fn=ESM3_structure_encoder_v0,
structure_decoder_fn=ESM3_structure_decoder_v0,
function_decoder_fn=ESM3_function_decoder_v0,
tokenizers=get_esm3_model_tokenizers(ESM3_OPEN_SMALL),
).eval()
state_dict = torch.load(
data_root("esm3") / "data/weights/esm3_sm_open_v1.pth", map_location=device
)
model.load_state_dict(state_dict)
return model
LOCAL_MODEL_REGISTRY: dict[str, ModelBuilder] = {
ESM3_OPEN_SMALL: ESM3_sm_open_v0,
ESM3_STRUCTURE_ENCODER_V0: ESM3_structure_encoder_v0,
ESM3_STRUCTURE_DECODER_V0: ESM3_structure_decoder_v0,
ESM3_FUNCTION_DECODER_V0: ESM3_function_decoder_v0,
ESMC_600M: ESMC_600M_202412,
ESMC_300M: ESMC_300M_202412,
}
def load_local_model(
model_name: str, device: torch.device = torch.device("cpu")
) -> nn.Module:
if model_name not in LOCAL_MODEL_REGISTRY:
raise ValueError(f"Model {model_name} not found in local model registry.")
return LOCAL_MODEL_REGISTRY[model_name](device)
# Register custom versions of ESM3 for use with the local inference API
def register_local_model(model_name: str, model_builder: ModelBuilder) -> None:
LOCAL_MODEL_REGISTRY[model_name] = model_builder
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