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# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
#
from abc import abstractmethod
from dataclasses import dataclass
from typing import Optional
from fairseq2.config_registry import ConfigRegistry
from fairseq2.logging import get_log_writer
from fairseq2.typing import DataType, Device
from torch.nn import Module
from lcm.models.sonar_normalizer import SonarNormalizer, load_sonar_normalizer_model
logger = get_log_writer(__name__)
"""
An abstract LCM model class for the bare minimum
"""
ABSTRACT_LCM_MODEL_TYPE = "abstract_lcm"
@dataclass
class AbstractLCModelConfig:
model_type: str = ABSTRACT_LCM_MODEL_TYPE
sonar_embed_dim: int = 1024
sonar_normalizer_name: Optional[str] = None
lcm_archs = ConfigRegistry[AbstractLCModelConfig]()
lcm_arch = lcm_archs.decorator
class AbstractLCModel(Module):
"""Asbtract Class for LCM models"""
def __init__(
self,
config: AbstractLCModelConfig,
) -> None:
"""
Asbtract LCM model
"""
super().__init__()
self.config = config
@property
def dtype(self):
return next(self.parameters()).dtype
@property
def device(self):
return next(self.parameters()).device
class AbstractLCModelBuilder:
"""Builds modules of an LCM"""
config: AbstractLCModelConfig
device: Optional[Device]
dtype: Optional[DataType]
def __init__(
self,
config: AbstractLCModelConfig,
*,
device: Optional[Device] = None,
dtype: Optional[DataType] = None,
) -> None:
"""
:param config:
The configuration.
:param device:
The device on which to initialize modules.
:param dtype:
The data type of module parameters and buffers.
"""
self.config = config
self.device, self.dtype = device, dtype
def build_sonar_normalizer(
self,
) -> Optional[SonarNormalizer]:
if self.config.sonar_normalizer_name is not None:
logger.info(
f"Building sonar_normalizer = {self.config.sonar_normalizer_name}"
)
return load_sonar_normalizer_model(
self.config.sonar_normalizer_name,
device=self.device,
dtype=self.dtype,
)
return None
@abstractmethod
def build_model(self) -> AbstractLCModel:
"""Build a model."""
...
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