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# Copyright (c) Meta Platforms, Inc. and affiliates
# All rights reserved.
#
# This source code is licensed under the license found in the
# MIT_LICENSE file in the root directory of this source tree.
from dataclasses import dataclass
from typing import Optional, Union
import torch
from fairseq2.assets.card import AssetCard
from fairseq2.data.vocabulary_info import VocabularyInfo
from fairseq2.models.utils.arch_registry import ArchitectureRegistry
from fairseq2.nn.embedding import StandardEmbedding, init_scaled_embedding
from fairseq2.typing import DataType, Device
from seamless_communication.models.aligner.model import (
UnitY2AlignmentEncoder,
UnitY2AlignmentFrontend,
UnitY2AlignmentModel,
)
from seamless_communication.models.unity.char_tokenizer import load_unity_char_tokenizer
from seamless_communication.models.unity.loader import load_unity_unit_tokenizer
@dataclass
class AlignmentEncoderConfig:
model_dim: int
feat_dim: int
num_text_layers: int
num_feat_layers: int
dropout: float
temperature: float
reduction_factor: int
@dataclass
class UnitY2AlignmentFrontendConfig:
unit_vocab_info: VocabularyInfo
text_vocab_size: int
@dataclass
class UnitY2AlignmentConfig:
model_name_or_card: Union[str, AssetCard]
alignment_encoder_config: AlignmentEncoderConfig
alignment_frontend_config: UnitY2AlignmentFrontendConfig
aligner_archs = ArchitectureRegistry[UnitY2AlignmentConfig]("unity2_aligner")
aligner_arch = aligner_archs.decorator
@aligner_arch("nar_t2u_aligner")
def _aligner_nar_t2u() -> UnitY2AlignmentConfig:
encoder_config = AlignmentEncoderConfig(
model_dim=1024,
feat_dim=1024,
num_text_layers=2,
num_feat_layers=3,
dropout=0.1,
temperature=1.0,
reduction_factor=1,
)
frontend_config = UnitY2AlignmentFrontendConfig(
unit_vocab_info=VocabularyInfo(
size=10082, unk_idx=3, bos_idx=0, eos_idx=2, pad_idx=1
),
text_vocab_size=10943,
)
return UnitY2AlignmentConfig(
model_name_or_card="nar_t2u_aligner",
alignment_encoder_config=encoder_config,
alignment_frontend_config=frontend_config,
)
class UnitY2AlignmentBuilder:
config: UnitY2AlignmentConfig
device: Optional[Device]
dtype: DataType
def __init__(
self,
config: UnitY2AlignmentConfig,
*,
device: Optional[Device] = None,
dtype: DataType = torch.float32,
) -> None:
"""
:param config:
The configuration to use.
: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_model(self) -> UnitY2AlignmentModel:
alignment_frontend = self.build_alignment_frontend()
alignment_encoder = self.build_alignment_encoder()
return UnitY2AlignmentModel(alignment_frontend, alignment_encoder)
def build_alignment_frontend(self) -> UnitY2AlignmentFrontend:
text_tokenizer = load_unity_char_tokenizer(self.config.model_name_or_card)
unit_tokenizer = load_unity_unit_tokenizer(self.config.model_name_or_card)
embed_text = StandardEmbedding(
num_embeddings=self.config.alignment_frontend_config.text_vocab_size,
embedding_dim=self.config.alignment_encoder_config.model_dim,
pad_idx=self.config.alignment_frontend_config.unit_vocab_info.pad_idx,
init_fn=init_scaled_embedding,
device=self.device,
dtype=self.dtype,
)
embed_unit = StandardEmbedding(
num_embeddings=self.config.alignment_frontend_config.unit_vocab_info.size,
embedding_dim=self.config.alignment_encoder_config.model_dim,
pad_idx=self.config.alignment_frontend_config.unit_vocab_info.pad_idx,
init_fn=init_scaled_embedding,
device=self.device,
dtype=self.dtype,
)
return UnitY2AlignmentFrontend(
embed_text, embed_unit, text_tokenizer, unit_tokenizer
)
def build_alignment_encoder(self, training: bool = False) -> UnitY2AlignmentEncoder:
cfg = self.config.alignment_encoder_config
alignment_encoder = UnitY2AlignmentEncoder(
embed_dim=cfg.model_dim,
feat_dim=cfg.feat_dim,
text_layers=cfg.num_text_layers,
feat_layers=cfg.num_feat_layers,
dropout=cfg.dropout,
temperature=cfg.temperature,
reduction_factor=cfg.reduction_factor,
dtype=self.dtype,
)
alignment_encoder.training = training
return alignment_encoder
def create_unity2_alignment_model(
config: UnitY2AlignmentConfig,
device: Optional[Device] = None,
dtype: DataType = torch.float32,
) -> UnitY2AlignmentModel:
"""Create a UnitY model.
:param config:
The configuration to use.
:param device:
The device on which to initialize modules.
:param dtype:
The data type of module parameters and buffers.
"""
unity2_aligner_builder = UnitY2AlignmentBuilder(
config,
device=device,
dtype=dtype,
)
return unity2_aligner_builder.build_model()
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