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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
from fairseq2.data import VocabularyInfo
from fairseq2.models.transformer import (
TransformerEmbeddingFrontend,
TransformerFrontend,
)
from fairseq2.models.utils.arch_registry import ArchitectureRegistry
from fairseq2.nn.embedding import Embedding, StandardEmbedding, init_scaled_embedding
from fairseq2.nn.position_encoder import SinusoidalPositionEncoder
from fairseq2.nn.projection import TiedProjection
from fairseq2.nn.transformer import (
FeedForwardNetwork,
MultiheadAttention,
StandardFeedForwardNetwork,
StandardMultiheadAttention,
TransformerNormOrder,
create_default_sdpa,
)
from fairseq2.typing import DataType, Device
from seamless_communication.models.monotonic_decoder.model import MonotonicDecoderModel
from seamless_communication.models.monotonic_decoder.monotonic_decoder import (
MonotonicTransformerDecoder,
)
from seamless_communication.models.monotonic_decoder.monotonic_decoder_layer import (
MonotonicTransformerDecoderLayer,
)
from seamless_communication.models.monotonic_decoder.p_choose import PChooseLayer
@dataclass
class MonotonicDecoderConfig:
"""Holds the configuration of an Monotonic Decoder model."""
model_dim: int
"""The dimensionality of the model."""
max_seq_len: int
"""The expected maximum sequence length."""
vocab_info: VocabularyInfo
"""The vocabulary information."""
num_decoder_layers: int
"""The number of Transformer decoder layers."""
num_decoder_attn_heads: int
"""The number of attention heads in Transformer decoder layers."""
ffn_inner_dim: int
"""The inner dimensionality of Transformer feed-forward networks."""
dropout_p: float
"""The dropout probability in Transformer layers."""
energy_bias_value: float
"""The value of the energy bias parameter to be added to the
monotonic energy in the PChooseLayer."""
monotonic_temperature: float
"""The parameter with which to divide the monotonic energy
to compute p_choose."""
num_monotonic_energy_layers: int
"""The number of layers in the EnergyProjection module."""
pre_decision_ratio: int
"""The kernel size and stride of the average pooling
in the PChooseLayer."""
monotonic_decoder_archs = ArchitectureRegistry[MonotonicDecoderConfig](
"monotonic_decoder"
)
monotonic_decoder_arch = monotonic_decoder_archs.decorator
@monotonic_decoder_arch("dense_1b")
def _dense_1b() -> MonotonicDecoderConfig:
return MonotonicDecoderConfig(
model_dim=1024,
max_seq_len=4096,
vocab_info=VocabularyInfo(
size=256102, unk_idx=1, bos_idx=2, eos_idx=3, pad_idx=0
),
num_decoder_layers=24,
num_decoder_attn_heads=16,
ffn_inner_dim=1024 * 8,
dropout_p=0.1,
energy_bias_value=-0.5,
monotonic_temperature=0.2,
num_monotonic_energy_layers=4,
pre_decision_ratio=2,
)
class MonotonicDecoderBuilder:
"""Builds modules of a Monotonic Decoder.
To tweak the architecture, you can derive from this class and override the
corresponding methods.
"""
config: MonotonicDecoderConfig
device: Optional[Device]
dtype: Optional[DataType]
def __init__(
self,
config: MonotonicDecoderConfig,
*,
device: Optional[Device] = None,
dtype: Optional[DataType] = None,
) -> 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) -> MonotonicDecoderModel:
text_embed = self.build_embedding()
text_decoder_frontend = self.build_frontend(text_embed)
text_decoder = self.build_decoder()
final_proj = TiedProjection(text_embed.weight, bias=None)
return MonotonicDecoderModel(
text_decoder_frontend,
text_decoder,
final_proj,
)
def build_embedding(self) -> StandardEmbedding:
"""Build an embedding table."""
return StandardEmbedding(
num_embeddings=self.config.vocab_info.size,
embedding_dim=self.config.model_dim,
pad_idx=self.config.vocab_info.pad_idx,
init_fn=init_scaled_embedding,
device=self.device,
dtype=self.dtype,
)
def build_frontend(self, embed: Embedding) -> TransformerFrontend:
"""Build a Transformer decoder front-end."""
pos_encoder = SinusoidalPositionEncoder(
self.config.model_dim,
self.config.max_seq_len,
_legacy_pad_idx=1,
device=self.device,
)
return TransformerEmbeddingFrontend(
embed,
pos_encoder,
dropout_p=self.config.dropout_p,
device=self.device,
dtype=self.dtype,
)
def build_decoder(self) -> MonotonicTransformerDecoder:
"""Build a Transformer decoder."""
num_layers = self.config.num_decoder_layers
layers = [self.build_decoder_layer() for _ in range(num_layers)]
return MonotonicTransformerDecoder(
layers,
device=self.device,
dtype=self.dtype,
)
def build_decoder_layer(self) -> MonotonicTransformerDecoderLayer:
"""Build a Transformer decoder layer."""
self_attn = self.build_attention(self.config.num_decoder_attn_heads)
encoder_decoder_attn = self.build_attention(self.config.num_decoder_attn_heads)
p_choose_layer = self.build_p_choose_layer(self.config.num_decoder_attn_heads)
ffn = self.build_ffn()
return MonotonicTransformerDecoderLayer(
self_attn,
encoder_decoder_attn,
p_choose_layer,
ffn,
dropout_p=self.config.dropout_p,
device=self.device,
dtype=self.dtype,
)
def build_attention(self, num_heads: int) -> MultiheadAttention:
"""Build a Transformer multi-head attention layer."""
sdpa = create_default_sdpa(attn_dropout_p=self.config.dropout_p)
return StandardMultiheadAttention(
self.config.model_dim,
num_heads,
sdpa=sdpa,
device=self.device,
dtype=self.dtype,
)
def build_p_choose_layer(self, num_heads: int) -> PChooseLayer:
"""Build a PChoose layer."""
return PChooseLayer(
self.config.model_dim,
num_heads,
self.config.energy_bias_value,
self.config.monotonic_temperature,
self.config.num_monotonic_energy_layers,
self.config.pre_decision_ratio,
device=self.device,
dtype=self.dtype,
)
def build_ffn(self) -> FeedForwardNetwork:
"""Build a Transformer feed-forward network."""
return StandardFeedForwardNetwork(
self.config.model_dim,
self.config.ffn_inner_dim,
bias=True,
norm_order=TransformerNormOrder.PRE,
device=self.device,
dtype=self.dtype,
)
def create_monotonic_decoder_model(
config: MonotonicDecoderConfig,
*,
device: Optional[Device] = None,
dtype: Optional[DataType] = None,
) -> MonotonicDecoderModel:
"""Create an Monotonic Decoder 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.
"""
return MonotonicDecoderBuilder(config, device=device, dtype=dtype).build_model()
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