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"""Transformer encoder module."""
from torch import Tensor, nn
from .attention import Attention
class Encoder(nn.Module):
def __init__(
self,
dim: int,
heads: int,
dim_head: int,
ff_mult: int,
depth: int,
dropout_rate: float = 0.0,
use_rotary_emb: bool = False,
one_kv_head: bool = False,
) -> None:
super().__init__()
self.norm = nn.LayerNorm(dim)
self.layers = nn.ModuleList(
[
Attention(
dim=dim,
heads=heads,
causal=False,
dim_head=dim_head,
ff_mult=ff_mult,
dropout_rate=dropout_rate,
use_flash=True,
norm_context=False,
use_rotary_emb=use_rotary_emb,
one_kv_head=one_kv_head,
)
for _ in range(depth)
]
)
def forward(
self,
x: Tensor,
) -> Tensor:
"""Applies decoder block on input signals."""
for self_attn in self.layers:
x = x + self_attn(x)
return self.norm(x)
|
