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6edd10d 2d52135 6edd10d 2d52135 6edd10d 2d52135 6edd10d 2d52135 6edd10d 2d52135 6edd10d 2d52135 6edd10d 2d52135 6edd10d 2d52135 6edd10d 2d52135 6edd10d | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 | """Encoder stack used by the transformer architecture."""
from __future__ import annotations
import torch
import torch.nn as nn
import torch.utils.checkpoint as ckpt
from torch import Tensor
from .attention import MultiHeadAttention
from .feedforward import FeedForwardLayer
__all__ = ["EncoderLayer", "TransformerEncoder"]
class EncoderLayer(nn.Module):
"""Self-attention + feed-forward block supporting pre/post layer normalisation."""
def __init__(
self,
d_model: int,
num_heads: int,
d_ff: int,
dropout_rate: float,
*,
layer_norm_style: str = "post",
) -> None:
super().__init__()
if not isinstance(d_model, int):
raise TypeError(f"d_model must be an int, got {type(d_model)}")
if not isinstance(num_heads, int):
raise TypeError(f"num_heads must be an int, got {type(num_heads)}")
if not isinstance(d_ff, int):
raise TypeError(f"d_ff must be an int, got {type(d_ff)}")
if not isinstance(dropout_rate, float):
raise TypeError(f"dropout_rate must be a float, got {type(dropout_rate)}")
if not isinstance(layer_norm_style, str):
raise TypeError(f"layer_norm_style must be a string, got {type(layer_norm_style)}")
if d_model <= 0:
raise ValueError("d_model must be strictly greater than 0")
if num_heads <= 0:
raise ValueError("num_heads must be strictly greater than 0")
if d_ff <= 0:
raise ValueError("d_ff must be strictly greater than 0")
if not 0.0 <= dropout_rate < 1.0:
raise ValueError("dropout_rate must be in [0, 1)")
style = layer_norm_style.lower()
if style not in {"pre", "post"}:
raise ValueError("layer_norm_style must be either 'pre' or 'post' (case-insensitive)")
self.layer_norm_style = style
self.pre_norm = style == "pre"
self.attention_layer = MultiHeadAttention(d_model, num_heads, dropout_rate)
self.feed_forward = FeedForwardLayer(d_model, d_ff, dropout_rate)
self.norm1 = nn.LayerNorm(d_model)
self.dropout1 = nn.Dropout(dropout_rate)
self.norm2 = nn.LayerNorm(d_model)
self.dropout2 = nn.Dropout(dropout_rate)
def forward(self, x: Tensor, src_padding_mask: Tensor) -> Tensor:
if not isinstance(x, torch.Tensor):
raise TypeError("x must be a torch.Tensor")
if x.dim() != 3:
raise ValueError(
f"x must be a 3D torch.Tensor of shape (B, S, D); got shape {tuple(x.shape)}"
)
if not isinstance(src_padding_mask, torch.Tensor):
raise TypeError("src_padding_mask must be a torch.Tensor")
if src_padding_mask.dtype != torch.bool or src_padding_mask.dim() != 4:
raise TypeError(
f"src_padding_mask must be boolean with shape (B, H, 1, S);"
f" got dtype {src_padding_mask.dtype} and shape {tuple(src_padding_mask.shape)}"
)
if self.pre_norm:
normed = self.norm1(x)
attn_out = self.attention_layer(
normed, normed, normed, src_padding_mask, src_padding_mask
)
x = x + self.dropout1(attn_out)
ff_out = self.feed_forward(self.norm2(x))
x = x + self.dropout2(ff_out)
return x
attn_out = self.attention_layer(x, x, x, src_padding_mask, src_padding_mask)
x = self.norm1(x + self.dropout1(attn_out))
ff_out = self.feed_forward(x)
return self.norm2(x + self.dropout2(ff_out))
class TransformerEncoder(nn.Module):
"""Stack of encoder layers with optional activation checkpointing."""
def __init__(
self,
d_model: int,
num_heads: int,
d_ff: int,
num_layers: int,
dropout_rate: float,
*,
layer_norm_style: str = "post",
) -> None:
super().__init__()
if not isinstance(num_layers, int):
raise TypeError(f"num_layers must be an int, got {type(num_layers)}")
if num_layers <= 0:
raise ValueError("num_layers must be strictly greater than 0")
if not isinstance(layer_norm_style, str):
raise TypeError(f"layer_norm_style must be a string, got {type(layer_norm_style)}")
style = layer_norm_style.lower()
if style not in {"pre", "post"}:
raise ValueError("layer_norm_style must be either 'pre' or 'post' (case-insensitive)")
self.layer_norm_style = style
self.layers = nn.ModuleList(
[
EncoderLayer(
d_model,
num_heads,
d_ff,
dropout_rate,
layer_norm_style=style,
)
for _ in range(num_layers)
]
)
self.use_ckpt = False
def forward(self, x: Tensor, src_padding_mask: Tensor) -> Tensor:
if not isinstance(x, torch.Tensor):
raise TypeError("x must be a torch.Tensor")
if x.dim() != 3:
raise ValueError(
f"x must be a 3D tensor of shape (B, S, D); got shape {tuple(x.shape)}"
)
for layer in self.layers:
if self.use_ckpt:
def _fn(x_, *, _layer=layer):
return _layer(x_, src_padding_mask)
x = ckpt.checkpoint(_fn, x, use_reentrant=False)
else:
x = layer(x, src_padding_mask)
return x
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