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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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 | """Decoder stack used by the encoder-decoder transformer."""
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__ = ["DecoderLayer", "TransformerDecoder"]
class DecoderLayer(nn.Module):
"""Single decoder block with self-attention, cross-attention, and feed-forward."""
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.self_attention_layer = MultiHeadAttention(d_model, num_heads, dropout_rate)
self.cross_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)
self.norm3 = nn.LayerNorm(d_model)
self.dropout3 = nn.Dropout(dropout_rate)
def forward(
self,
x: Tensor,
y: Tensor,
src_padding_mask: Tensor,
tgt_padding_mask: Tensor,
tgt_causal_mask: Tensor | None,
) -> Tensor:
"""Run one decoder layer using the configured layer-normalisation style."""
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(y, torch.Tensor):
raise TypeError("y must be a torch.Tensor")
if y.dim() != 3:
raise ValueError(
f"y must be a 3D torch.Tensor of shape (B, S, D); got shape {tuple(y.shape)}"
)
if x.shape[0] != y.shape[0] or x.shape[-1] != y.shape[-1]:
raise ValueError("Encoder memory and decoder input must match in batch and d_model")
for mask_name, mask in (
("src_padding_mask", src_padding_mask),
("tgt_padding_mask", tgt_padding_mask),
):
if not isinstance(mask, torch.Tensor):
raise TypeError(f"{mask_name} must be a torch.Tensor")
if mask.dtype != torch.bool or mask.dim() != 4:
raise TypeError(
f"{mask_name} must be boolean with shape (B, H, 1, S);"
f" got dtype {mask.dtype} and shape {tuple(mask.shape)}"
)
if self.pre_norm:
normed = self.norm1(y)
self_attn_out = self.self_attention_layer(
normed,
normed,
normed,
tgt_padding_mask,
tgt_padding_mask,
tgt_causal_mask,
)
y = y + self.dropout1(self_attn_out)
normed_y = self.norm2(y)
cross_attn_out = self.cross_attention_layer(
normed_y,
x,
x,
tgt_padding_mask,
src_padding_mask,
)
y = y + self.dropout2(cross_attn_out)
ff_out = self.feed_forward(self.norm3(y))
y = y + self.dropout3(ff_out)
return y
# Self-attention (post-LN)
self_attn_out = self.self_attention_layer(
y,
y,
y,
tgt_padding_mask,
tgt_padding_mask,
tgt_causal_mask,
)
y = self.norm1(y + self.dropout1(self_attn_out))
# Cross-attention (encoder memory as keys/values)
cross_attn_out = self.cross_attention_layer(
y,
x,
x,
tgt_padding_mask,
src_padding_mask,
)
y = self.norm2(y + self.dropout2(cross_attn_out))
# Feed-forward block
ff_out = self.feed_forward(y)
return self.norm3(y + self.dropout3(ff_out))
class TransformerDecoder(nn.Module):
"""Stack of decoder 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(
[
DecoderLayer(
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,
y: Tensor,
src_padding_mask: Tensor,
tgt_padding_mask: Tensor,
tgt_causal_mask: Tensor | None,
) -> Tensor:
"""Run the decoder stack for all time steps."""
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(y, torch.Tensor):
raise TypeError("y must be a torch.Tensor")
if y.dim() != 3:
raise ValueError(
f"y must be a 3D torch.Tensor of shape (B, S, D); got shape {tuple(y.shape)}"
)
if x.shape[0] != y.shape[0] or x.shape[-1] != y.shape[-1]:
raise ValueError("Encoder memory and decoder input must match in batch and d_model")
for layer in self.layers:
if self.use_ckpt:
def _fn(y_, *, _layer=layer):
return _layer(x, y_, src_padding_mask, tgt_padding_mask, tgt_causal_mask)
y = ckpt.checkpoint(_fn, y, use_reentrant=False)
else:
y = layer(x, y, src_padding_mask, tgt_padding_mask, tgt_causal_mask)
return y
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