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from torch import Tensor
import torch.nn as nn
from jaxtyping import Bool, Float
import math
class MultiHeadAttention(nn.Module):
"""
Terminology (jaxtyping):
B: batch_size
T_q: target sequence length (query)
T_k: source sequence length (key/value)
D: d_model (model dimension)
H: n_heads (number of heads)
d_k: dimension of each head (d_model / n_heads)
"""
def __init__(self, d_model: int, n_heads: int) -> None:
super().__init__()
assert d_model % n_heads == 0, "d_model must be divisible by n_heads"
self.d_model: int = d_model
self.n_heads: int = n_heads
self.d_k: int = d_model // n_heads
self.w_q: nn.Linear = nn.Linear(d_model, d_model, bias=False)
self.w_k: nn.Linear = nn.Linear(d_model, d_model, bias=False)
self.w_v: nn.Linear = nn.Linear(d_model, d_model, bias=False)
self.w_o: nn.Linear = nn.Linear(d_model, d_model, bias=False)
self.attention_weights: Tensor | None = None
@staticmethod
def attention(
query: Float[Tensor, "B H T_q d_k"],
key: Float[Tensor, "B H T_k d_k"],
value: Float[Tensor, "B H T_k d_k"],
mask: Bool[Tensor, "... 1 T_q T_k"] | None,
) -> tuple[Float[Tensor, "B H T_q d_k"], Float[Tensor, "B H T_q T_k"]]:
"""
Static method for Scaled Dot-Product Attention calculation.
This is pure, stateless logic, making it easy to test.
(Ref: "Attention Is All You Need", Equation 1)
Args:
query (Tensor): Query tensor
key (Tensor): Key tensor
value (Tensor): Value tensor
mask (Tensor | None): Optional mask (for padding or look-ahead).
Returns:
tuple[Tensor, Tensor]:
- context_vector: The output of the attention mechanism.
- attention_weights: The softmax-normalized attention weights.
"""
d_k: int = query.shape[-1]
# (B, H, T_q, d_k) @ (B, H, d_k, T_k) -> (B, H, T_q, T_k)
attention_scores: Tensor = (query @ key.transpose(-2, -1)) / math.sqrt(d_k)
if mask is not None:
attention_scores = attention_scores.masked_fill(
mask == 0, value=float("-inf")
)
attention_weights: Tensor = attention_scores.softmax(dim=-1)
# (B, H, T_q, T_k) @ (B, H, T_k, d_k) -> (B, H, T_q, d_k)
context_vector: Tensor = attention_weights @ value
return context_vector, attention_weights
def forward(
self,
q: Float[Tensor, "B T_q D"],
k: Float[Tensor, "B T_k D"],
v: Float[Tensor, "B T_k D"],
mask: Bool[Tensor, "... 1 T_q T_k"] | None = None, # Optional mask
) -> Float[Tensor, "B T_q D"]:
"""
Forward pass for Multi-Head Attention.
In Self-Attention (Encoder), q, k, and v are all the same tensor.
In Cross-Attention (Decoder), q comes from the Decoder, while k and v
come from the Encoder's output.
Args:
q: Query tensor
k: Key tensor
v: Value tensor
mask: Optional mask to apply (padding or look-ahead)
Returns:
The context vector after multi-head attention and output projection.
"""
B, T_q, _ = q.shape
_, T_k, _ = k.shape # T_k == T_v
# (B, T, D) -> (B, T, D)
Q: Tensor = self.w_q(q)
K: Tensor = self.w_k(k)
V: Tensor = self.w_v(v)
# (B, T, D) -> (B, T, H, d_k) -> (B, H, T, d_k)
Q = Q.view(B, T_q, self.n_heads, self.d_k).transpose(1, 2)
K = K.view(B, T_k, self.n_heads, self.d_k).transpose(1, 2)
V = V.view(B, T_k, self.n_heads, self.d_k).transpose(1, 2)
context_vector, self.attention_weights = self.attention(Q, K, V, mask)
# (B, H, T_q, d_k) -> (B, T_q, H, d_k)
context_vector = context_vector.transpose(1, 2).contiguous()
# (B, T_q, H, d_k) -> (B, T_q, D)
context_vector = context_vector.view(B, T_q, self.d_model)
# (B, T_q, D) -> (B, T_q, D)
output: Tensor = self.w_o(context_vector)
return output
class PositionwiseFeedForward(nn.Module):
"""
Implements the Position-wise Feed-Forward Network (FFN) sublayer.
(Ref: "Attention Is All You Need", Section 3.3)
This is a two-layer MLP (Multi-Layer Perceptron) applied independently
to each position in the sequence.
FFN(x) = max(0, x * W_1 + b_1) * W_2 + b_2
(Or using ReLU activation)
Terminology (jaxtyping):
B: batch_size
T: seq_len (context_length)
D: d_model (model dimension)
D_FF: d_ff (inner feed-forward dimension)
"""
def __init__(self, d_model: int, d_ff: int) -> None:
"""
Initializes the FFN.
Args:
d_model (int): Dimension of the model (e.g., 512).
d_ff (int): Inner dimension of the FFN (e.g., 2048).
Paper suggests d_ff = 4 * d_model.
dropout (float): Dropout probability (applied *before* the
second linear layer in some implementations,
or as part of ResidualConnection).
"""
super().__init__()
# (B, T, D) -> (B, T, D_FF)
self.linear_1: nn.Linear = nn.Linear(d_model, d_ff)
self.activation: nn.ReLU = nn.ReLU()
# (B, T, D_FF) -> (B, T, D)
self.linear_2: nn.Linear = nn.Linear(d_ff, d_model)
def forward(self, x: Float[Tensor, "B T D"]) -> Float[Tensor, "B T D"]:
"""
Forward pass for the FFN.
Applies two linear transformations with a ReLU activation in between.
Args:
x: Input tensor from the previous sublayer
(e.g., MultiHeadAttention output).
Returns:
Output tensor of the same shape.
"""
# (B, T, D) -> (B, T, D_FF)
x = self.linear_1(x)
# (B, T, D_FF) -> (B, T, D_FF)
x = self.activation(x)
# (B, T, D_FF) -> (B, T, D)
x = self.linear_2(x)
return x
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