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import math
from typing import Optional, Tuple
import torch
import torch.nn as nn
import torch.nn.functional as F
def precompute_rope_freqs(
head_dim: int,
max_seq_len: int,
base: float = 10_000.0,
) -> Tuple[torch.Tensor, torch.Tensor]:
"""Precompute cosine and sine tables for rotary embeddings."""
if head_dim % 2 != 0:
raise ValueError("RoPE requires an even head dimension.")
freqs = 1.0 / (base ** (torch.arange(0, head_dim, 2).float() / head_dim))
positions = torch.arange(max_seq_len).float()
angles = torch.outer(positions, freqs)
return torch.cos(angles), torch.sin(angles)
def apply_rope(
x: torch.Tensor,
cos: torch.Tensor,
sin: torch.Tensor,
) -> torch.Tensor:
"""Apply RoPE to a tensor shaped [batch, heads, seq_len, head_dim]."""
seq_len = x.shape[2]
cos = cos[:seq_len].unsqueeze(0).unsqueeze(0)
sin = sin[:seq_len].unsqueeze(0).unsqueeze(0)
x_even = x[..., ::2]
x_odd = x[..., 1::2]
rotated_even = x_even * cos - x_odd * sin
rotated_odd = x_even * sin + x_odd * cos
return torch.stack([rotated_even, rotated_odd], dim=-1).flatten(-2)
def repeat_kv(x: torch.Tensor, n_rep: int) -> torch.Tensor:
"""Repeat KV heads so they can be shared across more query heads."""
if n_rep == 1:
return x
batch, n_kv_heads, seq_len, head_dim = x.shape
return (
x[:, :, None, :, :]
.expand(batch, n_kv_heads, n_rep, seq_len, head_dim)
.reshape(batch, n_kv_heads * n_rep, seq_len, head_dim)
)
class RMSNorm(nn.Module):
"""Root Mean Square normalization used by modern LLMs."""
def __init__(self, dim: int, eps: float = 1e-6) -> None:
super().__init__()
self.eps = eps
self.weight = nn.Parameter(torch.ones(dim))
def forward(self, x: torch.Tensor) -> torch.Tensor:
rms = torch.sqrt(x.pow(2).mean(dim=-1, keepdim=True) + self.eps)
return (x / rms) * self.weight
class SwiGLU(nn.Module):
"""Feed-forward block with SiLU gating."""
def __init__(self, d_model: int, hidden_dim: int, dropout: float = 0.2) -> None:
super().__init__()
self.dropout = dropout
self.w_gate = nn.Linear(d_model, hidden_dim, bias=False)
self.w_up = nn.Linear(d_model, hidden_dim, bias=False)
self.w_down = nn.Linear(hidden_dim, d_model, bias=False)
def forward(self, x: torch.Tensor) -> torch.Tensor:
# SwiGLU splits the FFN into a gate branch and a value branch, then
# multiplies them elementwise before projecting back to model size.
gate = F.silu(self.w_gate(x))
up = self.w_up(x)
out = self.w_down(gate * up)
return F.dropout(out, p=self.dropout, training=self.training)
class GroupedQueryAttention(nn.Module):
"""Grouped-query self-attention with rotary embeddings and a causal mask."""
def __init__(
self,
d_model: int,
n_heads: int,
n_kv_heads: int,
dropout: float = 0.2,
) -> None:
super().__init__()
if d_model % n_heads != 0:
raise ValueError("d_model must be divisible by n_heads.")
if n_heads % n_kv_heads != 0:
raise ValueError("n_heads must be divisible by n_kv_heads.")
self.n_heads = n_heads
self.n_kv_heads = n_kv_heads
self.n_rep = n_heads // n_kv_heads
self.head_dim = d_model // n_heads
self.dropout = dropout
self.q_proj = nn.Linear(d_model, n_heads * self.head_dim, bias=False)
self.k_proj = nn.Linear(d_model, n_kv_heads * self.head_dim, bias=False)
self.v_proj = nn.Linear(d_model, n_kv_heads * self.head_dim, bias=False)
self.o_proj = nn.Linear(n_heads * self.head_dim, d_model, bias=False)
def forward(
self,
x: torch.Tensor,
rope_cos: torch.Tensor,
rope_sin: torch.Tensor,
) -> torch.Tensor:
batch, seq_len, _ = x.shape
# Queries use all attention heads, while keys/values use fewer shared
# heads. This is the grouped-query attention pattern.
q = self.q_proj(x)
k = self.k_proj(x)
v = self.v_proj(x)
q = q.view(batch, seq_len, self.n_heads, self.head_dim).transpose(1, 2)
k = k.view(batch, seq_len, self.n_kv_heads, self.head_dim).transpose(1, 2)
v = v.view(batch, seq_len, self.n_kv_heads, self.head_dim).transpose(1, 2)
# RoPE injects position information directly into the query/key vectors
# instead of adding a separate positional embedding to the tokens.
q = apply_rope(q, rope_cos, rope_sin)
k = apply_rope(k, rope_cos, rope_sin)
# Shared KV heads are repeated to line up with the full set of query heads.
k = repeat_kv(k, self.n_rep)
v = repeat_kv(v, self.n_rep)
scale = 1.0 / math.sqrt(self.head_dim)
scores = (q @ k.transpose(-2, -1)) * scale
mask = torch.triu(
torch.ones(seq_len, seq_len, device=x.device, dtype=torch.bool),
diagonal=1,
)
scores = scores.masked_fill(mask, float("-inf"))
# Causal self-attention only lets each position see itself and earlier tokens.
weights = F.softmax(scores, dim=-1)
weights = F.dropout(weights, p=self.dropout, training=self.training)
out = weights @ v
out = out.transpose(1, 2).contiguous().view(batch, seq_len, -1)
return self.o_proj(out)
class TransformerBlock(nn.Module):
"""Pre-norm transformer block with grouped-query attention and SwiGLU."""
def __init__(
self,
d_model: int,
n_heads: int,
n_kv_heads: int,
ffn_hidden_dim: int,
dropout: float = 0.2,
) -> None:
super().__init__()
# Pre-norm transformer block:
# x -> RMSNorm -> attention -> residual
# x -> RMSNorm -> FFN -> residual
self.attn_norm = RMSNorm(d_model)
self.attention = GroupedQueryAttention(
d_model=d_model,
n_heads=n_heads,
n_kv_heads=n_kv_heads,
dropout=dropout,
)
self.ffn_norm = RMSNorm(d_model)
self.ffn = SwiGLU(d_model=d_model, hidden_dim=ffn_hidden_dim, dropout=dropout)
def forward(
self,
x: torch.Tensor,
rope_cos: torch.Tensor,
rope_sin: torch.Tensor,
) -> torch.Tensor:
# Normalize before each sublayer, then add the sublayer output back to
# the running hidden state through a residual connection.
x = x + self.attention(self.attn_norm(x), rope_cos, rope_sin)
x = x + self.ffn(self.ffn_norm(x))
return x
class MiniLLM(nn.Module):
"""Small modern language model built from the extracted notebook blocks."""
def __init__(
self,
vocab_size: int,
d_model: int,
n_layers: int,
n_heads: int,
n_kv_heads: int,
ffn_hidden_dim: int,
max_seq_len: int,
dropout: float = 0.2,
) -> None:
super().__init__()
if d_model % n_heads != 0:
raise ValueError("d_model must be divisible by n_heads.")
self.d_model = d_model
self.max_seq_len = max_seq_len
# The full model is:
# tokens -> embedding -> N transformer blocks -> final RMSNorm -> LM head.
# RoPE handles positional information inside attention, so there is no
# separate learned positional embedding table.
self.token_emb = nn.Embedding(vocab_size, d_model)
self.layers = nn.ModuleList(
[
TransformerBlock(
d_model=d_model,
n_heads=n_heads,
n_kv_heads=n_kv_heads,
ffn_hidden_dim=ffn_hidden_dim,
dropout=dropout,
)
for _ in range(n_layers)
]
)
self.final_norm = RMSNorm(d_model)
self.lm_head = nn.Linear(d_model, vocab_size, bias=False)
# Tie input embeddings and output projection weights.
self.lm_head.weight = self.token_emb.weight
head_dim = d_model // n_heads
rope_cos, rope_sin = precompute_rope_freqs(head_dim, max_seq_len)
self.register_buffer("rope_cos", rope_cos)
self.register_buffer("rope_sin", rope_sin)
self.apply(self._init_weights)
def _init_weights(self, module: nn.Module) -> None:
if isinstance(module, nn.Linear):
nn.init.normal_(module.weight, mean=0.0, std=0.02)
elif isinstance(module, nn.Embedding):
nn.init.normal_(module.weight, mean=0.0, std=0.02)
def forward(
self,
idx: torch.Tensor,
targets: Optional[torch.Tensor] = None,
) -> Tuple[torch.Tensor, Optional[torch.Tensor]]:
_, seq_len = idx.shape
if seq_len > self.max_seq_len:
raise ValueError(
f"Sequence length {seq_len} exceeds max_seq_len={self.max_seq_len}."
)
# Convert token ids to dense vectors before passing them through the stack.
x = self.token_emb(idx)
for layer in self.layers:
x = layer(x, self.rope_cos, self.rope_sin)
# Final normalization plus vocabulary projection produces next-token logits.
logits = self.lm_head(self.final_norm(x))
loss = None
if targets is not None:
loss = F.cross_entropy(
logits.view(-1, logits.size(-1)),
targets.view(-1),
)
return logits, loss
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