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4a44c76 | 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 | """TinyModel v11 core architecture.
Decoder-only Gemma-shaped transformer: RMSNorm, RoPE, GQA, gated FFN,
tied embeddings. Shared across v11 weight variants (v11, v11a, …); a
new core ships only when the architecture itself changes (v12-core).
Default v11 shape: dim=512, layers=20, heads=8, kv=4, ffn=2048,
vocab=71261, max_seq=256. Authoritative per-weights values live in
`v11/config.json`.
"""
import math
import torch
import torch.nn as nn
import torch.nn.functional as F
class RMSNorm(nn.Module):
def __init__(self, dim: int, eps: float = 1e-6):
super().__init__()
self.weight = nn.Parameter(torch.ones(dim))
self.eps = eps
def forward(self, x: torch.Tensor) -> torch.Tensor:
norm = x.float().pow(2).mean(-1, keepdim=True).add(self.eps).rsqrt()
return (x.float() * norm).type_as(x) * self.weight
def precompute_rope(dim: int, max_seq: int, theta: float = 10000.0) -> torch.Tensor:
freqs = 1.0 / (theta ** (torch.arange(0, dim, 2).float() / dim))
t = torch.arange(max_seq).float()
freqs = torch.outer(t, freqs)
return torch.polar(torch.ones_like(freqs), freqs) # complex64
def apply_rope(x: torch.Tensor, freqs: torch.Tensor) -> torch.Tensor:
# x: (batch, seq, heads, head_dim)
xc = torch.view_as_complex(x.float().reshape(*x.shape[:-1], -1, 2))
freqs = freqs[:x.shape[1]].unsqueeze(0).unsqueeze(2) # (1, seq, 1, head_dim//2)
out = torch.view_as_real(xc * freqs).flatten(-2)
return out.type_as(x)
class GatedFFN(nn.Module):
"""Gated FFN: out = down(silu(gate(x)) * up(x))"""
def __init__(self, dim: int, ffn_dim: int):
super().__init__()
self.gate = nn.Linear(dim, ffn_dim, bias=False)
self.up = nn.Linear(dim, ffn_dim, bias=False)
self.down = nn.Linear(ffn_dim, dim, bias=False)
def forward(self, x: torch.Tensor) -> torch.Tensor:
return self.down(F.silu(self.gate(x)) * self.up(x))
class Attention(nn.Module):
def __init__(self, dim: int, n_heads: int, n_kv_heads: int):
super().__init__()
self.n_heads = n_heads
self.n_kv_heads = n_kv_heads
self.head_dim = dim // n_heads
self.gqa_ratio = n_heads // n_kv_heads
self.q_proj = nn.Linear(dim, n_heads * self.head_dim, bias=False)
self.k_proj = nn.Linear(dim, n_kv_heads * self.head_dim, bias=False)
self.v_proj = nn.Linear(dim, n_kv_heads * self.head_dim, bias=False)
self.o_proj = nn.Linear(n_heads * self.head_dim, dim, bias=False)
def forward(self, x: torch.Tensor, rope_freqs: torch.Tensor) -> torch.Tensor:
B, S, _ = x.shape
q = self.q_proj(x).view(B, S, self.n_heads, self.head_dim)
k = self.k_proj(x).view(B, S, self.n_kv_heads, self.head_dim)
v = self.v_proj(x).view(B, S, self.n_kv_heads, self.head_dim)
q = apply_rope(q, rope_freqs)
k = apply_rope(k, rope_freqs)
# GQA: repeat KV heads
if self.gqa_ratio > 1:
k = k.repeat_interleave(self.gqa_ratio, dim=2)
v = v.repeat_interleave(self.gqa_ratio, dim=2)
# (B, heads, S, head_dim)
q = q.transpose(1, 2)
k = k.transpose(1, 2)
v = v.transpose(1, 2)
# Scaled dot-product with causal mask
attn = F.scaled_dot_product_attention(q, k, v, is_causal=True)
out = attn.transpose(1, 2).contiguous().view(B, S, -1)
return self.o_proj(out)
class TransformerBlock(nn.Module):
def __init__(self, dim: int, ffn_dim: int, n_heads: int, n_kv_heads: int):
super().__init__()
self.attn_norm = RMSNorm(dim)
self.attn = Attention(dim, n_heads, n_kv_heads)
self.ffn_norm = RMSNorm(dim)
self.ffn = GatedFFN(dim, ffn_dim)
def forward(self, x: torch.Tensor, rope_freqs: torch.Tensor) -> torch.Tensor:
x = x + self.attn(self.attn_norm(x), rope_freqs)
x = x + self.ffn(self.ffn_norm(x))
return x
class TinyModel(nn.Module):
def __init__(
self,
vocab_size: int = 71261,
dim: int = 512,
n_layers: int = 20,
ffn_dim: int = 2048,
n_heads: int = 8,
n_kv_heads: int = 4,
max_seq: int = 256,
):
super().__init__()
self.dim = dim
self.n_layers = n_layers
self.ffn_dim = ffn_dim
self.vocab_size = vocab_size
self.embed = nn.Embedding(vocab_size, dim)
self.layers = nn.ModuleList([
TransformerBlock(dim, ffn_dim, n_heads, n_kv_heads)
for _ in range(n_layers)
])
self.norm = RMSNorm(dim)
self.lm_head = nn.Linear(dim, vocab_size, bias=False)
# Tie embeddings
self.lm_head.weight = self.embed.weight
# RoPE frequencies (not a parameter)
head_dim = dim // n_heads
self.register_buffer("rope_freqs", precompute_rope(head_dim, max_seq))
self._init_weights()
def _init_weights(self):
for p in self.parameters():
if p.dim() > 1:
nn.init.xavier_uniform_(p)
def forward(self, input_ids: torch.Tensor) -> torch.Tensor:
x = self.embed(input_ids) * math.sqrt(self.dim) # sqrt(dim) embed scaling (Gemma-style)
for layer in self.layers:
x = layer(x, self.rope_freqs)
x = self.norm(x)
return self.lm_head(x)
def param_count(self) -> int:
return sum(p.numel() for p in self.parameters())
if __name__ == "__main__":
model = TinyModel()
print(f"Parameters: {model.param_count():,}")
print(f"Layers: {model.n_layers}")
print(f"Hidden dim: {model.dim}")
print(f"FFN dim: {model.ffn_dim}")
print(f"Vocab: {model.vocab_size}")
x = torch.randint(0, model.vocab_size, (1, 64))
logits = model(x)
print(f"Input: {x.shape} → Logits: {logits.shape}")
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