File size: 5,618 Bytes
14c107a | 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 | """
Nano-SLM: a tiny decoder-only transformer (~1M params).
Architecture is intentionally minimal so every line is readable.
Mirrors the standard GPT recipe: token + position embeddings, N stacked
(causal self-attention -> MLP) blocks with pre-LayerNorm and residuals,
final LayerNorm, and a tied LM head.
"""
import math
import torch
import torch.nn as nn
import torch.nn.functional as F
class CausalSelfAttention(nn.Module):
"""Multi-head causal self-attention. Uses fused QKV and PyTorch's SDPA."""
def __init__(self, d_model, n_heads, dropout=0.1):
super().__init__()
assert d_model % n_heads == 0
self.n_heads = n_heads
self.head_dim = d_model // n_heads
# one big linear that produces Q, K, V at once
self.qkv = nn.Linear(d_model, 3 * d_model, bias=False)
self.proj = nn.Linear(d_model, d_model, bias=False)
self.attn_dropout_p = dropout
self.resid_dropout = nn.Dropout(dropout)
def forward(self, x):
B, T, C = x.shape
q, k, v = self.qkv(x).split(C, dim=-1)
# reshape to (B, n_heads, T, head_dim)
q = q.view(B, T, self.n_heads, self.head_dim).transpose(1, 2)
k = k.view(B, T, self.n_heads, self.head_dim).transpose(1, 2)
v = v.view(B, T, self.n_heads, self.head_dim).transpose(1, 2)
# Flash/SDPA: causal mask + scaling handled internally
y = F.scaled_dot_product_attention(
q, k, v,
is_causal=True,
dropout_p=self.attn_dropout_p if self.training else 0.0,
)
y = y.transpose(1, 2).contiguous().view(B, T, C)
return self.resid_dropout(self.proj(y))
class MLP(nn.Module):
"""Position-wise feed-forward (GELU)."""
def __init__(self, d_model, ffn_dim, dropout=0.1):
super().__init__()
self.fc1 = nn.Linear(d_model, ffn_dim, bias=False)
self.fc2 = nn.Linear(ffn_dim, d_model, bias=False)
self.dropout = nn.Dropout(dropout)
def forward(self, x):
return self.dropout(self.fc2(F.gelu(self.fc1(x))))
class Block(nn.Module):
"""Pre-LN transformer block: x = x + attn(LN(x)); x = x + mlp(LN(x))."""
def __init__(self, d_model, n_heads, ffn_dim, dropout=0.1):
super().__init__()
self.ln1 = nn.LayerNorm(d_model)
self.attn = CausalSelfAttention(d_model, n_heads, dropout)
self.ln2 = nn.LayerNorm(d_model)
self.mlp = MLP(d_model, ffn_dim, dropout)
def forward(self, x):
x = x + self.attn(self.ln1(x))
x = x + self.mlp(self.ln2(x))
return x
class NanoSLM(nn.Module):
def __init__(
self,
vocab_size=4096,
d_model=128,
n_heads=4,
n_layers=4,
ffn_dim=512,
ctx_len=256,
dropout=0.1,
):
super().__init__()
self.ctx_len = ctx_len
self.tok_emb = nn.Embedding(vocab_size, d_model)
self.pos_emb = nn.Embedding(ctx_len, d_model)
self.drop = nn.Dropout(dropout)
self.blocks = nn.ModuleList(
[Block(d_model, n_heads, ffn_dim, dropout) for _ in range(n_layers)]
)
self.ln_f = nn.LayerNorm(d_model)
self.head = nn.Linear(d_model, vocab_size, bias=False)
# weight tying: input embedding and output projection share weights.
# saves a lot of params at small vocab sizes and usually helps quality.
self.head.weight = self.tok_emb.weight
self.apply(self._init_weights)
# scaled init for residual projections (GPT-2 trick)
for name, p in self.named_parameters():
if name.endswith("proj.weight") or name.endswith("fc2.weight"):
nn.init.normal_(p, mean=0.0, std=0.02 / math.sqrt(2 * n_layers))
def _init_weights(self, m):
if isinstance(m, nn.Linear):
nn.init.normal_(m.weight, mean=0.0, std=0.02)
if m.bias is not None:
nn.init.zeros_(m.bias)
elif isinstance(m, nn.Embedding):
nn.init.normal_(m.weight, mean=0.0, std=0.02)
def num_params(self, non_embedding=False):
n = sum(p.numel() for p in self.parameters())
if non_embedding:
n -= self.tok_emb.weight.numel()
n -= self.pos_emb.weight.numel()
return n
def forward(self, idx, targets=None):
B, T = idx.shape
assert T <= self.ctx_len, f"sequence length {T} > ctx_len {self.ctx_len}"
pos = torch.arange(T, device=idx.device)
x = self.drop(self.tok_emb(idx) + self.pos_emb(pos))
for block in self.blocks:
x = block(x)
x = self.ln_f(x)
logits = self.head(x)
loss = None
if targets is not None:
loss = F.cross_entropy(
logits.view(-1, logits.size(-1)),
targets.view(-1),
ignore_index=-100,
)
return logits, loss
@torch.no_grad()
def generate(self, idx, max_new_tokens, temperature=1.0, top_k=None):
"""Autoregressive sampling. Slow on purpose: no KV cache (a great upgrade later)."""
for _ in range(max_new_tokens):
idx_cond = idx[:, -self.ctx_len:]
logits, _ = self(idx_cond)
logits = logits[:, -1, :] / temperature
if top_k is not None:
v, _ = torch.topk(logits, min(top_k, logits.size(-1)))
logits[logits < v[:, [-1]]] = -float("inf")
probs = F.softmax(logits, dim=-1)
next_tok = torch.multinomial(probs, num_samples=1)
idx = torch.cat([idx, next_tok], dim=1)
return idx
|