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a2ce935 | 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 | """Small but modern decoder-only transformer (~50M params).
Uses RoPE, RMSNorm, SwiGLU FFN, tied embeddings, and PyTorch SDPA
for causal attention (which lights up MPS fast-paths where available).
"""
import math
import torch
import torch.nn as nn
import torch.nn.functional as F
from config import ModelConfig
def precompute_rope(head_dim: int, seq_len: int, theta: float = 10000.0, device=None):
inv_freq = 1.0 / (theta ** (torch.arange(0, head_dim, 2, device=device).float() / head_dim))
t = torch.arange(seq_len, device=device).float()
freqs = torch.outer(t, inv_freq) # (T, head_dim/2)
return freqs.cos(), freqs.sin()
def apply_rope(x: torch.Tensor, cos: torch.Tensor, sin: torch.Tensor) -> torch.Tensor:
# x: (B, H, T, D); cos/sin: (T, D/2)
x1, x2 = x.chunk(2, dim=-1)
cos = cos[None, None, :, :]
sin = sin[None, None, :, :]
return torch.cat([x1 * cos - x2 * sin, x1 * sin + x2 * cos], dim=-1)
class RMSNorm(nn.Module):
def __init__(self, d: int, eps: float = 1e-5):
super().__init__()
self.weight = nn.Parameter(torch.ones(d))
self.eps = eps
def forward(self, x: torch.Tensor) -> torch.Tensor:
# Always compute the norm in fp32 for stability, then cast back.
dtype = x.dtype
x32 = x.float()
norm = torch.rsqrt(x32.pow(2).mean(-1, keepdim=True) + self.eps)
return (x32 * norm).to(dtype) * self.weight
class Attention(nn.Module):
def __init__(self, cfg: ModelConfig):
super().__init__()
assert cfg.d_model % cfg.n_heads == 0
self.n_heads = cfg.n_heads
self.head_dim = cfg.d_model // cfg.n_heads
self.qkv = nn.Linear(cfg.d_model, 3 * cfg.d_model, bias=False)
self.o = nn.Linear(cfg.d_model, cfg.d_model, bias=False)
self.dropout = cfg.dropout
def forward(self, x, cos, sin):
B, T, C = x.shape
q, k, v = self.qkv(x).chunk(3, dim=-1)
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)
q = apply_rope(q, cos[:T], sin[:T])
k = apply_rope(k, cos[:T], sin[:T])
y = F.scaled_dot_product_attention(
q, k, v,
is_causal=True,
dropout_p=self.dropout if self.training else 0.0,
)
y = y.transpose(1, 2).contiguous().view(B, T, C)
return self.o(y)
class SwiGLU(nn.Module):
def __init__(self, cfg: ModelConfig):
super().__init__()
self.w1 = nn.Linear(cfg.d_model, cfg.d_ff, bias=False) # gate
self.w2 = nn.Linear(cfg.d_ff, cfg.d_model, bias=False) # down
self.w3 = nn.Linear(cfg.d_model, cfg.d_ff, bias=False) # up
def forward(self, x):
return self.w2(F.silu(self.w1(x)) * self.w3(x))
class Block(nn.Module):
def __init__(self, cfg: ModelConfig):
super().__init__()
self.attn_norm = RMSNorm(cfg.d_model, cfg.norm_eps)
self.attn = Attention(cfg)
self.ffn_norm = RMSNorm(cfg.d_model, cfg.norm_eps)
self.ffn = SwiGLU(cfg)
def forward(self, x, cos, sin):
x = x + self.attn(self.attn_norm(x), cos, sin)
x = x + self.ffn(self.ffn_norm(x))
return x
class IntelliteGPT(nn.Module):
def __init__(self, cfg: ModelConfig):
super().__init__()
self.cfg = cfg
self.tok_emb = nn.Embedding(cfg.vocab_size, cfg.d_model)
self.blocks = nn.ModuleList([Block(cfg) for _ in range(cfg.n_layers)])
self.norm = RMSNorm(cfg.d_model, cfg.norm_eps)
self.lm_head = nn.Linear(cfg.d_model, cfg.vocab_size, bias=False)
if cfg.tie_embeddings:
self.lm_head.weight = self.tok_emb.weight
cos, sin = precompute_rope(cfg.d_model // cfg.n_heads, cfg.seq_len, cfg.rope_theta)
self.register_buffer("cos", cos, persistent=False)
self.register_buffer("sin", sin, persistent=False)
self.apply(self._init_weights)
# GPT-2 style: scale residual projections by 1/sqrt(2*n_layers)
scale = 0.02 / math.sqrt(2 * cfg.n_layers)
for n, p in self.named_parameters():
if n.endswith("attn.o.weight") or n.endswith("ffn.w2.weight"):
nn.init.normal_(p, mean=0.0, std=scale)
@staticmethod
def _init_weights(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, exclude_embedding: bool = False) -> int:
n = sum(p.numel() for p in self.parameters())
if exclude_embedding:
n -= self.tok_emb.weight.numel()
return n
def forward(self, idx: torch.Tensor, targets: torch.Tensor | None = None):
B, T = idx.shape
x = self.tok_emb(idx)
cos, sin = self.cos, self.sin
for block in self.blocks:
x = block(x, cos, sin)
x = self.norm(x)
logits = self.lm_head(x)
loss = None
if targets is not None:
loss = F.cross_entropy(
logits.view(-1, logits.size(-1)).float(),
targets.view(-1),
ignore_index=-1,
)
return logits, loss
@torch.no_grad()
def generate(self, idx, max_new_tokens, temperature=1.0, top_k=None):
for _ in range(max_new_tokens):
idx_cond = idx[:, -self.cfg.seq_len:]
logits, _ = self(idx_cond)
logits = logits[:, -1, :] / max(temperature, 1e-5)
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
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