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a8e5aec | 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 | """Compact decoder-only Transformer for the Part 2 chat experiment."""
from dataclasses import asdict, dataclass
import math
import torch
from torch import nn
from torch.nn import functional as F
@dataclass
class ModelConfig:
vocab_size: int = 8192
d_model: int = 192
n_layers: int = 6
n_heads: int = 6
n_kv_heads: int = 2
d_ff: int = 512
max_seq_len: int = 512
dropout: float = 0.0
def parameter_count(config: ModelConfig) -> int:
# tied token/output embedding; Q and output projections; grouped K/V.
d, h, kv, f, v = config.d_model, config.n_heads, config.n_kv_heads, config.d_ff, config.vocab_size
attn = d * d + 2 * d * (d * kv // h) + d * d
# SwiGLU has gate/up/down projections. RMSNorm has one scale per norm.
layer = attn + 3 * d * f + 2 * d
return v * d + config.n_layers * layer + d
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):
return F.rms_norm(x, (x.shape[-1],), self.weight, self.eps)
class Attention(nn.Module):
def __init__(self, c: ModelConfig):
super().__init__()
assert c.d_model % c.n_heads == 0 and c.n_heads % c.n_kv_heads == 0
self.head_dim = c.d_model // c.n_heads
self.n_heads, self.n_kv_heads = c.n_heads, c.n_kv_heads
self.q = nn.Linear(c.d_model, c.d_model, bias=False)
kv_dim = c.n_kv_heads * self.head_dim
self.k = nn.Linear(c.d_model, kv_dim, bias=False)
self.v = nn.Linear(c.d_model, kv_dim, bias=False)
self.out = nn.Linear(c.d_model, c.d_model, bias=False)
self.dropout = c.dropout
def forward(self, x):
b, t, _ = x.shape
q = self.q(x).view(b, t, self.n_heads, self.head_dim).transpose(1, 2)
k = self.k(x).view(b, t, self.n_kv_heads, self.head_dim).transpose(1, 2)
v = self.v(x).view(b, t, self.n_kv_heads, self.head_dim).transpose(1, 2)
if self.n_kv_heads != self.n_heads:
repeat = self.n_heads // self.n_kv_heads
k, v = k.repeat_interleave(repeat, dim=1), v.repeat_interleave(repeat, dim=1)
# RoPE, with interleaved even/odd pairs.
half = self.head_dim // 2
freq = torch.arange(half, device=x.device, dtype=torch.float32)
inv_freq = 1.0 / (10000 ** (freq / half))
pos = torch.arange(t, device=x.device, dtype=torch.float32)
angle = torch.outer(pos, inv_freq)
cos, sin = angle.cos()[None, None], angle.sin()[None, None]
def rotate(z):
z1, z2 = z[..., :half], z[..., half:2 * half]
return torch.cat((z1 * cos - z2 * sin, z1 * sin + z2 * cos, z[..., 2 * half:]), dim=-1).to(z.dtype)
q, k = rotate(q), rotate(k)
y = F.scaled_dot_product_attention(q, k, v, is_causal=True,
dropout_p=self.dropout if self.training else 0.0)
return self.out(y.transpose(1, 2).contiguous().view(b, t, -1))
class Block(nn.Module):
def __init__(self, c):
super().__init__()
self.attn_norm, self.ffn_norm = RMSNorm(c.d_model), RMSNorm(c.d_model)
self.attn = Attention(c)
self.gate = nn.Linear(c.d_model, c.d_ff, bias=False)
self.up = nn.Linear(c.d_model, c.d_ff, bias=False)
self.down = nn.Linear(c.d_ff, c.d_model, bias=False)
self.dropout = nn.Dropout(c.dropout)
def forward(self, x):
x = x + self.dropout(self.attn(self.attn_norm(x)))
x = x + self.dropout(self.down(F.silu(self.gate(self.ffn_norm(x))) * self.up(self.ffn_norm(x))))
return x
class TinyChatLM(nn.Module):
def __init__(self, config: ModelConfig):
super().__init__()
self.config = config
self.token_embedding = nn.Embedding(config.vocab_size, config.d_model)
self.blocks = nn.ModuleList([Block(config) for _ in range(config.n_layers)])
self.norm = RMSNorm(config.d_model)
self.lm_head = nn.Linear(config.d_model, config.vocab_size, bias=False)
self.lm_head.weight = self.token_embedding.weight
self.apply(self._init_weights)
for name, p in self.named_parameters():
if name.endswith("out.weight") or name.endswith("down.weight"):
nn.init.normal_(p, mean=0.0, std=0.02 / math.sqrt(2 * config.n_layers))
@staticmethod
def _init_weights(module):
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, ids, targets=None, loss_mask=None):
x = self.token_embedding(ids)
for block in self.blocks:
x = block(x)
logits = self.lm_head(self.norm(x))
loss = None
if targets is not None:
per_token = F.cross_entropy(logits.reshape(-1, logits.size(-1)), targets.reshape(-1), reduction="none")
if loss_mask is not None:
weights = loss_mask.reshape(-1).to(per_token.dtype)
loss = (per_token * weights).sum() / weights.sum().clamp_min(1)
else:
loss = per_token.mean()
return logits, loss
@torch.no_grad()
def generate(self, ids, max_new_tokens=100, temperature=0.8, top_k=40, eos_id=None):
self.eval()
for _ in range(max_new_tokens):
ctx = ids[:, -self.config.max_seq_len:]
logits, _ = self(ctx)
logits = logits[:, -1, :] / max(temperature, 1e-5)
if top_k:
cutoff = torch.topk(logits, min(top_k, logits.size(-1))).values[:, -1:]
logits = logits.masked_fill(logits < cutoff, -float("inf"))
nxt = torch.multinomial(F.softmax(logits, dim=-1), 1)
ids = torch.cat((ids, nxt), dim=1)
if eos_id is not None and (nxt == eos_id).all():
break
return ids
def config_dict(config):
return asdict(config)
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