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a330cfa dbb5d78 a330cfa dbb5d78 a330cfa | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 | """GPT model definition and checkpoint loading for exported smartwatch LM."""
from __future__ import annotations
import math
from pathlib import Path
import torch
import torch.nn as nn
import torch.nn.functional as F
from tokenizers import Tokenizer
import config as cfg
class CausalSelfAttention(nn.Module):
def __init__(self, n_head: int, n_embd: int, block_size: int, dropout: float, bias: bool):
super().__init__()
assert n_embd % n_head == 0
self.n_head = n_head
self.n_embd = n_embd
self.head_dim = n_embd // n_head
self.c_attn = nn.Linear(n_embd, 3 * n_embd, bias=bias)
self.c_proj = nn.Linear(n_embd, n_embd, bias=bias)
self.attn_dropout = nn.Dropout(dropout)
self.resid_dropout = nn.Dropout(dropout)
self.register_buffer(
"bias",
torch.tril(torch.ones(block_size, block_size)).view(1, 1, block_size, block_size),
)
def forward(self, x: torch.Tensor) -> torch.Tensor:
b, t, c = x.size()
q, k, v = self.c_attn(x).split(self.n_embd, dim=2)
k = k.view(b, t, self.n_head, self.head_dim).transpose(1, 2)
q = q.view(b, t, self.n_head, self.head_dim).transpose(1, 2)
v = v.view(b, t, self.n_head, self.head_dim).transpose(1, 2)
att = (q @ k.transpose(-2, -1)) * (1.0 / math.sqrt(self.head_dim))
att = att.masked_fill(self.bias[:, :, :t, :t] == 0, float("-inf"))
att = F.softmax(att, dim=-1)
att = self.attn_dropout(att)
y = att @ v
y = y.transpose(1, 2).contiguous().view(b, t, c)
return self.resid_dropout(self.c_proj(y))
class MLP(nn.Module):
def __init__(self, n_embd: int, dropout: float, bias: bool):
super().__init__()
self.c_fc = nn.Linear(n_embd, 4 * n_embd, bias=bias)
self.gelu = nn.GELU()
self.c_proj = nn.Linear(4 * n_embd, n_embd, bias=bias)
self.dropout = nn.Dropout(dropout)
def forward(self, x: torch.Tensor) -> torch.Tensor:
return self.dropout(self.c_proj(self.gelu(self.c_fc(x))))
class Block(nn.Module):
def __init__(self, n_head: int, n_embd: int, block_size: int, dropout: float, bias: bool):
super().__init__()
self.ln1 = nn.LayerNorm(n_embd)
self.attn = CausalSelfAttention(n_head, n_embd, block_size, dropout, bias)
self.ln2 = nn.LayerNorm(n_embd)
self.mlp = MLP(n_embd, dropout, bias)
def forward(self, x: torch.Tensor) -> torch.Tensor:
x = x + self.attn(self.ln1(x))
x = x + self.mlp(self.ln2(x))
return x
class GPT(nn.Module):
def __init__(
self,
vocab_size: int,
n_layer: int,
n_head: int,
n_embd: int,
block_size: int,
dropout: float,
bias: bool,
):
super().__init__()
self.block_size = block_size
self.transformer = nn.ModuleDict(
{
"wte": nn.Embedding(vocab_size, n_embd),
"wpe": nn.Embedding(block_size, n_embd),
"drop": nn.Dropout(dropout),
"h": nn.ModuleList(
[Block(n_head, n_embd, block_size, dropout, bias) for _ in range(n_layer)]
),
"ln_f": nn.LayerNorm(n_embd),
}
)
self.lm_head = nn.Linear(n_embd, vocab_size, bias=False)
self.transformer.wte.weight = self.lm_head.weight
self.apply(self._init_weights)
def _init_weights(self, module: nn.Module) -> None:
if isinstance(module, nn.Linear):
torch.nn.init.normal_(module.weight, mean=0.0, std=0.02)
if module.bias is not None:
torch.nn.init.zeros_(module.bias)
elif isinstance(module, nn.Embedding):
torch.nn.init.normal_(module.weight, mean=0.0, std=0.02)
def forward(self, idx: torch.Tensor, targets=None):
b, t = idx.size()
assert t <= self.block_size
pos = torch.arange(0, t, dtype=torch.long, device=idx.device)
x = self.transformer.drop(
self.transformer.wte(idx) + self.transformer.wpe(pos)
)
for block in self.transformer.h:
x = block(x)
x = self.transformer.ln_f(x)
logits = self.lm_head(x)
loss = None
if targets is not None:
loss = F.cross_entropy(logits.view(-1, logits.size(-1)), targets.view(-1))
return logits, loss
@torch.no_grad()
def generate(self, idx: torch.Tensor, max_new_tokens: int, temperature: float = 1.0, top_k=None):
for _ in range(max_new_tokens):
idx_cond = idx[:, -self.block_size :]
logits, _ = self(idx_cond)
logits = logits[:, -1, :] / max(temperature, 1e-8)
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)
idx_next = torch.multinomial(probs, num_samples=1)
idx = torch.cat((idx, idx_next), dim=1)
return idx
def resolve_checkpoint_paths(
checkpoint_path: Path | None = None,
tokenizer_path: Path | None = None,
) -> tuple[Path, Path]:
ckpt = checkpoint_path or cfg.OUTPUT_DIR / "checkpoint.pt"
tok = tokenizer_path or cfg.OUTPUT_DIR / "tokenizer.json"
if not ckpt.is_file():
raise FileNotFoundError(
f"Checkpoint not found at {ckpt}. Ensure checkpoint.pt is in this model folder."
)
if not tok.is_file():
raise FileNotFoundError(
f"Tokenizer not found at {tok}. Ensure tokenizer.json is in this model folder."
)
return ckpt, tok
def load_model(
checkpoint_path: Path | None = None,
tokenizer_path: Path | None = None,
device: str | None = None,
) -> tuple[GPT, Tokenizer, str]:
ckpt_path, tok_path = resolve_checkpoint_paths(checkpoint_path, tokenizer_path)
dev = device or ("cuda" if torch.cuda.is_available() else "cpu")
tokenizer = Tokenizer.from_file(str(tok_path))
checkpoint = torch.load(ckpt_path, map_location=dev, weights_only=False)
model_config = checkpoint["model_config"]
model = GPT(
vocab_size=model_config["vocab_size"],
n_layer=model_config["n_layer"],
n_head=model_config["n_head"],
n_embd=model_config["n_embd"],
block_size=model_config["block_size"],
dropout=model_config["dropout"],
bias=model_config["bias"],
)
model.load_state_dict(checkpoint["model_state_dict"])
model.to(dev)
model.eval()
return model, tokenizer, dev
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