File size: 3,272 Bytes
575bf55 | 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 | import json, os
import torch, torch.nn as nn, torch.nn.functional as F
from tokenizers import Tokenizer
class Block(nn.Module):
def __init__(self, d, n_head):
super().__init__()
self.n_head = n_head
self.ln1, self.ln2 = nn.LayerNorm(d), nn.LayerNorm(d)
self.qkv = nn.Linear(d, 3 * d, bias=False)
self.proj = nn.Linear(d, d, bias=False)
self.fc = nn.Linear(d, 4 * d, bias=False)
self.fc2 = nn.Linear(4 * d, d, bias=False)
def forward(self, x):
B, T, C = x.shape
q, k, v = self.qkv(self.ln1(x)).split(C, dim=2)
q, k, v = [t.view(B, T, self.n_head, C // self.n_head).transpose(1, 2) for t in (q, k, v)]
y = F.scaled_dot_product_attention(q, k, v, is_causal=True)
x = x + self.proj(y.transpose(1, 2).reshape(B, T, C))
return x + self.fc2(F.gelu(self.fc(self.ln2(x))))
class GPT(nn.Module):
def __init__(self, cfg):
super().__init__()
d = cfg["n_embd"]
self.ctx = cfg["ctx"]
self.tok_emb = nn.Embedding(cfg["vocab"], d)
self.pos_emb = nn.Embedding(cfg["ctx"], d)
self.blocks = nn.ModuleList([Block(d, cfg["n_head"]) for _ in range(cfg["n_layer"])])
self.ln_f = nn.LayerNorm(d)
self.head = nn.Linear(d, cfg["vocab"], bias=False)
self.head.weight = self.tok_emb.weight
def forward(self, idx):
x = self.tok_emb(idx) + self.pos_emb(torch.arange(idx.shape[1], device=idx.device))
for blk in self.blocks:
x = blk(x)
return self.head(self.ln_f(x))
@torch.no_grad()
def generate(self, idx, max_new=100, temperature=0.7, top_k=40, stop_id=None):
for _ in range(max_new):
logits = self(idx[:, -self.ctx:])[:, -1, :] / max(temperature, 1e-5)
if top_k:
v, _ = torch.topk(logits, min(top_k, logits.size(-1)))
logits[logits < v[:, [-1]]] = -float("inf")
nxt = torch.multinomial(F.softmax(logits, dim=-1), 1)
idx = torch.cat([idx, nxt], dim=1)
if stop_id is not None and nxt.item() == stop_id:
break
return idx
def load(path, device="cpu"):
with open(os.path.join(path, "config.json")) as f:
cfg = json.load(f)
model = GPT(cfg)
model.load_state_dict(torch.load(os.path.join(path, "model.pt"), map_location=device))
tok = Tokenizer.from_file(os.path.join(path, "tokenizer.json"))
return model.to(device).eval(), tok
def reply(model, tok, history, user_msg, temperature=0.7, top_k=40, max_new=120):
# history is a list of (user, assistant) string pairs
USER, ASSIST, END = [tok.token_to_id(t) for t in ("<|user|>", "<|assistant|>", "<|end|>")]
ids = []
for u, a in history:
ids += [USER] + tok.encode(u).ids + [END, ASSIST] + tok.encode(a).ids + [END]
ids += [USER] + tok.encode(user_msg).ids + [END, ASSIST]
ids = ids[-(model.ctx - 1):]
device = next(model.parameters()).device
out = model.generate(torch.tensor([ids], device=device), max_new=max_new,
temperature=temperature, top_k=top_k, stop_id=END)
new = out[0, len(ids):].tolist()
if new and new[-1] == END:
new = new[:-1]
return tok.decode(new).strip()
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