File size: 7,925 Bytes
b7599e3 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 |
import torch
import torch.nn as nn
import torch.nn.functional as F
import json, os, numpy as np
# ============================================================================
class ChunkTokenizer:
def __init__(self):
self.chunk_to_idx = {}
self.idx_to_chunk = {}
self.vocab_size = 0
def load(self, path):
with open(path, 'r') as f:
vocab_data = json.load(f)
self.chunk_to_idx = vocab_data['chunk_to_idx']
self.idx_to_chunk = {int(k): v for k, v in vocab_data['idx_to_chunk'].items()}
self.vocab_size = vocab_data['vocab_size']
print(f"Loaded tokenizer ({self.vocab_size} tokens)")
def encode(self, text):
text = text.lower()
pos, indices = 0, []
while pos < len(text):
for size in (3, 2, 1):
chunk = text[pos:pos+size]
if chunk in self.chunk_to_idx:
indices.append(self.chunk_to_idx[chunk])
pos += size
break
else:
pos += 1
return indices
def decode(self, indices):
return ''.join([self.idx_to_chunk.get(int(i), '') for i in indices])
# ============================================================================
class LoRPtLinear(nn.Module):
def __init__(self, in_features, out_features, rank=64):
super().__init__()
self.lora_A = nn.Parameter(torch.randn(out_features, rank) * 0.02)
self.lora_B = nn.Parameter(torch.randn(rank, in_features) * 0.02)
self.bias = nn.Parameter(torch.zeros(out_features))
def forward(self, x):
return F.linear(x, self.lora_A @ self.lora_B, self.bias)
class RWKVMambaHybrid(nn.Module):
def __init__(self, d_model, d_state=32):
super().__init__()
self.d_model = d_model
self.d_state = d_state
self.w_mix = nn.Parameter(torch.ones(d_model) * 0.5)
self.A = nn.Parameter(torch.randn(d_state, d_state) * 0.01)
self.B = nn.Parameter(torch.randn(d_state, d_model) * 0.01)
self.C = nn.Parameter(torch.randn(d_model, d_state) * 0.01)
self.D = nn.Parameter(torch.ones(d_model) * 0.1)
def forward(self, x):
B, T, C = x.shape
h = torch.zeros(B, C, device=x.device)
s = torch.zeros(B, self.d_state, device=x.device)
outputs = []
for t in range(T):
x_t = x[:, t, :]
h = self.w_mix * h + (1 - self.w_mix) * x_t
s = s @ self.A.T + x_t @ self.B.T
y_t = s @ self.C.T + h * self.D
outputs.append(y_t)
return torch.stack(outputs, dim=1)
class KQVAttention(nn.Module):
def __init__(self, d_model, n_heads=16, rank=64):
super().__init__()
self.d_model = d_model
self.n_heads = n_heads
self.head_dim = d_model // n_heads
self.q_down = nn.Linear(d_model, rank)
self.q_up = nn.Linear(rank, d_model)
self.k_down = nn.Linear(d_model, rank)
self.k_up = nn.Linear(rank, d_model)
self.v_down = nn.Linear(d_model, rank)
self.v_up = nn.Linear(rank, d_model)
self.out_proj = nn.Linear(d_model, d_model)
def forward(self, x, mask=None):
B, T, C = x.shape
q = self.q_up(self.q_down(x))
k = self.k_up(self.k_down(x))
v = self.v_up(self.v_down(x))
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)
attn = (q @ k.transpose(-2, -1)) / np.sqrt(self.head_dim)
if mask is not None:
attn = attn.masked_fill(mask == 0, float('-inf'))
attn = F.softmax(attn, dim=-1)
out = attn @ v
out = out.transpose(1, 2).contiguous().view(B, T, C)
return self.out_proj(out)
class i3Block(nn.Module):
def __init__(self, d_model, n_heads=16, d_state=32, rank=64, ffn_mult=4):
super().__init__()
self.hybrid = RWKVMambaHybrid(d_model, d_state)
self.ln1 = nn.LayerNorm(d_model)
self.attn = KQVAttention(d_model, n_heads, rank)
self.ln2 = nn.LayerNorm(d_model)
d_ff = d_model * ffn_mult
self.ffn = nn.Sequential(
LoRPtLinear(d_model, d_ff, rank),
nn.GELU(),
LoRPtLinear(d_ff, d_model, rank)
)
self.ln3 = nn.LayerNorm(d_model)
def forward(self, x, mask=None):
x = x + self.hybrid(self.ln1(x))
x = x + self.attn(self.ln2(x), mask)
x = x + self.ffn(self.ln3(x))
return x
class i3Model(nn.Module):
def __init__(self, vocab_size, d_model=512, n_layers=24, n_heads=16,
max_seq_len=256, rank=64, d_state=32):
super().__init__()
self.vocab_size = vocab_size
self.d_model = d_model
self.max_seq_len = max_seq_len
self.embed = nn.Embedding(vocab_size, d_model)
self.pos_embed = nn.Embedding(max_seq_len, d_model)
self.layers = nn.ModuleList([
i3Block(d_model, n_heads, d_state, rank)
for _ in range(n_layers)
])
self.ln_f = nn.LayerNorm(d_model)
self.head = LoRPtLinear(d_model, vocab_size, rank)
def forward(self, idx):
B, T = idx.shape
pos = torch.arange(0, T, device=idx.device).unsqueeze(0)
x = self.embed(idx) + self.pos_embed(pos)
mask = torch.tril(torch.ones(T, T, device=idx.device)).view(1, 1, T, T)
for layer in self.layers:
x = layer(x, mask)
x = self.ln_f(x)
return self.head(x)
@torch.no_grad()
def generate(self, idx, max_new_tokens=100, temperature=0.8, top_k=40):
for _ in range(max_new_tokens):
idx_cond = idx[:, -self.max_seq_len:]
logits = self(idx_cond)[:, -1, :] / temperature
v, _ = torch.topk(logits, top_k)
logits[logits < v[:, [-1]]] = -float("inf")
probs = F.softmax(logits, dim=-1)
idx_next = torch.multinomial(probs, 1)
idx = torch.cat((idx, idx_next), dim=1)
return idx
# ============================================================================
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
tokenizer = ChunkTokenizer()
tokenizer.load("tokenizer.json")
model = i3Model(
vocab_size=tokenizer.vocab_size,
d_model=512,
n_layers=24,
n_heads=16,
max_seq_len=256,
rank=64,
d_state=32
).to(device)
state_dict = torch.load("pytorch_model.bin", map_location=device)
model.load_state_dict(state_dict)
model.eval()
print("✓ Model loaded successfully")
# ============================================================================
@torch.no_grad()
def infer(prompt, max_new_tokens=100, temperature=0.8, top_k=40):
input_ids = torch.tensor([tokenizer.encode(prompt)], dtype=torch.long).to(device)
output = model.generate(input_ids, max_new_tokens=max_new_tokens,
temperature=temperature, top_k=top_k)
return tokenizer.decode(output[0].cpu().numpy())
def chat_loop():
print("=== i3 Interactive Chat ([INST] format) ===")
history = ""
while True:
user_input = input("[You] ")
if user_input.strip().lower() in {"quit", "exit"}:
break
prompt = f"{history}[INST] {user_input.strip()} [/INST]"
reply = infer(prompt, max_new_tokens=120)
reply_clean = reply.replace(prompt.lower(), "").strip()
print("[i3]:", reply_clean)
history += f"[INST] {user_input.strip()} [/INST] {reply_clean} "
# ============================================================================
print("\nExample:")
prompt = "[INST] What can we do to make people happier [/INST]"
print("Prompt:", prompt)
print("Generated:", infer(prompt))
# Optionally start a chat loop:
# chat_loop()
|