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import traceback
import os
print("🔮 Deep-NanoGPT Inference Script")
try:
import torch
import torch.nn as nn
from torch.nn import functional as F
import requests
# --- Config (must match training) ---
block_size = 256
n_embd = 128
n_head = 4
n_layer = 72
dropout = 0.1
device = 'cuda' if torch.cuda.is_available() else 'cpu'
# --- Storage ---
storage_dir = "/home/user/app/storage/deep_experiment_v2"
ckpt_path_a = os.path.join(storage_dir, 'ckpt_a.pt')
ckpt_path_b = os.path.join(storage_dir, 'ckpt_b.pt')
# --- Vocab (rebuild from data) ---
url = 'https://raw.githubusercontent.com/karpathy/char-rnn/master/data/tinyshakespeare/input.txt'
data = requests.get(url).text
chars = sorted(list(set(data)))
vocab_size = len(chars)
stoi = { ch:i for i,ch in enumerate(chars) }
itos = { i:ch for i,ch in enumerate(chars) }
encode = lambda s: [stoi.get(c, 0) for c in s]
decode = lambda l: ''.join([itos[i] for i in l])
# --- Model Classes ---
class Head(nn.Module):
def __init__(self, head_size):
super().__init__()
self.key = nn.Linear(n_embd, head_size, bias=False)
self.query = nn.Linear(n_embd, head_size, bias=False)
self.value = nn.Linear(n_embd, head_size, bias=False)
self.register_buffer('tril', torch.tril(torch.ones(block_size, block_size)))
self.dropout = nn.Dropout(dropout)
def forward(self, x):
B,T,C = x.shape
k = self.key(x)
q = self.query(x)
wei = q @ k.transpose(-2, -1) * C**-0.5
wei = wei.masked_fill(self.tril[:T, :T] == 0, float('-inf'))
wei = F.softmax(wei, dim=-1)
wei = self.dropout(wei)
v = self.value(x)
return wei @ v
class MultiHeadAttention(nn.Module):
def __init__(self, num_heads, head_size):
super().__init__()
self.heads = nn.ModuleList([Head(head_size) for _ in range(num_heads)])
self.proj = nn.Linear(n_embd, n_embd)
self.dropout = nn.Dropout(dropout)
def forward(self, x):
out = torch.cat([h(x) for h in self.heads], dim=-1)
return self.dropout(self.proj(out))
class FeedForward(nn.Module):
def __init__(self, n_embd):
super().__init__()
self.net = nn.Sequential(
nn.Linear(n_embd, 4 * n_embd),
nn.ReLU(),
nn.Linear(4 * n_embd, n_embd),
nn.Dropout(dropout),
)
def forward(self, x):
return self.net(x)
class BlockStandard(nn.Module):
def __init__(self, n_embd, n_head):
super().__init__()
head_size = n_embd // n_head
self.sa = MultiHeadAttention(n_head, head_size)
self.ffwd = FeedForward(n_embd)
self.ln1 = nn.LayerNorm(n_embd)
self.ln2 = nn.LayerNorm(n_embd)
def forward(self, x):
x = x + self.sa(self.ln1(x))
x = x + self.ffwd(self.ln2(x))
return x
class RMSNorm(nn.Module):
def __init__(self, dim, eps=1e-6):
super().__init__()
self.eps = eps
self.weight = nn.Parameter(torch.ones(dim))
def _norm(self, x):
return x * torch.rsqrt(x.pow(2).mean(-1, keepdim=True) + self.eps)
def forward(self, x):
return self._norm(x.float()).type_as(x) * self.weight
class BlockMHC(nn.Module):
def __init__(self, n_embd, n_head):
super().__init__()
head_size = n_embd // n_head
self.sa = MultiHeadAttention(n_head, head_size)
self.ffwd = FeedForward(n_embd)
self.alpha1 = nn.Parameter(torch.tensor(0.9))
self.beta1 = nn.Parameter(torch.tensor(0.1))
self.ln1 = RMSNorm(n_embd)
self.alpha2 = nn.Parameter(torch.tensor(0.9))
self.beta2 = nn.Parameter(torch.tensor(0.1))
self.ln2 = RMSNorm(n_embd)
def forward(self, x):
mix1 = self.alpha1 * x + self.beta1 * self.sa(x)
x = self.ln1(mix1)
mix2 = self.alpha2 * x + self.beta2 * self.ffwd(x)
x = self.ln2(mix2)
return x
class GPT(nn.Module):
def __init__(self, arch_type='standard'):
super().__init__()
self.arch_type = arch_type
self.token_embedding_table = nn.Embedding(vocab_size, n_embd)
self.position_embedding_table = nn.Embedding(block_size, n_embd)
if arch_type == 'standard':
self.blocks = nn.Sequential(*[BlockStandard(n_embd, n_head) for _ in range(n_layer)])
self.ln_f = nn.LayerNorm(n_embd)
elif arch_type == 'mhc':
self.blocks = nn.Sequential(*[BlockMHC(n_embd, n_head) for _ in range(n_layer)])
self.ln_f = RMSNorm(n_embd)
self.lm_head = nn.Linear(n_embd, vocab_size)
def forward(self, idx, targets=None):
B, T = idx.shape
tok_emb = self.token_embedding_table(idx)
pos_emb = self.position_embedding_table(torch.arange(T, device=device))
x = tok_emb + pos_emb
x = self.blocks(x)
x = self.ln_f(x)
logits = self.lm_head(x)
return logits, None
def generate(self, idx, max_new_tokens):
for _ in range(max_new_tokens):
idx_cond = idx[:, -block_size:]
logits, _ = self(idx_cond)
logits = logits[:, -1, :]
probs = F.softmax(logits, dim=-1)
idx_next = torch.multinomial(probs, num_samples=1)
idx = torch.cat((idx, idx_next), dim=1)
return idx
# --- Load Models ---
print(f"📦 Loading Model A (Standard) from {ckpt_path_a}...")
model_a = GPT(arch_type='standard').to(device)
model_a.load_state_dict(torch.load(ckpt_path_a, map_location=device))
model_a.eval()
print(f"📦 Loading Model B (mHC) from {ckpt_path_b}...")
model_b = GPT(arch_type='mhc').to(device)
model_b.load_state_dict(torch.load(ckpt_path_b, map_location=device))
model_b.eval()
# --- Inference ---
PROMPT = "ROMEO:" # Shakespearean prompt
MAX_TOKENS = 300
print(f"\n🎭 Prompt: '{PROMPT}'")
print(f"🔢 Max Tokens: {MAX_TOKENS}")
context = torch.tensor([encode(PROMPT)], dtype=torch.long, device=device)
print("\n--- MODEL A (Standard GPT, 72 Layers) ---")
with torch.no_grad():
out_a = model_a.generate(context.clone(), max_new_tokens=MAX_TOKENS)
print(decode(out_a[0].tolist()))
print("\n--- MODEL B (mHC GPT, 72 Layers) ---")
with torch.no_grad():
out_b = model_b.generate(context.clone(), max_new_tokens=MAX_TOKENS)
print(decode(out_b[0].tolist()))
print("\n✅ Inference Complete.")
except Exception as e:
print(f"\n❌ FATAL ERROR: {e}")
traceback.print_exc()
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