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"""
Interactive chat with the 1B Transformer.
Runs in an infinite conversation loop from the terminal.
Usage:
python chat.py # auto-find latest checkpoint
python chat.py /jfs/deepak-kumar/checkpoints/step_19000.pt # specific checkpoint
"""
import sys
import os
import glob
import time
import torch
import torch.nn.functional as F
import readline # enables arrow keys and history in input()
sys.path.insert(0, os.path.dirname(os.path.abspath(__file__)))
from model.config import ModelConfig
from model.transformer import Transformer
from model.data import get_tokenizer
def find_latest_checkpoint():
"""Look for DPO > SFT > pretrained checkpoint."""
dpo_dir = "/jfs/deepak-kumar/checkpoints_dpo"
sft_dir = "/jfs/deepak-kumar/checkpoints_sft"
pt_dir = "/jfs/deepak-kumar/checkpoints"
# Prefer DPO final
dpo_final = os.path.join(dpo_dir, "dpo_final.pt")
if os.path.exists(dpo_final):
return dpo_final, True
dpo_files = glob.glob(os.path.join(dpo_dir, "dpo_step_*.pt"))
if dpo_files:
return max(dpo_files, key=lambda f: int(f.split("dpo_step_")[1].split(".")[0])), True
# Then SFT
sft_final = os.path.join(sft_dir, "sft_final.pt")
if os.path.exists(sft_final):
return sft_final, True
sft_files = glob.glob(os.path.join(sft_dir, "sft_step_*.pt"))
if sft_files:
return max(sft_files, key=lambda f: int(f.split("sft_step_")[1].split(".")[0])), True
# Fall back to pretrained
pt_files = glob.glob(os.path.join(pt_dir, "step_*.pt"))
if pt_files:
return max(pt_files, key=lambda f: int(os.path.basename(f).split("_")[1].split(".")[0])), False
return None, False
def load_model(checkpoint_path, tokenizer, device="cuda:0"):
config = ModelConfig()
model = Transformer(config)
ckpt = torch.load(checkpoint_path, map_location="cpu", weights_only=False)
# Handle expanded vocab from SFT
saved_vocab = ckpt.get("vocab_size", config.vocab_size)
if saved_vocab > config.vocab_size:
config.vocab_size = saved_vocab
model = Transformer(config)
model.load_state_dict(ckpt["model"])
model = model.to(device).bfloat16().eval()
step = ckpt.get("step", "?")
loss = ckpt.get("loss", "?")
del ckpt
torch.cuda.empty_cache()
return model, config, step, loss
@torch.no_grad()
def generate_stream(model, tokenizer, prompt, max_new_tokens=512,
temperature=0.8, top_k=50, top_p=0.9,
repetition_penalty=1.15, device="cuda:0",
stop_token_ids=None):
"""Generate tokens one at a time, yielding each for streaming output."""
input_ids = tokenizer.encode(prompt, return_tensors="pt").to(device)
generated_ids = []
prev_decoded_len = 0
if stop_token_ids is None:
stop_token_ids = set()
else:
stop_token_ids = set(stop_token_ids)
stop_token_ids.add(tokenizer.eos_token_id)
for _ in range(max_new_tokens):
if input_ids.shape[1] >= model.config.max_seq_len:
break
with torch.autocast(device_type="cuda", dtype=torch.bfloat16):
logits, _ = model(input_ids)
logits = logits[:, -1, :]
if repetition_penalty != 1.0 and generated_ids:
prev_tokens = torch.tensor(generated_ids, device=device).unique()
for token_id in prev_tokens:
if logits[0, token_id] > 0:
logits[0, token_id] /= repetition_penalty
else:
logits[0, token_id] *= repetition_penalty
logits = logits / temperature
if top_k > 0:
topk_vals, _ = torch.topk(logits, top_k)
logits[logits < topk_vals[:, -1:]] = float("-inf")
if top_p < 1.0:
sorted_logits, sorted_idx = torch.sort(logits, descending=True)
cum_probs = torch.cumsum(F.softmax(sorted_logits, dim=-1), dim=-1)
mask = cum_probs - F.softmax(sorted_logits, dim=-1) >= top_p
sorted_logits[mask] = float("-inf")
logits = sorted_logits.scatter(1, sorted_idx, sorted_logits)
probs = F.softmax(logits, dim=-1)
next_token = torch.multinomial(probs, num_samples=1)
token_id = next_token.item()
# Stop on any stop token (EOS, <|end|>, <|user|>)
if token_id in stop_token_ids:
break
generated_ids.append(token_id)
input_ids = torch.cat([input_ids, next_token], dim=1)
full_decoded = tokenizer.decode(generated_ids, skip_special_tokens=True)
new_text = full_decoded[prev_decoded_len:]
prev_decoded_len = len(full_decoded)
yield new_text
return
def print_banner(step, loss, device):
print("\033[1;36m") # cyan bold
print("=" * 60)
print(" 1B TRANSFORMER — Interactive Chat")
print("=" * 60)
print(f"\033[0m Checkpoint : step {step}")
print(f" Loss : {loss}")
print(f" Device : {device}")
print(f" Parameters : 1.106B")
print()
print(" \033[90mCommands:\033[0m")
print(" \033[33m/quit\033[0m — exit")
print(" \033[33m/clear\033[0m — clear conversation context")
print(" \033[33m/temp N\033[0m — set temperature (default 0.8)")
print(" \033[33m/tokens N\033[0m — set max tokens (default 512)")
print(" \033[33m/topp N\033[0m — set top-p (default 0.9)")
print(" \033[33m/topk N\033[0m — set top-k (default 50)")
print(" \033[33m/rep N\033[0m — set repetition penalty (default 1.15)")
print()
print("\033[90m" + "─" * 60 + "\033[0m")
def main():
device = "cuda:0"
is_sft = False
if len(sys.argv) > 1:
checkpoint = sys.argv[1]
is_sft = "sft" in checkpoint.lower()
else:
result = find_latest_checkpoint()
if result[0] is None:
print("No checkpoint found!")
sys.exit(1)
checkpoint, is_sft = result
tokenizer = get_tokenizer()
# Add chat tokens for SFT models
if is_sft:
special_tokens = ["<|user|>", "<|assistant|>", "<|end|>"]
vocab = tokenizer.get_vocab()
new_tokens = [t for t in special_tokens if t not in vocab]
if new_tokens:
tokenizer.add_tokens(new_tokens, special_tokens=True)
print(f"\n Loading model from {checkpoint}...")
print(f" Mode: {'SFT (chat)' if is_sft else 'Base (completion)'}")
model, config, step, loss = load_model(checkpoint, tokenizer, device)
print(f" Model loaded!\n")
print_banner(step, loss, device)
if is_sft:
print(" \033[1;32mSFT mode: The model will respond as a chat assistant.\033[0m\n")
# Settings
temperature = 0.7 if is_sft else 0.8
max_tokens = 512
top_p = 0.9
top_k = 50
rep_penalty = 1.15
context = ""
# Chat template tokens for SFT
USER_START = "<|user|>\n"
ASST_START = "<|assistant|>\n"
TURN_END = "\n<|end|>\n"
# Build stop token IDs for generation
sft_stop_ids = []
if is_sft:
vocab = tokenizer.get_vocab()
for tok_str in ["<|end|>", "<|user|>"]:
if tok_str in vocab:
sft_stop_ids.append(vocab[tok_str])
while True:
try:
user_input = input("\n\033[1;32mYou:\033[0m ").strip()
except (KeyboardInterrupt, EOFError):
print("\n\nGoodbye!")
break
if not user_input:
continue
# Handle commands
if user_input.startswith("/"):
cmd = user_input.lower().split()
if cmd[0] == "/quit":
print("Goodbye!")
break
elif cmd[0] == "/clear":
context = ""
print("\033[90m [Context cleared]\033[0m")
continue
elif cmd[0] == "/temp" and len(cmd) > 1:
temperature = float(cmd[1])
print(f"\033[90m [Temperature set to {temperature}]\033[0m")
continue
elif cmd[0] == "/tokens" and len(cmd) > 1:
max_tokens = int(cmd[1])
print(f"\033[90m [Max tokens set to {max_tokens}]\033[0m")
continue
elif cmd[0] == "/topp" and len(cmd) > 1:
top_p = float(cmd[1])
print(f"\033[90m [Top-p set to {top_p}]\033[0m")
continue
elif cmd[0] == "/topk" and len(cmd) > 1:
top_k = int(cmd[1])
print(f"\033[90m [Top-k set to {top_k}]\033[0m")
continue
elif cmd[0] == "/rep" and len(cmd) > 1:
rep_penalty = float(cmd[1])
print(f"\033[90m [Repetition penalty set to {rep_penalty}]\033[0m")
continue
else:
print("\033[90m Unknown command. Try /quit, /clear, /temp, /tokens, /topp, /topk, /rep\033[0m")
continue
# Build prompt
if is_sft:
prompt = context + USER_START + user_input + TURN_END + ASST_START
else:
if context:
prompt = context + "\n" + user_input
else:
prompt = user_input
# Trim context if too long
while len(tokenizer.encode(prompt)) > config.max_seq_len - max_tokens:
if is_sft:
parts = context.split(TURN_END)
if len(parts) <= 2:
break
context = TURN_END.join(parts[2:])
prompt = context + USER_START + user_input + TURN_END + ASST_START
else:
lines = prompt.split("\n")
if len(lines) <= 2:
break
prompt = "\n".join(lines[1:])
# Generate with streaming
print("\033[1;34mModel:\033[0m ", end="", flush=True)
t0 = time.time()
full_response = ""
token_count = 0
for token_text in generate_stream(
model, tokenizer, prompt,
max_new_tokens=max_tokens,
temperature=temperature,
top_k=top_k,
top_p=top_p,
repetition_penalty=rep_penalty,
device=device,
stop_token_ids=sft_stop_ids if is_sft else None,
):
print(token_text, end="", flush=True)
full_response += token_text
token_count += 1
elapsed = time.time() - t0
tps = token_count / max(elapsed, 1e-9)
print(f"\n\033[90m [{token_count} tokens, {tps:.1f} tok/s, {elapsed:.1f}s]\033[0m")
# Append to context for multi-turn
if is_sft:
context = (context + USER_START + user_input + TURN_END +
ASST_START + full_response.strip() + TURN_END)
else:
context = prompt + full_response
if __name__ == "__main__":
main()
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