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# Copyright (c) Meta Platforms, Inc. and affiliates.
# This software may be used and distributed according to the terms of the GNU General Public License version 3.
from typing import Tuple
import os
import sys
import torch
import fire
import time
import json
from pathlib import Path
from llama import ModelArgs, Transformer, Tokenizer, LLaMA
def load(
ckpt_dir: str,
tokenizer_path: str,
max_seq_len: int,
max_batch_size: int,
) -> LLaMA:
print("Creating model...")
start_time = time.time()
checkpoints = sorted(Path(ckpt_dir).glob("*.pth"))
with open(Path(ckpt_dir) / "params.json", "r") as f:
params = json.loads(f.read())
model_args: ModelArgs = ModelArgs(
max_seq_len=max_seq_len, max_batch_size=max_batch_size, **params
)
tokenizer = Tokenizer(model_path=tokenizer_path)
model_args.vocab_size = tokenizer.n_words
model = Transformer(model_args)
model.to("cpu")
print("Loading merged checkpoint...")
checkpoint = torch.load(checkpoints[-1], map_location="cuda")
model.load_state_dict(checkpoint, strict=False)
del checkpoint
generator = LLaMA(model, tokenizer)
print(f"Loaded model in {time.time() - start_time:.2f} seconds")
return generator
def main(
ckpt_dir: str = './model',
tokenizer_path: str = './tokenizer/tokenizer.model',
temperature: float = 0.8,
top_p: float = 0.95,
max_seq_len: int = 256, # up to 2048
max_batch_size: int = 5,
):
# torch.manual_seed(1)
torch.set_default_dtype(torch.bfloat16)
generator = load(ckpt_dir, tokenizer_path, max_seq_len, max_batch_size)
while True:
prompt = input(f'prompt> ')
if len(prompt.strip()) > 0:
prompts = [prompt]
results = generator.generate(
prompts, max_gen_len=256, temperature=temperature, top_p=top_p
)
for result in results:
print(result)
if __name__ == "__main__":
fire.Fire(main)
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