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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
import pyarrow as pa
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:
start_time = time.time()
arrow_dir = Path(ckpt_dir).expanduser() / 'arrow'
if not arrow_dir.exists():
print('Converting checkpoints to arrow format')
checkpoints = sorted(Path(ckpt_dir).expanduser().glob("*.pth"))
for ckpt_file in checkpoints:
print(ckpt_file)
index = ckpt_file.parts[-1].split('.')[-2]
ckpt = torch.load(ckpt_file, map_location='cuda')
(arrow_dir / index).mkdir(parents=True, exist_ok=True)
for k, v in ckpt.items():
tens = pa.Tensor.from_numpy(v.numpy())
with pa.output_stream(arrow_dir / index / k) as f:
pa.ipc.write_tensor(tens, f)
ckpt = None
with open(Path(ckpt_dir) / "params.json", "r") as f:
params = json.loads(f.read())
print("Loading checkpoint")
segments = sorted((arrow_dir / '00').glob("*"))
checkpoint = {}
files = []
for seg in segments:
f = pa.memory_map(str(seg))
files.append(f)
t = pa.ipc.read_tensor(f).to_numpy()
t = torch.from_numpy(t)
checkpoint[seg.parts[-1]] = t
# torch.set_default_tensor_type(torch.cuda.HalfTensor)
torch.set_default_tensor_type(torch.BFloat16Tensor)
# torch.set_default_tensor_type(torch.FloatTensor)
model_args: ModelArgs = ModelArgs(
max_seq_len=max_seq_len, max_batch_size=max_batch_size, **params
)
print("Loading tokenizer")
tokenizer = Tokenizer(model_path=tokenizer_path)
model_args.vocab_size = tokenizer.n_words
print("Loading model")
model = Transformer(model_args)
checkpoints = sorted(Path(ckpt_dir).glob("*.pth"))
model.load_state_dict(torch.load(checkpoints[-1]), strict=False)
for f in files:
f.close()
files = None
generator = LLaMA(model, tokenizer)
print(f"Loaded in {time.time() - start_time:.2f} seconds")
return generator
def main(
ckpt_dir: str,
tokenizer_path: str,
temperature: float = 0.8,
top_p: float = 0.95,
max_seq_len: int = 2048,
max_batch_size: int = 1, # 16 for 13B, 4 for 30B and 65B, 2 for 1024 seq_len for 30B
):
generator = load(ckpt_dir, tokenizer_path, max_seq_len, max_batch_size)
ctx = """A dialog, where User interacts with AI. AI is helpful, kind, obedient, honest, and knows its own limits.
User: Hello, AI.
AI: Hello! How can I assist you today?
"""
while True:
prompt = input(f'User: ')
if ctx != "":
ctx = ctx + "User: " + prompt + "\n"
else:
ctx = prompt + "\n"
ctx = (ctx[-1920:]) if len(ctx) >= 2048 else ctx
if len(ctx.strip()) > 0:
prompts = [ctx]
results = generator.generate(
prompts, max_gen_len=2048, temperature=temperature, top_p=top_p
)
ctx = results[0]
if __name__ == "__main__":
fire.Fire(main)
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