example.py

# 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='cpu')
            (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("*"))
    # print(segments)

    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)
    model.load_state_dict(checkpoint, 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 = 512,
    max_batch_size: int = 16,
):
    generator = load(ckpt_dir, tokenizer_path, max_seq_len, max_batch_size)

    prompts = [
        # For these prompts, the expected answer is the natural continuation of the prompt
        "I believe the meaning of life is",
        "Simply put, the theory of relativity states that ",
        "Building a website can be done in 10 simple steps:\n",
        # Few shot prompts: https://huggingface.co/blog/few-shot-learning-gpt-neo-and-inference-api
        """Tweet: "I hate it when my phone battery dies."
Sentiment: Negative
###
Tweet: "My day has been 👍"
Sentiment: Positive
###
Tweet: "This is the link to the article"
Sentiment: Neutral
###
Tweet: "This new music video was incredibile"
Sentiment:""",
        """Translate English to French:

sea otter => loutre de mer

peppermint => menthe poivrée

plush girafe => girafe peluche

cheese =>""",
    ]
    results = generator.generate(
        prompts, max_gen_len=max_seq_len, temperature=temperature, top_p=top_p
    )

    for result in results:
        print("\n==================================\n")
        print(result)
        print("\n==================================\n")

    with open('example-output.json', 'w') as f:
        json.dump(results, f)


if __name__ == "__main__":
    fire.Fire(main)