Delete example.py

example.py

@@ -1,119 +0,0 @@
-# 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 fairscale.nn.model_parallel.initialize import initialize_model_parallel
-
-from llama import ModelArgs, Transformer, Tokenizer, LLaMA
-
-
-def setup_model_parallel() -> Tuple[int, int]:
-    local_rank = int(os.environ.get("LOCAL_RANK", -1))
-    world_size = int(os.environ.get("WORLD_SIZE", -1))
-
-    torch.distributed.init_process_group("nccl")
-    initialize_model_parallel(world_size)
-    torch.cuda.set_device(local_rank)
-
-    # seed must be the same in all processes
-    torch.manual_seed(1)
-    return local_rank, world_size
-
-
-def load(
-    ckpt_dir: str,
-    tokenizer_path: str,
-    local_rank: int,
-    world_size: int,
-    max_seq_len: int,
-    max_batch_size: int,
-) -> LLaMA:
-    start_time = time.time()
-    checkpoints = sorted(Path(ckpt_dir).glob("*.pth"))
-    assert world_size == len(
-        checkpoints
-    ), f"Loading a checkpoint for MP={len(checkpoints)} but world size is {world_size}"
-    ckpt_path = checkpoints[local_rank]
-    print("Loading")
-    checkpoint = torch.load(ckpt_path, map_location="cpu")
-    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
-    torch.set_default_tensor_type(torch.cuda.HalfTensor)
-    model = Transformer(model_args)
-    torch.set_default_tensor_type(torch.FloatTensor)
-    model.load_state_dict(checkpoint, strict=False)
-
-    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 = 32,
-):
-    local_rank, world_size = setup_model_parallel()
-    if local_rank > 0:
-        sys.stdout = open(os.devnull, "w")
-
-    generator = load(
-        ckpt_dir, tokenizer_path, local_rank, world_size, 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=256, temperature=temperature, top_p=top_p
-    )
-
-    for result in results:
-        print(result)
-        print("\n==================================\n")
-
-
-if __name__ == "__main__":
-    fire.Fire(main)