generate/full.py

import sys
import time
import warnings
from pathlib import Path
from typing import Optional

import lightning as L
import torch

# support running without installing as a package
wd = Path(__file__).absolute().parent.parent
sys.path.append(str(wd))

from lit_llama import LLaMA, Tokenizer
from lit_llama.utils import quantization
from scripts.prepare_alpaca import generate_prompt
from generate import generate


def main(
    prompt: str = "Hello, my name is",
    *,
    num_samples: int = 1,
    max_new_tokens: int = 50,
    top_k: int = 200,
    temperature: float = 0.8,
    checkpoint_path: Optional[Path] = None,
    tokenizer_path: Path = Path("checkpoints/lit-llama/tokenizer.model"),
    model_size: str = "7B",
    quantize: Optional[str] = None,
) -> None:
    """Generates text samples based on a pre-trained LLaMA model and tokenizer.

    Args:
        prompt: The prompt string to use for generating the samples.
        num_samples: The number of text samples to generate.
        max_new_tokens: The number of generation steps to take.
        top_k: The number of top most probable tokens to consider in the sampling process.
        temperature: A value controlling the randomness of the sampling process. Higher values result in more random
            samples.
        checkpoint_path: The checkpoint path to load.
        tokenizer_path: The tokenizer path to load.
        model_size: The model size to load.
        quantize: Whether to quantize the model and using which method:
            ``"llm.int8"``: LLM.int8() mode,
            ``"gptq.int4"``: GPTQ 4-bit mode.
    """
    if not checkpoint_path:
        checkpoint_path = Path(f"checkpoints/lit-llama/{model_size}/lit-llama.pth")
    assert checkpoint_path.is_file(), checkpoint_path
    assert tokenizer_path.is_file(), tokenizer_path

    precision = "bf16-true" if torch.cuda.is_available() and torch.cuda.is_bf16_supported() else "32-true"
    fabric = L.Fabric(devices=1, precision=precision)

    print("Loading model ...", file=sys.stderr)
    t0 = time.time()
    
    with fabric.init_module(empty_init=True), quantization(mode=quantize):
        model = LLaMA.from_name(model_size)

    checkpoint = torch.load(checkpoint_path)
    model.load_state_dict(checkpoint)
    print(f"Time to load model: {time.time() - t0:.02f} seconds.", file=sys.stderr)

    model.eval()
    model = fabric.setup(model)

    tokenizer = Tokenizer(tokenizer_path)
    sample = {"instruction": prompt, "input": input}
    prompt = generate_prompt(sample)
    encoded = tokenizer.encode(prompt, bos=True, eos=False, device=fabric.device)
    prompt_length = encoded.size(0)

    L.seed_everything(1234)
    for i in range(num_samples):
        t0 = time.perf_counter()
        y = generate(model, encoded, max_new_tokens, temperature=temperature, top_k=top_k)
        t = time.perf_counter() - t0

        model.reset_cache()
        print(tokenizer.decode(y))
        tokens_generated = y.size(0) - prompt_length
        print(f"Time for inference {i + 1}: {t:.02f} sec total, {tokens_generated / t:.02f} tokens/sec", file=sys.stderr)
    if fabric.device.type == "cuda":
        print(f"Memory used: {torch.cuda.max_memory_reserved() / 1e9:.02f} GB", file=sys.stderr)


if __name__ == "__main__":
    from jsonargparse import CLI

    torch.set_float32_matmul_precision("high")
    warnings.filterwarnings(
        # Triggered internally at ../aten/src/ATen/EmptyTensor.cpp:31
        "ignore", 
        message="ComplexHalf support is experimental and many operators don't support it yet"
    )
    warnings.filterwarnings(
        # Triggered in bitsandbytes/autograd/_functions.py:298
        "ignore", 
        message="MatMul8bitLt: inputs will be cast from torch.bfloat16 to float16 during quantization",
    )
    CLI(main)