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import sys
import time
import warnings
from pathlib import Path
from typing import Optional
import lightning as L
import torch
from generate import generate
from lit_llama import Tokenizer
from lit_llama.adapter import LLaMA
from lit_llama.utils import EmptyInitOnDevice, lazy_load, llama_model_lookup
from scripts.prepare_alpaca import generate_prompt
def main(
prompt: str = "What food do lamas eat?",
input: str = "",
adapter_path: Optional[Path] = None,
pretrained_path: Optional[Path] = None,
tokenizer_path: Optional[Path] = None,
quantize: Optional[str] = None,
max_new_tokens: int = 100,
top_k: int = 200,
temperature: float = 0.8,
) -> None:
"""Generates a response based on a given instruction and an optional input.
This script will only work with checkpoints from the instruction-tuned LLaMA-Adapter model.
See `finetune_adapter.py`.
Args:
prompt: The prompt/instruction (Alpaca style).
adapter_path: Path to the checkpoint with trained adapter weights, which are the output of
`finetune_adapter.py`.
input: Optional input (Alpaca style).
pretrained_path: The path to the checkpoint with pretrained LLaMA weights.
tokenizer_path: The tokenizer path to load.
quantize: Whether to quantize the model and using which method:
``"llm.int8"``: LLM.int8() mode,
``"gptq.int4"``: GPTQ 4-bit mode.
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.
"""
if not adapter_path:
adapter_path = Path("out/adapter/alpaca/lit-llama-adapter-finetuned.pth")
if not pretrained_path:
pretrained_path = Path(f"./checkpoints/lit-llama/7B/lit-llama.pth")
if not tokenizer_path:
tokenizer_path = Path("./checkpoints/lit-llama/tokenizer.model")
assert adapter_path.is_file()
assert pretrained_path.is_file()
assert tokenizer_path.is_file()
fabric = L.Fabric(devices=1)
dtype = torch.bfloat16 if fabric.device.type == "cuda" and torch.cuda.is_bf16_supported() else torch.float32
print("Loading model ...", file=sys.stderr)
t0 = time.time()
with (lazy_load(pretrained_path) as pretrained_checkpoint,
lazy_load(adapter_path) as adapter_checkpoint):
name = llama_model_lookup(pretrained_checkpoint)
with EmptyInitOnDevice(
device=fabric.device, dtype=dtype, quantization_mode=quantize
):
model = LLaMA.from_name(name)
# 1. Load the pretrained weights
model.load_state_dict(pretrained_checkpoint, strict=False)
# 2. Load the fine-tuned adapter weights
model.load_state_dict(adapter_checkpoint, strict=False)
print(f"Time to load model: {time.time() - t0:.02f} seconds.", file=sys.stderr)
model.eval()
model = fabric.setup_module(model)
tokenizer = Tokenizer(tokenizer_path)
sample = {"instruction": prompt, "input": input}
prompt = generate_prompt(sample)
encoded = tokenizer.encode(prompt, bos=True, eos=False, device=model.device)
t0 = time.perf_counter()
output = generate(
model,
idx=encoded,
max_seq_length=max_new_tokens,
max_new_tokens=max_new_tokens,
temperature=temperature,
top_k=top_k,
eos_id=tokenizer.eos_id
)
t = time.perf_counter() - t0
output = tokenizer.decode(output)
output = output.split("### Response:")[1].strip()
print(output)
print(f"\n\nTime for inference: {t:.02f} sec total, {max_new_tokens / 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"
)
CLI(main)
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