TAPA / generate /adapter.py
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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__).parent.parent.resolve()
sys.path.append(str(wd))
from generate import generate
from lit_llama import Tokenizer
from lit_llama.adapter import LLaMA
from lit_llama.utils import lazy_load, llama_model_lookup, quantization
from scripts.prepare_alpaca import generate_prompt
def main(
prompt: str = "What food do lamas eat?",
input: str = "",
adapter_path: Path = Path("out/adapter/alpaca/lit-llama-adapter-finetuned.pth"),
pretrained_path: Path = Path("checkpoints/lit-llama/7B/lit-llama.pth"),
tokenizer_path: Path = Path("checkpoints/lit-llama/tokenizer.model"),
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.
"""
assert adapter_path.is_file()
assert pretrained_path.is_file()
assert tokenizer_path.is_file()
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 lazy_load(pretrained_path) as pretrained_checkpoint, lazy_load(adapter_path) as adapter_checkpoint:
name = llama_model_lookup(pretrained_checkpoint)
with fabric.init_module(empty_init=True), 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(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)
prompt_length = encoded.size(0)
t0 = time.perf_counter()
y = generate(model, encoded, max_new_tokens, temperature=temperature, top_k=top_k, eos_id=tokenizer.eos_id)
t = time.perf_counter() - t0
model.reset_cache()
output = tokenizer.decode(y)
output = output.split("### Response:")[1].strip()
print(output)
tokens_generated = y.size(0) - prompt_length
print(f"\n\nTime for inference: {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"
)
CLI(main)