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# This mimics GPTQ's evaluation metrics: https://github.com/IST-DASLab/gptq/
# Thanks to E. Frantar et al GPTQ: Accurate Post-training Compression for GPT, arXiv:2210.17323
import math
import sys
import time
from pathlib import Path
from typing import Optional
import lightning as L
import torch
import tqdm
from lit_llama import LLaMA, Tokenizer
from lit_llama.utils import EmptyInitOnDevice, lazy_load, llama_model_lookup
from lit_llama.lora import lora
from scripts.prepare_alpaca import generate_prompt
from datasets import load_dataset
lora_r = 8
lora_alpha = 16
lora_dropout = 0.05
def load_eval_data(dataset_name: str) -> str:
# this mimics gptq datautils
if dataset_name == "wikitext":
# traindata = load_dataset('wikitext', 'wikitext-2-raw-v1', split='train')
testdata = load_dataset("wikitext", "wikitext-2-raw-v1", split="test")
testdata = "\n\n".join(testdata["text"])
elif dataset_name == "ptb":
testdata = load_dataset("ptb_text_only", "penn_treebank", split="test")
testdata = "\n\n".join(testdata["sentence"])
elif dataset_name == "c4":
testdata = load_dataset(
"allenai/c4",
"allenai--c4",
data_files={"validation": "en/c4-validation.00000-of-00008.json.gz"},
split="validation",
)
testdata = " ".join(testdata[:1100]["text"])
else:
raise ValueError("invalid dataset name (wikitext, ptb, c4 are allowed)")
return testdata
def main(
datasets: str = "wikitext,ptb,c4",
*,
# compilation fails as it does not support torch.complex64 for RoPE
# compile: bool = False,
accelerator: str = "auto",
lora_path: Optional[Path] = None,
checkpoint_path: Optional[Path] = None,
tokenizer_path: Optional[Path] = None,
dtype: str = "float32",
quantize: Optional[str] = None,
) -> None:
"""Generates text samples based on a pre-trained LLaMA model and tokenizer
finetuned with LoRA.
Args:
datasets: The datasets to use as a comma separated string
# compile: Whether to compile the model.
accelerator: The hardware to run on. Possible choices are:
``"cpu"``, ``"cuda"``, ``"mps"``, ``"gpu"``, ``"tpu"``, ``"auto"``.
lora_path: Path to the checkpoint with trained LoRA weights, which are the output of
`finetune_lora.py`.
checkpoint_path: The checkpoint path to load.
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.
"""
if not lora_path:
lora_path = Path("out/lora/alpaca/lit-llama-lora-finetuned.pth")
if not checkpoint_path:
checkpoint_path = Path(f"./checkpoints/lit-llama/7B/lit-llama.pth")
if not tokenizer_path:
tokenizer_path = Path("./checkpoints/lit-llama/tokenizer.model")
assert lora_path.is_file()
assert checkpoint_path.is_file()
assert tokenizer_path.is_file()
if quantize is not None:
raise NotImplementedError("Quantization in LoRA is not supported yet")
fabric = L.Fabric(accelerator=accelerator, devices=1)
dt = getattr(torch, dtype, None)
if not isinstance(dt, torch.dtype):
raise ValueError(f"{dtype} is not a valid dtype.")
dtype = dt
print("Loading model ...", file=sys.stderr)
t0 = time.time()
pretrained_checkpoint = lazy_load(checkpoint_path)
adapter_checkpoint = lazy_load(lora_path)
name = llama_model_lookup(pretrained_checkpoint)
with EmptyInitOnDevice(
device=fabric.device, dtype=dtype, quantization_mode=quantize
), lora(r=lora_r, alpha=lora_alpha, dropout=lora_dropout, enabled=True):
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()
# if compile:
# model = torch.compile(model)
total_toks = 0
model = fabric.setup_module(model)
tokenizer = Tokenizer(tokenizer_path)
for dsname in datasets.split(","):
test_string = load_eval_data(dsname)
sample = {"instruction": test_string, "input": input}
test_string = generate_prompt(sample)
encoded_text = tokenizer.encode(
test_string, bos=True, eos=False, device=fabric.device
)
encoded_text = encoded_text[
None, : 256 * model.config.block_size
] # add batch dimension, trim like gptq implementation
t0 = time.perf_counter()
nlls = 0
toks = 0
with torch.inference_mode():
block_size = 2048 # this is for compat with gptq, and indeed we get much worse beyond this (https://github.com/facebookresearch/llama/blob/57b0eb62de0636e75af471e49e2f1862d908d9d8/llama/model.py#L30)
for i in tqdm.tqdm(range(0, encoded_text.shape[1], block_size)):
inp = encoded_text[:, i : i + block_size]
logits = model(inp)[0]
nll = torch.nn.functional.cross_entropy(
logits[:-1], inp[0, 1:].to(dtype=torch.long), reduction="sum"
)
toks += inp.size(1) - 1
nlls += nll.item()
print(encoded_text.shape, logits.shape)
encoded_text = encoded_text[:, : logits.shape[0]]
ppl = math.exp(nlls / toks)
print(f"Perplexity on {dsname}: {ppl:.2f}")
total_toks += toks
t = time.perf_counter() - t0
print(
f"\n\nTime for inference: {t:.02f} sec total, {total_toks / t:.02f} tokens/sec",
file=sys.stderr,
)
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")
CLI(main)
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