LLM: Update ppl tests (#10092)
* update ppl tests * use load_dataset api * add exception handling * add language argument * address comments
This commit is contained in:
Ovo233
GitHub
parent
3a46b57253
commit
2aaa21c41d
3 changed files with 124 additions and 87 deletions
|
|
@ -1,11 +1,20 @@
|
|||
# Perplexity
|
||||
Perplexity (PPL) is one of the most common metrics for evaluating language models. This benchmark implementation was from [transformers/perplexity](https://huggingface.co/docs/transformers/perplexity#perplexity-of-fixed-length-models) and [llm_perplexity.py](https://github.com/luo-cheng2021/ov.cpu.llm.experimental/blob/main/llm_perplexity.py)
|
||||
Perplexity (PPL) is one of the most common metrics for evaluating language models. This benchmark implementation was from [transformers/perplexity](https://huggingface.co/docs/transformers/perplexity#perplexity-of-fixed-length-models) and [benchmark_patch_llm.py](https://github.com/insuhan/hyper-attn/blob/main/benchmark_patch_llm.py)
|
||||
|
||||
## HOW TO RUN
|
||||
```python
|
||||
python run.py --model_path <path/to/model> --precisions sym_int4 fp4 mixed_fp4 sym_int8 fp8_e5m2 fp8_e4m3 mixed_fp8 --device xpu --dataset path=<dataset_path>,name=<dataset_name>
|
||||
```bash
|
||||
python run.py --model_path <path/to/model> --precisions sym_int4 fp4 mixed_fp4 sym_int8 fp8_e5m2 fp8_e4m3 mixed_fp8 --device xpu --datasets dataset_names --dataset_path <path/to/dataset> --language en
|
||||
```
|
||||
A more specific example to run perplexity on Llama2-7B and wikitext:
|
||||
```python
|
||||
python run.py --model_path meta-llama/Llama-2-7b-chat-hf --precisions float16 sym_int4 --device xpu --dataset path=wikitext,name=wikitext-2-raw-v1
|
||||
```
|
||||
A more specific example to run perplexity on Llama2-7B using the default English datasets:
|
||||
```bash
|
||||
python run.py --model_path meta-llama/Llama-2-7b-chat-hf --precisions float16 sym_int4 --device xpu --language en
|
||||
```
|
||||
|
||||
> Note: We currently only support the `THUDM/LongBench` [dataset](https://github.com/THUDM/LongBench)
|
||||
|
||||
- If you want to test model perplexity on a few selected datasets from the `LongBench` dataset, please use the format below.
|
||||
```bash
|
||||
--datasets narrativeqa qasper ...
|
||||
```
|
||||
- The `language` argument will only take effect if `datasets` is `None`. The choices for this argument are `en, zh, all`, which stands for all the English datasets, all the Chinese datasets and all the datasets respectively during testing.
|
||||
- If you want to test perplexity on pre-downloaded datasets, please specify the `<path/to/dataset>` in the `dataset_path` argument in your command.
|
||||
|
|
|
|||
|
|
@ -14,77 +14,69 @@
|
|||
# limitations under the License.
|
||||
#
|
||||
|
||||
import intel_extension_for_pytorch as ipex
|
||||
import numpy as np
|
||||
import torch
|
||||
from torch.nn import CrossEntropyLoss
|
||||
from tqdm import tqdm
|
||||
from transformers import AutoTokenizer
|
||||
import gc
|
||||
|
||||
from bigdl.llm.transformers import AutoModelForCausalLM
|
||||
|
||||
class PPL:
|
||||
def __init__(self):
|
||||
self.nll = 0
|
||||
self.cnt = 0
|
||||
def __call__(self, all_logits, labels):
|
||||
'''
|
||||
all_logits [seq_length, vocab_size]
|
||||
labels [seq_length]
|
||||
'''
|
||||
seq_length = all_logits.shape[0]
|
||||
for i in range(0, seq_length - 1):
|
||||
logits = all_logits[i, :]
|
||||
max_logit = np.amax(logits)
|
||||
sum_exp = np.sum(np.exp(logits - max_logit))
|
||||
# logits at time-step i is for predicting token at time-step (i+1)
|
||||
next_tok = labels[i + 1]
|
||||
log_softmax_of_tok = (logits[next_tok] - max_logit) - np.log(sum_exp)
|
||||
self.nll += -log_softmax_of_tok
|
||||
self.cnt += 1
|
||||
return np.exp(self.nll / self.cnt)
|
||||
|
||||
def result(self):
|
||||
return np.exp(self.nll / self.cnt)
|
||||
|
||||
def __str__(self):
|
||||
return f"PPL: {np.exp(self.nll / self.cnt):.3f}"
|
||||
|
||||
from bigdl.llm.transformers import AutoModelForCausalLM, AutoModel
|
||||
|
||||
class BigDLPPL:
|
||||
def __init__(self, model_path, device, **model_kwargs) -> None:
|
||||
model_kwargs['trust_remote_code'] = model_kwargs.get('trust_remote_code', True)
|
||||
model_kwargs['optimize_model'] = model_kwargs.get('optimize_model', True)
|
||||
self.device = device
|
||||
self.tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
|
||||
self.model = AutoModelForCausalLM.from_pretrained(model_path, **model_kwargs)
|
||||
if 'xpu' in device:
|
||||
import intel_extension_for_pytorch as ipex
|
||||
|
||||
if 'chatglm' in model_path.lower():
|
||||
self.model = AutoModel.from_pretrained(model_path, **model_kwargs)
|
||||
else:
|
||||
self.model = AutoModelForCausalLM.from_pretrained(model_path, **model_kwargs)
|
||||
self.model.to(device)
|
||||
self.ppl_evaluator = PPL()
|
||||
|
||||
def perplexity_hf(self, text):
|
||||
inputs = self.tokenizer('\n\n'.join(text), return_tensors='pt').to(self.device)
|
||||
input_ids = inputs['input_ids']
|
||||
# attention_mask = inputs['attention_mask']
|
||||
progress_bar = tqdm(range(0, input_ids.shape[1], 512))
|
||||
|
||||
for i0 in progress_bar:
|
||||
input_ids_chunks = input_ids[:, i0:(i0+512)]
|
||||
input_ids_chunks[:, 0] = 1
|
||||
with torch.no_grad():
|
||||
result = self.model.forward(input_ids_chunks, labels = input_ids_chunks, return_dict=True)
|
||||
#print(f"ppl = {torch.exp(result.loss)}")
|
||||
seq_len = result.logits.shape[1]
|
||||
data = result.logits
|
||||
data = data.to('cpu')
|
||||
input_ids_chunks = input_ids_chunks.to('cpu')
|
||||
self.ppl_evaluator(data.numpy()[0, seq_len//2:, :], input_ids_chunks.numpy()[0, seq_len//2:])
|
||||
progress_bar.set_description(f"{self.ppl_evaluator}")
|
||||
|
||||
torch.xpu.synchronize()
|
||||
torch.xpu.empty_cache()
|
||||
del self.model
|
||||
gc.collect()
|
||||
def perplexity_hf(self, encoded_texts):
|
||||
self.model.eval()
|
||||
loss_fct = CrossEntropyLoss(reduction="none")
|
||||
ppls = []
|
||||
|
||||
try:
|
||||
pbar = tqdm(range(len(encoded_texts)))
|
||||
for bid in pbar:
|
||||
encoded_batch = encoded_texts[bid:bid+1]
|
||||
if type(encoded_batch) == dict:
|
||||
attn_mask = encoded_batch['attention_mask'] if 'attention_mask' in encoded_batch.keys() else None
|
||||
encoded_batch = encoded_batch['input_ids']
|
||||
elif type(encoded_batch) == list:
|
||||
encoded_batch = encoded_batch[0]
|
||||
|
||||
encoded_batch = encoded_batch.to(self.device)
|
||||
attn_mask = torch.ones_like(encoded_batch)
|
||||
|
||||
out_logits = self.model(encoded_batch).logits
|
||||
|
||||
labels = encoded_batch
|
||||
|
||||
shift_logits = out_logits[..., :-1, :].contiguous()
|
||||
shift_labels = labels[..., 1:].contiguous()
|
||||
shift_attention_mask_batch = attn_mask[..., 1:].contiguous()
|
||||
|
||||
loss_ = loss_fct(shift_logits.transpose(1, 2), shift_labels).float()
|
||||
perplexity_batch = torch.exp2(
|
||||
(loss_ * shift_attention_mask_batch).sum(1)
|
||||
/ shift_attention_mask_batch.sum(1)
|
||||
)
|
||||
ppls += perplexity_batch.tolist()
|
||||
|
||||
pbar.set_description(f"[{bid:<4}/{len(encoded_texts)}] avg_ppls: {np.mean(np.array(ppls)[~np.isnan(np.array(ppls))]):.4f}")
|
||||
|
||||
del out_logits, encoded_batch, attn_mask, shift_logits, shift_labels, shift_attention_mask_batch, perplexity_batch
|
||||
|
||||
ppl_mean = np.mean(np.array(ppls)[~np.isnan(np.array(ppls))])
|
||||
finally:
|
||||
torch.xpu.synchronize()
|
||||
torch.xpu.empty_cache()
|
||||
del self.model
|
||||
gc.collect()
|
||||
|
||||
return self.ppl_evaluator.result()
|
||||
return ppl_mean
|
||||
|
|
@ -14,45 +14,81 @@
|
|||
# limitations under the License.
|
||||
#
|
||||
|
||||
import torch
|
||||
from bigdl.llm.ggml.quantize import ggml_tensor_qtype
|
||||
from ppl import BigDLPPL
|
||||
from datasets import load_dataset
|
||||
import argparse
|
||||
from tqdm import tqdm
|
||||
import torch
|
||||
from datasets import concatenate_datasets, load_dataset
|
||||
from transformers import AutoTokenizer
|
||||
|
||||
from ppl import BigDLPPL
|
||||
from bigdl.llm.ggml.quantize import ggml_tensor_qtype
|
||||
|
||||
def parse_kwargs(kwstr):
|
||||
kvpair = [item.split('=') for item in kwstr.split(',') if item != ""]
|
||||
return {k:v for k, v in kvpair}
|
||||
import os
|
||||
|
||||
|
||||
def parse_args():
|
||||
def get_arguments():
|
||||
parser = argparse.ArgumentParser()
|
||||
parser.add_argument("--seq_len", type=int, default=512)
|
||||
parser.add_argument("--model_path", required=True, type=str)
|
||||
parser.add_argument("--datasets", required=False, type=str, default=None, nargs='*')
|
||||
parser.add_argument("--dataset_path", required=False, type=str, default=None)
|
||||
parser.add_argument("--language", required=False, type=str, default="en", choices=['en', 'zh', 'all'])
|
||||
parser.add_argument("--precisions", required=False, type=str, default=None, nargs='+')
|
||||
parser.add_argument("--model_kwargs", required=False, type=str, default="")
|
||||
parser.add_argument("--torch_dtype", type=str, default=None)
|
||||
parser.add_argument("--device", type=str, default=None)
|
||||
parser.add_argument("--dataset", type=str, default='path=wikitext,name=wikitext-2-raw-v1')
|
||||
|
||||
parser.add_argument("--device", type=str, default="xpu")
|
||||
return parser.parse_args()
|
||||
|
||||
|
||||
|
||||
@torch.no_grad()
|
||||
def main():
|
||||
args = parse_args()
|
||||
text = load_dataset(**parse_kwargs(args.dataset), split="test")["text"]
|
||||
additional_model_kwargs = parse_kwargs(args.model_kwargs)
|
||||
args = get_arguments()
|
||||
for arg_name, arg_var in args.__dict__.items():
|
||||
print(f"{arg_name:<16} : {arg_var}")
|
||||
|
||||
tokenizer = AutoTokenizer.from_pretrained(args.model_path, trust_remote_code=True)
|
||||
tokenizer.model_max_length = args.seq_len
|
||||
|
||||
en_datasets = ["narrativeqa", "qasper", "multifieldqa_en", "hotpotqa", "2wikimqa", "musique", "gov_report",
|
||||
"qmsum", "multi_news", "trec", "triviaqa", "samsum", "passage_count", "passage_retrieval_en"]
|
||||
zh_datasets = ["multifieldqa_zh", "dureader", "vcsum", "lsht", "passage_retrieval_zh"]
|
||||
|
||||
if args.datasets is None:
|
||||
if args.language == 'en':
|
||||
datasets = en_datasets
|
||||
elif args.language == 'zh':
|
||||
datasets = zh_datasets
|
||||
else:
|
||||
datasets = en_datasets + zh_datasets
|
||||
else:
|
||||
datasets = args.datasets
|
||||
|
||||
dataset_all = []
|
||||
for dataset_name in datasets:
|
||||
data_ = load_dataset(os.path.join(args.dataset_path, dataset_name), split='test') if args.dataset_path \
|
||||
else load_dataset('THUDM/LongBench', f'{dataset_name}', split='test')
|
||||
dataset_all.append(data_)
|
||||
data = concatenate_datasets(dataset_all)
|
||||
|
||||
encoded_texts = []
|
||||
pbar = tqdm(data)
|
||||
for i, data_i in enumerate(pbar):
|
||||
encoded_text = tokenizer.encode(data_i['context'], return_tensors='pt', truncation=True)
|
||||
pbar.set_description(f"seq_len: {len(encoded_text[0])}, n_data: {len(encoded_texts)}")
|
||||
if len(encoded_text[0]) < args.seq_len:
|
||||
continue
|
||||
encoded_texts.append(encoded_text)
|
||||
|
||||
summary = {}
|
||||
for precision in args.precisions:
|
||||
model_kwargs = additional_model_kwargs.copy()
|
||||
model_kwargs = {}
|
||||
if precision in ggml_tensor_qtype.keys():
|
||||
model_kwargs['load_in_low_bit'] = precision
|
||||
else:
|
||||
model_kwargs['torch_dtype'] = getattr(torch, precision)
|
||||
print(model_kwargs)
|
||||
|
||||
ppl_evaluator = BigDLPPL(model_path=args.model_path, device=args.device, **model_kwargs)
|
||||
ppl = ppl_evaluator.perplexity_hf(text)
|
||||
ppl = ppl_evaluator.perplexity_hf(encoded_texts)
|
||||
summary[precision] = ppl
|
||||
print(summary)
|
||||
|
||||
main()
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
Loading…
Reference in a new issue