704a896e90
* add xpu latency job * update install way * remove duplicated workflow * add perf upload
75 lines
2.7 KiB
Python
75 lines
2.7 KiB
Python
#
|
|
# Copyright 2016 The BigDL Authors.
|
|
#
|
|
# Licensed under the Apache License, Version 2.0 (the "License");
|
|
# you may not use this file except in compliance with the License.
|
|
# You may obtain a copy of the License at
|
|
#
|
|
# http://www.apache.org/licenses/LICENSE-2.0
|
|
#
|
|
# Unless required by applicable law or agreed to in writing, software
|
|
# distributed under the License is distributed on an "AS IS" BASIS,
|
|
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
# See the License for the specific language governing permissions and
|
|
# limitations under the License.
|
|
#
|
|
|
|
import argparse
|
|
import intel_extension_for_pytorch as ipex
|
|
from transformers import WhisperProcessor
|
|
import torch
|
|
import time
|
|
from benchmark_util import BenchmarkWrapper
|
|
from bigdl.llm.transformers import AutoModelForSpeechSeq2Seq
|
|
from datasets import load_dataset, load_from_disk
|
|
|
|
if __name__ == '__main__':
|
|
parser = argparse.ArgumentParser('OPT generation script', add_help=False)
|
|
parser.add_argument('-m', '--model-dir',
|
|
default="/mnt/disk1/models/whisper-medium", type=str)
|
|
args = parser.parse_args()
|
|
print(args)
|
|
|
|
model_path = args.model_dir
|
|
dataset_path = "hf-internal-testing/librispeech_asr_dummy"
|
|
|
|
# load model and processor
|
|
ds = load_dataset(dataset_path, "clean", split="validation")
|
|
print("pass")
|
|
processor = WhisperProcessor.from_pretrained(model_path)
|
|
print("model loaded")
|
|
# load dummy dataset and read audio files
|
|
sample = ds[0]["audio"]
|
|
# for transformer == 4.30.2
|
|
input_features = processor(
|
|
sample["array"], sampling_rate=sample["sampling_rate"], return_tensors="pt").input_features
|
|
|
|
input_features = input_features.half().contiguous()
|
|
input_features = input_features.to('xpu')
|
|
print(input_features.shape)
|
|
print(input_features.is_contiguous())
|
|
|
|
# generate token ids
|
|
whisper = AutoModelForSpeechSeq2Seq.from_pretrained(
|
|
model_path, load_in_4bit=True, optimize_model=False)
|
|
whisper.config.forced_decoder_ids = None
|
|
whisper = whisper.half().to('xpu')
|
|
whisper = BenchmarkWrapper(whisper)
|
|
|
|
with torch.inference_mode():
|
|
e2e_time = []
|
|
for i in range(10):
|
|
torch.xpu.synchronize()
|
|
st = time.time()
|
|
predicted_ids = whisper.generate(input_features)
|
|
# print(len(predicted_ids[0]))
|
|
torch.xpu.synchronize()
|
|
output_str = processor.batch_decode(
|
|
predicted_ids, skip_special_tokens=True)
|
|
end = time.time()
|
|
e2e_time.append(end-st)
|
|
|
|
print(f'Inference time: {end-st} s')
|
|
print('Output:', output_str)
|
|
print(f'Inference time: {end-st} s')
|
|
print(e2e_time)
|