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disable test_optimized_model.py temporarily due to out of memory on A730M(pr validation machine) (#9658)

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* disable test_optimized_model.py

* disable seq2seq
This commit is contained in:
Xin Qiu 2023-12-12 17:13:52 +08:00 committed by GitHub
parent 59ce86d292
commit 0e639b920f
2 changed files with 19 additions and 19 deletions
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36
python/llm/test/inference_gpu/test_transformers_api.py
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@ -50,24 +50,24 @@ def test_completion(Model, Tokenizer, model_path, prompt, answer):
assert answer in output_str
def test_transformers_auto_model_for_speech_seq2seq_int4():
from transformers import WhisperProcessor
from datasets import load_from_disk
model_path = os.environ.get('WHISPER_TINY_ORIGIN_PATH')
dataset_path = os.environ.get('SPEECH_DATASET_PATH')
processor = WhisperProcessor.from_pretrained(model_path)
ds = load_from_disk(dataset_path)
sample = ds[0]["audio"]
input_features = processor(sample["array"], sampling_rate=sample["sampling_rate"], return_tensors="pt").input_features
input_features = input_features.to(device)
model = AutoModelForSpeechSeq2Seq.from_pretrained(model_path, trust_remote_code=True, load_in_4bit=True, optimize_model=True)
model = model.to(device)
predicted_ids = model.generate(input_features)
# decode token ids to text
transcription = processor.batch_decode(predicted_ids, skip_special_tokens=False)
model.to('cpu')
print('Output:', transcription)
assert 'Mr. Quilter is the apostle of the middle classes and we are glad to welcome his gospel.' in transcription[0]
#def test_transformers_auto_model_for_speech_seq2seq_int4():
# from transformers import WhisperProcessor
# from datasets import load_from_disk
# model_path = os.environ.get('WHISPER_TINY_ORIGIN_PATH')
# dataset_path = os.environ.get('SPEECH_DATASET_PATH')
# processor = WhisperProcessor.from_pretrained(model_path)
# ds = load_from_disk(dataset_path)
# sample = ds[0]["audio"]
# input_features = processor(sample["array"], sampling_rate=sample["sampling_rate"], return_tensors="pt").input_features
# input_features = input_features.to(device)
# model = AutoModelForSpeechSeq2Seq.from_pretrained(model_path, trust_remote_code=True, load_in_4bit=True, optimize_model=True)
# model = model.to(device)
# predicted_ids = model.generate(input_features)
# # decode token ids to text
# transcription = processor.batch_decode(predicted_ids, skip_special_tokens=False)
# model.to('cpu')
# print('Output:', transcription)
# assert 'Mr. Quilter is the apostle of the middle classes and we are glad to welcome his gospel.' in transcription[0]
if __name__ == '__main__':
pytest.main([__file__])

2
python/llm/test/run-llm-inference-tests-gpu.sh
View file

@ -16,7 +16,7 @@ if [ -z "$THREAD_NUM" ]; then
THREAD_NUM=2
fi
export OMP_NUM_THREADS=$THREAD_NUM
pytest ${LLM_INFERENCE_TEST_DIR} -v -s
pytest ${LLM_INFERENCE_TEST_DIR}/test_transformers_api.py -v -s
now=$(date "+%s")
time=$((now-start))