9df70d95eb
* Rename bigdl/llm to ipex_llm * rm python/llm/src/bigdl * from bigdl.llm to from ipex_llm
170 lines
7.6 KiB
Python
170 lines
7.6 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 unittest
|
|
import os
|
|
import tempfile
|
|
import time
|
|
import torch
|
|
import pytest
|
|
|
|
from ipex_llm.transformers import AutoModel, AutoModelForCausalLM, AutoModelForSpeechSeq2Seq
|
|
from transformers import AutoTokenizer, LlamaTokenizer
|
|
|
|
class TestTransformersAPI(unittest.TestCase):
|
|
|
|
def setUp(self):
|
|
thread_num = os.environ.get('THREAD_NUM')
|
|
if thread_num is not None:
|
|
self.n_threads = int(thread_num)
|
|
else:
|
|
self.n_threads = 2
|
|
|
|
def test_transformers_auto_model_int4(self):
|
|
model_path = os.environ.get('ORIGINAL_CHATGLM2_6B_PATH')
|
|
model = AutoModel.from_pretrained(model_path, trust_remote_code=True, load_in_4bit=True)
|
|
tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
|
|
input_str = "Tell me the capital of France.\n\n"
|
|
|
|
with torch.inference_mode():
|
|
st = time.time()
|
|
input_ids = tokenizer.encode(input_str, return_tensors="pt")
|
|
output = model.generate(input_ids, do_sample=False, max_new_tokens=32)
|
|
output_str = tokenizer.decode(output[0], skip_special_tokens=True)
|
|
end = time.time()
|
|
print('Prompt:', input_str)
|
|
print('Output:', output_str)
|
|
print(f'Inference time: {end-st} s')
|
|
res = 'Paris' in output_str
|
|
self.assertTrue(res)
|
|
|
|
def test_transformers_auto_model_for_causal_lm_int4(self):
|
|
model_path = os.environ.get('ORIGINAL_REPLIT_CODE_PATH')
|
|
tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
|
|
input_str = 'def hello():\n print("hello world")\n'
|
|
model = AutoModelForCausalLM.from_pretrained(model_path, trust_remote_code=True, load_in_4bit=True)
|
|
with torch.inference_mode():
|
|
|
|
st = time.time()
|
|
input_ids = tokenizer.encode(input_str, return_tensors="pt")
|
|
output = model.generate(input_ids, do_sample=False, max_new_tokens=32)
|
|
output_str = tokenizer.decode(output[0], skip_special_tokens=True)
|
|
end = time.time()
|
|
print('Prompt:', input_str)
|
|
print('Output:', output_str)
|
|
print(f'Inference time: {end-st} s')
|
|
res = '\nhello()' in output_str
|
|
self.assertTrue(res)
|
|
|
|
|
|
def test_transformers_auto_model_for_speech_seq2seq_int4(self):
|
|
from transformers import WhisperProcessor, WhisperForConditionalGeneration
|
|
from datasets import load_from_disk
|
|
model_path = os.environ.get('ORIGINAL_WHISPER_TINY_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
|
|
model = AutoModelForSpeechSeq2Seq.from_pretrained(model_path, trust_remote_code=True, load_in_4bit=True)
|
|
with torch.inference_mode():
|
|
st = time.time()
|
|
predicted_ids = model.generate(input_features)
|
|
# decode token ids to text
|
|
transcription = processor.batch_decode(predicted_ids, skip_special_tokens=False)
|
|
end = time.time()
|
|
print('Output:', transcription)
|
|
print(f'Inference time: {end-st} s')
|
|
res = 'Mr. Quilter is the apostle of the middle classes and we are glad to welcome his gospel.' in transcription[0]
|
|
self.assertTrue(res)
|
|
|
|
def test_transformers_chatglm_for_causallm(self):
|
|
from ipex_llm.transformers import ChatGLMForCausalLM
|
|
model_path = os.environ.get('ORIGINAL_CHATGLM2_6B_PATH')
|
|
model = ChatGLMForCausalLM.from_pretrained(model_path, native=False, trust_remote_code=True, load_in_4bit=True)
|
|
tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
|
|
input_str = "Tell me the capital of France.\n\n"
|
|
|
|
with torch.inference_mode():
|
|
st = time.time()
|
|
input_ids = tokenizer.encode(input_str, return_tensors="pt")
|
|
output = model.generate(input_ids, do_sample=False, max_new_tokens=32)
|
|
output_str = tokenizer.decode(output[0], skip_special_tokens=True)
|
|
end = time.time()
|
|
print('Prompt:', input_str)
|
|
print('Output:', output_str)
|
|
print(f'Inference time: {end-st} s')
|
|
res = 'Paris' in output_str
|
|
self.assertTrue(res)
|
|
|
|
@pytest.mark.parametrize('prompt, answer', [
|
|
('What is the capital of France?\n\n', 'Paris')
|
|
])
|
|
@pytest.mark.parametrize('Model, Tokenizer, model_path',[
|
|
(AutoModel, AutoTokenizer, os.environ.get('ORIGINAL_CHATGLM2_6B_PATH')),
|
|
])
|
|
def test_load_low_bit_completion(Model, Tokenizer, model_path, prompt, answer):
|
|
tokenizer = Tokenizer.from_pretrained(model_path, trust_remote_code=True)
|
|
model = Model.from_pretrained(model_path,
|
|
load_in_4bit=True,
|
|
optimize_model=True,
|
|
trust_remote_code=True)
|
|
|
|
with tempfile.TemporaryDirectory() as tempdir:
|
|
model.save_low_bit(tempdir)
|
|
loaded_model = Model.load_low_bit(tempdir,
|
|
optimize_model=True,
|
|
trust_remote_code=True)
|
|
|
|
with torch.inference_mode():
|
|
input_ids = tokenizer.encode(prompt, return_tensors="pt")
|
|
output = loaded_model.generate(input_ids, max_new_tokens=32)
|
|
output_str = tokenizer.decode(output[0], skip_special_tokens=True)
|
|
|
|
assert answer in output_str
|
|
|
|
prompt = "Once upon a time, there existed a little girl who liked to have adventures. She wanted to go to places and meet new people, and have fun"
|
|
|
|
@pytest.mark.parametrize("Model, Tokenizer, model_path, prompt", [
|
|
(AutoModelForCausalLM, LlamaTokenizer, os.environ.get('LLAMA_ORIGIN_PATH'), prompt),
|
|
(AutoModelForCausalLM, AutoTokenizer, os.environ.get('BLOOM_ORIGIN_PATH'), prompt),
|
|
(AutoModel, AutoTokenizer, os.environ.get('ORIGINAL_CHATGLM2_6B_PATH'), prompt),
|
|
(AutoModelForCausalLM, AutoTokenizer, os.environ.get('ORIGINAL_REPLIT_CODE_PATH'), prompt)
|
|
])
|
|
|
|
def test_optimize_model(Model, Tokenizer, model_path, prompt):
|
|
tokenizer = Tokenizer.from_pretrained(model_path, trust_remote_code=True)
|
|
input_ids = tokenizer.encode(prompt, return_tensors="pt")
|
|
|
|
model = Model.from_pretrained(model_path,
|
|
load_in_4bit=True,
|
|
optimize_model=False,
|
|
trust_remote_code=True)
|
|
logits_base_model = (model(input_ids)).logits
|
|
|
|
model = Model.from_pretrained(model_path,
|
|
load_in_4bit=True,
|
|
optimize_model=True,
|
|
trust_remote_code=True)
|
|
logits_optimized_model = (model(input_ids)).logits
|
|
diff = abs(logits_base_model - logits_optimized_model).flatten()
|
|
|
|
assert any(diff) is False
|
|
|
|
|
|
if __name__ == '__main__':
|
|
pytest.main([__file__])
|