ayo/ipex-llm
1
Fork 0
Code Issues Pull requests Projects Releases Packages Wiki Activity Actions 1

LLM: separate arc ut for disable XMX (#9953)

Browse source 
* separate test_optimize_model api with disabled xmx

* delete test_optimize_model in test_transformers_api.py

* set env variable in .sh/ put back test_optimize_model

* unset env variable

* remove env setting in .py

* address errors in action

* remove import ipex

* lower tolerance
This commit is contained in:
Mingyu Wei 2024-01-23 19:04:47 +08:00 committed by GitHub
parent 8d28aa8e2b
commit 50a851e3b3
3 changed files with 88 additions and 25 deletions
Show stats Download patch file Download diff file Expand all files Collapse all files

52
python/llm/test/inference_gpu/test_transformers_api.py
View file

@ -24,8 +24,6 @@ from transformers import LlamaTokenizer, AutoTokenizer
device = os.environ['DEVICE']
print(f'Running on {device}')
if device == 'xpu':
import intel_extension_for_pytorch as ipex
@pytest.mark.parametrize('prompt, answer', [
('What is the capital of France?\n\n', 'Paris')
@ -75,32 +73,36 @@ def test_transformers_auto_model_for_speech_seq2seq_int4():
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',[
(AutoModelForCausalLM, AutoTokenizer, os.environ.get('MPT_7B_ORIGIN_PATH')),
(AutoModelForCausalLM, AutoTokenizer, os.environ.get('LLAMA2_7B_ORIGIN_PATH'))
])
def test_optimize_model(Model, Tokenizer, model_path):
with torch.inference_mode():
tokenizer = Tokenizer.from_pretrained(model_path, trust_remote_code=True)
input_ids = tokenizer.encode(prompt, return_tensors="pt").to(device)
# @pytest.mark.parametrize('Model, Tokenizer, model_path',[
# (AutoModelForCausalLM, AutoTokenizer, os.environ.get('MPT_7B_ORIGIN_PATH')),
# (AutoModelForCausalLM, AutoTokenizer, os.environ.get('LLAMA2_7B_ORIGIN_PATH'))
# ])
# def test_optimize_model(Model, Tokenizer, model_path):
# with torch.inference_mode():
# tokenizer = Tokenizer.from_pretrained(model_path, trust_remote_code=True)
# input_ids = tokenizer.encode(prompt, return_tensors="pt").to(device)
model = Model.from_pretrained(model_path,
load_in_4bit=True,
optimize_model=False,
trust_remote_code=True)
model = model.to(device)
logits_base_model = (model(input_ids)).logits
model.to('cpu') # deallocate gpu memory
# model = Model.from_pretrained(model_path,
# load_in_4bit=True,
# optimize_model=False,
# trust_remote_code=True)
# model = model.to(device)
# logits_base_model = (model(input_ids)).logits
# model.to('cpu') # deallocate gpu memory
model = Model.from_pretrained(model_path,
load_in_4bit=True,
optimize_model=True,
trust_remote_code=True)
model = model.to(device)
logits_optimized_model = (model(input_ids)).logits
model.to('cpu')
# model = Model.from_pretrained(model_path,
# load_in_4bit=True,
# optimize_model=True,
# trust_remote_code=True)
# model = model.to(device)
# logits_optimized_model = (model(input_ids)).logits
# model.to('cpu')
assert all(torch.isclose(logits_optimized_model, logits_base_model).tolist())
# tol = 1e-02
# num_false = torch.isclose(logits_optimized_model, logits_base_model, rtol=tol, atol=tol)\
# .flatten().tolist().count(False)
# percent_false = num_false / logits_optimized_model.numel()
# assert percent_false < 1e-02
class Test_Optimize_Gpu_Model:
def setup(self):

58
python/llm/test/inference_gpu/test_transformers_api_disable_xmx.py Normal file
View file

@ -0,0 +1,58 @@
#
# 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 os
import pytest
import torch
from bigdl.llm.transformers import AutoModelForCausalLM
from transformers import AutoTokenizer
device = os.environ['DEVICE']
print(f'Running on {device}')
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',[
(AutoModelForCausalLM, AutoTokenizer, os.environ.get('MPT_7B_ORIGIN_PATH')),
(AutoModelForCausalLM, AutoTokenizer, os.environ.get('LLAMA2_7B_ORIGIN_PATH'))
])
def test_optimize_model(Model, Tokenizer, model_path):
with torch.inference_mode():
tokenizer = Tokenizer.from_pretrained(model_path, trust_remote_code=True)
input_ids = tokenizer.encode(prompt, return_tensors="pt").to(device)
model = Model.from_pretrained(model_path,
load_in_4bit=True,
optimize_model=False,
trust_remote_code=True)
model = model.to(device)
logits_base_model = (model(input_ids)).logits
model.to('cpu') # deallocate gpu memory
model = Model.from_pretrained(model_path,
load_in_4bit=True,
optimize_model=True,
trust_remote_code=True)
model = model.to(device)
logits_optimized_model = (model(input_ids)).logits
model.to('cpu')
tol = 1e-03
num_false = torch.isclose(logits_optimized_model, logits_base_model, rtol=tol, atol=tol)\
.flatten().tolist().count(False)
percent_false = num_false / logits_optimized_model.numel()
assert percent_false < 1e-02

3
python/llm/test/run-llm-inference-tests-gpu.sh
View file

@ -18,6 +18,9 @@ if [ -z "$THREAD_NUM" ]; then
fi
export OMP_NUM_THREADS=$THREAD_NUM
pytest ${LLM_INFERENCE_TEST_DIR}/test_transformers_api.py -v -s
export BIGDL_LLM_XMX_DISABLED=1
pytest ${LLM_INFERENCE_TEST_DIR}/test_transformers_api_disable_xmx.py -v -s
unset BIGDL_LLM_XMX_DISABLED
now=$(date "+%s")
time=$((now-start))