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Update all-in-one benchmark (#12272)

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* Update all-in-one benchmark

* Small fix

* Small fix

* Small fix
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Yuwen Hu 2024-10-25 16:52:59 +08:00 committed by GitHub
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27
python/llm/dev/benchmark/all-in-one/README.md
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@ -2,9 +2,7 @@
All in one benchmark test allows users to test all the benchmarks and record them in a result CSV. Users can provide models and related information in `config.yaml`.
Before running, make sure you have [ipex-llm](../../../../../README.md) installed.
If you would like to use all-in-one benchmark for testing OpenVINO, please directly refer to [this section](#optional-save-model-for-openvino) for environment setup.
Before running, make sure to have [ipex-llm](../../../../../README.md) installed.
> The prompts for benchmarking are from datasets [abisee/cnn_dailymail](https://huggingface.co/datasets/abisee/cnn_dailymail), [Open-Orca/OpenOrca](https://huggingface.co/datasets/Open-Orca/OpenOrca), [THUDM/LongBench](https://huggingface.co/datasets/THUDM/LongBench), etc.
@ -62,12 +60,11 @@ test_api:
# - "speculative_cpu" # on Intel CPU, inference with self-speculative decoding
# - "deepspeed_transformer_int4_cpu" # on Intel CPU, deepspeed autotp inference
# - "transformers_int4_npu_win" # on Intel NPU for Windows, transformer-like API, (qtype=int4)
# - "transformers_openvino" # on Intel GPU, use OpenVINO. Please make sure you have used the save_openvino.py to save the converted OpenVINO model
cpu_embedding: False # whether put embedding to CPU
streaming: False # whether output in streaming way (only available now for gpu win related test_api)
use_fp16_torch_dtype: True # whether use fp16 for non-linear layer (only available now for "pipeline_parallel_gpu" test_api)
task: 'continuation' # task can be 'continuation', 'QA' and 'summarize'
group_size: 64 # group_size when converting OpenVINO model (only available or "transformers_openvino" test_api)
```
## (Optional) Save model in low bit
@ -75,25 +72,11 @@ If you choose the `transformer_int4_loadlowbit_gpu_win` or `transformer_int4_fp1
Running `python save.py` will save all models declared in `repo_id` list into low bit models under `local_model_hub` folder.
## (Optional) Save model for OpenVINO
If you choose the `transformers_openvino` test API, you will need to convert the model with OpenVINO first.
Follow commands below to set up the environment for testing OpenVINO on Intel GPU, in which `requirements.txt` should be downloaded from [here](Download the requirements txt from https://github.com/openvino-dev-samples/Qwen2.openvino/blob/main/requirements.txt):
```bash
conda create -n test-ov python=3.11
pip install -r requirements.txt
pip install --pre --upgrade ipex-llm # only for IPEX-LLM BenchmarkWrapper
pip install accelerate omegaconf pandas
```
Then, running `python save_openvino.py` will save all models declared in `repo_id` list into OpenVINO models with `low_bit` precision under `local_model_hub` folder.
## Run
run `python run.py`, this will output results to `results.csv`.
For IPEX-LLM SPR performance, run `bash run-spr.sh`.
For SPR performance, run `bash run-spr.sh`.
> **Note**
>
@ -103,6 +86,6 @@ For IPEX-LLM SPR performance, run `bash run-spr.sh`.
>
> Please install torch nightly version to avoid `Illegal instruction (core dumped)` issue, you can follow the following command to install: `pip install --pre --upgrade torch --index-url https://download.pytorch.org/whl/nightly/cpu`
For IPEX-LLM ARC performance, run `bash run-arc.sh`.
For ARC performance, run `bash run-arc.sh`.
For IPEX-LLM MAX GPU performance, run `bash run-max-gpu.sh`.
For MAX GPU performance, run `bash run-max-gpu.sh`.

2
python/llm/dev/benchmark/all-in-one/config.yaml
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@ -37,11 +37,9 @@ test_api:
# - "deepspeed_transformer_int4_cpu" # on Intel CPU, deepspeed autotp inference
# - "transformers_int4_npu_win" # on Intel NPU for Windows, transformer-like API, (qtype=int4)
# - "transformers_int4_loadlowbit_npu_win" # on Intel NPU for Windows, transformer-like API, (qtype=int4), use load_low_bit API. Please make sure you have used the save_npu.py to save the converted low bit model
# - "transformers_openvino" # on Intel GPU, use OpenVINO. Please make sure you have used the save_openvino.py to save the converted OpenVINO model
cpu_embedding: False # whether put embedding to CPU
streaming: False # whether output in streaming way (only available now for gpu win related test_api)
optimize_model: False # whether apply further optimization on NPU (only available now for transformers_int4_npu_win test_api)
use_fp16_torch_dtype: True # whether use fp16 for non-linear layer (only available now for "pipeline_parallel_gpu" test_api)
task: 'continuation' # task can be 'continuation', 'QA' and 'summarize'
transpose_value_cache: True # whether apply transposed v_cache optimization on NPU (only available now for transformers_int4_npu_win test_api)
group_size: 64 # group_size when converting OpenVINO model (only available or "transformers_openvino" test_api)

107
python/llm/dev/benchmark/all-in-one/save_openvino.py
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@ -1,107 +0,0 @@
#
# 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.
#
# Some parts of this file is adapted from
# https://github.com/openvino-dev-samples/Qwen2.openvino/blob/main/convert.py
import os
from pathlib import Path
import warnings
from transformers import AutoTokenizer, LlamaTokenizer
from optimum.intel import OVWeightQuantizationConfig
from optimum.intel.openvino import OVModelForCausalLM
from run import LLAMA_IDS, get_model_path
current_dir = os.path.dirname(os.path.realpath(__file__))
def save_model_to_openvino(repo_id,
local_model_hub,
low_bit,
group_size,
):
model_path = get_model_path(repo_id, local_model_hub)
ir_repo_id = (repo_id.split(
"/")[1] + '-ov-' + low_bit + '-' +str(group_size))
if local_model_hub:
repo_model_name = repo_id.split(
"/")[1] + '-ov-' + low_bit + '-' +str(group_size)
ir_model_path = local_model_hub + os.path.sep + repo_model_name
ir_model_path = Path(ir_model_path)
else:
ir_model_path = Path(ir_repo_id)
if not ir_model_path.exists():
os.mkdir(ir_model_path)
compression_configs = {
"sym": True,
"group_size": group_size,
"ratio": 1.0,
}
print(">> Exporting IR")
if low_bit == "sym_int4":
compression_configs['sym'] = True
ov_model = OVModelForCausalLM.from_pretrained(model_path, export=True,
trust_remote_code=True,
compile=False, quantization_config=OVWeightQuantizationConfig(
bits=4, **compression_configs)).eval()
elif low_bit == "asym_int4":
compression_configs['sym'] = False
ov_model = OVModelForCausalLM.from_pretrained(model_path, export=True,
trust_remote_code=True,
compile=False, quantization_config=OVWeightQuantizationConfig(
bits=4, **compression_configs)).eval()
print(">> Saving IR")
ov_model.save_pretrained(ir_model_path)
print(">> Exporting tokenizer")
if repo_id in LLAMA_IDS:
tokenizer = LlamaTokenizer.from_pretrained(model_path,
trust_remote_code=True)
else:
tokenizer = AutoTokenizer.from_pretrained(model_path,
trust_remote_code=True)
tokenizer.save_pretrained(ir_model_path)
print(">> Exporting IR tokenizer")
from optimum.exporters.openvino.convert import export_tokenizer
export_tokenizer(tokenizer, ir_model_path)
print(f">> Finished saving OpenVINO IR for {repo_id} in {low_bit} with group size {group_size}")
del ov_model
del model_path
if __name__ == '__main__':
supported_precision = ["sym_int4", "asym_int4"]
from omegaconf import OmegaConf
conf = OmegaConf.load(f'{current_dir}/config.yaml')
if conf['low_bit'] in supported_precision:
for model in conf.repo_id:
save_model_to_openvino(repo_id=model,
local_model_hub=conf['local_model_hub'],
low_bit=conf['low_bit'],
group_size=conf['group_size'],)
else:
warnings.warn(f"low_bit {conf['low_bit']} is not supported "
"in all-in-one benchmark for OpenVINO tests. Only "
'sym_int4 and asym_int4 is currently supported for "transformers_openvino" test api.')