e37f951cce
* dq divide * fix * support attn divide * update qwen2 7b * divide down_proj & other linear * use concat & reduce sum * support scale after * support qwen2 * w/ mm * update reshape * spda * split * split 2+ * update * lm head-> 28 * no scale * update * update * update * fix style * fix style * to split linear * update * update code * address comments * fix style & remove redundant code & revert benchmark scripts * fix style & remove code * update save & load --------- Co-authored-by: Yang Wang <yang3.wang@intel.com>
88 lines
3.6 KiB
Python
88 lines
3.6 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.
|
|
#
|
|
|
|
# this code is to support converting of model in load bit
|
|
# for performance tests using load_low_bit
|
|
|
|
import time
|
|
import torch
|
|
import os
|
|
import argparse
|
|
from ipex_llm.transformers.npu_model import AutoModelForCausalLM
|
|
from transformers import AutoTokenizer
|
|
from run import get_model_path
|
|
|
|
current_dir = os.path.dirname(os.path.realpath(__file__))
|
|
|
|
def save_npu_model_in_low_bit(repo_id,
|
|
local_model_hub,
|
|
low_bit,
|
|
max_output_len, max_prompt_len, intra_pp, inter_pp,
|
|
disable_transpose_value_cache,
|
|
quantization_group_size):
|
|
model_path = get_model_path(repo_id, local_model_hub)
|
|
# Load model in 4 bit,
|
|
# which convert the relevant layers in the model into INT4 format
|
|
st = time.perf_counter()
|
|
model = AutoModelForCausalLM.from_pretrained(
|
|
model_path,
|
|
torch_dtype=torch.float16,
|
|
trust_remote_code=True,
|
|
attn_implementation="eager",
|
|
load_in_low_bit="sym_int4",
|
|
optimize_model=True,
|
|
max_output_len=max_output_len,
|
|
max_prompt_len=max_prompt_len,
|
|
intra_pp=intra_pp,
|
|
inter_pp=inter_pp,
|
|
transpose_value_cache=not disable_transpose_value_cache,
|
|
quantization_group_size=quantization_group_size
|
|
)
|
|
tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
|
|
end = time.perf_counter()
|
|
print(">> loading of and converting of model costs {}s".format(end - st))
|
|
|
|
model.save_low_bit(model_path+'-npu-'+low_bit)
|
|
tokenizer.save_pretrained(model_path+'-npu-'+low_bit)
|
|
print(f"Model saved to {model_path+'-npu-'+low_bit}")
|
|
|
|
|
|
if __name__ == "__main__":
|
|
parser = argparse.ArgumentParser(
|
|
description="Predict Tokens using `generate()` API for npu model"
|
|
)
|
|
parser.add_argument("--max-output-len", type=int, default=1024)
|
|
parser.add_argument("--max-prompt-len", type=int, default=512)
|
|
parser.add_argument("--disable-transpose-value-cache", action="store_true", default=False)
|
|
parser.add_argument("--intra-pp", type=int, default=2)
|
|
parser.add_argument("--inter-pp", type=int, default=2)
|
|
parser.add_argument("--quantization_group_size", type=int, default=0)
|
|
|
|
args = parser.parse_args()
|
|
from omegaconf import OmegaConf
|
|
conf = OmegaConf.load(f'{current_dir}/config.yaml')
|
|
|
|
for model in conf.repo_id:
|
|
save_npu_model_in_low_bit(repo_id=model,
|
|
local_model_hub=conf['local_model_hub'],
|
|
low_bit=conf['low_bit'],
|
|
max_output_len=args.max_output_len,
|
|
max_prompt_len=args.max_prompt_len,
|
|
intra_pp=args.intra_pp,
|
|
inter_pp=args.inter_pp,
|
|
disable_transpose_value_cache=args.disable_transpose_value_cache,
|
|
quantization_group_size=args.quantization_group_size,
|
|
)
|