123 lines
4.7 KiB
Python
123 lines
4.7 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 os
|
|
import torch
|
|
import time
|
|
import argparse
|
|
|
|
from ipex_llm.transformers.npu_model import AutoModelForCausalLM
|
|
from transformers import AutoTokenizer, TextStreamer
|
|
|
|
from transformers.utils import logging
|
|
|
|
logger = logging.get_logger(__name__)
|
|
|
|
if __name__ == "__main__":
|
|
parser = argparse.ArgumentParser(
|
|
description="Predict Tokens using `generate()` API for npu model"
|
|
)
|
|
parser.add_argument(
|
|
"--repo-id-or-model-path",
|
|
type=str,
|
|
default="THUDM/glm-edge-1.5b-chat",
|
|
help="The huggingface repo id for the glm-edge model to be downloaded"
|
|
", or the path to the huggingface checkpoint folder",
|
|
)
|
|
parser.add_argument('--prompt', type=str, default="What is AI?",
|
|
help='Prompt to infer')
|
|
parser.add_argument("--n-predict", type=int, default=32, help="Max tokens to predict")
|
|
parser.add_argument("--max-context-len", type=int, default=1024)
|
|
parser.add_argument("--max-prompt-len", type=int, default=512)
|
|
parser.add_argument('--low-bit', type=str, default="sym_int4",
|
|
help='Load in low bit to use')
|
|
parser.add_argument("--disable-streaming", action="store_true", default=False)
|
|
parser.add_argument("--disable-transpose-value-cache", action="store_true", default=False)
|
|
parser.add_argument("--save-directory", type=str,
|
|
required=True,
|
|
help="The path of folder to save converted model, "
|
|
"If path not exists, lowbit model will be saved there. "
|
|
"Else, lowbit model will be loaded.",
|
|
)
|
|
|
|
args = parser.parse_args()
|
|
model_path = args.repo_id_or_model_path
|
|
|
|
if not os.path.exists(args.save_directory):
|
|
model = AutoModelForCausalLM.from_pretrained(
|
|
model_path,
|
|
torch_dtype=torch.float16,
|
|
trust_remote_code=True,
|
|
attn_implementation="eager",
|
|
load_in_low_bit=args.low_bit,
|
|
optimize_model=True,
|
|
max_context_len=args.max_context_len,
|
|
max_prompt_len=args.max_prompt_len,
|
|
transpose_value_cache=not args.disable_transpose_value_cache,
|
|
save_directory=args.save_directory
|
|
)
|
|
tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
|
|
tokenizer.save_pretrained(args.save_directory)
|
|
else:
|
|
model = AutoModelForCausalLM.load_low_bit(
|
|
args.save_directory,
|
|
attn_implementation="eager",
|
|
torch_dtype=torch.float16,
|
|
optimize_model=True,
|
|
max_context_len=args.max_context_len,
|
|
max_prompt_len=args.max_prompt_len,
|
|
transpose_value_cache=not args.disable_transpose_value_cache,
|
|
)
|
|
tokenizer = AutoTokenizer.from_pretrained(args.save_directory, trust_remote_code=True)
|
|
|
|
if args.disable_streaming:
|
|
streamer = None
|
|
else:
|
|
streamer = TextStreamer(tokenizer=tokenizer, skip_special_tokens=True)
|
|
|
|
print("-" * 80)
|
|
print("done")
|
|
with torch.inference_mode():
|
|
print("finish to load")
|
|
for i in range(3):
|
|
message = [{"role": "user", "content": args.prompt}]
|
|
|
|
inputs = tokenizer.apply_chat_template(
|
|
message,
|
|
return_tensors="pt",
|
|
add_generation_prompt=True,
|
|
return_dict=True,
|
|
)
|
|
_input_ids = inputs["input_ids"]
|
|
|
|
print("-" * 20, "Input", "-" * 20)
|
|
print("input length:", len(_input_ids[0]))
|
|
input_str = tokenizer.decode(_input_ids[0], skip_special_tokens=False)
|
|
print(input_str)
|
|
print("-" * 20, "Output", "-" * 20)
|
|
st = time.time()
|
|
output = model.generate(
|
|
_input_ids, num_beams=1, do_sample=False, max_new_tokens=args.n_predict, streamer=streamer
|
|
)
|
|
end = time.time()
|
|
if args.disable_streaming:
|
|
output_str = tokenizer.decode(output[0], skip_special_tokens=False)
|
|
print(output_str)
|
|
print(f"Inference time: {end-st} s")
|
|
|
|
print("-" * 80)
|
|
print("done")
|
|
print("success shut down")
|