308e637d0d
* dsmoe-hf add * add dsmoe pytorch * update README * modify comment * remove GPU example * update model name * format code
62 lines
2.7 KiB
Python
62 lines
2.7 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 torch
|
|
import time
|
|
import argparse
|
|
import numpy as np
|
|
from transformers import AutoTokenizer, GenerationConfig
|
|
|
|
|
|
if __name__ == '__main__':
|
|
parser = argparse.ArgumentParser(description='Predict Tokens using `generate()` API for DeepSeek-MoE model')
|
|
parser.add_argument('--repo-id-or-model-path', type=str, default="/mnt/disk1/models/deepseek-moe-16b-chat",
|
|
help='The huggingface repo id for the CodeShell 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')
|
|
|
|
args = parser.parse_args()
|
|
model_path = args.repo_id_or_model_path
|
|
|
|
|
|
from transformers import AutoModelForCausalLM
|
|
from bigdl.llm import optimize_model
|
|
model = AutoModelForCausalLM.from_pretrained(model_path, trust_remote_code=True, torch_dtype = torch.bfloat16, device_map = "auto", attn_implementation="eager")
|
|
model.generation_config = GenerationConfig.from_pretrained(model_path)
|
|
model.generation_config.pad_token_id = model.generation_config.eos_token_id
|
|
model = optimize_model(model)
|
|
tokenizer = AutoTokenizer.from_pretrained(model_path,
|
|
trust_remote_code=True)
|
|
|
|
# Generate predicted tokens
|
|
with torch.inference_mode():
|
|
messages = [
|
|
{"role": "user", "content": args.prompt}
|
|
]
|
|
prompt = tokenizer.apply_chat_template(messages, add_generation_prompt=True, tokenize=False)
|
|
input_tensor = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt")
|
|
st = time.time()
|
|
outputs = model.generate(input_tensor.to(model.device), max_new_tokens=args.n_predict)
|
|
end = time.time()
|
|
result = tokenizer.decode(outputs[0][input_tensor.shape[1]:], skip_special_tokens=True)
|
|
print(f'Inference time: {end-st} s')
|
|
print('-'*20, 'Prompt', '-'*20)
|
|
print(prompt)
|
|
print('-'*20, 'Output', '-'*20)
|
|
print(result)
|