fix: adjust bigdl-llm to ipex-llm base intel cpu

Intel_device_demo/README.md

@@ -10,7 +10,7 @@
 - 其他理论支持 OpenVINO 加速的Intel 工具套件。
 
 ## 2. 文件目录
-- BigDl_demo: BigDL-LLM 是一个为Intel XPU(Xeon/Core/Flex/Arc/PVC)打造的低精度轻量级大语言模型库，在Intel平台上具有广泛的模型支持、最低的延迟和最小的内存占用，实现加速模型部署示例。
+- IPEX_demo: IPEX-LLM 是一个为Intel XPU(Xeon/Core/Flex/Arc/PVC)打造的低精度轻量级大语言模型库，在Intel平台上具有广泛的模型支持、最低的延迟和最小的内存占用，实现加速模型部署示例。
 - OpenVINO_demo: 使用 Intel OpenVINO 推理加速框架，实现加速模型部署示例。
 - Pytorch_demo (暂未推出) : 使用 Intel Pytorch Extension 实现在 Pytorch 环境上开发（适用于 Intel Arc 系列 GPU）
 

Intel_device_demo/bigdl_demo/api_server.py → Intel_device_demo/ipex_cpu_demo/api_server.py

@@ -38,7 +38,7 @@
 from typing import List, Literal, Optional, Union
 from loguru import logger
 from pydantic import BaseModel, Field
-from bigdl.llm.transformers import AutoModel
+from ipex_llm.transformers import AutoModel
 from transformers import AutoTokenizer
 from utils import process_response, generate_chatglm3, generate_stream_chatglm3
 # from sentence_transformers import SentenceTransformer

Intel_device_demo/bigdl_demo/chatglm3_infer.py → Intel_device_demo/ipex_cpu_demo/chatglm3_infer.py

@@ -1,5 +1,5 @@
 import time
-from bigdl.llm.transformers import AutoModel
+from ipex_llm.transformers import AutoModel
 from transformers import AutoTokenizer
 
 CHATGLM_V3_PROMPT_FORMAT = "\n{prompt}\n"

Intel_device_demo/bigdl_demo/chatglm3_web_demo.py → Intel_device_demo/ipex_cpu_demo/chatglm3_web_demo.py

@@ -18,7 +18,7 @@
 
 import os
 import streamlit as st
-from bigdl.llm.transformers import AutoModel
+from ipex_llm.transformers import AutoModel
 from transformers import AutoTokenizer
 
 

Intel_device_demo/ipex_cpu_demo/generate.py

@@ -0,0 +1,72 @@
+#
+# 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 ipex_llm.transformers import AutoModel
+from modelscope import AutoTokenizer
+from transformers import AutoTokenizer
+
+# you could tune the prompt based on your own model,
+# here the prompt tuning refers to https://github.com/THUDM/ChatGLM3/blob/main/PROMPT.md
+CHATGLM_V3_PROMPT_FORMAT = "<|user|>\n{prompt}\n<|assistant|>"
+
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser(description='Predict Tokens using `generate()` API for ModelScope ChatGLM3 model')
+    parser.add_argument('--repo-id-or-model-path', type=str, default="ZhipuAI/chatglm3-6b",
+                        help='The ModelScope repo id for the ChatGLM3 model to be downloaded'
+                             ', or the path to the ModelScope checkpoint folder')
+    parser.add_argument('--prompt', type=str, default="AI是什么？",
+                        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
+
+    # Load model in 4 bit,
+    # which convert the relevant layers in the model into INT4 format
+    # It is important to set `model_hub='modelscope'`, otherwise model hub is default to be huggingface
+    model = AutoModel.from_pretrained(model_path,
+                                      load_in_4bit=True,
+                                      trust_remote_code=True,
+                                      model_hub='modelscope')
+
+    # Load tokenizer
+    tokenizer = AutoTokenizer.from_pretrained(model_path,
+                                              trust_remote_code=True)
+
+    # Generate predicted tokens
+    with torch.inference_mode():
+        prompt = CHATGLM_V3_PROMPT_FORMAT.format(prompt=args.prompt)
+        input_ids = tokenizer.encode(prompt, return_tensors="pt")
+        st = time.time()
+        # if your selected model is capable of utilizing previous key/value attentions
+        # to enhance decoding speed, but has `"use_cache": false` in its model config,
+        # it is important to set `use_cache=True` explicitly in the `generate` function
+        # to obtain optimal performance with BigDL-LLM INT4 optimizations
+        output = model.generate(input_ids,
+                                max_new_tokens=args.n_predict)
+        end = time.time()
+        output_str = tokenizer.decode(output[0], skip_special_tokens=True)
+        print(f'Inference time: {end - st} s')
+        print('-' * 20, 'Prompt', '-' * 20)
+        print(prompt)
+        print('-' * 20, 'Output', '-' * 20)
+        print(output_str)