LLM: add mpt example on arc (#8723)

python/llm/example/transformers/transformers_int4/GPU/mpt/README.md

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+# MPT
+In this directory, you will find examples on how you could apply BigDL-LLM INT4 optimizations on Llama2 models on any Intel® Arc™ A-Series Graphics. For illustration purposes, we utilize the [mosaicml/mpt-7b-chat](https://huggingface.co/mosaicml/mpt-7b-chat) as a reference MPT model.
+
+## 0. Requirements
+To run these examples with BigDL-LLM on Intel® Arc™ A-Series Graphics, we have some recommended requirements for your machine, please refer to [here](../README.md#recommended-requirements) for more information.
+
+## Example: Predict Tokens using `generate()` API
+In the example [generate.py](./generate.py), we show a basic use case for an MPT model to predict the next N tokens using `generate()` API, with BigDL-LLM INT4 optimizations on Intel® Arc™ A-Series Graphics.
+### 1. Install
+We suggest using conda to manage environment:
+```bash
+conda create -n llm python=3.9
+conda activate llm
+# below command will install intel_extension_for_pytorch==2.0.110+xpu as default
+# you can install specific ipex/torch version for your need
+pip install --pre --upgrade bigdl-llm[xpu] -f https://developer.intel.com/ipex-whl-stable-xpu
+pip install einops  # additional package required for mpt-7b-chat and mpt-30b-chat to conduct generation
+```
+### 2. Configures OneAPI environment variables
+```bash
+source /opt/intel/oneapi/setvars.sh
+```
+
+### 3. Run
+
+For optimal performance on Arc, it is recommended to set several environment variables.
+
+```bash
+export USE_XETLA=OFF
+export SYCL_PI_LEVEL_ZERO_USE_IMMEDIATE_COMMANDLISTS=1
+```
+
+```
+python ./generate.py --repo-id-or-model-path REPO_ID_OR_MODEL_PATH --prompt PROMPT --n-predict N_PREDICT
+```
+
+Arguments info:
+- `--repo-id-or-model-path REPO_ID_OR_MODEL_PATH`: argument defining the huggingface repo id for the MPT model (e.g. `mosaicml/mpt-7b-chat`) to be downloaded, or the path to the huggingface checkpoint folder. It is default to be `'mosaicml/mpt-7b-chat'`.
+- `--prompt PROMPT`: argument defining the prompt to be infered (with integrated prompt format for chat). It is default to be `'What is AI?'`.
+- `--n-predict N_PREDICT`: argument defining the max number of tokens to predict. It is default to be `32`.
+
+#### Sample Output
+#### [mosaicml/mpt-7b-chat](https://huggingface.co/mosaicml/mpt-7b-chat)
+```log
+Inference time: xxxx s
+-------------------- Prompt --------------------
+<|im_start|>user
+What is AI?<|im_end|>
+<|im_start|>assistant
+
+-------------------- Output --------------------
+user
+What is AI?
+assistant
+AI, or artificial intelligence, is the simulation of human intelligence in machines that are programmed to think and learn like humans. AI systems can perform tasks that typically require
+```

python/llm/example/transformers/transformers_int4/GPU/mpt/generate.py

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+#
+# 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
+
+from bigdl.llm.transformers import AutoModelForCausalLM
+from transformers import AutoTokenizer, GenerationConfig
+import intel_extension_for_pytorch as ipex
+
+# you could tune the prompt based on your own model,
+# here the prompt tuning refers to https://huggingface.co/spaces/mosaicml/mpt-30b-chat/blob/main/app.py
+MPT_PROMPT_FORMAT = "<|im_start|>user\n{prompt}<|im_end|>\n<|im_start|>assistant\n"
+
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser(description='Predict Tokens using `generate()` API for MPT model')
+    parser.add_argument('--repo-id-or-model-path', type=str, default="mosaicml/mpt-7b-chat",
+                        help='The huggingface repo id for the MPT models'
+                             '(e.g. `mosaicml/mpt-7b-chat`) 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
+
+    # Load model in 4 bit,
+    # which convert the relevant layers in the model into INT4 format
+    model = AutoModelForCausalLM.from_pretrained(model_path,
+                                                 load_in_4bit=True,
+                                                 optimize_model=False,
+                                                 trust_remote_code=True)
+    model = model.half().to('xpu')
+
+    # Load tokenizer
+    tokenizer = AutoTokenizer.from_pretrained(model_path,
+                                              trust_remote_code=True)
+    
+    # Generate predicted tokens
+    with torch.inference_mode():
+        prompt = MPT_PROMPT_FORMAT.format(prompt=args.prompt)
+        input_ids = tokenizer.encode(prompt, return_tensors="pt").to('xpu')
+        # enabling `use_cache=True` allows the model to utilize the previous
+        # key/values attentions to speed up decoding;
+        # to obtain optimal performance with BigDL-LLM INT4 optimizations,
+        # it is important to set use_cache=True for MPT models
+        mpt_generation_config = GenerationConfig(
+            max_new_tokens=args.n_predict, 
+            use_cache=True, 
+            pad_token_id=tokenizer.eos_token_id, 
+            eos_token_id=tokenizer.eos_token_id
+        )
+        st = time.time()
+        output = model.generate(input_ids,
+                                generation_config=mpt_generation_config)
+        end = time.time()
+        output = output.cpu()
+        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)