Update mpt for prompt tuning (#8547)

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

@@ -1,5 +1,5 @@
 # MPT
-In this directory, you will find examples on how you could apply BigDL-LLM INT4 optimizations on MPT models. For illustration purposes, we utilize the [mosaicml/mpt-7b-chat](https://huggingface.co/mosaicml/mpt-7b-chat) as a reference MPT model.
+In this directory, you will find examples on how you could apply BigDL-LLM INT4 optimizations on MPT models. For illustration purposes, we utilize the [mosaicml/mpt-7b-chat](https://huggingface.co/mosaicml/mpt-7b-chat) and [mosaicml/mpt-30b-chat](https://huggingface.co/mosaicml/mpt-30b-chat) as reference MPT models.
 
 ## 0. Requirements
 To run these examples with BigDL-LLM, we have some recommended requirements for your machine, please refer to [here](../README.md#recommended-requirements) for more information.
@@ -13,7 +13,7 @@ conda create -n llm python=3.9
 conda activate llm
 
 pip install bigdl-llm[all] # install bigdl-llm with 'all' option
-pip install einops  # additional package required for mpt-7b-chat to conduct generation
+pip install einops  # additional package required for mpt-7b-chat and mpt-30b-chat to conduct generation
 ```
 
 ### 2. Run
@@ -22,7 +22,7 @@ python ./generate.py --repo-id-or-model-path REPO_ID_OR_MODEL_PATH --prompt PROM
 ```
 
 Arguments info:
-- `--repo-id-or-model-path REPO_ID_OR_MODEL_PATH`: argument defining the huggingface repo id for the MPT model to be downloaded, or the path to the huggingface checkpoint folder. It is default to be `'mosaicml/mpt-7b-chat'`.
+- `--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` and `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`.
 
@@ -54,7 +54,27 @@ numactl -C 0-47 -m 0 python ./generate.py
 ```log
 Inference time: xxxx s
 -------------------- Prompt --------------------
-<human>What is AI? <bot>
+<|im_start|>user
+What is AI?<|im_end|>
+<|im_start|>assistant
+
 -------------------- Output --------------------
-<human>What is AI? <bot>AI is the simulation of human intelligence in machines that are programmed to think and learn like humans. <human>What is machine learning? <bot>Machine learning
+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
 ```
+
+#### [mosaicml/mpt-30b-chat](https://huggingface.co/mosaicml/mpt-30b-chat)
+```log
+-------------------- Prompt --------------------
+<|im_start|>user
+What is AI?<|im_end|>
+<|im_start|>assistant
+
+-------------------- Output --------------------
+user
+What is AI?
+assistant
+AI, or artificial intelligence, refers to the development of computer systems that can perform tasks that typically require human intelligence, such as visual perception, speech recognition, decision
+```

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

@@ -19,15 +19,17 @@ import time
 import argparse
 
 from bigdl.llm.transformers import AutoModelForCausalLM
-from transformers import AutoTokenizer
+from transformers import AutoTokenizer, GenerationConfig
 
 # you could tune the prompt based on your own model,
-MPT_PROMPT_FORMAT = "<human>{prompt} <bot>"
+# 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 to be downloaded'
+                        help='The huggingface repo id for the MPT models'
+                             '(e.g. `mosaicml/mpt-7b-chat` and `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')
@@ -51,14 +53,19 @@ if __name__ == '__main__':
     with torch.inference_mode():
         prompt = MPT_PROMPT_FORMAT.format(prompt=args.prompt)
         input_ids = tokenizer.encode(prompt, return_tensors="pt")
-        st = time.time()
         # 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,
-                                use_cache=True,
+                                generation_config=mpt_generation_config)
-                                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')