Duplicate from chansung/Alpaca-LoRA-Serve

Co-authored-by: chansung park <[email protected]>

.gitattributes

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README.md

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+---
+title: Alpaca-LoRA-Serve
+emoji: 🦙🚀
+sdk: gradio
+app_file: app.py
+pinned: true
+license: gpl-3.0
+colorFrom: yellow
+colorTo: green
+duplicated_from: chansung/Alpaca-LoRA-Serve
+---
+
+# 🦙 🚀 Alpaca-LoRA as a Chatbot Service
+
+🚧 This project is still under development process. While serving the project, I noticed there are some bugs emitted by the model itself such as too many line breaks which causes OOM eventually. You can propose PR, but I will merge any improvement at any time as soon as I spot any problems.
+
+🔗 **Demo link**: [Batch Mode](https://notebooksf.jarvislabs.ai/43j3x9FSS8Tg0sqvMlDgKPo9vsoSTTKRsX4RIdC3tNd6qeQ6ktlA0tyWRAR3fe_l) and [Streaming Mode](https://notebookse.jarvislabs.ai/BuOu_VbEuUHb09VEVHhfnFq4-PMhBRVCcfHBRCOrq7c4O9GI4dIGoidvNf76UsRL/) (both are running on a single A6000 instance)
+
+The **easiest way** to run this project is to use Colab. Just open up the [alpaca_lora_in_colab](https://github.com/deep-diver/Alpaca-LoRA-Serve/blob/main/notebooks/alpaca_lora_in_colab.ipynb) notebook in Colab (there is a button `open in colab`), and run every cell sequentially. With the standard GPU instance(___T4___), you can run 7B and 13B models. With the premium GPU instance(___A100 40GB___), you can even run 30B model! Screenshot👇🏼 Just note that the connection could be somewhat unstable, so I recommend you to use Colab for development purpose.
+
+![](https://i.ibb.co/hZ3771L/Screen-Shot-2023-03-22-at-9-36-15-PM.png)
+
+This repository demonstrates Alpaca-LoRA as a Chatbot service with [Alpaca-LoRA](https://github.com/tloen/alpaca-lora) and [Gradio](https://gradio.app/). It comes with the following features:
+
+### Mode
+
+**1. Batch Generation Mode**: batch generation mode aggregates requests up to `batch_size`, and pass the prompts in the requests to the model. It waits the current requests are fully handled. For instance, with `batch_size=4`, if a user sends a request, that is under processing. While it is under processing, if other users are connected, up to 4 requests from the users are aggregated and processed as soon as the current one is done.
+
+**2. Streaming Mode**: streaming mode handles multiple requests in a interleaving way with threads. For instance, if there are two users (A and B) are connected, A's request is handled, and then B's request is handled, and then A's request is handled again.... This is because of the nature of streaming mode which generates and `yield` tokens in one by one manner. 
+
+### Context management
+
+- Alpaca-LoRA as a Chatbot Service manages context in two ways. First of all, it remembers(stores) every history of the conversations by default as in the following code snippet. `context_string` is set as ___"Below is a history of instructions that describe tasks, paired with an input that provides further context. Write a response that appropriately completes the request by remembering the conversation history."___ by default, but it could be set manually via the `Context` field on top of the screen. 
+  - additionally, there is a `Summarize` button in the middle (you need to expand the component labeled as ___"Helper Buttons"___). If you click this button, it automatically input ___"summarize our conversations so far in three sentences."___ as a prompt, and the resulting generated text will be inserted into the `Context` field. THen all the conversation history up to this point will be ignored. That means the conversation fresh restarts with the below code snippet except `context_string` will be filled up with the model generated text.
+  - _NOTE: the only last 2,000 characters are kept, and this number can be configured in `constants.py`_
+
+```python
+f"""{context_string}
+
+### Input: {input} # Surrounding information to AI
+
+### Instruction: {prompt1} # First instruction/prompt given by user
+
+### Response {response1} # First response on the first prompt by AI
+
+### Instruction: {prompt2} # Second instruction/prompt given by user
+
+### Response: {response2} # Second response on the first prompt by AI
+....
+"""
+```
+
+### misc.
+
+- There is a `continue` button in the middle of screen. What it does is to simply send ___"continue."___ prompt to the model. This is useful if you get incomplete previous response from the model. With the ___"continue."___, the model tries to complete the response. Also, since this is a continuation of the response, the ___"continue."___ prompt will be hidden to make chatting history more natural.
+
+### Currently supported LoRA checkpoints
+  - [tloen/alpaca-lora-7b](https://huggingface.co/tloen/alpaca-lora-7b): the original 7B Alpaca-LoRA checkpoint by tloen
+  - [chansung/alpaca-lora-13b](https://huggingface.co/chansung/alpaca-lora-13b): the 13B Alpaca-LoRA checkpoint by myself(chansung) with the same script to tune the original 7B model
+  - [chansung/koalpaca-lora-13b](https://huggingface.co/chansung/koalpaca-lora-13b): the 13B Alpaca-LoRA checkpoint by myself(chansung) with the Korean dataset created by [KoAlpaca project](https://github.com/Beomi/KoAlpaca) by Beomi. It works for English(user) to Korean(AI) conversations.
+  - [chansung/alpaca-lora-30b](https://huggingface.co/chansung/alpaca-lora-30b): the 30B Alpaca-LoRA checkpoint by myself(chansung) with the same script to tune the original 7B model
+
+## Instructions
+
+0. Prerequisites
+
+Note that the code only works `Python >= 3.9`
+
+```console
+$ conda create -n alpaca-serve python=3.9
+$ conda activate alpaca-serve
+```
+
+1. Install dependencies
+```console
+$ cd Alpaca-LoRA-Serve
+$ pip install -r requirements.txt
+```
+
+2. Run Gradio application
+```console
+$ BASE_URL=decapoda-research/llama-7b-hf
+$ FINETUNED_CKPT_URL=tloen/alpaca-lora-7b
+
+$ python app.py --base_url $BASE_URL --ft_ckpt_url $FINETUNED_CKPT_URL --port 6006
+```
+
+the following flags are supported
+
+```console
+usage: app.py [-h] [--base_url BASE_URL] [--ft_ckpt_url FT_CKPT_URL] [--port PORT] [--batch_size BATCH_SIZE]
+              [--api_open API_OPEN] [--share SHARE] [--gen_config_path GEN_CONFIG_PATH]
+
+Gradio Application for Alpaca-LoRA as a chatbot service
+
+optional arguments:
+  -h, --help            show this help message and exit
+  --base_url BASE_URL   Hugging Face Hub URL
+  --ft_ckpt_url FT_CKPT_URL
+                        Hugging Face Hub URL
+  --port PORT           port number where the app is served
+  --batch_size BATCH_SIZE
+                        how many requests to handle at the same time
+                        default is set to 1 which enables streaming mode
+  --api_open API_OPEN   do you want to open as API
+  --share SHARE         do you want to share temporarily (useful in Colab env)
+  --gen_config_path GEN_CONFIG_PATH
+                        which config to use for GenerationConfig
+```
+
+## Design figure
+
+<p align="center">
+  <img src="https://i.ibb.co/w069GYg/Screenshot-2023-03-20-at-1-25-29-PM.png" />
+</p>
+
+## Acknowledgements
+
+I am thankful to [Jarvislabs.ai](https://jarvislabs.ai/) who generously provided free GPU resources to experiment with Alpaca-LoRA deployment and share it to communities to try out.

app.py

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+from strings import TITLE, ABSTRACT, BOTTOM_LINE
+from strings import DEFAULT_EXAMPLES
+from strings import SPECIAL_STRS
+from styles import PARENT_BLOCK_CSS
+
+import time
+import gradio as gr
+
+from model import load_model
+from gen import get_output_batch, StreamModel
+from utils import generate_prompt, post_processes_batch, post_process_stream, get_generation_config, common_post_process
+
+model, tokenizer = load_model(
+    base="decapoda-research/llama-13b-hf",
+    finetuned="chansung/alpaca-lora-13b"
+)    
+
+model = StreamModel(model, tokenizer)
+
+def chat_stream(
+    context,
+    instruction,
+    state_chatbot,
+):
+    # print(instruction)
+
+    # user input should be appropriately formatted (don't be confused by the function name)
+    instruction_display = common_post_process(instruction)
+    instruction_prompt = generate_prompt(instruction, state_chatbot, context)    
+    bot_response = model(
+        instruction_prompt,
+        max_tokens=128,
+        temperature=1,
+        top_p=0.9
+    )
+    
+    instruction_display = None if instruction_display == SPECIAL_STRS["continue"] else instruction_display
+    state_chatbot = state_chatbot + [(instruction_display, None)]
+    
+    prev_index = 0
+    agg_tokens = ""
+    cutoff_idx = 0
+    for tokens in bot_response:
+        tokens = tokens.strip()
+        cur_token = tokens[prev_index:]
+        
+        if "#" in cur_token and agg_tokens == "":
+            cutoff_idx = tokens.find("#")
+            agg_tokens = tokens[cutoff_idx:]
+
+        if agg_tokens != "":
+            if len(agg_tokens) < len("### Instruction:") :
+                agg_tokens = agg_tokens + cur_token
+            elif len(agg_tokens) >= len("### Instruction:"):
+                if tokens.find("### Instruction:") > -1:
+                    processed_response, _ = post_process_stream(tokens[:tokens.find("### Instruction:")].strip())
+
+                    state_chatbot[-1] = (
+                        instruction_display, 
+                        processed_response
+                    )
+                    yield (state_chatbot, state_chatbot, context)
+                    break
+                else:
+                    agg_tokens = ""
+                    cutoff_idx = 0
+
+        if agg_tokens == "":
+            processed_response, to_exit = post_process_stream(tokens)
+            state_chatbot[-1] = (instruction_display, processed_response)
+            yield (state_chatbot, state_chatbot, context)
+
+            if to_exit:
+                break
+
+        prev_index = len(tokens)
+
+    yield (
+        state_chatbot,
+        state_chatbot,
+        gr.Textbox.update(value=tokens) if instruction_display == SPECIAL_STRS["summarize"] else context
+    )
+
+def chat_batch(
+    contexts,
+    instructions, 
+    state_chatbots,
+):
+    state_results = []
+    ctx_results = []
+
+    instruct_prompts = [
+        generate_prompt(instruct, histories, ctx) 
+        for ctx, instruct, histories in zip(contexts, instructions, state_chatbots)
+    ]
+        
+    bot_responses = get_output_batch(
+        model, tokenizer, instruct_prompts, generation_config
+    )
+    bot_responses = post_processes_batch(bot_responses)
+
+    for ctx, instruction, bot_response, state_chatbot in zip(contexts, instructions, bot_responses, state_chatbots):
+        new_state_chatbot = state_chatbot + [('' if instruction == SPECIAL_STRS["continue"] else instruction, bot_response)]
+        ctx_results.append(gr.Textbox.update(value=bot_response) if instruction == SPECIAL_STRS["summarize"] else ctx)
+        state_results.append(new_state_chatbot)
+
+    return (state_results, state_results, ctx_results)
+
+def reset_textbox():
+    return gr.Textbox.update(value='')
+
+with gr.Blocks(css=PARENT_BLOCK_CSS) as demo:
+    state_chatbot = gr.State([])
+
+    with gr.Column(elem_id='col_container'):
+        gr.Markdown(f"## {TITLE}\n\n\n{ABSTRACT}")
+
+        with gr.Accordion("Context Setting", open=False):
+            context_txtbox = gr.Textbox(placeholder="Surrounding information to AI", label="Enter Context")
+            hidden_txtbox = gr.Textbox(placeholder="", label="Order", visible=False)
+
+        chatbot = gr.Chatbot(elem_id='chatbot', label="Alpaca-LoRA")
+        instruction_txtbox = gr.Textbox(placeholder="What do you want to say to AI?", label="Instruction")
+        send_prompt_btn = gr.Button(value="Send Prompt")
+
+        with gr.Accordion("Helper Buttons", open=False):
+            gr.Markdown(f"`Continue` lets AI to complete the previous incomplete answers. `Summarize` lets AI to summarize the conversations so far.")
+            continue_txtbox = gr.Textbox(value=SPECIAL_STRS["continue"], visible=False)
+            summrize_txtbox = gr.Textbox(value=SPECIAL_STRS["summarize"], visible=False)
+
+            continue_btn = gr.Button(value="Continue")
+            summarize_btn = gr.Button(value="Summarize")
+
+        gr.Markdown("#### Examples")
+        for idx, examples in enumerate(DEFAULT_EXAMPLES):
+            with gr.Accordion(examples["title"], open=False):
+                gr.Examples(
+                    examples=examples["examples"], 
+                    inputs=[
+                        hidden_txtbox, instruction_txtbox
+                    ],
+                    label=None
+                )
+
+        gr.Markdown(f"{BOTTOM_LINE}")
+
+    send_prompt_btn.click(
+        chat_stream, 
+        [context_txtbox, instruction_txtbox, state_chatbot],
+        [state_chatbot, chatbot, context_txtbox],
+    )
+    send_prompt_btn.click(
+        reset_textbox, 
+        [], 
+        [instruction_txtbox],
+    )
+
+    continue_btn.click(
+        chat_stream, 
+        [context_txtbox, continue_txtbox, state_chatbot],
+        [state_chatbot, chatbot, context_txtbox],
+    )
+    continue_btn.click(
+        reset_textbox, 
+        [], 
+        [instruction_txtbox],
+    )
+
+    summarize_btn.click(
+        chat_stream, 
+        [context_txtbox, summrize_txtbox, state_chatbot],
+        [state_chatbot, chatbot, context_txtbox],
+    )
+    summarize_btn.click(
+        reset_textbox, 
+        [], 
+        [instruction_txtbox],
+    )              
+
+demo.queue(
+    concurrency_count=1,
+    max_size=100,
+).launch(
+    max_threads=5,
+    server_name="0.0.0.0",
+)

constants.py

@@ -0,0 +1,16 @@
+import re
+
+# constants
+num_of_characters_to_keep = 2000
+
+# regex
+html_tag_pattern = re.compile(r"<.*?>")
+multi_line_pattern = re.compile(r"\n+")
+multi_space_pattern = re.compile(r"(  )")
+multi_br_tag_pattern = re.compile(re.compile(r'<br>\s*(<br>\s*)*'))
+    
+# repl is short for replacement
+repl_linebreak = "\n"
+repl_empty_str = ""
+repl_br_tag = "<br>"
+repl_span_tag_multispace = '<span class="chat_wrap_space">  <span>'

gen.py

@@ -0,0 +1,207 @@
+import gc
+import copy
+import time
+from tenacity import RetryError
+from tenacity import retry, stop_after_attempt, wait_fixed
+
+import torch
+
+from transformers import (
+    AutoModelForCausalLM,
+    AutoModelForSeq2SeqLM,
+    AutoTokenizer,
+    LogitsProcessorList,
+    MinNewTokensLengthLogitsProcessor,
+    TemperatureLogitsWarper,
+    TopPLogitsWarper,
+    MinLengthLogitsProcessor
+)
+
+def get_output_batch(
+    model, tokenizer, prompts, generation_config
+):
+    if len(prompts) == 1:
+        encoding = tokenizer(prompts, return_tensors="pt")
+        input_ids = encoding["input_ids"].cuda()
+        generated_id = model.generate(
+            input_ids=input_ids,
+            generation_config=generation_config,
+            max_new_tokens=256
+        )
+
+        decoded = tokenizer.batch_decode(generated_id)
+        del input_ids, generated_id
+        torch.cuda.empty_cache()
+        return decoded
+    else:
+        encodings = tokenizer(prompts, padding=True, return_tensors="pt").to('cuda')
+        generated_ids = model.generate(
+            **encodings,
+            generation_config=generation_config,
+            max_new_tokens=256
+        )
+
+        decoded = tokenizer.batch_decode(generated_ids)
+        del encodings, generated_ids
+        torch.cuda.empty_cache()
+        return decoded
+
+
+# StreamModel is borrowed from basaran project
+# please find more info about it -> https://github.com/hyperonym/basaran
+class StreamModel:
+    """StreamModel wraps around a language model to provide stream decoding."""
+
+    def __init__(self, model, tokenizer):
+        super().__init__()
+        self.model = model
+        self.tokenizer = tokenizer
+        self.device = "cuda" if torch.cuda.is_available() else "cpu"
+
+        self.processor = LogitsProcessorList()
+        self.processor.append(TemperatureLogitsWarper(0.9))
+        self.processor.append(TopPLogitsWarper(0.75))
+
+
+    def __call__(
+        self,
+        prompt,
+        min_tokens=0,
+        max_tokens=16,
+        temperature=1.0,
+        top_p=1.0,
+        n=1,
+        logprobs=0,
+    ):
+        """Create a completion stream for the provided prompt."""
+        input_ids = self.tokenize(prompt)
+        logprobs = max(logprobs, 0)
+
+        # bigger than 1
+        chunk_size = 2
+        chunk_count = 0
+        
+        # Generate completion tokens.
+        final_tokens = torch.empty(0)
+        
+        for tokens in self.generate(
+            input_ids[None, :].repeat(n, 1),
+            logprobs=logprobs,
+            min_new_tokens=min_tokens,
+            max_new_tokens=max_tokens,
+            temperature=temperature,
+            top_p=top_p,
+        ):
+            if chunk_count < chunk_size:
+                chunk_count = chunk_count + 1        
+            
+            final_tokens = torch.cat((final_tokens, tokens.to("cpu")))
+
+            if chunk_count == chunk_size-1:
+                chunk_count = 0
+                yield self.tokenizer.decode(final_tokens, skip_special_tokens=True)
+
+        if chunk_count > 0:
+            yield self.tokenizer.decode(final_tokens, skip_special_tokens=True)
+                
+        del final_tokens, input_ids
+        if self.device == "cuda": 
+            torch.cuda.empty_cache()
+
+    def _infer(self, model_fn, **kwargs):
+        with torch.inference_mode():
+            return model_fn(**kwargs)
+
+    def tokenize(self, text):
+        """Tokenize a string into a tensor of token IDs."""
+        batch = self.tokenizer.encode(text, return_tensors="pt")
+        return batch[0].to(self.device)
+
+    def generate(self, input_ids, logprobs=0, **kwargs):
+        """Generate a stream of predicted tokens using the language model."""
+
+        # Store the original batch size and input length.
+        batch_size = input_ids.shape[0]
+        input_length = input_ids.shape[-1]
+
+        # Separate model arguments from generation config.
+        config = self.model.generation_config
+        config = copy.deepcopy(config)
+        kwargs = config.update(**kwargs)
+        kwargs["output_attentions"] = False
+        kwargs["output_hidden_states"] = False
+        kwargs["use_cache"] = True
+
+        # Collect special token IDs.
+        pad_token_id = config.pad_token_id
+        bos_token_id = config.bos_token_id
+        eos_token_id = config.eos_token_id
+        if isinstance(eos_token_id, int):
+            eos_token_id = [eos_token_id]
+        if pad_token_id is None and eos_token_id is not None:
+            pad_token_id = eos_token_id[0]
+
+        # Generate from eos if no input is specified.
+        if input_length == 0:
+            input_ids = input_ids.new_ones((batch_size, 1)).long()
+            if eos_token_id is not None:
+                input_ids = input_ids * eos_token_id[0]
+            input_length = 1
+
+        # Keep track of which sequences are already finished.
+        unfinished = input_ids.new_ones(batch_size)
+
+        # Start auto-regressive generation.
+        while True:
+            inputs = self.model.prepare_inputs_for_generation(
+                input_ids, **kwargs
+            )  # noqa: E501
+
+            outputs = self._infer(
+                self.model,
+                **inputs,
+                # return_dict=True,
+                output_attentions=False,
+                output_hidden_states=False,
+            )
+
+            # Pre-process the probability distribution of the next tokens.
+            logits = outputs.logits[:, -1, :]
+            with torch.inference_mode():
+                logits = self.processor(input_ids, logits)
+            probs = torch.nn.functional.softmax(logits, dim=-1)
+
+            # Select deterministic or stochastic decoding strategy.
+            if (config.top_p is not None and config.top_p <= 0) or (
+                config.temperature is not None and config.temperature <= 0
+            ):
+                tokens = torch.argmax(probs, dim=-1)[:, None]
+            else:
+                tokens = torch.multinomial(probs, num_samples=1)
+
+            tokens = tokens.squeeze(1)
+
+            # Finished sequences should have their next token be a padding.
+            if pad_token_id is not None:
+                tokens = tokens * unfinished + pad_token_id * (1 - unfinished)
+
+            # Append selected tokens to the inputs.
+            input_ids = torch.cat([input_ids, tokens[:, None]], dim=-1)
+
+            # Mark sequences with eos tokens as finished.
+            if eos_token_id is not None:
+                not_eos = sum(tokens != i for i in eos_token_id)
+                unfinished = unfinished.mul(not_eos.long())
+
+            # Set status to -1 if exceeded the max length.
+            status = unfinished.clone()
+            if input_ids.shape[-1] - input_length >= config.max_new_tokens:
+                status = 0 - status
+
+            # Yield predictions and status.
+            yield tokens
+
+            # Stop when finished or exceeded the max length.
+            if status.max() <= 0:
+                break
+    

generation_config_default.yaml

@@ -0,0 +1,7 @@
+generation_config:
+    temperature: 0.90
+    top_p: 0.75
+    num_beams: 1
+    use_cache: True
+    max_length: 1000
+    min_length: 0

model.py

@@ -0,0 +1,20 @@
+from peft import PeftModel
+from transformers import LlamaTokenizer, LlamaForCausalLM
+
+def load_model(
+    base="decapoda-research/llama-7b-hf",
+    finetuned="tloen/alpaca-lora-7b",
+):
+    tokenizer = LlamaTokenizer.from_pretrained(base)
+    tokenizer.pad_token_id = 0
+    tokenizer.padding_side = "left"
+
+    model = LlamaForCausalLM.from_pretrained(
+        base,
+        load_in_8bit=True,
+        device_map="auto",
+    )
+    
+    model = PeftModel.from_pretrained(model, finetuned, device_map={'': 0})
+    return model, tokenizer
+

notebooks/alpaca_lora_in_colab.ipynb

@@ -0,0 +1,218 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": [],
+      "machine_shape": "hm"
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    },
+    "accelerator": "GPU",
+    "gpuClass": "premium"
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# Check GPU's Memory Capacity\n",
+        "\n",
+        "By running `nvidia-smi` command, you can find out the GPU's memory capacity on the current system. \n",
+        "\n",
+        "With the standard GPU instance(___T4___) which is free, you can run 7B and 13B models. With the premium GPU instance(___A100 40GB___) which is paid with the compute unit that you own, you can even run 30B model! Choose the instance at the menu `Runtime` -> `Change runtime type` -> `Hardware accelerator (GPU)` -> `GPU class (Standard or Premium)`"
+      ],
+      "metadata": {
+        "id": "xf3pUNyVO3WS"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "!nvidia-smi"
+      ],
+      "metadata": {
+        "id": "L2MoM27rfaKK",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "53175950-3269-4296-9425-3652c81ce9b7"
+      },
+      "execution_count": 1,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Wed Mar 22 12:11:41 2023       \n",
+            "+-----------------------------------------------------------------------------+\n",
+            "| NVIDIA-SMI 525.85.12    Driver Version: 525.85.12    CUDA Version: 12.0     |\n",
+            "|-------------------------------+----------------------+----------------------+\n",
+            "| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |\n",
+            "| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |\n",
+            "|                               |                      |               MIG M. |\n",
+            "|===============================+======================+======================|\n",
+            "|   0  Tesla T4            Off  | 00000000:00:04.0 Off |                    0 |\n",
+            "| N/A   41C    P0    24W /  70W |      0MiB / 15360MiB |      0%      Default |\n",
+            "|                               |                      |                  N/A |\n",
+            "+-------------------------------+----------------------+----------------------+\n",
+            "                                                                               \n",
+            "+-----------------------------------------------------------------------------+\n",
+            "| Processes:                                                                  |\n",
+            "|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |\n",
+            "|        ID   ID                                                   Usage      |\n",
+            "|=============================================================================|\n",
+            "|  No running processes found                                                 |\n",
+            "+-----------------------------------------------------------------------------+\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# Clone the repository"
+      ],
+      "metadata": {
+        "id": "N0MDD9TuPTfJ"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "!git clone https://github.com/deep-diver/Alpaca-LoRA-Serve.git"
+      ],
+      "metadata": {
+        "id": "a_i5DKBNnzAK"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# Move into the directory of the cloned repository"
+      ],
+      "metadata": {
+        "id": "HUuzxWGuPYLq"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "%cd Alpaca-LoRA-Serve"
+      ],
+      "metadata": {
+        "id": "wR-M8u7gsQqg",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "eb7b24ba-10e4-46d5-cf8f-852d9fac8170"
+      },
+      "execution_count": 3,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "/content/Alpaca-LoRA-Serve\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# Install dependencies"
+      ],
+      "metadata": {
+        "id": "XG8oy7BBPdMh"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "!pip install -r requirements.txt"
+      ],
+      "metadata": {
+        "id": "moN-15x_ifHE",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "a7ec61ff-28cb-4ac4-a0ca-6a5cba060579"
+      },
+      "execution_count": 4,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "  Building wheel for transformers (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
+            "  Created wheel for transformers: filename=transformers-4.28.0.dev0-py3-none-any.whl size=6758864 sha256=028619344608e01338ac944ad0d4e6496fe5c743c90a15dd20c2e436e56106a9\n",
+            "  Stored in directory: /tmp/pip-ephem-wheel-cache-vqcgstta/wheels/f7/92/8c/752ff3bfcd3439805d8bbf641614da38ef3226e127ebea86ee\n",
+            "  Building wheel for peft (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
+            "  Created wheel for peft: filename=peft-0.3.0.dev0-py3-none-any.whl size=40669 sha256=bb0afa4164ac44e0a604c781f61767ea3e7255b85b70e2d4cf76a4252119ac27\n",
+            "  Stored in directory: /tmp/pip-ephem-wheel-cache-vqcgstta/wheels/2d/60/1b/0edd9dc0f0c489738b1166bc1b0b560ee368f7721f89d06e3a\n",
+            "  Building wheel for ffmpy (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
+            "  Created wheel for ffmpy: filename=ffmpy-0.3.0-py3-none-any.whl size=4707 sha256=5f7dae7c29ab50f6251f5c864c70d4e485a4338a98c5cc1ee51523ace2758bf1\n",
+            "  Stored in directory: /root/.cache/pip/wheels/91/e2/96/f676aa08bfd789328c6576cd0f1fde4a3d686703bb0c247697\n",
+            "Successfully built transformers peft ffmpy\n",
+            "Installing collected packages: tokenizers, sentencepiece, rfc3986, pydub, ffmpy, bitsandbytes, xxhash, websockets, uc-micro-py, python-multipart, pycryptodome, orjson, multidict, mdurl, loralib, h11, frozenlist, dill, async-timeout, aiofiles, yarl, uvicorn, starlette, responses, multiprocess, markdown-it-py, linkify-it-py, huggingface-hub, httpcore, aiosignal, accelerate, transformers, mdit-py-plugins, httpx, fastapi, aiohttp, peft, gradio, datasets\n",
+            "Successfully installed accelerate-0.17.1 aiofiles-23.1.0 aiohttp-3.8.4 aiosignal-1.3.1 async-timeout-4.0.2 bitsandbytes-0.37.2 datasets-2.10.1 dill-0.3.6 fastapi-0.95.0 ffmpy-0.3.0 frozenlist-1.3.3 gradio-3.20.0 h11-0.14.0 httpcore-0.16.3 httpx-0.23.3 huggingface-hub-0.13.3 linkify-it-py-2.0.0 loralib-0.1.1 markdown-it-py-2.2.0 mdit-py-plugins-0.3.3 mdurl-0.1.2 multidict-6.0.4 multiprocess-0.70.14 orjson-3.8.8 peft-0.3.0.dev0 pycryptodome-3.17 pydub-0.25.1 python-multipart-0.0.6 responses-0.18.0 rfc3986-1.5.0 sentencepiece-0.1.97 starlette-0.26.1 tokenizers-0.13.2 transformers-4.28.0.dev0 uc-micro-py-1.0.1 uvicorn-0.21.1 websockets-10.4 xxhash-3.2.0 yarl-1.8.2\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# Run the application"
+      ],
+      "metadata": {
+        "id": "Cr3bQkSePfrG"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#@title Choose models\n",
+        "\n",
+        "base_model = 'decapoda-research/llama-13b-hf' #@param [\"decapoda-research/llama-7b-hf\", \"decapoda-research/llama-13b-hf\", \"decapoda-research/llama-30b-hf\"]\n",
+        "finetuned_model = 'chansung/alpaca-lora-13b' #@param [\"tloen/alpaca-lora-7b\", \"chansung/alpaca-lora-13b\", \"chansung/koalpaca-lora-13b\", \"chansung/alpaca-lora-30b\"]\n"
+      ],
+      "metadata": {
+        "id": "4Wg0eqnkPnq-"
+      },
+      "execution_count": 14,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## Run the application\n",
+        "\n",
+        "It will take some time since LLaMA weights are huge. \n",
+        "\n",
+        "Click the URL appeared in the `Running on public URL:` field from the log. That will bring you to a new browser tab which opens up the running application."
+      ],
+      "metadata": {
+        "id": "b81jhdtcQyOP"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "!python app.py --base_url $base_model --ft_ckpt_url $finetuned_model --share yes"
+      ],
+      "metadata": {
+        "id": "y3qpzBw2jMHq"
+      },
+      "execution_count": null,
+      "outputs": []
+    }
+  ]
+}

requirements.txt

@@ -0,0 +1,8 @@
+bitsandbytes
+datasets
+loralib 
+sentencepiece
+git+https://github.com/huggingface/transformers.git
+git+https://github.com/huggingface/peft.git
+gradio==3.20.0
+tenacity

scripts/hparams_explore.py

@@ -0,0 +1,66 @@
+import time
+import itertools
+import wandb
+from transformers import GenerationConfig
+
+wandb.login(key="")
+
+PROJECT="txt_gen_test_project"
+
+generation_configs = {
+    "temperature": [0.5, 0.7, 0.8, 0.9, 1.0],
+    "top_p": [0.5, 0.75, 0.85, 0.95, 1.0],
+    "num_beams": [1, 2, 3, 4]
+}
+
+num_gens = 1
+
+# token initialization
+# model initialization
+
+for comb in itertools.product(generation_configs['temperature'], 
+                              generation_configs['top_p'],
+                              generation_configs['num_beams']):
+  temperature = comb[0]
+  top_p = comb[1]
+  num_beams = comb[2]
+
+  generation_config = GenerationConfig(
+      temperature=temperature,
+      top_p=top_p,
+      num_beams=num_beams,
+  )
+
+  first_columns = [f"gen_txt_{num}" for num in range(num_gens)]
+  columns = first_columns + ["temperature", "top_p", "num_beams", "time_delta"]
+  
+  avg_time_delta = 0
+  txt_gens = []
+  for i in range(num_gens):
+    start = time.time()
+    # text generation
+    text = "dummy text"
+    txt_gens.append(text)
+
+    # decode outputs
+    end = time.time()
+    t_delta = end - start
+    avg_time_delta = avg_time_delta + t_delta
+
+  avg_time_delta = round(avg_time_delta / num_gens, 4)
+    
+  wandb.init(
+      project=PROJECT,
+      name=f"t@{temperature}-tp@{top_p}-nb@{num_beams}",
+      config=generation_config,
+  )
+
+  text_table = wandb.Table(columns=columns)
+  text_table.add_data(*txt_gens, temperature, top_p, num_beams, avg_time_delta)
+
+  wandb.log({
+      "avg_t_delta": avg_time_delta,
+      "results": text_table
+  })
+
+  wandb.finish()

scripts/requirements.txt

@@ -0,0 +1,2 @@
+transformers
+wandb

strings.py

@@ -0,0 +1,53 @@
+TITLE = "Alpaca-LoRA Playground"
+
+ABSTRACT = """
+Thanks to [tolen](https://github.com/tloen/alpaca-lora), this simple application runs Alpaca-LoRA which is instruction fine-tuned version of [LLaMA](https://ai.facebook.com/blog/large-language-model-llama-meta-ai/) from Meta AI. Alpaca-LoRA is *Low-Rank LLaMA Instruct-Tuning* which is inspired by [Stanford Alpaca project](https://github.com/tatsu-lab/stanford_alpaca). This demo application currently runs 13B version on a A10 instance.
+"""
+
+BOTTOM_LINE = """
+
+This demo application runs the open source project, [Alpaca-LoRA-Serve](https://github.com/deep-diver/Alpaca-LoRA-Serve). By default, it runs with streaming mode, but you can also run with dynamic batch generation model. Please visit the repo, find more information, and contribute if you can.
+
+Alpaca-LoRA is built on the same concept as Standford Alpaca project, but it lets us train and inference on a smaller GPUs such as RTX4090 for 7B version. Also, we could build very small size of checkpoints on top of base models thanks to [🤗 transformers](https://huggingface.co/docs/transformers/index), [🤗 peft](https://github.com/huggingface/peft), and [bitsandbytes](https://github.com/TimDettmers/bitsandbytes/tree/main) libraries.
+"""
+
+DEFAULT_EXAMPLES = [
+    {
+        "title": "1️⃣ List all Canadian provinces in alphabetical order.",
+        "examples": [
+            ["1", "List all Canadian provinces in alphabetical order."],
+            ["2", "Which ones are on the east side?"],
+            ["3", "What foods are famous in each province on the east side?"],
+            ["4", "What about sightseeing? or landmarks? list one per province"],
+        ],
+    },
+    {
+        "title": "2️⃣ Tell me about Alpacas.",
+        "examples": [
+            ["1", "Tell me about alpacas in two sentences"],
+            ["2", "What other animals are living in the same area?"],
+            ["3", "Are they the same species?"],
+            ["4", "Write a Python program to return those species"],
+        ],
+    },
+    {
+        "title": "3️⃣ Tell me about the king of France in 2019.",
+        "examples": [
+            ["1", "Tell me about the king of France in 2019."],
+            ["2", "What about before him?"],
+        ]
+    },
+    {
+        "title": "4️⃣ Write a Python program that prints the first 10 Fibonacci numbers.",
+        "examples": [
+            ["1", "Write a Python program that prints the first 10 Fibonacci numbers."],
+            ["2", "Could you explain how the code works?"],
+            ["3", "What is recursion?"],
+        ]
+    }
+]
+
+SPECIAL_STRS = {
+    "continue": "continue.",
+    "summarize": "summarize our conversations so far in three sentences."
+}

styles.py

@@ -0,0 +1,16 @@
+PARENT_BLOCK_CSS = """
+#col_container {
+    width: 95%; 
+    margin-left: auto; 
+    margin-right: auto;
+}
+
+#chatbot {
+    height: 500px; 
+    overflow: auto;
+}
+
+.chat_wrap_space {
+    margin-left: 0.5em
+}
+"""

utils.py

@@ -0,0 +1,80 @@
+import re
+import yaml
+
+from transformers import GenerationConfig
+
+from strings import SPECIAL_STRS
+from constants import num_of_characters_to_keep
+from constants import html_tag_pattern, multi_line_pattern, multi_space_pattern
+from constants import repl_empty_str, repl_br_tag, repl_span_tag_multispace, repl_linebreak
+
+def get_generation_config(path):
+    with open(path, 'rb') as f:
+        generation_config = yaml.safe_load(f.read())
+
+    return GenerationConfig(**generation_config["generation_config"])
+
+def generate_prompt(prompt, histories, ctx=None):
+    convs = f"""Below is a history of instructions that describe tasks, paired with an input that provides further context. Write a response that appropriately completes the request by remembering the conversation history.
+    
+"""
+    if ctx is not None:
+        convs = f"""{ctx}
+
+"""
+    
+    start_idx = 0
+    
+    for idx, history in enumerate(histories):
+        history_prompt = history[0]
+        if history_prompt == SPECIAL_STRS["summarize"]:
+            start_idx = idx
+
+    # drop the previous conversations if user has summarized
+    for history in histories[start_idx if start_idx == 0 else start_idx+1:]:
+        history_prompt = history[0]
+        history_response = history[1]
+        
+        history_response = history_response.replace("<br>", "\n")
+        history_response = re.sub(
+            html_tag_pattern, repl_empty_str, history_response
+        )
+
+        convs = convs + f"""### Instruction:{history_prompt}
+
+### Response:{history_response}
+
+"""
+
+    convs = convs + f"""### Instruction:{prompt}
+
+### Response:"""
+
+    return convs[-num_of_characters_to_keep:]
+
+# applicable to instruction to be displayed as well
+def common_post_process(original_str):
+    original_str = re.sub(
+        multi_line_pattern, repl_br_tag, original_str
+    )
+    original_str = re.sub(
+        multi_space_pattern, repl_span_tag_multispace, original_str
+    )
+    
+    return original_str
+
+def post_process_stream(bot_response):
+    # sometimes model spits out text containing 
+    # "### Response:" and "### Instruction: -> in this case, we want to stop generating
+    if "### Response:" in bot_response or "### Input:" in bot_response:
+        bot_response = bot_response.replace("### Response:", '').replace("### Input:", '').strip()
+        return bot_response, True
+    
+    return common_post_process(bot_response), False
+
+def post_process_batch(bot_response):
+    bot_response = bot_response.split("### Response:")[-1].strip()
+    return common_post_process(bot_response)
+
+def post_processes_batch(bot_responses):
+    return [post_process_batch(r) for r in bot_responses]