Deploy Quantized version of SOLAR 10.7B-Instruct using Inferless

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Introduction

SOLAR-10.7B, an advanced large language model (LLM) with 10.7 billion parameters, demonstrates superior performance in various natural language processing (NLP) tasks. They have presented a methodology for scaling LLMs called depth up-scaling (DUS), which encompasses architectural modifications and continued pretraining. They have integrated Mistral 7B weights into the upscaled layers, and finally, continued pre-training for the entire model.

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Our Observations

We utilized AutoGPTQ to quantize SOLAR-10.7B-Instruct-v1.0 into a 4-bit quantized GPTQ version. In the inference process, we deployed the quantized model on an A100 GPU (80GB) using vLLM. We also tried deploying via Auto-GPTQ as mentioned in our observations below:

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Getting Started with Quantization

Quantization techniques reduces the model’s computation cost and memory. It represent the model’s weights and activations in lower precision data-types while trying not to reduce in the accuracy.

We have quantized the model using GPTQ algorithm, GPTQ is a quantization algorithms for LLMs. We have used AutoGPTQ for 4-bit GPTQ quantization.

Install the AutoGPTQ library:

pip install auto-gptq

Import the following libraries, and initialize the model and the tokenizer. For GPTQ calibration phase we are using VMware/open-instruct dataset.

from auto_gptq import AutoGPTQForCausalLM, BaseQuantizeConfig
from transformers import AutoTokenizer
from datasets import load_dataset
import random
import numpy as np
import torch

model_id = "upstage/SOLAR-10.7B-Instruct-v1.0"
quantized_model_dir = "SOLAR-10.7B-Instruct-v1.0"

tokenizer = AutoTokenizer.from_pretrained("upstage/SOLAR-10.7B-Instruct-v1.0", use_fast=True)

train_data = load_dataset('VMware/open-instruct')
tokenized_data = tokenizer("\n\n".join(train_data['train']['response']), return_tensors='pt')

def generate_data(nsamples,seed,seqlen):
    random.seed(seed)
    np.random.seed(0)
    torch.random.manual_seed(0)

    train_dataset = []

    for _ in range(nsamples):
        i = random.randint(0, tokenized_data.input_ids.shape[1] - seqlen - 1)
        j = i + seqlen
        inp = tokenized_data.input_ids[:, i:j]
        attention_mask = torch.ones_like(inp)
        train_dataset.append({'input_ids':inp,'attention_mask': attention_mask})
    return train_dataset

Now you can start the quantization process, it will create a new directory where it will store the quantized model. The quantized model is 5.98 GB, which is approximately 27.85% of the original model 21.47 GB. Here’s the link to our quanitized model.

training_dataset = generate_data(1000,4040,2048)
quantize_config = BaseQuantizeConfig(
    bits=4,
    group_size=128,
    desc_act=False)

model = AutoGPTQForCausalLM.from_pretrained(model_id,quantize_config)
model.quantize(training_dataset)

model.save_quantized(quantized_model_dir, use_safetensors=True)
tokenizer.save_pretrained(quantized_model_dir)

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Defining Dependencies

We are using the vLLM library, which enables you to run LLM on low memory. We deploy a GPTQ 4bit quantized version of the model.

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Constructing the GitHub/GitLab Template

Now quickly construct the GitHub/GitLab template, this process is mandatory and make sure you don’t add any file named model.py

SOLAR-10.7B-Instruct/
├── app.py
├── inferless-runtime-config.yaml
├── inferless.yaml
└── input_schema.py

You can also add other files to this directory.

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Create the class for inference

In the app.py we will define the class and import all the required functions

1. def initialize: In this function, you will initialize your model and define any variable that you want to use during inference.

2. def infer: This function gets called for every request that you send. Here you can define all the steps that are required for the inference. You can also pass custom values for inference and pass it through inputs(dict) parameter.

3. def finalize: This function cleans up all the allocated memory.

from vllm import LLM, SamplingParams

class InferlessPythonModel:
    def initialize(self):

        self.sampling_params = SamplingParams(temperature=0.7, top_p=0.95,max_tokens=256)
        self.llm = LLM(model="Inferless/SOLAR-10.7B-Instruct-v1.0-GPTQ", quantization="gptq", dtype="float16")

    def infer(self, inputs):
        prompts = inputs["prompt"]
        result = self.llm.generate(prompts, self.sampling_params)
        result_output = [[[output.outputs[0].text,output.outputs[0].token_ids] for output in result]

        return {'generated_result': result_output[0]}

    def finalize(self):
        pass

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Creating the Custom Runtime

This is a mandatory step where we allow the users to upload their custom runtime through inferless-runtime-config.yaml.

build:
  cuda_version: "12.1.1"
  system_packages:
    - "libssl-dev"
  python_packages:
    - "torch==2.1.2"
    - "vllm==0.2.6"
    - "transformers==4.36.2"
    - "accelerate==0.25.0"

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Method A: Deploying the model on Inferless Platform

Inferless supports multiple ways of importing your model. For this tutorial, we will use GitHub.

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Step 1: Login to the inferless dashboard can click on Import model button

Navigate to your desired workspace in Inferless and Click on Add a custom model button that you see on the top right. An import wizard will open up.

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Step 2: Follow the UI to complete the model Import

• Select the GitHub/GitLab Integration option to connect your source code repository with the deployment environment.
• Navigate to the specific GitHub repository that contains your model’s code. Here, you will need to identify and enter the name of the model you wish to import.
• Choose the appropriate type of machine that suits your model’s requirements. Additionally, specify the minimum and maximum number of replicas to define the scalability range for deploying your model.
• Optionally, you have the option to enable automatic build and deployment. This feature triggers a new deployment automatically whenever there is a new code push to your repository.
• If your model requires additional software packages, configure the Custom Runtime settings by including necessary pip or apt packages. Also, set up environment variables such as Inference Timeout, Container Concurrency, and Scale Down Timeout to tailor the runtime environment according to your needs.
• Wait for the validation process to complete, ensuring that all settings are correct and functional. Once validation is successful, click on the “Import” button to finalize the import of your model.

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Step 3: Wait for the model build to complete usually takes ~5-10 minutes

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Step 4: Use the APIs to call the model

Once the model is in ‘Active’ status you can click on the ‘API’ page to call the model

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Here is the Demo:

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Method B: Deploying the model on Inferless CLI

Inferless allows you to deploy your model using Inferless-CLI. Follow the steps to deploy using Inferless CLI.

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Initialization of the model

Create the app.py and inferless-runtime-config.yaml, move the files to the working directory. Run the following command to initialize your model:

inferless init

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Upload the custom runtime

Once you have created the inferless-runtime-config.yaml file, you can run the following command:

inferless runtime upload

Upon entering this command, you will be prompted to provide the configuration file name. Enter the name and ensure to update it in the inferless.yaml file. Now you are ready for the deployment.

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Deploy the Model

Execute the following command to deploy your model. Once deployed, you can track the build logs on the Inferless platform:

inferless deploy