Deploy a CodeLlama-Python-34B Model using Inferless

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Experimentation with different libraries

1. Hugging Face: Transformer provides an easy-to-use pipeline for quick deployment of LLM but is not a good choice for LLM inference. We have used BitsandBytes with 4bit quantization, however it didn’t help in improving the inference latency.
2. AutoGPTQ: AutoGPTQ enables you to run LLM on low memory. We deploy a GPTQ 4bit quantized model and are able to achieve better inference latency and token/sec than Hugging Face(unquantized and bitsandbytes).
3. Text Generation Inference (TGI): TGI allows you to deploy and serve LLM. We have deployed and tested different type of quantized versions where 4bit quantized AWQ perform better.
4. vLLM: vLLM is a library use for deployment and serving LLM. We have deployed both quantized and unquantized versions, both using vLLM and vLLM as a backend with the Triton inference server. We have used vLLM-GPTQ(vLLM GPTQ brach) for deploying GPTQ quantized model, as of 24/11/23 vLLM doesn’t support GPTQ quantization.

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Our Observations

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GPU Recommendation

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Results with Different Experimentation

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Defining Dependencies

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Constructing the Github/Gitlab Template

codellama-34b-python/
├── app.py
├── inferless-runtime-config.yaml
├── inferless.yaml
└── input_schema.py

• The app.py (Check the Github URL) file will load and serve the model.
• The inferless-runtime-config.yaml (Check the Github URL) file will have all the software and Python dependencies.
• You can also have any additional dependency files.

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Creating the class for inference

1. def initialize: This function will load the model and tokenizer into the memory whenever the container starts.
2. def infer: This function helps to serve the loaded model. You can create a complex inference pipeline or chain multiple models together here.
3. def finalize: This function deallocates the allocated memory for the model and tokenizer whenever the container shuts down.

import inferless
from vllm import SamplingParams, LLM

app = inferless.Cls(gpu="A100")

class InferlessPythonModel:

    @app.load
    def initialize(self):
      self.llm = LLM(model="TheBloke/CodeLlama-34B-Python-GPTQ",quantization="gptq")
    @app.infer
    def infer(self, inputs):
        prompts = inputs["prompt"]
        sampling_params = SamplingParams(
            temperature=1.0,
            top_p=1,
            max_tokens=512
        )
        result = self.llm.generate(prompts, sampling_params)
        result_output = [output.outputs[0].text for output in result]

        return {"result": result_output[0]}

    def finalize(self):
        self.llm = None

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Creating the custom runtime

build:
  cuda_version: "12.1.1"
  python_packages:
    - "vllm==0.8.2"
    - "inferless-cli==2.0.9"
    - "hf-transfer==0.1.9"
    - "huggingface-hub==0.27.1"

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Test your model with Remote Run

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Step 1: Add the Decorators and local entry point

1. Import the inferless library and initialize Cls(gpu="A100"). The available GPU options are T4, A10 and A100.
2. Decorated the initialize and infer functions with @app.load and @app.infer respectively.
3. Create the Local Entry Point by decorating a function (for example, my_local_entry) with @inferless.local_entry_point. Within this function, instantiate your model class, convert any incoming parameters into a RequestObjects object, and invoke the model’s infer method.

import inferless
from vllm import SamplingParams, LLM
from pydantic import BaseModel, Field
from typing import Optional


@inferless.request
class RequestObjects(BaseModel):
    prompt: str = Field(default="Explain Deep Learning.")
    temperature: Optional[float] = 0.7
    top_p: Optional[float] = 0.1
    repetition_penalty: Optional[float] = 1.18
    top_k: Optional[int] = 40
    max_tokens: Optional[int] = 256

@inferless.response
class ResponseObjects(BaseModel):
    generated_text: str = Field(default='Test output')

app = inferless.Cls(gpu="A100")

class InferlessPythonModel:

    @app.load
    def initialize(self):
      self.llm = LLM(model="TheBloke/CodeLlama-34B-Python-GPTQ",quantization="gptq")
    @app.infer
    def infer(self, request: RequestObjects) -> ResponseObjects:
        sampling_params = SamplingParams(temperature=request.temperature,top_p=request.top_p,repetition_penalty=request.repetition_penalty,
                                         top_k=request.top_k,max_tokens=request.max_tokens)

        result = self.llm.generate(request.prompt, sampling_params)
        result_output = [output.outputs[0].text for output in result]

        generateObject = ResponseObjects(generated_text = result_output[0])
        return generateObject

    def finalize(self):
        self.llm = None

@inferless.local_entry_point
def my_local_entry(dynamic_params):
    request_objects = RequestObjects(**dynamic_params)
    model_instance = InferlessPythonModel()

    return model_instance.infer(request_objects)

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Step 2: Run with Remote GPU

inferless remote-run app.py -c inferless-runtime-config.yaml --prompt "def factorial(int n):"

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Method A: Deploying the model on Inferless Platform

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

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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

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Here is the Demo:

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Method B: Deploying the model on Inferless CLI

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Clone the repository of the model

git clone https://github.com/inferless/Codellama-34B.git

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Deploy the Model

inferless deploy --gpu A100 --runtime inferless-runtime-config.yaml

• --gpu A100: Specifies the GPU type for deployment. Available options include A10, A100, and T4.
• --runtime inferless-runtime-config.yaml: Defines the runtime configuration file. If not specified, the default Inferless runtime is used.