InferyLLM

Table of Contents

1. Introduction
2. Installation
3. Serving
4. Generation
5. Advanced Usage:
   • CLI
   • Lowering loading time
   • Benchmarking models and serving configurations

Introduction

InferyLLM is a high-performance engine and server for running LLM inference.

InferyLLM is fast

• Optimized CUDA kernels for MQA, GQA and MHA
• Continuous batching using a paged KV cache and custom paged attention kernels
• Kernel autotuning capabilities with automatic selection of the optimal kernels and parameters on the given HW
• Support for extremely efficient LLMs, designed to reach SOTA throughput

InferyLLM is easy to use

• Containerized OR local entrypoint servers
• Simple, minimal-dependency python client
• Seamless integration with 🤗 model hub

Model support

• deci/decilm-6b
• deci/decilm-7b
• deci/decicoder-1b
• meta-llama/llama-2-7b-hf
• mistralai/Mistral-7B-Instruct-v0.1, mistralai/Mistral-7B-Instruct-v0.2
• All fine-tuned variants of the above (ex: the instruct variants like Deci/DeciLM-7B-instruct).
• More models coming soon... (Mixtral, Falcon, MPT etc)

Supported GPUs

• Compute capability >= 8.0 (e.g. A100, A10, L4, ...). See full list here
  
• Memory requirements depends on the model size. For example:

1. DeciLM-7B - at least 24G.
2. DeciCoder-1B - 16G is more than enough.

Installation

Prerequisites

Before you begin, in order to use InferyLLM you need some details from Deci.ai:

1. Artifactory credentials (referred to as ARTIFACTORY USER and ARTIFACTORY TOKEN) in order to download and update the package in the future.
2. Authentication token for running the server. This will be referred to as INFERY_LLM_DECI_TOKEN below.

Then, ensure you have met the following system requirements:

• General requirements:
  • Python >= 3.11
  • CUDA ToolKit >= 12.1
• For local serving:
  • GLIBC >= 2.31
  • GCC, G++ >= 11.3
  • gcc, g++ and nvcc in your $PATH at the time of installation
• For containerized serving:
  • nvidia-container-runtime >= 1.13.4

Installing locally

InferyLLM may be used with a lean (client-only) installation or a full (client+server) installation.

Client Installation

# Install InferyLLM (along with LLMClient)
pip install --extra-index-url=https://[ARTIFACTORY USER]:[ARTIFACTORY TOKEN]@deci.jfrog.io/artifactory/api/pypi/deciExternal/simple infery-llm

Server Installation

# Install InferyLLM Server
INFERY_LLM_DECI_TOKEN=[DECI TOKEN] infery-llm install

For a more thorough explanation, please refer to the Advanced Usage and check out the install CLI command.

Pulling the InferyLLM container

To pull an InferyLLM container from Deci's container registry:

# Log in to Deci's container registry
docker login --username [ARTIFACTORY USER] --password [ARTIFACTORY TOKEN] deci.jfrog.io

# Pull the container. You may be specify a version instead of "latest" (e.g. 0.0.7)
docker pull deci.jfrog.io/deci-external-docker-local/infery-llm:latest

Serving

Note: remember you need an authentication token to run the server (INFERY_LLM_DECI_TOKEN)

There are two ways to serve an LLM with InferyLLM:

1. Through a local entrypoint
2. By using the InferyLLM container

By default, InferyLLM serves at 0.0.0.0:8080 this is configurable through passing the --host and --port flags.

Serving with a container (suggested)

Assuming you have pulled the container as shown in the Installation section, running the server is a simple one-liner. You can also use the container to query the serving CLI help for all available serving flags and defaults:

Note: In order to mount any local models already downloaded to the local machine add -v </path/to/local/model>:</path/to/container/destination>.
In the next example, the ~/.cache/deci/ directory will be mounted to /deci/ inside the container.

# Serve Deci/DeciLM-6b (from HF hub) on port 9000
docker run --runtime=nvidia -e INFERY_LLM_DECI_TOKEN=[DECI TOKEN] -v ~/.cache/deci/:/deci/ -p 9000:9000 deci.jfrog.io/deci-external-docker-local/infery-llm:[VERSION TAG] --model-name Deci/DeciLM-6b --port 9000

# Serve Mistral-7B. Set the Max Batch Size to 16 and limit the Maximum Sequence (input + generation) to 2048
docker run --runtime=nvidia -e INFERY_LLM_DECI_TOKEN=[DECI TOKEN] -v ~/.cache/deci/:/deci/ -p 9000:9000 deci.jfrog.io/deci-external-docker-local/infery-llm:[VERSION TAG] --model-name mistralai/Mistral-7B-Instruct-v0.2 --max-seq-length 2048 --max-batch-size 16 

# See all serving CLI options and defaults
docker run --rm --runtime=nvidia -e INFERY_LLM_DECI_TOKEN=[DECI TOKEN] deci.jfrog.io/deci-external-docker-local/infery-llm:[VERSION TAG] --help

Notice that a HuggingFace token may be passed as an environment variable (using the docker -e flag) or as a CLI parameter

Serving with a local entry point

Assuming you have installed the infery-llm local serving requirements, you may use the InferyLLM CLI as a server entrypoint:

# Serve Deci/DeciLM-7b (from HF hub) on port 9000
infery-llm serve --model-name Deci/DeciLM-7b --port 9000

# See all serving options
infery-llm serve --help

Generation

Assuming you have a running server listening at 0.0.0.0:9000, you may submit generation requests in two ways:

1. Through InferyLLM's LLMClient:

import asyncio
from infery_llm.client import LLMClient, GenerationParams

client = LLMClient("http://0.0.0.0:9000")

# Prepare GenerationParams (max_new_tokens, temperature, top_p, top_k, stop_tokens, ...)
gen_params = GenerationParams(max_new_tokens=50, top_p=0.95, top_k=100, temperature=0.1)

# Submit a single prompt and query results (along with metadata in this case)
result = client.generate("Large language models are ", generation_params=gen_params, return_metadata=True)
print(f"Output: {result.output}.\nGenerated Tokens :{result.metadata[0]['generated_token_count']}")

# Submit a batch of prompts (a list of results will be returned this time)
prompts = ["A receipe for making spaghetti: ", "5 interesting facts about the President of France are: ", "Write a short story about a dog named Snoopy: "]
result = client.generate(prompts, generation_params=gen_params)
[print(f"Prompt: {output['prompt']}\nGeneration: {output['output']}") for output in result.outputs]

# Use stop tokens
gen_params = GenerationParams(stop_str_tokens=[1524], stop_strs=["add tomatoes"], skip_special_tokens=True)
result = client.generate("A receipe for making spaghetti: ", generation_params=gen_params)

# Stream results
for text in client.generate("Will the real Slim Shady please ", generation_params=gen_params, stream=True):
    print(text, end="")

# Async generation is also supported from within async code:
async def example():
    result = await client.generate_async("AsyncIO is fun because ", generation_params=gen_params)
    print(result.output)
asyncio.run(example())

2. Through a curl command (assuming you have cURL installed)

curl -X POST http://0.0.0.0:9000/generate \
-H 'Content-Type: application/json' \
-d '{"prompts":["def factorial(n: int) -> int:"], "generation_params":{"max_new_tokens": 500, "temperature":0.5, "top_k":50, "top_p":0.8}, "stream":true}'

Advanced Usage

CLI

InferyLLM and its CLI are rapidly accumulating more features. For example, the infery-llm CLI already allows to benchmark with numerous configurations, to prepare model artifacts before serving in order to cut down loading time, and more. To see the available features you may simply pass --help to the infery-llm CLI or any of its subcommands:

For container users:

# Query infery-llm CLI help menu
docker run --entrypoint infery-llm --runtime=nvidia deci.jfrog.io/deci-external-docker-local/infery-llm:latest --help

# Query the infery-llm CLI's `benchmark` subcommand help menu
docker run --entrypoint infery-llm --runtime=nvidia deci.jfrog.io/deci-external-docker-local/infery-llm:latest benchmark --help

For local installation users:

# Query infery-llm CLI help menu
infery-llm --help

# Query the infery-llm CLI's `benchmark` subcommand help menu
infery-llm benchmark --help

Lowering loading time (the prepare command)

InferyLLM has its own internal format and per-model artifact requirements. While the required artifacts are automatically generated by the infery-llm serve command, you can also generate them ahead of time with the infery-llm prepare command, thus drastically cutting down server start-time.

For container users:

# Create artifacts for serving a Deci/DeciCoder-1b and place the result in ~ on the host machine
docker run --rm --entrypoint infery-llm -v ~/:/models --runtime=nvidia deci.jfrog.io/deci-external-docker-local/infery-llm:latest prepare --hf-model Deci/DeciCoder-1b --output-dir /models/infery_llm_model

# Now serve the created artifact (specifically here on port 9000)
docker run --runtime=nvidia -e INFERY_LLM_DECI_TOKEN=[DECI TOKEN] -p 9000:9000 -v ~/:/models deci.jfrog.io/deci-external-docker-local/infery-llm:latest --infery-model-dir /models/infery_llm_model --port 9000

For local installation users:

# Create artifacts for serving a Deci/DeciCoder-1b and place the result in ~ on the host machine
infery-llm prepare --hf-model Deci/DeciCoder-1b --output-dir /models/infery_llm_model

# Now serve the created artifact (specifically here on port 9000)
infery-llm serve --infery-model-dir /models/infery_llm_model --port 9000

Important note on caching: Just like 🤗 caches downloaded model weights in ~/.cache/huggingface, InferyLLM caches the mentioned artifacts in ~/.cache/deci. It does so for every model served. This means that relaunching a local or containerized server (if it is the same container, not just the same image) will automatically lower the loading time.

Benchmarking

By using InferyLLM's benchmark cli, you can test different combinations of parameters of serving parameters for your different models. Test different batch sizes, sequence lengths, max generated tokens, and more. Each GPU and memory size will also change the performance.

Starting a new project with benchmark should help you find the correct combination for you model deployment.

The metrics benchmark will output:

1. E2E Time - time it took to process the number of requests that were sent
2. E2E Throughput - How many tokens/s were generated
3. Mean Latency - the mean latency for 1 request

Below are some more examples for benchmarking different models with several serving configurations:

Benchmarking on an A10 GPU

In order to use benchmark cli, you need a json file with a list of prompts, in the following format:
[{"prompt": "Your prompt here..", "max_new_tokens": 512}, {"prompt": "Your second prompt..", "max_new_tokens": 256}, {"prompt": "Your 3rd prompt"}].

In the next benchmarking example, lets compare Mistral7B and DeciLM7B on a batch size of 16 with sequence lengths that aren't above 1024 tokens (larger prompts will be skipped, and not tested). The prompts file will be saved at /prompts.json, the local prepared models are saved at /models/Mistral-7B and /models/DeciLM-7B.
The test will run on a A10 GPU instance with 24GB of memory:

$ docker run --entrypoint infery-llm --runtime=nvidia -v /prompts.json:/prompts.json  -v /models/:/models/  deci.jfrog.io/deci-external-docker-local/infery-llm:0.0.8 benchmark --infery-model-dir /models/DeciLM-7B --num-prompts 100 --prompts-json /prompts.json --verbose --max-batch-size 16 --input-seq-len=1024 --verbose
INFO: 2024-04-11 08:21:46,272 - INFERY_LLM - Found autotune results (/models/DeciLM-7B/autotune_benchmarks.pkl)
Completed warmup requests: 100%|██████████| 1/1 [00:20<00:00, 20.51s/it]
Completed Requests: 100%|██████████| 100/100 [03:37<00:00,  2.18s/it]
INFO: 2024-04-11 08:25:45,044 - INFERY_LLM - 
============================== BENCHMARK SUMMARY ==============================
PARAMETERS:
	Num Prompts:       100
	Input Tokens:      1025
	Generated Tokens:  512
	Max Batch Size:    16
	Block Size:        Model Config Default
RESULTS:
	E2E Time:          163.709 [s]
	E2E Throughput:    312.751 [Tokens/s]
	Mean Latency:      1.837 [s]
===============================================================================

We can see that the latency mean of 1 request is 1.837 seconds for this combination.
Now let's test Mistral7B:

$ docker run --entrypoint infery-llm --runtime=nvidia -v /prompts.json:/prompts.json  -v /models/:/models/  deci.jfrog.io/deci-external-docker-local/infery-llm:0.0.8 benchmark --infery-model-dir /models/Mistral-7B --num-prompts 100 --prompts-json /prompts.json --verbose --max-batch-size 16 --input-seq-len=1024 --verbose
INFO: 2024-04-11 09:21:46,272 - INFERY_LLM - Found autotune results (/models/Mistral-7B/autotune_benchmarks.pkl)
Completed warmup requests: 100%|██████████| 1/1 [00:20<00:00, 20.51s/it]
Completed Requests: 100%|██████████| 100/100 [03:37<00:00,  2.18s/it]
INFO: 2024-04-11 09:25:45,044 - INFERY_LLM - 
============================== BENCHMARK SUMMARY ==============================
PARAMETERS:
	Num Prompts:       100
	Input Tokens:      1025
	Generated Tokens:  512
	Max Batch Size:    16
	Block Size:        Model Config Default
RESULTS:
	E2E Time:          217.893 [s]
	E2E Throughput:    234.977 [Tokens/s]
	Mean Latency:      2.706 [s]
===============================================================================

The Tokens per second for batch-size=16 and sequence-len=1024 was 234 TKS.
We now know that DeciLM-7B is 1.5X faster than Mistral-7B in the exact same setup and has 33% better throughput!

What will be the result for Mistral7B if our sequences are much longer (2048), but we use a smaller batch size (8):

$ docker run --entrypoint infery-llm --runtime=nvidia -v /prompts.json:/prompts.json  -v /models/:/models/  deci.jfrog.io/deci-external-docker-local/infery-llm:0.0.8 benchmark --infery-model-dir /models/Mistral-7B --num-prompts 100 --prompts-json /prompts.json --verbose --max-batch-size 8 --input-seq-len=2048 --verbose
INFO: 2024-04-11 09:16:46,272 - INFERY_LLM - Found autotune results (/models/Mistral-7B/autotune_benchmarks.pkl)
Completed warmup requests: 100%|██████████| 1/1 [00:20<00:00, 20.51s/it]
Completed Requests: 100%|██████████| 100/100 [03:37<00:00,  2.18s/it]
INFO: 2024-04-11 09:20:45,044 - INFERY_LLM - 
============================== BENCHMARK SUMMARY ==============================
PARAMETERS:
	Num Prompts:       100
	Input Tokens:      1501
	Generated Tokens:  512
	Max Batch Size:    8
	Block Size:        Model Config Default
RESULTS:
	E2E Time:          330.644 [s]
	E2E Throughput:    154.849 [Tokens/s]
	Mean Latency:      3.306 [s]
===============================================================================

We can see that the total time was longer than before, and that the Mean Latency is longer by almost ~1 second.