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# LLM Deployment Examples using TensorRT-LLM

This directory contains examples and reference implementations for deploying Large Language Models (LLMs) in various configurations using TensorRT-LLM.

## Use the Latest Release

We recommend using the latest stable release of dynamo to avoid breaking changes:

[![GitHub Release](https://img.shields.io/github/v/release/ai-dynamo/dynamo)](https://github.com/ai-dynamo/dynamo/releases/latest)

You can find the latest release [here](https://github.com/ai-dynamo/dynamo/releases/latest) and check out the corresponding branch with:

```bash
git checkout $(git describe --tags $(git rev-list --tags --max-count=1))
```

## Deployment Architectures

See [Deployment Architectures](llm_deployment.md#deployment-architectures) to learn about the general idea of the architecture.
Note that this TensorRT-LLM version does not support all the options yet.

```{note}
TensorRT-LLM disaggregation does not support conditional disaggregation yet. You can only configure the deployment to always use aggregate or disaggregated serving.
```

## Getting Started

1. Choose a deployment architecture based on your requirements
2. Configure the components as needed
3. Deploy using the provided scripts

### Prerequisites

Start required services (etcd and NATS) using [Docker Compose](../../deploy/metrics/docker-compose.yml)
```bash
docker compose -f deploy/docker-compose.yml up -d
```

### Build docker

#### Step 1: Build TensorRT-LLM base container image

Because of the known issue of C++11 ABI compatibility within the NGC pytorch container, we rebuild TensorRT-LLM from source.
See [here](https://nvidia.github.io/TensorRT-LLM/installation/linux.html) for more information.

Use the helper script to build a TensorRT-LLM container base image. The script uses a specific commit id from TensorRT-LLM main branch.

```bash
# TensorRT-LLM uses git-lfs, which needs to be installed in advance.
apt-get update && apt-get -y install git git-lfs

# The script uses python packages like docker-squash to squash image
# layers within trtllm base image
DEBIAN_FRONTEND=noninteractive TZ=America/Los_Angeles apt-get -y install python3 python3-pip python3-venv

./container/build_trtllm_base_image.sh
```

For more information see [here](https://nvidia.github.io/TensorRT-LLM/installation/build-from-source-linux.html#option-1-build-tensorrt-llm-in-one-step) for more details on building from source.
If you already have a TensorRT-LLM container image, you can skip this step.

#### Step 2: Build the Dynamo container

```
# On an x86 machine:
./container/build.sh --framework tensorrtllm

# On an ARM machine:
./container/build.sh --framework tensorrtllm --platform linux/arm64
```

This build script internally points to the base container image built with step 1. If you skipped previous step because you already have the container image available, you can run the build script with that image as a base.


```bash
# Build dynamo image with other TRTLLM base image.
./container/build.sh --framework TENSORRTLLM --base-image <trtllm-base-image> --base-image-tag <trtllm-base-image-tag>
```

### Run container

```
./container/run.sh --framework tensorrtllm -it
```
## Run Deployment

This figure shows an overview of the major components to deploy:



```

+------+      +-----------+      +------------------+             +---------------+
| HTTP |----->| processor |----->|      Worker      |------------>|     Prefill   |
|      |<-----|           |<-----|                  |<------------|     Worker    |
+------+      +-----------+      +------------------+             +---------------+
                  |    ^                  |
       query best |    | return           | publish kv events
           worker |    | worker_id        v
                  |    |         +------------------+
                  |    +---------|     kv-router    |
                  +------------->|                  |
                                 +------------------+

```

Note: The above architecture illustrates all the components. The final components
that get spawned depend upon the chosen graph.

### Example architectures

#### Aggregated serving
```bash
cd /workspace/examples/tensorrt_llm
dynamo serve graphs.agg:Frontend -f ./configs/agg.yaml
```

#### Aggregated serving with KV Routing
```bash
cd /workspace/examples/tensorrt_llm
dynamo serve graphs.agg_router:Frontend -f ./configs/agg_router.yaml
```

#### Disaggregated serving
```bash
cd /workspace/examples/tensorrt_llm
dynamo serve graphs.disagg:Frontend -f ./configs/disagg.yaml
```

We are defining TRTLLM_USE_UCX_KVCACHE so that TRTLLM uses UCX for transfering the KV
cache between the context and generation workers.

#### Disaggregated serving with KV Routing
```bash
cd /workspace/examples/tensorrt_llm
dynamo serve graphs.disagg_router:Frontend -f ./configs/disagg_router.yaml
```

We are defining TRTLLM_USE_UCX_KVCACHE so that TRTLLM uses UCX for transfering the KV
cache between the context and generation workers.

### Client

See [client](llm_deployment.md#client) section to learn how to send request to the deployment.

### Close deployment

See [close deployment](../../docs/guides/dynamo_serve.md#close-deployment) section to learn about how to close the deployment.

Remaining tasks:

- [x] Add support for the disaggregated serving.
- [ ] Add integration test coverage.
- [ ] Add instructions for benchmarking.
- [ ] Add multi-node support.
- [ ] Merge the code base with llm example to reduce the code duplication.
- [ ] Use processor from dynamo-llm framework.
- [ ] Enable NIXL integration with TensorRT-LLM once available. Currently, TensorRT-LLM uses UCX to transfer KV cache.
