README.md

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# Multimodal Deployment Examples

This directory provides example workflows and reference implementations for deploying a multimodal model using Dynamo.
The examples are based on the [llava-1.5-7b-hf](https://huggingface.co/llava-hf/llava-1.5-7b-hf) model.

## Multimodal Aggregated Serving

### Components

- workers: For aggregated serving, we have two workers, [encode_worker](components/encode_worker.py) for encoding and [vllm_worker](components/worker.py) for prefilling and decoding.
- processor: Tokenizes the prompt and passes it to the vllm worker.
- frontend: Http endpoint to handle incoming requests.

### Deployment

In this deployment, we have two workers, [encode_worker](components/encode_worker.py) and [vllm_worker](components/worker.py).
The encode worker is responsible for encoding the image and passing the embeddings to the vllm worker via NATS.
The vllm worker then prefills and decodes the prompt, just like the [LLM aggregated serving](../llm/README.md) example.
By separating the encode from the prefill and decode stages, we can have a more flexible deployment and scale the
encode worker independently from the prefill and decode workers if needed.

This figure shows the flow of the deployment:
```

+------+      +-----------+      +------------------+      image url       +---------------+
| HTTP |----->| processor |----->|   vllm worker    |--------------------->| encode worker |
|      |<-----|           |<-----|                  |<---------------------|               |
+------+      +-----------+      +------------------+   image embeddings   +---------------+

```

```bash
cd $DYNAMO_HOME/examples/multimodal
dynamo serve graphs.agg:Frontend -f ./configs/agg.yaml
```

### Client

In another terminal:
```bash
curl http://localhost:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "llava-hf/llava-1.5-7b-hf",
    "messages": [
      {
        "role": "user",
        "content": [
          {
            "type": "text",
            "text": "What is in this image?"
          },
          {
            "type": "image_url",
            "image_url": {
              "url": "http://images.cocodataset.org/test2017/000000155781.jpg"
            }
          }
        ]
      }
    ],
    "max_tokens": 300,
    "stream": false
  }'
```

You should see a response similar to this:
```
{"id": "c37b946e-9e58-4d54-88c8-2dbd92c47b0c", "object": "chat.completion", "created": 1747725277, "model": "llava-hf/llava-1.5-7b-hf", "choices": [{"index": 0, "message": {"role": "assistant", "content": " In the image, there is a city bus parked on a street, with a street sign nearby on the right side. The bus appears to be stopped out of service. The setting is in a foggy city, giving it a slightly moody atmosphere."}, "finish_reason": "stop"}]}
```

## Multimodal Disaggregated serving

### Components

- workers: For disaggregated serving, we have three workers, [encode_worker](components/encode_worker.py) for encoding, [vllm_worker](components/worker.py) for decoding, and [prefill_worker](components/prefill_worker.py) for prefilling.
- processor: Tokenizes the prompt and passes it to the vllm worker.
- frontend: Http endpoint to handle incoming requests.

### Deployment

In this deployment, we have three workers, [encode_worker](components/encode_worker.py), [vllm_worker](components/worker.py), and [prefill_worker](components/prefill_worker.py).
For the Llava model, embeddings are only required during the prefill stage. As such, the encode worker is connected directly to the prefill worker.
The encode worker handles image encoding and transmits the resulting embeddings to the prefill worker via NATS.
The prefill worker performs the prefilling step and forwards the KV cache to the vllm worker for decoding.
For more details on the roles of the prefill and vllm workers, refer to the [LLM disaggregated serving](../llm/README.md) example.

This figure shows the flow of the deployment:
```

+------+      +-----------+      +------------------+      +------------------+      image url       +---------------+
| HTTP |----->| processor |----->|   vllm worker    |----->|  prefill worker  |--------------------->| encode worker |
|      |<-----|           |<-----|  (decode worker) |<-----|                  |<---------------------|               |
+------+      +-----------+      +------------------+      +------------------+   image embeddings   +---------------+

```


```bash
cd $DYNAMO_HOME/examples/multimodal
dynamo serve graphs.disagg:Frontend -f configs/disagg.yaml
```

### Client

In another terminal:
```bash
curl http://localhost:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "llava-hf/llava-1.5-7b-hf",
    "messages": [
      {
        "role": "user",
        "content": [
          {
            "type": "text",
            "text": "What is in this image?"
          },
          {
            "type": "image_url",
            "image_url": {
              "url": "http://images.cocodataset.org/test2017/000000155781.jpg"
            }
          }
        ]
      }
    ],
    "max_tokens": 300,
    "stream": false
  }'
```

You should see a response similar to this:
```
{"id": "c1774d61-3299-4aa3-bea1-a0af6c055ba8", "object": "chat.completion", "created": 1747725645, "model": "llava-hf/llava-1.5-7b-hf", "choices": [{"index": 0, "message": {"role": "assistant", "content": " This image shows a passenger bus traveling down the road near power lines and trees. The bus displays a sign that says \"OUT OF SERVICE\" on its front."}, "finish_reason": "stop"}]}
```