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whoisj merged 1 commit into from
May 29, 2025
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docs: Update Multimodal Example README #1275

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examples/multimodal: update README 
This change corrects the README.md file in the examples/multimodal folder:
- Correct "vllm worker" to "decode worker"
- Correct assertion that data is moved via NATS when embeddings are moved via RDMA.

Additionally, this change updates the textual graphs with Mermaid graphs for improved presentation on github.com.
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@whoisj
whoisj committed May 29, 2025
commit 0c4a230cd6bf8d70c13a126d10d7c38b824b4e30
144 changes: 75 additions & 69 deletions examples/multimodal/README.md
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Expand Up @@ -24,26 +24,29 @@ The examples are based on the [llava-1.5-7b-hf](https://huggingface.co/llava-hf/

### 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.
- workers: For aggregated serving, we have two workers, [encode_worker](components/encode_worker.py) for encoding and [decode_worker](components/decode_worker.py) for prefilling and decoding.
- processor: Tokenizes the prompt and passes it to the decode 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.
In this deployment, we have two workers, [encode_worker](components/encode_worker.py) and [decode_worker](components/decode_worker.py).
The encode worker is responsible for encoding the image and passing the embeddings to the decode worker via a combination of NATS and RDMA.
The work complete event is sent via NATS, while the embeddings tensor is transferred via RDMA through the NIXL interface.
Its decode 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:
```mermaid
flowchart LR
HTTP --> processor
processor --> HTTP
processor --> decode_worker
decode_worker --> processor
decode_worker --image_url--> encode_worker
encode_worker --embeddings--> decode_worker
```

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

```

```bash
Expand All @@ -58,61 +61,64 @@ In another terminal:
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"
"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
}'
]
}
],
"max_tokens": 300,
"stream": false
}'
```

You should see a response similar to this:
```
```json
{"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.
- workers: For disaggregated serving, we have three workers, [encode_worker](components/encode_worker.py) for encoding, [decode_worker](components/decode_worker.py) for decoding, and [prefill_worker](components/prefill_worker.py) for prefilling.
- processor: Tokenizes the prompt and passes it to the decode 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).
In this deployment, we have three workers, [encode_worker](components/encode_worker.py), [decode_worker](components/decode_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.
The encode worker is responsible for encoding the image and passing the embeddings to the prefill worker via a combination of NATS and RDMA.
Its work complete event is sent via NATS, while the embeddings tensor is transferred via RDMA through the NIXL interface.
The prefill worker performs the prefilling step and forwards the KV cache to the decode worker for decoding.
For more details on the roles of the prefill and decode workers, refer to the [LLM disaggregated serving](../llm/README.md) example.

This figure shows the flow of the deployment:
```mermaid
flowchart LR
HTTP --> processor
processor --> HTTP
processor --> decode_worker
decode_worker --> processor
decode_worker --> prefill_worker
prefill_worker --> decode_worker
prefill_worker --image_url--> encode_worker
encode_worker --embeddings--> prefill_worker
```

+------+ +-----------+ +------------------+ +------------------+ 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
Expand All @@ -125,30 +131,30 @@ In another terminal:
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"
"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
}'
]
}
],
"max_tokens": 300,
"stream": false
}'
```

You should see a response similar to this:
```
```json
{"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"}]}
```
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