Add model server protocol proposal

docs/proposals/003-model-server-protocol/protocol.md

@@ -0,0 +1,106 @@
+# Model Server Protocol for Gateway API Inference Extension
+
+## Inference API Protocol
+
+The model server MUST implement OpenAI’s [Completions](https://platform.openai.com/docs/api-reference/completions)
+and [Chat](https://platform.openai.com/docs/api-reference/chat) API. In the future we are open to
+supporting more API protocols.
+
+<details>
+<summary>Why?</summary>
+The extension makes intelligent request scheduling decisions based on certain information from the
+request body, such as the `model` field.
+</details>
+
+## Metrics Reporting
+
+The inference extension scrapes metrics from the model servers to make optimal request scheduling
+decisions. The model servers SHOULD provide the following metrics via a Prometheus endpoint. While
+the metric names may differ slightly in different model servers, the metric types MUST be the same.
+We will align with the
+[model server metrics standardization](https://docs.google.com/document/d/1SpSp1E6moa4HSrJnS4x3NpLuj88sMXr2tbofKlzTZpk)
+effort.
+
+We also show the metrics in vLLM, which is already integrated into the inference extension. We will
+add more model server once they are integrated.
+
+| Metric | Type | Description | vLLM metric |
+| ----- | ---- | ---- | ---- |
+| TotalQueuedRequests         | Gauge     | The current total number of requests in the queue.| `vllm:num_requests_waiting`|
+| KVCacheUtilization| Gauge     | The current KV cache utilization in percentage.| `vllm:gpu_cache_usage_perc`|
+
+
+### Future Metrics
+The following metrics MAY be needed in the future for further optimization.
+
+| Metric |Type | Description | vLLM   metric |
+| ----- | ---- | ---- | ---- |
+| TotalTokensInCurrentBatch   | Gauge     | Number of tokens in the current batch.| `vllm:num_tokens_running`|
+| TotalQueuedTokens| Gauge     | The current total number of tokens in the queued requests.| `vllm:num_tokens_waiting` (need to be added)|
+| MaxTokenCapacity| Gauge     | The total size of the KV cache in number of tokens.| `vllm:max_token_capacity` <br> NOTE: This info is available indirectly in [`cache_config_info`](https://github.com/vllm-project/vllm/blob/15702038642192002cd8973cf8948751b750fd07/vllm/engine/metrics.py#L551) metric already , and also  proposed in [model server metrics standardization](https://docs.google.com/document/d/1SpSp1E6moa4HSrJnS4x3NpLuj88sMXr2tbofKlzTZpk). |
+| TimePerPrefillToken | Histogram | The prefill latency per token in the last W seconds. W will be decided by simulation/benchmarking.  In time series metric the latency is typically reported as Histogram and we can derive the average from the Histogram. | `vllm:time_to_first_token_seconds` | 
+| TimePerDecodeToken | Histogram | The decode latency per token in the last W seconds. W will be decided by simulation/benchmarking. | `vllm:time_per_output_token_seconds` | 
+
+## LoRA Adapter Serving
+
+### Dynamic LoRA Serving
+
+Model servers that support dynamic LoRA serving can gain additional benefit from the inference
+extension's LoRA affinity algorithm. While dynamic LoRA serving is quite new and evolving, and there
+is no common standard, the inference extension generally expects the following behavior.
+
+* Support running multiple LoRA adapters in parallel in the same decode batch.
+* Dynamically load/unload adapters in GPU memory from/to a cahe (e.g., in host memory) depending on
+  the requested adapters in the current batch.
+
+The model server SHOULD expose the following information via an API:
+
+* AdapterConfig 
+  * LoRAEnabled: Whether dynamic LoRA serving is enabled.
+  *  MaxActiveAdapter: Maximum number of adapters that can be loaded to GPU memory to serve a batch.
+  Requests will be queued if the model server has reached MaxActiveAdapter and cannot load the
+  requested adapter. In vLLM, this is currently exposed as a string label `max_lora` in the
+  `vllm:lora_requests_info` metric.
+* AdapterState
+  * ActiveAdapters: A list of adapters that are currently loaded in GPU memory and ready to servce
+  requests. In vLLM, this is currently exposed as a comma separated string label `running_lora_adapters`
+  in the `vllm:lora_requests_info` metric.
+
+The API MAY look like this:
+```
+GET ${server_endpoint}/adapters/info
+```
+
+And the response MAY look like this:
+```
+{
+    "config": {
+        "enabled": true,
+        "maxActiveAdapters": 4,
+    },
+    "state": {
+        "activeAdapters": ["adapter1", "adapter2"]
+    }
+}
+```
+
+#### Dynamically Register/Unregister Adapters
+
+Model servers SHOULD have APIs to dynamically register/unregister models (usually LoRA adapters).
+This enables platform teams to multiplex multiple LoRA adapters on shared model servers and
+dynamically rollout LoRA adapters. 
+
+NOTE this is not a strict requirement from the inference extension, but a critical feature for CI/CD
+integration.
+
+While we don’t intend to dictate how model servers should implement this API, a reference REST API
+MAY look this:
+
+```
+POST ${server_endpoint}/adapters/{adapter-id}
+{
+        "path": "path/to/my/adapter"
+}
+
+DELETE ${server_endpoint}/adapters/{adapter-id}
+```