Add prefix cache plugin configuration guide

mkdocs.yml

@@ -68,6 +68,8 @@ nav:
         - Adapter Rollout: guides/adapter-rollout.md
         - InferencePool Rollout: guides/inferencepool-rollout.md
       - Metrics: guides/metrics.md
+      - Configuration Guide:
+          - Prefix Cache Aware Plugin: guides/epp-configuration/prefix-aware.md
     - Implementer's Guide: guides/implementers.md
   - Performance:
     - Benchmark: performance/benchmark/index.md

site-src/guides/epp-configuration/prefix-aware.md

@@ -0,0 +1,89 @@
+# Prefix Cache Aware Plugin Configuration
+
+The [prefix cache plugin](https://github.com/kubernetes-sigs/gateway-api-inference-extension/blob/7617439188b410670ed0f1ff805a3b7f9918a75b/pkg/epp/scheduling/framework/plugins/multi/prefix/plugin.go#L63)
+takes advantage of the prefix caching (e.g., [vllm APC](https://docs.vllm.ai/en/latest/features/automatic_prefix_caching.html))
+feature of model servers, and optimizes request scheduling by placing requests sharing the longest
+prefixes to the same server as much as possible, while balancing the server load by considering kv-cache
+and queue depth. 
+
+## Enable the prefix cache plugin
+
+Currently prefix cache aware plugin is implemented in the V2 scheduler as an experimental feature.
+To enable it, set the following environment variables when starting the EndpointPicker(EPP).
+
+```
+EXPERIMENTAL_USE_SCHEDULER_V2: true
+ENABLE_PREFIX_CACHE_SCHEDULING: true
+```
+
+See the [Use Helm section](#helm) to install an inferencepool with the environment variables.
+
+
+## Customize the prefix cache plugin
+
+The prefix cache plugin exposes the following advanced configuration options via environment variables:
+
+* `PREFIX_CACHE_HASH_BLOCK_SIZE`: The plugin matches prefixes in the unit of blocks. This is the size
+of each block in number of bytes. vLLM default block size is 16 tokens. Assume 4 characters per token, the default
+is set to 64 in EPP. The default is recommended unless performance is critical for use cases with
+extremely long inputs.
+
+* `PREFIX_CACHE_MAX_PREFIX_BLOCKS`: The maximum number of blocks to find prefix match. The default is
+128 (or 128*64=8192 characters, or roughly 2048 tokens). This is useful to tradeoff prefix match accuracy
+for performance.
+
+* `PREFIX_CACHE_LRU_CAPACITY`: Maximum capacity the prefix LRU indexer in number of block hashes. Below
+shows a detailed analysis on how to estimate this.
+
+    The prefix cache plugin estimates the prefix cache indexes in model server HBMs.  In the perfect
+    scenario, EPP has the exact same prefix cache entries per model server as their HBM cache entries. If
+    the EPP cache is smaller than HBM cache, a positive EPP cache match is more accurate, but there are more
+    false cache misses. If the EPP cache is larger than the HBM cache, then there are more false cache hits.
+    Therefore **the EPP prefix cache indexer size should be as close as possible to the HBM cache size.**
+
+    NOTE: EPP builds prefix cache based on characters, while model server maintains prefix cache entries 
+    in tokens, a conversion between character <-> token is needed.
+
+    Below are the formulas to estimate the EPP prefix indexer size:
+
+    ```
+    max_kv_tokens_per_server = (HBM_size - model_size)/ kv_size_per_token
+    lru_indexer_capacity_per_server = (max_kv_tokens_per_server * avg_chars_per_token)/prefix_indexer_hash_block_size
+    lru_indexer_capacity_total = max_num_servers * lru_indexer_capacity_per_server
+    ```
+
+    Let's take an example:
+
+    * Model: llama3 8B
+    * Accelerator: Nvidia H100 80GB
+    * Num replicas: 3
+    * Estimated # characters per token: 4 ([source](https://genai.stackexchange.com/questions/34/how-long-is-a-token))
+
+    ```
+    max_kv_tokens_per_server = (80GB - 16GB) / 128KB = 500,000
+    # assume avg_chars_per_token = 4, prefix_indexer_hash_block_size = 64 (default)
+    # each entry is about 358KB, so the memory footrpint is abut 11 MB per server
+    lru_indexer_capacity_per_server = 500,000*4/64 = 31250 
+    lru_indexer_capacity_total = 3 * 31250 = 93750
+    ```
+
+See the [Use Helm section](#helm) to install an inferencepool with the environment variables.
+
+
+<a id="helm"></a>
+## Use Helm
+
+Use the following reference command to install an inferencepool with the prefix
+cache plugin environment variable configurations:
+
+```txt
+$ helm install triton-llama3-8b-instruct \
+  --set inferencePool.modelServers.matchLabels.app=triton-llama3-8b-instruct \
+  --set inferencePool.modelServerType=triton-tensorrt-llm \
+  --set provider.name=[none|gke] \
+  --set inferenceExtension.env.EXPERIMENTAL_USE_SCHEDULER_V2=true \
+  --set inferenceExtension.env.ENABLE_PREFIX_CACHE_SCHEDULING=true \
+  --set inferenceExtension.env.PREFIX_CACHE_LRU_CAPACITY=93750 \
+  --set inferenceExtension.env.PREFIX_CACHE_MAX_PREFIX_BLOCKS=1024 \
+  oci://us-central1-docker.pkg.dev/k8s-staging-images/gateway-api-inference-extension/charts/inferencepool --version v0
+```