Refactor(PrefixCache): New load API, per-layer Tries, async ops & stats

jetstream/core/orchestrator.py

@@ -730,32 +730,18 @@ def _do_chunked_prefill_with_prefix_cache(
     chunk_size = prefill_engine.prefill_chunk_size
 
     # 1. Load the longest possible prefix from the cache
-    load_result = prefix_cache.load_existing_prefix(
-        self._prefix_cache, tuple_tokens, chunk_size
+    existing_prefix, remain_tokens = (
+        prefix_cache.load_existing_prefix_and_get_remain_tokens(
+            self._prefix_cache, tokens, chunk_size
+        )
     )
 
-    existing_prefix = None
-    remain_tokens = tokens  # Assume full prefill initially
-    original_common_prefix_len = 0
-
-    if load_result:
-      existing_prefix, original_common_prefix_len = load_result
-      # Calculate the tokens that still need to be prefilled
-      # common_prefix_tokens is already truncated to chunk_size multiple
-      # and ensures at least one token remains.
-      truncated_len = existing_prefix.common_prefix_tokens.shape[0]
-      remain_tokens = tokens[truncated_len:]
+    if existing_prefix is not None:
       logger.debug(
-          "Prefix cache hit. Original common length: %d, Truncated length: %d,"
-          " Remaining tokens to prefill: %d",
-          original_common_prefix_len,
-          truncated_len,
+          "Prefix cache hit length: %d, Remaining tokens len to prefill: %d",
+          len(existing_prefix.common_prefix_tokens),
           len(remain_tokens),
       )
-    else:
-      logger.debug(
-          "Prefix cache miss or prefix too short. Prefilling all tokens."
-      )
 
     # 2. Perform chunked prefill on the remaining tokens
     prefill_result, first_token = self._do_chunked_prefill(