Refactor SchedulePolicy to improve code organization

python/sglang/srt/managers/schedule_policy.py

@@ -18,7 +18,7 @@
 from collections import defaultdict
 from contextlib import contextmanager
 from enum import Enum, auto
-from typing import Dict, List, Optional
+from typing import Dict, List, Optional, Set, Union
 
 import torch
 
@@ -50,124 +50,193 @@
 )
 
 
+class CacheAwarePolicy(Enum):
+    """Scheduling policies that are aware of the tree cache."""
+
+    LPM = "lpm"  # longest prefix match
+    DFS_WEIGHT = "dfs-weight"  # depth-first search weighting
+
+
+class CacheAgnosticPolicy(Enum):
+    """Scheduling policies that are not aware of the tree cache."""
+
+    FCFS = "fcfs"  # first come first serve
+    LOF = "lof"  # longest output first
+    RANDOM = "random"
+
+
 class SchedulePolicy:
-    def __init__(self, policy: str, tree_cache: BasePrefixCache):
-        if tree_cache.disable and policy in ["lpm", "dfs-weight"]:
-            # LPM and DFS-weight is meaningless when the tree cache is disabled.
-            policy = "fcfs"
+    Policy = Union[CacheAwarePolicy, CacheAgnosticPolicy]
 
-        self.policy = policy
+    def __init__(self, policy: str, tree_cache: BasePrefixCache):
+        self.policy = self._validate_and_adjust_policy(policy, tree_cache)
         self.tree_cache = tree_cache
 
         # It is used to find the matching prefix for in-batch prefix caching.
         self.waiting_queue_radix_tree = RadixCache(
             req_to_token_pool=None, token_to_kv_pool=None, disable=False
         )
 
-    def calc_priority(self, waiting_queue: List[Req]):
-        if len(waiting_queue) > 128 and self.policy == "lpm":
-            # Turn off the expensive prefix matching and sorting when the #queue is large.
-            policy = "fcfs"
-        else:
-            policy = self.policy
+    def calc_priority(self, waiting_queue: List[Req]) -> bool:
+        policy = self._determine_active_policy(waiting_queue)
 
-        # Compute matched prefix length
         prefix_computed = False
-        if policy == "lpm" or policy == "dfs-weight":
-            # rid to deprioritize in the current run for in-batch prefix caching.
-            temporary_deprioritized = set()
-            self.waiting_queue_radix_tree.reset()
-
-            for r in waiting_queue:
-                prefix_ids = r.adjust_max_prefix_ids()
-
-                # NOTE: the prefix_indices must always be aligned with last_node
-                r.prefix_indices, r.last_node = self.tree_cache.match_prefix(
-                    rid=r.rid, key=prefix_ids
+        if isinstance(policy, CacheAwarePolicy):
+            prefix_computed = True
+            temporary_deprioritized = self._compute_prefix_matches(
+                waiting_queue, policy
+            )
+            if policy == CacheAwarePolicy.LPM:
+                SchedulePolicy._sort_by_longest_prefix(
+                    waiting_queue, temporary_deprioritized
                 )
+            elif policy == CacheAwarePolicy.DFS_WEIGHT:
+                SchedulePolicy._sort_by_dfs_weight(waiting_queue, self.tree_cache)
+            else:
+                raise ValueError(f"Unknown CacheAware Policy: {policy=}")
+        else:
+            if policy == CacheAgnosticPolicy.FCFS:
+                pass
+            elif policy == CacheAgnosticPolicy.LOF:
+                SchedulePolicy._sort_by_longest_output(waiting_queue)
+            elif policy == CacheAgnosticPolicy.RANDOM:
+                SchedulePolicy._sort_randomly(waiting_queue)
+            else:
+                raise ValueError(f"Unknown CacheAgnostic Policy: {policy=}")
 
-                # NOTE(sang): This logic is for in-batch prefix caching;
-                # If there are more than 1 request that have small matching prefix from
-                # existing cache, but all those requests share the same prefix, we prefer
-                # to schedule only one of them so that we can increase the cache hit rate.
-                # We prefer to set IN_BATCH_PREFIX_CACHING_CHECK_THRESHOLD > 0 because too small
-                # threshold means we cannot use in-batch prefix caching for short prefixes.
-                # It is kind of common when the engine is long running (e.g., imagine the prefix "the").
-                if len(r.prefix_indices) <= IN_BATCH_PREFIX_CACHING_CHECK_THRESHOLD:
-                    in_batch_matching_prefixes, _ = (
-                        self.waiting_queue_radix_tree.match_prefix(
-                            rid=r.rid, key=prefix_ids
-                        )
-                    )
-                    if (
-                        len(in_batch_matching_prefixes)
-                        >= IN_BATCH_PREFIX_CACHING_DEPRIORITIZE_THRESHOLD
-                    ):
-                        temporary_deprioritized.add(r.rid)
-                    else:
-                        # Insert with a dummy key
-                        self.waiting_queue_radix_tree.insert(
-                            prefix_ids, torch.empty(len(prefix_ids), dtype=torch.bool)
-                        )
+        return prefix_computed
 
-            prefix_computed = True
+    def _determine_active_policy(self, waiting_queue: List[Req]) -> Policy:
+        if len(waiting_queue) > 128 and self.policy == CacheAwarePolicy.LPM:
+            # Turn off the expensive prefix matching and sorting when the #queue is large.
+            return CacheAgnosticPolicy.FCFS
+        return self.policy
+
+    def _validate_and_adjust_policy(
+        self, policy: str, tree_cache: BasePrefixCache
+    ) -> Policy:
+        """
+        Validates the policy and adjusts it if necessary based on tree cache settings.
+        """
+        try:
+            policy_enum = CacheAwarePolicy(policy)
+            if tree_cache.disable:
+                # If tree_cache is disabled, using CacheAgnosticPolicy policy
+                return CacheAgnosticPolicy.FCFS
+            return policy_enum
+        except ValueError:
+            try:
+                return CacheAgnosticPolicy(policy)
+            except ValueError:
+                raise ValueError(f"Unknown schedule_policy: {policy=}")
+
+    def _compute_prefix_matches(
+        self, waiting_queue: List[Req], policy: CacheAwarePolicy
+    ) -> Set[int]:
+        """
+        Computes and caches the matching prefixes for requests in the waiting queue,
+            and handles in-batch prefix caching logic.
+        """
+        temporary_deprioritized: Set[int] = set()
+        self.waiting_queue_radix_tree.reset()
+
+        for r in waiting_queue:
+            prefix_ids = r.adjust_max_prefix_ids()
+
+            # NOTE: the prefix_indices must always be aligned with last_node
+            r.prefix_indices, r.last_node = self.tree_cache.match_prefix(
+                rid=r.rid, key=prefix_ids
+            )
 
-        if policy == "lpm":
-            # Longest Prefix Match
-            waiting_queue.sort(
-                key=lambda r: (
-                    -len(r.prefix_indices)
-                    if r.rid not in temporary_deprioritized
-                    else float("inf")
+            # NOTE(sang): This logic is for in-batch prefix caching;
+            # If there are more than 1 request that have small matching prefix from
+            # existing cache, but all those requests share the same prefix, we prefer
+            # to schedule only one of them so that we can increase the cache hit rate.
+            # We prefer to set IN_BATCH_PREFIX_CACHING_CHECK_THRESHOLD > 0 because too small
+            # threshold means we cannot use in-batch prefix caching for short prefixes.
+            # It is kind of common when the engine is long running (e.g., imagine the prefix "the").
+            if len(r.prefix_indices) <= IN_BATCH_PREFIX_CACHING_CHECK_THRESHOLD:
+                in_batch_matching_prefixes, _ = (
+                    self.waiting_queue_radix_tree.match_prefix(
+                        rid=r.rid, key=prefix_ids
+                    )
                 )
+                if (
+                    len(in_batch_matching_prefixes)
+                    >= IN_BATCH_PREFIX_CACHING_DEPRIORITIZE_THRESHOLD
+                ):
+                    temporary_deprioritized.add(r.rid)
+                else:
+                    # Insert with a dummy key
+                    self.waiting_queue_radix_tree.insert(
+                        prefix_ids, torch.empty(len(prefix_ids), dtype=torch.bool)
+                    )
+        return temporary_deprioritized
+
+    @staticmethod
+    def _sort_by_longest_prefix(
+        waiting_queue: List[Req], temporary_deprioritized: Set[int]
+    ) -> None:
+        """Sorts the waiting queue based on the longest prefix match."""
+        waiting_queue.sort(
+            key=lambda r: (
+                -len(r.prefix_indices)
+                if r.rid not in temporary_deprioritized
+                else float("inf")
             )
-        elif policy == "fcfs":
-            # first come first serve
-            pass
-        elif policy == "lof":
-            # longest output first
-            waiting_queue.sort(key=lambda x: -x.sampling_params.max_new_tokens)
-        elif policy == "random":
-            random.shuffle(waiting_queue)
-        elif policy == "dfs-weight":
-            # Experimental policy based on custom weights
-            last_node_to_reqs = defaultdict(list)
-            for req in waiting_queue:
-                last_node_to_reqs[req.last_node].append(req)
-
-            node_to_weight = defaultdict(int)
-            for node in last_node_to_reqs:
-                node_to_weight[node] = len(last_node_to_reqs[node])
-            self.calc_weight(self.tree_cache.root_node, node_to_weight)
-
-            waiting_queue.clear()
-            self.get_dfs_priority(
-                self.tree_cache.root_node,
-                node_to_weight,
-                last_node_to_reqs,
-                waiting_queue,
-            )
-        else:
-            raise ValueError(f"Unknown schedule_policy: {policy=}")
+        )
 
-        return prefix_computed
+    @staticmethod
+    def _sort_by_dfs_weight(
+        waiting_queue: List[Req], tree_cache: BasePrefixCache
+    ) -> None:
+        """Sorts the waiting queue based on a depth-first search weighting."""
+        last_node_to_reqs = defaultdict(list)
+        for req in waiting_queue:
+            last_node_to_reqs[req.last_node].append(req)
+
+        node_to_weight = defaultdict(int)
+        for node in last_node_to_reqs:
+            node_to_weight[node] = len(last_node_to_reqs[node])
+        SchedulePolicy._calc_weight(tree_cache.root_node, node_to_weight)
+
+        waiting_queue.clear()
+        SchedulePolicy._get_dfs_priority(
+            tree_cache.root_node,
+            node_to_weight,
+            last_node_to_reqs,
+            waiting_queue,
+        )
+
+    @staticmethod
+    def _sort_by_longest_output(waiting_queue: List[Req]) -> None:
+        """Sorts the waiting queue based on the longest output (max_new_tokens)."""
+        waiting_queue.sort(key=lambda x: -x.sampling_params.max_new_tokens)
 
-    def calc_weight(self, cur_node: TreeNode, node_to_weight: Dict):
+    @staticmethod
+    def _sort_randomly(waiting_queue: List[Req]) -> None:
+        """Shuffles the waiting queue randomly."""
+        random.shuffle(waiting_queue)
+
+    @staticmethod
+    def _calc_weight(cur_node: TreeNode, node_to_weight: Dict[TreeNode, int]) -> None:
         for child in cur_node.children.values():
-            self.calc_weight(child, node_to_weight)
+            SchedulePolicy._calc_weight(child, node_to_weight)
             node_to_weight[cur_node] += node_to_weight[child]
 
-    def get_dfs_priority(
-        self,
+    @staticmethod
+    def _get_dfs_priority(
         cur_node: TreeNode,
         node_to_priority: Dict[TreeNode, int],
         last_node_to_reqs: Dict[TreeNode, List[Req]],
         q: List,
-    ):
+    ) -> None:
         childs = [child for child in cur_node.children.values()]
         childs.sort(key=lambda x: -node_to_priority[x])
         for child in childs:
-            self.get_dfs_priority(child, node_to_priority, last_node_to_reqs, q)
+            SchedulePolicy._get_dfs_priority(
+                child, node_to_priority, last_node_to_reqs, q
+            )
         q.extend(last_node_to_reqs[cur_node])
 
 

test/srt/test_schedule_policy.py

@@ -0,0 +1,52 @@
+import unittest
+
+from sglang.srt.managers.schedule_batch import Req
+from sglang.srt.managers.schedule_policy import (
+    CacheAgnosticPolicy,
+    CacheAwarePolicy,
+    SchedulePolicy,
+)
+from sglang.srt.mem_cache.radix_cache import RadixCache, TreeNode
+from sglang.srt.sampling.sampling_params import SamplingParams
+
+
+class TestSchedulePolicy(unittest.TestCase):
+
+    def setUp(self):
+        self.tree_cache = RadixCache(None, None, False)
+
+    def test_init_with_cache_aware_policy(self):
+        policy = SchedulePolicy(policy="lpm", tree_cache=self.tree_cache)
+        self.assertEqual(policy.policy, CacheAwarePolicy.LPM)
+
+    def test_init_with_cache_agnostic_policy(self):
+        policy = SchedulePolicy(policy="fcfs", tree_cache=self.tree_cache)
+        self.assertEqual(policy.policy, CacheAgnosticPolicy.FCFS)
+
+    def test_init_with_unknown_policy(self):
+        with self.assertRaises(ValueError):
+            SchedulePolicy(policy="invalid", tree_cache=self.tree_cache)
+
+    def test_init_with_disabled_cache(self):
+        disabled_tree_cache = RadixCache(None, None, disable=True)
+        policy = SchedulePolicy(policy="lpm", tree_cache=disabled_tree_cache)
+        self.assertEqual(policy.policy, CacheAgnosticPolicy.FCFS)
+
+    def test_calc_priority_fcfs(self):
+        tree_cache = RadixCache(None, None, False)
+        waiting_queue = [
+            Req(1, "a b", [1, 2], SamplingParams()),
+            Req(3, "a b c", [1, 2, 3], SamplingParams()),
+            Req(2, "a", [1], SamplingParams()),
+        ]
+
+        policy = SchedulePolicy(policy="fcfs", tree_cache=tree_cache)
+        policy.calc_priority(waiting_queue)
+        # Check if FCFS keeps the original order
+        self.assertEqual(waiting_queue[0].rid, 1)
+        self.assertEqual(waiting_queue[1].rid, 3)
+        self.assertEqual(waiting_queue[2].rid, 2)
+
+
+if __name__ == "__main__":
+    unittest.main()