Configurable ReplayBuffer

src/forge/actors/replay_buffer.py

@@ -6,7 +6,9 @@
 
 import logging
 import random
+from collections import deque
 from dataclasses import dataclass
+from operator import itemgetter
 from typing import Any, Callable
 
 from monarch.actor import endpoint
@@ -19,89 +21,107 @@
 logger.setLevel(logging.INFO)
 
 
+@dataclass
+class BufferEntry:
+    data: "Episode"
+    sample_count: int = 0
+
+
+def default_evict(buffer, policy_version, max_samples=None, max_age=None):
+    """Default buffer eviction policy"""
+    indices = []
+    for i, entry in enumerate(buffer):
+        if max_age and policy_version - entry.data.policy_version > max_age:
+            continue
+        if max_samples and entry.sample_count >= max_samples:
+            continue
+        indices.append(i)
+    return indices
+
+
+def default_sample(buffer, sample_size, sampler, policy_version):
+    """Default buffer sampling policy"""
+    if sample_size > len(buffer):
+        return None
+    return sampler(range(len(buffer)), k=sample_size)
+
+
 @dataclass
 class ReplayBuffer(ForgeActor):
     """Simple in-memory replay buffer implementation."""
 
     batch_size: int
-    max_policy_age: int
     dp_size: int = 1
+    max_policy_age: int | None = None
+    max_buffer_size: int | None = None
+    max_resample_count: int | None = 0
     seed: int | None = None
     collate: Callable = lambda batch: batch
-
-    def __post_init__(self):
-        super().__init__()
+    eviction_policy: Callable = default_evict
+    sample_policy: Callable = default_sample
 
     @endpoint
     async def setup(self) -> None:
-        self.buffer: list = []
+        self.buffer: deque = deque(maxlen=self.max_buffer_size)
         if self.seed is None:
             self.seed = random.randint(0, 2**32)
         random.seed(self.seed)
         self.sampler = random.sample
 
     @endpoint
     async def add(self, episode: "Episode") -> None:
-        self.buffer.append(episode)
+        self.buffer.append(BufferEntry(episode))
         record_metric("buffer/add/count_episodes_added", 1, Reduce.SUM)
 
     @endpoint
     @trace("buffer_perf/sample", track_memory=False)
     async def sample(
-        self, curr_policy_version: int, batch_size: int | None = None
+        self, curr_policy_version: int
     ) -> tuple[tuple[Any, ...], ...] | None:
         """Sample from the replay buffer.
 
         Args:
             curr_policy_version (int): The current policy version.
-            batch_size (int, optional): Number of episodes to sample. If none, defaults to batch size
-                passed in at initialization.
 
         Returns:
             A list of sampled episodes with shape (dp_size, bsz, ...) or None if there are not enough episodes in the buffer.
         """
         # Record sample request metric
         record_metric("buffer/sample/count_sample_requests", 1, Reduce.SUM)
 
-        bsz = batch_size if batch_size is not None else self.batch_size
-        total_samples = self.dp_size * bsz
+        total_samples = self.dp_size * self.batch_size
 
-        # Evict old episodes
+        # Evict episodes
         self._evict(curr_policy_version)
 
-        if total_samples > len(self.buffer):
-            return None
-
-        # Calculate buffer utilization
-        utilization_pct = (
-            (total_samples / len(self.buffer)) * 100 if len(self.buffer) > 0 else 0
-        )
-
-        record_metric(
-            "buffer/sample/avg_buffer_utilization",
-            len(self.buffer),
-            Reduce.MEAN,
-        )
-
-        record_metric(
-            "buffer/sample/avg_buffer_utilization_pct",
-            utilization_pct,
-            Reduce.MEAN,
-        )
+        # Calculate metrics
+        if len(self.buffer) > 0:
+            record_metric(
+                "buffer/sample/avg_data_utilization",
+                total_samples / len(self.buffer),
+                Reduce.MEAN,
+            )
+        if self.max_buffer_size:
+            record_metric(
+                "buffer/sample/avg_buffer_utilization",
+                len(self.buffer) / self.max_buffer_size,
+                Reduce.MEAN,
+            )
 
         # TODO: prefetch samples in advance
-        idx_to_sample = self.sampler(range(len(self.buffer)), k=total_samples)
-        # Pop episodes in descending order to avoid shifting issues
-        popped = [self.buffer.pop(i) for i in sorted(idx_to_sample, reverse=True)]
-
-        # Reorder popped episodes to match the original random sample order
-        sorted_idxs = sorted(idx_to_sample, reverse=True)
-        idx_to_popped = dict(zip(sorted_idxs, popped))
-        sampled_episodes = [idx_to_popped[i] for i in idx_to_sample]
+        sampled_indices = self.sample_policy(
+            self.buffer, total_samples, self.sampler, curr_policy_version
+        )
+        if sampled_indices is None:
+            return None
+        sampled_episodes = []
+        for entry in self._collect(sampled_indices):
+            entry.sample_count += 1
+            sampled_episodes.append(entry.data)
 
         # Reshape into (dp_size, bsz, ...)
         reshaped_episodes = [
-            sampled_episodes[dp_idx * bsz : (dp_idx + 1) * bsz]
+            sampled_episodes[dp_idx * self.batch_size : (dp_idx + 1) * self.batch_size]
             for dp_idx in range(self.dp_size)
         ]
 
@@ -117,46 +137,69 @@ async def evict(self, curr_policy_version: int) -> None:
         """
         self._evict(curr_policy_version)
 
-    def _evict(self, curr_policy_version: int) -> None:
+    def _evict(self, curr_policy_version):
         buffer_len_before_evict = len(self.buffer)
-        self.buffer = [
-            trajectory
-            for trajectory in self.buffer
-            if (curr_policy_version - trajectory.policy_version) <= self.max_policy_age
-        ]
-        buffer_len_after_evict = len(self.buffer)
+        indices = self.eviction_policy(
+            self.buffer,
+            curr_policy_version,
+            self.max_resample_count + 1,
+            self.max_policy_age,
+        )
+        self.buffer = deque(self._collect(indices))
 
         # Record evict metrics
-        policy_staleness = [
-            curr_policy_version - ep.policy_version for ep in self.buffer
+        policy_age = [
+            curr_policy_version - ep.data.policy_version for ep in self.buffer
         ]
-        if policy_staleness:
+        if policy_age:
             record_metric(
-                "buffer/evict/avg_policy_staleness",
-                sum(policy_staleness) / len(policy_staleness),
+                "buffer/evict/avg_policy_age",
+                sum(policy_age) / len(policy_age),
                 Reduce.MEAN,
             )
             record_metric(
-                "buffer/evict/max_policy_staleness",
-                max(policy_staleness),
+                "buffer/evict/max_policy_age",
+                max(policy_age),
                 Reduce.MAX,
             )
 
-        # Record eviction metrics
-        evicted_count = buffer_len_before_evict - buffer_len_after_evict
-        if evicted_count > 0:
-            record_metric(
-                "buffer/evict/sum_episodes_evicted", evicted_count, Reduce.SUM
-            )
+        evicted_count = buffer_len_before_evict - len(self.buffer)
+        record_metric("buffer/evict/sum_episodes_evicted", evicted_count, Reduce.SUM)
 
         logger.debug(
             f"maximum policy age: {self.max_policy_age}, current policy version: {curr_policy_version}, "
-            f"{evicted_count} episodes expired, {buffer_len_after_evict} episodes left"
+            f"{evicted_count} episodes expired, {len(self.buffer)} episodes left"
         )
 
+    def _collect(self, indices: list[int]):
+        """Efficiently traverse deque and collect elements at each requested index"""
+        n = len(self.buffer)
+        if n == 0 or len(indices) == 0:
+            return []
+
+        # Normalize indices and store with their original order
+        indexed = [(pos, idx % n) for pos, idx in enumerate(indices)]
+        indexed.sort(key=itemgetter(1))
+
+        result = [None] * len(indices)
+        rotations = 0  # logical current index
+        total_rotation = 0  # total net rotation applied
+
+        for orig_pos, idx in indexed:
+            move = idx - rotations
+            self.buffer.rotate(-move)
+            total_rotation += move
+            rotations = idx
+            result[orig_pos] = self.buffer[0]
+
+        # Restore original deque orientation
+        self.buffer.rotate(total_rotation)
+
+        return result
+
     @endpoint
     async def _getitem(self, idx: int):
-        return self.buffer[idx]
+        return self.buffer[idx].data
 
     @endpoint
     async def _numel(self) -> int:

tests/unit_tests/test_replay_buffer.py

@@ -70,40 +70,35 @@ async def test_sample(self, replay_buffer) -> None:
         await replay_buffer.add.call_one(trajectory_1)
         assert replay_buffer._numel.call_one().get() == 2
 
-        # Test a simple sampling w/ no evictions
+        # Test a simple sampling
         samples = await replay_buffer.sample.call_one(curr_policy_version=1)
         assert samples is not None
         assert len(samples[0]) == 2
+        assert replay_buffer._numel.call_one().get() == 2
 
-        # Test sampling with overriding batch size
-        await replay_buffer.add.call_one(trajectory_0)
-        samples = await replay_buffer.sample.call_one(
-            curr_policy_version=1, batch_size=1
-        )
-        assert samples is not None
-        assert len(samples[0]) == 1
-
-        # Test sampling w/ overriding batch size (not enough samples in buffer, returns None)
+        # Test sampling (not enough samples in buffer, returns None)
         await replay_buffer.add.call_one(trajectory_0)
-        samples = await replay_buffer.sample.call_one(
-            curr_policy_version=1, batch_size=3
-        )
+        samples = await replay_buffer.sample.call_one(curr_policy_version=1)
         assert samples is None
         replay_buffer.clear.call_one().get()
 
     @pytest.mark.asyncio
     async def test_sample_with_evictions(self, replay_buffer) -> None:
         trajectory_0 = Trajectory(policy_version=0)
         trajectory_1 = Trajectory(policy_version=1)
+        trajectory_2 = Trajectory(policy_version=2)
         await replay_buffer.add.call_one(trajectory_0)
         await replay_buffer.add.call_one(trajectory_1)
-        assert replay_buffer._numel.call_one().get() == 2
+        await replay_buffer.add.call_one(trajectory_2)
+        assert replay_buffer._numel.call_one().get() == 3
         samples = await replay_buffer.sample.call_one(
-            curr_policy_version=2, batch_size=1
+            curr_policy_version=2,
         )
         assert samples is not None
-        assert len(samples[0]) == 1
-        assert samples[0][0] == trajectory_1
+        assert len(samples[0]) == 2
+        assert samples[0][0].policy_version > 0
+        assert samples[0][1].policy_version > 0
+        assert replay_buffer._numel.call_one().get() == 2
         replay_buffer.clear.call_one().get()
 
     @pytest.mark.asyncio