[Feature] replay_buffer_chunk

ghstack-source-id: b7db61465f251e645e9b6e346297f38258c3acf3
Pull Request resolved: #2388

test/test_collector.py

@@ -69,6 +69,7 @@
 from torchrl.collectors.utils import split_trajectories
 from torchrl.data import (
     Composite,
+    LazyMemmapStorage,
     LazyTensorStorage,
     NonTensor,
     ReplayBuffer,
@@ -2806,45 +2807,87 @@ def test_collector_rb_sync(self):
         assert assert_allclose_td(rbdata0, rbdata1)
 
     @pytest.mark.skipif(not _has_gym, reason="requires gym.")
-    def test_collector_rb_multisync(self):
-        env = GymEnv(CARTPOLE_VERSIONED())
-        env.set_seed(0)
+    @pytest.mark.parametrize("replay_buffer_chunk", [False, True])
+    @pytest.mark.parametrize("env_creator", [False, True])
+    @pytest.mark.parametrize("storagetype", [LazyTensorStorage, LazyMemmapStorage])
+    def test_collector_rb_multisync(
+        self, replay_buffer_chunk, env_creator, storagetype, tmpdir
+    ):
+        if not env_creator:
+            env = GymEnv(CARTPOLE_VERSIONED()).append_transform(StepCounter())
+            env.set_seed(0)
+            action_spec = env.action_spec
+            env = lambda env=env: env
+        else:
+            env = EnvCreator(
+                lambda cp=CARTPOLE_VERSIONED(): GymEnv(cp).append_transform(
+                    StepCounter()
+                )
+            )
+            action_spec = env.meta_data.specs["input_spec", "full_action_spec"]
 
-        rb = ReplayBuffer(storage=LazyTensorStorage(256), batch_size=5)
-        rb.add(env.rand_step(env.reset()))
-        rb.empty()
+        if storagetype == LazyMemmapStorage:
+            storagetype = functools.partial(LazyMemmapStorage, scratch_dir=tmpdir)
+        rb = ReplayBuffer(storage=storagetype(256), batch_size=5)
 
         collector = MultiSyncDataCollector(
-            [lambda: env, lambda: env],
-            RandomPolicy(env.action_spec),
+            [env, env],
+            RandomPolicy(action_spec),
             replay_buffer=rb,
             total_frames=256,
-            frames_per_batch=16,
+            frames_per_batch=32,
+            replay_buffer_chunk=replay_buffer_chunk,
         )
         torch.manual_seed(0)
         pred_len = 0
         for c in collector:
-            pred_len += 16
+            pred_len += 32
             assert c is None
             assert len(rb) == pred_len
         collector.shutdown()
         assert len(rb) == 256
+        if not replay_buffer_chunk:
+            steps_counts = rb["step_count"].squeeze().split(16)
+            collector_ids = rb["collector", "traj_ids"].squeeze().split(16)
+            for step_count, ids in zip(steps_counts, collector_ids):
+                step_countdiff = step_count.diff()
+                idsdiff = ids.diff()
+                assert (
+                    (step_countdiff == 1) | (step_countdiff < 0)
+                ).all(), steps_counts
+                assert (idsdiff >= 0).all()
 
     @pytest.mark.skipif(not _has_gym, reason="requires gym.")
-    def test_collector_rb_multiasync(self):
-        env = GymEnv(CARTPOLE_VERSIONED())
-        env.set_seed(0)
+    @pytest.mark.parametrize("replay_buffer_chunk", [False, True])
+    @pytest.mark.parametrize("env_creator", [False, True])
+    @pytest.mark.parametrize("storagetype", [LazyTensorStorage, LazyMemmapStorage])
+    def test_collector_rb_multiasync(
+        self, replay_buffer_chunk, env_creator, storagetype, tmpdir
+    ):
+        if not env_creator:
+            env = GymEnv(CARTPOLE_VERSIONED()).append_transform(StepCounter())
+            env.set_seed(0)
+            action_spec = env.action_spec
+            env = lambda env=env: env
+        else:
+            env = EnvCreator(
+                lambda cp=CARTPOLE_VERSIONED(): GymEnv(cp).append_transform(
+                    StepCounter()
+                )
+            )
+            action_spec = env.meta_data.specs["input_spec", "full_action_spec"]
 
-        rb = ReplayBuffer(storage=LazyTensorStorage(256), batch_size=5)
-        rb.add(env.rand_step(env.reset()))
-        rb.empty()
+        if storagetype == LazyMemmapStorage:
+            storagetype = functools.partial(LazyMemmapStorage, scratch_dir=tmpdir)
+        rb = ReplayBuffer(storage=storagetype(256), batch_size=5)
 
         collector = MultiaSyncDataCollector(
-            [lambda: env, lambda: env],
-            RandomPolicy(env.action_spec),
+            [env, env],
+            RandomPolicy(action_spec),
             replay_buffer=rb,
             total_frames=256,
             frames_per_batch=16,
+            replay_buffer_chunk=replay_buffer_chunk,
         )
         torch.manual_seed(0)
         pred_len = 0
@@ -2854,6 +2897,16 @@ def test_collector_rb_multiasync(self):
             assert len(rb) >= pred_len
         collector.shutdown()
         assert len(rb) == 256
+        if not replay_buffer_chunk:
+            steps_counts = rb["step_count"].squeeze().split(16)
+            collector_ids = rb["collector", "traj_ids"].squeeze().split(16)
+            for step_count, ids in zip(steps_counts, collector_ids):
+                step_countdiff = step_count.diff()
+                idsdiff = ids.diff()
+                assert (
+                    (step_countdiff == 1) | (step_countdiff < 0)
+                ).all(), steps_counts
+                assert (idsdiff >= 0).all()
 
 
 if __name__ == "__main__":

torchrl/collectors/collectors.py

@@ -54,6 +54,7 @@
 from torchrl.data.tensor_specs import TensorSpec
 from torchrl.data.utils import CloudpickleWrapper, DEVICE_TYPING
 from torchrl.envs.common import _do_nothing, EnvBase
+from torchrl.envs.env_creator import EnvCreator
 from torchrl.envs.transforms import StepCounter, TransformedEnv
 from torchrl.envs.utils import (
     _aggregate_end_of_traj,
@@ -1470,6 +1471,7 @@ def __init__(
         set_truncated: bool = False,
         use_buffers: bool | None = None,
         replay_buffer: ReplayBuffer | None = None,
+        replay_buffer_chunk: bool = True,
     ):
         exploration_type = _convert_exploration_type(
             exploration_mode=exploration_mode, exploration_type=exploration_type
@@ -1514,6 +1516,8 @@ def __init__(
 
         self._use_buffers = use_buffers
         self.replay_buffer = replay_buffer
+        self._check_replay_buffer_init()
+        self.replay_buffer_chunk = replay_buffer_chunk
         if (
             replay_buffer is not None
             and hasattr(replay_buffer, "shared")
@@ -1660,6 +1664,21 @@ def _get_weight_fn(weights=policy_weights):
             )
         self.cat_results = cat_results
 
+    def _check_replay_buffer_init(self):
+        try:
+            if not self.replay_buffer._storage.initialized:
+                if isinstance(self.create_env_fn, EnvCreator):
+                    fake_td = self.create_env_fn.tensordict
+                else:
+                    fake_td = self.create_env_fn[0](
+                        **self.create_env_kwargs[0]
+                    ).fake_tensordict()
+                fake_td["collector", "traj_ids"] = torch.zeros((), dtype=torch.long)
+
+                self.replay_buffer._storage._init(fake_td)
+        except AttributeError:
+            pass
+
     @classmethod
     def _total_workers_from_env(cls, env_creators):
         if isinstance(env_creators, (tuple, list)):
@@ -1795,6 +1814,7 @@ def _run_processes(self) -> None:
                     "set_truncated": self.set_truncated,
                     "use_buffers": self._use_buffers,
                     "replay_buffer": self.replay_buffer,
+                    "replay_buffer_chunk": self.replay_buffer_chunk,
                     "traj_pool": self._traj_pool,
                 }
                 proc = _ProcessNoWarn(
@@ -2804,6 +2824,7 @@ def _main_async_collector(
     set_truncated: bool = False,
     use_buffers: bool | None = None,
     replay_buffer: ReplayBuffer | None = None,
+    replay_buffer_chunk: bool = True,
     traj_pool: _TrajectoryPool = None,
 ) -> None:
     pipe_parent.close()
@@ -2825,11 +2846,11 @@ def _main_async_collector(
         env_device=env_device,
         exploration_type=exploration_type,
         reset_when_done=reset_when_done,
-        return_same_td=True,
+        return_same_td=replay_buffer is None,
         interruptor=interruptor,
         set_truncated=set_truncated,
         use_buffers=use_buffers,
-        replay_buffer=replay_buffer,
+        replay_buffer=replay_buffer if replay_buffer_chunk else None,
         traj_pool=traj_pool,
     )
     use_buffers = inner_collector._use_buffers
@@ -2895,6 +2916,10 @@ def _main_async_collector(
                 continue
 
             if replay_buffer is not None:
+                if not replay_buffer_chunk:
+                    next_data.names = None
+                    replay_buffer.extend(next_data)
+
                 try:
                     queue_out.put((idx, j), timeout=_TIMEOUT)
                     if verbose:

torchrl/data/replay_buffers/replay_buffers.py

@@ -364,6 +364,11 @@ def __len__(self) -> int:
         with self._replay_lock:
             return len(self._storage)
 
+    @property
+    def write_count(self):
+        """The total number of items written so far in the buffer through add and extend."""
+        return self._writer._write_count
+
     def __repr__(self) -> str:
         from torchrl.envs.transforms import Compose
 

torchrl/data/replay_buffers/storages.py

@@ -562,7 +562,7 @@ def assert_is_sharable(tensor):
                 raise RuntimeError(STORAGE_ERR)
 
             if is_tensor_collection(storage):
-                storage.apply(assert_is_sharable)
+                storage.apply(assert_is_sharable, filter_empty=True)
             else:
                 tree_map(storage, assert_is_sharable)
 

torchrl/data/replay_buffers/writers.py

@@ -163,6 +163,7 @@ def add(self, data: Any) -> int | torch.Tensor:
         self._cursor = (self._cursor + 1) % self._storage._max_size_along_dim0(
             single_data=data
         )
+        self._write_count += 1
         # Replicate index requires the shape of the storage to be known
         # Other than that, a "flat" (1d) index is ok to write the data
         self._storage.set(_cursor, data)
@@ -191,6 +192,7 @@ def extend(self, data: Sequence) -> torch.Tensor:
         )
         # we need to update the cursor first to avoid race conditions between workers
         self._cursor = (batch_size + cur_size) % max_size_along0
+        self._write_count += batch_size
         # Replicate index requires the shape of the storage to be known
         # Other than that, a "flat" (1d) index is ok to write the data
         self._storage.set(index, data)
@@ -222,6 +224,20 @@ def _cursor(self, value):
             _cursor_value = self._cursor_value = mp.Value("i", 0)
         _cursor_value.value = value
 
+    @property
+    def _write_count(self):
+        _write_count = self.__dict__.get("_write_count_value", None)
+        if _write_count is None:
+            _write_count = self._write_count_value = mp.Value("i", 0)
+        return _write_count.value
+
+    @_write_count.setter
+    def _write_count(self, value):
+        _write_count = self.__dict__.get("_write_count_value", None)
+        if _write_count is None:
+            _write_count = self._write_count_value = mp.Value("i", 0)
+        _write_count.value = value
+
     def __getstate__(self):
         state = super().__getstate__()
         if get_spawning_popen() is None:
@@ -249,6 +265,7 @@ def add(self, data: Any) -> int | torch.Tensor:
         # we need to update the cursor first to avoid race conditions between workers
         max_size_along_dim0 = self._storage._max_size_along_dim0(single_data=data)
         self._cursor = (index + 1) % max_size_along_dim0
+        self._write_count += 1
         if not is_tensorclass(data):
             data.set(
                 "index",
@@ -275,6 +292,7 @@ def extend(self, data: Sequence) -> torch.Tensor:
         )
         # we need to update the cursor first to avoid race conditions between workers
         self._cursor = (batch_size + cur_size) % max_size_along_dim0
+        self._write_count += batch_size
         # storage must convert the data to the appropriate format if needed
         if not is_tensorclass(data):
             data.set(
@@ -460,6 +478,20 @@ def get_insert_index(self, data: Any) -> int:
 
         return ret
 
+    @property
+    def _write_count(self):
+        _write_count = self.__dict__.get("_write_count_value", None)
+        if _write_count is None:
+            _write_count = self._write_count_value = mp.Value("i", 0)
+        return _write_count.value
+
+    @_write_count.setter
+    def _write_count(self, value):
+        _write_count = self.__dict__.get("_write_count_value", None)
+        if _write_count is None:
+            _write_count = self._write_count_value = mp.Value("i", 0)
+        _write_count.value = value
+
     def add(self, data: Any) -> int | torch.Tensor:
         """Inserts a single element of data at an appropriate index, and returns that index.
 
@@ -469,6 +501,7 @@ def add(self, data: Any) -> int | torch.Tensor:
         index = self.get_insert_index(data)
         if index is not None:
             data.set("index", index)
+            self._write_count += 1
             # Replicate index requires the shape of the storage to be known
             # Other than that, a "flat" (1d) index is ok to write the data
             self._storage.set(index, data)
@@ -488,6 +521,7 @@ def extend(self, data: TensorDictBase) -> None:
         for data_idx, sample in enumerate(data):
             storage_idx = self.get_insert_index(sample)
             if storage_idx is not None:
+                self._write_count += 1
                 data_to_replace[storage_idx] = data_idx
 
         # -1 will be interpreted as invalid by prioritized buffers
@@ -517,7 +551,8 @@ def _empty(self) -> None:
     def __getstate__(self):
         if get_spawning_popen() is not None:
             raise RuntimeError(
-                f"Writers of type {type(self)} cannot be shared between processes."
+                f"Writers of type {type(self)} cannot be shared between processes. "
+                f"Please submit an issue at https://github.com/pytorch/rl if this feature is needed."
             )
         state = super().__getstate__()
         return state

torchrl/envs/env_creator.py

@@ -109,7 +109,7 @@ def share_memory(self, state_dict: OrderedDict) -> None:
                 del state_dict[key]
 
     @property
-    def meta_data(self):
+    def meta_data(self) -> EnvMetaData:
         if self._meta_data is None:
             raise RuntimeError(
                 "meta_data is None in EnvCreator. " "Make sure init_() has been called."