[BugFix,Feature,Doc] Fix replay buffers sampling info, docstrings and iteration

examples/discrete_sac/discrete_sac.py

@@ -44,15 +44,15 @@ def make_replay_buffer(
     batch_size=256,
     buffer_scratch_dir="/tmp/",
     device="cpu",
-    make_replay_buffer=3,
+    prefetch=3,
 ):
     if prb:
         replay_buffer = TensorDictPrioritizedReplayBuffer(
             alpha=0.7,
             beta=0.5,
             pin_memory=False,
             batch_size=batch_size,
-            prefetch=make_replay_buffer,
+            prefetch=prefetch,
             storage=LazyMemmapStorage(
                 buffer_size,
                 scratch_dir=buffer_scratch_dir,
@@ -63,7 +63,7 @@ def make_replay_buffer(
         replay_buffer = TensorDictReplayBuffer(
             pin_memory=False,
             batch_size=batch_size,
-            prefetch=make_replay_buffer,
+            prefetch=prefetch,
             storage=LazyMemmapStorage(
                 buffer_size,
                 scratch_dir=buffer_scratch_dir,

examples/iql/iql_online.py

@@ -36,33 +36,36 @@ def env_maker(env_name, frame_skip=1, device="cpu", from_pixels=False):
 
 
 def make_replay_buffer(
+    batch_size,
     prb=False,
     buffer_size=1000000,
     buffer_scratch_dir="/tmp/",
     device="cpu",
-    make_replay_buffer=3,
+    prefetch=3,
 ):
     if prb:
         replay_buffer = TensorDictPrioritizedReplayBuffer(
             alpha=0.7,
             beta=0.5,
             pin_memory=False,
-            prefetch=make_replay_buffer,
+            prefetch=prefetch,
             storage=LazyMemmapStorage(
                 buffer_size,
                 scratch_dir=buffer_scratch_dir,
                 device=device,
             ),
+            batch_size=batch_size,
         )
     else:
         replay_buffer = TensorDictReplayBuffer(
             pin_memory=False,
-            prefetch=make_replay_buffer,
+            prefetch=prefetch,
             storage=LazyMemmapStorage(
                 buffer_size,
                 scratch_dir=buffer_scratch_dir,
                 device=device,
             ),
+            batch_size=batch_size,
         )
     return replay_buffer
 
@@ -218,7 +221,9 @@ def env_factory(num_workers):
     collector.set_seed(cfg.seed)
 
     # Make Replay Buffer
-    replay_buffer = make_replay_buffer(buffer_size=cfg.buffer_size, device=device)
+    replay_buffer = make_replay_buffer(
+        buffer_size=cfg.buffer_size, device=device, batch_size=cfg.batch_size
+    )
 
     # Optimizers
     params = list(loss_module.parameters())

examples/td3/td3.py

@@ -60,33 +60,36 @@ def apply_env_transforms(env, reward_scaling=1.0):
 
 
 def make_replay_buffer(
+    batch_size,
     prb=False,
     buffer_size=1000000,
     buffer_scratch_dir="/tmp/",
     device="cpu",
-    make_replay_buffer=3,
+    prefetch=3,
 ):
     if prb:
         replay_buffer = TensorDictPrioritizedReplayBuffer(
             alpha=0.7,
             beta=0.5,
             pin_memory=False,
-            prefetch=make_replay_buffer,
+            prefetch=prefetch,
             storage=LazyMemmapStorage(
                 buffer_size,
                 scratch_dir=buffer_scratch_dir,
                 device=device,
             ),
+            batch_size=batch_size,
         )
     else:
         replay_buffer = TensorDictReplayBuffer(
             pin_memory=False,
-            prefetch=make_replay_buffer,
+            prefetch=prefetch,
             storage=LazyMemmapStorage(
                 buffer_size,
                 scratch_dir=buffer_scratch_dir,
                 device=device,
             ),
+            batch_size=batch_size,
         )
     return replay_buffer
 
@@ -239,7 +242,10 @@ def main(cfg: "DictConfig"):  # noqa: F821
 
     # Make Replay Buffer
     replay_buffer = make_replay_buffer(
-        prb=cfg.prb, buffer_size=cfg.buffer_size, device=device
+        batch_size=cfg.batch_size,
+        prb=cfg.prb,
+        buffer_size=cfg.buffer_size,
+        device=device,
     )
 
     # Optimizers

test/test_rb.py

@@ -580,6 +580,54 @@ def test_index(self, rbtype, storage, size, prefetch):
         assert b
 
 
+def test_multi_loops():
+    """Tests that one can iterate multiple times over a buffer without rep."""
+    rb = ReplayBuffer(
+        batch_size=5, storage=ListStorage(10), sampler=SamplerWithoutReplacement()
+    )
+    rb.extend(torch.zeros(10))
+    for i, d in enumerate(rb):  # noqa: B007
+        assert (d == 0).all()
+    assert i == 1
+    for i, d in enumerate(rb):  # noqa: B007
+        assert (d == 0).all()
+    assert i == 1
+
+
+def test_batch_errors():
+    """Tests error messages related to batch-size"""
+    rb = ReplayBuffer(
+        storage=ListStorage(10), sampler=SamplerWithoutReplacement(drop_last=False)
+    )
+    rb.extend(torch.zeros(10))
+    rb.sample(3)  # that works
+    with pytest.raises(
+        RuntimeError,
+        match="Cannot iterate over the replay buffer. Batch_size was not specified",
+    ):
+        for _ in rb:
+            pass
+    with pytest.raises(RuntimeError, match="batch_size not specified"):
+        rb.sample()
+    with pytest.raises(ValueError, match="Samplers with drop_last=True"):
+        ReplayBuffer(
+            storage=ListStorage(10), sampler=SamplerWithoutReplacement(drop_last=True)
+        )
+    # that works
+    ReplayBuffer(
+        storage=ListStorage(10),
+    )
+    rb = ReplayBuffer(
+        storage=ListStorage(10),
+        sampler=SamplerWithoutReplacement(drop_last=False),
+        batch_size=3,
+    )
+    rb.extend(torch.zeros(10))
+    for _ in rb:
+        pass
+    rb.sample()
+
+
 @pytest.mark.parametrize("priority_key", ["pk", "td_error"])
 @pytest.mark.parametrize("contiguous", [True, False])
 @pytest.mark.parametrize("device", get_available_devices())

test/test_rb_distributed.py

@@ -53,7 +53,7 @@ def sample_from_buffer_remotely_returns_correct_tensordict_test(rank, name, worl
         _, inserted = _add_random_tensor_dict_to_buffer(buffer)
         sampled = _sample_from_buffer(buffer, 1)
         assert type(sampled) is type(inserted) is TensorDict
-        assert (sampled == inserted)["a"].item()
+        assert (sampled["a"] == inserted["a"]).all()
 
 
 @pytest.mark.skipif(

test/test_trainer.py

@@ -203,7 +203,9 @@ def test_rb_trainer(self, prioritized):
         S = 100
         storage = ListStorage(S)
         if prioritized:
-            replay_buffer = TensorDictPrioritizedReplayBuffer(1.1, 0.9, storage=storage)
+            replay_buffer = TensorDictPrioritizedReplayBuffer(
+                alpha=1.1, beta=0.9, storage=storage
+            )
         else:
             replay_buffer = TensorDictReplayBuffer(storage=storage)
 
@@ -260,8 +262,8 @@ def test_rb_trainer_state_dict(self, prioritized, storage_type):
 
         if prioritized:
             replay_buffer = TensorDictPrioritizedReplayBuffer(
-                1.1,
-                0.9,
+                alpha=1.1,
+                beta=0.9,
                 storage=storage,
             )
         else:
@@ -293,7 +295,7 @@ def test_rb_trainer_state_dict(self, prioritized, storage_type):
         trainer2 = mocking_trainer()
         if prioritized:
             replay_buffer2 = TensorDictPrioritizedReplayBuffer(
-                1.1, 0.9, storage=storage
+                alpha=1.1, beta=0.9, storage=storage
             )
         else:
             replay_buffer2 = TensorDictReplayBuffer(storage=storage)
@@ -398,8 +400,8 @@ def make_storage():
             storage = make_storage()
             if prioritized:
                 replay_buffer = TensorDictPrioritizedReplayBuffer(
-                    1.1,
-                    0.9,
+                    alpha=1.1,
+                    beta=0.9,
                     storage=storage,
                 )
             else:
@@ -430,8 +432,8 @@ def make_storage():
             storage2 = make_storage()
             if prioritized:
                 replay_buffer2 = TensorDictPrioritizedReplayBuffer(
-                    1.1,
-                    0.9,
+                    alpha=1.1,
+                    beta=0.9,
                     storage=storage2,
                 )
             else: