Merge remote-tracking branch 'origin/main' into extend-rb-dim1

test/test_cost.py

@@ -6100,7 +6100,7 @@ def zero_param(p):
             if isinstance(p, nn.Parameter):
                 p.data.zero_()
 
-        params.apply(zero_param)
+        params.apply(zero_param, filter_empty=True)
 
         # assert len(list(floss_fn.parameters())) == 0
         with params.to_module(loss_fn):

test/test_env.py

@@ -624,6 +624,29 @@ def test_parallel_env_with_policy(
         # env_serial.close()
         env0.close()
 
+    @pytest.mark.skipif(not torch.cuda.is_available(), reason="CUDA required")
+    @pytest.mark.parametrize("heterogeneous", [False, True])
+    def test_transform_env_transform_no_device(self, heterogeneous):
+        # Tests non-regression on 1865
+        def make_env():
+            return TransformedEnv(
+                ContinuousActionVecMockEnv(), StepCounter(max_steps=3)
+            )
+
+        if heterogeneous:
+            make_envs = [EnvCreator(make_env), EnvCreator(make_env)]
+        else:
+            make_envs = make_env
+        penv = ParallelEnv(2, make_envs)
+        r = penv.rollout(6, break_when_any_done=False)
+        assert r.shape == (2, 6)
+        try:
+            env = TransformedEnv(penv)
+            r = env.rollout(6, break_when_any_done=False)
+            assert r.shape == (2, 6)
+        finally:
+            penv.close()
+
     @pytest.mark.skipif(not _has_gym, reason="no gym")
     @pytest.mark.parametrize(
         "env_name",

test/test_rb.py

@@ -834,7 +834,7 @@ def test_set_tensorclass(self, max_size, shape, storage):
 @pytest.mark.parametrize("priority_key", ["pk", "td_error"])
 @pytest.mark.parametrize("contiguous", [True, False])
 @pytest.mark.parametrize("device", get_default_devices())
-def test_prototype_prb(priority_key, contiguous, device):
+def test_ptdrb(priority_key, contiguous, device):
     torch.manual_seed(0)
     np.random.seed(0)
     rb = TensorDictReplayBuffer(

torchrl/collectors/collectors.py

@@ -316,12 +316,14 @@ def _map_to_device_params(param, device):
 
             # Create a stateless policy, then populate this copy with params on device
             with param_and_buf.apply(
-                functools.partial(_map_to_device_params, device="meta")
+                functools.partial(_map_to_device_params, device="meta"),
+                filter_empty=False,
             ).to_module(policy):
                 policy = deepcopy(policy)
 
             param_and_buf.apply(
-                functools.partial(_map_to_device_params, device=self.policy_device)
+                functools.partial(_map_to_device_params, device=self.policy_device),
+                filter_empty=False,
             ).to_module(policy)
 
         return policy, get_weights_fn
@@ -801,22 +803,30 @@ def check_exclusive(val):
                             "Consider using a placeholder for missing keys."
                         )
 
-                policy_output._fast_apply(check_exclusive, call_on_nested=True)
+                policy_output._fast_apply(
+                    check_exclusive, call_on_nested=True, filter_empty=True
+                )
+
                 # Use apply, because it works well with lazy stacks
                 # Edge-case of this approach: the policy may change the values in-place and only by a tiny bit
                 # or occasionally. In these cases, the keys will be missed (we can't detect if the policy has
                 # changed them here).
                 # This will cause a failure to update entries when policy and env device mismatch and
                 # casting is necessary.
+                def filter_policy(value_output, value_input, value_input_clone):
+                    if (
+                        (value_input is None)
+                        or (value_output is not value_input)
+                        or ~torch.isclose(value_output, value_input_clone).any()
+                    ):
+                        return value_output
+
                 filtered_policy_output = policy_output.apply(
-                    lambda value_output, value_input, value_input_clone: value_output
-                    if (value_input is None)
-                    or (value_output is not value_input)
-                    or ~torch.isclose(value_output, value_input_clone).any()
-                    else None,
+                    filter_policy,
                     policy_input_copy,
                     policy_input_clone,
                     default=None,
+                    filter_empty=True,
                 )
                 self._policy_output_keys = list(
                     self._policy_output_keys.union(
@@ -933,7 +943,7 @@ def cuda_check(tensor: torch.Tensor):
                 if tensor.is_cuda:
                     cuda_devices.add(tensor.device)
 
-            self._final_rollout.apply(cuda_check)
+            self._final_rollout.apply(cuda_check, filter_empty=True)
             for device in cuda_devices:
                 streams.append(torch.cuda.Stream(device, priority=-1))
                 events.append(streams[-1].record_event())
@@ -1487,7 +1497,9 @@ def map_weight(
                         weight = nn.Parameter(weight, requires_grad=False)
                     return weight
 
-            local_policy_weights = TensorDictParams(policy_weights.apply(map_weight))
+            local_policy_weights = TensorDictParams(
+                policy_weights.apply(map_weight, filter_empty=False)
+            )
 
             def _get_weight_fn(weights=policy_weights):
                 # This function will give the local_policy_weight the original weights.

torchrl/data/replay_buffers/replay_buffers.py

@@ -929,7 +929,7 @@ def update_tensordict_priority(self, data: TensorDictBase) -> None:
         if data.ndim:
             priority = self._get_priority_vector(data)
         else:
-            priority = self._get_priority_item(data)
+            priority = torch.as_tensor(self._get_priority_item(data))
         index = data.get("index")
         while index.shape != priority.shape:
             # reduce index

torchrl/data/replay_buffers/samplers.py

@@ -24,7 +24,6 @@
 
 from torchrl._utils import _replace_last
 from torchrl.data.replay_buffers.storages import Storage, StorageEnsemble, TensorStorage
-from torchrl.data.replay_buffers.utils import _to_numpy, INT_CLASSES
 
 try:
     from torchrl._torchrl import (
@@ -250,11 +249,10 @@ def load_state_dict(self, state_dict: Dict[str, Any]) -> None:
 class PrioritizedSampler(Sampler):
     """Prioritized sampler for replay buffer.
 
-    Presented in "Schaul, T.; Quan, J.; Antonoglou, I.; and Silver, D. 2015.
-        Prioritized experience replay."
-        (https://arxiv.org/abs/1511.05952)
+    Presented in "Schaul, T.; Quan, J.; Antonoglou, I.; and Silver, D. 2015. Prioritized experience replay." (https://arxiv.org/abs/1511.05952)
 
     Args:
+        max_capacity (int): maximum capacity of the buffer.
         alpha (float): exponent α determines how much prioritization is used,
             with α = 0 corresponding to the uniform case.
         beta (float): importance sampling negative exponent.
@@ -264,6 +262,51 @@ class PrioritizedSampler(Sampler):
             tensordicts (ie stored trajectory). Can be one of "max", "min",
             "median" or "mean".
 
+    Examples:
+        >>> from torchrl.data.replay_buffers import ReplayBuffer, LazyTensorStorage, PrioritizedSampler
+        >>> from tensordict import TensorDict
+        >>> rb = ReplayBuffer(storage=LazyTensorStorage(10), sampler=PrioritizedSampler(max_capacity=10, alpha=1.0, beta=1.0))
+        >>> priority = torch.tensor([0, 1000])
+        >>> data_0 = TensorDict({"reward": 0, "obs": [0], "action": [0], "priority": priority[0]}, [])
+        >>> data_1 = TensorDict({"reward": 1, "obs": [1], "action": [2], "priority": priority[1]}, [])
+        >>> rb.add(data_0)
+        >>> rb.add(data_1)
+        >>> rb.update_priority(torch.tensor([0, 1]), priority=priority)
+        >>> sample, info = rb.sample(10, return_info=True)
+        >>> print(sample)
+        TensorDict(
+                fields={
+                    action: Tensor(shape=torch.Size([10, 1]), device=cpu, dtype=torch.int64, is_shared=False),
+                    obs: Tensor(shape=torch.Size([10, 1]), device=cpu, dtype=torch.int64, is_shared=False),
+                    priority: Tensor(shape=torch.Size([10]), device=cpu, dtype=torch.int64, is_shared=False),
+                    reward: Tensor(shape=torch.Size([10]), device=cpu, dtype=torch.int64, is_shared=False)},
+                batch_size=torch.Size([10]),
+                device=cpu,
+                is_shared=False)
+        >>> print(info)
+        {'_weight': array([1.e-11, 1.e-11, 1.e-11, 1.e-11, 1.e-11, 1.e-11, 1.e-11, 1.e-11,
+               1.e-11, 1.e-11], dtype=float32), 'index': array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1])}
+
+    .. note:: Using a :class:`~torchrl.data.replay_buffers.TensorDictReplayBuffer` can smoothen the
+        process of updating the priorities:
+
+            >>> from torchrl.data.replay_buffers import TensorDictReplayBuffer as TDRB, LazyTensorStorage, PrioritizedSampler
+            >>> from tensordict import TensorDict
+            >>> rb = TDRB(
+            ...     storage=LazyTensorStorage(10),
+            ...     sampler=PrioritizedSampler(max_capacity=10, alpha=1.0, beta=1.0),
+            ...     priority_key="priority",  # This kwarg isn't present in regular RBs
+            ... )
+            >>> priority = torch.tensor([0, 1000])
+            >>> data_0 = TensorDict({"reward": 0, "obs": [0], "action": [0], "priority": priority[0]}, [])
+            >>> data_1 = TensorDict({"reward": 1, "obs": [1], "action": [2], "priority": priority[1]}, [])
+            >>> data = torch.stack([data_0, data_1])
+            >>> rb.extend(data)
+            >>> rb.update_priority(data)  # Reads the "priority" key as indicated in the constructor
+            >>> sample, info = rb.sample(10, return_info=True)
+            >>> print(sample['index'])  # The index is packed with the tensordict
+            tensor([1, 1, 1, 1, 1, 1, 1, 1, 1, 1])
+
     """
 
     def __init__(
@@ -327,15 +370,17 @@ def sample(self, storage: Storage, batch_size: int) -> torch.Tensor:
             raise RuntimeError("negative p_sum")
         if p_min <= 0:
             raise RuntimeError("negative p_min")
+        # For some undefined reason, only np.random works here.
+        # All PT attempts fail, even when subsequently transformed into numpy
         mass = np.random.uniform(0.0, p_sum, size=batch_size)
+        # mass = torch.zeros(batch_size, dtype=torch.double).uniform_(0.0, p_sum)
+        # mass = torch.rand(batch_size).mul_(p_sum)
         index = self._sum_tree.scan_lower_bound(mass)
-        if not isinstance(index, np.ndarray):
-            index = np.array([index])
-        if isinstance(index, torch.Tensor):
-            index.clamp_max_(len(storage) - 1)
-        else:
-            index = np.clip(index, None, len(storage) - 1)
-        weight = self._sum_tree[index]
+        index = torch.as_tensor(index)
+        if not index.ndim:
+            index = index.unsqueeze(0)
+        index.clamp_max_(len(storage) - 1)
+        weight = torch.as_tensor(self._sum_tree[index])
 
         # Importance sampling weight formula:
         #   w_i = (p_i / sum(p) * N) ^ (-beta)
@@ -345,9 +390,10 @@ def sample(self, storage: Storage, batch_size: int) -> torch.Tensor:
         #   weight_i = ((p_i / sum(p) * N) / (min(p) / sum(p) * N)) ^ (-beta)
         #   weight_i = (p_i / min(p)) ^ (-beta)
         # weight = np.power(weight / (p_min + self._eps), -self._beta)
-        weight = np.power(weight / p_min, -self._beta)
+        weight = torch.pow(weight / p_min, -self._beta)
         return index, {"_weight": weight}
 
+    @torch.no_grad()
     def _add_or_extend(self, index: Union[int, torch.Tensor]) -> None:
         priority = self.default_priority
 
@@ -360,6 +406,11 @@ def _add_or_extend(self, index: Union[int, torch.Tensor]) -> None:
                 "priority should be a scalar or an iterable of the same "
                 "length as index"
             )
+        # make sure everything is cast to cpu
+        if isinstance(index, torch.Tensor) and not index.is_cpu:
+            index = index.cpu()
+        if isinstance(priority, torch.Tensor) and not priority.is_cpu:
+            priority = priority.cpu()
 
         self._sum_tree[index] = priority
         self._min_tree[index] = priority
@@ -377,6 +428,7 @@ def extend(self, index: torch.Tensor) -> None:
             index = index.cpu()
             self._add_or_extend(index)
 
+    @torch.no_grad()
     def update_priority(
         self, index: Union[int, torch.Tensor], priority: Union[float, torch.Tensor]
     ) -> None:
@@ -389,28 +441,26 @@ def update_priority(
                 indexed elements.
 
         """
-        if isinstance(index, INT_CLASSES):
-            if not isinstance(priority, float):
-                if len(priority) != 1:
-                    raise RuntimeError(
-                        f"priority length should be 1, got {len(priority)}"
-                    )
-                priority = priority.item()
-        else:
-            if not (
-                isinstance(priority, float)
-                or len(priority) == 1
-                or len(index) == len(priority)
-            ):
+        priority = torch.as_tensor(priority, device=torch.device("cpu")).detach()
+        index = torch.as_tensor(
+            index, dtype=torch.long, device=torch.device("cpu")
+        ).detach()
+        # we need to reshape priority if it has more than one elements or if it has
+        # a different shape than index
+        if priority.numel() > 1 and priority.shape != index.shape:
+            try:
+                priority = priority.reshape(index.shape[:1])
+            except Exception as err:
                 raise RuntimeError(
                     "priority should be a number or an iterable of the same "
-                    "length as index"
-                )
-            index = _to_numpy(index)
-            priority = _to_numpy(priority)
-
-        self._max_priority = max(self._max_priority, np.max(priority))
-        priority = np.power(priority + self._eps, self._alpha)
+                    f"length as index. Got priority of shape {priority.shape} and index "
+                    f"{index.shape}."
+                ) from err
+        elif priority.numel() <= 1:
+            priority = priority.squeeze()
+
+        self._max_priority = priority.max().clamp_min(self._max_priority).item()
+        priority = torch.pow(priority + self._eps, self._alpha)
         self._sum_tree[index] = priority
         self._min_tree[index] = priority
 
@@ -867,7 +917,7 @@ def _sample_slices(
                 truncated[seq_length.cumsum(0) - 1] = 1
             traj_terminated = stop_idx[traj_idx] == start_idx[traj_idx] + seq_length - 1
             terminated = torch.zeros_like(truncated)
-            if terminated.any():
+            if traj_terminated.any():
                 if isinstance(seq_length, int):
                     truncated.view(num_slices, -1)[traj_terminated] = 1
                 else:
@@ -1233,7 +1283,7 @@ def __getitem__(self, index):
         if isinstance(index, slice) and index == slice(None):
             return self
         if isinstance(index, (list, range, np.ndarray)):
-            index = torch.tensor(index)
+            index = torch.as_tensor(index)
         if isinstance(index, torch.Tensor):
             if index.ndim > 1:
                 raise RuntimeError(