[BugFix] Fix old deps tests

ghstack-source-id: 134d129514f39538fd9c9f94ce07ad939831c8aa Pull Request resolved: #2500
pytorch · Oct 21, 2024 · 56b0b9a · 56b0b9a · github-actions · Oct 21, 2024
1 parent 9f6c21f
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Showing 8 changed files with 58 additions and 17 deletions.
diff --git a/test/test_collector.py b/test/test_collector.py
@@ -43,6 +43,7 @@
     MultiKeyCountingEnvPolicy,
     NestedCountingEnv,
 )
+from packaging import version
 from tensordict import (
     assert_allclose_td,
     LazyStackedTensorDict,
@@ -106,6 +107,7 @@
 IS_OSX = sys.platform == "darwin"
 PYTHON_3_10 = sys.version_info.major == 3 and sys.version_info.minor == 10
 PYTHON_3_7 = sys.version_info.major == 3 and sys.version_info.minor == 7
+TORCH_VERSION = version.parse(version.parse(torch.__version__).base_version)
 
 
 class WrappablePolicy(nn.Module):
@@ -2654,6 +2656,9 @@ def test_dynamic_multiasync_collector(self):
             assert data.names[-1] == "time"
 
 
+@pytest.mark.skipif(
+    TORCH_VERSION < version.parse("2.5.0"), reason="requires Torch >= 2.5.0"
+)
 class TestCompile:
     @pytest.mark.parametrize(
         "collector_cls",
@@ -2996,8 +3001,9 @@ def __deepcopy_error__(*args, **kwargs):
     raise RuntimeError("deepcopy not allowed")
 
 
-@pytest.mark.filterwarnings("error")
-@pytest.mark.filterwarnings("ignore:Tensordict is registered in PyTree")
+@pytest.mark.filterwarnings(
+    "error::UserWarning", "ignore:Tensordict is registered in PyTree:UserWarning"
+)
 @pytest.mark.parametrize(
     "collector_type",
     [
@@ -3016,6 +3022,8 @@ def test_no_deepcopy_policy(collector_type):
     # If the policy is not a nn.Module or has no parameter, policy_device should warn (we don't know what to do but we
     # can trust that the user knows what to do).
 
+    # warnings.warn("Tensordict is registered in PyTree", category=UserWarning)
+
     shared_device = torch.device("cpu")
     if torch.cuda.is_available():
         original_device = torch.device("cuda:0")

diff --git a/test/test_cost.py b/test/test_cost.py
@@ -46,6 +46,7 @@
     get_default_devices,
 )
 from mocking_classes import ContinuousActionConvMockEnv
+from packaging import version
 
 # from torchrl.data.postprocs.utils import expand_as_right
 from tensordict import assert_allclose_td, TensorDict, TensorDictBase
@@ -146,7 +147,7 @@
     _split_and_pad_sequence,
 )
 
-TORCH_VERSION = torch.__version__
+TORCH_VERSION = version.parse(version.parse(torch.__version__).base_version)
 
 # Capture all warnings
 pytestmark = [
@@ -15731,7 +15732,9 @@ def __init__(self):
                 assert p.device == dest
 
 
-@pytest.mark.skipif(TORCH_VERSION < "2.5", reason="requires torch>=2.5")
+@pytest.mark.skipif(
+    TORCH_VERSION < version.parse("2.5.0"), reason="requires torch>=2.5"
+)
 def test_exploration_compile():
     m = ProbabilisticTensorDictModule(
         in_keys=["loc", "scale"],

diff --git a/test/test_distributions.py b/test/test_distributions.py
@@ -13,6 +13,7 @@
 from _utils_internal import get_default_devices
 from tensordict import TensorDictBase
 from torch import autograd, nn
+from torch.utils._pytree import tree_map
 from torchrl.modules import (
     NormalParamWrapper,
     OneHotCategorical,
@@ -182,7 +183,7 @@ class TestTruncatedNormal:
     @pytest.mark.parametrize("device", get_default_devices())
     def test_truncnormal(self, min, max, vecs, upscale, shape, device):
         torch.manual_seed(0)
-        *vecs, min, max, vecs, upscale = torch.utils._pytree.tree_map(
+        *vecs, min, max, vecs, upscale = tree_map(
             lambda t: torch.as_tensor(t, device=device),
             (*vecs, min, max, vecs, upscale),
         )

diff --git a/test/test_exploration.py b/test/test_exploration.py
@@ -757,7 +757,7 @@ def test_consistent_dropout(self, dropout_p, parallel_spec, device):
 
         # NOTE: Please only put a module with one dropout layer.
         # That's how this test is constructed anyways.
-        @torch.no_grad
+        @torch.no_grad()
         def inner_verify_routine(module, env):
             # Perform transitions.
             collector = SyncDataCollector(

diff --git a/test/test_helpers.py b/test/test_helpers.py
@@ -50,7 +50,7 @@
     make_dqn_actor,
 )
 
-TORCH_VERSION = version.parse(torch.__version__)
+TORCH_VERSION = version.parse(version.parse(torch.__version__).base_version)
 if TORCH_VERSION < version.parse("1.12.0"):
     UNSQUEEZE_SINGLETON = True
 else:

diff --git a/torchrl/collectors/collectors.py b/torchrl/collectors/collectors.py
@@ -35,7 +35,9 @@
 )
 from tensordict.base import NO_DEFAULT
 from tensordict.nn import CudaGraphModule, TensorDictModule
+from tensordict.utils import Buffer
 from torch import multiprocessing as mp
+from torch.nn import Parameter
 from torch.utils.data import IterableDataset
 
 from torchrl._utils import (
@@ -202,17 +204,17 @@ def map_weight(
             policy_device=policy_device,
         ):
 
-            is_param = isinstance(weight, nn.Parameter)
-            is_buffer = isinstance(weight, nn.Buffer)
+            is_param = isinstance(weight, Parameter)
+            is_buffer = isinstance(weight, Buffer)
             weight = weight.data
             if weight.device != policy_device:
                 weight = weight.to(policy_device)
             elif weight.device.type in ("cpu", "mps"):
                 weight = weight.share_memory_()
             if is_param:
-                weight = nn.Parameter(weight, requires_grad=False)
+                weight = Parameter(weight, requires_grad=False)
             elif is_buffer:
-                weight = nn.Buffer(weight)
+                weight = Buffer(weight)
             return weight
 
         # Create a stateless policy, then populate this copy with params on device
@@ -3089,12 +3091,12 @@ def cast_tensor(x, MPS_ERROR=MPS_ERROR):
 
 
 def _make_meta_params(param):
-    is_param = isinstance(param, nn.Parameter)
+    is_param = isinstance(param, Parameter)
 
     pd = param.detach().to("meta")
 
     if is_param:
-        pd = nn.Parameter(pd, requires_grad=False)
+        pd = Parameter(pd, requires_grad=False)
     return pd
 
 

diff --git a/torchrl/data/replay_buffers/replay_buffers.py b/torchrl/data/replay_buffers/replay_buffers.py
@@ -31,6 +31,7 @@
 from tensordict.nn.utils import _set_dispatch_td_nn_modules
 from tensordict.utils import expand_as_right, expand_right
 from torch import Tensor
+from torch.utils._pytree import tree_map
 
 from torchrl._utils import _make_ordinal_device, accept_remote_rref_udf_invocation
 from torchrl.data.replay_buffers.samplers import (
@@ -319,9 +320,7 @@ def dim_extend(self, value):
     def _transpose(self, data):
         if is_tensor_collection(data):
             return data.transpose(self.dim_extend, 0)
-        return torch.utils._pytree.tree_map(
-            lambda x: x.transpose(self.dim_extend, 0), data
-        )
+        return tree_map(lambda x: x.transpose(self.dim_extend, 0), data)
 
     def _get_collate_fn(self, collate_fn):
         self._collate_fn = (

diff --git a/torchrl/data/replay_buffers/storages.py b/torchrl/data/replay_buffers/storages.py
@@ -1367,16 +1367,44 @@ def _collate_list_tensordict(x):
     return out
 
 
+@implement_for("torch", "2.4")
 def _stack_anything(data):
     if is_tensor_collection(data[0]):
         return LazyStackedTensorDict.maybe_dense_stack(data)
-    return torch.utils._pytree.tree_map(
+    return tree_map(
         lambda *x: torch.stack(x),
         *data,
         is_leaf=lambda x: isinstance(x, torch.Tensor) or is_tensor_collection(x),
     )
 
 
+@implement_for("torch", None, "2.4")
+def _stack_anything(data):  # noqa: F811
+    from tensordict import _pytree
+
+    if not _pytree.PYTREE_REGISTERED_TDS:
+        raise RuntimeError(
+            "TensorDict is not registered within PyTree. "
+            "If you see this error, it means tensordicts instances cannot be natively stacked using tree_map. "
+            "To solve this issue, (a) upgrade pytorch to a version > 2.4, or (b) make sure TensorDict is registered in PyTree. "
+            "If this error persists, open an issue on https://github.com/pytorch/rl/issues"
+        )
+    if is_tensor_collection(data[0]):
+        return LazyStackedTensorDict.maybe_dense_stack(data)
+    flat_trees = []
+    spec = None
+    for d in data:
+        flat_tree, spec = tree_flatten(d)
+        flat_trees.append(flat_tree)
+
+    leaves = []
+    for leaf in zip(*flat_trees):
+        leaf = torch.stack(leaf)
+        leaves.append(leaf)
+
+    return tree_unflatten(leaves, spec)
+
+
 def _collate_id(x):
     return x