amend

pytorch · Sep 10, 2024 · fdb7a75 · fdb7a75
1 parent 6aa4b53
commit fdb7a75
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Showing 3 changed files with 6 additions and 4 deletions.
diff --git a/.github/unittest/linux_libs/scripts_gym/environment.yml b/.github/unittest/linux_libs/scripts_gym/environment.yml
@@ -7,7 +7,6 @@ dependencies:
   - pip:
     # Initial version is required to install Atari ROMS in setup_env.sh
     - gym[atari]==0.13
-    - minigrid
     - hypothesis
     - future
     - cloudpickle

diff --git a/.github/unittest/linux_olddeps/scripts_gym_0_13/install.sh b/.github/unittest/linux_olddeps/scripts_gym_0_13/install.sh
@@ -44,6 +44,8 @@ fi
 
 # Solving circular import: https://stackoverflow.com/questions/75501048/how-to-fix-attributeerror-partially-initialized-module-charset-normalizer-has
 pip install -U charset-normalizer
+# numpy upgrade can be a pain
+pip install "numpy<2.0"
 
 # install tensordict
 if [[ "$RELEASE" == 0 ]]; then

diff --git a/torchrl/collectors/collectors.py b/torchrl/collectors/collectors.py
@@ -507,7 +507,8 @@ def __init__(
             # Cuda handles sync
             if torch.cuda.is_available():
                 self._sync_storage = torch.cuda.synchronize
-            elif torch.backends.mps.is_available():
+            elif torch.backends.mps.is_available() and hasattr(torch, "mps"):
+                # Will break for older PT versions which don't have torch.mps
                 self._sync_storage = torch.mps.synchronize
             elif self.storing_device.type == "cpu":
                 self._sync_storage = _do_nothing
@@ -521,7 +522,7 @@ def __init__(
             # Cuda handles sync
             if torch.cuda.is_available():
                 self._sync_env = torch.cuda.synchronize
-            elif torch.backends.mps.is_available():
+            elif torch.backends.mps.is_available() and hasattr(torch, "mps"):
                 self._sync_env = torch.mps.synchronize
             elif self.env_device.type == "cpu":
                 self._sync_env = _do_nothing
@@ -534,7 +535,7 @@ def __init__(
             # Cuda handles sync
             if torch.cuda.is_available():
                 self._sync_policy = torch.cuda.synchronize
-            elif torch.backends.mps.is_available():
+            elif torch.backends.mps.is_available() and hasattr(torch, "mps"):
                 self._sync_policy = torch.mps.synchronize
             elif self.policy_device.type == "cpu":
                 self._sync_policy = _do_nothing