[CI] Fix broken workflows (#2428)

.github/unittest/linux_libs/scripts_gym/batch_scripts.sh

@@ -6,6 +6,7 @@
 DIR="$(cd "$(dirname "$0")" && pwd)"
 
 set -e
+set -v
 
 eval "$(./conda/bin/conda shell.bash hook)"
 conda activate ./env

.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

.github/unittest/linux_libs/scripts_gym/install.sh

@@ -7,6 +7,7 @@ unset PYTORCH_VERSION
 apt-get update && apt-get install -y git wget gcc g++
 
 set -e
+set -v
 
 eval "$(./conda/bin/conda shell.bash hook)"
 conda activate ./env
@@ -39,7 +40,7 @@ printf "Installing PyTorch with %s\n" "${CU_VERSION}"
 if [ "${CU_VERSION:-}" == cpu ] ; then
     conda install pytorch==2.0 torchvision==0.15 cpuonly -c pytorch -y
 else
-    conda install pytorch==2.0.1 torchvision==0.15.2 torchaudio==2.0.2 pytorch-cuda=11.8 -c pytorch -c nvidia -y
+    conda install pytorch==2.0.1 torchvision==0.15.2 torchaudio==2.0.2 pytorch-cuda=11.8 numpy==1.26 numpy-base==1.26 -c pytorch -c nvidia -y
 fi
 
 # Solving circular import: https://stackoverflow.com/questions/75501048/how-to-fix-attributeerror-partially-initialized-module-charset-normalizer-has

.github/unittest/linux_libs/scripts_habitat/setup_env.sh

@@ -67,8 +67,9 @@ pip install pip --upgrade
 
 conda env update --file "${this_dir}/environment.yml" --prune
 
-#conda install habitat-sim withbullet headless -c conda-forge -c aihabitat -y
 conda install habitat-sim withbullet headless -c conda-forge -c aihabitat -y
-conda run python -m pip install git+https://github.com/facebookresearch/habitat-lab.git@stable#subdirectory=habitat-lab
-#conda run python -m pip install git+https://github.com/facebookresearch/habitat-lab.git#subdirectory=habitat-baselines
+git clone https://github.com/facebookresearch/habitat-lab.git
+cd habitat-lab
+pip3 install -e habitat-lab
+pip3 install -e habitat-baselines  # install habitat_baselines
 conda run python -m pip install "gym[atari,accept-rom-license]" pygame

.github/unittest/linux_olddeps/scripts_gym_0_13/install.sh

@@ -39,7 +39,7 @@ printf "Installing PyTorch with %s\n" "${CU_VERSION}"
 if [ "${CU_VERSION:-}" == cpu ] ; then
     conda install pytorch==2.0 torchvision==0.15 cpuonly -c pytorch -y
 else
-    conda install pytorch==2.0.1 torchvision==0.15.2 torchaudio==2.0.2 pytorch-cuda=11.8 -c pytorch -c nvidia -y
+    conda install pytorch==2.0.1 torchvision==0.15.2 torchaudio==2.0.2 pytorch-cuda=11.8 numpy==1.26 numpy-base==1.26 -c pytorch -c nvidia -y
 fi
 
 # Solving circular import: https://stackoverflow.com/questions/75501048/how-to-fix-attributeerror-partially-initialized-module-charset-normalizer-has

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