Support for device=auto buffers and set it as default value (DLR-RM…

…#1009) * Default device is "auto" for buffer + auto device support in BufferBaseClass * Update docstring * Update tests * Unify tests * Update changelog * Fix tests on CUDA device Co-authored-by: Antonin RAFFIN <[email protected]> Co-authored-by: Antonin Raffin <[email protected]>
DavideLuc · Aug 16, 2022 · 73822c3 · 73822c3
1 parent 792e3bc
commit 73822c3
Show file tree

Hide file tree

Showing 4 changed files with 61 additions and 13 deletions.
diff --git a/docs/misc/changelog.rst b/docs/misc/changelog.rst
@@ -29,6 +29,8 @@ Others:
 ^^^^^^^
 - Fixed ``DictReplayBuffer.next_observations`` typing (@qgallouedec)
 
+- Added support for ``device="auto"`` in buffers and made it default (@qgallouedec)
+
 Documentation:
 ^^^^^^^^^^^^^^
 - Fixed typo in docstring "nature" -> "Nature" (@Melanol)

diff --git a/stable_baselines3/common/buffers.py b/stable_baselines3/common/buffers.py
@@ -13,6 +13,7 @@
     ReplayBufferSamples,
     RolloutBufferSamples,
 )
+from stable_baselines3.common.utils import get_device
 from stable_baselines3.common.vec_env import VecNormalize
 
 try:
@@ -39,7 +40,7 @@ def __init__(
         buffer_size: int,
         observation_space: spaces.Space,
         action_space: spaces.Space,
-        device: Union[th.device, str] = "cpu",
+        device: Union[th.device, str] = "auto",
         n_envs: int = 1,
     ):
         super().__init__()
@@ -51,7 +52,7 @@ def __init__(
         self.action_dim = get_action_dim(action_space)
         self.pos = 0
         self.full = False
-        self.device = device
+        self.device = get_device(device)
         self.n_envs = n_envs
 
     @staticmethod
@@ -157,7 +158,7 @@ class ReplayBuffer(BaseBuffer):
     :param buffer_size: Max number of element in the buffer
     :param observation_space: Observation space
     :param action_space: Action space
-    :param device:
+    :param device: PyTorch device
     :param n_envs: Number of parallel environments
     :param optimize_memory_usage: Enable a memory efficient variant
         of the replay buffer which reduces by almost a factor two the memory used,
@@ -175,7 +176,7 @@ def __init__(
         buffer_size: int,
         observation_space: spaces.Space,
         action_space: spaces.Space,
-        device: Union[th.device, str] = "cpu",
+        device: Union[th.device, str] = "auto",
         n_envs: int = 1,
         optimize_memory_usage: bool = False,
         handle_timeout_termination: bool = True,
@@ -328,7 +329,7 @@ class RolloutBuffer(BaseBuffer):
     :param buffer_size: Max number of element in the buffer
     :param observation_space: Observation space
     :param action_space: Action space
-    :param device:
+    :param device: PyTorch device
     :param gae_lambda: Factor for trade-off of bias vs variance for Generalized Advantage Estimator
         Equivalent to classic advantage when set to 1.
     :param gamma: Discount factor
@@ -340,7 +341,7 @@ def __init__(
         buffer_size: int,
         observation_space: spaces.Space,
         action_space: spaces.Space,
-        device: Union[th.device, str] = "cpu",
+        device: Union[th.device, str] = "auto",
         gae_lambda: float = 1,
         gamma: float = 0.99,
         n_envs: int = 1,
@@ -493,7 +494,7 @@ class DictReplayBuffer(ReplayBuffer):
     :param buffer_size: Max number of element in the buffer
     :param observation_space: Observation space
     :param action_space: Action space
-    :param device:
+    :param device: PyTorch device
     :param n_envs: Number of parallel environments
     :param optimize_memory_usage: Enable a memory efficient variant
         Disabled for now (see https://github.com/DLR-RM/stable-baselines3/pull/243#discussion_r531535702)
@@ -507,7 +508,7 @@ def __init__(
         buffer_size: int,
         observation_space: spaces.Space,
         action_space: spaces.Space,
-        device: Union[th.device, str] = "cpu",
+        device: Union[th.device, str] = "auto",
         n_envs: int = 1,
         optimize_memory_usage: bool = False,
         handle_timeout_termination: bool = True,
@@ -658,7 +659,7 @@ class DictRolloutBuffer(RolloutBuffer):
     :param buffer_size: Max number of element in the buffer
     :param observation_space: Observation space
     :param action_space: Action space
-    :param device:
+    :param device: PyTorch device
     :param gae_lambda: Factor for trade-off of bias vs variance for Generalized Advantage Estimator
         Equivalent to Monte-Carlo advantage estimate when set to 1.
     :param gamma: Discount factor
@@ -670,7 +671,7 @@ def __init__(
         buffer_size: int,
         observation_space: spaces.Space,
         action_space: spaces.Space,
-        device: Union[th.device, str] = "cpu",
+        device: Union[th.device, str] = "auto",
         gae_lambda: float = 1,
         gamma: float = 0.99,
         n_envs: int = 1,

diff --git a/stable_baselines3/her/her_replay_buffer.py b/stable_baselines3/her/her_replay_buffer.py
@@ -73,7 +73,7 @@ def __init__(
         self,
         env: VecEnv,
         buffer_size: int,
-        device: Union[th.device, str] = "cpu",
+        device: Union[th.device, str] = "auto",
         replay_buffer: Optional[DictReplayBuffer] = None,
         max_episode_length: Optional[int] = None,
         n_sampled_goal: int = 4,

diff --git a/tests/test_buffers.py b/tests/test_buffers.py
@@ -4,9 +4,10 @@
 import torch as th
 from gym import spaces
 
-from stable_baselines3.common.buffers import DictReplayBuffer, ReplayBuffer
+from stable_baselines3.common.buffers import DictReplayBuffer, DictRolloutBuffer, ReplayBuffer, RolloutBuffer
 from stable_baselines3.common.env_util import make_vec_env
 from stable_baselines3.common.type_aliases import DictReplayBufferSamples, ReplayBufferSamples
+from stable_baselines3.common.utils import get_device
 from stable_baselines3.common.vec_env import VecNormalize
 
 
@@ -71,7 +72,7 @@ def test_replay_buffer_normalization(replay_buffer_cls):
     env = make_vec_env(env)
     env = VecNormalize(env)
 
-    buffer = replay_buffer_cls(100, env.observation_space, env.action_space)
+    buffer = replay_buffer_cls(100, env.observation_space, env.action_space, device="cpu")
 
     # Interract and store transitions
     env.reset()
@@ -94,3 +95,47 @@ def test_replay_buffer_normalization(replay_buffer_cls):
             assert th.allclose(observations.mean(0), th.zeros(1), atol=1)
     # Test reward normalization
     assert np.allclose(sample.rewards.mean(0), np.zeros(1), atol=1)
+
+
+@pytest.mark.parametrize("replay_buffer_cls", [DictReplayBuffer, DictRolloutBuffer, ReplayBuffer, RolloutBuffer])
+@pytest.mark.parametrize("device", ["cpu", "cuda", "auto"])
+def test_device_buffer(replay_buffer_cls, device):
+    if device == "cuda" and not th.cuda.is_available():
+        pytest.skip("CUDA not available")
+
+    env = {
+        RolloutBuffer: DummyEnv,
+        DictRolloutBuffer: DummyDictEnv,
+        ReplayBuffer: DummyEnv,
+        DictReplayBuffer: DummyDictEnv,
+    }[replay_buffer_cls]
+    env = make_vec_env(env)
+
+    buffer = replay_buffer_cls(100, env.observation_space, env.action_space, device=device)
+
+    # Interract and store transitions
+    obs = env.reset()
+    for _ in range(100):
+        action = env.action_space.sample()
+        next_obs, reward, done, info = env.step(action)
+        if replay_buffer_cls in [RolloutBuffer, DictRolloutBuffer]:
+            episode_start, values, log_prob = np.zeros(1), th.zeros(1), th.ones(1)
+            buffer.add(obs, action, reward, episode_start, values, log_prob)
+        else:
+            buffer.add(obs, next_obs, action, reward, done, info)
+        obs = next_obs
+
+    # Get data from the buffer
+    if replay_buffer_cls in [RolloutBuffer, DictRolloutBuffer]:
+        data = buffer.get(50)
+    elif replay_buffer_cls in [ReplayBuffer, DictReplayBuffer]:
+        data = buffer.sample(50)
+
+    # Check that all data are on the desired device
+    desired_device = get_device(device).type
+    for value in list(data):
+        if isinstance(value, dict):
+            for key in value.keys():
+                assert value[key].device.type == desired_device
+        elif isinstance(value, th.Tensor):
+            assert value.device.type == desired_device