Fix lint errors

d3rlpy/__init__.py

@@ -1,3 +1,4 @@
+# pylint: disable=protected-access
 import random
 
 import gymnasium
@@ -68,9 +69,10 @@ def seed(n: int) -> None:
 # run healthcheck
 run_healthcheck()
 
-# enable autograd compilation
-torch._dynamo.config.compiled_autograd = True
-torch.set_float32_matmul_precision("high")
+if torch.cuda.is_available():
+    # enable autograd compilation
+    torch._dynamo.config.compiled_autograd = True
+    torch.set_float32_matmul_precision("high")
 
 # register Shimmy if available
 try:

d3rlpy/algos/qlearning/awac.py

@@ -70,7 +70,7 @@ class AWACConfig(LearnableConfig):
         n_action_samples (int): Number of sampled actions to calculate
             :math:`A^\pi(s_t, a_t)`.
         n_critics (int): Number of Q functions for ensemble.
-        compile (bool): Flag to enable JIT compilation and CUDAGraph.
+        compile_graph (bool): Flag to enable JIT compilation and CUDAGraph.
     """
 
     actor_learning_rate: float = 3e-4
@@ -86,7 +86,7 @@ class AWACConfig(LearnableConfig):
     lam: float = 1.0
     n_action_samples: int = 1
     n_critics: int = 2
-    compile: bool = False
+    compile_graph: bool = False
 
     def create(
         self, device: DeviceArg = False, enable_ddp: bool = False
@@ -160,7 +160,7 @@ def inner_create_impl(
             tau=self._config.tau,
             lam=self._config.lam,
             n_action_samples=self._config.n_action_samples,
-            compile=self._config.compile and "cuda" in self._device,
+            compile_graph=self._config.compile_graph and "cuda" in self._device,
             device=self._device,
         )
 

d3rlpy/algos/qlearning/bcq.py

@@ -137,7 +137,7 @@ class BCQConfig(LearnableConfig):
         rl_start_step (int): Steps to start to update policy function and Q
             functions. If this is large, RL training would be more stabilized.
         beta (float): KL reguralization term for Conditional VAE.
-        compile (bool): Flag to enable JIT compilation and CUDAGraph.
+        compile_graph (bool): Flag to enable JIT compilation and CUDAGraph.
     """
 
     actor_learning_rate: float = 1e-3
@@ -160,7 +160,7 @@ class BCQConfig(LearnableConfig):
     action_flexibility: float = 0.05
     rl_start_step: int = 0
     beta: float = 0.5
-    compile: bool = False
+    compile_graph: bool = False
 
     def create(
         self, device: DeviceArg = False, enable_ddp: bool = False
@@ -266,7 +266,7 @@ def inner_create_impl(
             action_flexibility=self._config.action_flexibility,
             beta=self._config.beta,
             rl_start_step=self._config.rl_start_step,
-            compile=self._config.compile and "cuda" in self._device,
+            compile_graph=self._config.compile_graph and "cuda" in self._device,
             device=self._device,
         )
 
@@ -334,7 +334,7 @@ class DiscreteBCQConfig(LearnableConfig):
         target_update_interval (int): Interval to update the target network.
         share_encoder (bool): Flag to share encoder between Q-function and
             imitation models.
-        compile (bool): Flag to enable JIT compilation and CUDAGraph.
+        compile_graph (bool): Flag to enable JIT compilation and CUDAGraph.
     """
 
     learning_rate: float = 6.25e-5
@@ -348,7 +348,7 @@ class DiscreteBCQConfig(LearnableConfig):
     beta: float = 0.5
     target_update_interval: int = 8000
     share_encoder: bool = True
-    compile: bool = False
+    compile_graph: bool = False
 
     def create(
         self, device: DeviceArg = False, enable_ddp: bool = False
@@ -427,7 +427,7 @@ def inner_create_impl(
             gamma=self._config.gamma,
             action_flexibility=self._config.action_flexibility,
             beta=self._config.beta,
-            compile=self._config.compile and "cuda" in self._device,
+            compile_graph=self._config.compile_graph and "cuda" in self._device,
             device=self._device,
         )
 

d3rlpy/algos/qlearning/bear.py

@@ -114,7 +114,7 @@ class BEARConfig(LearnableConfig):
             policy training.
         warmup_steps (int): Number of steps to warmup the policy
             function.
-        compile (bool): Flag to enable JIT compilation and CUDAGraph.
+        compile_graph (bool): Flag to enable JIT compilation and CUDAGraph.
     """
 
     actor_learning_rate: float = 1e-4
@@ -146,7 +146,7 @@ class BEARConfig(LearnableConfig):
     mmd_sigma: float = 20.0
     vae_kl_weight: float = 0.5
     warmup_steps: int = 40000
-    compile: bool = False
+    compile_graph: bool = False
 
     def create(
         self, device: DeviceArg = False, enable_ddp: bool = False
@@ -268,7 +268,7 @@ def inner_create_impl(
             mmd_sigma=self._config.mmd_sigma,
             vae_kl_weight=self._config.vae_kl_weight,
             warmup_steps=self._config.warmup_steps,
-            compile=self._config.compile and "cuda" in self._device,
+            compile_graph=self._config.compile_graph and "cuda" in self._device,
             device=self._device,
         )
 

d3rlpy/algos/qlearning/cal_ql.py

@@ -69,7 +69,7 @@ class CalQLConfig(CQLConfig):
             :math:`\log{\sum_a \exp{Q(s, a)}}`.
         soft_q_backup (bool): Flag to use SAC-style backup.
         max_q_backup (bool): Flag to sample max Q-values for target.
-        compile (bool): Flag to enable JIT compilation and CUDAGraph.
+        compile_graph (bool): Flag to enable JIT compilation and CUDAGraph.
     """
 
     def create(
@@ -172,7 +172,7 @@ def inner_create_impl(
             n_action_samples=self._config.n_action_samples,
             soft_q_backup=self._config.soft_q_backup,
             max_q_backup=self._config.max_q_backup,
-            compile=self._config.compile,
+            compile_graph=self._config.compile_graph and "cuda" in self._device,
             device=self._device,
         )
 

d3rlpy/algos/qlearning/cql.py

@@ -100,7 +100,7 @@ class CQLConfig(LearnableConfig):
             :math:`\log{\sum_a \exp{Q(s, a)}}`.
         soft_q_backup (bool): Flag to use SAC-style backup.
         max_q_backup (bool): Flag to sample max Q-values for target.
-        compile (bool): Flag to enable JIT compilation and CUDAGraph.
+        compile_graph (bool): Flag to enable JIT compilation and CUDAGraph.
     """
 
     actor_learning_rate: float = 1e-4
@@ -125,7 +125,7 @@ class CQLConfig(LearnableConfig):
     n_action_samples: int = 10
     soft_q_backup: bool = False
     max_q_backup: bool = False
-    compile: bool = False
+    compile_graph: bool = False
 
     def create(
         self, device: DeviceArg = False, enable_ddp: bool = False
@@ -227,7 +227,7 @@ def inner_create_impl(
             n_action_samples=self._config.n_action_samples,
             soft_q_backup=self._config.soft_q_backup,
             max_q_backup=self._config.max_q_backup,
-            compile=self._config.compile and "cuda" in self._device,
+            compile_graph=self._config.compile_graph and "cuda" in self._device,
             device=self._device,
         )
 
@@ -275,7 +275,7 @@ class DiscreteCQLConfig(LearnableConfig):
         target_update_interval (int): Interval to synchronize the target
             network.
         alpha (float): math:`\alpha` value above.
-        compile (bool): Flag to enable JIT compilation and CUDAGraph.
+        compile_graph (bool): Flag to enable JIT compilation and CUDAGraph.
     """
 
     learning_rate: float = 6.25e-5
@@ -287,7 +287,7 @@ class DiscreteCQLConfig(LearnableConfig):
     n_critics: int = 1
     target_update_interval: int = 8000
     alpha: float = 1.0
-    compile: bool = False
+    compile_graph: bool = False
 
     def create(
         self, device: DeviceArg = False, enable_ddp: bool = False
@@ -341,7 +341,7 @@ def inner_create_impl(
             target_update_interval=self._config.target_update_interval,
             gamma=self._config.gamma,
             alpha=self._config.alpha,
-            compile=self._config.compile and "cuda" in self._device,
+            compile_graph=self._config.compile_graph and "cuda" in self._device,
             device=self._device,
         )
 

d3rlpy/algos/qlearning/crr.py

@@ -99,6 +99,7 @@ class CRRConfig(LearnableConfig):
             ``soft`` target update.
         update_actor_interval (int): Interval to update policy function used
             with ``hard`` target update.
+        compile_graph (bool): Flag to enable JIT compilation and CUDAGraph.
     """
 
     actor_learning_rate: float = 3e-4
@@ -120,6 +121,7 @@ class CRRConfig(LearnableConfig):
     tau: float = 5e-3
     target_update_interval: int = 100
     update_actor_interval: int = 1
+    compile_graph: bool = False
 
     def create(
         self, device: DeviceArg = False, enable_ddp: bool = False
@@ -199,6 +201,7 @@ def inner_create_impl(
             tau=self._config.tau,
             target_update_type=self._config.target_update_type,
             target_update_interval=self._config.target_update_interval,
+            compile_graph=self._config.compile_graph and "cuda" in self._device,
             device=self._device,
         )
 

d3rlpy/algos/qlearning/ddpg.py

@@ -69,7 +69,7 @@ class DDPGConfig(LearnableConfig):
         gamma (float): Discount factor.
         tau (float): Target network synchronization coefficiency.
         n_critics (int): Number of Q functions for ensemble.
-        compile (bool): Flag to enable JIT compilation and CUDAGraph.
+        compile_graph (bool): Flag to enable JIT compilation and CUDAGraph.
     """
 
     batch_size: int = 256
@@ -82,7 +82,7 @@ class DDPGConfig(LearnableConfig):
     q_func_factory: QFunctionFactory = make_q_func_field()
     tau: float = 0.005
     n_critics: int = 1
-    compile: bool = False
+    compile_graph: bool = False
 
     def create(
         self, device: DeviceArg = False, enable_ddp: bool = False
@@ -155,7 +155,7 @@ def inner_create_impl(
             targ_q_func_forwarder=targ_q_func_forwarder,
             gamma=self._config.gamma,
             tau=self._config.tau,
-            compile=self._config.compile,
+            compile_graph=self._config.compile_graph,
             device=self._device,
         )
 

d3rlpy/algos/qlearning/dqn.py

@@ -44,7 +44,7 @@ class DQNConfig(LearnableConfig):
         gamma (float): Discount factor.
         n_critics (int): Number of Q functions for ensemble.
         target_update_interval (int): Interval to update the target network.
-        compile (bool): Flag to enable JIT compilation and CUDAGraph.
+        compile_graph (bool): Flag to enable JIT compilation and CUDAGraph.
     """
 
     batch_size: int = 32
@@ -55,7 +55,7 @@ class DQNConfig(LearnableConfig):
     gamma: float = 0.99
     n_critics: int = 1
     target_update_interval: int = 8000
-    compile: bool = False
+    compile_graph: bool = False
 
     def create(
         self, device: DeviceArg = False, enable_ddp: bool = False
@@ -108,7 +108,7 @@ def inner_create_impl(
             target_update_interval=self._config.target_update_interval,
             modules=modules,
             gamma=self._config.gamma,
-            compile=self._config.compile,
+            compile_graph=self._config.compile_graph and "cuda" in self._device,
             device=self._device,
         )
 
@@ -154,7 +154,7 @@ class DoubleDQNConfig(DQNConfig):
         n_critics (int): Number of Q functions.
         target_update_interval (int): Interval to synchronize the target
             network.
-        compile (bool): Flag to enable JIT compilation and CUDAGraph.
+        compile_graph (bool): Flag to enable JIT compilation and CUDAGraph.
     """
 
     batch_size: int = 32
@@ -165,7 +165,7 @@ class DoubleDQNConfig(DQNConfig):
     gamma: float = 0.99
     n_critics: int = 1
     target_update_interval: int = 8000
-    compile: bool = False
+    compile_graph: bool = False
 
     def create(
         self, device: DeviceArg = False, enable_ddp: bool = False
@@ -218,7 +218,7 @@ def inner_create_impl(
             targ_q_func_forwarder=targ_forwarder,
             target_update_interval=self._config.target_update_interval,
             gamma=self._config.gamma,
-            compile=self._config.compile,
+            compile_graph=self._config.compile_graph and "cuda" in self._device,
             device=self._device,
         )
 

d3rlpy/algos/qlearning/iql.py

@@ -80,7 +80,7 @@ class IQLConfig(LearnableConfig):
         weight_temp (float): Inverse temperature value represented as
             :math:`\beta`.
         max_weight (float): Maximum advantage weight value to clip.
-        compile (bool): Flag to enable JIT compilation and CUDAGraph.
+        compile_graph (bool): Flag to enable JIT compilation and CUDAGraph.
     """
 
     actor_learning_rate: float = 3e-4
@@ -97,7 +97,7 @@ class IQLConfig(LearnableConfig):
     expectile: float = 0.7
     weight_temp: float = 3.0
     max_weight: float = 100.0
-    compile: bool = False
+    compile_graph: bool = False
 
     def create(
         self, device: DeviceArg = False, enable_ddp: bool = False
@@ -177,7 +177,7 @@ def inner_create_impl(
             expectile=self._config.expectile,
             weight_temp=self._config.weight_temp,
             max_weight=self._config.max_weight,
-            compile=self._config.compile and "cuda" in self._device,
+            compile_graph=self._config.compile_graph and "cuda" in self._device,
             device=self._device,
         )
 

d3rlpy/algos/qlearning/nfq.py

@@ -47,6 +47,7 @@ class NFQConfig(LearnableConfig):
         batch_size (int): Mini-batch size.
         gamma (float): Discount factor.
         n_critics (int): Number of Q functions for ensemble.
+        compile_graph (bool): Flag to enable JIT compilation and CUDAGraph.
     """
 
     learning_rate: float = 6.25e-5
@@ -56,6 +57,7 @@ class NFQConfig(LearnableConfig):
     batch_size: int = 32
     gamma: float = 0.99
     n_critics: int = 1
+    compile_graph: bool = False
 
     def create(
         self, device: DeviceArg = False, enable_ddp: bool = False
@@ -108,6 +110,7 @@ def inner_create_impl(
             targ_q_func_forwarder=targ_q_func_forwarder,
             target_update_interval=1,
             gamma=self._config.gamma,
+            compile_graph=self._config.compile_graph and "cuda" in self._device,
             device=self._device,
         )
 

d3rlpy/algos/qlearning/plas.py

@@ -77,7 +77,7 @@ class PLASConfig(LearnableConfig):
         lam (float): Weight factor for critic ensemble.
         warmup_steps (int): Number of steps to warmup the VAE.
         beta (float): KL reguralization term for Conditional VAE.
-        compile (bool): Flag to enable JIT compilation and CUDAGraph.
+        compile_graph (bool): Flag to enable JIT compilation and CUDAGraph.
     """
 
     actor_learning_rate: float = 1e-4
@@ -97,7 +97,7 @@ class PLASConfig(LearnableConfig):
     lam: float = 0.75
     warmup_steps: int = 500000
     beta: float = 0.5
-    compile: bool = False
+    compile_graph: bool = False
 
     def create(
         self, device: DeviceArg = False, enable_ddp: bool = False
@@ -199,7 +199,7 @@ def inner_create_impl(
             lam=self._config.lam,
             beta=self._config.beta,
             warmup_steps=self._config.warmup_steps,
-            compile=self._config.compile and "cuda" in self._device,
+            compile_graph=self._config.compile_graph and "cuda" in self._device,
             device=self._device,
         )
 
@@ -250,7 +250,7 @@ class PLASWithPerturbationConfig(PLASConfig):
         action_flexibility (float): Output scale of perturbation layer.
         warmup_steps (int): Number of steps to warmup the VAE.
         beta (float): KL reguralization term for Conditional VAE.
-        compile (bool): Flag to enable JIT compilation and CUDAGraph.
+        compile_graph (bool): Flag to enable JIT compilation and CUDAGraph.
     """
 
     action_flexibility: float = 0.05
@@ -377,7 +377,7 @@ def inner_create_impl(
             lam=self._config.lam,
             beta=self._config.beta,
             warmup_steps=self._config.warmup_steps,
-            compile=self._config.compile and "cuda" in self._device,
+            compile_graph=self._config.compile_graph and "cuda" in self._device,
             device=self._device,
         )