Support BC

d3rlpy/algos/qlearning/bc.py

@@ -49,6 +49,7 @@ class BCConfig(LearnableConfig):
         observation_scaler (d3rlpy.preprocessing.ObservationScaler):
             Observation preprocessor.
         action_scaler (d3rlpy.preprocessing.ActionScaler): Action preprocessor.
+        compile_graph (bool): Flag to enable JIT compilation and CUDAGraph.
     """
 
     batch_size: int = 100
@@ -95,7 +96,7 @@ def inner_create_impl(
         optim = self._config.optim_factory.create(
             imitator.named_modules(),
             lr=self._config.learning_rate,
-            compiled=False,
+            compiled=self.compiled,
         )
 
         modules = BCModules(optim=optim, imitator=imitator)
@@ -105,6 +106,7 @@ def inner_create_impl(
             action_size=action_size,
             modules=modules,
             policy_type=self._config.policy_type,
+            compiled=self.compiled,
             device=self._device,
         )
 
@@ -139,6 +141,7 @@ class DiscreteBCConfig(LearnableConfig):
         beta (float): Reguralization factor.
         observation_scaler (d3rlpy.preprocessing.ObservationScaler):
             Observation preprocessor.
+        compile_graph (bool): Flag to enable JIT compilation and CUDAGraph.
     """
 
     batch_size: int = 100
@@ -172,7 +175,7 @@ def inner_create_impl(
         optim = self._config.optim_factory.create(
             imitator.named_modules(),
             lr=self._config.learning_rate,
-            compiled=False,
+            compiled=self.compiled,
         )
 
         modules = DiscreteBCModules(optim=optim, imitator=imitator)
@@ -182,6 +185,7 @@ def inner_create_impl(
             action_size=action_size,
             modules=modules,
             beta=self._config.beta,
+            compiled=self.compiled,
             device=self._device,
         )
 

d3rlpy/algos/qlearning/torch/bc_impl.py

@@ -1,6 +1,6 @@
 import dataclasses
 from abc import ABCMeta, abstractmethod
-from typing import Dict, Union
+from typing import Callable, Dict, Union
 
 import torch
 from torch.optim import Optimizer
@@ -18,7 +18,7 @@
     compute_stochastic_imitation_loss,
 )
 from ....optimizers import OptimizerWrapper
-from ....torch_utility import Modules, TorchMiniBatch
+from ....torch_utility import CudaGraphWrapper, Modules, TorchMiniBatch
 from ....types import Shape, TorchObservation
 from ..base import QLearningAlgoImplBase
 
@@ -32,12 +32,14 @@ class BCBaseModules(Modules):
 
 class BCBaseImpl(QLearningAlgoImplBase, metaclass=ABCMeta):
     _modules: BCBaseModules
+    _compute_imitator_grad: Callable[[TorchMiniBatch], ImitationLoss]
 
     def __init__(
         self,
         observation_shape: Shape,
         action_size: int,
         modules: BCBaseModules,
+        compiled: bool,
         device: str,
     ):
         super().__init__(
@@ -46,15 +48,21 @@ def __init__(
             modules=modules,
             device=device,
         )
+        self._compute_imitator_grad = (
+            CudaGraphWrapper(self.compute_imitator_grad)
+            if compiled
+            else self.compute_imitator_grad
+        )
 
-    def update_imitator(self, batch: TorchMiniBatch) -> Dict[str, float]:
+    def compute_imitator_grad(self, batch: TorchMiniBatch) -> ImitationLoss:
         self._modules.optim.zero_grad()
-
         loss = self.compute_loss(batch.observations, batch.actions)
-
         loss.loss.backward()
-        self._modules.optim.step()
+        return loss
 
+    def update_imitator(self, batch: TorchMiniBatch) -> Dict[str, float]:
+        loss = self._compute_imitator_grad(batch)
+        self._modules.optim.step()
         return asdict_as_float(loss)
 
     @abstractmethod
@@ -92,12 +100,14 @@ def __init__(
         action_size: int,
         modules: BCModules,
         policy_type: str,
+        compiled: bool,
         device: str,
     ):
         super().__init__(
             observation_shape=observation_shape,
             action_size=action_size,
             modules=modules,
+            compiled=compiled,
             device=device,
         )
         self._policy_type = policy_type
@@ -145,12 +155,14 @@ def __init__(
         action_size: int,
         modules: DiscreteBCModules,
         beta: float,
+        compiled: bool,
         device: str,
     ):
         super().__init__(
             observation_shape=observation_shape,
             action_size=action_size,
             modules=modules,
+            compiled=compiled,
             device=device,
         )
         self._beta = beta