[RLlib] PPO torch RLTrainer

rllib/BUILD

@@ -1133,6 +1133,14 @@ py_test(
     srcs = ["algorithms/ppo/tests/test_ppo_rl_module.py"]
 )
 
+
+py_test(
+    name = "test_ppo_rl_trainer",
+    tags = ["team:rllib", "algorithms_dir"],
+    size = "medium",
+    srcs = ["algorithms/ppo/tests/test_ppo_rl_trainer.py"]
+)
+
 # PPO Reproducibility
 py_test(
     name = "test_repro_ppo",
@@ -3840,6 +3848,44 @@ py_test(
     ]
 )
 
+# --------------------------------------------------------------------
+# examples/rl_trainer directory
+#
+# 
+# Description: These are RLlib tests for the new multi-gpu enabled 
+# training stack via RLTrainers.
+#
+# NOTE: Add tests alphabetically to this list.
+# --------------------------------------------------------------------
+
+py_test(
+    name = "examples/rl_trainer/multi_agent_cartpole_ppo_torch",
+    main = "examples/rl_trainer/multi_agent_cartpole_ppo.py",
+    tags = ["team:rllib", "exclusive", "examples", "no-gpu"],
+    size = "medium",
+    srcs = ["examples/rl_trainer/multi_agent_cartpole_ppo.py"],
+    args = ["--as-test", "--framework=torch", "--num-gpus=0"]
+)
+
+py_test(
+    name = "examples/rl_trainer/multi_agent_cartpole_ppo_torch_gpu",
+    main = "examples/rl_trainer/multi_agent_cartpole_ppo.py",
+    tags = ["team:rllib", "exclusive", "examples", "gpu"],
+    size = "medium",
+    srcs = ["examples/rl_trainer/multi_agent_cartpole_ppo.py"],
+    args = ["--as-test", "--framework=torch", "--num-gpus=1"]
+)
+
+
+py_test(
+    name = "examples/rl_trainer/multi_agent_cartpole_ppo_torch_multi_gpu",
+    main = "examples/rl_trainer/multi_agent_cartpole_ppo.py",
+    tags = ["team:rllib", "exclusive", "examples", "multi-gpu"],
+    size = "medium",
+    srcs = ["examples/rl_trainer/multi_agent_cartpole_ppo.py"],
+    args = ["--as-test", "--framework=torch", "--num-gpus=2"]
+)
+
 # --------------------------------------------------------------------
 # examples/documentation directory
 #

rllib/algorithms/algorithm.py

@@ -695,7 +695,7 @@ def setup(self, config: AlgorithmConfig) -> None:
             # TODO (Kourosh): This is an interim solution where policies and modules
             # co-exist. In this world we have both policy_map and MARLModule that need
             # to be consistent with one another. To make a consistent parity between
-            # the two we need to loop throught the policy modules and create a simple
+            # the two we need to loop through the policy modules and create a simple
             # MARLModule from the RLModule within each policy.
             local_worker = self.workers.local_worker()
             module_specs = {}
@@ -715,6 +715,10 @@ def setup(self, config: AlgorithmConfig) -> None:
             trainer_runner_config = self.config.get_trainer_runner_config(module_spec)
             self.trainer_runner = trainer_runner_config.build()
 
+            # sync the weights from local rollout worker to trainers
+            weights = local_worker.get_weights()
+            self.trainer_runner.set_weights(weights)
+
         # Run `on_algorithm_init` callback after initialization is done.
         self.callbacks.on_algorithm_init(algorithm=self)
 
@@ -858,7 +862,7 @@ def evaluate(
         # Sync weights to the evaluation WorkerSet.
         if self.evaluation_workers is not None:
             self.evaluation_workers.sync_weights(
-                from_worker=self.workers.local_worker()
+                from_worker_or_trainer=self.workers.local_worker()
             )
             self._sync_filters_if_needed(
                 from_worker=self.workers.local_worker(),
@@ -1376,11 +1380,11 @@ def training_step(self) -> ResultDict:
             # TODO (Avnish): Implement this on trainer_runner.get_weights().
             # TODO (Kourosh): figure out how we are going to sync MARLModule
             # weights to MARLModule weights under the policy_map objects?
-            from_worker = None
+            from_worker_or_trainer = None
             if self.config._enable_rl_trainer_api:
-                from_worker = self.trainer_runner
+                from_worker_or_trainer = self.trainer_runner
             self.workers.sync_weights(
-                from_worker=from_worker,
+                from_worker_or_trainer=from_worker_or_trainer,
                 policies=list(train_results.keys()),
                 global_vars=global_vars,
             )
@@ -2132,10 +2136,13 @@ def default_resource_request(
         eval_cf.freeze()
 
         # resources for local worker
-        local_worker = {
-            "CPU": cf.num_cpus_for_local_worker,
-            "GPU": 0 if cf._fake_gpus else cf.num_gpus,
-        }
+        if cf._enable_rl_trainer_api:
+            local_worker = {"CPU": cf.num_cpus_for_local_worker, "GPU": 0}
+        else:
+            local_worker = {
+                "CPU": cf.num_cpus_for_local_worker,
+                "GPU": 0 if cf._fake_gpus else cf.num_gpus,
+            }
 
         bundles = [local_worker]
 
@@ -2179,6 +2186,28 @@ def default_resource_request(
 
         bundles += rollout_workers + evaluation_bundle
 
+        if cf._enable_rl_trainer_api:
+            # resources for the trainer
+            if cf.num_trainer_workers == 0:
+                # if num_trainer_workers is 0, then we need to allocate one gpu if
+                # num_gpus_per_trainer_worker is greater than 0.
+                trainer_bundle = [
+                    {
+                        "CPU": cf.num_cpus_per_trainer_worker,
+                        "GPU": cf.num_gpus_per_trainer_worker,
+                    }
+                ]
+            else:
+                trainer_bundle = [
+                    {
+                        "CPU": cf.num_cpus_per_trainer_worker,
+                        "GPU": cf.num_gpus_per_trainer_worker,
+                    }
+                    for _ in range(cf.num_trainer_workers)
+                ]
+
+            bundles += trainer_bundle
+
         # Return PlacementGroupFactory containing all needed resources
         # (already properly defined as device bundles).
         return PlacementGroupFactory(

rllib/algorithms/algorithm_config.py

@@ -883,6 +883,13 @@ def validate(self) -> None:
             rl_module_class_path = self.get_default_rl_module_class()
             self.rl_module_class = _resolve_class_path(rl_module_class_path)
 
+        # make sure the resource requirements for trainer runner is valid
+        if self.num_trainer_workers == 0 and self.num_gpus_per_worker > 1:
+            raise ValueError(
+                "num_gpus_per_worker must be 0 (cpu) or 1 (gpu) when using local mode "
+                "(i.e. num_trainer_workers = 0)"
+            )
+
         # resolve rl_trainer class
         if self._enable_rl_trainer_api and self.rl_trainer_class is None:
             rl_trainer_class_path = self.get_default_rl_trainer_class()

rllib/algorithms/ppo/ppo.py

@@ -16,6 +16,7 @@
 from ray.rllib.algorithms.algorithm import Algorithm
 from ray.rllib.algorithms.algorithm_config import AlgorithmConfig, NotProvided
 from ray.rllib.algorithms.pg import PGConfig
+from ray.rllib.algorithms.ppo.ppo_rl_trainer_config import PPORLTrainerHPs
 from ray.rllib.execution.rollout_ops import (
     standardize_fields,
 )
@@ -42,6 +43,8 @@
 
 if TYPE_CHECKING:
     from ray.rllib.core.rl_module import RLModule
+    from ray.rllib.core.rl_trainer.rl_trainer import RLTrainer
+
 
 logger = logging.getLogger(__name__)
 
@@ -89,6 +92,7 @@ def __init__(self, algo_class=None):
         # fmt: off
         # __sphinx_doc_begin__
         # PPO specific settings:
+        self._rl_trainer_hps = PPORLTrainerHPs()
         self.use_critic = True
         self.use_gae = True
         self.lambda_ = 1.0
@@ -131,6 +135,17 @@ def get_default_rl_module_class(self) -> Union[Type["RLModule"], str]:
         else:
             raise ValueError(f"The framework {self.framework_str} is not supported.")
 
+    @override(AlgorithmConfig)
+    def get_default_rl_trainer_class(self) -> Union[Type["RLTrainer"], str]:
+        if self.framework_str == "torch":
+            from ray.rllib.algorithms.ppo.torch.ppo_torch_rl_trainer import (
+                PPOTorchRLTrainer,
+            )
+
+            return PPOTorchRLTrainer
+        else:
+            raise ValueError(f"The framework {self.framework_str} is not supported.")
+
     @override(AlgorithmConfig)
     def training(
         self,
@@ -201,12 +216,16 @@ def training(
             self.lr_schedule = lr_schedule
         if use_critic is not NotProvided:
             self.use_critic = use_critic
+            # TODO (Kourosh) This is experimental. Set rl_trainer_hps parameters as
+            # well. Don't forget to remove .use_critic from algorithm config.
+            self._rl_trainer_hps.use_critic = use_critic
         if use_gae is not NotProvided:
             self.use_gae = use_gae
         if lambda_ is not NotProvided:
             self.lambda_ = lambda_
         if kl_coeff is not NotProvided:
             self.kl_coeff = kl_coeff
+            self._rl_trainer_hps.kl_coeff = kl_coeff
         if sgd_minibatch_size is not NotProvided:
             self.sgd_minibatch_size = sgd_minibatch_size
         if num_sgd_iter is not NotProvided:
@@ -215,18 +234,24 @@ def training(
             self.shuffle_sequences = shuffle_sequences
         if vf_loss_coeff is not NotProvided:
             self.vf_loss_coeff = vf_loss_coeff
+            self._rl_trainer_hps.vf_loss_coeff = vf_loss_coeff
         if entropy_coeff is not NotProvided:
             self.entropy_coeff = entropy_coeff
+            self._rl_trainer_hps.entropy_coeff = entropy_coeff
         if entropy_coeff_schedule is not NotProvided:
             self.entropy_coeff_schedule = entropy_coeff_schedule
+            self._rl_trainer_hps.entropy_coeff_schedule = entropy_coeff_schedule
         if clip_param is not NotProvided:
             self.clip_param = clip_param
+            self._rl_trainer_hps.clip_param = clip_param
         if vf_clip_param is not NotProvided:
             self.vf_clip_param = vf_clip_param
+            self._rl_trainer_hps.vf_clip_param = vf_clip_param
         if grad_clip is not NotProvided:
             self.grad_clip = grad_clip
         if kl_target is not NotProvided:
             self.kl_target = kl_target
+            self._rl_trainer_hps.kl_target = kl_target
 
         return self
 
@@ -366,14 +391,39 @@ def training_step(self) -> ResultDict:
         train_batch = standardize_fields(train_batch, ["advantages"])
         # Train
         if self.config._enable_rl_trainer_api:
-            train_results = self.trainer_runner.update(train_batch)
+            # TODO (Kourosh) Clearly define what train_batch_size
+            # vs. sgd_minibatch_size and num_sgd_iter is in the config.
+            # TODO (Kourosh) Do this inside the RL Trainer so
+            # that we don't have to do this back and forth
+            # communication between driver and the remote
+            # trainer workers
+
+            train_results = self.trainer_runner.fit(
+                train_batch,
+                minibatch_size=self.config.sgd_minibatch_size,
+                num_iters=self.config.num_sgd_iter,
+            )
+
         elif self.config.simple_optimizer:
             train_results = train_one_step(self, train_batch)
         else:
             train_results = multi_gpu_train_one_step(self, train_batch)
 
-        policies_to_update = list(train_results.keys())
+        if self.config._enable_rl_trainer_api:
+            # the train results's loss keys are pids to their loss values. But we also
+            # return a total_loss key at the same level as the pid keys. So we need to
+            # subtract that to get the total set of pids to update.
+            # TODO (Kourosh): We need to make a better design for the hierarchy of the
+            # train results, so that all the policy ids end up in the same level.
+            # TODO (Kourosh): We should also not be using train_results as a message
+            # passing medium to infer whcih policies to update. We could use
+            # policies_to_train variable that is given by the user to infer this.
+            policies_to_update = set(train_results["loss"].keys()) - {"total_loss"}
+        else:
+            policies_to_update = list(train_results.keys())
 
+        # TODO (Kourosh): num_grad_updates per each policy should be accessible via
+        # train_results
         global_vars = {
             "timestep": self._counters[NUM_AGENT_STEPS_SAMPLED],
             "num_grad_updates_per_policy": {
@@ -384,24 +434,34 @@ def training_step(self) -> ResultDict:
 
         # Update weights - after learning on the local worker - on all remote
         # workers.
-        if self.workers.num_remote_workers() > 0:
-            with self._timers[SYNCH_WORKER_WEIGHTS_TIMER]:
-                from_worker = None
+        with self._timers[SYNCH_WORKER_WEIGHTS_TIMER]:
+            if self.workers.num_remote_workers() > 0:
+                from_worker_or_trainer = None
                 if self.config._enable_rl_trainer_api:
-                    from_worker = self.trainer_runner
+                    # sync weights from trainer_runner to all rollout workers
+                    from_worker_or_trainer = self.trainer_runner
                 self.workers.sync_weights(
-                    from_worker=from_worker,
-                    policies=list(train_results.keys()),
+                    from_worker_or_trainer=from_worker_or_trainer,
+                    policies=policies_to_update,
                     global_vars=global_vars,
                 )
+            elif self.config._enable_rl_trainer_api:
+                weights = self.trainer_runner.get_weights()
+                self.workers.local_worker().set_weights(weights)
 
         if self.config._enable_rl_trainer_api:
             kl_dict = {
-                pid: pinfo[LEARNER_STATS_KEY].get("kl")
-                for pid, pinfo in train_results.items()
+                # TODO (Kourosh): Train results don't match the old format. The thing
+                # that used to be under `kl` is now under `mean_kl_loss`. Fix this. Do
+                # we need get here?
+                pid: train_results["loss"][pid].get("mean_kl_loss")
+                for pid in policies_to_update
             }
             # triggers a special update method on RLOptimizer to update the KL values.
-            self.trainer_runner.additional_update(kl_values=kl_dict)
+            self.trainer_runner.additional_update(
+                sampled_kl_values=kl_dict,
+                timestep=self._counters[NUM_AGENT_STEPS_SAMPLED],
+            )
 
             return train_results
 

rllib/algorithms/ppo/ppo_rl_trainer_config.py

@@ -0,0 +1,21 @@
+from dataclasses import dataclass
+from typing import List, Optional, Union
+
+from ray.rllib.core.rl_trainer.rl_trainer import RLTrainerHPs
+
+
+@dataclass
+class PPORLTrainerHPs(RLTrainerHPs):
+    """Hyperparameters for the PPO RL Trainer"""
+
+    kl_coeff: float = 0.2
+    kl_target: float = 0.01
+    use_critic: bool = True
+    clip_param: float = 0.3
+    vf_clip_param: float = 10.0
+    entropy_coeff: float = 0.0
+    vf_loss_coeff: float = 1.0
+
+    # experimental placeholder for things that could be part of the base RLTrainerHPs
+    lr_schedule: Optional[List[List[Union[int, float]]]] = None
+    entropy_coeff_schedule: Optional[List[List[Union[int, float]]]] = None