[RLlib; new API stack by default] Switch on new API stack by default …

…for SAC and DQN. (ray-project#47217)

Signed-off-by: ujjawal-khare <[email protected]>

doc/source/rllib/doc_code/training.py

@@ -38,7 +38,6 @@
     .api_stack(
         enable_rl_module_and_learner=False, enable_env_runner_and_connector_v2=False
     )
-    .framework("torch")
     .environment("CartPole-v1")
     .env_runners(num_env_runners=0)
     .training(
@@ -113,7 +112,6 @@
     .api_stack(
         enable_rl_module_and_learner=False, enable_env_runner_and_connector_v2=False
     )
-    .framework("torch")
     .environment("CartPole-v1")
     .training(
         replay_buffer_config={

rllib/BUILD

@@ -944,6 +944,13 @@ py_test(
     srcs = ["algorithms/tests/test_callbacks_old_api_stack.py"]
 )
 
+py_test(
+    name = "test_node_failure",
+    tags = ["team:rllib", "tests_dir", "exclusive"],
+    size = "medium",
+    srcs = ["tests/test_node_failure.py"],
+)
+
 py_test(
     name = "test_registry",
     tags = ["team:rllib", "algorithms_dir", "algorithms_dir_generic"],
@@ -988,7 +995,7 @@ py_test(
     name = "test_bc",
     tags = ["team:rllib", "algorithms_dir"],
     size = "medium",
-    # Include the offline data files.
+    # Include the parquet data files.
     data = ["tests/data/cartpole/cartpole-v1_large"],
     srcs = ["algorithms/bc/tests/test_bc.py"]
 )
@@ -1053,7 +1060,7 @@ py_test(
     name = "test_marwil",
     tags = ["team:rllib", "algorithms_dir"],
     size = "large",
-    # Include the offline data files.
+    # Include the parquet data folder.
     data = [
         "tests/data/cartpole/cartpole-v1_large",
         "tests/data/pendulum/pendulum-v1_large",

rllib/algorithms/cql/cql.py

@@ -107,12 +107,6 @@ def __init__(self, algo_class=None):
         }
 
         # Changes to Algorithm's/SACConfig's default:
-
-        # `.api_stack()`
-        self.api_stack(
-            enable_rl_module_and_learner=False,
-            enable_env_runner_and_connector_v2=False,
-        )
         # .reporting()
         self.min_sample_timesteps_per_iteration = 0
         self.min_train_timesteps_per_iteration = 100

rllib/algorithms/dqn/dqn.py

@@ -424,15 +424,23 @@ def validate(self) -> None:
         # Call super's validation method.
         super().validate()
 
-        # Warn about new API stack on by default.
+        # Disallow hybrid API stack for DQN/SAC.
         if self.enable_rl_module_and_learner:
-            logger.warning(
-                "You are running DQN on the new API stack! This is the new default "
-                "behavior for this algorithm. If you don't want to use the new API "
-                "stack, set `config.api_stack(enable_rl_module_and_learner=False, "
-                "enable_env_runner_and_connector_v2=False)`. For a detailed "
-                "migration guide, see here: https://docs.ray.io/en/master/rllib/new-api-stack-migration-guide.html"  # noqa
-            )
+            if not self.enable_env_runner_and_connector_v2:
+                raise ValueError(
+                    "Hybrid API stack (`enable_rl_module_and_learner=True` and "
+                    "`enable_env_runner_and_connector_v2=False`) no longer supported "
+                    "for DQN! Set both to True (recommended new API stack) or both to "
+                    "False (old API stack)."
+                )
+            else:
+                logger.warning(
+                    "You are running DQN on the new API stack! This is the new default "
+                    "behavior for this algorithm. If you don't want to use the new API "
+                    "stack, set `config.api_stack(enable_rl_module_and_learner=False, "
+                    "enable_env_runner_and_connector_v2=False)`. For a detailed "
+                    "migration guide, see here: https://docs.ray.io/en/master/rllib/new-api-stack-migration-guide.html"  # noqa
+                )
 
         if (
             not self.enable_rl_module_and_learner

rllib/algorithms/impala/tests/test_vtrace_v2.py

@@ -7,6 +7,7 @@
     vtrace_torch,
     make_time_major,
 )
+from ray.rllib.algorithms.impala.torch.vtrace_torch_v2 import vtrace_torch
 from ray.rllib.algorithms.impala.tests.test_vtrace_old_api_stack import (
     _ground_truth_vtrace_calculation,
 )

rllib/algorithms/ppo/tests/test_ppo.py

@@ -8,12 +8,12 @@
 from ray.rllib.algorithms.callbacks import DefaultCallbacks
 from ray.rllib.core import DEFAULT_MODULE_ID
 from ray.rllib.core.learner.learner import DEFAULT_OPTIMIZER, LR_KEY
-from ray.rllib.core.rl_module.default_model_config import DefaultModelConfig
+
 from ray.rllib.utils.metrics import LEARNER_RESULTS
 from ray.rllib.utils.test_utils import check, check_train_results_new_api_stack
 
 
-def get_model_config(lstm=False):
+def get_model_config(framework, lstm=False):
     return (
         dict(
             use_lstm=True,
@@ -60,93 +60,16 @@ def setUpClass(cls):
     def tearDownClass(cls):
         ray.shutdown()
 
-    def test_ppo_compilation_w_connectors(self):
-        """Test whether PPO can be built with all frameworks w/ connectors."""
-
-        # Build a PPOConfig object.
-        config = (
-            ppo.PPOConfig()
-            .training(
-                num_epochs=2,
-                # Setup lr schedule for testing.
-                lr_schedule=[[0, 5e-5], [128, 0.0]],
-                # Set entropy_coeff to a faulty value to proof that it'll get
-                # overridden by the schedule below (which is expected).
-                entropy_coeff=100.0,
-                entropy_coeff_schedule=[[0, 0.1], [256, 0.0]],
-                train_batch_size=128,
-                model=dict(
-                    # Settings in case we use an LSTM.
-                    lstm_cell_size=10,
-                    max_seq_len=20,
-                ),
-            )
-            .env_runners(
-                num_env_runners=1,
-                # Test with compression.
-                compress_observations=True,
-                enable_connectors=True,
-            )
-            .callbacks(MyCallbacks)
-            .evaluation(
-                evaluation_duration=2,
-                evaluation_duration_unit="episodes",
-                evaluation_num_env_runners=1,
-            )
-        )  # For checking lr-schedule correctness.
-
-        num_iterations = 2
-
-        for env in ["FrozenLake-v1", "ALE/MsPacman-v5"]:
-            print("Env={}".format(env))
-            for lstm in [False, True]:
-                print("LSTM={}".format(lstm))
-                config.training(
-                    model=dict(
-                        use_lstm=lstm,
-                        lstm_use_prev_action=lstm,
-                        lstm_use_prev_reward=lstm,
-                    )
-                )
-
-                algo = config.build(env=env)
-                policy = algo.get_policy()
-                entropy_coeff = algo.get_policy().entropy_coeff
-                lr = policy.cur_lr
-                check(entropy_coeff, 0.1)
-                check(lr, config.lr)
-
-                for i in range(num_iterations):
-                    results = algo.train()
-                    check_train_results(results)
-                    print(results)
-
-                algo.evaluate()
-
-                check_inference_w_connectors(policy, env_name=env)
-                algo.stop()
-
     def test_ppo_compilation_and_schedule_mixins(self):
         """Test whether PPO can be built with all frameworks."""
 
         # Build a PPOConfig object with the `SingleAgentEnvRunner` class.
         config = (
             ppo.PPOConfig()
-            .training(
-                # Setup lr schedule for testing.
-                lr_schedule=[[0, 5e-5], [256, 0.0]],
-                # Set entropy_coeff to a faulty value to proof that it'll get
-                # overridden by the schedule below (which is expected).
-                entropy_coeff=100.0,
-                entropy_coeff_schedule=[[0, 0.1], [512, 0.0]],
-                train_batch_size=256,
-                minibatch_size=128,
-                num_epochs=2,
-                model=dict(
-                    # Settings in case we use an LSTM.
-                    lstm_cell_size=10,
-                    max_seq_len=20,
-                ),
+            # Enable new API stack and use EnvRunner.
+            .api_stack(
+                enable_rl_module_and_learner=True,
+                enable_env_runner_and_connector_v2=True,
             )
             .env_runners(num_env_runners=0)
             .training(
@@ -179,7 +102,9 @@ def test_ppo_compilation_and_schedule_mixins(self):
             print("Env={}".format(env))
             for lstm in [False]:
                 print("LSTM={}".format(lstm))
-                config.rl_module(model_config=get_model_config(lstm=lstm))
+                config.rl_module(
+                    model_config_dict=get_model_config("torch", lstm=lstm)
+                ).framework(eager_tracing=False)
 
                 algo = config.build(env=env)
                 # TODO: Maybe add an API to get the Learner(s) instances within
@@ -205,52 +130,6 @@ def test_ppo_compilation_and_schedule_mixins(self):
                 # algo.evaluate()
                 algo.stop()
 
-    def test_ppo_free_log_std(self):
-        """Tests the free log std option works."""
-        config = (
-            ppo.PPOConfig()
-            .api_stack(
-                enable_rl_module_and_learner=True,
-                enable_env_runner_and_connector_v2=True,
-            )
-            .environment("Pendulum-v1")
-            .env_runners(
-                num_env_runners=1,
-            )
-            .rl_module(
-                model_config=DefaultModelConfig(
-                    fcnet_hiddens=[10],
-                    fcnet_activation="linear",
-                    free_log_std=True,
-                    vf_share_layers=True,
-                ),
-            )
-            .training(
-                gamma=0.99,
-            )
-        )
-
-        algo = config.build()
-        module = algo.get_module(DEFAULT_MODULE_ID)
-
-        # Check the free log std var is created.
-        matching = [v for (n, v) in module.named_parameters() if "log_std" in n]
-        assert len(matching) == 1, matching
-        log_std_var = matching[0]
-
-        def get_value(log_std_var=log_std_var):
-            return log_std_var.detach().cpu().numpy()[0]
-
-        # Check the variable is initially zero.
-        init_std = get_value()
-        assert init_std == 0.0, init_std
-        algo.train()
-
-        # Check the variable is updated.
-        post_std = get_value()
-        assert post_std != 0.0, post_std
-        algo.stop()
-
 
 if __name__ == "__main__":
     import pytest

rllib/algorithms/ppo/tests/test_ppo_old_api_stack.py

@@ -144,6 +144,7 @@ def test_ppo_compilation_w_connectors(self):
                 num_env_runners=1,
                 # Test with compression.
                 compress_observations=True,
+                enable_connectors=True,
             )
             .callbacks(MyCallbacks)
             .evaluation(

rllib/algorithms/sac/sac.py

@@ -485,14 +485,21 @@ def validate(self) -> None:
                     "and `alpha_lr`, for the actor, critic, and the hyperparameter "
                     "`alpha`, respectively and set `config.lr` to None."
                 )
-            # Warn about new API stack on by default.
-            logger.warning(
-                "You are running SAC on the new API stack! This is the new default "
-                "behavior for this algorithm. If you don't want to use the new API "
-                "stack, set `config.api_stack(enable_rl_module_and_learner=False, "
-                "enable_env_runner_and_connector_v2=False)`. For a detailed "
-                "migration guide, see here: https://docs.ray.io/en/master/rllib/new-api-stack-migration-guide.html"  # noqa
-            )
+            elif not self.enable_env_runner_and_connector_v2:
+                raise ValueError(
+                    "Hybrid API stack (`enable_rl_module_and_learner=True` and "
+                    "`enable_env_runner_and_connector_v2=False`) no longer supported "
+                    "for SAC! Set both to True (recommended new API stack) or both to "
+                    "False (old API stack)."
+                )
+            else:
+                logger.warning(
+                    "You are running SAC on the new API stack! This is the new default "
+                    "behavior for this algorithm. If you don't want to use the new API "
+                    "stack, set `config.api_stack(enable_rl_module_and_learner=False, "
+                    "enable_env_runner_and_connector_v2=False)`. For a detailed "
+                    "migration guide, see here: https://docs.ray.io/en/master/rllib/new-api-stack-migration-guide.html"  # noqa
+                )
 
     @override(AlgorithmConfig)
     def get_rollout_fragment_length(self, worker_index: int = 0) -> int: