[RLlib] JAXPolicy prep PR #2 (move get_activation_fn, minor fixes and preparations).

rllib/agents/ddpg/ddpg_torch_model.py

@@ -2,7 +2,8 @@
 
 from ray.rllib.models.torch.misc import SlimFC
 from ray.rllib.models.torch.torch_modelv2 import TorchModelV2
-from ray.rllib.utils.framework import get_activation_fn, try_import_torch
+from ray.rllib.models.utils import get_activation_fn
+from ray.rllib.utils.framework import try_import_torch
 
 torch, nn = try_import_torch()
 

rllib/agents/maml/maml_tf_policy.py

@@ -5,10 +5,10 @@
     vf_preds_fetches, compute_and_clip_gradients, setup_config, \
     ValueNetworkMixin
 from ray.rllib.evaluation.postprocessing import Postprocessing
+from ray.rllib.models.utils import get_activation_fn
 from ray.rllib.policy.sample_batch import SampleBatch
 from ray.rllib.policy.tf_policy_template import build_tf_policy
 from ray.rllib.utils import try_import_tf
-from ray.rllib.utils.framework import get_activation_fn
 
 tf1, tf, tfv = try_import_tf()
 

rllib/agents/sac/sac_torch_model.py

@@ -5,7 +5,8 @@
 
 from ray.rllib.models.torch.misc import SlimFC
 from ray.rllib.models.torch.torch_modelv2 import TorchModelV2
-from ray.rllib.utils.framework import get_activation_fn, try_import_torch
+from ray.rllib.models.utils import get_activation_fn
+from ray.rllib.utils.framework import try_import_torch
 from ray.rllib.utils.spaces.simplex import Simplex
 from ray.rllib.utils.typing import ModelConfigDict, TensorType
 

rllib/models/tf/fcnet.py

@@ -3,7 +3,8 @@
 
 from ray.rllib.models.tf.misc import normc_initializer
 from ray.rllib.models.tf.tf_modelv2 import TFModelV2
-from ray.rllib.utils.framework import get_activation_fn, try_import_tf
+from ray.rllib.models.utils import get_activation_fn
+from ray.rllib.utils.framework import try_import_tf
 from ray.rllib.utils.typing import Dict, TensorType, List, ModelConfigDict
 
 tf1, tf, tfv = try_import_tf()

rllib/models/tf/layers/noisy_layer.py

@@ -1,8 +1,8 @@
 import numpy as np
 
-from ray.rllib.utils.framework import get_activation_fn, get_variable, \
-    try_import_tf
-from ray.rllib.utils.framework import TensorType, TensorShape
+from ray.rllib.models.utils import get_activation_fn
+from ray.rllib.utils.framework import get_variable, try_import_tf, \
+    TensorType, TensorShape
 
 tf1, tf, tfv = try_import_tf()
 

rllib/models/tf/visionnet.py

@@ -3,8 +3,8 @@
 
 from ray.rllib.models.tf.tf_modelv2 import TFModelV2
 from ray.rllib.models.tf.misc import normc_initializer
-from ray.rllib.models.utils import get_filter_config
-from ray.rllib.utils.framework import get_activation_fn, try_import_tf
+from ray.rllib.models.utils import get_activation_fn, get_filter_config
+from ray.rllib.utils.framework import try_import_tf
 from ray.rllib.utils.typing import ModelConfigDict, TensorType
 
 tf1, tf, tfv = try_import_tf()

rllib/models/torch/misc.py

@@ -2,7 +2,8 @@
 import numpy as np
 from typing import Union, Tuple, Any, List
 
-from ray.rllib.utils.framework import get_activation_fn, try_import_torch
+from ray.rllib.models.utils import get_activation_fn
+from ray.rllib.utils.framework import try_import_torch
 from ray.rllib.utils.typing import TensorType
 
 torch, nn = try_import_torch()

rllib/models/torch/modules/convtranspose2d_stack.py

@@ -1,8 +1,8 @@
 from typing import Tuple
 
 from ray.rllib.models.torch.misc import Reshape
-from ray.rllib.models.utils import get_initializer
-from ray.rllib.utils.framework import get_activation_fn, try_import_torch
+from ray.rllib.models.utils import get_activation_fn, get_initializer
+from ray.rllib.utils.framework import try_import_torch
 
 torch, nn = try_import_torch()
 if torch:

rllib/models/torch/modules/noisy_layer.py

@@ -1,7 +1,7 @@
 import numpy as np
 
-from ray.rllib.utils.framework import get_activation_fn, try_import_torch
-from ray.rllib.utils.framework import TensorType
+from ray.rllib.models.utils import get_activation_fn
+from ray.rllib.utils.framework import try_import_torch, TensorType
 
 torch, nn = try_import_torch()
 

rllib/models/utils.py

@@ -1,4 +1,62 @@
-from ray.rllib.utils.framework import try_import_tf, try_import_torch
+from typing import Optional
+
+from ray.rllib.utils.framework import try_import_jax, try_import_tf, \
+    try_import_torch
+
+
+def get_activation_fn(name: Optional[str] = None, framework: str = "tf"):
+    """Returns a framework specific activation function, given a name string.
+
+    Args:
+        name (Optional[str]): One of "relu" (default), "tanh", "swish", or
+            "linear" or None.
+        framework (str): One of "jax", "tf|tfe|tf2" or "torch".
+
+    Returns:
+        A framework-specific activtion function. e.g. tf.nn.tanh or
+            torch.nn.ReLU. None if name in ["linear", None].
+
+    Raises:
+        ValueError: If name is an unknown activation function.
+    """
+    # Already a callable, return as-is.
+    if callable(name):
+        return name
+
+    # Infer the correct activation function from the string specifier.
+    if framework == "torch":
+        if name in ["linear", None]:
+            return None
+        if name == "swish":
+            from ray.rllib.utils.torch_ops import Swish
+            return Swish
+        _, nn = try_import_torch()
+        if name == "relu":
+            return nn.ReLU
+        elif name == "tanh":
+            return nn.Tanh
+    elif framework == "jax":
+        if name in ["linear", None]:
+            return None
+        jax, _ = try_import_jax()
+        if name == "swish":
+            return jax.nn.swish
+        if name == "relu":
+            return jax.nn.relu
+        elif name == "tanh":
+            return jax.nn.hard_tanh
+    else:
+        assert framework in ["tf", "tfe", "tf2"],\
+            "Unsupported framework `{}`!".format(framework)
+        if name in ["linear", None]:
+            return None
+        tf1, tf, tfv = try_import_tf()
+        fn = getattr(tf.nn, name, None)
+        if fn is not None:
+            return fn
+
+    raise ValueError("Unknown activation ({}) for framework={}!".format(
+        name, framework))
 
 
 def get_filter_config(shape):
@@ -40,7 +98,7 @@ def get_initializer(name, framework="tf"):
 
     Args:
         name (str): One of "xavier_uniform" (default), "xavier_normal".
-        framework (str): One of "tf" or "torch".
+        framework (str): One of "jax", "tf|tfe|tf2" or "torch".
 
     Returns:
         A framework-specific initializer function, e.g.
@@ -50,14 +108,33 @@ def get_initializer(name, framework="tf"):
     Raises:
         ValueError: If name is an unknown initializer.
     """
+    # Already a callable, return as-is.
+    if callable(name):
+        return name
+
+    if framework == "jax":
+        _, flax = try_import_jax()
+        assert flax is not None,\
+            "`flax` not installed. Try `pip install jax flax`."
+        import flax.linen as nn
+        if name in [None, "default", "xavier_uniform"]:
+            return nn.initializers.xavier_uniform()
+        elif name == "xavier_normal":
+            return nn.initializers.xavier_normal()
     if framework == "torch":
         _, nn = try_import_torch()
+        assert nn is not None,\
+            "`torch` not installed. Try `pip install torch`."
         if name in [None, "default", "xavier_uniform"]:
             return nn.init.xavier_uniform_
         elif name == "xavier_normal":
             return nn.init.xavier_normal_
     else:
+        assert framework in ["tf", "tfe", "tf2"],\
+            "Unsupported framework `{}`!".format(framework)
         tf1, tf, tfv = try_import_tf()
+        assert tf is not None,\
+            "`tensorflow` not installed. Try `pip install tensorflow`."
         if name in [None, "default", "xavier_uniform"]:
             return tf.keras.initializers.GlorotUniform
         elif name == "xavier_normal":

rllib/tests/run_regression_tests.py

@@ -25,17 +25,25 @@
 import ray
 from ray.tune import run_experiments
 from ray.rllib import _register_all
+from ray.rllib.utils.deprecation import deprecation_warning
 
 parser = argparse.ArgumentParser()
 parser.add_argument(
-    "--torch",
-    action="store_true",
-    help="Runs all tests with PyTorch enabled.")
+    "--framework",
+    choices=["jax", "tf2", "tf", "tfe", "torch"],
+    default="tf",
+    help="The deep learning framework to use.")
 parser.add_argument(
     "--yaml-dir",
     type=str,
     help="The directory in which to find all yamls to test.")
 
+# Obsoleted arg, use --framework=torch instead.
+parser.add_argument(
+    "--torch",
+    action="store_true",
+    help="Runs all tests with PyTorch enabled.")
+
 if __name__ == "__main__":
     args = parser.parse_args()
 
@@ -69,8 +77,11 @@
 
         # Add torch option to exp configs.
         for exp in experiments.values():
+            exp["config"]["framework"] = args.framework
             if args.torch:
+                deprecation_warning(old="--torch", new="--framework=torch")
                 exp["config"]["framework"] = "torch"
+                args.framework = "torch"
 
         # Print out the actual config.
         print("== Test config ==")
@@ -82,7 +93,7 @@
         for i in range(3):
             try:
                 ray.init(num_cpus=5)
-                trials = run_experiments(experiments, resume=False, verbose=1)
+                trials = run_experiments(experiments, resume=False, verbose=2)
             finally:
                 ray.shutdown()
                 _register_all()

rllib/utils/deprecation.py

@@ -15,8 +15,8 @@ def deprecation_warning(old, new=None, error=None):
     Args:
         old (str): A description of the "thing" that is to be deprecated.
         new (Optional[str]): A description of the new "thing" that replaces it.
-        error (Optional[bool,Exception]): Whether or which exception to throw.
-            If True, throw ValueError.
+        error (Optional[Union[bool,Exception]]): Whether or which exception to
+            throw. If True, throw ValueError.
     """
     msg = "`{}` has been deprecated.{}".format(
         old, (" Use `{}` instead.".format(new) if new else ""))

rllib/utils/exploration/curiosity.py

@@ -9,11 +9,12 @@
 from ray.rllib.models.torch.misc import SlimFC
 from ray.rllib.models.torch.torch_action_dist import TorchCategorical, \
     TorchMultiCategorical
+from ray.rllib.models.utils import get_activation_fn
 from ray.rllib.policy.sample_batch import SampleBatch
 from ray.rllib.utils import NullContextManager
 from ray.rllib.utils.annotations import override
 from ray.rllib.utils.exploration.exploration import Exploration
-from ray.rllib.utils.framework import get_activation_fn, try_import_tf, \
+from ray.rllib.utils.framework import try_import_tf, \
     try_import_torch
 from ray.rllib.utils.from_config import from_config
 from ray.rllib.utils.tf_ops import get_placeholder, one_hot as tf_one_hot

rllib/utils/framework.py

@@ -4,6 +4,7 @@
 import sys
 from typing import Any, Optional
 
+from ray.rllib.utils.deprecation import deprecation_warning
 from ray.rllib.utils.typing import TensorStructType, TensorShape, TensorType
 
 logger = logging.getLogger(__name__)
@@ -252,7 +253,7 @@ def get_variable(value,
     return value
 
 
-# TODO: (sven) move to models/utils.py
+# Deprecated: Use rllib.models.utils::get_activation_fn instead.
 def get_activation_fn(name: Optional[str] = None, framework: str = "tf"):
     """Returns a framework specific activation function, given a name string.
 
@@ -268,6 +269,10 @@ def get_activation_fn(name: Optional[str] = None, framework: str = "tf"):
     Raises:
         ValueError: If name is an unknown activation function.
     """
+    deprecation_warning(
+        "rllib/utils/framework.py::get_activation_fn",
+        "rllib/models/utils.py::get_activation_fn",
+        error=False)
     if framework == "torch":
         if name in ["linear", None]:
             return None

rllib/utils/test_utils.py

@@ -5,7 +5,7 @@
 from ray.rllib.utils.framework import try_import_jax, try_import_tf, \
     try_import_torch
 
-jax, flax = try_import_jax()
+jax, _ = try_import_jax()
 tf1, tf, tfv = try_import_tf()
 if tf1:
     eager_mode = None