[BugFix, Feature] Vmap randomness in losses

test/test_cost.py

@@ -120,6 +120,7 @@
 from torchrl.objectives.redq import REDQLoss
 from torchrl.objectives.reinforce import ReinforceLoss
 from torchrl.objectives.utils import (
+    _vmap_func,
     HardUpdate,
     hold_out_net,
     SoftUpdate,
@@ -233,6 +234,38 @@ def set_advantage_keys_through_loss_test(
             )
 
 
+@pytest.mark.parametrize("device", get_default_devices())
+@pytest.mark.parametrize("vmap_randomness", (None, "different", "same"))
+@pytest.mark.parametrize("dropout", (0.0, 0.1))
+def test_loss_vmap_random(device, vmap_randomness, dropout):
+    class VmapTestLoss(LossModule):
+        def __init__(self):
+            super().__init__()
+            layers = [nn.Linear(4, 4), nn.ReLU()]
+            if dropout > 0.0:
+                layers.append(nn.Dropout(dropout))
+            layers.append(nn.Linear(4, 4))
+            net = nn.Sequential(*layers).to(device)
+            model = TensorDictModule(net, in_keys=["obs"], out_keys=["action"])
+            self.convert_to_functional(model, "model", expand_dim=4)
+            self.vmap_model = _vmap_func(
+                self.model, (None, 0), randomness=self.vmap_randomness
+            )
+
+        def forward(self, td):
+            out = self.vmap_model(td, self.model_params)
+            return {"loss": out["action"].mean()}
+
+    loss_module = VmapTestLoss()
+    td = TensorDict({"obs": torch.randn(3, 4).to(device)}, [3])
+
+    # If user sets vmap randomness to a specific value
+    if vmap_randomness in ("different", "same") and dropout > 0.0:
+        loss_module.set_vmap_randomness(vmap_randomness)
+
+    loss_module(td)["loss"]
+
+
 class TestDQN(LossModuleTestBase):
     seed = 0
 
@@ -1803,12 +1836,17 @@ def _create_mock_actor(
         device="cpu",
         in_keys=None,
         out_keys=None,
+        dropout=0.0,
     ):
         # Actor
         action_spec = BoundedTensorSpec(
             -torch.ones(action_dim), torch.ones(action_dim), (action_dim,)
         )
-        module = nn.Linear(obs_dim, action_dim)
+        module = nn.Sequential(
+            nn.Linear(obs_dim, obs_dim),
+            nn.Dropout(dropout),
+            nn.Linear(obs_dim, action_dim),
+        )
         actor = Actor(
             spec=action_spec, module=module, in_keys=in_keys, out_keys=out_keys
         )
@@ -1984,6 +2022,7 @@ def _create_seq_mock_data_td3(
     @pytest.mark.parametrize("noise_clip", [0.1, 1.0])
     @pytest.mark.parametrize("td_est", list(ValueEstimators) + [None])
     @pytest.mark.parametrize("use_action_spec", [True, False])
+    @pytest.mark.parametrize("dropout", [0.0, 0.1])
     def test_td3(
         self,
         delay_actor,
@@ -1993,9 +2032,10 @@ def test_td3(
         noise_clip,
         td_est,
         use_action_spec,
+        dropout,
     ):
         torch.manual_seed(self.seed)
-        actor = self._create_mock_actor(device=device)
+        actor = self._create_mock_actor(device=device, dropout=dropout)
         value = self._create_mock_value(device=device)
         td = self._create_mock_data_td3(device=device)
         if use_action_spec:
@@ -4876,7 +4916,6 @@ def test_cql(
         device,
         td_est,
     ):
-
         torch.manual_seed(self.seed)
         td = self._create_mock_data_cql(device=device)
 
@@ -6075,7 +6114,7 @@ def zero_param(p):
             p.grad = None
         loss_objective.backward()
         named_parameters = loss_fn.named_parameters()
-        for (name, other_p) in named_parameters:
+        for name, other_p in named_parameters:
             p = params.get(tuple(name.split(".")))
             assert other_p.shape == p.shape
             assert other_p.dtype == p.dtype
@@ -11137,7 +11176,6 @@ def test_set_deprecated_keys(self, adv, kwargs):
         )
 
         with pytest.warns(DeprecationWarning):
-
             if adv is VTrace:
                 actor_net = TensorDictModule(
                     nn.Linear(3, 4), in_keys=["obs"], out_keys=["logits"]

torchrl/objectives/common.py

@@ -16,10 +16,10 @@
 from tensordict.nn import TensorDictModule, TensorDictModuleBase, TensorDictParams
 from torch import nn
 from torch.nn import Parameter
-
 from torchrl._utils import RL_WARNINGS
 from torchrl.envs.utils import ExplorationType, set_exploration_type
-from torchrl.objectives.utils import ValueEstimators
+
+from torchrl.objectives.utils import RANDOM_MODULE_LIST, ValueEstimators
 from torchrl.objectives.value import ValueEstimatorBase
 
 
@@ -81,6 +81,7 @@ class _AcceptedKeys:
 
         pass
 
+    _vmap_randomness = None
     default_value_estimator: ValueEstimators = None
     SEP = "."
     TARGET_NET_WARNING = (
@@ -429,6 +430,28 @@ def make_value_estimator(self, value_type: ValueEstimators = None, **hyperparams
 
         return self
 
+    @property
+    def vmap_randomness(self):
+        if self._vmap_randomness is None:
+            do_break = False
+            for val in self.__dict__.values():
+                if isinstance(val, torch.nn.Module):
+                    for module in val.modules():
+                        if isinstance(module, RANDOM_MODULE_LIST):
+                            self._vmap_randomness = "different"
+                            do_break = True
+                            break
+                if do_break:
+                    # double break
+                    break
+            else:
+                self._vmap_randomness = "error"
+
+        return self._vmap_randomness
+
+    def set_vmap_randomness(self, value):
+        self._vmap_randomness = value
+
     @staticmethod
     def _make_meta_params(param):
         is_param = isinstance(param, nn.Parameter)

torchrl/objectives/cql.py

@@ -349,8 +349,12 @@ def __init__(
                 torch.nn.Parameter(torch.tensor(math.log(1.0), device=device)),
             )
 
-        self._vmap_qvalue_networkN0 = _vmap_func(self.qvalue_network, (None, 0))
-        self._vmap_qvalue_network00 = _vmap_func(self.qvalue_network)
+        self._vmap_qvalue_networkN0 = _vmap_func(
+            self.qvalue_network, (None, 0), randomness=self.vmap_randomness
+        )
+        self._vmap_qvalue_network00 = _vmap_func(
+            self.qvalue_network, randomness=self.vmap_randomness
+        )
 
     @property
     def target_entropy(self):

torchrl/objectives/iql.py

@@ -286,7 +286,9 @@ def __init__(
         if gamma is not None:
             warnings.warn(_GAMMA_LMBDA_DEPREC_WARNING, category=DeprecationWarning)
             self.gamma = gamma
-        self._vmap_qvalue_networkN0 = _vmap_func(self.qvalue_network, (None, 0))
+        self._vmap_qvalue_networkN0 = _vmap_func(
+            self.qvalue_network, (None, 0), randomness=self.vmap_randomness
+        )
 
     @property
     def device(self) -> torch.device:

torchrl/objectives/redq.py

@@ -255,7 +255,6 @@ def __init__(
         priority_key: str = None,
         separate_losses: bool = False,
     ):
-
         super().__init__()
         self._in_keys = None
         self._set_deprecated_ctor_keys(priority_key=priority_key)
@@ -318,8 +317,12 @@ def __init__(
             warnings.warn(_GAMMA_LMBDA_DEPREC_WARNING, category=DeprecationWarning)
             self.gamma = gamma
 
-        self._vmap_qvalue_network00 = _vmap_func(self.qvalue_network)
-        self._vmap_getdist = _vmap_func(self.actor_network, func="get_dist_params")
+        self._vmap_qvalue_network00 = _vmap_func(
+            self.qvalue_network, randomness=self.vmap_randomness
+        )
+        self._vmap_getdist = _vmap_func(
+            self.actor_network, func="get_dist_params", randomness=self.vmap_randomness
+        )
 
     @property
     def target_entropy(self):

torchrl/objectives/sac.py

@@ -376,9 +376,13 @@ def __init__(
         if gamma is not None:
             warnings.warn(_GAMMA_LMBDA_DEPREC_WARNING, category=DeprecationWarning)
             self.gamma = gamma
-        self._vmap_qnetworkN0 = _vmap_func(self.qvalue_network, (None, 0))
+        self._vmap_qnetworkN0 = _vmap_func(
+            self.qvalue_network, (None, 0), randomness=self.vmap_randomness
+        )
         if self._version == 1:
-            self._vmap_qnetwork00 = _vmap_func(qvalue_network)
+            self._vmap_qnetwork00 = _vmap_func(
+                qvalue_network, randomness=self.vmap_randomness
+            )
 
     @property
     def target_entropy_buffer(self):
@@ -411,7 +415,6 @@ def target_entropy(self):
                 isinstance(self.tensor_keys.action, tuple)
                 and len(self.tensor_keys.action) > 1
             ):
-
                 action_container_shape = action_spec[self.tensor_keys.action[:-1]].shape
             else:
                 action_container_shape = action_spec.shape
@@ -753,7 +756,6 @@ def _value_loss(
         return loss_value, {}
 
     def _alpha_loss(self, log_prob: Tensor) -> Tensor:
-
         if self.target_entropy is not None:
             # we can compute this loss even if log_alpha is not a parameter
             alpha_loss = -self.log_alpha * (log_prob + self.target_entropy)
@@ -1049,7 +1051,9 @@ def __init__(
         self.register_buffer(
             "target_entropy", torch.tensor(target_entropy, device=device)
         )
-        self._vmap_qnetworkN0 = _vmap_func(self.qvalue_network, (None, 0))
+        self._vmap_qnetworkN0 = _vmap_func(
+            self.qvalue_network, (None, 0), randomness=self.vmap_randomness
+        )
 
     def _forward_value_estimator_keys(self, **kwargs) -> None:
         if self._value_estimator is not None:

torchrl/objectives/td3.py

@@ -219,7 +219,6 @@ def __init__(
         priority_key: str = None,
         separate_losses: bool = False,
     ) -> None:
-
         super().__init__()
         self._in_keys = None
         self._set_deprecated_ctor_keys(priority=priority_key)
@@ -296,8 +295,12 @@ def __init__(
         if gamma is not None:
             warnings.warn(_GAMMA_LMBDA_DEPREC_WARNING, category=DeprecationWarning)
             self.gamma = gamma
-        self._vmap_qvalue_network00 = _vmap_func(self.qvalue_network)
-        self._vmap_actor_network00 = _vmap_func(self.actor_network)
+        self._vmap_qvalue_network00 = _vmap_func(
+            self.qvalue_network, randomness=self.vmap_randomness
+        )
+        self._vmap_actor_network00 = _vmap_func(
+            self.actor_network, randomness=self.vmap_randomness
+        )
 
     def _forward_value_estimator_keys(self, **kwargs) -> None:
         if self._value_estimator is not None:

torchrl/objectives/utils.py

@@ -4,6 +4,7 @@
 # LICENSE file in the root directory of this source tree.
 
 import functools
+import re
 import warnings
 from enum import Enum
 from typing import Iterable, Optional, Union
@@ -29,6 +30,14 @@
     "run `loss_module.make_value_estimator(ValueEstimators.<value_fun>, gamma=val)`."
 )
 
+RANDOM_MODULE_LIST = (
+    nn.Dropout,
+    nn.Dropout2d,
+    nn.Dropout3d,
+    nn.AlphaDropout,
+    nn.FeatureAlphaDropout,
+)
+
 
 class ValueEstimators(Enum):
     """Value function enumerator for custom-built estimators.
@@ -478,13 +487,23 @@ def new_fun(self, netname=None):
 
 
 def _vmap_func(module, *args, func=None, **kwargs):
-    def decorated_module(*module_args_params):
-        params = module_args_params[-1]
-        module_args = module_args_params[:-1]
-        with params.to_module(module):
-            if func is None:
-                return module(*module_args)
-            else:
-                return getattr(module, func)(*module_args)
+    try:
 
-    return vmap(decorated_module, *args, **kwargs)  # noqa: TOR101
+        def decorated_module(*module_args_params):
+            params = module_args_params[-1]
+            module_args = module_args_params[:-1]
+            with params.to_module(module):
+                if func is None:
+                    return module(*module_args)
+                else:
+                    return getattr(module, func)(*module_args)
+
+        return vmap(decorated_module, *args, **kwargs)  # noqa: TOR101
+
+    except RuntimeError as err:
+        if re.match(
+            r"vmap: called random operation while in randomness error mode", str(err)
+        ):
+            raise RuntimeError(
+                "Please use <loss_module>.set_vmap_randomness('different') to handle random operations during vmap."
+            ) from err