Merge branch 'main' into fix-ci

# Conflicts:
#	torchrl/modules/distributions/continuous.py

test/test_actors.py

@@ -130,8 +130,8 @@ def test_probabilistic_actor_nested_normal(log_prob_key, nested_dim=5, n_actions
         out_keys=[("data", "action")],
         distribution_class=TanhNormal,
         distribution_kwargs={
-            "min": action_spec.space.low,
-            "max": action_spec.space.high,
+            "low": action_spec.space.low,
+            "high": action_spec.space.high,
         },
         log_prob_key=log_prob_key,
         return_log_prob=True,
@@ -153,8 +153,8 @@ def test_probabilistic_actor_nested_normal(log_prob_key, nested_dim=5, n_actions
         out_keys=[("data", "action")],
         distribution_class=TanhNormal,
         distribution_kwargs={
-            "min": action_spec.space.low,
-            "max": action_spec.space.high,
+            "low": action_spec.space.low,
+            "high": action_spec.space.high,
         },
         log_prob_key=log_prob_key,
         return_log_prob=True,

test/test_cost.py

@@ -13519,17 +13519,36 @@ def __init__(self):
 
 def test_loss_exploration():
     class DummyLoss(LossModule):
-        def forward(self, td):
-            assert exploration_type() == InteractionType.MODE
+        def forward(self, td, mode):
+            if mode is None:
+                mode = self.deterministic_sampling_mode
+            assert exploration_type() == mode
             with set_exploration_type(ExplorationType.RANDOM):
                 assert exploration_type() == ExplorationType.RANDOM
-            assert exploration_type() == ExplorationType.MODE
+            assert exploration_type() == mode
             return td
 
     loss_fn = DummyLoss()
     with set_exploration_type(ExplorationType.RANDOM):
         assert exploration_type() == ExplorationType.RANDOM
-        loss_fn(None)
+        loss_fn(None, None)
+        assert exploration_type() == ExplorationType.RANDOM
+
+    with set_exploration_type(ExplorationType.RANDOM):
+        assert exploration_type() == ExplorationType.RANDOM
+        loss_fn(None, ExplorationType.DETERMINISTIC)
+        assert exploration_type() == ExplorationType.RANDOM
+
+    loss_fn.deterministic_sampling_mode = ExplorationType.MODE
+    with set_exploration_type(ExplorationType.RANDOM):
+        assert exploration_type() == ExplorationType.RANDOM
+        loss_fn(None, ExplorationType.MODE)
+        assert exploration_type() == ExplorationType.RANDOM
+
+    loss_fn.deterministic_sampling_mode = ExplorationType.MEAN
+    with set_exploration_type(ExplorationType.RANDOM):
+        assert exploration_type() == ExplorationType.RANDOM
+        loss_fn(None, ExplorationType.MEAN)
         assert exploration_type() == ExplorationType.RANDOM
 
 

test/test_distributions.py

@@ -85,10 +85,10 @@ def _map_all(*tensors_or_other, device):
 
 class TestTanhNormal:
     @pytest.mark.parametrize(
-        "min", [-torch.ones(3), -1, 3 * torch.tensor([-1.0, -2.0, -0.5]), -0.1]
+        "low", [-torch.ones(3), -1, 3 * torch.tensor([-1.0, -2.0, -0.5]), -0.1]
     )
     @pytest.mark.parametrize(
-        "max", [torch.ones(3), 1, 3 * torch.tensor([1.0, 2.0, 0.5]), 0.1]
+        "high", [torch.ones(3), 1, 3 * torch.tensor([1.0, 2.0, 0.5]), 0.1]
     )
     @pytest.mark.parametrize(
         "vecs",
@@ -102,25 +102,64 @@ class TestTanhNormal:
     )
     @pytest.mark.parametrize("shape", [torch.Size([]), torch.Size([3, 4])])
     @pytest.mark.parametrize("device", get_default_devices())
-    def test_tanhnormal(self, min, max, vecs, upscale, shape, device):
-        min, max, vecs, upscale, shape = _map_all(
-            min, max, vecs, upscale, shape, device=device
+    def test_tanhnormal(self, low, high, vecs, upscale, shape, device):
+        torch.manual_seed(0)
+        low, high, vecs, upscale, shape = _map_all(
+            low, high, vecs, upscale, shape, device=device
         )
         torch.manual_seed(0)
         d = TanhNormal(
             *vecs,
             upscale=upscale,
-            min=min,
-            max=max,
+            low=low,
+            high=high,
         )
         for _ in range(100):
             a = d.rsample(shape)
             assert a.shape[: len(shape)] == shape
-            assert (a >= d.min).all()
-            assert (a <= d.max).all()
+            assert (a >= d.low).all()
+            assert (a <= d.high).all()
             lp = d.log_prob(a)
             assert torch.isfinite(lp).all()
 
+    def test_tanhnormal_mode(self):
+        # Checks that the std of the mode computed by tanh normal is within a certain range
+        # when starting from close points
+
+        torch.manual_seed(0)
+        # 10 start points with 1000 jitters around that
+        # std of the loc is about 1e-4
+        loc = torch.randn(10) + torch.randn(1000, 10) / 10000
+
+        t = TanhNormal(loc=loc, scale=0.5, low=-1, high=1, event_dims=0)
+
+        mode = t.get_mode()
+        assert mode.shape == loc.shape
+        empirical_mode, empirical_mode_lp = torch.zeros_like(loc), -float("inf")
+        for v in torch.arange(-1, 1, step=0.01):
+            lp = t.log_prob(v.expand_as(t.loc))
+            empirical_mode = torch.where(lp > empirical_mode_lp, v, empirical_mode)
+            empirical_mode_lp = torch.where(
+                lp > empirical_mode_lp, lp, empirical_mode_lp
+            )
+        assert abs(empirical_mode - mode).max() < 0.1, abs(empirical_mode - mode).max()
+        assert mode.shape == loc.shape
+        assert (mode.std(0).max() < 0.1).all(), mode.std(0)
+
+    @pytest.mark.parametrize("event_dims", [0, 1, 2])
+    def test_tanhnormal_event_dims(self, event_dims):
+        scale = 1
+        loc = torch.randn(1, 2, 3, 4)
+        t = TanhNormal(loc=loc, scale=scale, event_dims=event_dims)
+        sample = t.sample()
+        assert sample.shape == loc.shape
+        exp_shape = loc.shape[:-event_dims] if event_dims > 0 else loc.shape
+        assert t.log_prob(sample).shape == exp_shape, (
+            t.log_prob(sample).shape,
+            event_dims,
+            exp_shape,
+        )
+
 
 class TestTruncatedNormal:
     @pytest.mark.parametrize(
@@ -159,13 +198,13 @@ def test_truncnormal(self, min, max, vecs, upscale, shape, device):
             a = d.rsample(shape)
             assert a.device == device
             assert a.shape[: len(shape)] == shape
-            assert (a >= d.min).all()
-            assert (a <= d.max).all()
+            assert (a >= d.low).all()
+            assert (a <= d.high).all()
             lp = d.log_prob(a)
             assert torch.isfinite(lp).all()
-        oob_min = d.min.expand((*d.batch_shape, *d.event_shape)) - 1e-2
+        oob_min = d.low.expand((*d.batch_shape, *d.event_shape)) - 1e-2
         assert not torch.isfinite(d.log_prob(oob_min)).any()
-        oob_max = d.max.expand((*d.batch_shape, *d.event_shape)) + 1e-2
+        oob_max = d.high.expand((*d.batch_shape, *d.event_shape)) + 1e-2
         assert not torch.isfinite(d.log_prob(oob_max)).any()
 
     @pytest.mark.skipif(not _has_scipy, reason="scipy not installed")

test/test_exploration.py

@@ -585,7 +585,7 @@ def test_gsde(
         wrapper = NormalParamWrapper(model)
         module = SafeModule(wrapper, in_keys=in_keys, out_keys=["loc", "scale"])
         distribution_class = TanhNormal
-        distribution_kwargs = {"min": -bound, "max": bound}
+        distribution_kwargs = {"low": -bound, "high": bound}
     spec = BoundedTensorSpec(
         -torch.ones(action_dim) * bound, torch.ones(action_dim) * bound, (action_dim,)
     ).to(device)

test/test_tensordictmodules.py

@@ -1416,8 +1416,8 @@ def test_dt_inference_wrapper(self, online):
             )
             dist_class = TanhDelta
         dist_kwargs = {
-            "min": -1.0,
-            "max": 1.0,
+            "low": -1.0,
+            "high": 1.0,
         }
         actor = ProbabilisticActor(
             in_keys=in_keys,

torchrl/collectors/collectors.py

@@ -335,9 +335,9 @@ class SyncDataCollector(DataCollectorBase):
             information.
             Defaults to ``False``.
         exploration_type (ExplorationType, optional): interaction mode to be used when
-            collecting data. Must be one of ``torchrl.envs.utils.ExplorationType.RANDOM``,
-            ``torchrl.envs.utils.ExplorationType.MODE`` or ``torchrl.envs.utils.ExplorationType.MEAN``.
-            Defaults to ``torchrl.envs.utils.ExplorationType.RANDOM``.
+            collecting data. Must be one of ``torchrl.envs.utils.ExplorationType.DETERMINISTIC``,
+            ``torchrl.envs.utils.ExplorationType.RANDOM``, ``torchrl.envs.utils.ExplorationType.MODE``
+            or ``torchrl.envs.utils.ExplorationType.MEAN``.
         return_same_td (bool, optional): if ``True``, the same TensorDict
             will be returned at each iteration, with its values
             updated. This feature should be used cautiously: if the same
@@ -1336,9 +1336,9 @@ class _MultiDataCollector(DataCollectorBase):
             information.
             Defaults to ``False``.
         exploration_type (ExplorationType, optional): interaction mode to be used when
-            collecting data. Must be one of ``torchrl.envs.utils.ExplorationType.RANDOM``,
-            ``torchrl.envs.utils.ExplorationType.MODE`` or ``torchrl.envs.utils.ExplorationType.MEAN``.
-            Defaults to ``torchrl.envs.utils.ExplorationType.RANDOM``.
+            collecting data. Must be one of ``torchrl.envs.utils.ExplorationType.DETERMINISTIC``,
+            ``torchrl.envs.utils.ExplorationType.RANDOM``, ``torchrl.envs.utils.ExplorationType.MODE``
+            or ``torchrl.envs.utils.ExplorationType.MEAN``.
         reset_when_done (bool, optional): if ``True`` (default), an environment
             that return a ``True`` value in its ``"done"`` or ``"truncated"``
             entry will be reset at the corresponding indices.
@@ -2635,9 +2635,9 @@ class aSyncDataCollector(MultiaSyncDataCollector):
             information.
             Defaults to ``False``.
         exploration_type (ExplorationType, optional): interaction mode to be used when
-            collecting data. Must be one of ``torchrl.envs.utils.ExplorationType.RANDOM``,
-            ``torchrl.envs.utils.ExplorationType.MODE`` or ``torchrl.envs.utils.ExplorationType.MEAN``.
-            Defaults to ``torchrl.envs.utils.ExplorationType.RANDOM``.
+            collecting data. Must be one of ``torchrl.envs.utils.ExplorationType.DETERMINISTIC``,
+            ``torchrl.envs.utils.ExplorationType.RANDOM``, ``torchrl.envs.utils.ExplorationType.MODE``
+            or ``torchrl.envs.utils.ExplorationType.MEAN``.
         reset_when_done (bool, optional): if ``True`` (default), an environment
             that return a ``True`` value in its ``"done"`` or ``"truncated"``
             entry will be reset at the corresponding indices.

torchrl/collectors/distributed/generic.py

@@ -346,9 +346,9 @@ class DistributedDataCollector(DataCollectorBase):
             information.
             Defaults to ``False``.
         exploration_type (ExplorationType, optional): interaction mode to be used when
-            collecting data. Must be one of ``torchrl.envs.utils.ExplorationType.RANDOM``,
-            ``torchrl.envs.utils.ExplorationType.MODE`` or ``torchrl.envs.utils.ExplorationType.MEAN``.
-            Defaults to ``torchrl.envs.utils.ExplorationType.RANDOM``.
+            collecting data. Must be one of ``torchrl.envs.utils.ExplorationType.DETERMINISTIC``,
+            ``torchrl.envs.utils.ExplorationType.RANDOM``, ``torchrl.envs.utils.ExplorationType.MODE``
+            or ``torchrl.envs.utils.ExplorationType.MEAN``.
         collector_class (type or str, optional): a collector class for the remote node. Can be
             :class:`~torchrl.collectors.SyncDataCollector`,
             :class:`~torchrl.collectors.MultiSyncDataCollector`,

torchrl/collectors/distributed/ray.py

@@ -211,9 +211,9 @@ class RayCollector(DataCollectorBase):
             information.
             Defaults to ``False``.
         exploration_type (ExplorationType, optional): interaction mode to be used when
-            collecting data. Must be one of ``torchrl.envs.utils.ExplorationType.RANDOM``,
-            ``torchrl.envs.utils.ExplorationType.MODE`` or ``torchrl.envs.utils.ExplorationType.MEAN``.
-            Defaults to ``torchrl.envs.utils.ExplorationType.RANDOM``.
+            collecting data. Must be one of ``torchrl.envs.utils.ExplorationType.DETERMINISTIC``,
+            ``torchrl.envs.utils.ExplorationType.RANDOM``, ``torchrl.envs.utils.ExplorationType.MODE``
+            or ``torchrl.envs.utils.ExplorationType.MEAN``.
         collector_class (Python class): a collector class to be remotely instantiated. Can be
             :class:`~torchrl.collectors.SyncDataCollector`,
             :class:`~torchrl.collectors.MultiSyncDataCollector`,

torchrl/collectors/distributed/rpc.py

@@ -187,8 +187,9 @@ class RPCDataCollector(DataCollectorBase):
             information.
             Defaults to ``False``.
         exploration_type (ExplorationType, optional): interaction mode to be used when
-            collecting data. Must be one of ``torchrl.envs.utils.ExplorationType.RANDOM``,
-            ``torchrl.envs.utils.ExplorationType.MODE`` or ``torchrl.envs.utils.ExplorationType.MEAN``.
+            collecting data. Must be one of ``torchrl.envs.utils.ExplorationType.DETERMINISTIC``,
+            ``torchrl.envs.utils.ExplorationType.RANDOM``, ``torchrl.envs.utils.ExplorationType.MODE``
+            or ``torchrl.envs.utils.ExplorationType.MEAN``.
             Defaults to ``torchrl.envs.utils.ExplorationType.RANDOM``.
         collector_class (type or str, optional): a collector class for the remote node. Can be
             :class:`~torchrl.collectors.SyncDataCollector`,

torchrl/collectors/distributed/sync.py

@@ -226,9 +226,9 @@ class DistributedSyncDataCollector(DataCollectorBase):
             information.
             Defaults to ``False``.
         exploration_type (ExplorationType, optional): interaction mode to be used when
-            collecting data. Must be one of ``torchrl.envs.utils.ExplorationType.RANDOM``,
-            ``torchrl.envs.utils.ExplorationType.MODE`` or ``torchrl.envs.utils.ExplorationType.MEAN``.
-            Defaults to ``torchrl.envs.utils.ExplorationType.RANDOM``.
+            collecting data. Must be one of ``torchrl.envs.utils.ExplorationType.DETERMINISTIC``,
+            ``torchrl.envs.utils.ExplorationType.RANDOM``, ``torchrl.envs.utils.ExplorationType.MODE``
+            or ``torchrl.envs.utils.ExplorationType.MEAN``.
         collector_class (type or str, optional): a collector class for the remote node. Can be
             :class:`~torchrl.collectors.SyncDataCollector`,
             :class:`~torchrl.collectors.MultiSyncDataCollector`,