[Feature] Port test_fake_tensordict to torchrl

docs/source/reference/envs.rst

@@ -111,12 +111,20 @@ It is also possible to reset some but not all of the environments:
             is_shared=True)
 
 
-A note on performance: launching a :obj:`ParallelEnv` can take quite some time
+*A note on performance*: launching a :obj:`ParallelEnv` can take quite some time
 as it requires to launch as many python instances as there are processes. Due to
 the time that it takes to run :obj:`import torch` (and other imports), starting the
 parallel env can be a bottleneck. This is why, for instance, TorchRL tests are so slow.
 Once the environment is launched, a great speedup should be observed.
 
+Another thing to take in consideration is that :obj:`ParallelEnv`s (as well as data collectors)
+will create data buffers based on the environment specs to pass data from one process
+to another. This means that a misspecified spec (input, observation or reward) will
+cause a breakage at runtime as the data can't be written on the preallocated buffer.
+In general, an environment should be tested using the :obj:`check_env_specs`
+test function before being used in a :obj:`ParallelEnv`. This function will raise
+an assertion error whenever the preallocated buffer and the collected data mismatch.
+
 We also offer the :obj:`SerialEnv` class that enjoys the exact same API but is executed
 serially. This is mostly useful for testing purposes, when one wants to assess the
 behaviour of a :obj:`ParallelEnv` without launching the subprocesses.
@@ -210,6 +218,7 @@ in the environment. The keys to be included in this inverse transform are passed
     TensorDictPrimer
     R3MTransform
     VIPTransform
+    VIPRewardTransform
 
 Helpers
 -------
@@ -223,6 +232,7 @@ Helpers
     get_available_libraries
     set_exploration_mode
     exploration_mode
+    check_env_specs
 
 Domain-specific
 ---------------

test/_utils_internal.py

@@ -12,7 +12,6 @@
 import pytest
 import torch.cuda
 from torchrl._utils import implement_for, seed_generator
-from torchrl.envs import EnvBase
 from torchrl.envs.libs.gym import _has_gym
 
 # Specified for test_utils.py
@@ -70,45 +69,6 @@ def generate_seeds(seed, repeat):
     return seeds
 
 
-def _test_fake_tensordict(env: EnvBase):
-    fake_tensordict = env.fake_tensordict().flatten_keys(".")
-    real_tensordict = env.rollout(3).flatten_keys(".")
-
-    keys1 = set(fake_tensordict.keys())
-    keys2 = set(real_tensordict.keys())
-    assert keys1 == keys2
-    fake_tensordict = fake_tensordict.unsqueeze(real_tensordict.batch_dims - 1)
-    fake_tensordict = fake_tensordict.expand(*real_tensordict.shape)
-    fake_tensordict = fake_tensordict.to_tensordict()
-    assert (
-        fake_tensordict.apply(lambda x: torch.zeros_like(x))
-        == real_tensordict.apply(lambda x: torch.zeros_like(x))
-    ).all()
-    for key in keys2:
-        assert fake_tensordict[key].shape == real_tensordict[key].shape
-
-    # test dtypes
-    for key, value in real_tensordict.unflatten_keys(".").items():
-        _check_dtype(key, value, env.observation_spec, env.input_spec)
-
-
-def _check_dtype(key, value, obs_spec, input_spec):
-    if key in {"reward", "done"}:
-        return
-    elif key == "next":
-        for _key, _value in value.items():
-            _check_dtype(_key, _value, obs_spec, input_spec)
-        return
-    elif key in input_spec.keys(yield_nesting_keys=True):
-        assert input_spec[key].is_in(value), (input_spec[key], value)
-        return
-    elif key in obs_spec.keys(yield_nesting_keys=True):
-        assert obs_spec[key].is_in(value), (input_spec[key], value)
-        return
-    else:
-        raise KeyError(key)
-
-
 # Decorator to retry upon certain Exceptions.
 def retry(ExceptionToCheck, tries=3, delay=3, skip_after_retries=False):
     def deco_retry(f):

test/test_libs.py

@@ -9,7 +9,6 @@
 import pytest
 import torch
 from _utils_internal import (
-    _test_fake_tensordict,
     get_available_devices,
     HALFCHEETAH_VERSIONED,
     PENDULUM_VERSIONED,
@@ -25,6 +24,7 @@
 from torchrl.envs.libs.gym import _has_gym, _is_from_pixels, GymEnv, GymWrapper
 from torchrl.envs.libs.habitat import _has_habitat, HabitatEnv
 from torchrl.envs.libs.jumanji import _has_jumanji, JumanjiEnv
+from torchrl.envs.utils import check_env_specs
 
 if _has_gym:
     import gym
@@ -136,7 +136,7 @@ def test_gym_fake_td(self, env_name, frame_skip, from_pixels, pixels_only):
             from_pixels=from_pixels,
             pixels_only=pixels_only,
         )
-        _test_fake_tensordict(env)
+        check_env_specs(env)
 
 
 @implement_for("gym", None, "0.26")
@@ -243,7 +243,7 @@ def test_faketd(self, env_name, task, frame_skip, from_pixels, pixels_only):
             from_pixels=from_pixels,
             pixels_only=pixels_only,
         )
-        _test_fake_tensordict(env)
+        check_env_specs(env)
 
 
 @pytest.mark.skipif(
@@ -337,7 +337,7 @@ class TestHabitat:
     def test_habitat(self, envname):
         env = HabitatEnv(envname)
         rollout = env.rollout(3)
-        _test_fake_tensordict(env)
+        check_env_specs(env)
 
 
 @pytest.mark.skipif(not _has_jumanji, reason="jumanji not installed")
@@ -375,7 +375,7 @@ def test_jumanji_batch_size(self, envname, batch_size):
     def test_jumanji_spec_rollout(self, envname, batch_size):
         env = JumanjiEnv(envname, batch_size=batch_size)
         env.set_seed(0)
-        _test_fake_tensordict(env)
+        check_env_specs(env)
 
     @pytest.mark.parametrize("batch_size", [(), (5,), (5, 4)])
     def test_jumanji_consistency(self, envname, batch_size):

torchrl/envs/utils.py

@@ -4,6 +4,7 @@
 # LICENSE file in the root directory of this source tree.
 
 import pkg_resources
+import torch
 from tensordict.nn.probabilistic import (  # noqa
     interaction_mode as exploration_mode,
     set_interaction_mode as set_exploration_mode,
@@ -151,3 +152,52 @@ def _check_dmlab():
     # "screeps": None,  # https://github.com/screeps/screeps
     # "ml-agents": None,
 }
+
+
+def check_env_specs(env):
+    """Tests an environment specs against the results of short rollout.
+
+    This test function should be used as a sanity check for an env wrapped with
+    torchrl's EnvBase subclasses: any discrepency between the expected data and
+    the data collected should raise an assertion error.
+
+    A broken environment spec will likely make it impossible to use parallel
+    environments.
+
+    """
+    fake_tensordict = env.fake_tensordict().flatten_keys(".")
+    real_tensordict = env.rollout(3).flatten_keys(".")
+
+    keys1 = set(fake_tensordict.keys())
+    keys2 = set(real_tensordict.keys())
+    assert keys1 == keys2
+    fake_tensordict = fake_tensordict.unsqueeze(real_tensordict.batch_dims - 1)
+    fake_tensordict = fake_tensordict.expand(*real_tensordict.shape)
+    fake_tensordict = fake_tensordict.to_tensordict()
+    assert (
+        fake_tensordict.apply(lambda x: torch.zeros_like(x))
+        == real_tensordict.apply(lambda x: torch.zeros_like(x))
+    ).all()
+    for key in keys2:
+        assert fake_tensordict[key].shape == real_tensordict[key].shape
+
+    # test dtypes
+    for key, value in real_tensordict.unflatten_keys(".").items():
+        _check_dtype(key, value, env.observation_spec, env.input_spec)
+
+
+def _check_dtype(key, value, obs_spec, input_spec):
+    if key in {"reward", "done"}:
+        return
+    elif key == "next":
+        for _key, _value in value.items():
+            _check_dtype(_key, _value, obs_spec, input_spec)
+        return
+    elif key in input_spec.keys(yield_nesting_keys=True):
+        assert input_spec[key].is_in(value), (input_spec[key], value)
+        return
+    elif key in obs_spec.keys(yield_nesting_keys=True):
+        assert obs_spec[key].is_in(value), (input_spec[key], value)
+        return
+    else:
+        raise KeyError(key)