[Feature] LLMHashingEnv

ghstack-source-id: d1a20ec
Pull Request resolved: #2635

Author: Vincent Moens

docs/source/reference/envs.rst

@@ -347,6 +347,8 @@ TorchRL offers a series of custom built-in environments.
 
     PendulumEnv
     TicTacToeEnv
+    LLMHashingEnv
+
 
 Multi-agent environments
 ------------------------

docs/source/reference/trainers.rst

@@ -79,7 +79,7 @@ Hooks can be split into 3 categories: **data processing** (``"batch_process"`` a
 
 - **Logging** hooks take a batch of data presented as a ``TensorDict`` and write in the logger
   some information retrieved from that data. Examples include the ``LogValidationReward`` hook, the reward
-  logger (``LogScaler``) and such. Hooks should return a dictionary (or a None value) containing the
+  logger (``LogScalar``) and such. Hooks should return a dictionary (or a None value) containing the
   data to log. The key ``"log_pbar"`` is reserved to boolean values indicating if the logged value
   should be displayed on the progression bar printed on the training log.
 
@@ -174,7 +174,7 @@ Trainer and hooks
     BatchSubSampler
     ClearCudaCache
     CountFramesLog
-    LogScaler
+    LogScalar
     OptimizerHook
     LogValidationReward
     ReplayBufferTrainer

test/test_env.py

@@ -8,6 +8,7 @@
 import functools
 import gc
 import os.path
+import random
 import re
 from collections import defaultdict
 from functools import partial
@@ -114,6 +115,7 @@
     DoubleToFloat,
     EnvBase,
     EnvCreator,
+    LLMHashingEnv,
     ParallelEnv,
     PendulumEnv,
     SerialEnv,
@@ -3419,6 +3421,29 @@ def test_pendulum_env(self, device):
             r = env.rollout(10, tensordict=TensorDict(batch_size=[5], device=device))
             assert r.shape == torch.Size((5, 10))
 
+    def test_llm_hashing_env(self):
+        vocab_size = 5
+
+        class Tokenizer:
+            def __call__(self, obj):
+                return torch.randint(vocab_size, (len(obj.split(" ")),)).tolist()
+
+            def decode(self, obj):
+                words = ["apple", "banana", "cherry", "date", "elderberry"]
+                return " ".join(random.choice(words) for _ in obj)
+
+            def batch_decode(self, obj):
+                return [self.decode(_obj) for _obj in obj]
+
+            def encode(self, obj):
+                return self(obj)
+
+        tokenizer = Tokenizer()
+        env = LLMHashingEnv(tokenizer=tokenizer, vocab_size=vocab_size)
+        td = env.make_tensordict("some sentence")
+        assert isinstance(td, TensorDict)
+        env.check_env_specs(tensordict=td)
+
 
 @pytest.mark.parametrize("device", [None, *get_default_devices()])
 @pytest.mark.parametrize("env_device", [None, *get_default_devices()])

torchrl/data/map/tree.py

@@ -135,35 +135,40 @@ def make_node(
     def full_observation_spec(self):
         """The observation spec of the tree.
 
-        This is an alias for `Tree.specs['output_spec', 'full_observation_spec']`."""
+        This is an alias for `Tree.specs['output_spec', 'full_observation_spec']`.
+        """
         return self.specs["output_spec", "full_observation_spec"]
 
     @property
     def full_reward_spec(self):
         """The reward spec of the tree.
 
-        This is an alias for `Tree.specs['output_spec', 'full_reward_spec']`."""
+        This is an alias for `Tree.specs['output_spec', 'full_reward_spec']`.
+        """
         return self.specs["output_spec", "full_reward_spec"]
 
     @property
     def full_done_spec(self):
         """The done spec of the tree.
 
-        This is an alias for `Tree.specs['output_spec', 'full_done_spec']`."""
+        This is an alias for `Tree.specs['output_spec', 'full_done_spec']`.
+        """
         return self.specs["output_spec", "full_done_spec"]
 
     @property
     def full_state_spec(self):
         """The state spec of the tree.
 
-        This is an alias for `Tree.specs['input_spec', 'full_state_spec']`."""
+        This is an alias for `Tree.specs['input_spec', 'full_state_spec']`.
+        """
         return self.specs["input_spec", "full_state_spec"]
 
     @property
     def full_action_spec(self):
         """The action spec of the tree.
 
-        This is an alias for `Tree.specs['input_spec', 'full_action_spec']`."""
+        This is an alias for `Tree.specs['input_spec', 'full_action_spec']`.
+        """
         return self.specs["input_spec", "full_action_spec"]
 
     @property

torchrl/envs/__init__.py

@@ -5,7 +5,7 @@
 
 from .batched_envs import ParallelEnv, SerialEnv
 from .common import EnvBase, EnvMetaData, make_tensordict
-from .custom import PendulumEnv, TicTacToeEnv
+from .custom import LLMHashingEnv, PendulumEnv, TicTacToeEnv
 from .env_creator import env_creator, EnvCreator, get_env_metadata
 from .gym_like import default_info_dict_reader, GymLikeEnv
 from .libs import (

torchrl/envs/common.py

@@ -14,8 +14,14 @@
 import numpy as np
 import torch
 import torch.nn as nn
-from tensordict import LazyStackedTensorDict, TensorDictBase, unravel_key
-from tensordict.utils import NestedKey
+from tensordict import (
+    is_tensor_collection,
+    LazyStackedTensorDict,
+    TensorDictBase,
+    unravel_key,
+)
+from tensordict.base import _is_leaf_nontensor
+from tensordict.utils import is_non_tensor, NestedKey
 from torchrl._utils import (
     _ends_with,
     _make_ordinal_device,
@@ -25,7 +31,13 @@
     seed_generator,
 )
 
-from torchrl.data.tensor_specs import Categorical, Composite, TensorSpec, Unbounded
+from torchrl.data.tensor_specs import (
+    Categorical,
+    Composite,
+    NonTensor,
+    TensorSpec,
+    Unbounded,
+)
 from torchrl.data.utils import DEVICE_TYPING
 from torchrl.envs.utils import (
     _make_compatible_policy,
@@ -430,7 +442,6 @@ def auto_specs_(
         done_key: NestedKey | List[NestedKey] | None = None,
         observation_key: NestedKey | List[NestedKey] = "observation",
         reward_key: NestedKey | List[NestedKey] = "reward",
-        batch_size: torch.Size | None = None,
     ):
         """Automatically sets the specifications (specs) of the environment based on a random rollout using a given policy.
 
@@ -484,6 +495,7 @@ def auto_specs_(
             tensordict2,
             named=True,
             nested_keys=True,
+            is_leaf=_is_leaf_nontensor,
         )
         input_spec = Composite(input_spec_stack, batch_size=batch_size)
         if not self.batch_locked and batch_size != self.batch_size:
@@ -501,6 +513,7 @@ def auto_specs_(
             nexts_1,
             named=True,
             nested_keys=True,
+            is_leaf=_is_leaf_nontensor,
         )
 
         output_spec = Composite(output_spec_stack, batch_size=batch_size)
@@ -523,7 +536,8 @@ def auto_specs_(
         full_observation_spec = output_spec.separates(*observation_key, default=None)
         if not output_spec.is_empty(recurse=True):
             raise RuntimeError(
-                f"Keys {list(output_spec.keys(True, True))} are unaccounted for."
+                f"Keys {list(output_spec.keys(True, True))} are unaccounted for. "
+                f"Make sure you have passed all the leaf names to the auto_specs_ method."
             )
 
         if full_action_spec is not None:
@@ -541,6 +555,8 @@ def auto_specs_(
 
     @wraps(check_env_specs_func)
     def check_env_specs(self, *args, **kwargs):
+        return_contiguous = kwargs.pop("return_contiguous", not self._has_dynamic_specs)
+        kwargs["return_contiguous"] = return_contiguous
         return check_env_specs_func(self, *args, **kwargs)
 
     check_env_specs.__doc__ = check_env_specs_func.__doc__
@@ -3206,7 +3222,10 @@ def maybe_reset(self, tensordict: TensorDictBase) -> TensorDictBase:
         """
         if self._simple_done:
             done = tensordict._get_str("done", default=None)
-            any_done = done.any()
+            if done is not None:
+                any_done = done.any()
+            else:
+                any_done = False
             if any_done:
                 tensordict._set_str(
                     "_reset",
@@ -3572,6 +3591,12 @@ def _has_dynamic_specs(spec: Composite):
 
 
 def _tensor_to_spec(name, leaf, leaf_compare=None, *, stack):
+    if not (isinstance(leaf, torch.Tensor) or is_tensor_collection(leaf)):
+        stack[name] = NonTensor(shape=())
+        return
+    elif is_non_tensor(leaf):
+        stack[name] = NonTensor(shape=leaf.shape)
+        return
     shape = leaf.shape
     if leaf_compare is not None:
         shape_compare = leaf_compare.shape

torchrl/envs/custom/__init__.py

@@ -3,5 +3,6 @@
 # This source code is licensed under the MIT license found in the
 # LICENSE file in the root directory of this source tree.
 
+from .llm import LLMHashingEnv
 from .pendulum import PendulumEnv
 from .tictactoeenv import TicTacToeEnv