Update

[ghstack-poisoned]

Author: Vincent Moens

test/test_env.py

@@ -131,6 +131,7 @@
 from torchrl.envs.transforms.transforms import (
     AutoResetEnv,
     AutoResetTransform,
+    Tokenizer,
     Transform,
 )
 from torchrl.envs.utils import (
@@ -3346,10 +3347,6 @@ def test_batched_dynamic(self, break_when_any_done):
         )
         del env_no_buffers
         gc.collect()
-        # print(dummy_rollouts)
-        # print(rollout_no_buffers_serial)
-        # # for a, b in zip(dummy_rollouts.exclude("action").unbind(0), rollout_no_buffers_serial.exclude("action").unbind(0)):
-        #     assert_allclose_td(a, b)
         assert_allclose_td(
             dummy_rollouts.exclude("action"),
             rollout_no_buffers_serial.exclude("action"),
@@ -3463,6 +3460,8 @@ def test_env(self, stateful, include_pgn, include_fen, include_hash, include_san
                 include_hash=include_hash,
                 include_san=include_san,
             )
+            # Because we always use mask_actions=True
+            assert isinstance(env, TransformedEnv)
             check_env_specs(env)
             if include_hash:
                 if include_fen:
@@ -3560,8 +3559,8 @@ def test_reset_white_to_move(self, stateful, include_pgn, include_fen):
         )
         fen = "5k2/4r3/8/8/8/1Q6/2K5/8 w - - 0 1"
         td = env.reset(TensorDict({"fen": fen}))
-        assert td["fen"] == fen
         if include_fen:
+            assert td["fen"] == fen
             assert env.board.fen() == fen
         assert td["turn"] == env.lib.WHITE
         assert not td["done"]
@@ -3666,6 +3665,27 @@ def test_reward(
         assert td["reward"] == expected_reward
         assert td["turn"] == (not expected_turn)
 
+    def test_chess_tokenized(self):
+        env = ChessEnv(include_fen=True, stateful=True, include_san=True)
+        assert isinstance(env.observation_spec["fen"], NonTensor)
+        env = env.append_transform(
+            Tokenizer(in_keys=["fen"], out_keys=["fen_tokenized"])
+        )
+        assert isinstance(env.observation_spec["fen"], NonTensor)
+        env.transform.transform_output_spec(env.base_env.output_spec)
+        env.transform.transform_input_spec(env.base_env.input_spec)
+        r = env.rollout(10, return_contiguous=False)
+        assert "fen_tokenized" in r
+        assert "fen" in r
+        assert "fen_tokenized" in r["next"]
+        assert "fen" in r["next"]
+        ftd = env.fake_tensordict()
+        assert "fen_tokenized" in ftd
+        assert "fen" in ftd
+        assert "fen_tokenized" in ftd["next"]
+        assert "fen" in ftd["next"]
+        env.check_env_specs()
+
 
 class TestCustomEnvs:
     def test_tictactoe_env(self):

torchrl/data/tensor_specs.py

@@ -5042,7 +5042,7 @@ def zero(self, shape: torch.Size = None) -> TensorDictBase:
 
     def __eq__(self, other):
         return (
-            type(self) is type(other)
+            type(self) == type(other)
             and self.shape == other.shape
             and self._device == other._device
             and set(self._specs.keys()) == set(other._specs.keys())

torchrl/envs/custom/chess.py

@@ -7,20 +7,20 @@
 import importlib.util
 import io
 import pathlib
-from typing import Dict, Optional
+from typing import Dict
 
 import torch
 from PIL import Image
 from tensordict import TensorDict, TensorDictBase
-from torchrl.data import Bounded, Categorical, Composite, NonTensor, Unbounded
+from torchrl.data import Binary, Bounded, Categorical, Composite, NonTensor, Unbounded
 
 from torchrl.envs import EnvBase
 from torchrl.envs.common import _EnvPostInit
 
 from torchrl.envs.utils import _classproperty
 
 
-class _HashMeta(_EnvPostInit):
+class _ChessMeta(_EnvPostInit):
     def __call__(cls, *args, **kwargs):
         instance = super().__call__(*args, **kwargs)
         if kwargs.get("include_hash"):
@@ -37,11 +37,15 @@ def __call__(cls, *args, **kwargs):
             if instance.include_pgn:
                 in_keys.append("pgn")
                 out_keys.append("pgn_hash")
-            return instance.append_transform(Hash(in_keys, out_keys))
+            instance = instance.append_transform(Hash(in_keys, out_keys))
+        if kwargs.get("mask_actions", True):
+            from torchrl.envs import ActionMask
+
+            instance = instance.append_transform(ActionMask())
         return instance
 
 
-class ChessEnv(EnvBase, metaclass=_HashMeta):
+class ChessEnv(EnvBase, metaclass=_ChessMeta):
     r"""A chess environment that follows the TorchRL API.
 
     This environment simulates a chess game using the `chess` library. It supports various state representations
@@ -63,6 +67,8 @@ class ChessEnv(EnvBase, metaclass=_HashMeta):
         include_pgn (bool): Whether to include PGN (Portable Game Notation) in the observations. Default: ``False``.
         include_legal_moves (bool): Whether to include legal moves in the observations. Default: ``False``.
         include_hash (bool): Whether to include hash transformations in the environment. Default: ``False``.
+        mask_actions (bool): if ``True``, a :class:`~torchrl.envs.ActionMask` transform will be appended
+            to the env to make sure that the actions are properly masked. Default: ``True``.
         pixels (bool): Whether to include pixel-based observations of the board. Default: ``False``.
 
     .. note:: The action spec is a :class:`~torchrl.data.Categorical` with a number of actions equal to the number of possible SAN moves.
@@ -200,16 +206,15 @@ def _legal_moves_to_index(
     ) -> torch.Tensor:
         if not self.stateful:
             if tensordict is None:
-                raise RuntimeError(
-                    "rand_action requires a tensordict when stateful is False."
-                )
-            if self.include_fen:
-                fen = self._get_fen(tensordict)
+                # trust the board
+                pass
+            elif self.include_fen:
+                fen = tensordict.get("fen", None)
                 fen = fen.data
                 self.board.set_fen(fen)
                 board = self.board
             elif self.include_pgn:
-                pgn = self._get_pgn(tensordict)
+                pgn = tensordict.get("pgn")
                 pgn = pgn.data
                 board = self._pgn_to_board(pgn, self.board)
 
@@ -222,15 +227,19 @@ def _legal_moves_to_index(
         )
 
         if return_mask:
-            return torch.zeros(len(self.san_moves), dtype=torch.bool).index_fill_(
-                0, indices, True
-            )
+            return self._move_index_to_mask(indices)
         if pad:
             indices = torch.nn.functional.pad(
                 indices, [0, 218 - indices.numel() + 1], value=len(self.san_moves)
             )
         return indices
 
+    @classmethod
+    def _move_index_to_mask(cls, indices: torch.Tensor) -> torch.Tensor:
+        return torch.zeros(len(cls.san_moves), dtype=torch.bool).index_fill_(
+            0, indices, True
+        )
+
     def __init__(
         self,
         *,
@@ -240,6 +249,7 @@ def __init__(
         include_pgn: bool = False,
         include_legal_moves: bool = False,
         include_hash: bool = False,
+        mask_actions: bool = True,
         pixels: bool = False,
     ):
         chess = self.lib
@@ -250,6 +260,7 @@ def __init__(
         self.include_san = include_san
         self.include_fen = include_fen
         self.include_pgn = include_pgn
+        self.mask_actions = mask_actions
         self.include_legal_moves = include_legal_moves
         if include_legal_moves:
             # 218 max possible legal moves per chess board position
@@ -274,8 +285,10 @@ def __init__(
 
         self.stateful = stateful
 
-        if not self.stateful:
-            self.full_state_spec = self.full_observation_spec.clone()
+        # state_spec is loosely defined as such - it's not really an issue that extra keys
+        # can go missing but it allows us to reset the env using fen passed to the reset
+        # method.
+        self.full_state_spec = self.full_observation_spec.clone()
 
         self.pixels = pixels
         if pixels:
@@ -295,16 +308,16 @@ def __init__(
         self.full_reward_spec = Composite(
             reward=Unbounded(shape=(1,), dtype=torch.float32)
         )
+        if self.mask_actions:
+            self.full_observation_spec["action_mask"] = Binary(
+                n=len(self.san_moves), dtype=torch.bool
+            )
+
         # done spec generated automatically
         self.board = chess.Board()
         if self.stateful:
             self.action_spec.set_provisional_n(len(list(self.board.legal_moves)))
 
-    def rand_action(self, tensordict: Optional[TensorDictBase] = None):
-        mask = self._legal_moves_to_index(tensordict, return_mask=True)
-        self.action_spec.update_mask(mask)
-        return super().rand_action(tensordict)
-
     def _is_done(self, board):
         return board.is_game_over() | board.is_fifty_moves()
 
@@ -314,11 +327,11 @@ def _reset(self, tensordict=None):
         if tensordict is not None:
             dest = tensordict.empty()
             if self.include_fen:
-                fen = self._get_fen(tensordict)
+                fen = tensordict.get("fen", None)
                 if fen is not None:
                     fen = fen.data
             elif self.include_pgn:
-                pgn = self._get_pgn(tensordict)
+                pgn = tensordict.get("pgn", None)
                 if pgn is not None:
                     pgn = pgn.data
         else:
@@ -358,13 +371,18 @@ def _reset(self, tensordict=None):
         if self.include_legal_moves:
             moves_idx = self._legal_moves_to_index(board=self.board, pad=True)
             dest.set("legal_moves", moves_idx)
+            if self.mask_actions:
+                dest.set("action_mask", self._move_index_to_mask(moves_idx))
+        elif self.mask_actions:
+            dest.set(
+                "action_mask",
+                self._legal_moves_to_index(
+                    board=self.board, pad=True, return_mask=True
+                ),
+            )
+
         if self.pixels:
             dest.set("pixels", self._get_tensor_image(board=self.board))
-
-        if self.stateful:
-            mask = self._legal_moves_to_index(dest, return_mask=True)
-            self.action_spec.update_mask(mask)
-
         return dest
 
     _cairosvg_lib = None
@@ -435,16 +453,6 @@ def _board_to_pgn(cls, board: "chess.Board") -> str:  # noqa: F821
         pgn_string = str(game)
         return pgn_string
 
-    @classmethod
-    def _get_fen(cls, tensordict):
-        fen = tensordict.get("fen", None)
-        return fen
-
-    @classmethod
-    def _get_pgn(cls, tensordict):
-        pgn = tensordict.get("pgn", None)
-        return pgn
-
     def get_legal_moves(self, tensordict=None, uci=False):
         """List the legal moves in a position.
 
@@ -468,7 +476,7 @@ def get_legal_moves(self, tensordict=None, uci=False):
                 raise ValueError(
                     "tensordict must be given since this env is not stateful"
                 )
-            fen = self._get_fen(tensordict).data
+            fen = tensordict.get("fen").data
             board.set_fen(fen)
         moves = board.legal_moves
 
@@ -486,10 +494,10 @@ def _step(self, tensordict):
         fen = None
         if not self.stateful:
             if self.include_fen:
-                fen = self._get_fen(tensordict).data
+                fen = tensordict.get("fen").data
                 board.set_fen(fen)
             elif self.include_pgn:
-                pgn = self._get_pgn(tensordict).data
+                pgn = tensordict.get("pgn").data
                 board = self._pgn_to_board(pgn, board)
             else:
                 raise RuntimeError(
@@ -519,6 +527,15 @@ def _step(self, tensordict):
         if self.include_legal_moves:
             moves_idx = self._legal_moves_to_index(board=board, pad=True)
             dest.set("legal_moves", moves_idx)
+            if self.mask_actions:
+                dest.set("action_mask", self._move_index_to_mask(moves_idx))
+        elif self.mask_actions:
+            dest.set(
+                "action_mask",
+                self._legal_moves_to_index(
+                    board=self.board, pad=True, return_mask=True
+                ),
+            )
 
         turn = torch.tensor(board.turn)
         done = self._is_done(board)
@@ -538,11 +555,6 @@ def _step(self, tensordict):
         dest.set("terminated", [done])
         if self.pixels:
             dest.set("pixels", self._get_tensor_image(board=self.board))
-
-        if self.stateful:
-            mask = self._legal_moves_to_index(dest, return_mask=True)
-            self.action_spec.update_mask(mask)
-
         return dest
 
     def _set_seed(self, *args, **kwargs):