Merge pull request #253 from GFNOrg/graph-state-pyg

switch from TensorDict to torch_geometric

pyproject.toml

@@ -2,9 +2,6 @@
 requires = ["poetry-core>=1.0.8"]
 build-backend = "poetry.core.masonry.api"
 
-[project]
-name = "torchgfn"
-
 [tool.poetry]
 name = "torchgfn"
 packages = [{include = "gfn", from = "src"}]
@@ -25,8 +22,9 @@ classifiers = [
 einops = ">=0.6.1"
 numpy = ">=1.21.2"
 python = "^3.10"
-tensordict = ">=0.6.1"
 torch = ">=2.6.0"
+tensordict = ">=0.6.1"
+torch_geometric = ">=2.6.1"
 
 # dev dependencies.
 black = { version = "24.3", optional = true }
@@ -49,7 +47,6 @@ wandb = { version = "*", optional = true }
 scikit-learn = {version = "*", optional = true }
 scipy = { version = "*", optional = true }
 matplotlib = { version = "*", optional = true }
-torch_geometric = { version = ">=2.6.1", optional = true }
 
 [tool.poetry.extras]
 dev = [
@@ -66,7 +63,6 @@ dev = [
     "sphinx",
     "tox",
     "flake8",
-    "torch_geometric",
 ]
 
 scripts = ["tqdm", "wandb", "scikit-learn", "scipy", "matplotlib"]
@@ -89,7 +85,6 @@ all = [
     "tox",
     "tqdm",
     "wandb",
-    "torch_geometric",
 ]
 
 [tool.poetry.urls]
@@ -103,17 +98,22 @@ include = '\.pyi?$'
 extend-exclude = '''/(\.git|\.hg|\.mypy_cache|\.ipynb|\.tox|\.venv|build)/g'''
 
 [tool.pyright]
+pythonVersion = "3.10"
 include = ["src/gfn", "tutorials/examples", "testing"]  # Removed ** globstars
 exclude = [
     "**/node_modules",
     "**/__pycache__",
     "**/.*", # Exclude dot files and folders
 ]
 
+strict = [
+
+]
+# This is required as the CI pre-commit does not dl the module (i.e. numpy)
+# Therefore, we have to ignore missing imports
 # Removed "strict": [], as it's redundant with typeCheckingMode
 
 typeCheckingMode = "basic"
-pythonVersion = "3.10"
 
 # Removed enableTypeIgnoreComments, not available in pyproject.toml, and bad practice.
 
@@ -128,6 +128,9 @@ reportUntypedFunctionDecorator = "none"
 reportMissingTypeStubs = false
 reportUnboundVariable = "warning"
 reportGeneralTypeIssues = "none"
+reportAttributeAccessIssue = false
+
+[tool.pytest.ini_options]
 reportOptionalMemberAccess = "error"
 reportArgumentType = "error" #This setting doesn't exist, removed.
 

src/gfn/actions.py

@@ -204,8 +204,9 @@ def __init__(self, tensor: TensorDict):
 
         Args:
             action: a GraphActionType indicating the type of action.
-            features: a tensor of shape (batch_shape, feature_shape) representing the features of the nodes or of the edges, depending on the action type.
-                In case of EXIT action, this can be None.
+            features: a tensor of shape (batch_shape, feature_shape) representing the features
+                of the nodes or of the edges, depending on the action type. In case of EXIT
+                action, this can be None.
             edge_index: an tensor of shape (batch_shape, 2) representing the edge to add.
                 This must defined if and only if the action type is GraphActionType.AddEdge.
         """
@@ -245,12 +246,16 @@ def __len__(self) -> int:
         """Returns the number of actions in the batch."""
         return int(prod(self.batch_shape))
 
-    def __getitem__(self, index: int | Sequence[int] | Sequence[bool]) -> GraphActions:
+    def __getitem__(
+        self, index: int | List[int] | List[bool] | slice | torch.Tensor
+    ) -> GraphActions:
         """Get particular actions of the batch."""
         return GraphActions(self.tensor[index])
 
     def __setitem__(
-        self, index: int | Sequence[int] | Sequence[bool], action: GraphActions
+        self,
+        index: int | List[int] | List[bool] | slice | torch.Tensor,
+        action: GraphActions,
     ) -> None:
         """Set particular actions of the batch."""
         self.tensor[index] = action.tensor
@@ -263,15 +268,18 @@ def compare(self, other: GraphActions) -> torch.Tensor:
 
         Returns: boolean tensor of shape batch_shape indicating whether the actions are equal.
         """
-        compare = torch.all(self.tensor == other.tensor, dim=-1)
-        return (
-            compare["action_type"]
-            & (compare["action_type"] == GraphActionType.EXIT | compare["features"])
-            & (
-                compare["action_type"]
-                != GraphActionType.ADD_EDGE | compare["edge_index"]
-            )
+        action_compare = torch.all(
+            self.tensor["action_type"] == other.tensor["action_type"]
+        )
+        exit_compare = (
+            torch.all(self.tensor["features"] == other.tensor["features"])
+            | action_compare
+            == GraphActionType.EXIT
+        )
+        edge_compare = (action_compare != GraphActionType.ADD_EDGE) | (
+            torch.all(self.tensor["edge_index"] == other.tensor["edge_index"])
         )
+        return action_compare & exit_compare & edge_compare
 
     @property
     def is_exit(self) -> torch.Tensor:

src/gfn/containers/trajectories.py

@@ -33,7 +33,8 @@ class Trajectories(Container):
         when_is_done: Tensor of shape (n_trajectories,) indicating the time step at which each trajectory ends.
         is_backward: Whether the trajectories are backward or forward.
         log_rewards: Tensor of shape (n_trajectories,) containing the log rewards of the trajectories.
-        log_probs: Tensor of shape (max_length, n_trajectories) indicating the log probabilities of the trajectories' actions.
+        log_probs: Tensor of shape (max_length, n_trajectories) indicating the log probabilities of the
+            trajectories' actions.
 
     """
 
@@ -57,7 +58,8 @@ def __init__(
             when_is_done: Tensor of shape (n_trajectories,) indicating the time step at which each trajectory ends.
             is_backward: Whether the trajectories are backward or forward.
             log_rewards: Tensor of shape (n_trajectories,) containing the log rewards of the trajectories.
-            log_probs: Tensor of shape (max_length, n_trajectories) indicating the log probabilities of the trajectories' actions.
+            log_probs: Tensor of shape (max_length, n_trajectories) indicating the log probabilities of
+                the trajectories' actions.
             estimator_outputs: Tensor of shape (batch_shape, output_dim).
                 When forward sampling off-policy for an n-step trajectory,
                 n forward passes will be made on some function approximator,
@@ -103,25 +105,37 @@ def __init__(
             assert (
                 log_probs.shape == (self.max_length, self.n_trajectories)
                 and log_probs.dtype == torch.float
-            ), f"log_probs.shape={log_probs.shape}, self.max_length={self.max_length}, self.n_trajectories={self.n_trajectories}"
+            ), f"log_probs.shape={log_probs.shape}, "
+            f"self.max_length={self.max_length}, "
+            f"self.n_trajectories={self.n_trajectories}"
         else:
             log_probs = torch.full(size=(0, 0), fill_value=0, dtype=torch.float)
         self.log_probs: torch.Tensor = log_probs
 
         self.estimator_outputs = estimator_outputs
         if self.estimator_outputs is not None:
-            # assert self.estimator_outputs.shape[:len(self.states.batch_shape)] == self.states.batch_shape TODO: check why fails
+            #  TODO: check why this fails.
+            # assert self.estimator_outputs.shape[:len(self.states.batch_shape)] == self.states.batch_shape
             assert self.estimator_outputs.dtype == torch.float
 
     def __repr__(self) -> str:
         states = self.states.tensor.transpose(0, 1)
         assert states.ndim == 3
         trajectories_representation = ""
+        assert isinstance(
+            self.env.s0, torch.Tensor
+        ), "not supported for Graph trajectories."
+        assert isinstance(
+            self.env.sf, torch.Tensor
+        ), "not supported for Graph trajectories."
+
         for traj in states[:10]:
             one_traj_repr = []
             for step in traj:
                 one_traj_repr.append(str(step.cpu().numpy()))
-                if step.equal(self.env.s0 if self.is_backward else self.env.sf):
+                if self.is_backward and step.equal(self.env.s0):
+                    break
+                elif not self.is_backward and step.equal(self.env.sf):
                     break
             trajectories_representation += "-> ".join(one_traj_repr) + "\n"
         return (
@@ -482,6 +496,10 @@ def reverse_backward_trajectories(self, debug: bool = False) -> Trajectories:
         new_actions[torch.arange(len(self)), seq_lengths] = self.env.exit_action
 
         # Assign reversed states to new_states
+        assert isinstance(states[:, -1], torch.Tensor)
+        assert isinstance(
+            self.env.s0, torch.Tensor
+        ), "reverse_backward_trajectories not supported for Graph trajectories"
         assert torch.all(states[:, -1] == self.env.s0), "Last state must be s0"
         new_states[:, 0] = self.env.s0
         new_states[:, 1:-1][mask] = states[:, :-1][mask][rev_idx[mask]]
@@ -536,7 +554,7 @@ def reverse_backward_trajectories(self, debug: bool = False) -> Trajectories:
 
 
 def pad_dim0_to_target(a: torch.Tensor, target_dim0: int) -> torch.Tensor:
-    """Pads tensor a to match the dimention of b."""
+    """Pads tensor a to match the dimension of b."""
     assert a.shape[0] < target_dim0, "a is already larger than target_dim0!"
     pad_dim = target_dim0 - a.shape[0]
     pad_dim_full = (pad_dim,) + tuple(a.shape[1:])

src/gfn/containers/transitions.py

@@ -56,8 +56,8 @@ def __init__(
                 the children of the transitions.
             is_backward: Whether the transitions are backward transitions (i.e.
                 `next_states` is the parent of states).
-            log_rewards: Tensor of shape (n_transitions,) containing the log-rewards of the transitions (using a default value like
-                `-float('inf')` for non-terminating transitions).
+            log_rewards: Tensor of shape (n_transitions,) containing the log-rewards of the transitions (using a
+                default value like `-float('inf')` for non-terminating transitions).
             log_probs: Tensor of shape (n_transitions,) containing the log-probabilities of the actions.
 
         Raises:

src/gfn/env.py

@@ -2,7 +2,8 @@
 from typing import Optional, Tuple, Union, cast
 
 import torch
-from tensordict import TensorDict
+from torch_geometric.data import Batch as GeometricBatch
+from torch_geometric.data import Data as GeometricData
 
 from gfn.actions import Actions, GraphActions
 from gfn.preprocessors import IdentityPreprocessor, Preprocessor
@@ -23,12 +24,12 @@ class Env(ABC):
 
     def __init__(
         self,
-        s0: torch.Tensor | TensorDict,
+        s0: torch.Tensor | GeometricData,
         state_shape: Tuple,
         action_shape: Tuple,
         dummy_action: torch.Tensor,
         exit_action: torch.Tensor,
-        sf: Optional[torch.Tensor | TensorDict] = None,
+        sf: Optional[torch.Tensor | GeometricData] = None,
         device_str: Optional[str] = None,
         preprocessor: Optional[Preprocessor] = None,
     ):
@@ -51,7 +52,7 @@ def __init__(
         """
         self.device = get_device(device_str, default_device=s0.device)
 
-        self.s0 = s0.to(self.device)
+        self.s0 = s0.to(self.device)  # pyright: ignore
         assert s0.shape == state_shape
         if sf is None:
             sf = torch.full(s0.shape, -float("inf")).to(self.device)
@@ -229,20 +230,7 @@ def reset(
             batch_shape=batch_shape, random=random, sink=sink
         )
 
-    def validate_actions(
-        self, states: States, actions: Actions, backward: bool = False
-    ) -> bool:
-        """First, asserts that states and actions have the same batch_shape.
-        Then, uses `is_action_valid`.
-        Returns a boolean indicating whether states/actions pairs are valid."""
-        assert states.batch_shape == actions.batch_shape
-        return self.is_action_valid(states, actions, backward)
-
-    def _step(
-        self,
-        states: States,
-        actions: Actions,
-    ) -> States:
+    def _step(self, states: States, actions: Actions) -> States:
         """Core step function. Calls the user-defined self.step() function.
 
         Function that takes a batch of states and actions and returns a batch of next
@@ -256,7 +244,7 @@ def _step(
         valid_actions = actions[valid_states_idx]
         valid_states = states[valid_states_idx]
 
-        if not self.validate_actions(valid_states, valid_actions):
+        if not self.is_action_valid(valid_states, valid_actions):
             raise NonValidActionsError(
                 "Some actions are not valid in the given states. See `is_action_valid`."
             )
@@ -275,19 +263,15 @@ def _step(
 
         new_not_done_states_tensor = self.step(not_done_states, not_done_actions)
 
-        if not isinstance(new_not_done_states_tensor, (torch.Tensor, TensorDict)):
+        if not isinstance(new_not_done_states_tensor, (torch.Tensor, GeometricBatch)):
             raise Exception(
                 "User implemented env.step function *must* return a torch.Tensor!"
             )
 
         new_states[~new_sink_states_idx] = self.States(new_not_done_states_tensor)
         return new_states
 
-    def _backward_step(
-        self,
-        states: States,
-        actions: Actions,
-    ) -> States:
+    def _backward_step(self, states: States, actions: Actions) -> States:
         """Core backward_step function. Calls the user-defined self.backward_step fn.
 
         This function takes a batch of states and actions and returns a batch of next
@@ -301,7 +285,7 @@ def _backward_step(
         valid_actions = actions[valid_states_idx]
         valid_states = states[valid_states_idx]
 
-        if not self.validate_actions(valid_states, valid_actions, backward=True):
+        if not self.is_action_valid(valid_states, valid_actions, backward=True):
             raise NonValidActionsError(
                 "Some actions are not valid in the given states. See `is_action_valid`."
             )
@@ -590,12 +574,12 @@ def terminating_states(self) -> DiscreteStates:
 class GraphEnv(Env):
     """Base class for graph-based environments."""
 
-    sf: TensorDict  # this tells the type checker that sf is a TensorDict
+    sf: GeometricData  # this tells the type checker that sf is a GeometricData
 
     def __init__(
         self,
-        s0: TensorDict,
-        sf: TensorDict,
+        s0: GeometricData,
+        sf: GeometricData,
         device_str: Optional[str] = None,
         preprocessor: Optional[Preprocessor] = None,
     ):
@@ -604,14 +588,16 @@ def __init__(
         Args:
             s0: The initial graph state.
             sf: The sink graph state.
-            device_str: 'cpu' or 'cuda'. Defaults to None, in which case the device is
-                inferred from s0.
+            device_str: String representation of the device.
             preprocessor: a Preprocessor object that converts raw graph states to a tensor
                 that can be fed into a neural network. Defaults to None, in which case
                 the IdentityPreprocessor is used.
         """
-        self.s0 = s0.to(device_str)
-        self.features_dim = s0["node_feature"].shape[-1]
+        device = get_device(device_str, default_device=s0.device)
+        assert s0.x is not None
+
+        self.s0 = s0.to(device)  # pyright: ignore
+        self.features_dim = s0.x.shape[-1]
         self.sf = sf
 
         self.States = self.make_states_class()

src/gfn/gym/__init__.py

@@ -1,5 +1,6 @@
 from .box import Box
 from .discrete_ebm import DiscreteEBM
+from .graph_building import GraphBuilding
 from .hypergrid import HyperGrid
 from .line import Line
 
@@ -8,4 +9,5 @@
     "DiscreteEBM",
     "HyperGrid",
     "Line",
+    "GraphBuilding",
 ]