From 3fbde19010ffaa3009a79c8874fb801f7fed9789 Mon Sep 17 00:00:00 2001
From: Aasheesh Singh <aasheeshdtu@gmail.com>
Date: Tue, 20 Aug 2024 16:43:16 -0400
Subject: [PATCH] add GraphState class with State containing Timesteps as Batch

---
 src/gfn/states.py | 141 +++++++++++++++++++++++++++++++++++++++++++++-
 1 file changed, 140 insertions(+), 1 deletion(-)

diff --git a/src/gfn/states.py b/src/gfn/states.py
index f4fa1a20..167c59de 100644
--- a/src/gfn/states.py
+++ b/src/gfn/states.py
@@ -3,10 +3,11 @@
 from abc import ABC
 from copy import deepcopy
 from math import prod
-from typing import Callable, ClassVar, List, Optional, Sequence, cast
+from typing import Callable, ClassVar, List, Optional, Sequence, cast, Tuple
 
 import torch
 from torchtyping import TensorType as TT
+from torch_geometric.data import Batch, Data
 
 
 class States(ABC):
@@ -492,3 +493,141 @@ def stack_states(states: List[States]):
     ) + state_example.batch_shape
 
     return stacked_states
+
+
+class GraphStates(ABC):
+    """
+    Base class for Graph as a state representation. The `GraphStates` object is a batched collection of
+    multiple graph objects. The `Batch` object from PyTorch Geometric is used to represent the batch of
+    graph objects as states.
+    """
+
+
+    s0: ClassVar[Data]
+    sf: ClassVar[Data]
+    node_feature_dim: ClassVar[int]
+    edge_feature_dim: ClassVar[int]
+    make_random_states_graph: Callable = lambda x: (_ for _ in ()).throw(
+        NotImplementedError(
+            "The environment does not support initialization of random Graph states."
+        )
+    )
+
+    def __init__(self, graphs: Batch):
+        self.data: Batch = graphs
+        self.batch_shape: int = self.data.num_graphs
+        self._log_rewards: float = None
+
+    @classmethod
+    def from_batch_shape(cls, batch_shape: int, random: bool = False, sink: bool=False) -> GraphStates:
+        if random and sink:
+            raise ValueError("Only one of `random` and `sink` should be True.")
+        if random:
+            data = cls.make_random_states_graph(batch_shape)
+        elif sink:
+            data = cls.make_sink_states_graph(batch_shape)
+        else:
+            data = cls.make_initial_states_graph(batch_shape)
+        return cls(data)
+
+    @classmethod
+    def make_initial_states_graph(cls, batch_shape: int) -> Batch:
+        data = Batch.from_data_list([cls.s0 for _ in range(batch_shape)])
+        return data
+
+    @classmethod
+    def make_sink_states_graph(cls, batch_shape: int) -> Batch:
+        data = Batch.from_data_list([cls.sf for _ in range(batch_shape)])
+        return data
+
+    @classmethod
+    def make_random_states_graph(cls, batch_shape: int) -> Batch:
+        data = Batch.from_data_list([cls.make_random_states_graph() for _ in range(batch_shape)])
+        return data
+
+    def __len__(self):
+        return self.data.batch_size
+
+    def __repr__(self):
+        return (f"{self.__class__.__name__} object of batch shape {self.batch_shape} and "
+                f"node feature dim {self.node_feature_dim} and edge feature dim {self.edge_feature_dim}")
+
+    def __getitem__(self, index: int | Sequence[int] | slice) -> GraphStates:
+        if isinstance(index, int):
+            out = self.__class__(Batch.from_data_list([self.data[index]]))
+        elif isinstance(index, (Sequence, slice)):
+            out = self.__class__(Batch.from_data_list(self.data.index_select(index)))
+        else:
+            raise NotImplementedError("Indexing with type {} is not implemented".format(type(index)))
+
+        if self._log_rewards is not None:
+            out._log_rewards = self._log_rewards[index]
+
+        return out
+
+    def __setitem__(self, index: int | Sequence[int], graph: GraphStates):
+        """
+        Set particular states of the Batch
+        """
+        data_list = self.data.to_data_list()
+        if isinstance(index, int):
+            assert len(graph) == 1,  "GraphStates must have a batch size of 1 for single index assignment"
+            data_list[index] = graph.data[0]
+            self.data = Batch.from_data_list(data_list)
+        elif isinstance(index, Sequence):
+            assert len(index) == len(graph), "Index and GraphState must have the same length"
+            for i, idx in enumerate(index):
+                data_list[idx] = graph.data[i]
+            self.data = Batch.from_data_list(data_list)
+        elif isinstance(index, slice):
+            assert index.stop - index.start == len(graph), "Index slice and GraphStates must have the same length"
+            data_list[index] = graph.data.to_data_list()
+            self.data = Batch.from_data_list(data_list)
+        else:
+            raise NotImplementedError("Setters with type {} is not implemented".format(type(index)))
+
+    @property
+    def device(self) -> torch.device:
+        return self.data.get_example(0).x.device
+
+    def to(self, device: torch.device) -> GraphStates:
+        """
+        Moves and/or casts the graph states to the specified device
+        """
+        if self.device != device:
+            self.data = self.data.to(device)
+        return self
+
+    def clone(self) -> States:
+        """Returns a *detached* clone of the current instance using deepcopy."""
+        return deepcopy(self)
+
+    def extend(self, other: GraphStates):
+        """Concatenates to another GraphStates object along the batch dimension"""
+        self.data = Batch.from_data_list(self.data.to_data_list() + other.data.to_data_list())
+        if self._log_rewards is not None:
+            assert other._log_rewards is not None
+            self._log_rewards = torch.cat(
+                (self._log_rewards, other._log_rewards), dim=0
+            )
+
+
+    @property
+    def log_rewards(self) -> TT["batch_shape", torch.float]:
+        return self._log_rewards
+
+    @log_rewards.setter
+    def log_rewards(self, log_rewards: TT["batch_shape", torch.float]) -> None:
+        self._log_rewards = log_rewards
+
+
+
+
+
+
+
+
+
+
+
+