[BugFix, Feature] GNN position_key and velocity_key not in `obser…

…vation_spec` (#125)

* amend

* amend

* amend

* amend

* amend

* amend

* amend

* amend

* amend

benchmarl/conf/model/layers/gnn.yaml

@@ -8,7 +8,9 @@ gnn_kwargs:
   aggr: "add"
 
 position_key: null
+pos_features: 0
 velocity_key: null
+vel_features: 0
 
 exclude_pos_from_node_features: False
 edge_radius: null

benchmarl/experiment/experiment.py

@@ -715,7 +715,6 @@ def _get_excluded_keys(self, group: str):
         for other_group in self.group_map.keys():
             if other_group != group:
                 excluded_keys += [other_group, ("next", other_group)]
-        excluded_keys += ["info", (group, "info"), ("next", group, "info")]
         return excluded_keys
 
     def _optimizer_loop(self, group: str) -> TensorDictBase:

benchmarl/models/gnn.py

@@ -11,11 +11,11 @@
 import warnings
 from dataclasses import dataclass, MISSING
 from math import prod
-from typing import Optional, Type
+from typing import List, Optional, Type
 
 import torch
 from tensordict import TensorDictBase
-from tensordict.utils import _unravel_key_to_tuple
+from tensordict.utils import _unravel_key_to_tuple, NestedKey
 from torch import nn, Tensor
 
 from benchmarl.models.common import Model, ModelConfig
@@ -59,16 +59,21 @@ class Gnn(Model):
         self_loops (str): Whether the resulting adjacency matrix will have self loops.
         gnn_class (Type[torch_geometric.nn.MessagePassing]): the gnn convolution class to use
         gnn_kwargs (dict, optional): the dict of arguments to pass to the gnn conv class
-        position_key (str, optional): if provided, it will need to match a leaf key in the env observation spec
-            representing the agent position. This key will not be processed as a node feature, but it will used to construct
-            edge features. In particular it be used to compute relative positions (``pos_node_1 - pos_node_2``) and a
+        position_key (str, optional): if provided, it will need to match a leaf key in the tensordict coming from the env
+            (we suggest to use the "info" dict) representing the agent position. This key will be processed as a
+            node feature (unless exclude_pos_from_node_features=True) and it will be used to construct edge features.
+            In particular, it will be used to compute relative positions (``pos_node_1 - pos_node_2``) and a
             one-dimensional distance for all neighbours in the graph.
+        pos_features (int, optional): Needed when position_key is specified.
+            It has to match to the last element of the shape the tensor under position_key.
         exclude_pos_from_node_features (optional, bool): If ``position_key`` is provided,
             wether to use it just to compute edge features or also include it in node features.
-        velocity_key (str, optional): if provided, it will need to match a leaf key in the env observation spec
-            representing the agent velocity. This key will not be processed as a node feature, but it will used to construct
-            edge features. In particular it be used to compute relative velocities (``vel_node_1 - vel_node_2``) for all neighbours
-            in the graph.
+        velocity_key (str, optional): if provided, it will need to match a leaf key in the tensordict coming from the env
+            (we suggest to use the "info" dict) representing the agent velocity. This key will be processed as a node feature, and
+            it will be used to construct edge features. In particular, it will be used to compute relative velocities
+            (``vel_node_1 - vel_node_2``) for all neighbours in the graph.
+        vel_features (int, optional): Needed when velocity_key is specified.
+            It has to match to the last element of the shape the tensor under velocity_key.
         edge_radius (float, optional): If topology is ``"from_pos"`` the radius to use to build the agent graph.
             Agents within this radius distance will be neighnours.
 
@@ -120,6 +125,8 @@ def __init__(
         exclude_pos_from_node_features: Optional[bool],
         velocity_key: Optional[str],
         edge_radius: Optional[float],
+        pos_features: Optional[int],
+        vel_features: Optional[int],
         **kwargs,
     ):
         self.topology = topology
@@ -128,34 +135,26 @@ def __init__(
         self.velocity_key = velocity_key
         self.exclude_pos_from_node_features = exclude_pos_from_node_features
         self.edge_radius = edge_radius
+        self.pos_features = pos_features
+        self.vel_features = vel_features
 
         super().__init__(**kwargs)
 
-        self.pos_features = sum(
-            [
-                spec.shape[-1]
-                for key, spec in self.input_spec.items(True, True)
-                if _unravel_key_to_tuple(key)[-1] == position_key
-            ]
-        )  # Input keys ending with `position_key`
         if self.pos_features > 0:
             self.pos_features += 1  # We will add also 1-dimensional distance
-        self.vel_features = sum(
-            [
-                spec.shape[-1]
-                for key, spec in self.input_spec.items(True, True)
-                if _unravel_key_to_tuple(key)[-1] == velocity_key
-            ]
-        )  # Input keys ending with `velocity_key`
         self.edge_features = self.pos_features + self.vel_features
         self.input_features = sum(
             [
                 spec.shape[-1]
                 for key, spec in self.input_spec.items(True, True)
-                if _unravel_key_to_tuple(key)[-1]
-                not in ((position_key) if self.exclude_pos_from_node_features else ())
+                if _unravel_key_to_tuple(key)[-1] not in (position_key, velocity_key)
             ]
-        )  # Input keys not ending with `velocity_key` and `position_key`
+        )  # Input keys
+        if self.position_key is not None and not self.exclude_pos_from_node_features:
+            self.input_features += self.pos_features - 1
+        if self.velocity_key is not None:
+            self.input_features += self.vel_features
+
         self.output_features = self.output_leaf_spec.shape[-1]
 
         if gnn_kwargs is None:
@@ -191,6 +190,8 @@ def __init__(
             device=self.device,
             n_agents=self.n_agents,
         )
+        self._full_position_key = None
+        self._full_velocity_key = None
 
     def _perform_checks(self):
         super()._perform_checks()
@@ -208,6 +209,22 @@ def _perform_checks(self):
             raise ValueError(
                 "exclude_pos_from_node_features needs to be specified when position_key is provided"
             )
+        if self.position_key is not None and self.pos_features <= 0:
+            raise ValueError(
+                f"Position key specified but pos_features is {self.pos_features}"
+            )
+        elif self.position_key is None and self.pos_features > 0:
+            raise ValueError(
+                f"If no position_key is given, pos_features needs to be 0, got: {self.pos_features}"
+            )
+        if self.velocity_key is not None and self.vel_features <= 0:
+            raise ValueError(
+                f"Velocity key specified but vel_features is {self.vel_features}"
+            )
+        elif self.velocity_key is None and self.vel_features > 0:
+            raise ValueError(
+                f"If no velocity_key is given, vel_features needs to be 0, got: {self.vel_features}"
+            )
 
         if not self.input_has_agent_dim:
             raise ValueError(
@@ -247,40 +264,51 @@ def _perform_checks(self):
 
     def _forward(self, tensordict: TensorDictBase) -> TensorDictBase:
         # Gather in_key
-        input = torch.cat(
-            [
-                tensordict.get(in_key)
-                for in_key in self.in_keys
-                if _unravel_key_to_tuple(in_key)[-1]
-                not in (
-                    (self.position_key) if self.exclude_pos_from_node_features else ()
-                )
-            ],
-            dim=-1,
-        )
+        input = [
+            tensordict.get(in_key)
+            for in_key in self.in_keys
+            if _unravel_key_to_tuple(in_key)[-1]
+            not in (self.position_key, self.velocity_key)
+        ]
+
+        # Retrieve position
         if self.position_key is not None:
-            pos = torch.cat(
-                [
-                    tensordict.get(in_key)
-                    for in_key in self.in_keys
-                    if _unravel_key_to_tuple(in_key)[-1] == self.position_key
-                ],
-                dim=-1,
-            )
+            if self._full_position_key is None:  # Run once to find full key
+                self._full_position_key = self._get_key_terminating_with(
+                    list(tensordict.keys(True, True)), self.position_key
+                )
+                pos = tensordict.get(self._full_position_key)
+                if pos.shape[-1] != self.pos_features - 1:
+                    raise ValueError(
+                        f"Position key in tensordict is {pos.shape[-1]}-dimensional, "
+                        f"while model was configured with pos_features={self.pos_features-1}"
+                    )
+            else:
+                pos = tensordict.get(self._full_position_key)
+            if not self.exclude_pos_from_node_features:
+                input.append(pos)
         else:
             pos = None
+
+        # Retrieve velocity
         if self.velocity_key is not None:
-            vel = torch.cat(
-                [
-                    tensordict.get(in_key)
-                    for in_key in self.in_keys
-                    if _unravel_key_to_tuple(in_key)[-1] == self.velocity_key
-                ],
-                dim=-1,
-            )
+            if self._full_velocity_key is None:  # Run once to find full key
+                self._full_velocity_key = self._get_key_terminating_with(
+                    list(tensordict.keys(True, True)), self.velocity_key
+                )
+                vel = tensordict.get(self._full_velocity_key)
+                if vel.shape[-1] != self.vel_features:
+                    raise ValueError(
+                        f"Velocity key in tensordict is {vel.shape[-1]}-dimensional, "
+                        f"while model was configured with vel_features={self.vel_features}"
+                    )
+            else:
+                vel = tensordict.get(self._full_velocity_key)
+            input.append(vel)
         else:
             vel = None
 
+        input = torch.cat(input, dim=-1)
         batch_size = input.shape[:-2]
 
         graph = _batch_from_dense_to_ptg(
@@ -338,6 +366,15 @@ def _forward(self, tensordict: TensorDictBase) -> TensorDictBase:
         tensordict.set(self.out_key, res)
         return tensordict
 
+    def _get_key_terminating_with(self, keys: List[NestedKey], key: str) -> NestedKey:
+        for k in keys:
+            k_tuple = _unravel_key_to_tuple(k)
+            if k_tuple[-1] == key and self.agent_group in k_tuple:
+                return k
+        raise KeyError(
+            f"Key terminating with {key} and containing {self.agent_group} not found in keys: {keys}"
+        )
+
 
 def _get_edge_index(topology: str, self_loops: bool, n_agents: int, device: str):
     if topology == "full":
@@ -426,7 +463,9 @@ class GnnConfig(ModelConfig):
     gnn_kwargs: Optional[dict] = None
 
     position_key: Optional[str] = None
+    pos_features: Optional[int] = 0
     velocity_key: Optional[str] = None
+    vel_features: Optional[int] = 0
     exclude_pos_from_node_features: Optional[bool] = None
     edge_radius: Optional[float] = None
 

test/test_models.py

@@ -331,7 +331,7 @@ def test_gnn_edge_attrs(
                             shape=multi_agent_obs.shape[len(batch_size) :]
                         ),
                         "pos": UnboundedContinuousTensorSpec(
-                            shape=multi_agent_obs.shape[len(batch_size) :]
+                            shape=multi_agent_pos.shape[len(batch_size) :]
                         ),
                     },
                     shape=(n_agents,),
@@ -360,6 +360,7 @@ def test_gnn_edge_attrs(
             gnn_kwargs=None,
             position_key=position_key,
             exclude_pos_from_node_features=False,
+            pos_features=pos_size if position_key is not None else 0,
         ).get_model(
             input_spec=input_spec,
             output_spec=output_spec,
@@ -391,6 +392,7 @@ def test_gnn_edge_attrs(
                 gnn_kwargs=None,
                 position_key=position_key,
                 exclude_pos_from_node_features=False,
+                pos_features=pos_size if position_key is not None else 0,
             ).get_model(
                 input_spec=input_spec,
                 output_spec=output_spec,