add weights call to subtensor

bittensor/_metagraph/__init__.py

@@ -114,7 +114,8 @@ def sync ( self, block: Optional[int] = None, lite: bool = True, subtensor: Opti
         if lite:
             self.neurons = subtensor.neurons_lite( block = block, netuid = self.netuid )
         else:
-            self.neurons = subtensor.neurons(block = block, netuid = self.netuid )
+            self.neurons = subtensor.neurons( block = block, netuid = self.netuid )
+
         self.lite = lite
         self.n = torch.nn.Parameter( torch.tensor( len(self.neurons), dtype=torch.int64 ), requires_grad=False )
         self.version = torch.nn.Parameter( torch.tensor( [bittensor.__version_as_int__], dtype=torch.int64 ), requires_grad=False )

bittensor/_subtensor/chain_data.py

@@ -288,6 +288,14 @@ def _null_neuron() -> 'NeuronInfo':
             pruning_score = 0,
         )
         return neuron
+
+    @classmethod
+    def from_weights_bonds_and_neuron_lite( cls, neuron_lite: 'NeuronInfoLite', weights_as_dict: Dict[int, List[Tuple[int, int]]], bonds_as_dict: Dict[int, List[Tuple[int, int]]] ) -> 'NeuronInfo':
+        n_dict = neuron_lite.__dict__
+        n_dict['weights'] = weights_as_dict.get(neuron_lite.uid, [])
+        n_dict['bonds'] = bonds_as_dict.get(neuron_lite.uid, [])
+
+        return cls( **n_dict )
 
     @staticmethod
     def _neuron_dict_to_namespace(neuron_dict) -> 'NeuronInfo':

bittensor/_subtensor/subtensor_impl.py

@@ -1312,25 +1312,23 @@ def neurons(self, netuid: int, block: Optional[int] = None ) -> List[NeuronInfo]
             neuron (List[NeuronInfo]):
                 List of neuron metadata objects.
         """
-        @retry(delay=2, tries=3, backoff=2, max_delay=4)
-        def make_substrate_call_with_retry():
-            with self.substrate as substrate:
-                block_hash = None if block == None else substrate.get_block_hash( block )
-                params = [netuid]
-                if block_hash:
-                    params = params + [block_hash]
-                return substrate.rpc_request(
-                    method="neuronInfo_getNeurons", # custom rpc method
-                    params=params
-                )
+        neurons_lite = self.neurons_lite( netuid = netuid, block = block )
+        weights = self.weights( block = block, netuid = netuid )
+        bonds = self.bonds( block = block, netuid = netuid )
 
-        json_body = make_substrate_call_with_retry()
-        result = json_body['result']
+        weights_as_dict = {
+            uid: w for uid, w in weights
+        }
+        bonds_as_dict = {
+            uid: b for uid, b in bonds
+        }
 
-        if result in (None, []):
-            return []
+        neurons = [
+            NeuronInfo.from_weights_bonds_and_neuron_lite( neuron_lite, weights_as_dict, bonds_as_dict ) for neuron_lite in neurons_lite
+        ]
 
-        return NeuronInfo.list_from_vec_u8( result )
+        return neurons
+
 
     def neuron_for_uid_lite( self, uid: int, netuid: int, block: Optional[int] = None ) -> Optional[NeuronInfoLite]:
         r""" Returns a list of neuron lite from the chain.
@@ -1414,6 +1412,24 @@ def metagraph( self, netuid: int, lite: bool = True, block: Optional[int] = None
 
         return metagraph_
 
+    def weights(self, netuid: int, block: Optional[int] = None) -> List[Tuple[int, List[Tuple[int, int]]]]:
+        w_map = []
+        w_map_encoded = self.query_map_subtensor(name="Weights", block=block, params = [netuid])
+        if w_map_encoded.records:
+            for uid, w in w_map_encoded:
+                w_map.append((uid.serialize(), w.serialize()))
+
+        return w_map
+
+    def bonds(self, netuid: int, block: Optional[int] = None) -> List[Tuple[int, List[Tuple[int, int]]]]:
+        b_map = []
+        b_map_encoded = self.query_map_subtensor(name="Bonds", block=block, params = [netuid])
+        if b_map_encoded.records:
+            for uid, b in b_map_encoded:
+                b_map.append((uid.serialize(), b.serialize()))
+
+        return b_map
+
     ################
     ## Extrinsics ##
     ################