Implement CodeRabit Feedback

Author: Xeratec

Deeploy/Targets/PULPOpen/Parsers.py

@@ -355,7 +355,7 @@ def parseNode(self, node: gs.Node) -> bool:
 
         M = node.inputs[0].shape[-1 if node.attrs["transA"] else -2]
         batch = math.prod(node.inputs[0].shape[:-2])
-        return super().parseNode(node) and M == 1 and batch >= 8
+        return M == 1 and batch >= 8
 
 
 class PULPTallGEMMParser(PULPGEMMParser):
@@ -367,4 +367,4 @@ def parseNode(self, node: gs.Node) -> bool:
         M = node.inputs[0].shape[-1 if node.attrs["transA"] else -2]
         N = node.inputs[1].shape[-2 if node.attrs["transB"] else -1]
         batch = math.prod(node.inputs[0].shape[:-2])
-        return super().parseNode(node) and M >= 8 and (M % 8) < (N % 8) and batch < 8
+        return M >= 8 and (M % 8) < (N % 8) and batch < 8

Deeploy/Targets/Snitch/Layers.py

@@ -18,7 +18,7 @@ def computeOps(self):
         return self.mapper.parser.operatorRepresentation['size'] * 4  # 2 mul, 1 add, 1 right shift
 
     def computeShapes(self, inputShapes: Shape, outputShapes: Shape, operatorRepresentation: OperatorRepresentation,
-                      channels_first: bool) -> Tuple[Shape]:
+                      channels_first: bool) -> Tuple[Shape, Shape]:
         # JUNGVI: Broadcast the weights and bias to have as many dimensions as the inputs
         shape = np.broadcast_shapes(*inputShapes)
         return ([shape] * len(inputShapes), outputShapes)