PR fixes

Author: diaconuccalin

Deeploy/CommonExtensions/CodeTransformationPasses/Closure.py

@@ -156,7 +156,7 @@ def apply(self,
               name: str,
               verbose: CodeGenVerbosity = _NoVerbosity) -> Tuple[NetworkContext, ExecutionBlock]:
         # Add underscore to avoid name issues when beginning with problematic characters (like numbers)
-        self.closureName = "op_" + name + self.closureSuffix
+        self.closureName = "_" + name + self.closureSuffix
         self.functionCall = executionBlock.generate(ctxt)
         self._generateClosureStruct(ctxt, executionBlock)
         ctxt = self._generateClosureCtxt(ctxt, name)

Deeploy/DeeployTypes.py

@@ -453,9 +453,9 @@ class ConstantBuffer(VariableBuffer):
 
     """
 
-    def __init__(self, name: str = '', shape = [1], values = [0], alias_of: Optional[List[str]] = None):
+    def __init__(self, name: str = '', shape = [1], values = [0]):
         # Pass a copy of alias_of to avoid shared references
-        super().__init__(name, shape, list(alias_of) if alias_of is not None else None)
+        super().__init__(name, shape)
 
         values = np.asarray(values)
         # intArray = values.astype(int)
@@ -495,7 +495,6 @@ def fromVariableBuffer(cls, buffer: VariableBuffer, values):
             name = buffer.name,
             shape = buffer.shape,
             values = values,
-            alias_of = buffer.alias_of,
         )
 
         return ret
@@ -595,7 +594,6 @@ def __init__(self,
         self.ConstantBuffer = constantBuffer
         self.StructBuffer = structBuffer
         self.TransientBuffer = transientBuffer
-        self.n_cores = n_cores
         self.name = name
         self.n_cores = n_cores
 

Deeploy/Targets/Generic/Parsers.py

@@ -1090,7 +1090,7 @@ def parseNodeCtxt(self,
         output_node.add_aliases(aliases_to_add = new_output_node_aliases)
 
         # Add output node as alias to its aliases (alias relationship is symmetric)
-        for alias in new_output_node_aliases:
+        for alias in input_node.get_aliases_of():
             alias_node = ctxt.lookup(alias)
             alias_node.add_aliases(aliases_to_add = [
                 output_node.name,

Deeploy/Targets/PULPOpen/Deployer.py

@@ -66,7 +66,6 @@ def __init__(self,
         ]
 
         self.extNameCount = 0
-        self.n_cores = n_cores
 
     def bind(self):
         # SCHEREMO: THIS IS A STOP GAP SOLUTION. DONT REUSE. I MEAN IT. I WILL FIND YOU.

Deeploy/Targets/PULPOpen/Parsers.py

@@ -77,6 +77,7 @@ def parseNode(self, node: gs.Node) -> (bool):
                 self.operatorRepresentation['pads'][0] == self.operatorRepresentation['pads'][2],
                 self.operatorRepresentation['pads'][1] == self.operatorRepresentation['pads'][3],
                 self.operatorRepresentation['pads'][0] == self.operatorRepresentation['pads'][1],
+                len(node.inputs) in [2, 3],
             ])
 
             self.operatorRepresentation['dim_kernel_x'] = int(self.operatorRepresentation['kernel_shape'][0])
@@ -104,12 +105,12 @@ def parseNodeCtxt(self,
             inputs = ['data_in', 'weight']
 
             # Handle bias, if present
-            if len(node.inputs) > 2:
-                inputs.append("bias")
-                self.operatorRepresentation["has_bias"] = "true"
-            else:
+            if len(node.inputs) == 2:
                 self.operatorRepresentation["has_bias"] = "false"
                 self.operatorRepresentation["bias"] = "NULL"
+            else:
+                inputs.append("bias")
+                self.operatorRepresentation["has_bias"] = "true"
 
             for idx, inputNode in enumerate(node.inputs):
                 self.operatorRepresentation[inputs[idx]] = ctxt.lookup(inputNode.name).name
@@ -170,12 +171,12 @@ def parseNodeCtxt(self,
             inputs = ['data_in', 'weight']
 
             # Handle bias, if present
-            if len(node.inputs) > 2:
-                inputs.append("bias")
-                self.operatorRepresentation["has_bias"] = "true"
-            else:
+            if len(node.inputs) == 2:
                 self.operatorRepresentation["has_bias"] = "false"
                 self.operatorRepresentation["bias"] = "NULL"
+            else:
+                inputs.append("bias")
+                self.operatorRepresentation["has_bias"] = "true"
 
             # Map input nodes to operator representation
             for idx, inputNode in enumerate(node.inputs):