[examples][MLIRPython] Fix var_mean_op and remove unused operator

examples/MLIRPython/buddy/operators_gen.py

@@ -110,7 +110,6 @@ def addmm_op(node: torch.fx.Node,
   add_result_tensor_type = ir.RankedTensorType.get(final_result_shape,
                                                    result_element_type)
 
-
   op = _gen_arith_binary_op(input_, matmul_result_reshape_op.result, tosa.AddOp)
   return op
 
@@ -128,12 +127,6 @@ def bmm_op(node: torch.fx.Node,
   return op
 
 
-def gt_op(node, symbol_table):
-  input1 = symbol_table.get((str(node.args[0]), 0), node.args[0])
-  input2 = symbol_table.get((str(node.args[1]), 0), node.args[1])
-  return _gen_arith_binary_op(input1, input2, tosa.GreaterOp)
-
-
 def add_op(node, symbol_table):
   input1 = symbol_table.get((str(node.args[0]), 0), node.args[0])
   input2 = symbol_table.get((str(node.args[1]), 0), node.args[1])
@@ -253,7 +246,6 @@ def unsqueeze_op(node, symbol_table):
   sizes.insert(dim, 1)
   new_shape_content = array.array("i", sizes)
   new_shape_content = memoryview(new_shape_content)
-  # new_shape_attr = ir.DenseElementsAttr.get(new_shape_content)
   op = tosa.ReshapeOp(input_tensor, new_shape_content)
   return op
 
@@ -361,7 +353,7 @@ def mean_dim_op(_input_tensor: ir.Value, _dim) -> ir.Operation:
     reduce_sum_result = _input_tensor
 
     # reduce along each dimension in `_dim`
-    for _dim_item, _ in enumerate(_dim):
+    for _dim_item in _dim:
       reduce_dim_attr = ir.IntegerAttr.get(ir.IntegerType.get_signless(64),
                                            _dim_item)
       reduce_sum_op: ir.Operation = tosa.ReduceSumOp(reduce_sum_result,
@@ -408,7 +400,7 @@ def var_dim_op(_input_tensor: ir.Value, _mean_tensor: ir.Value, _dim,
 
     # the result of `mul_op` is the first tensor we need to reduce
     reduce_sum_op = mul_op
-    for _dim_item, _ in enumerate(_dim):
+    for _dim_item in _dim:
       reduce_dim_attr = ir.IntegerAttr.get(ir.IntegerType.get_signless(64),
                                            _dim_item)
       reduce_sum_op: ir.Operation = tosa.ReduceSumOp(reduce_sum_op.results[0],
@@ -438,7 +430,7 @@ def var_dim_op(_input_tensor: ir.Value, _mean_tensor: ir.Value, _dim,
   var_input_tensor = symbol_table.get((str(node.args[0]), 0))
 
   kwargs = node.kwargs
-  keep_dim = kwargs.get("keep_dim", False)
+  keepdim = kwargs.get("keepdim", False)
   correction = kwargs.get("correction", 1.0)
 
   mean_op = None
@@ -447,14 +439,14 @@ def var_dim_op(_input_tensor: ir.Value, _mean_tensor: ir.Value, _dim,
     calc_dims = range(len(ir.RankedTensorType(mean_input_tensor.type).shape))
   else:
     calc_dims = node.args[1]
-
+  
   mean_op = mean_dim_op(mean_input_tensor, calc_dims)
   var_op = var_dim_op(var_input_tensor, mean_op.results[0], calc_dims,
                       correction)
   mean_input_tensor = mean_op.results[0]
   var_input_tensor = var_op.results[0]
 
-  if not keep_dim:
+  if not keepdim:
     result_shp = ir.RankedTensorType(var_op.results[0].type).shape
     result_shp = [siz for siz in result_shp if siz != 1]
     var_op = tosa.ReshapeOp(var_op.results[0],
@@ -583,4 +575,4 @@ def sum_op(node, symbol_table):
     "convert_element_type.default": convert_element_type_op,
     "permute.default": permute_op,
     "unsqueeze.default": unsqueeze_op,
-}
+}