diff --git a/chunk_codegen.py b/chunk_codegen.py
index aa9d7ecd8..a14f7c134 100644
--- a/chunk_codegen.py
+++ b/chunk_codegen.py
@@ -120,6 +120,13 @@ class NodeIndexTracer(object):
         return self.idx_trace_list[node_idx]['compute']
     
     def assign_index_as_input(self, node, node_idx):
+        """
+        Assign node's trace as its input node.
+
+        Args:
+            node (node)
+            node_idx (int)
+        """        
         input_node_idx = _find_idx_by_name(node.args[0].name, self.nodes_list)
         input_node_idx_trace = self.idx_trace_list[input_node_idx]['idx']
         
@@ -127,6 +134,13 @@ class NodeIndexTracer(object):
         self.idx_trace_list[node_idx]['idx'] = new_idx_trace
     
     def assign_all_index(self, node, node_idx):
+        """
+        Add new index for all node's dims.
+
+        Args:
+            node (node)
+            node_idx (int)
+        """  
         shape = node.meta['tensor_meta'].shape
         new_trace = []
         for _ in shape:
@@ -134,6 +148,15 @@ class NodeIndexTracer(object):
         self.idx_trace_list[node_idx]['idx'] = new_trace   
 
     def assign_transpose_index(self, node, node_idx):
+        """
+        Assign index for transpose op.
+        1. swap input's dim according to transpose args
+        2. inherit input's computation
+
+        Args:
+            node (node)
+            node_idx (int)
+        """  
         tranpose_dim = node.args[1:]
         input_node_idx_trace = self.find_idx_trace_from_node(node.args[0])
         
@@ -145,6 +168,15 @@ class NodeIndexTracer(object):
         self.inherit_computation(node.args[0], node)
         
     def assign_permute_index(self, node, node_idx):
+        """
+        Assign index for permute op.
+        1. swap input's dim according to permute args
+        2. inherit input's computation
+
+        Args:
+            node (node)
+            node_idx (int)
+        """  
         permute_dim = node.args[1:]
         input_node_idx_trace = self.find_idx_trace_from_node(node.args[0])
         
@@ -156,6 +188,16 @@ class NodeIndexTracer(object):
         self.inherit_computation(node.args[0], node)
         
     def assign_linear_index(self, node, node_idx):
+        """
+        Assign index for linear op.
+        1. copy trace from input node and change last index accroding to weight
+        2. mark equal for input node last index, weight first dim and bias dim.
+        3. inherit input's computation, mark computation for last dim.
+
+        Args:
+            node (node)
+            node_idx (int)
+        """  
         input_node, weight, bias = node.args
         input_node_idx_trace = self.find_idx_trace_from_node(input_node)
         weight_idx_trace = self.find_idx_trace_from_node(weight)
@@ -173,6 +215,16 @@ class NodeIndexTracer(object):
             self.mark_idx_equal(input_node_idx_trace[-1], bias_idx_trace[0])
 
     def assign_matmul_index(self, node, node_idx):
+        """
+        Assign index for matmul op.
+        1. copy trace from matmul_left and change last index accroding to matmul_right. (assert they have same length)
+        2. mark equal for input matmul_left -1 index and matmul_right -2 dim.
+        3. inherit matmul_left and matmul_right computation, mark computation for last dim.
+
+        Args:
+            node (node)
+            node_idx (int)
+        """  
         matmul_left, matmul_right = node.args
         matmul_left_idx_trace = self.find_idx_trace_from_node(matmul_left)
         matmul_right_idx_trace = self.find_idx_trace_from_node(matmul_right)
@@ -188,21 +240,63 @@ class NodeIndexTracer(object):
         self.mark_idx_equal(matmul_left_idx_trace[-1], matmul_right_idx_trace[-2])
 
     def assign_layernorm_index(self, node, idx):
+        """
+        Assign index for layernorm op.
+        1. assign index as input node
+        2. inherit computation and mark last 2 dims as computed.
+
+        Args:
+            node (node)
+            node_idx (int)
+        """
         self.assign_index_as_input(node, idx)
         self.inherit_computation(node.args[0], node)
         self.mark_computation(node, idx, [-1, -2])
     
     def assign_elementwise_index(self, node, idx):
+        """
+        Assign index for element-wise op (eg. relu sigmoid add mul).
+        1. assign index as input node
+        2. inherit computation from all input nodes.
+
+        Args:
+            node (node)
+            node_idx (int)
+        """  
         self.assign_index_as_input(node, idx)
         for node_in in node.args:
             if type(node_in) not in (int, float):
                 self.inherit_computation(node_in, node)
                 
     def assign_softmax_index(self, node, idx):
+        """
+        Assign index for softmax op.
+        1. assign index as input node
+        2. inherit computation and mark softmax dim as computed.
+
+        Args:
+            node (node)
+            node_idx (int)
+        """  
         self.assign_index_as_input(node, idx)
+        self.inherit_computation(node.args[0], node)
         self.mark_computation(node, idx, [node.kwargs['dim']])
 
     def assign_view_reshape_index(self, node, node_idx):
+        """
+        Assign index for view and reshape op.
+        1. get origin shape and target shape by meta info.
+        2. compute the real value of -1 in target shape.
+        3. determine changed dim, and assgin index for generated dim.
+        4. log changed dim and generated dim for restore
+        5. look into view list to see whether the view is associated with other,
+           if so assgin equal dim according to previous view.
+        6. inherit computation.
+
+        Args:
+            node (node)
+            node_idx (int)
+        """  
         # get data, turn into number
         origin_node = node.args[0]
         origin_shape = origin_node.meta['tensor_meta'].shape
@@ -305,6 +399,7 @@ class NodeIndexTracer(object):
             else:
                 raise NotImplementedError(node.op, "op not implemented yet!")
 
+
 def _get_meta_node_size(x):
     x = x.meta['tensor_meta']
     x = x.numel * torch.tensor([], dtype=x.dtype).element_size()