!9591 Assignment enables negative index

From: @liangzhibo
Reviewed-by: @ginfung,@zh_qh
Signed-off-by: @zh_qh

mindspore/ccsrc/frontend/optimizer/clean.cc

@@ -330,7 +330,7 @@ AnfNodePtr EraseExtractKeywordArg(const CNodePtr &node) {
 ValueTuplePtr ConvertValueListToValueTuple(const ValueListPtr &value_list, int64_t depth) {
   const int64_t DEPTH_MAX = 5;
   if (depth > DEPTH_MAX) {
-    MS_LOG(EXCEPTION) << "List nesting is not allowed more than 5 levels.";
+    MS_LOG(EXCEPTION) << "List nesting is not allowed more than 6 levels.";
   }
   std::vector<ValuePtr> elements;
   for (const auto &it : value_list->value()) {

mindspore/core/abstract/prim_structures.cc

@@ -163,18 +163,14 @@ AbstractBasePtr InferTupleOrListSetItem(const std::string &op_name, const Abstra
                              << index_value->ToString();
   }
   int64_t idx_v = GetValue<int64_t>(index_value);
-  if (idx_v < 0) {
-    MS_EXCEPTION(IndexError) << "The index of " << typeid(T).name() << " should be positive number, but got " << idx_v
-                             << ".";
-  }
-
-  size_t uidx_v = LongToSize(idx_v);
   AbstractBasePtrList elements = queue->elements();
   std::size_t nelems = elements.size();
-  if (uidx_v >= nelems) {
-    MS_EXCEPTION(IndexError) << op_name << " evaluator the index: " << uidx_v << " to set out of range: " << nelems - 1
-                             << ".";
+  int64_t idx_t = idx_v >= 0 ? idx_v : idx_v + SizeToLong(nelems);
+  if (idx_t < 0 || idx_t >= SizeToLong(nelems)) {
+    MS_EXCEPTION(IndexError) << op_name << " evaluator the index: " << idx_v << " to set out of range: [-" << nelems
+                             << "," << nelems - 1 << "].";
   }
+  size_t uidx_v = LongToSize(idx_t);
   elements[uidx_v] = args_spec_list[2];
   return std::make_shared<T>(elements);
 }

mindspore/core/ir/pattern_matcher.h

@@ -755,6 +755,9 @@ class PConstant : public PBase<PConstant<T> > {
       ShapeVector tensor_shape = tensor_abstract->shape()->shape();
       auto new_tensor_ptr = std::make_shared<tensor::Tensor>(tensor_type_ptr->type_id(), tensor_shape);
       size_t mem_size = GetTypeByte(tensor_type_ptr) * IntToSize(new_tensor_ptr->ElementsNum());
+      if (new_tensor_ptr->DataSize() < tensor_ptr->DataSize()) {
+        MS_EXCEPTION(ValueError) << "DataSize of new_tensor_ptr is smaller than DataSize of tensor_ptr";
+      }
       if ((tensor_type == TypeId::kNumberTypeFloat32) || (tensor_type == TypeId::kNumberTypeFloat) ||
           (tensor_type == TypeId::kNumberTypeFloat64)) {
         float *data = reinterpret_cast<float *>(tensor_ptr->data_c());

tests/ut/python/pipeline/parse/test_sequence_assign.py

@@ -37,6 +37,23 @@ def test_list_index_1D():
     assert out[2] == [3, 3, 3]
 
 
+def test_list_neg_index_1D():
+    class Net(nn.Cell):
+        def __init__(self):
+            super(Net, self).__init__()
+
+        def construct(self):
+            list_ = [[1], [2, 2], [3, 3, 3]]
+            list_[-3] = [100]
+            return list_
+
+    net = Net()
+    out = net()
+    assert out[0] == [100]
+    assert out[1] == [2, 2]
+    assert out[2] == [3, 3, 3]
+
+
 def test_list_index_2D():
     class Net(nn.Cell):
         def __init__(self):
@@ -55,6 +72,24 @@ def test_list_index_2D():
     assert out[2] == [3, 3, 3]
 
 
+def test_list_neg_index_2D():
+    class Net(nn.Cell):
+        def __init__(self):
+            super(Net, self).__init__()
+
+        def construct(self):
+            list_ = [[1], [2, 2], [3, 3, 3]]
+            list_[1][-2] = 200
+            list_[1][-1] = 201
+            return list_
+
+    net = Net()
+    out = net()
+    assert out[0] == [1]
+    assert out[1] == [200, 201]
+    assert out[2] == [3, 3, 3]
+
+
 def test_list_index_3D():
     class Net(nn.Cell):
         def __init__(self):
@@ -74,6 +109,25 @@ def test_list_index_3D():
     assert out[2] == [[300, 301, 302]]
 
 
+def test_list_neg_index_3D():
+    class Net(nn.Cell):
+        def __init__(self):
+            super(Net, self).__init__()
+
+        def construct(self):
+            list_ = [[1], [2, 2], [[3, 3, 3]]]
+            list_[2][0][-3] = 300
+            list_[2][0][-2] = 301
+            list_[2][0][-1] = 302
+            return list_
+
+    net = Net()
+    out = net()
+    assert out[0] == [1]
+    assert out[1] == [2, 2]
+    assert out[2] == [[300, 301, 302]]
+
+
 def test_list_index_1D_parameter():
     class Net(nn.Cell):
         def __init__(self):