support non tensor inputs

mindspore/ccsrc/frontend/operator/composite/composite.cc

@@ -62,8 +62,6 @@ ElemwiseMap kElemwiseMap = {{"__add__", kPrimScalarAdd}, {"__sub__", kPrimScalar
                             {"__gt__", kPrimScalarGt},   {"__ne__", kPrimScalarNe},   {"__le__", kPrimScalarLe},
                             {"__ge__", kPrimScalarGe}};
 
-const MetaFuncGraphPtr kTail = std::make_shared<Tail>("tail");
-
 // copy from python API: reduce.
 // Apply a function of two arguments cumulatively to the items of a sequence,
 // from left to right, so as to reduce the sequence to a single value.For example,
@@ -384,8 +382,8 @@ REGISTER_PYBIND_DEFINE(HyperMap_, ([](const py::module *m) {
                            .def(py::init<>());
                        }));
 
-FuncGraphPtr Tail::GenerateTupleFuncGraph(const abstract::AbstractTuplePtr &a_tuple) {
-  MS_EXCEPTION_IF_NULL(a_tuple);
+FuncGraphPtr Tail::GenerateSequeueFuncGraph(const abstract::AbstractSequeuePtr &sequeue) {
+  MS_EXCEPTION_IF_NULL(sequeue);
 
   FuncGraphPtr ret = std::make_shared<FuncGraph>();
   ret->set_flag(FUNC_GRAPH_FLAG_CORE, true);
@@ -393,31 +391,24 @@ FuncGraphPtr Tail::GenerateTupleFuncGraph(const abstract::AbstractTuplePtr &a_tu
   AnfNodePtr ptrTup = ret->add_parameter();
 
   std::vector<AnfNodePtr> elems;
-  elems.push_back(NewValueNode(prim::kPrimMakeTuple));
-
-  int64_t tuple_size = SizeToLong(a_tuple->size());
-  for (int64_t i = 1; i < tuple_size; ++i) {
-    elems.push_back(ret->NewCNode({NewValueNode(prim::kPrimTupleGetItem), ptrTup, NewValueNode(i)}));
+  PrimitivePtr op = nullptr;
+  if (sequeue->isa<AbstractTuple>()) {
+    elems.push_back(NewValueNode(prim::kPrimMakeTuple));
+    op = prim::kPrimTupleGetItem;
+  } else {
+    elems.push_back(NewValueNode(prim::kPrimMakeList));
+    op = prim::kPrimListGetItem;
   }
 
-  ret->set_output(ret->NewCNode(elems));
-  return ret;
-}
-
-FuncGraphPtr Tail::GenerateListFuncGraph(const abstract::AbstractListPtr &a_list) {
-  MS_EXCEPTION_IF_NULL(a_list);
-
-  FuncGraphPtr ret = std::make_shared<FuncGraph>();
-  ret->set_flag(FUNC_GRAPH_FLAG_CORE, true);
-  ret->debug_info()->set_name("tail");
-  AnfNodePtr ptrList = ret->add_parameter();
-
-  std::vector<AnfNodePtr> elems;
-  elems.push_back(NewValueNode(prim::kPrimMakeList));
-
-  int64_t list_size = SizeToLong(a_list->size());
-  for (int64_t i = 1; i < list_size; ++i) {
-    elems.push_back(ret->NewCNode({NewValueNode(prim::kPrimListGetItem), ptrList, NewValueNode(i)}));
+  for (size_t i = 1; i < sequeue->size(); ++i) {
+    if (do_grad_) {
+      MS_EXCEPTION_IF_NULL((*sequeue)[i]);
+      if ((*sequeue)[i]->isa<abstract::AbstractUndetermined>()) {
+        elems.push_back(ret->NewCNode({NewValueNode(op), ptrTup, NewValueNode(SizeToLong(i))}));
+      }
+    } else {
+      elems.push_back(ret->NewCNode({NewValueNode(op), ptrTup, NewValueNode(SizeToLong(i))}));
+    }
   }
 
   ret->set_output(ret->NewCNode(elems));
@@ -430,14 +421,8 @@ FuncGraphPtr Tail::GenerateFuncGraph(const AbstractBasePtrList &args_spec_list)
   }
 
   AbstractBasePtr a = args_spec_list[0];
-  abstract::AbstractTuplePtr a_tuple = dyn_cast<AbstractTuple>(a);
-  if (a_tuple != nullptr) {
-    return GenerateTupleFuncGraph(a_tuple);
-  }
-
-  abstract::AbstractListPtr a_list = dyn_cast<AbstractList>(a);
-  if (a_list != nullptr) {
-    return GenerateListFuncGraph(a_list);
+  if (a->isa<AbstractTuple>() || a->isa<AbstractList>()) {
+    return GenerateSequeueFuncGraph(a->cast<abstract::AbstractSequeuePtr>());
   }
 
   MS_LOG(EXCEPTION) << "arg0 must be AbstractTuple or AbstractList, but: " << a->ToString();
@@ -614,7 +599,8 @@ void GradOperation::doGetGrad(const FuncGraphPtr &func_graph, AnfNodePtr out, An
 
   CNodePtr inputs_bprop = nullptr;
   if (get_all_) {
-    inputs_bprop = func_graph->NewCNode({NewValueNode(kTail), ptr_bapp});
+    TailPtr tail = std::make_shared<Tail>("tail", true);
+    inputs_bprop = func_graph->NewCNode({NewValueNode(tail), ptr_bapp});
   }
 
   // Gradients wrt inputs and parameters

mindspore/ccsrc/frontend/operator/composite/composite.h

@@ -99,15 +99,17 @@ extern ValuePtr kCompositeHyperMap;
 
 class Tail : public MetaFuncGraph {
  public:
-  explicit Tail(const std::string &name) : MetaFuncGraph(name) {}
+  explicit Tail(const std::string &name, bool do_grad = false) : MetaFuncGraph(name), do_grad_(do_grad) {}
   ~Tail() override = default;
   MS_DECLARE_PARENT(Tail, MetaFuncGraph)
 
   FuncGraphPtr GenerateFuncGraph(const AbstractBasePtrList &args_spec_list) override;
-  FuncGraphPtr GenerateTupleFuncGraph(const abstract::AbstractTuplePtr &a_tuple);
-  FuncGraphPtr GenerateListFuncGraph(const abstract::AbstractListPtr &a_list);
+  FuncGraphPtr GenerateSequeueFuncGraph(const abstract::AbstractSequeuePtr &sequeue);
 
   friend bool operator==(const Tail &lhs, const Tail &rhs) { return lhs.name_ == rhs.name_; }
+
+ private:
+  bool do_grad_;
 };
 using TailPtr = std::shared_ptr<Tail>;
 

mindspore/ccsrc/pipeline/jit/pass.cc

@@ -446,10 +446,28 @@ bool TransformTopGraphPass(const ResourcePtr &res) {
 
 bool PipelineSplitPass(const ResourcePtr &res) { return PipelineSplit(res); }
 
+void UpdateFuncGraphParameter(const FuncGraphPtr &func_graph) {
+  MS_EXCEPTION_IF_NULL(func_graph);
+  std::vector<AnfNodePtr> new_paras;
+  for (const auto &param : func_graph->parameters()) {
+    auto param_node = param->cast<ParameterPtr>();
+    if (param_node->has_default()) {
+      new_paras.push_back(param_node);
+      continue;
+    }
+    AbstractBasePtr par_abs = param_node->abstract();
+    if (par_abs->isa<abstract::AbstractUndetermined>()) {
+      new_paras.push_back(param_node);
+    }
+  }
+  func_graph->set_parameters(new_paras);
+}
+
 bool ValidatePass(const ResourcePtr &res) {
   MS_EXCEPTION_IF_NULL(res->func_graph());
   FuncGraphPtr func_graph = res->func_graph();
   Validate(func_graph);
+  UpdateFuncGraphParameter(func_graph);
   return true;
 }
 

mindspore/ccsrc/pipeline/jit/pipeline.cc

@@ -69,6 +69,10 @@ namespace pipeline {
 using Tensor = mindspore::tensor::Tensor;
 using MetaTensor = mindspore::tensor::MetaTensor;
 using TensorOrderMap = std::map<std::string, std::shared_ptr<Tensor>>;
+using mindspore::abstract::AbstractDictionary;
+using mindspore::abstract::AbstractDictionaryPtr;
+using mindspore::abstract::AbstractList;
+using mindspore::abstract::AbstractListPtr;
 using mindspore::abstract::AbstractTensor;
 using mindspore::abstract::AbstractTensorPtr;
 using mindspore::abstract::AbstractTuple;
@@ -93,15 +97,10 @@ std::string GetBaseNameForIR(int64_t stage_idx, const std::string &action_name)
   return oss.str();
 }
 
-void CheckArgIsTensor(const ValuePtr &arg, std::size_t idx) {
-  MS_EXCEPTION_IF_NULL(arg);
-  auto tensor_arg = arg->cast<TensorPtr>();
-  if (tensor_arg == nullptr) {
-    MS_EXCEPTION(TypeError) << "For 'graph mode', the " << idx << "th arg: " << arg->ToString() << " is not a tensor.";
-  }
-  if (tensor_arg->is_parameter()) {
-    MS_EXCEPTION(TypeError) << "The inputs could not be Parameter.";
-  }
+AbstractBasePtr ArgsToAbstract(const ValuePtr &value) {
+  MS_EXCEPTION_IF_NULL(value);
+  bool broaden = value->isa<MetaTensor>();
+  return abstract::FromValue(value, broaden);
 }
 }  // namespace
 
@@ -117,8 +116,7 @@ py::tuple GenerateKey(const std::string &name, const std::unordered_map<std::str
     if (!parse::ConvertData(arg.second, &converted)) {
       MS_LOG(EXCEPTION) << "GenerateKey convert arg failed";
     }
-    bool broaden = converted->isa<Tensor>() || converted->isa<MetaTensor>();
-    args_spec.push_back(abstract::FromValue(converted, broaden));
+    args_spec.push_back(ArgsToAbstract(converted));
   }
   if (g_args_cache.count(args_spec) == 0) {
     static int64_t key = 0;
@@ -484,11 +482,7 @@ bool ExecutorPy::CompileInner(const py::object &obj, const py::tuple &args, cons
     if (!succ) {
       MS_LOG(EXCEPTION) << "Args convert error";
     }
-    if (MsContext::GetInstance()->get_param<int>(MS_CTX_EXECUTION_MODE) == kGraphMode) {
-      CheckArgIsTensor(converted, i);
-    }
-    bool broaden = true;
-    args_spec.push_back(abstract::FromValue(converted, broaden));
+    args_spec.push_back(ArgsToAbstract(converted));
   }
 
   resource->set_args_spec(args_spec);
@@ -814,9 +808,6 @@ py::object ExecutorPy::Run(const py::tuple &args, const py::object &phase) {
           if (!parse::ConvertData(args[i], &converted)) {
             MS_LOG(EXCEPTION) << "The " << i << "th arg convert failed.";
           }
-          if (!converted->isa<tensor::Tensor>()) {
-            MS_EXCEPTION(TypeError) << "The " << i << "th arg: " << converted->ToString() << " is not tensor.";
-          }
         }
       }
       return *ret_val;

mindspore/common/api.py

@@ -208,7 +208,11 @@ class _MindSporeFunction:
 
         if context.get_context("precompile_only"):
             return None
-        return self._executor(args_list, phase)
+        new_inputs = []
+        for i in args_list:
+            if isinstance(i, Tensor):
+                new_inputs.append(i)
+        return self._executor(tuple(new_inputs), phase)
 
 
 def ms_function(fn=None, obj=None, input_signature=None):

mindspore/ops/composite/multitype_ops/zeros_like_impl.py

@@ -18,7 +18,6 @@
 from ...composite import base
 from ... import functional as F
 
-
 zeros_like_leaf = base.MultitypeFuncGraph('zeros_like_leaf', True)
 """
 `zeros_like_leaf` is a metafuncgraph object which will generate a tensor filled with one according to its input type
@@ -31,11 +30,13 @@ def _zeros_like_scala(x):
     """Returns 0 which has the same dtype as x where x is a scalar."""
     return 0
 
+
 @zeros_like_leaf.register("Bool")
 def _zeros_like_bool(x):
     """Returns False if x is a bool."""
     return False
 
+
 newenv = base.EnvInstance_()
 
 
@@ -100,6 +101,25 @@ def _zeros_like_abstract_error(x):
     return x
 
 
+@zeros_like_leaf.register("Dictionary")
+def _zeros_like_dict(x):
+    """
+    Derivation of a AbstractError.
+
+    Args:
+        x (dict): the input
+
+    Returns:
+        dict, keys are same as input's keys, and value are same as zeros_like of input'value.
+    """
+    keys = x.keys()
+    values = x.values()
+    new_values = ()
+    for ele in values:
+        new_values += (zeros_like_leaf(ele),)
+    return F.make_dict(keys, new_values)
+
+
 # zeros_like is an object that will generate graph of zero_like operation for different type
 zeros_like = base.HyperMap(zeros_like_leaf)
 """`zeros_like` is an object that will generate graph of `zero_like` operation for different type."""

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

@@ -0,0 +1,60 @@
+# Copyright 2021 Huawei Technologies Co., Ltd
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ============================================================================
+""" test ms_function pass non_tensor inputs"""
+import numpy as np
+
+from mindspore import Tensor, ms_function, Parameter
+from mindspore import context
+from mindspore.ops import operations as P
+
+context.set_context(mode=context.PYNATIVE_MODE, save_graphs=True)
+
+
+@ms_function
+def compute(x, y, tuple_p, list_q, dict_w):
+    return x + y - tuple_p[0] + list_q[1] - dict_w["x"]
+
+
+def test_scalar_compute():
+    int_x = 1
+    int_y = 2
+    p = (3, 4)
+    q = [5, 6]
+    w = {"x": 7, "y": 8}
+    ret = compute(int_x, int_y, p, q, w)
+    assert ret == -1
+
+
+def test_tensor_compute():
+    tensor_x = Tensor(np.ones((2, 3, 4), np.float32))
+    tensor_y = Tensor(np.ones((2, 3, 4), np.float32) * 2)
+    p = (Tensor(np.ones((2, 3, 4), np.float32) * 3), Tensor(np.ones((2, 3, 4), np.float32) * 4))
+    q = [Tensor(np.ones((2, 3, 4), np.float32) * 5), Tensor(np.ones((2, 3, 4), np.float32) * 6)]
+    w = {"x": Tensor(np.ones((2, 3, 4), np.float32) * 7), "y": Tensor(np.ones((2, 3, 4), np.float32) * 8)}
+    compute(tensor_x, tensor_y, p, q, w)
+
+
+@ms_function
+def tensor_reduce(tensor_x, axis, tensor_y):
+    reduce_sum = P.ReduceSum()
+    ret = reduce_sum(tensor_x, axis) + tensor_y
+    return ret
+
+
+def test_tensor_reduce():
+    tensor_x = Tensor(np.ones((2, 3, 4, 5), np.float32))
+    axis = (0, 1)
+    tensor_y = Parameter(Tensor(np.ones((4, 5), np.float32) * 2))
+    tensor_reduce(tensor_x, axis, tensor_y)

tests/ut/python/pipeline/parse/test_outermost_net_pass_scalar_tuple_list_dict.py → tests/ut/python/pipeline/parse/test_outermost_net_pass_non_tensor_inputs.py

@@ -12,8 +12,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ============================================================================
-""" test outermost net pass scalar tuple list dict"""
-import pytest
+""" test outermost net pass non_tensor inputs"""
 import numpy as np
 
 import mindspore.nn as nn
@@ -28,7 +27,7 @@ def test_outermost_net_pass_scalar_tuple_list_dict():
     class TestNet(nn.Cell):
         def __init__(self):
             super(TestNet, self).__init__()
-            self.support_non_tensor_inputs = True
+            self.support_non_tensor_inputs = False
 
         def construct(self, tuple_a, z, list_m, w, s, dict_n):
             return z - tuple_a[2] + list_m[1][1]["x"] - w + s - dict_n["y"]
@@ -58,12 +57,5 @@ def test_outermost_net_pass_scalar_tuple_list_dict():
     forward_net(arg_t1, z, arg_l1, x, 6, args_d1)
 
     grad_net = GradNet(forward_net)
-    with pytest.raises(TypeError) as err:
-        grad_net(arg_t0, z, arg_l0, w, 6, args_d0)
-    assert "For 'graph mode', the 0th arg" in str(err.value)
-
-    grad_net.support_non_tensor_inputs = True
-    with pytest.raises(ValueError) as err:
-        grad_net(arg_t0, z, arg_l0, w, 6, args_d0)
-    assert  "Not support set 'support_non_tensor_inputs' to the 'True' for grad net, only support forward net." \
-            in str(err.value)
+    grad_net(arg_t0, z, arg_l0, w, 6, args_d0)
+    grad_net(arg_t1, z, arg_l1, x, 6, args_d1)

tests/ut/python/pynative_mode/ops/test_grad.py

@@ -20,9 +20,9 @@ import mindspore.ops.operations as P
 from mindspore import Tensor, context
 from mindspore.common.api import ms_function
 from mindspore.ops import composite as C
-from mindspore.ops import functional as F
 from ...ut_filter import non_graph_engine
 
+
 # pylint: disable=unused-argument
 def setup_module(module):
     context.set_context(mode=context.PYNATIVE_MODE)
@@ -86,24 +86,6 @@ def test_cast_grad():
     assert np.all(gout[0].asnumpy() == expect)
 
 
-def test_scalar_cast_grad():
-    """ test_scalar_cast_grad """
-    input_x = 255.5
-    input_t = ms.int8
-
-    def fx_cast(x):
-        output = F.scalar_cast(x, input_t)
-        return output
-
-    @ms_function
-    def grad_fx_cast(input_x):
-        return grad(fx_cast)(input_x)
-
-    gfn = grad_fx_cast(input_x)
-    expect_dx = 1
-    assert gfn == expect_dx
-
-
 @non_graph_engine
 def test_reshape_grad():
     """ test_reshape_grad """

tests/ut/python/pynative_mode/test_framstruct.py

@@ -14,31 +14,28 @@
 # ============================================================================
 """ test_framstruct """
 import numpy as np
-import pytest
 
 import mindspore as ms
 import mindspore.nn as nn
 from mindspore import context
 from mindspore.common import dtype as mstype
 from mindspore.common.parameter import Parameter, ParameterTuple
-from mindspore.common.tensor import Tensor
 from mindspore.ops import composite as C
 from mindspore.ops import operations as P
 from ..ut_filter import non_graph_engine
 from ....mindspore_test_framework.utils.check_gradient import (
     ms_function, check_jacobian, Tensor, NNGradChecker,
-    OperationGradChecker, check_gradient, ScalarGradChecker)
+    OperationGradChecker, check_gradient)
 
 context.set_context(mode=context.PYNATIVE_MODE)
 
+
 def setup_module(module):
     context.set_context(mode=context.PYNATIVE_MODE)
 
 
-grad = C.GradOperation()
 grad_all = C.GradOperation(get_all=True)
 grad_by_list = C.GradOperation(get_by_list=True)
-grad_all_with_sens = C.GradOperation(get_all=True, sens_param=True)
 
 
 @ms_function
@@ -79,9 +76,7 @@ def dynamic_make_tuple(x, lower, upper):
 
 
 def test_dynamic_make_tuple():
-    # Dynamicly recursively creating static type is invalid in mindspore, as mindspore is a static language.
-    with pytest.raises(RuntimeError):
-        dynamic_make_tuple(2, 1, 5)
+    assert dynamic_make_tuple(2, 1, 5) == (2, 2, 2, 2)
 
 
 def test_make_tuple():
@@ -273,15 +268,6 @@ def rec(x):
         return rec(x - 1)
     return x
 
-@ms_function
-def grad_rec(input_x):
-    return grad(rec)(input_x)
-
-def test_grad_rec():
-    """ test_grad_rec """
-    res = grad_rec(3)
-    assert res == 1
-
 
 def test_me_rec():
     """ test_me_rec """
@@ -303,13 +289,6 @@ def test_while2():
     assert res == 6
 
 
-def test_grad_while2():
-    @ms_function
-    def df_t2_while(input_x, input_y):
-        return grad(t2_while)(input_x, input_y)
-    assert df_t2_while(2, 3) == 3
-
-
 def if_test(a, b):
     """ if_test """
     if a > b:
@@ -327,24 +306,6 @@ def test_grad_if():
     assert grad_if(Tensor(5, dtype=ms.int32), Tensor(4, dtype=ms.int32)) == (3, 0)
 
 
-# While loop is not unrolled in forward and backward graphs.
-def test_dont_unroll_while():
-    def dont_unroll_while(x, y):
-        i = 2
-        out = y - x
-        while i < 10:
-            out = mul(x, y)
-            i = i + 1
-        return out
-
-    @ms_function()
-    def invoke_while(x, y):
-        return grad(dont_unroll_while)(x, y)
-
-    res = invoke_while(2, 3)
-    assert res == 3
-
-
 class ConvNet(nn.Cell):
     def __init__(self):
         super(ConvNet, self).__init__()
@@ -445,13 +406,6 @@ def test_factorial():
     assert res == 6
 
 
-def test_grad_factorial():
-    @ms_function
-    def df_factorial(x):
-        return grad(factorial)(x)
-    assert df_factorial(3) == 11
-
-
 @ms_function
 def factorial2(n):
     """ factorial """
@@ -523,17 +477,13 @@ def _for(x):
         ret = ret * i
     return ret
 
+
 @ms_function
 def grad_for(x):
     """ grad_for """
     return grad_all(_for)(x)
 
 
-def test_grad_for():
-    """ test_grad_for """
-    assert grad_for(5) == (60,)
-
-
 @ms_function
 def try_tail(x):
     """ try_tail """
@@ -675,15 +625,6 @@ def test_arithmetic_simplify_08():
     assert np.all(res.asnumpy() == expect)
 
 
-def test_ScalarGradChecker():
-    """ test_ScalarGradChecker """
-
-    def scalar_f(x, y):
-        return x * y
-
-    check_gradient(scalar_f, 1.0, 4.0, grad_checker_class=ScalarGradChecker, sampling_times=1)
-
-
 def test_GradCheckerPrimitive():
     """ test_GradCheckerPrimitive """
     matmul = P.MatMul()
@@ -737,15 +678,6 @@ def test_OperationGradChecker():
                    input_selector=[1], sampling_times=2)
 
 
-def test_ScalarJacobianChecker():
-    """ test_ScalarJacobianChecker """
-
-    def scalar_f(x, y):
-        return x * y
-
-    check_jacobian(scalar_f, 1.0, 4.0, grad_checker_class=ScalarGradChecker, input_selector=[0])
-
-
 def test_OperationJacobianChecker():
     """ test_OperationJacobianChecker """
 
@@ -795,13 +727,6 @@ def multi_outputs(x, y):
     return 2 * z, 2 * z
 
 
-def test_grad_multi_outputs():
-    @ms_function
-    def df_multi_outputs(x, y):
-        return grad_all_with_sens(multi_outputs)(x, y, (1, 1))
-    assert df_multi_outputs(2, 3) == (4, 4)
-
-
 @ms_function
 def while_sp(x, y, z):
     out = x
@@ -874,13 +799,6 @@ def grad_refactor_3(a):
     return 3 * a
 
 
-def test_grad_refactor_3():
-    @ms_function
-    def df_refactor_3(x):
-        return grad_all(grad_refactor_3)(x)
-    assert df_refactor_3(3) == (3,)
-
-
 def grad_refactor_4(a):
     """ if_test """
     if a > 3:
@@ -899,13 +817,6 @@ def grad_refactor_5(a):
     return a
 
 
-def test_grad_refactor_5():
-    @ms_function
-    def df_refactor_5(x):
-        return grad_all(grad_refactor_5)(x)
-    assert df_refactor_5(1) == (1,)
-
-
 def grad_refactor_6(a, b):
     """ if_test """
     if a > b:
@@ -925,13 +836,6 @@ def grad_refactor_while(x):
     return rval
 
 
-def test_grad_refactor_9():
-    @ms_function
-    def df_refactor_while(input_x):
-        return grad_all(grad_refactor_while)(input_x)
-    assert df_refactor_while(3) == (6,)
-
-
 def grad_refactor__while_1(x):
     """ _while """
     ret = x * x
@@ -1009,13 +913,6 @@ def grad_refactor_14(a, b):
     return inner1(b) + inner2(a) + inner3(a)
 
 
-def test_grad_refactor_14():
-    @ms_function
-    def df_refactor_14(x, y):
-        return grad_all(grad_refactor_14)(x, y)
-    assert df_refactor_14(2, 3) == (3, 9)
-
-
 # pylint: disable=using-constant-test
 class IfDeferInline(nn.Cell):
     def __init__(self, mul_size):
@@ -1044,6 +941,8 @@ def test_dict_const():
         def __init__(self):
             super(Net, self).__init__()
             self.res = {'1': 10}
+
         def construct(self):
             return self.res
+
     Net()()

tests/ut/python/pynative_mode/test_high_order_grad.py

@@ -109,25 +109,3 @@ def first_derivative_if(x):
 def second_derivative_if(x):
     """ second_derivative_if """
     return grad(first_derivative_if)(x)
-
-
-def test_high_order_grad_1():
-    """ test_high_order_grad_1 """
-    # 18
-    assert third_derivative(2) == 18
-    # 18 * y * y * y, 18 * x * x * x
-    assert third_derivative_dual(4, 5) == (2250, 1152)
-    # 18 * x
-    assert second_derivative_all(3) == 54
-
-
-def test_high_order_grad_2():
-    """ test_high_order_grad_2 """
-    # 2
-    assert second_derivative_if(12) == 2
-
-
-def test_high_order_grad_3():
-    """ test_high_order_grad_2 """
-    # 6 * x
-    assert second_derivative_if(4) == 24

tests/ut/python/pynative_mode/test_parse_method.py

@@ -325,7 +325,7 @@ def invoke_dataclass2(x, y):
 def test_access_attr_error():
     """ test_access """
     with pytest.raises(AttributeError):
-        invoke_dataclass2(1, 2)
+        invoke_dataclass2(2, 1)
 
 
 def myfunc(x):