!16072 Fix cpu kernel scatter_nd_update

From: @irmo
Reviewed-by: @jjfeing,@zhoufeng54
Signed-off-by: @zhoufeng54

mindspore/ccsrc/backend/kernel_compiler/cpu/scatter_nd_update_cpu_kernel.cc

@@ -85,11 +85,12 @@ void ScatterNdUpdateCPUKernel::InitKernel(const CNodePtr &kernel_node) {
     out_stride *= shape[i + 1];
     out_strides_.push_back(out_stride);
   }
+  reverse(out_strides_.begin(), out_strides_.end());
   dtype_ = AnfAlgo::GetPrevNodeOutputInferDataType(kernel_node, 0);
 }
 
 bool ScatterNdUpdateCPUKernel::Launch(const std::vector<kernel::AddressPtr> &inputs,
-                                      const std::vector<kernel::AddressPtr> & /*workspace*/,
+                                      const std::vector<kernel::AddressPtr> &,
                                       const std::vector<kernel::AddressPtr> &outputs) {
   if (dtype_ == kNumberTypeFloat16) {
     LaunchKernel<float16>(inputs, outputs);

tests/st/ops/cpu/test_scatter_nd_update_op.py

@@ -26,46 +26,25 @@ from mindspore.ops import operations as P
 context.set_context(mode=context.GRAPH_MODE, device_target='CPU', save_graphs=False)
 
 
-class ScatterNdUpdate1(nn.Cell):
-    def __init__(self):
-        super(ScatterNdUpdate1, self).__init__()
-        self.scatter_nd_update = P.ScatterNdUpdate()
-        self.x = Parameter(Tensor(np.array([[-0.1, 0.3, 3.6], [0.4, 0.5, -3.2]]), mstype.float32), name="x")
-
-    def construct(self, indices, update):
-        return self.scatter_nd_update(self.x, indices, update)
-
-
-class ScatterNdUpdate2(nn.Cell):
-    def __init__(self):
-        super(ScatterNdUpdate2, self).__init__()
-        self.scatter_nd_update = P.ScatterNdUpdate()
-        self.x = Parameter(Tensor(np.array([1, 2, 3, 4, 5, 6, 7, 8]), mstype.float32), name="x")
-
-    def construct(self, indices, update):
-        return self.scatter_nd_update(self.x, indices, update)
-
-
-class ScatterNdUpdate3(nn.Cell):
-    def __init__(self):
-        super(ScatterNdUpdate3, self).__init__()
-        self.scatter_nd_update = P.ScatterNdUpdate()
-        self.x = Parameter(Tensor(np.zeros((4, 4, 4)), mstype.float32), name="x")
-
-    def construct(self, indices, update):
-        return self.scatter_nd_update(self.x, indices, update)
-
-
 @pytest.mark.level0
 @pytest.mark.platform_x86_cpu
 @pytest.mark.env_onecard
 def test_op1():
+    class ScatterNdUpdate(nn.Cell):
+        def __init__(self):
+            super(ScatterNdUpdate, self).__init__()
+            self.scatter_nd_update = P.ScatterNdUpdate()
+            self.x = Parameter(Tensor(np.array([[-0.1, 0.3, 3.6], [0.4, 0.5, -3.2]]), mstype.float32), name="x")
+
+        def construct(self, indices, update):
+            return self.scatter_nd_update(self.x, indices, update)
+
     indices = Tensor(np.array([[0, 0], [1, 1]]), mstype.int32)
     update = Tensor(np.array([1.0, 2.2]), mstype.float32)
 
-    scatter_nd_update = ScatterNdUpdate1()
+    scatter_nd_update = ScatterNdUpdate()
     scatter_nd_update(indices, update)
-    print("x:\n", scatter_nd_update.x.data)
+    print("x:\n", scatter_nd_update.x.data.asnumpy())
     expect = [[1.0, 0.3, 3.6], [0.4, 2.2, -3.2]]
     assert np.allclose(scatter_nd_update.x.data.asnumpy(), np.array(expect, np.float))
 
@@ -74,12 +53,21 @@ def test_op1():
 @pytest.mark.platform_x86_cpu
 @pytest.mark.env_onecard
 def test_op2():
+    class ScatterNdUpdate(nn.Cell):
+        def __init__(self):
+            super(ScatterNdUpdate, self).__init__()
+            self.scatter_nd_update = P.ScatterNdUpdate()
+            self.x = Parameter(Tensor(np.array([1, 2, 3, 4, 5, 6, 7, 8]), mstype.float32), name="x")
+
+        def construct(self, indices, update):
+            return self.scatter_nd_update(self.x, indices, update)
+
     indices = Tensor(np.array([[4], [3], [1], [7]]), mstype.int32)
     update = Tensor(np.array([9, 10, 11, 12]), mstype.float32)
 
-    scatter_nd_update = ScatterNdUpdate2()
+    scatter_nd_update = ScatterNdUpdate()
     scatter_nd_update(indices, update)
-    print("x:\n", scatter_nd_update.x.data)
+    print("x:\n", scatter_nd_update.x.data.asnumpy())
     expect = [1, 11, 3, 10, 9, 6, 7, 12]
     assert np.allclose(scatter_nd_update.x.data.asnumpy(), np.array(expect, dtype=float))
 
@@ -88,17 +76,50 @@ def test_op2():
 @pytest.mark.platform_x86_cpu
 @pytest.mark.env_onecard
 def test_op3():
+    class ScatterNdUpdate(nn.Cell):
+        def __init__(self):
+            super(ScatterNdUpdate, self).__init__()
+            self.scatter_nd_update = P.ScatterNdUpdate()
+            self.x = Parameter(Tensor(np.zeros((4, 4, 4)), mstype.float32), name="x")
+
+        def construct(self, indices, update):
+            return self.scatter_nd_update(self.x, indices, update)
+
     indices = Tensor(np.array([[0], [2]]), mstype.int32)
     update = Tensor(np.array([[[5, 5, 5, 5], [6, 6, 6, 6],
                                [7, 7, 7, 7], [8, 8, 8, 8]],
                               [[5, 5, 5, 5], [6, 6, 6, 6],
                                [7, 7, 7, 7], [8, 8, 8, 8]]]), mstype.float32)
 
-    scatter_nd_update = ScatterNdUpdate3()
+    scatter_nd_update = ScatterNdUpdate()
     scatter_nd_update(indices, update)
-    print("x:\n", scatter_nd_update.x.data)
+    print("x:\n", scatter_nd_update.x.data.asnumpy())
     expect = [[[5, 5, 5, 5], [6, 6, 6, 6], [7, 7, 7, 7], [8, 8, 8, 8]],
               [[0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0]],
               [[5, 5, 5, 5], [6, 6, 6, 6], [7, 7, 7, 7], [8, 8, 8, 8]],
               [[0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0]]]
     assert np.allclose(scatter_nd_update.x.data.asnumpy(), np.array(expect, dtype=float))
+
+
+@pytest.mark.level0
+@pytest.mark.platform_x86_cpu
+@pytest.mark.env_onecard
+def test_op4():
+    class ScatterNdUpdate(nn.Cell):
+        def __init__(self):
+            super(ScatterNdUpdate, self).__init__()
+            self.scatter_nd_update = P.ScatterNdUpdate()
+            self.x = Parameter(Tensor(np.array([[-0.1, 0.3, 3.6], [0.4, 0.5, -3.2]]), mstype.float32), name="x")
+
+        def construct(self, indices, update):
+            return self.scatter_nd_update(self.x, indices, update)
+
+    indices = Tensor(np.array([[0, 1]]), mstype.int32)
+    update = Tensor(np.array([1.0]), mstype.float32)
+
+    scatter_nd_update = ScatterNdUpdate()
+    out = scatter_nd_update(indices, update)
+    print("x:\n", out)
+    assert np.allclose(out.asnumpy(), scatter_nd_update.x.data.asnumpy())
+    expect = [[-0.1, 1.0, 3.6], [0.4, 0.5, -3.2]]
+    assert np.allclose(out.asnumpy(), np.array(expect, np.float))