support more high rank for activation op

mindspore/ccsrc/backend/kernel_compiler/cpu/mkldnn/mkl_cpu_kernel.cc

@@ -111,22 +111,16 @@ bool MKLCPUKernel::BinaryBroadCast(std::vector<size_t> *src0_shape, std::vector<
 }
 
 dnnl::memory::format_tag MKLCPUKernel::GetDefaultFormatTag(const dnnl::memory::dims &dims) const {
-  dnnl::memory::format_tag mem_tag;
-  auto dim_size = dims.size();
-  if (dim_size == 5) {
-    mem_tag = dnnl::memory::format_tag::abcde;
-  } else if (dim_size == 4) {
-    mem_tag = dnnl::memory::format_tag::abcd;
-  } else if (dim_size == 3) {
-    mem_tag = dnnl::memory::format_tag::abc;
-  } else if (dim_size == 2) {
-    mem_tag = dnnl::memory::format_tag::ab;
-  } else if (dim_size == 1) {
-    mem_tag = dnnl::memory::format_tag::a;
-  } else {
-    MS_LOG(EXCEPTION) << "Kernel dims invalid " << dim_size;
+  static const std::vector<dnnl::memory::format_tag> tag_vec = {
+    dnnl::memory::format_tag::a,      dnnl::memory::format_tag::ab,    dnnl::memory::format_tag::abc,
+    dnnl::memory::format_tag::abcd,   dnnl::memory::format_tag::abcde, dnnl::memory::format_tag::abcdef,
+    dnnl::memory::format_tag::abcdefg};
+
+  auto rank = dims.size();
+  if (rank > tag_vec.size()) {
+    MS_LOG(EXCEPTION) << "The kernel does not support construct " << rank << "-D tensor dnnl memory format_tag.";
   }
-  return mem_tag;
+  return tag_vec[rank - 1];
 }
 
 dnnl::memory::desc MKLCPUKernel::GetDefaultMemDesc(const std::vector<size_t> &shape) {

mindspore/ops/operations/nn_ops.py

@@ -346,14 +346,14 @@ class Softplus(Primitive):
 
     Inputs:
         - **input_x** (Tensor) - Tensor of shape :math:`(N, *)`, where :math:`*` means, any number of
-          additional dimensions, with float16 or float32 data type.
+          additional dimensions, with float16, float32 or float64 data type.
 
     Outputs:
         Tensor, with the same type and shape as the `input_x`.
 
     Raises:
         TypeError: If `input_x` is not a Tensor.
-        TypeError: If dtype of `input_x` is neither float16 nor float32.
+        TypeError: If dtype of `input_x` does not belong to any of [float16, float32, float64].
 
     Supported Platforms:
         ``Ascend``  ``GPU``

tests/st/ops/cpu/test_softplus_grad_op.py

@@ -76,3 +76,17 @@ def test_softplus_grad_fp16():
     output = grad(Tensor(x_np), Tensor(dy_np))
     expect = dy_np * np.exp(x_np) / (1 + np.exp(x_np))
     assert np.allclose(output[0].asnumpy(), expect, rtol=1e-2)
+
+
+@pytest.mark.level0
+@pytest.mark.platform_x86_cpu
+@pytest.mark.env_onecard
+def test_softplus_grad_7d_fp64():
+    np.random.seed(20)
+    x_np = np.random.randn(5, 3, 6, 3, 4, 5, 6).astype(np.float64)
+    dy_np = np.random.randn(5, 3, 6, 3, 4, 5, 6).astype(np.float64)
+    net = SoftplusNet()
+    grad = Grad(net)
+    output = grad(Tensor(x_np), Tensor(dy_np))
+    expect = dy_np * np.exp(x_np) / (1 + np.exp(x_np))
+    assert np.allclose(output[0].asnumpy(), expect, rtol=1e-2)

tests/st/ops/cpu/test_softplus_op.py

@@ -37,6 +37,20 @@ def SoftplusCompute(x):
     return np.log(1 + np.exp(x))
 
 
+@pytest.mark.level0
+@pytest.mark.platform_x86_cpu
+@pytest.mark.env_onecard
+def test_softplus_0d():
+    x_np = np.array(1.2, np.float64)
+    y_np = SoftplusCompute(x_np)
+
+    x_ms = Tensor(x_np)
+    net = SoftplusNet()
+    y_ms = net(x_ms)
+
+    assert np.allclose(y_np, y_ms.asnumpy())
+
+
 @pytest.mark.level0
 @pytest.mark.platform_x86_cpu
 @pytest.mark.env_onecard
@@ -105,3 +119,17 @@ def test_softplus_4d_fp16():
     y_ms = net(x_ms)
 
     assert np.allclose(y_np, y_ms.asnumpy(), rtol=5e-3)
+
+
+@pytest.mark.level0
+@pytest.mark.platform_x86_cpu
+@pytest.mark.env_onecard
+def test_softplus_7d_fp64():
+    x_np = np.random.random((32, 3, 20, 20, 20, 10, 10)).astype(np.float16)
+    y_np = SoftplusCompute(x_np)
+
+    x_ms = Tensor(x_np)
+    net = SoftplusNet()
+    y_ms = net(x_ms)
+
+    assert np.allclose(y_np, y_ms.asnumpy(), rtol=5e-3)