!11401 Add Elu For CPU

From: @xukailun_1
Reviewed-by: @wuxuejian,@liangchenghui
Signed-off-by: @wuxuejian

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

@@ -43,6 +43,8 @@ dnnl::eltwise_forward::desc EltWiseCPUKernel::GetForwardEltwiseDesc(const CNodeP
     return dnnl::eltwise_forward::desc(DnnlForward, dnnl::algorithm::eltwise_square, src_desc);
   } else if (kernel_name == "Tanh") {
     return dnnl::eltwise_forward::desc(DnnlForward, dnnl::algorithm::eltwise_tanh, src_desc);
+  } else if (kernel_name == "Elu") {
+    return dnnl::eltwise_forward::desc(DnnlForward, dnnl::algorithm::eltwise_elu, src_desc, 1.0);
   } else {
     MS_LOG(EXCEPTION) << "Eltwise operators don't support " << kernel_name;
   }

mindspore/ccsrc/backend/kernel_compiler/cpu/mkldnn/eltwise_cpu_kernel.h

@@ -36,6 +36,11 @@ class EltWiseCPUKernel : public MKLCPUKernel {
   dnnl::prop_kind DnnlForward = dnnl::prop_kind::forward_training;
 };
 
+MS_REG_CPU_KERNEL(Elu, KernelAttr().AddInputAttr(kNumberTypeFloat16).AddOutputAttr(kNumberTypeFloat16),
+                  EltWiseCPUKernel);
+MS_REG_CPU_KERNEL(Elu, KernelAttr().AddInputAttr(kNumberTypeFloat32).AddOutputAttr(kNumberTypeFloat32),
+                  EltWiseCPUKernel);
+MS_REG_CPU_KERNEL(Elu, KernelAttr().AddInputAttr(kNumberTypeFloat).AddOutputAttr(kNumberTypeFloat), EltWiseCPUKernel);
 MS_REG_CPU_KERNEL(ReLU, KernelAttr().AddInputAttr(kNumberTypeFloat32).AddOutputAttr(kNumberTypeFloat32),
                   EltWiseCPUKernel);
 MS_REG_CPU_KERNEL(ReLU6, KernelAttr().AddInputAttr(kNumberTypeFloat32).AddOutputAttr(kNumberTypeFloat32),

tests/st/ops/cpu/test_elu_op.py

@@ -0,0 +1,59 @@
+# 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.
+# ============================================================================
+
+import numpy as np
+import pytest
+
+import mindspore.context as context
+import mindspore.nn as nn
+from mindspore import Tensor
+from mindspore.ops import operations as P
+
+class NetElu(nn.Cell):
+    def __init__(self):
+        super(NetElu, self).__init__()
+        self.elu = P.Elu()
+
+    def construct(self, x):
+        return self.elu(x)
+
+
+@pytest.mark.level0
+@pytest.mark.platform_x86_cpu
+@pytest.mark.env_onecard
+def test_elu_fp16():
+    x = Tensor(np.array([[-1.0, 4.0, -8.0], [2.0, -5.0, 9.0]]).astype(np.float16))
+    expect = np.array([[-0.632, 4.0, -0.999], [2.0, -0.993, 9.0]]).astype(np.float16)
+    error = np.ones(shape=[2, 3]) * 1.0e-6
+
+    context.set_context(mode=context.GRAPH_MODE, device_target="CPU")
+    elu = NetElu()
+    output = elu(x)
+    diff = output.asnumpy() - expect
+    assert np.all(diff < error)
+
+@pytest.mark.level0
+@pytest.mark.platform_x86_cpu
+@pytest.mark.env_onecard
+def test_elu_fp32():
+    x = Tensor(np.array([[-1.0, 4.0, -8.0], [2.0, -5.0, 9.0]]).astype(np.float32))
+    expect = np.array([[-0.632, 4.0, -0.999], [2.0, -0.993, 9.0]]).astype(np.float32)
+    error = np.ones(shape=[2, 3]) * 1.0e-6
+
+    context.set_context(mode=context.GRAPH_MODE, device_target="CPU")
+    elu = NetElu()
+    output = elu(x)
+    diff = output.asnumpy() - expect
+    assert np.all(diff < error)