!8630 fix mul broadcast and tanhgrad bug

From: @zhao_ting_v
Reviewed-by: @kisnwang,@wuxuejian
Signed-off-by: @liangchenghui

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

@@ -73,8 +73,8 @@ void EltWiseGradCPUKernel::SqrtGrad(const T *input1, const T *input2, T *out, si
 template <typename T>
 void EltWiseGradCPUKernel::TanhGrad(const T *input1, const T *input2, T *out, size_t start, size_t end) {
   for (size_t i = start; i < end; i++) {
-    T tmp = (1 - input1[i]);
-    out[i] = input2[i] * tmp * tmp;
+    T tmp = input1[i] * input1[i];
+    out[i] = input2[i] * (1 - tmp);
   }
 }
 

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

@@ -25,14 +25,27 @@ void MulCPUKernel::InitKernel(const CNodePtr &kernel_node) {
   std::vector<size_t> src0_shape = AnfAlgo::GetInputDeviceShape(kernel_node, 0);
   std::vector<size_t> src1_shape = AnfAlgo::GetInputDeviceShape(kernel_node, 1);
   std::vector<size_t> dst_shape = AnfAlgo::GetOutputDeviceShape(kernel_node, 0);
+  if (dst_shape.size() == 0) {
+    dst_shape.emplace_back(1);
+    src0_shape.emplace_back(1);
+    src1_shape.emplace_back(1);
+  }
+  size_t src0_length = 1;
+  size_t src1_length = 1;
+  for (size_t i = 0; i < src0_shape.size(); ++i) {
+    src0_length = src0_length * src0_shape[i];
+  }
+  for (size_t i = 0; i < src1_shape.size(); ++i) {
+    src1_length = src1_length * src1_shape[i];
+  }
   if (src1_shape.size() != src0_shape.size()) {
-    if (src0_shape.size() == 0) {
+    if (src0_length == 1 && src0_shape.size() != dst_shape.size()) {
       need_swap_ = true;
-      for (size_t i = 0; i < src1_shape.size(); ++i) {
+      for (size_t i = src0_shape.size(); i < src1_shape.size(); ++i) {
         src0_shape.emplace_back(1);
       }
-    } else if (src1_shape.size() == 0) {
-      for (size_t i = 0; i < src0_shape.size(); ++i) {
+    } else if (src1_length == 1 && src1_shape.size() != dst_shape.size()) {
+      for (size_t i = src1_shape.size(); i < src0_shape.size(); ++i) {
         src1_shape.emplace_back(1);
       }
     } else {

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

@@ -25,14 +25,27 @@ void TensorAddCPUKernel::InitKernel(const CNodePtr &kernel_node) {
   std::vector<size_t> src0_shape = AnfAlgo::GetInputDeviceShape(kernel_node, 0);
   std::vector<size_t> src1_shape = AnfAlgo::GetInputDeviceShape(kernel_node, 1);
   std::vector<size_t> dst_shape = AnfAlgo::GetOutputDeviceShape(kernel_node, 0);
+  if (dst_shape.size() == 0) {
+    dst_shape.emplace_back(1);
+    src0_shape.emplace_back(1);
+    src1_shape.emplace_back(1);
+  }
+  size_t src0_length = 1;
+  size_t src1_length = 1;
+  for (size_t i = 0; i < src0_shape.size(); ++i) {
+    src0_length = src0_length * src0_shape[i];
+  }
+  for (size_t i = 0; i < src1_shape.size(); ++i) {
+    src1_length = src1_length * src1_shape[i];
+  }
   if (src1_shape.size() != src0_shape.size()) {
-    if (src0_shape.size() == 0) {
+    if (src0_length == 1 && src0_shape.size() != dst_shape.size()) {
       need_swap_ = true;
-      for (size_t i = 0; i < src1_shape.size(); ++i) {
+      for (size_t i = src0_shape.size(); i < src1_shape.size(); ++i) {
         src0_shape.emplace_back(1);
       }
-    } else if (src1_shape.size() == 0) {
-      for (size_t i = 0; i < src0_shape.size(); ++i) {
+    } else if (src1_length == 1 && src1_shape.size() != dst_shape.size()) {
+      for (size_t i = src1_shape.size(); i < src0_shape.size(); ++i) {
         src1_shape.emplace_back(1);
       }
     } else {

tests/st/ops/cpu/test_mul_op.py

@@ -45,6 +45,8 @@ def test_mul():
     y1 = Tensor(np.random.uniform(-2, 2, (2, 3, 4, 4)).astype(np.float32))
     x2 = Tensor(np.random.uniform(-2, 2, (2, 3, 4, 4)).astype(np.float32))
     y2 = Tensor(2, mstype.float32)
+    x3 = Tensor(2, mstype.float32)
+    y3 = Tensor(2, mstype.float32)
     mul = Net()
     out = mul(x0, y0).asnumpy()
     exp = x0.asnumpy() * y0.asnumpy()
@@ -66,3 +68,10 @@ def test_mul():
     err = np.ones(shape=exp.shape) * 1.0e-5
     assert np.all(diff < err)
     assert out.shape == exp.shape
+
+    out = mul(x3, y3).asnumpy()
+    exp = x3.asnumpy() * y3.asnumpy()
+    diff = np.abs(out - exp)
+    err = np.ones(shape=exp.shape) * 1.0e-5
+    assert np.all(diff < err)
+    assert out.shape == exp.shape

tests/st/ops/cpu/test_tensoradd.py

@@ -43,6 +43,8 @@ def test_tensor_add():
     y1 = Tensor(np.random.uniform(-2, 2, (2, 3, 4, 4)).astype(np.float32))
     x2 = Tensor(np.random.uniform(-2, 2, (2, 3, 4, 4)).astype(np.float32))
     y2 = Tensor(2, mstype.float32)
+    x3 = Tensor(2, mstype.float32)
+    y3 = Tensor(2, mstype.float32)
     add = TensorAdd()
     out = add(x0, y0).asnumpy()
     exp = x0.asnumpy() + y0.asnumpy()
@@ -64,3 +66,10 @@ def test_tensor_add():
     err = np.ones(shape=exp.shape) * 1.0e-5
     assert np.all(diff < err)
     assert out.shape == exp.shape
+
+    out = add(x3, y3).asnumpy()
+    exp = x3.asnumpy() + y3.asnumpy()
+    diff = np.abs(out - exp)
+    err = np.ones(shape=exp.shape) * 1.0e-5
+    assert np.all(diff < err)
+    assert out.shape == exp.shape