[Tensor Operants & Prim] Tensor arithmetic operants support right scalar type (#50563)

* polish namespace

* change static_tensor_operants

* polish namespace

* support add, subtract, divide

* add unit test

* polish unittest

* fix cmake error

* solve conflicts, merge auto code-gen

* add scalar operator in tensor.h

* tensorbase

* static prim full support more datatype

* fix prim unittest

* polish codes

* fix cmake error

paddle/fluid/prim/api/api.yaml

+- add
+- subtract
+- multiply
+- divide
 - unsqueeze
 - pow
 - exp
 - scale
-- multiply
 - matmul
 - expand
-- divide
 - sum
-- add
 - abs
 - assign
 - concat
@@ -24,4 +25,3 @@
 - scatter_nd_add
 - tile
 - transpose
-- subtract

paddle/fluid/prim/api/auto_code_generated/tensor_operants_gen.py

@@ -48,6 +48,14 @@ class EagerTensorOperants : public TensorOperantsBase {
  public:
   EagerTensorOperants() = default;
 
+  Tensor add(const Tensor& x, const Scalar& y);
+
+  Tensor subtract(const Tensor& x, const Scalar& y);
+
+  Tensor multiply(const Tensor& x, const Scalar& y);
+
+  Tensor divide(const Tensor& x, const Scalar& y);
+
 """
 
 
@@ -73,6 +81,22 @@ namespace paddle {
 
 namespace prim {
 
+Tensor EagerTensorOperants::add(const Tensor& x, const Scalar& y) {
+  return ::add_ad_func(x, ::full_like_ad_func(x, y));
+}
+
+Tensor EagerTensorOperants::subtract(const Tensor& x, const Scalar& y) {
+  return ::subtract_ad_func(x, ::full_like_ad_func(x, y));
+}
+
+Tensor EagerTensorOperants::multiply(const Tensor& x, const Scalar& y) {
+  return ::multiply_ad_func(x, ::full_like_ad_func(x, y));
+}
+
+Tensor EagerTensorOperants::divide(const Tensor& x, const Scalar& y) {
+  return ::divide_ad_func(x, ::full_like_ad_func(x, y));
+}
+
 """
 
 
@@ -112,6 +136,14 @@ class StaticTensorOperants : public TensorOperantsBase {
  public:
   StaticTensorOperants() = default;
 
+  Tensor add(const Tensor& x, const Scalar& y);
+
+  Tensor subtract(const Tensor& x, const Scalar& y);
+
+  Tensor multiply(const Tensor& x, const Scalar& y);
+
+  Tensor divide(const Tensor& x, const Scalar& y);
+
 """
 
 
@@ -128,6 +160,7 @@ static_source_include = """// Generated by paddle/fluid/prim/api/auto_code_gener
 #include "paddle/fluid/prim/utils/static/static_tensor_operants.h"
 
 #include "paddle/fluid/prim/api/generated_prim/prim_generated_api.h"
+#include "paddle/fluid/prim/api/manual_prim/prim_manual_api.h"
 #include "paddle/fluid/prim/utils/static/desc_tensor.h"
 
 """
@@ -139,6 +172,22 @@ namespace paddle {
 namespace prim {
 using DescTensor = paddle::prim::DescTensor;
 
+Tensor StaticTensorOperants::add(const Tensor& x, const Scalar& y) {
+  return paddle::prim::add<DescTensor>(x, paddle::prim::full<DescTensor>(x.shape(), y, x.dtype(), x.place()));
+}
+
+Tensor StaticTensorOperants::subtract(const Tensor& x, const Scalar& y) {
+  return paddle::prim::subtract<DescTensor>(x, paddle::prim::full<DescTensor>(x.shape(), y, x.dtype(), x.place()));
+}
+
+Tensor StaticTensorOperants::multiply(const Tensor& x, const Scalar& y) {
+  return paddle::prim::multiply<DescTensor>(x, paddle::prim::full<DescTensor>(x.shape(), y, x.dtype(), x.place()));
+}
+
+Tensor StaticTensorOperants::divide(const Tensor& x, const Scalar& y) {
+  return paddle::prim::divide<DescTensor>(x, paddle::prim::full<DescTensor>(x.shape(), y, x.dtype(), x.place()));
+}
+
 """
 
 

paddle/fluid/prim/api/composite_backward/composite_backward_api.h

@@ -61,9 +61,7 @@ void gather_grad(const Tensor& x,
 template <typename T>
 void tanh_grad(const Tensor& out, const Tensor& grad_out, Tensor* grad_x) {
   if (!grad_x) return;
-  auto tmp = out.pow(2.0);
-  tmp = scale<T>(tmp, -1.0, 1.0, true);
-  auto grad_x_tmp = grad_out * tmp;
+  auto grad_x_tmp = grad_out * (out.pow(2.0) * -1.0 + 1.0);
   set_output<T>(grad_x_tmp, grad_x);
 }
 
@@ -203,10 +201,7 @@ void divide_grad(const Tensor& x,
                  Tensor* dy) {
   if (dy) {
     // dy = -(x/y^2) * dout
-    auto tmp0 = y.pow(2.0);
-    auto tmp1 = x / tmp0;
-    auto tmp2 = scale<T>(tmp1, -1.0, 0.0, true);
-    auto dy_res = tmp2 * out_grad;
+    auto dy_res = x / y.pow(2.0) * -1.0 * out_grad;
     if (x.dims() != y.dims()) {
       // Maybe need reduce here
       phi::DDim reduce_dim = get_reduce_dims(y.dims(), x.dims());
@@ -247,8 +242,7 @@ void divide_grad(const Tensor& x,
 template <typename T>
 void sqrt_grad(const Tensor& out, const Tensor& out_grad, Tensor* x_grad) {
   if (x_grad) {
-    auto div_x = full<T>(phi::vectorize(out.dims()), 0.5);
-    auto x_grad_tmp = out_grad * div_x / out;
+    auto x_grad_tmp = out_grad / 2.0 / out;
     set_output<T>(x_grad_tmp, x_grad);
   }
 }

paddle/fluid/prim/api/manual_prim/static_prim_api.cc

@@ -67,23 +67,51 @@ Tensor full<DescTensor>(const IntArray& shape,
   framework::OpDesc* op = block->AppendOp();
   op->SetType("fill_constant");
   op->SetAttr("shape", shape.GetData());
-  PADDLE_ENFORCE_EQ(
-      ((dtype == DataType::FLOAT32) || (dtype == DataType::FLOAT64) ||
-       (dtype == DataType::FLOAT16)),
-      true,
-      phi::errors::InvalidArgument(
-          "We only support float32/float16 for full, but we got data type: %s",
+  switch (dtype) {
+    case phi::DataType::FLOAT16:
+      op->SetAttr("str_value", std::to_string(value.to<float>()));
+      break;
+    case phi::DataType::FLOAT32:
+      op->SetAttr("value", value.to<float>());
+      break;
+    case phi::DataType::FLOAT64:
+      op->SetAttr("str_value", std::to_string(value.to<double>()));
+      break;
+    case phi::DataType::BOOL:
+      op->SetAttr("str_value", std::to_string(value.to<bool>()));
+      break;
+    case phi::DataType::INT8:
+      op->SetAttr("str_value", std::to_string(value.to<int8_t>()));
+      break;
+    case phi::DataType::UINT8:
+      op->SetAttr("str_value", std::to_string(value.to<uint8_t>()));
+      break;
+    case phi::DataType::INT16:
+      op->SetAttr("str_value", std::to_string(value.to<int16_t>()));
+      break;
+    case phi::DataType::UINT16:
+      op->SetAttr("str_value", std::to_string(value.to<uint16_t>()));
+      break;
+    case phi::DataType::INT32:
+      op->SetAttr("str_value", std::to_string(value.to<int32_t>()));
+      break;
+    case phi::DataType::UINT32:
+      op->SetAttr("str_value", std::to_string(value.to<uint32_t>()));
+      break;
+    case phi::DataType::INT64:
+      op->SetAttr("str_value", std::to_string(value.to<int64_t>()));
+      break;
+    case phi::DataType::UINT64:
+      op->SetAttr("str_value", std::to_string(value.to<uint64_t>()));
+      break;
+    default:
+      PADDLE_THROW(phi::errors::Unimplemented(
+          "We support "
+          "bool/float16/float32/float64/int8/int16/int32/int64/uint8/uint16/"
+          "uint32/uint64 for full, but we got data type: %s",
           phi::DataTypeToString(dtype)));
-  if (dtype == phi::DataType::FLOAT32) {
-    op->SetAttr("value", value.to<float>());
-  } else if (dtype == phi::DataType::FLOAT64) {
-    op->SetAttr("str_value", std::to_string(value.to<double>()));
-  } else if (dtype == phi::DataType::FLOAT16) {
-    op->SetAttr("str_value", std::to_string(value.to<float>()));
-  } else {
-    PADDLE_THROW(phi::errors::Unimplemented(
-        "We only support float64/float32/float16 for full"));
   }
+
   op->SetAttr("dtype", paddle::framework::TransToProtoVarType(dtype));
   op->SetOutput(
       "Out", {std::static_pointer_cast<prim::DescTensor>(out.impl())->Name()});

paddle/fluid/prim/tests/CMakeLists.txt

@@ -29,6 +29,8 @@ cc_test_old(
   prim_utils
   operator
   elementwise_mul_op
+  elementwise_add_op
+  fill_constant_op
   activation_op
   phi_api
   phi_dygraph_api

paddle/fluid/prim/tests/test_static_prim.cc

@@ -35,6 +35,7 @@ PD_DECLARE_KERNEL(tanh, CPU, ALL_LAYOUT);
 PD_DECLARE_KERNEL(tanh_grad, CPU, ALL_LAYOUT);
 PD_DECLARE_KERNEL(pow, CPU, ALL_LAYOUT);
 PD_DECLARE_KERNEL(scale, CPU, ALL_LAYOUT);
+PD_DECLARE_KERNEL(add, CPU, ALL_LAYOUT);
 PD_DECLARE_KERNEL(multiply, CPU, ALL_LAYOUT);
 PD_DECLARE_KERNEL(concat, CPU, ALL_LAYOUT);
 #if defined(PADDLE_WITH_CUDA) || defined(PADDLE_WITH_HIP)
@@ -43,6 +44,7 @@ PD_DECLARE_KERNEL(tanh, GPU, ALL_LAYOUT);
 PD_DECLARE_KERNEL(tanh_grad, GPU, ALL_LAYOUT);
 PD_DECLARE_KERNEL(pow, GPU, ALL_LAYOUT);
 PD_DECLARE_KERNEL(scale, GPU, ALL_LAYOUT);
+PD_DECLARE_KERNEL(add, KPS, ALL_LAYOUT);
 PD_DECLARE_KERNEL(multiply, KPS, ALL_LAYOUT);
 PD_DECLARE_KERNEL(concat, GPU, ALL_LAYOUT);
 #endif
@@ -192,7 +194,7 @@ TEST(StaticPrim, TanhBackwardComposite) {
                                        target_block,
                                        grad_sub_block));
   ASSERT_EQ(target_block->AllOps().size(), static_cast<std::size_t>(1));
-  ASSERT_EQ(grad_ops.size(), static_cast<std::size_t>(3));
+  ASSERT_EQ(grad_ops.size(), static_cast<std::size_t>(6));
   ASSERT_EQ(target_block->AllOps()[0]->Type(), "tanh");
   ASSERT_EQ(target_block->AllOps()[0]->Inputs().at("X").size(),
             static_cast<std::size_t>(1));
@@ -210,14 +212,9 @@ TEST(StaticPrim, TanhBackwardComposite) {
   ASSERT_EQ(grad_ops[0]->Outputs().at("Out").size(),
             static_cast<std::size_t>(1));
 
-  ASSERT_EQ(grad_ops[1]->Type(), "scale");
-  ASSERT_EQ(grad_ops[1]->Inputs().at("X").size(), static_cast<std::size_t>(1));
-  ASSERT_EQ(grad_ops[1]->Inputs().at("X")[0],
-            grad_ops[0]->Outputs().at("Out")[0]);
-  ASSERT_EQ(PADDLE_GET_CONST(float, grad_ops[1]->GetAttr("scale")),
-            static_cast<float>(-1.0));
-  ASSERT_EQ(PADDLE_GET_CONST(float, grad_ops[1]->GetAttr("bias")),
-            static_cast<float>(1.0));
+  ASSERT_EQ(grad_ops[1]->Type(), "fill_constant");
+  ASSERT_EQ(PADDLE_GET_CONST(int, grad_ops[1]->GetAttr("dtype")),
+            static_cast<int>(5));  // ProtoDataType::FP32
   ASSERT_EQ(grad_ops[1]->Outputs().at("Out").size(),
             static_cast<std::size_t>(1));
 
@@ -226,9 +223,31 @@ TEST(StaticPrim, TanhBackwardComposite) {
   ASSERT_EQ(grad_ops[2]->Inputs().at("Y").size(), static_cast<std::size_t>(1));
   ASSERT_EQ(grad_ops[2]->Inputs().at("Y")[0],
             grad_ops[1]->Outputs().at("Out")[0]);
-  ASSERT_EQ(grad_ops[2]->Inputs().at("X")[0], "b@GRAD");
   ASSERT_EQ(grad_ops[2]->Outputs().at("Out").size(),
             static_cast<std::size_t>(1));
+
+  ASSERT_EQ(grad_ops[3]->Type(), "fill_constant");
+  ASSERT_EQ(PADDLE_GET_CONST(int, grad_ops[3]->GetAttr("dtype")),
+            static_cast<int>(5));  // ProtoDataType::FP32
+  ASSERT_EQ(grad_ops[3]->Outputs().at("Out").size(),
+            static_cast<std::size_t>(1));
+
+  ASSERT_EQ(grad_ops[4]->Type(), "elementwise_add");
+  ASSERT_EQ(grad_ops[4]->Inputs().at("X").size(), static_cast<std::size_t>(1));
+  ASSERT_EQ(grad_ops[4]->Inputs().at("Y").size(), static_cast<std::size_t>(1));
+  ASSERT_EQ(grad_ops[4]->Inputs().at("Y")[0],
+            grad_ops[3]->Outputs().at("Out")[0]);
+  ASSERT_EQ(grad_ops[4]->Outputs().at("Out").size(),
+            static_cast<std::size_t>(1));
+
+  ASSERT_EQ(grad_ops[5]->Type(), "elementwise_mul");
+  ASSERT_EQ(grad_ops[5]->Inputs().at("X").size(), static_cast<std::size_t>(1));
+  ASSERT_EQ(grad_ops[5]->Inputs().at("Y").size(), static_cast<std::size_t>(1));
+  ASSERT_EQ(grad_ops[5]->Inputs().at("Y")[0],
+            grad_ops[4]->Outputs().at("Out")[0]);
+  ASSERT_EQ(grad_ops[5]->Inputs().at("X")[0], "b@GRAD");
+  ASSERT_EQ(grad_ops[5]->Outputs().at("Out").size(),
+            static_cast<std::size_t>(1));
 }
 
 TEST(StaticCompositeGradMaker, TestMutiInputMethod) {
@@ -368,8 +387,10 @@ TEST(StaticPrim, TestFlags) {
 
 }  // namespace prim
 }  // namespace paddle
+USE_OP_ITSELF(fill_constant);
 USE_OP_ITSELF(tanh);
 USE_OP_ITSELF(tanh_grad);
 USE_OP_ITSELF(pow);
 USE_OP_ITSELF(elementwise_mul);
+USE_OP_ITSELF(elementwise_add);
 USE_OP_ITSELF(scale);

paddle/phi/api/include/tensor.h

@@ -548,6 +548,14 @@ class PADDLE_API Tensor final {
 
   Tensor operator/(const Tensor& other) const;
 
+  Tensor operator+(const Scalar& other) const;
+
+  Tensor operator-(const Scalar& other) const;
+
+  Tensor operator*(const Scalar& other) const;
+
+  Tensor operator/(const Scalar& other) const;
+
   /* Part 8: Autograd methods */
 
   /**
@@ -663,6 +671,11 @@ class PADDLE_API Tensor final {
   Tensor divide(const Tensor& y) const;
   Tensor multiply(const Tensor& y) const;
   Tensor subtract(const Tensor& y) const;
+  Tensor add(const Scalar& y) const;
+  Tensor divide(const Scalar& y) const;
+  Tensor multiply(const Scalar& y) const;
+  Tensor subtract(const Scalar& y) const;
+
   Tensor exp() const;
   Tensor floor() const;
   Tensor gather_nd(const Tensor& index) const;

paddle/phi/api/yaml/generator/tensor_operants_gen.py

@@ -52,6 +52,14 @@ using IntArray = paddle::experimental::IntArray;
 class TensorOperantsBase {
  public:
   virtual ~TensorOperantsBase() = default;
+
+  virtual Tensor add(const Tensor& x, const Scalar& y) = 0;
+
+  virtual Tensor divide(const Tensor& x, const Scalar& y) = 0;
+
+  virtual Tensor multiply(const Tensor& x, const Scalar& y) = 0;
+
+  virtual Tensor subtract(const Tensor& x, const Scalar& y) = 0;
 """
 
 
@@ -90,6 +98,38 @@ Tensor Tensor::operator*(const Tensor &other) const {
 Tensor Tensor::operator/(const Tensor &other) const {
   return divide(other);
 }
+
+Tensor Tensor::operator+(const Scalar &other) const {
+  return add(other);
+}
+
+Tensor Tensor::operator-(const Scalar &other) const {
+  return subtract(other);
+}
+
+Tensor Tensor::operator*(const Scalar &other) const {
+  return multiply(other);
+}
+
+Tensor Tensor::operator/(const Scalar &other) const {
+  return divide(other);
+}
+
+Tensor Tensor::add(const Scalar& y) const {
+  return paddle::OperantsManager::Instance().add(static_cast<const Tensor &>(*this), y);
+}
+
+Tensor Tensor::divide(const Scalar& y) const {
+  return paddle::OperantsManager::Instance().divide(static_cast<const Tensor &>(*this), y);
+}
+
+Tensor Tensor::multiply(const Scalar& y) const {
+  return paddle::OperantsManager::Instance().multiply(static_cast<const Tensor &>(*this), y);
+}
+
+Tensor Tensor::subtract(const Scalar& y) const {
+  return paddle::OperantsManager::Instance().subtract(static_cast<const Tensor &>(*this), y);
+}
 """
 
 
@@ -126,6 +166,15 @@ class PhiTensorOperants : public TensorOperantsBase {
 
  public:
   PhiTensorOperants() = default;
+
+  Tensor add(const Tensor& x, const Scalar& y);
+
+  Tensor subtract(const Tensor& x, const Scalar& y);
+
+  Tensor multiply(const Tensor& x, const Scalar& y);
+
+  Tensor divide(const Tensor& x, const Scalar& y);
+
 """
 
 
@@ -150,6 +199,22 @@ operants_source_start = """
 namespace paddle {
 
 namespace operants {
+
+Tensor PhiTensorOperants::add(const Tensor& x, const Scalar& y) {
+  return paddle::experimental::add(x, paddle::experimental::full_like(x, y));
+}
+
+Tensor PhiTensorOperants::subtract(const Tensor& x, const Scalar& y) {
+  return paddle::experimental::subtract(x, paddle::experimental::full_like(x, y));
+}
+
+Tensor PhiTensorOperants::multiply(const Tensor& x, const Scalar& y) {
+  return paddle::experimental::multiply(x, paddle::experimental::full_like(x, y));
+}
+
+Tensor PhiTensorOperants::divide(const Tensor& x, const Scalar& y) {
+  return paddle::experimental::divide(x, paddle::experimental::full_like(x, y));
+}
 """
 
 
@@ -225,6 +290,15 @@ class OperantsManager {
 
  public:
   static OperantsManager& Instance();
+
+  Tensor add(const Tensor& x, const Scalar& y);
+
+  Tensor subtract(const Tensor& x, const Scalar& y);
+
+  Tensor multiply(const Tensor& x, const Scalar& y);
+
+  Tensor divide(const Tensor& x, const Scalar& y);
+
 """
 
 
@@ -395,17 +469,28 @@ class OperantsAPI(ForwardAPI):
 
     def gene_operants_manager_implementation(self):
         func_name = self.get_api_func_name()
+        final_code = ""
+        if func_name in ["add", "subtract", "multiply", "divide"]:
+            final_code += f"""
+{self.get_return_type()} OperantsManager::{func_name}(const Tensor& x, const Scalar& y) {{{self.gene_operants_manager_code()}}}
+"""
         # func decalaration
         if func_name[-1] != '_':
-            return f"""
+            return (
+                final_code
+                + f"""
 {self.get_return_type()} OperantsManager::{func_name}({self.get_define_args()}) {{{self.gene_operants_manager_code()}}}
 """
+            )
         else:
-            return f"""
+            return (
+                final_code
+                + f"""
 {self.get_return_type(inplace_flag=True)} OperantsManager::{func_name}({self.get_define_args(inplace_flag=True)}) {{
 {self.gene_operants_manager_code()}
 }}
 """
+            )
 
 
 def generate_tensor_operants_api(

python/paddle/fluid/tests/custom_op/custom_tensor_operator.cc

@@ -49,6 +49,36 @@ PD_BUILD_GRAD_OP(custom_add)
     .Outputs({paddle::Grad("X")})
     .SetKernelFn(PD_KERNEL(AddBackward));
 
+// y = x + 1
+std::vector<paddle::Tensor> ScalarAddForward(const paddle::Tensor& x) {
+  if (x.is_cpu() || x.is_gpu()) {
+    return {x + 1};
+  } else {
+    PD_THROW("Not implemented.");
+  }
+}
+
+// dy / dx = 1 * grad_out
+std::vector<paddle::Tensor> ScalarAddBackward(const paddle::Tensor& x,
+                                              const paddle::Tensor& out,
+                                              const paddle::Tensor& grad_out) {
+  if (x.is_cpu() || x.is_gpu()) {
+    return {grad_out * 1};
+  } else {
+    PD_THROW("Not implemented.");
+  }
+}
+
+PD_BUILD_OP(custom_scalar_add)
+    .Inputs({"X"})
+    .Outputs({"Out"})
+    .SetKernelFn(PD_KERNEL(ScalarAddForward));
+
+PD_BUILD_GRAD_OP(custom_scalar_add)
+    .Inputs({"X", "Out", paddle::Grad("Out")})
+    .Outputs({paddle::Grad("X")})
+    .SetKernelFn(PD_KERNEL(ScalarAddBackward));
+
 // y = x - 1
 std::vector<paddle::Tensor> SubtractForward(const paddle::Tensor& x) {
   if (x.is_cpu() || x.is_gpu()) {
@@ -80,6 +110,37 @@ PD_BUILD_GRAD_OP(custom_subtract)
     .Outputs({paddle::Grad("X")})
     .SetKernelFn(PD_KERNEL(SubtractBackward));
 
+// y = x - 1
+std::vector<paddle::Tensor> ScalarSubtractForward(const paddle::Tensor& x) {
+  if (x.is_cpu() || x.is_gpu()) {
+    return {x - 1};
+  } else {
+    PD_THROW("Not implemented.");
+  }
+}
+
+// dy / dx = 1 * grad_out
+std::vector<paddle::Tensor> ScalarSubtractBackward(
+    const paddle::Tensor& x,
+    const paddle::Tensor& out,
+    const paddle::Tensor& grad_out) {
+  if (x.is_cpu() || x.is_gpu()) {
+    return {grad_out * 1};
+  } else {
+    PD_THROW("Not implemented.");
+  }
+}
+
+PD_BUILD_OP(custom_scalar_subtract)
+    .Inputs({"X"})
+    .Outputs({"Out"})
+    .SetKernelFn(PD_KERNEL(ScalarSubtractForward));
+
+PD_BUILD_GRAD_OP(custom_scalar_subtract)
+    .Inputs({"X", "Out", paddle::Grad("Out")})
+    .Outputs({paddle::Grad("X")})
+    .SetKernelFn(PD_KERNEL(ScalarSubtractBackward));
+
 // y = x * 5
 std::vector<paddle::Tensor> MultiplyForward(const paddle::Tensor& x) {
   if (x.is_cpu() || x.is_gpu()) {
@@ -114,6 +175,37 @@ PD_BUILD_GRAD_OP(custom_multiply)
     .Outputs({paddle::Grad("X")})
     .SetKernelFn(PD_KERNEL(MultiplyBackward));
 
+// y = x * 5
+std::vector<paddle::Tensor> ScalarMultiplyForward(const paddle::Tensor& x) {
+  if (x.is_cpu() || x.is_gpu()) {
+    return {x * 5};
+  } else {
+    PD_THROW("Not implemented.");
+  }
+}
+
+// dy / dx = grad_out * 5
+std::vector<paddle::Tensor> ScalarMultiplyBackward(
+    const paddle::Tensor& x,
+    const paddle::Tensor& out,
+    const paddle::Tensor& grad_out) {
+  if (x.is_cpu() || x.is_gpu()) {
+    return {grad_out * 5};
+  } else {
+    PD_THROW("Not implemented.");
+  }
+}
+
+PD_BUILD_OP(custom_scalar_multiply)
+    .Inputs({"X"})
+    .Outputs({"Out"})
+    .SetKernelFn(PD_KERNEL(ScalarMultiplyForward));
+
+PD_BUILD_GRAD_OP(custom_scalar_multiply)
+    .Inputs({"X", "Out", paddle::Grad("Out")})
+    .Outputs({paddle::Grad("X")})
+    .SetKernelFn(PD_KERNEL(ScalarMultiplyBackward));
+
 // y = 1 / x
 std::vector<paddle::Tensor> DivideForward(const paddle::Tensor& x) {
   if (x.is_cpu() || x.is_gpu()) {
@@ -145,3 +237,36 @@ PD_BUILD_GRAD_OP(custom_divide)
     .Inputs({"X", "Out", paddle::Grad("Out")})
     .Outputs({paddle::Grad("X")})
     .SetKernelFn(PD_KERNEL(DivideBackward));
+
+// y = 1 / x / 1
+std::vector<paddle::Tensor> ScalarDivideForward(const paddle::Tensor& x) {
+  if (x.is_cpu() || x.is_gpu()) {
+    paddle::Tensor ones = paddle::full(x.shape(), 1.0, x.dtype(), x.place());
+    return {ones / x / 1};
+  } else {
+    PD_THROW("Not implemented.");
+  }
+}
+
+// dy / dx = - (1 / x / x) * grad_out
+std::vector<paddle::Tensor> ScalarDivideBackward(
+    const paddle::Tensor& x,
+    const paddle::Tensor& out,
+    const paddle::Tensor& grad_out) {
+  if (x.is_cpu() || x.is_gpu()) {
+    paddle::Tensor zeros = paddle::full(x.shape(), 0.0, x.dtype(), x.place());
+    return {zeros - grad_out / (x * x)};
+  } else {
+    PD_THROW("Not implemented.");
+  }
+}
+
+PD_BUILD_OP(custom_scalar_divide)
+    .Inputs({"X"})
+    .Outputs({"Out"})
+    .SetKernelFn(PD_KERNEL(ScalarDivideForward));
+
+PD_BUILD_GRAD_OP(custom_scalar_divide)
+    .Inputs({"X", "Out", paddle::Grad("Out")})
+    .Outputs({paddle::Grad("X")})
+    .SetKernelFn(PD_KERNEL(ScalarDivideBackward));

python/paddle/fluid/tests/custom_op/test_custom_tensor_operator.py

@@ -228,6 +228,16 @@ class TestJITLoad(unittest.TestCase):
             self.dtypes.append('float16')
 
     def test_all(self):
+        self.add = self.custom_module.custom_add
+        self.subtract = self.custom_module.custom_subtract
+        self.multiply = self.custom_module.custom_multiply
+        self.divide = self.custom_module.custom_divide
+        self._test_static()
+        self._test_dynamic()
+        self.add = self.custom_module.custom_scalar_add
+        self.subtract = self.custom_module.custom_scalar_subtract
+        self.multiply = self.custom_module.custom_scalar_multiply
+        self.divide = self.custom_module.custom_scalar_divide
         self._test_static()
         self._test_dynamic()
 
@@ -238,35 +248,31 @@ class TestJITLoad(unittest.TestCase):
                     continue
                 x = np.random.uniform(-1, 1, [4, 8]).astype(dtype)
 
-                out = test_custom_add_static(
-                    self.custom_module.custom_add, device, dtype, x
-                )
+                out = test_custom_add_static(self.add, device, dtype, x)
                 pd_out = test_custom_add_static(
-                    self.custom_module.custom_add, device, dtype, x, False
+                    self.add, device, dtype, x, False
                 )
                 np.testing.assert_allclose(out, pd_out, rtol=1e-5, atol=1e-8)
 
                 out = test_custom_subtract_static(
-                    self.custom_module.custom_subtract, device, dtype, x
+                    self.subtract, device, dtype, x
                 )
                 pd_out = test_custom_subtract_static(
-                    self.custom_module.custom_subtract, device, dtype, x, False
+                    self.subtract, device, dtype, x, False
                 )
                 np.testing.assert_allclose(out, pd_out, rtol=1e-5, atol=1e-8)
 
                 out = test_custom_multiply_static(
-                    self.custom_module.custom_multiply, device, dtype, x
+                    self.multiply, device, dtype, x
                 )
                 pd_out = test_custom_multiply_static(
-                    self.custom_module.custom_multiply, device, dtype, x, False
+                    self.multiply, device, dtype, x, False
                 )
                 np.testing.assert_allclose(out, pd_out, rtol=1e-5, atol=1e-8)
 
-                out = test_custom_divide_static(
-                    self.custom_module.custom_divide, device, dtype, x
-                )
+                out = test_custom_divide_static(self.divide, device, dtype, x)
                 pd_out = test_custom_divide_static(
-                    self.custom_module.custom_divide, device, dtype, x, False
+                    self.divide, device, dtype, x, False
                 )
                 np.testing.assert_allclose(out, pd_out, rtol=1e-5, atol=1e-8)
 
@@ -278,10 +284,10 @@ class TestJITLoad(unittest.TestCase):
                 x = np.random.uniform(-1, 1, [4, 8]).astype(dtype)
 
                 out, x_grad = test_custom_add_dynamic(
-                    self.custom_module.custom_add, device, dtype, x
+                    self.add, device, dtype, x
                 )
                 pd_out, pd_x_grad = test_custom_add_dynamic(
-                    self.custom_module.custom_add, device, dtype, x, False
+                    self.add, device, dtype, x, False
                 )
                 np.testing.assert_allclose(out, pd_out, rtol=1e-5, atol=1e-8)
                 np.testing.assert_allclose(
@@ -289,10 +295,10 @@ class TestJITLoad(unittest.TestCase):
                 )
 
                 out, x_grad = test_custom_subtract_dynamic(
-                    self.custom_module.custom_subtract, device, dtype, x
+                    self.subtract, device, dtype, x
                 )
                 pd_out, pd_x_grad = test_custom_subtract_dynamic(
-                    self.custom_module.custom_subtract, device, dtype, x, False
+                    self.subtract, device, dtype, x, False
                 )
                 np.testing.assert_allclose(out, pd_out, rtol=1e-5, atol=1e-8)
                 np.testing.assert_allclose(
@@ -300,10 +306,10 @@ class TestJITLoad(unittest.TestCase):
                 )
 
                 out, x_grad = test_custom_multiply_dynamic(
-                    self.custom_module.custom_multiply, device, dtype, x
+                    self.multiply, device, dtype, x
                 )
                 pd_out, pd_x_grad = test_custom_multiply_dynamic(
-                    self.custom_module.custom_multiply, device, dtype, x, False
+                    self.multiply, device, dtype, x, False
                 )
                 np.testing.assert_allclose(out, pd_out, rtol=1e-5, atol=1e-8)
                 np.testing.assert_allclose(
@@ -311,10 +317,10 @@ class TestJITLoad(unittest.TestCase):
                 )
 
                 out, x_grad = test_custom_divide_dynamic(
-                    self.custom_module.custom_divide, device, dtype, x
+                    self.divide, device, dtype, x
                 )
                 pd_out, pd_x_grad = test_custom_divide_dynamic(
-                    self.custom_module.custom_divide, device, dtype, x, False
+                    self.divide, device, dtype, x, False
                 )
                 np.testing.assert_allclose(out, pd_out, rtol=1e-5, atol=1e-8)
 

python/paddle/fluid/tests/unittests/prim/test_comp_get_grad_op_desc_prim_enabled.py

@@ -41,7 +41,14 @@ from paddle.fluid import core, framework
             {'Out': ['y']},
             set(),
             tuple(),
-            ('pow', 'scale', 'elementwise_mul'),
+            (
+                'pow',
+                'fill_constant',
+                'elementwise_mul',
+                'fill_constant',
+                'elementwise_add',
+                'elementwise_mul',
+            ),
         ),
         ('empty', {}, {'Out': ['y']}, set(), tuple(), tuple()),
     ),

python/paddle/fluid/tests/unittests/prim/test_comp_skip_op_set.py

@@ -27,7 +27,14 @@ class TestGetGradOpDescPrimEnabled(unittest.TestCase):
         self.no_grad_var = set()
         self.grad_sub_block = tuple()
         self.desired_ops = 'tanh_grad'
-        self.desired_ops_no_skip = ('pow', 'scale', 'elementwise_mul')
+        self.desired_ops_no_skip = (
+            'pow',
+            'fill_constant',
+            'elementwise_mul',
+            'fill_constant',
+            'elementwise_add',
+            'elementwise_mul',
+        )
         paddle.enable_static()
         block = framework.Block(framework.Program(), 0)
         block.append_op(