Create Tensor by paddle::empty in custom operator (#41840)

* create tensor by empty in custom op

* fix some bug

paddle/fluid/eager/auto_code_generator/final_state_generator/codegen_utils.py

@@ -45,7 +45,7 @@ yaml_types_mapping = {
     'int' : 'int', 'int32_t' : 'int32_t', 'int64_t' : 'int64_t',  'size_t' : 'size_t', \
     'float' : 'float', 'double' : 'double', 'bool' : 'bool', \
     'str' : 'std::string', \
-    'Place' : 'paddle::experimental::Place', 'DataLayout' : 'paddle::experimental::DataLayout', 'DataType' : 'paddle::experimental::DataType', \
+    'Place' : 'paddle::Place', 'DataLayout' : 'paddle::experimental::DataLayout', 'DataType' : 'paddle::experimental::DataType', \
     'int64_t[]' : 'std::vector<int64_t>', 'int[]' : 'std::vector<int>',
     'Tensor' : 'Tensor',
     'Tensor[]' : 'std::vector<Tensor>',

paddle/fluid/eager/auto_code_generator/final_state_generator/python_c_gen.py

@@ -46,7 +46,7 @@ atype_to_parsing_function = {
     "std::vector<std::string>": "CastPyArg2Strings",
     "paddle::experimental::Scalar": "CastPyArg2Scalar",
     "paddle::experimental::IntArray": "CastPyArg2IntArray",
-    "paddle::experimental::Place": "CastPyArg2Place",
+    "paddle::Place": "CastPyArg2Place",
     "paddle::experimental::DataType": "CastPyArg2DataType",
 }
 

paddle/fluid/pybind/eager_utils.cc

@@ -1151,14 +1151,12 @@ std::vector<paddle::framework::Scope*> GetScopePtrListFromArgs(
   return result;
 }
 
-paddle::experimental::Place CastPyArg2Place(PyObject* obj,
-                                            const std::string& op_type,
+paddle::Place CastPyArg2Place(PyObject* obj, const std::string& op_type,
                               ssize_t arg_pos) {
   return CastPyArg2Place(obj, arg_pos);
 }
 
-paddle::experimental::DataType CastPyArg2DataType(PyObject* obj,
-                                                  const std::string& op_type,
+paddle::DataType CastPyArg2DataType(PyObject* obj, const std::string& op_type,
                                     ssize_t arg_pos) {
   if (obj == Py_None) {
     PADDLE_THROW(platform::errors::InvalidArgument(

paddle/fluid/pybind/eager_utils.h

@@ -162,12 +162,10 @@ paddle::experimental::IntArray CastPyArg2IntArray(PyObject* obj,
                                                   const std::string& op_type,
                                                   ssize_t arg_pos);
 
-paddle::experimental::Place CastPyArg2Place(PyObject* obj,
-                                            const std::string& op_type,
+paddle::Place CastPyArg2Place(PyObject* obj, const std::string& op_type,
                               ssize_t arg_pos);
 
-paddle::experimental::DataType CastPyArg2DataType(PyObject* obj,
-                                                  const std::string& op_type,
+paddle::DataType CastPyArg2DataType(PyObject* obj, const std::string& op_type,
                                     ssize_t arg_pos);
 
 paddle::optional<const paddle::experimental::Tensor&> GetOptionalTensorFromArgs(

paddle/phi/common/place.h

@@ -213,9 +213,6 @@ std::ostream& operator<<(std::ostream&, const Place&);
 namespace paddle {
 namespace experimental {
 using AllocationType = phi::AllocationType;
-using Place = phi::Place;
-using CPUPlace = phi::CPUPlace;
-using GPUPlace = phi::GPUPlace;
 using GPUPinnedPlace = phi::GPUPinnedPlace;
 using XPUPlace = phi::XPUPlace;
 using NPUPlace = phi::NPUPlace;

paddle/phi/tests/api/test_data_transform.cc

@@ -37,13 +37,11 @@ namespace tests {
 // TODO(chenweihang): Remove this test after the API is used in the dygraph
 TEST(API, data_transform_same_place) {
   // 1. create tensor
-  auto x = paddle::experimental::full({3, 3},
-                                      1.0,
-                                      experimental::DataType::COMPLEX128,
-                                      experimental::CPUPlace());
+  auto x =
+      paddle::experimental::full({3, 3}, 1.0, DataType::COMPLEX128, CPUPlace());
 
-  auto y = paddle::experimental::full(
-      {3, 3}, 2.0, experimental::DataType::FLOAT32, experimental::CPUPlace());
+  auto y =
+      paddle::experimental::full({3, 3}, 2.0, DataType::FLOAT32, CPUPlace());
 
   std::vector<phi::dtype::complex<double>> sum(9, 6.0);
 
@@ -75,10 +73,10 @@ TEST(API, data_transform_same_place) {
 TEST(Tensor, data_transform_diff_place) {
   // 1. create tensor
   auto x = paddle::experimental::full(
-      {3, 3}, 1.0, experimental::DataType::FLOAT64, experimental::CPUPlace());
+      {3, 3}, 1.0, experimental::DataType::FLOAT64, CPUPlace());
 
   auto y = paddle::experimental::full(
-      {3, 3}, 2.0, experimental::DataType::FLOAT64, experimental::GPUPlace());
+      {3, 3}, 2.0, experimental::DataType::FLOAT64, GPUPlace());
 
   std::vector<float> sum(9, 6.0);
 
@@ -93,10 +91,9 @@ TEST(Tensor, data_transform_diff_place) {
   ASSERT_EQ(out.dtype(), phi::DataType::FLOAT64);
   ASSERT_EQ(out.layout(), phi::DataLayout::NCHW);
   ASSERT_EQ(out.initialized(), true);
-  ASSERT_EQ(out.impl()->place(),
-            phi::TransToPhiPlace(experimental::Backend::GPU));
+  ASSERT_EQ(out.impl()->place(), phi::TransToPhiPlace(phi::Backend::GPU));
 
-  auto ref_out = experimental::copy_to(out, experimental::CPUPlace(), true);
+  auto ref_out = experimental::copy_to(out, CPUPlace(), true);
 
   auto dense_out = std::dynamic_pointer_cast<phi::DenseTensor>(ref_out.impl());
   for (size_t i = 0; i < 9; i++) {

paddle/phi/tests/api/test_scale_benchmark.cc

@@ -30,7 +30,7 @@ namespace tests {
 
 TEST(API, scale) {
   auto x = experimental::full(
-      {3, 4}, 1.0, experimental::DataType::FLOAT32, experimental::CPUPlace());
+      {3, 4}, 1.0, experimental::DataType::FLOAT32, CPUPlace());
 
   const size_t cycles = 300;
   phi::tests::Timer timer;

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

@@ -22,8 +22,7 @@
 
 std::vector<paddle::Tensor> ContextPoolTest(const paddle::Tensor& x) {
   // 1. test cpu context
-  paddle::experimental::Place cpu_place(
-      paddle::experimental::AllocationType::CPU);
+  paddle::Place cpu_place(paddle::experimental::AllocationType::CPU);
   auto* cpu_ctx =
       paddle::experimental::DeviceContextPool::Instance()
           .Get<paddle::experimental::AllocationType::CPU>(cpu_place);
@@ -34,8 +33,7 @@ std::vector<paddle::Tensor> ContextPoolTest(const paddle::Tensor& x) {
 
 #if defined(PADDLE_WITH_CUDA) || defined(PADDLE_WITH_HIP)
   // 2. test gpu context
-  paddle::experimental::Place gpu_place(
-      paddle::experimental::AllocationType::GPU);
+  paddle::Place gpu_place(paddle::experimental::AllocationType::GPU);
   auto* gpu_ctx =
       paddle::experimental::DeviceContextPool::Instance()
           .Get<paddle::experimental::AllocationType::GPU>(gpu_place);

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

@@ -75,7 +75,7 @@ std::vector<paddle::Tensor> ConcatForwardDynamicAxis(
   auto out_shape = ComputeOutShape(in_shapes, axis);
 
   // create output
-  auto out = paddle::Tensor(paddle::PlaceType::kCPU, out_shape);
+  auto out = paddle::empty(out_shape, inputs[0].type(), paddle::CPUPlace());
 
   // calc
   PD_DISPATCH_FLOATING_AND_INTEGRAL_TYPES(
@@ -106,7 +106,7 @@ std::vector<paddle::Tensor> ConcatBackwardDynamicAxis(
   // create outputs
   std::vector<paddle::Tensor> grad_inputs;
   for (auto& t : inputs) {
-    auto grad = paddle::Tensor(paddle::PlaceType::kCPU, t.shape());
+    auto grad = paddle::empty(t.shape(), t.dtype(), t.place());
     grad_inputs.emplace_back(grad);
   }
 
@@ -161,7 +161,7 @@ std::vector<paddle::Tensor> ConcatForwardStaticAxis(
   auto out_shape = ComputeOutShape(in_shapes, final_axis);
 
   // create output
-  auto out = paddle::Tensor(paddle::PlaceType::kCPU, out_shape);
+  auto out = paddle::empty(out_shape, inputs[0].type(), paddle::CPUPlace());
 
   // calc
   PD_DISPATCH_FLOATING_AND_INTEGRAL_TYPES(
@@ -190,7 +190,7 @@ std::vector<paddle::Tensor> ConcatBackwardStaticAxis(
   // create outputs
   std::vector<paddle::Tensor> grad_inputs;
   for (auto& t : inputs) {
-    auto grad = paddle::Tensor(paddle::PlaceType::kCPU, t.shape());
+    auto grad = paddle::empty(t.shape(), t.dtype(), t.place());
     grad_inputs.emplace_back(grad);
   }
 

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

@@ -71,7 +71,7 @@ void ConjCPUKernel(const data_t* x_data, int64_t numel, data_t* out_data) {
 std::vector<paddle::Tensor> ConjFunction(const paddle::Tensor& x) {
   CHECK_INPUT(x);
 
-  paddle::Tensor out(x.place(), x.shape());
+  paddle::Tensor out = paddle::empty(x.shape(), x.dtype(), x.place());
 
   PD_DISPATCH_FLOATING_AND_COMPLEX_TYPES(
       x.type(), "ConjCPUKernel", ([&] {

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

@@ -54,7 +54,7 @@ void relu_cpu_double_backward_kernel(const data_t* out_data,
 }
 
 std::vector<paddle::Tensor> relu_cpu_forward(const paddle::Tensor& x) {
-  auto out = paddle::Tensor(paddle::PlaceType::kCPU, x.shape());
+  auto out = paddle::empty(x.shape(), x.dtype(), x.place());
 
   PD_DISPATCH_FLOATING_TYPES(
       x.type(), "relu_cpu_forward", ([&] {
@@ -68,7 +68,7 @@ std::vector<paddle::Tensor> relu_cpu_forward(const paddle::Tensor& x) {
 std::vector<paddle::Tensor> relu_cpu_backward(const paddle::Tensor& x,
                                               const paddle::Tensor& out,
                                               const paddle::Tensor& grad_out) {
-  auto grad_x = paddle::Tensor(paddle::PlaceType::kCPU, x.shape());
+  auto grad_x = paddle::empty(x.shape(), x.dtype(), x.place());
 
   PD_DISPATCH_FLOATING_TYPES(out.type(), "relu_cpu_backward", ([&] {
                                relu_cpu_backward_kernel<data_t>(
@@ -85,7 +85,7 @@ std::vector<paddle::Tensor> relu_cpu_double_backward(
     const paddle::Tensor& out, const paddle::Tensor& ddx) {
   CHECK_CPU_INPUT(out);
   CHECK_CPU_INPUT(ddx);
-  auto ddout = paddle::Tensor(paddle::PlaceType::kCPU, out.shape());
+  auto ddout = paddle::empty(out.shape(), out.dtype(), out.place());
 
   PD_DISPATCH_FLOATING_TYPES(out.type(), "relu_cpu_double_backward", ([&] {
                                relu_cpu_double_backward_kernel<data_t>(
@@ -165,7 +165,7 @@ PD_BUILD_DOUBLE_GRAD_OP(custom_relu)
 
 std::vector<paddle::Tensor> relu_cpu_backward_without_x(
     const paddle::Tensor& out, const paddle::Tensor& grad_out) {
-  auto grad_x = paddle::Tensor(paddle::PlaceType::kCPU, out.shape());
+  auto grad_x = paddle::empty(out.shape(), out.dtype(), out.place());
 
   PD_DISPATCH_FLOATING_TYPES(out.type(), "relu_cpu_backward", ([&] {
                                relu_cpu_backward_kernel<data_t>(

python/paddle/fluid/tests/custom_op/custom_relu_op.cu

@@ -54,7 +54,7 @@ __global__ void relu_cuda_double_backward_kernel(const data_t* out_data,
 
 std::vector<paddle::Tensor> relu_cuda_forward(const paddle::Tensor& x) {
   CHECK_GPU_INPUT(x);
-  auto out = paddle::Tensor(paddle::PlaceType::kGPU, x.shape());
+  auto out = paddle::empty(x.shape(), x.dtype(), x.place());
 
   int numel = x.size();
   int block = 512;
@@ -74,7 +74,7 @@ std::vector<paddle::Tensor> relu_cuda_backward(const paddle::Tensor& x,
   CHECK_GPU_INPUT(x);
   CHECK_GPU_INPUT(out);
   CHECK_GPU_INPUT(grad_out);
-  auto grad_x = paddle::Tensor(paddle::PlaceType::kGPU, x.shape());
+  auto grad_x = paddle::empty(x.shape(), x.dtype(), x.place());
 
   int numel = out.size();
   int block = 512;
@@ -95,7 +95,7 @@ std::vector<paddle::Tensor> relu_cuda_double_backward(
     const paddle::Tensor& out, const paddle::Tensor& ddx) {
   CHECK_GPU_INPUT(out);
   CHECK_GPU_INPUT(ddx);
-  auto ddout = paddle::Tensor(paddle::PlaceType::kGPU, out.shape());
+  auto ddout = paddle::empty(out.shape(), out.dtype(), out.place());
 
   int64_t numel = out.size();
   int64_t block = 512;
@@ -117,7 +117,7 @@ std::vector<paddle::Tensor> relu_cuda_double_backward(
 
 std::vector<paddle::Tensor> relu_cuda_backward_without_x(
     const paddle::Tensor& out, const paddle::Tensor& grad_out) {
-  auto grad_x = paddle::Tensor(paddle::PlaceType::kGPU, out.shape());
+  auto grad_x = paddle::empty(out.shape(), out.dtype(), out.place());
 
   int numel = out.size();
   int block = 512;

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

@@ -68,7 +68,7 @@ void tanh_cpu_double_backward_kernel(const data_t* out_data,
 
 std::vector<paddle::Tensor> TanhForward(const paddle::Tensor& x) {
   CHECK_CPU_INPUT(x);
-  auto out = paddle::Tensor(paddle::PlaceType::kCPU, x.shape());
+  auto out = paddle::empty(x.shape(), x.dtype(), x.place());
 
   PD_DISPATCH_FLOATING_TYPES(
       x.dtype(), "tanh_cpu_forward", ([&] {
@@ -82,7 +82,7 @@ std::vector<paddle::Tensor> TanhForward(const paddle::Tensor& x) {
 std::vector<paddle::Tensor> TanhBackward(const paddle::Tensor& out,
                                          const paddle::Tensor& grad_out) {
   CHECK_CPU_INPUT(out);
-  auto grad_x = paddle::Tensor(paddle::PlaceType::kCPU, out.shape());
+  auto grad_x = paddle::empty(out.shape(), out.dtype(), out.place());
 
   PD_DISPATCH_FLOATING_TYPES(out.dtype(), "tanh_cpu_backward", ([&] {
                                tanh_cpu_backward_kernel<data_t>(
@@ -101,8 +101,8 @@ std::vector<paddle::Tensor> TanhDoubleBackward(const paddle::Tensor& out,
   CHECK_CPU_INPUT(out);
   CHECK_CPU_INPUT(ddx);
   CHECK_CPU_INPUT(dout);
-  auto dout_new = paddle::Tensor(paddle::PlaceType::kCPU, out.shape());
-  auto ddout = paddle::Tensor(paddle::PlaceType::kCPU, out.shape());
+  auto dout_new = paddle::empty(out.shape(), out.dtype(), out.place());
+  auto ddout = paddle::empty(out.shape(), out.dtype(), out.place());
 
   PD_DISPATCH_FLOATING_TYPES(out.dtype(), "tanh_cpu_double_backward", ([&] {
                                tanh_cpu_double_backward_kernel<data_t>(