tensor.set() supports array list and remove unused code, test=develop (#20959)

paddle/fluid/pybind/tensor_py.h

@@ -140,7 +140,8 @@ void TensorSetElement(framework::Tensor *self, size_t offset, T elem) {
 template <typename T, typename P>
 void SetTensorFromPyArrayT(
     framework::Tensor *self,
-    py::array_t<T, py::array::c_style | py::array::forcecast> array, P place) {
+    const py::array_t<T, py::array::c_style | py::array::forcecast> &array,
+    const P &place) {
   std::vector<int64_t> dims;
   dims.reserve(array.ndim());
   for (decltype(array.ndim()) i = 0; i < array.ndim(); ++i) {
@@ -171,8 +172,9 @@ void SetTensorFromPyArrayT(
 }
 
 template <typename P>
-void SetTensorFromPyArray(framework::Tensor *self, pybind11::array array,
-                          P place) {
+void SetTensorFromPyArray(framework::Tensor *self, const py::object &obj,
+                          const P &place) {
+  auto array = obj.cast<py::array>();
   if (py::isinstance<py::array_t<float>>(array)) {
     SetTensorFromPyArrayT<float, P>(self, array, place);
   } else if (py::isinstance<py::array_t<int>>(array)) {
@@ -202,42 +204,6 @@ void SetTensorFromPyArray(framework::Tensor *self, pybind11::array array,
   }
 }
 
-template <typename T>
-void PyCPUTensorSetFromArray(
-    framework::Tensor *self,
-    pybind11::array_t<T, pybind11::array::c_style | pybind11::array::forcecast>
-        array,
-    paddle::platform::CPUPlace place) {
-  std::vector<int64_t> dims;
-  dims.reserve(array.ndim());
-  for (decltype(array.ndim()) i = 0; i < array.ndim(); ++i) {
-    dims.push_back(static_cast<int>(array.shape()[i]));
-  }
-
-  self->Resize(framework::make_ddim(dims));
-  auto *dst = self->mutable_data<T>(place);
-  std::memcpy(dst, array.data(), sizeof(T) * array.size());
-}
-
-template <>
-// This following specialization maps uint16_t in the parameter type to
-// platform::float16.
-inline void PyCPUTensorSetFromArray(
-    framework::Tensor *self,
-    pybind11::array_t<uint16_t,
-                      pybind11::array::c_style | pybind11::array::forcecast>
-        array,
-    paddle::platform::CPUPlace place) {
-  std::vector<int64_t> dims;
-  dims.reserve(array.ndim());
-  for (decltype(array.ndim()) i = 0; i < array.ndim(); ++i) {
-    dims.push_back(static_cast<int>(array.shape()[i]));
-  }
-  self->Resize(framework::make_ddim(dims));
-  auto *dst = self->mutable_data<platform::float16>(place);
-  std::memcpy(dst, array.data(), sizeof(uint16_t) * array.size());
-}
-
 template <typename T, size_t D>
 void _sliceCompute(const framework::Tensor *in, framework::Tensor *out,
                    const platform::CPUDeviceContext &ctx,
@@ -485,84 +451,6 @@ inline framework::Tensor *PySliceTensor(const framework::Tensor &self,
   }
 }
 
-#ifdef PADDLE_WITH_CUDA
-template <typename T>
-void PyCUDATensorSetFromArray(
-    framework::Tensor *self,
-    pybind11::array_t<T, pybind11::array::c_style | pybind11::array::forcecast>
-        array,
-    paddle::platform::CUDAPlace place) {
-  std::vector<int64_t> dims;
-  dims.reserve(array.ndim());
-  for (decltype(array.ndim()) i = 0; i < array.ndim(); ++i) {
-    dims.push_back(static_cast<int>(array.shape()[i]));
-  }
-  self->Resize(framework::make_ddim(dims));
-  auto *dst = self->mutable_data<T>(place);
-  paddle::platform::GpuMemcpySync(dst, array.data(), sizeof(T) * array.size(),
-                                  cudaMemcpyHostToDevice);
-}
-
-template <>
-// This following specialization maps uint16_t in the parameter type to
-// platform::float16.
-inline void PyCUDATensorSetFromArray(
-    framework::Tensor *self,
-    pybind11::array_t<uint16_t,
-                      pybind11::array::c_style | pybind11::array::forcecast>
-        array,
-    paddle::platform::CUDAPlace place) {
-  std::vector<int64_t> dims;
-  dims.reserve(array.ndim());
-  for (decltype(array.ndim()) i = 0; i < array.ndim(); ++i) {
-    dims.push_back(static_cast<int>(array.shape()[i]));
-  }
-
-  self->Resize(framework::make_ddim(dims));
-  auto *dst = self->mutable_data<platform::float16>(place);
-  paddle::platform::GpuMemcpySync(dst, array.data(),
-                                  sizeof(uint16_t) * array.size(),
-                                  cudaMemcpyHostToDevice);
-}
-
-template <typename T>
-void PyCUDAPinnedTensorSetFromArray(
-    framework::Tensor *self,
-    pybind11::array_t<T, pybind11::array::c_style | pybind11::array::forcecast>
-        array,
-    const paddle::platform::CUDAPinnedPlace &place) {
-  std::vector<int64_t> dims;
-  dims.reserve(array.ndim());
-  for (decltype(array.ndim()) i = 0; i < array.ndim(); ++i) {
-    dims.push_back(static_cast<int>(array.shape()[i]));
-  }
-
-  self->Resize(framework::make_ddim(dims));
-  auto *dst = self->mutable_data<T>(place);
-  std::memcpy(dst, array.data(), sizeof(T) * array.size());
-}
-
-template <>
-// This following specialization maps uint16_t in the parameter type to
-// platform::float16.
-inline void PyCUDAPinnedTensorSetFromArray(
-    framework::Tensor *self,
-    pybind11::array_t<uint16_t,
-                      pybind11::array::c_style | pybind11::array::forcecast>
-        array,
-    const paddle::platform::CUDAPinnedPlace &place) {
-  std::vector<int64_t> dims;
-  dims.reserve(array.ndim());
-  for (decltype(array.ndim()) i = 0; i < array.ndim(); ++i) {
-    dims.push_back(static_cast<int>(array.shape()[i]));
-  }
-
-  self->Resize(framework::make_ddim(dims));
-  auto *dst = self->mutable_data<platform::float16>(place);
-  std::memcpy(dst, array.data(), sizeof(uint16_t) * array.size());
-}
-#endif
-
 inline py::array TensorToPyArray(const framework::Tensor &tensor) {
   if (!tensor.IsInitialized()) {
     return py::array();

python/paddle/fluid/tests/unittests/test_tensor.py

@@ -280,6 +280,45 @@ class TestTensor(unittest.TestCase):
                 isinstance(
                     tensor._mutable_data(places[0], dtype), numbers.Integral))
 
+    def test_tensor_set_fp16(self):
+        array = numpy.random.random((300, 500)).astype("float16")
+        tensor = fluid.Tensor()
+        place = core.CPUPlace()
+        tensor.set(array, place)
+        self.assertEqual(tensor._dtype(), core.VarDesc.VarType.FP16)
+        self.assertTrue(numpy.array_equal(numpy.array(tensor), array))
+
+        if core.is_compiled_with_cuda():
+            place = core.CUDAPlace(0)
+            tensor.set(array, place)
+            self.assertEqual(tensor._dtype(), core.VarDesc.VarType.FP16)
+            self.assertTrue(numpy.array_equal(numpy.array(tensor), array))
+
+            place = core.CUDAPinnedPlace()
+            tensor.set(array, place)
+            self.assertEqual(tensor._dtype(), core.VarDesc.VarType.FP16)
+            self.assertTrue(numpy.array_equal(numpy.array(tensor), array))
+
+    def test_tensor_set_from_array_list(self):
+        array = numpy.random.randint(1000, size=(200, 300))
+        list_array = [array, array]
+        tensor = fluid.Tensor()
+        place = core.CPUPlace()
+        tensor.set(list_array, place)
+        self.assertEqual([2, 200, 300], tensor.shape())
+        self.assertTrue(numpy.array_equal(numpy.array(tensor), list_array))
+
+        if core.is_compiled_with_cuda():
+            place = core.CUDAPlace(0)
+            tensor.set(list_array, place)
+            self.assertEqual([2, 200, 300], tensor.shape())
+            self.assertTrue(numpy.array_equal(numpy.array(tensor), list_array))
+
+            place = core.CUDAPinnedPlace()
+            tensor.set(list_array, place)
+            self.assertEqual([2, 200, 300], tensor.shape())
+            self.assertTrue(numpy.array_equal(numpy.array(tensor), list_array))
+
 
 if __name__ == '__main__':
     unittest.main()