Add Tensor::CopyFrom and Tensor::mutable_data(Place place)

1. Add `Tensor::CopyFrom`. Current version can only support CPU memory
copy. The support of GPU will be provided later by `paddle::memory`.
The current implementation of `Tensor::CopyFrom` is a little inefficient:
Every time `CopyFrom` is called, tensor will re-allocate its memory. However, if
we try to check and reuse `placeholder_`, we have to provide a template
parameter for `CopyFrom` to indicate the data type. It seems strange for
a simple copy function.

2. Add `Tensor::mutable_data(Place place)`, which directly use member
variable `dims_` as its dim parameter. This interface is required by
`Op::InferShape`.

paddle/framework/tensor.h

@@ -15,6 +15,7 @@ limitations under the License. */
 #pragma once
 
 #include <cstdint>
+#include <cstring>
 #include <memory>
 #include <type_traits>
 #include "paddle/framework/ddim.h"
@@ -44,11 +45,17 @@ class Tensor {
             typename std::enable_if<std::is_pod<T>::value>::type* = nullptr>
   T* mutable_data(DDim dims, paddle::platform::Place place) {
     dims_ = dims;
+    return mutable_data<T>(place);
+  }
+
+  template <typename T,  // must be POD types
+            typename std::enable_if<std::is_pod<T>::value>::type* = nullptr>
+  T* mutable_data(paddle::platform::Place place) {
     if (holder_ == nullptr ||
         !(holder_->Place() ==
           place) /* some versions of boost::variant don't have operator!= */
-        || holder_->Size() < product(dims) * sizeof(T) + offset_) {
-      holder_.reset(new PlaceholderImpl<T>(place, product(dims) * sizeof(T)));
+        || holder_->Size() < product(dims_) * sizeof(T) + offset_) {
+      holder_.reset(new PlaceholderImpl<T>(place, product(dims_) * sizeof(T)));
       offset_ = 0;
     }
     return reinterpret_cast<T*>(reinterpret_cast<uintptr_t>(holder_->Ptr()) +
@@ -63,6 +70,15 @@ class Tensor {
     offset_ = src.offset_;
   }
 
+  void CopyFrom(const Tensor& src, paddle::platform::Place dst_place) {
+    PADDLE_ENFORCE(src.holder_ != nullptr,
+                   "Can not copy from an uninitialized tensor.");
+    size_t size = product(src.dims()) * src.holder_->TypeSize();
+    holder_.reset(src.holder_->Clone(src.offset_, size, dst_place));
+    dims_ = src.dims();
+    offset_ = 0;
+  }
+
   Tensor Slice(const int& begin_idx, const int& end_idx) const {
     PADDLE_ENFORCE(holder_ != nullptr,
                    "The sliced tenosr has not been initialized.");
@@ -95,6 +111,8 @@ class Tensor {
     virtual paddle::platform::Place Place() const = 0;
     virtual size_t Size() const = 0;
     virtual size_t TypeSize() const = 0;
+    virtual Placeholder* Clone(size_t begin, size_t size,
+                               paddle::platform::Place place) const = 0;
   };
 
   template <typename T>
@@ -122,6 +140,18 @@ class Tensor {
     virtual size_t Size() const { return size_; }
     virtual paddle::platform::Place Place() const { return place_; }
     virtual size_t TypeSize() const { return sizeof(T); }
+    // TODO: Clone only support CPU now. GPU support is needed.
+    virtual Placeholder* Clone(size_t begin, size_t size,
+                               paddle::platform::Place place) const {
+      PADDLE_ENFORCE(paddle::platform::is_cpu_place(place_) &&
+                         paddle::platform::is_cpu_place(place),
+                     "PlaceholderImpl::Clone only support CPU now.");
+      PlaceholderImpl<T>* dst = new PlaceholderImpl<T>(place, size);
+      void* begin_ptr =
+          reinterpret_cast<void*>(reinterpret_cast<uintptr_t>(Ptr()) + begin);
+      memcpy(dst->Ptr(), begin_ptr, size);
+      return dst;
+    }
 
     std::unique_ptr<T, Deleter> ptr_;
     paddle::platform::Place place_;  // record the place of ptr_.

paddle/framework/tensor_test.cc

@@ -178,4 +178,29 @@ TEST(Tensor, Slice) {
   }
 }
 
+TEST(Tensor, CopyFrom) {
+  using namespace paddle::framework;
+  using namespace paddle::platform;
+
+  Tensor src_tensor;
+  int* src_ptr = src_tensor.mutable_data<int>(make_ddim({3, 3}), CPUPlace());
+  int arr[9] = {1, 2, 3, 4, 5, 6, 7, 8, 9};
+  memcpy(src_ptr, arr, 9 * sizeof(int));
+  Tensor dst_tensor;
+  dst_tensor.CopyFrom(src_tensor, CPUPlace());
+  const int* dst_ptr = dst_tensor.data<int>();
+  ASSERT_NE(src_ptr, dst_ptr);
+  for (size_t i = 0; i < 9; ++i) {
+    EXPECT_EQ(src_ptr[i], dst_ptr[i]);
+  }
+
+  Tensor slice_tensor = src_tensor.Slice(1, 2);
+  dst_tensor.CopyFrom(slice_tensor, CPUPlace());
+  const int* slice_ptr = slice_tensor.data<int>();
+  dst_ptr = dst_tensor.data<int>();
+  ASSERT_NE(dst_ptr, slice_ptr);
+  for (size_t i = 0; i < 3; ++i) {
+    EXPECT_EQ(dst_ptr[i], slice_ptr[i]);
+  }
+}
 */
\ No newline at end of file