Allow TFE_TensorHandleCopyToDevice to have the same device as src and

destination. It will reuse the same underlying buffer in those cases.

PiperOrigin-RevId: 164909906

tensorflow/c/eager/c_api.cc

@@ -201,39 +201,25 @@ TFE_TensorHandle* TFE_TensorHandleCopyToDevice(TFE_TensorHandle* h,
   if (!status->status.ok()) return nullptr;
 
   tensorflow::Device* srcd = h->d == nullptr ? ctx->devices()[0] : h->d;
-  const bool src_cpu = IsCPU(srcd);
+  bool is_same_device =
+      (srcd == dstd) || (DeviceName(srcd) == DeviceName(dstd));
   const bool dst_cpu = IsCPU(dstd);
-  if (!src_cpu && !dst_cpu) {
+  if (is_same_device) {
+    return new TFE_TensorHandle(h->t, dst_cpu ? nullptr : dstd);
+  }
+  const bool src_cpu = IsCPU(srcd);
+  if (src_cpu == dst_cpu) {
     TF_SetStatus(
         status, TF_INVALID_ARGUMENT,
         tensorflow::strings::StrCat(
             "TFE_TensorHandleCopyToDevice requires either the source "
-            "TFE_TensorHandle be on or the destination device be CPU (they "
-            "are ",
+            "TFE_TensorHandle be on or the destination device be on CPU "
+            "or be the same (they are ",
             DeviceName(srcd), " and ", DeviceName(dstd), " in this call)")
             .c_str());
     return nullptr;
   }
   tensorflow::Tensor* src = &(h->t);
-  if (src_cpu && dst_cpu) {
-    // There must be a better way, but for now redirect through proto to ensure
-    // that the underlying buffers are not shared.
-    tensorflow::TensorProto proto;
-    src->AsProtoTensorContent(&proto);
-    tensorflow::Tensor dst(src->dtype(), src->shape());
-    if (!dst.FromProto(proto)) {
-      TF_SetStatus(
-          status, TF_INTERNAL,
-          tensorflow::strings::StrCat(
-              "error copying between TFE_TensorHandles on CPU. Consider filing "
-              "a bug report at https://github.com/tensorflow/tensorflow/issues "
-              "mentioning version: ",
-              TF_Version(), " and ", __FILE__, ":", __LINE__)
-              .c_str());
-      return nullptr;
-    }
-    return new TFE_TensorHandle(dst, nullptr);
-  }
   if (src_cpu) {
     tensorflow::Tensor dst(
         dstd->GetAllocator(tensorflow::AllocatorAttributes()), src->dtype(),

tensorflow/c/eager/c_api.h

@@ -54,10 +54,10 @@ extern TF_Tensor* TFE_TensorHandleResolve(TFE_TensorHandle* h,
 
 // Create a new TFE_TensorHandle with the same contents as 'h' but placed
 // in the memory of the device name 'device_name'.
-//
-// Currently requires at least one of the source or destination devices to
-// be CPU (i.e., for the source or destination tensor to be placed in
-// host memory).
+// If source and destination are the same device, then this creates a new handle
+// that shares the underlying buffer. Otherwise, it currently requires at least
+// one of the source or destination devices to be CPU (i.e., for the source or
+// destination tensor to be placed in host memory).
 extern TFE_TensorHandle* TFE_TensorHandleCopyToDevice(TFE_TensorHandle* h,
                                                       TFE_Context* ctx,
                                                       const char* device_name,

tensorflow/c/eager/c_api_test.cc

@@ -170,14 +170,22 @@ TEST(CAPI, TensorHandleCopyBetweenDevices) {
       ADD_FAILURE() << tag << " -- " << TF_Message(status.get());
       continue;
     }
-    // Copy back to CPU
-    TFE_TensorHandle* hcopy =
-        TFE_TensorHandleCopyToDevice(hdevice, ctx, kCPUDevice, status.get());
+    // Copy from device to the same device.
+    TFE_TensorHandle* hdevice2 =
+        TFE_TensorHandleCopyToDevice(hdevice, ctx, name.c_str(), status.get());
     if (TF_GetCode(status.get()) != TF_OK) {
       ADD_FAILURE() << tag << " -- " << TF_Message(status.get());
       continue;
     }
     TFE_DeleteTensorHandle(hdevice);
+    // Copy back to CPU
+    TFE_TensorHandle* hcopy =
+        TFE_TensorHandleCopyToDevice(hdevice2, ctx, kCPUDevice, status.get());
+    if (TF_GetCode(status.get()) != TF_OK) {
+      ADD_FAILURE() << tag << " -- " << TF_Message(status.get());
+      continue;
+    }
+    TFE_DeleteTensorHandle(hdevice2);
 
     // Ensure that the contents are the same!
     TF_Tensor* tcopy = TFE_TensorHandleResolve(hcopy, status.get());

tensorflow/python/eager/core_test.py

@@ -150,21 +150,15 @@ class TFETest(test_util.TensorFlowTestCase):
     if not context.context().num_gpus():
       self.skipTest('No GPUs found')
 
-    cpu = tensor.Tensor([[1., 2.], [3., 4.]])
-    c2g = cpu.as_gpu_tensor()
-    # Exercise a copy from GPU to CPU, even though we ignore the value.
-    _ = c2g.as_cpu_tensor()
-
-    with self.assertRaises(errors.InvalidArgumentError):
-      # c2g is on GPU. Copying between GPU devices fails
-      # (must redirect through CPU for now).
-      # TODO(ashankar): Perhaps the function should not fail and instead
-      # faciliate the copy through host memory?
-      c2g.as_gpu_tensor()
+    x = tensor.Tensor([[1., 2.], [3., 4.]])
+    x = x.as_cpu_tensor()
+    x = x.as_gpu_tensor()
+    x = x.as_gpu_tensor()
+    x = x.as_cpu_tensor()
 
     # Invalid device
     with self.assertRaises(errors.InvalidArgumentError):
-      cpu.as_gpu_tensor(context.context().num_gpus() + 1)
+      x.as_gpu_tensor(context.context().num_gpus() + 1)
 
   def testNumpyForceCPU(self):
     if not context.context().num_gpus():
@@ -274,7 +268,8 @@ class TFETest(test_util.TensorFlowTestCase):
     product = execute.execute(
         'MatMul',
         num_outputs=1,
-        inputs=[tensor.Tensor([[3]]), tensor.Tensor([[5]])],
+        inputs=[tensor.Tensor([[3]]),
+                tensor.Tensor([[5]])],
         attrs=('transpose_a', True, 'transpose_b', False, 'T',
                dtypes.int32.as_datatype_enum))[0]
     self.assertEqual([[15]], product.numpy())
@@ -475,8 +470,8 @@ class TFETest(test_util.TensorFlowTestCase):
     with context.device('gpu:0'):
       y = truncated_normal(shape)
     # Add would fail if x and y were not on the same device.
-    execute.execute('Add', 1, inputs=[x, y],
-                    attrs=('T', x.dtype.as_datatype_enum))
+    execute.execute(
+        'Add', 1, inputs=[x, y], attrs=('T', x.dtype.as_datatype_enum))
 
   def testInvalidDevice(self):
     with self.assertRaises(ValueError):