Fix the logic of VarBase _to func

python/paddle/fluid/dygraph/varbase_patch_methods.py

@@ -386,21 +386,18 @@ def transform(t, device, dtype, blocking):
  device = t.place
  if dtype is None:
  dtype = t.dtype
+ if type(dtype) is str:
+ dtype = framework.convert_np_dtype_to_dtype_(dtype)
 
  # 1. gpu place need to determine whether the memory is sufficient for allocation.
  if t.place.is_gpu_place():
- gpu_memory_available = core.gpu_memory_available()
- # for gpu, minimum memory allocation unit is 256 bytes.
- if type(dtype) is str:
- size_dtype = core.size_of_dtype(
- framework.convert_np_dtype_to_dtype_(dtype))
- else:
- size_dtype = core.size_of_dtype(dtype)
+ size_dtype = core.size_of_dtype(dtype)
  # Note(weilong wu): Paddle GPU minimum memory allocation unit is 256 bytes,
  # waiting_alloc_memory will compute the memory space occupied by 't'.
  # Coefficient 1.2 is used to avoid OOM that may occur in this critical state when the memory is just enough.
  waiting_alloc_memory = (
  (t._numel() * size_dtype) / 256 + 1) * 256 * 1.2
+ gpu_memory_available = core.gpu_memory_available()
  if gpu_memory_available < waiting_alloc_memory:
  # Copy Tensor to cpu
  t_used = t._copy_to(paddle.CPUPlace(), blocking)
@@ -414,12 +411,17 @@ def transform(t, device, dtype, blocking):
 
  # 2. cast Tensor to dtype
  if dtype is not None and dtype != t_used.dtype:
- t_casted = t_used.cast(dtype=dtype)
+ with paddle.fluid.framework._dygraph_place_guard(
+ place=t_used.place):
+ t_casted = t_used.cast(dtype=dtype)
  else:
  t_casted = t_used
 
  # 3. Copy casted Tensor(in CPU or GPU) to device
- new_t = t_casted._copy_to(device, blocking)
+ if device is not None and not t_casted.place._equals(device):
+ new_t = t_casted._copy_to(device, blocking)
+ else:
+ new_t = t_casted
 
  # 4. Share Tensor to origin Tensor
  dst_tensor = t.value().get_tensor()