refine code

Author: zhangbo9674

python/paddle/fluid/dygraph/layers.py

@@ -1556,19 +1556,18 @@ def transform(t, device, dtype, blocking):
  if dtype is None:
  dtype = t.dtype
 
+ if type(dtype) is str:
+ dtype = 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(
- convert_np_dtype_to_dtype_(dtype))
- else:
- size_dtype = core.size_of_dtype(dtype)
+ size_dtype = core.size_of_dtype(dtype)
  # Note(zhangbo): Paddle GPU minimum memory allocation unit is 256 bytes, waiting_alloc_memory will comput ‘t’ occupied memory space.
  # 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().numpy()[0] * size_dtype) / 256 + 1) * 256 * 1.2
+ (np.prod(t.shape) * size_dtype) / 256 + 1) * 256 * 1.2
+ gpu_memory_available = core.gpu_memory_available()
  if gpu_memory_available < waiting_alloc_memory:
  # Copy param / Tensor to cpu
  t_used = t._copy_to(paddle.CPUPlace(),
@@ -1582,26 +1581,17 @@ def transform(t, device, dtype, blocking):
 
  # 2. cast param / Tensor to dtype
  if dtype is not None and dtype != t_used.dtype:
- if isinstance(t_used, framework.ParamBase):
- from paddle.fluid.layer_helper import LayerHelper
- helper = LayerHelper("cast", **locals())
- t_casted = helper.create_variable_for_type_inference(
- dtype=dtype)
- framework._dygraph_tracer().trace_op(
- type='cast',
- inputs={'X': t_used},
- outputs={'Out': t_casted},
- attrs={
- 'in_dtype': t_used.dtype,
- 'out_dtype': convert_np_dtype_to_dtype_(dtype)
- })
- else:
+ 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 cpu param / Tensor 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 param / Tensor
  dst_tensor = t.value().get_tensor()