def __del__(self):
if not self._requires_grad or self._static_shape:
if self._own_storage and self._dg_tensor:
# Always reuse the leaf variables or
# tensors that do not require grad
# PyGC will detect them automatically
TPool.put(self.name)
def backward(self, gradient=None):
if not self._requires_grad:
raise RuntimeError('This variable does not require grads.'
'\nCan not backward from this variable.')
# 1. Expressions -> Forward-Ops
# We should sort out the topology of these operators before using
all_expressions = sorted(self._expr._ops.items(), key=lambda d: d[0])
forward_ops = [v for k, v in all_expressions]
# 2. Forward-Ops + Targets + InputGrads + IgnoredGrads -> Backward-Ops
targets = [self.name]; input_grads = []
ignored_grads = list(self._ignored_grads) if self._ignored_grads else []
if gradient is not None:
if not isinstance(gradient, Tensor):
raise TypeError('gradients can be either Tensors, Variables or None,'
' but got {}'.format(type(gradient)))
tensor_utils.FromPyArray(gradient.cpu().numpy(), self.name + '_grad')
input_grads.append(self.name + '_grad')
# 3. Flow or Flow or Flow
ws.RunGradientFlow(forward_ops, targets, input_grads, ignored_grads)
# 4. Release resources
# We should release both the anchors and tensors
for forward_op in forward_ops:
APool.put(forward_op.name)
for output in forward_op.output:
if output not in forward_op.input:
TPool.put(output)