def broadcast(tensor):
"""Returns a tensor that can be efficiently transferred to other devices.
Args:
tensor: The tensor to send; must be assigned to a GPU device.
Returns:
A tensor with the value of `src_tensor`, which can be used as input to
ops on other GPU devices.
"""
_check_graph_mode()
_check_device(tensor)
with ops.device(tensor.device):
return gen_nccl_ops.nccl_broadcast(input=tensor, shape=tensor.shape)
def broadcast(tensor):
"""Returns a tensor that can be efficiently transferred to other devices.
Args:
tensor: The tensor to send; must be assigned to a GPU device.
Returns:
A tensor with the value of `src_tensor`, which can be used as input to
ops on other GPU devices.
"""
_validate_and_load_nccl_so()
_check_device(tensor)
with ops.device(tensor.device):
return gen_nccl_ops.nccl_broadcast(input=tensor, shape=tensor.shape)
def _reduce_sum_grad(op, grad):
"""The gradients for input `Operation` of `reduce_sum`.
Args:
op: The `sum send` `Operation` that we are differentiating.
grad: Gradient with respect to the output of the `reduce_sum` op.
Returns:
The gradient with respect to the input of `reduce_sum` op.
Raises:
LookupError: If the reduction attribute of op is not `sum`.
"""
if op.get_attr('reduction') != 'sum':
raise LookupError('No gradient defined for NcclReduce except sum.')
_check_device(grad, expected=op.device)
with ops.device(op.device):
result = gen_nccl_ops.nccl_broadcast(input=grad, shape=grad.shape)
return [result] * len(op.inputs)
def _reduce_sum_grad(op, grad):
"""The gradients for input `Operation` of `reduce_sum`.
Args:
op: The `sum send` `Operation` that we are differentiating.
grad: Gradient with respect to the output of the `reduce_sum` op.
Returns:
The gradient with respect to the input of `reduce_sum` op.
Raises:
LookupError: If the reduction attribute of op is not `sum`.
"""
if op.get_attr('reduction') != b'sum':
raise LookupError('No gradient defined for NcclReduce except sum.')
_check_device(grad, expected=op.device)
with ops.device(op.device):
result = gen_nccl_ops.nccl_broadcast(input=grad, shape=grad.shape)
return [result] * len(op.inputs)
