def get_reduction_of_data(aggregation_name, tensor_data, tensor_name, abs=False):
reduction_name = aggregation_name
# If tensor_data is of np.ndarray type invoke np operators.
if isinstance(tensor_data, np.ndarray):
return get_numpy_reduction(reduction_name, tensor_data, abs)
if abs:
tensor_data = mx.ndarray.abs(tensor_data)
if reduction_name in ALLOWED_REDUCTIONS:
if hasattr(mx.ndarray, aggregation_name):
f = getattr(mx.ndarray, aggregation_name)
op = f(tensor_data, name=tensor_name)
else:
# If aggregation is not supported by mxnet, we convert data into np.ndarray and invoke numpy operators
tensor_data_np = make_numpy_array(tensor_data)
op = get_numpy_reduction(aggregation_name, numpy_data=tensor_data_np, abs=abs)
return op
elif reduction_name in ALLOWED_NORMS:
if reduction_name in ["l1", "l2"]:
op = mx.ndarray.norm(data=tensor_data, ord=int(reduction_name[1]))
return op
else:
raise RuntimeError(
"Invalid normalization operation {0} for mx.NDArray".format(reduction_name)
)
elif hasattr(mx, reduction_name):
f = getattr(mx, reduction_name)
op = f(tensor_data, name=tensor_name)
return op
elif hasattr(np, aggregation_name):
f = getattr(np, aggregation_name)
op = f(tensor_data)
return op
raise RuntimeError("Invalid aggregation_name {0} for mx.NDArray".format(aggregation_name))
def get_reduction_of_data(reduction_name, tensor_data, tensor_name, abs=False):
if isinstance(tensor_data, np.ndarray):
return get_numpy_reduction(reduction_name, tensor_data, abs)
if abs:
tensor_data = torch.abs(tensor_data)
if reduction_name in ALLOWED_REDUCTIONS:
if reduction_name == "variance":
reduction_name = "var"
assert hasattr(torch.Tensor, reduction_name)
f = getattr(torch.Tensor, reduction_name)
op = f(tensor_data)
return op
elif reduction_name in ALLOWED_NORMS:
if reduction_name in ["l1", "l2"]:
ord = int(reduction_name[1])
else:
raise RuntimeError(
"Invalid normalization operation {0} for torch.Tensor".format(
reduction_name))
op = torch.norm(tensor_data, p=ord)
return op
elif hasattr(torch, reduction_name):
f = getattr(torch, reduction_name)
op = f(tensor_data)
return op
raise RuntimeError("Invalid reduction_name {0}".format(reduction_name))
def _get_reduction_of_data(reduction_name, tensor_value, tensor_name, abs):
return get_numpy_reduction(reduction_name, tensor_value, abs)
def _get_reduction_of_data(reduction_name, tensor_value, tensor_name, abs):
if hasattr(tensor_value, "numpy"):
tensor_value = tensor_value.numpy()
return get_numpy_reduction(reduction_name, tensor_value, abs)
