def _apply_loss_reduction(loss: TensorLikeType, reduction: str) -> TensorLikeType:
if reduction == "sum":
return refs.sum(loss)
elif reduction == "mean":
return refs.mean(loss)
else: # reduction == "none"
return loss
def vector_norm(
x: TensorLikeType,
ord: float = 2.0,
dim: Optional[DimsType] = None,
keepdim: bool = False,
*,
dtype: Optional[torch.dtype] = None,
) -> Tensor:
# Checks
check_fp_or_complex(x.dtype, "linalg.vector_norm")
if isinstance(dim, int):
dim = [dim] # type: ignore[assignment]
elif not isinstance(dim, List) and dim is not None:
# refs.amin just accepts List rather than DimType (Tuple)
dim = list(dim) # type: ignore[assignment]
if x.numel() == 0 and (ord < 0.0 or ord == float("inf")):
check(
dim is not None and len(dim) != 0,
lambda: f"linalg.vector_norm cannot compute the {ord} norm on an empty tensor "
"because the operation does not have an identity",
)
shape = x.shape
assert dim is not None # mypy does not seem to be able to see through check?
for d in dim:
check(
shape[d] != 0,
lambda: f"linalg.vector_norm cannot compute the {ord} norm on the "
f"dimension {d} because this dimension is empty and the "
"operation does not have an identity",
)
check_norm_dtype(dtype, x.dtype, "linalg.vector_norm")
computation_dtype, result_dtype = reduction_dtypes(
x, REDUCTION_OUTPUT_TYPE_KIND.COMPLEX_TO_FLOAT, dtype
)
to_result_dtype = partial(prims.convert_element_type, dtype=result_dtype)
# Implementation
if ord == 0.0:
return refs.sum(refs.ne(x, 0.0), dim=dim, keepdim=keepdim, dtype=result_dtype)
elif ord == float("inf"):
return to_result_dtype(refs.amax(torch.abs(x), dim=dim, keepdim=keepdim))
elif ord == float("-inf"):
return to_result_dtype(refs.amin(torch.abs(x), dim=dim, keepdim=keepdim))
else:
# From here on the computation dtype is important as the reduction is non-trivial
x = prims.convert_element_type(x, computation_dtype)
reduce_sum = partial(refs.sum, dim=dim, keepdim=keepdim)
# Avoid computing a sqrt in abs and then squaring (more stable)
# This could potentially be done for complex dtypes as
# x = torch.real(torch.conj(x) * x))
# and it should be more stable, but it's not clear whether it'll be faster on, say
# CPU (abs is 1 vectorised operation), so leaving it just for real dtypes for now
if not (ord % 2.0 == 0.0 and is_float_dtype(x.dtype)):
x = torch.abs(x)
return to_result_dtype(torch.pow(reduce_sum(torch.pow(x, ord)), 1.0 / ord))
