tmp,
left_side=True,
transpose_a=False,
lower=True)),
precision=lax.Precision.HIGHEST)
return L, L_dot
def cholesky_batching_rule(batched_args, batch_dims):
x, = batched_args
bd, = batch_dims
x = batching.moveaxis(x, bd, 0)
return cholesky(x), 0
cholesky_p = standard_unop(_float | _complex, 'cholesky')
ad.primitive_jvps[cholesky_p] = cholesky_jvp_rule
batching.primitive_batchers[cholesky_p] = cholesky_batching_rule
def _nan_like(c, operand):
shape = c.GetShape(operand)
dtype = shape.element_type()
if np.issubdtype(dtype, onp.complexfloating):
nan = xb.constant(c, onp.array(onp.nan * (1. + 1j), dtype=dtype))
else:
nan = xb.constant(c, onp.array(onp.nan, dtype=dtype))
return xops.Broadcast(nan, shape.dimensions())
def _cholesky_cpu_gpu_translation_rule(potrf_impl, c, operand):
sigma_dot,
left_side=False,
transpose_a=True,
lower=True)
L_dot = lax.dot(
L,
phi(
triangular_solve(L,
tmp,
left_side=True,
transpose_a=False,
lower=True)))
return L, L_dot
cholesky_p = standard_unop(_float, 'cholesky')
ad.primitive_jvps[cholesky_p] = cholesky_jvp_rule
triangular_solve_dtype_rule = partial(binop_dtype_rule, _input_dtype,
(_float | _complex, _float | _complex),
'triangular_solve')
def triangular_solve_shape_rule(a, b, left_side=False, **unused_kwargs):
if a.ndim < 2:
msg = "triangular_solve requires a.ndim to be at least 2, got {}."
raise TypeError(msg.format(a.ndim))
if a.shape[-1] != a.shape[-2]:
msg = (
"triangular_solve requires the last two dimensions of a to be equal "
"in size, got a.shape of {}.")
