def svd_jvp_rule(primals, tangents, full_matrices, compute_uv):
A, = primals
dA, = tangents
s, U, Vt = svd_p.bind(A, full_matrices=False, compute_uv=True)
if compute_uv and full_matrices:
# TODO: implement full matrices case, documented here: https://people.maths.ox.ac.uk/gilesm/files/NA-08-01.pdf
raise NotImplementedError(
"Singular value decomposition JVP not implemented for full matrices")
k = s.shape[-1]
Ut, V = _H(U), _H(Vt)
s_dim = s[..., None, :]
dS = jnp.matmul(jnp.matmul(Ut, dA), V)
ds = jnp.real(jnp.diagonal(dS, 0, -2, -1))
if not compute_uv:
return (s,), (ds,)
F = 1 / (jnp.square(s_dim) - jnp.square(_T(s_dim)) + jnp.eye(k, dtype=A.dtype))
F = F - jnp.eye(k, dtype=A.dtype)
dSS = s_dim * dS
SdS = _T(s_dim) * dS
dU = jnp.matmul(U, F * (dSS + _T(dSS)))
dV = jnp.matmul(V, F * (SdS + _T(SdS)))
m, n = A.shape[-2:]
if m > n:
dU = dU + jnp.matmul(jnp.eye(m, dtype=A.dtype) - jnp.matmul(U, Ut), jnp.matmul(dA, V)) / s_dim
if n > m:
dV = dV + jnp.matmul(jnp.eye(n, dtype=A.dtype) - jnp.matmul(V, Vt), jnp.matmul(_H(dA), U)) / s_dim
return (s, U, Vt), (ds, dU, _T(dV))
def _lu_jvp_rule(primals, tangents):
a, = primals
a_dot, = tangents
lu, pivots, permutation = lu_p.bind(a)
a_shape = jnp.shape(a)
m, n = a_shape[-2:]
dtype = lax.dtype(a)
k = min(m, n)
batch_dims = a_shape[:-2]
iotas = jnp.ix_(*(lax.iota(jnp.int32, b) for b in batch_dims + (1, )))
x = a_dot[iotas[:-1] + (permutation, slice(None))]
# Differentiation of Matrix Functionals Using Triangular Factorization
# F. R. De Hoog, R. S. Anderssen, and M. A. Lukas
#
# LU = A
# ==> L'U + LU' = A'
# ==> inv(L) . L' + U' . inv(U) = inv(L) A' inv(U)
# ==> L' = L . tril(inv(L) . A' . inv(U), -1)
# U' = triu(inv(L) . A' . inv(U)) . U
ndims = len(a_shape)
l_padding = [(0, 0, 0)] * ndims
l_padding[-1] = (0, m - k, 0)
zero = jnp._constant_like(lu, 0)
l = lax.pad(jnp.tril(lu[..., :, :k], -1), zero, l_padding)
l = l + jnp.eye(m, m, dtype=dtype)
u_eye = lax.pad(jnp.eye(n - k, n - k, dtype=dtype), zero,
((k, 0, 0), (k, 0, 0)))
u_padding = [(0, 0, 0)] * ndims
u_padding[-2] = (0, n - k, 0)
u = lax.pad(jnp.triu(lu[..., :k, :]), zero, u_padding) + u_eye
la = triangular_solve(l,
x,
left_side=True,
transpose_a=False,
lower=True,
unit_diagonal=True)
lau = triangular_solve(u,
la,
left_side=False,
transpose_a=False,
lower=False)
l_dot = jnp.matmul(l, jnp.tril(lau, -1))
u_dot = jnp.matmul(jnp.triu(lau), u)
lu_dot = l_dot + u_dot
return (lu, pivots, permutation), (lu_dot, ad_util.Zero.from_value(pivots),
ad_util.Zero.from_value(permutation))
def detrend(data, axis=-1, type='linear', bp=0, overwrite_data=None):
if overwrite_data is not None:
raise NotImplementedError("overwrite_data argument not implemented.")
if type not in ['constant', 'linear']:
raise ValueError("Trend type must be 'linear' or 'constant'.")
data, = _promote_dtypes_inexact(jnp.asarray(data))
if type == 'constant':
return data - data.mean(axis, keepdims=True)
else:
N = data.shape[axis]
# bp is static, so we use np operations to avoid pushing to device.
bp = np.sort(np.unique(np.r_[0, bp, N]))
if bp[0] < 0 or bp[-1] > N:
raise ValueError(
"Breakpoints must be non-negative and less than length of data along given axis."
)
data = jnp.moveaxis(data, axis, 0)
shape = data.shape
data = data.reshape(N, -1)
for m in range(len(bp) - 1):
Npts = bp[m + 1] - bp[m]
A = jnp.vstack([
jnp.ones(Npts, dtype=data.dtype),
jnp.arange(1, Npts + 1, dtype=data.dtype) / Npts
]).T
sl = slice(bp[m], bp[m + 1])
coef, *_ = linalg.lstsq(A, data[sl])
data = data.at[sl].add(
-jnp.matmul(A, coef, precision=lax.Precision.HIGHEST))
return jnp.moveaxis(data.reshape(shape), 0, axis)
def qr_jvp_rule(primals, tangents, full_matrices):
# See j-towns.github.io/papers/qr-derivative.pdf for a terse derivation.
x, = primals
dx, = tangents
q, r = qr_p.bind(x, full_matrices=False)
*_, m, n = x.shape
if full_matrices or m < n:
raise NotImplementedError(
"Unimplemented case of QR decomposition derivative")
dx_rinv = triangular_solve(r, dx) # Right side solve by default
qt_dx_rinv = jnp.matmul(_H(q), dx_rinv)
qt_dx_rinv_lower = jnp.tril(qt_dx_rinv, -1)
do = qt_dx_rinv_lower - _H(qt_dx_rinv_lower) # This is skew-symmetric
# The following correction is necessary for complex inputs
do = do + jnp.eye(n, dtype=do.dtype) * (qt_dx_rinv - jnp.real(qt_dx_rinv))
dq = jnp.matmul(q, do - qt_dx_rinv) + dx_rinv
dr = jnp.matmul(qt_dx_rinv - do, r)
return (q, r), (dq, dr)
def _lstsq(a, b, rcond, *, numpy_resid=False):
# TODO: add lstsq to lax_linalg and implement this function via those wrappers.
# TODO: add custom jvp rule for more robust lstsq differentiation
a, b = _promote_arg_dtypes(a, b)
if a.shape[0] != b.shape[0]:
raise ValueError("Leading dimensions of input arrays must match")
b_orig_ndim = b.ndim
if b_orig_ndim == 1:
b = b[:, None]
if a.ndim != 2:
raise TypeError(
f"{a.ndim}-dimensional array given. Array must be two-dimensional")
if b.ndim != 2:
raise TypeError(
f"{b.ndim}-dimensional array given. Array must be one or two-dimensional"
)
m, n = a.shape
dtype = a.dtype
if rcond is None:
rcond = jnp.finfo(dtype).eps * max(n, m)
else:
rcond = jnp.where(rcond < 0, jnp.finfo(dtype).eps, rcond)
u, s, vt = svd(a, full_matrices=False)
mask = s >= rcond * s[0]
rank = mask.sum()
safe_s = jnp.where(mask, s, 1)
s_inv = jnp.where(mask, 1 / safe_s, 0)[:, jnp.newaxis]
uTb = jnp.matmul(u.conj().T, b, precision=lax.Precision.HIGHEST)
x = jnp.matmul(vt.conj().T, s_inv * uTb, precision=lax.Precision.HIGHEST)
# Numpy returns empty residuals in some cases. To allow compilation, we
# default to returning full residuals in all cases.
if numpy_resid and (rank < n or m <= n):
resid = jnp.asarray([])
else:
b_estimate = jnp.matmul(a, x, precision=lax.Precision.HIGHEST)
resid = norm(b - b_estimate, axis=0)**2
if b_orig_ndim == 1:
x = x.ravel()
return x, resid, rank, s
def _lu(a, permute_l):
a = np_linalg._promote_arg_dtypes(jnp.asarray(a))
lu, pivots, permutation = lax_linalg.lu(a)
dtype = lax.dtype(a)
m, n = jnp.shape(a)
p = jnp.real(jnp.array(permutation == jnp.arange(m)[:, None], dtype=dtype))
k = min(m, n)
l = jnp.tril(lu, -1)[:, :k] + jnp.eye(m, k, dtype=dtype)
u = jnp.triu(lu)[:k, :]
if permute_l:
return jnp.matmul(p, l), u
else:
return p, l, u
def svd_jvp_rule(primals, tangents, full_matrices, compute_uv):
A, = primals
dA, = tangents
s, U, Vt = svd_p.bind(A, full_matrices=False, compute_uv=True)
if compute_uv and full_matrices:
# TODO: implement full matrices case, documented here: https://people.maths.ox.ac.uk/gilesm/files/NA-08-01.pdf
raise NotImplementedError(
"Singular value decomposition JVP not implemented for full matrices")
Ut, V = _H(U), _H(Vt)
s_dim = s[..., None, :]
dS = jnp.matmul(jnp.matmul(Ut, dA), V)
ds = jnp.real(jnp.diagonal(dS, 0, -2, -1))
if not compute_uv:
return (s,), (ds,)
s_diffs = jnp.square(s_dim) - jnp.square(_T(s_dim))
s_diffs_zeros = jnp.eye(s.shape[-1], dtype=A.dtype) # jnp.ones((), dtype=A.dtype) * (s_diffs == 0.) # is 1. where s_diffs is 0. and is 0. everywhere else
F = 1 / (s_diffs + s_diffs_zeros) - s_diffs_zeros
dSS = s_dim * dS # dS.dot(jnp.diag(s))
SdS = _T(s_dim) * dS # jnp.diag(s).dot(dS)
s_zeros = jnp.ones((), dtype=A.dtype) * (s == 0.)
s_inv = 1 / (s + s_zeros) - s_zeros
s_inv_mat = jnp.vectorize(jnp.diag, signature='(k)->(k,k)')(s_inv)
dUdV_diag = .5 * (dS - _H(dS)) * s_inv_mat
dU = jnp.matmul(U, F * (dSS + _H(dSS)) + dUdV_diag)
dV = jnp.matmul(V, F * (SdS + _H(SdS)))
m, n = A.shape[-2:]
if m > n:
dU = dU + jnp.matmul(jnp.eye(m, dtype=A.dtype) - jnp.matmul(U, Ut), jnp.matmul(dA, V)) / s_dim
if n > m:
dV = dV + jnp.matmul(jnp.eye(n, dtype=A.dtype) - jnp.matmul(V, Vt), jnp.matmul(_H(dA), U)) / s_dim
return (s, U, Vt), (ds, dU, _H(dV))
def pinv(a, rcond=None):
# Uses same algorithm as
# https://github.com/numpy/numpy/blob/v1.17.0/numpy/linalg/linalg.py#L1890-L1979
a = jnp.conj(a)
if rcond is None:
max_rows_cols = max(a.shape[-2:])
rcond = 10. * max_rows_cols * jnp.finfo(a.dtype).eps
rcond = jnp.asarray(rcond)
u, s, vh = svd(a, full_matrices=False)
# Singular values less than or equal to ``rcond * largest_singular_value``
# are set to zero.
cutoff = rcond[..., jnp.newaxis] * jnp.amax(
s, axis=-1, keepdims=True, initial=-jnp.inf)
s = jnp.where(s > cutoff, s, jnp.inf)
res = jnp.matmul(_T(vh), jnp.divide(_T(u), s[..., jnp.newaxis]))
return lax.convert_element_type(res, a.dtype)
def _lu(a, permute_l):
a, = _promote_dtypes_inexact(jnp.asarray(a))
lu, _, permutation = lax_linalg.lu(a)
dtype = lax.dtype(a)
m, n = jnp.shape(a)
p = jnp.real(
jnp.array(permutation[None, :] == jnp.arange(
m, dtype=permutation.dtype)[:, None],
dtype=dtype))
k = min(m, n)
l = jnp.tril(lu, -1)[:, :k] + jnp.eye(m, k, dtype=dtype)
u = jnp.triu(lu)[:k, :]
if permute_l:
return jnp.matmul(p, l), u
else:
return p, l, u
def _sqrtm(A):
T, Z = schur(A, output='complex')
sqrt_T = _sqrtm_triu(T)
return jnp.matmul(jnp.matmul(Z, sqrt_T, precision=lax.Precision.HIGHEST),
jnp.conj(Z.T),
precision=lax.Precision.HIGHEST)
