def conjugate_gradient(self,
A,
y,
x0,
solve_params=LinearSolve(),
callback=None):
bs_y = self.staticshape(y)[0]
bs_x0 = self.staticshape(x0)[0]
batch_size = combined_dim(bs_y, bs_x0)
if isinstance(A, (tuple, list)) or self.ndims(A) == 3:
batch_size = combined_dim(batch_size, self.staticshape(A)[0])
results = []
for batch in range(batch_size):
y_ = y[min(batch, bs_y - 1)]
x0_ = x0[min(batch, bs_x0 - 1)]
x, ret_val = cg(A,
y_,
x0_,
tol=solve_params.relative_tolerance,
atol=solve_params.absolute_tolerance,
maxiter=solve_params.max_iterations)
results.append(x)
solve_params.result = SolveResult(success=True, iterations=-1)
return self.stack(results)
def cg_fixed_point_solve(linear_op, bvec, init_x, max_iter=1000, tol=1e-10):
sol, _ = linalg.cg(
lambda x: tree_util.tree_multimap(jax.lax.sub, x, linear_op(x)),
bvec,
init_x,
tol=tol,
maxiter=max_iter,
)
return sol
def _jax_cg_solve(x0, mat_vec, oks, grad, diag_shift, sparse_tol, sparse_maxiter):
r"""
Solves the SR flow equation using the conjugate gradient method
"""
_mat_vec = partial(mat_vec, oks=oks, diag_shift=diag_shift)
out, _ = cg(_mat_vec, grad, x0=x0, tol=sparse_tol, maxiter=sparse_maxiter)
return out
def _jax_cg_solve_onthefly(
x0,
forward_fn,
params,
samples,
grad,
diag_shift,
sparse_tol,
sparse_maxiter,
):
_mat_vec = partial(
_mat_vec_onthefly,
forward_fn=forward_fn,
params=params,
samples=samples,
diag_shift=diag_shift,
)
out, _ = cg(_mat_vec, grad, x0=x0, tol=sparse_tol, maxiter=sparse_maxiter)
return out
def test_cg():
# also tests if matvec can be jitted and be differentiated with AD
diag_shift = 0.001
sparse_tol = 1.0e-5
sparse_maxiter = None
actual = _jax_cg_solve_onthefly(v, f, params, samples, grad, diag_shift,
sparse_tol, sparse_maxiter)
def mv_real(v):
return S_real @ v + diag_shift * v
expected = reassemble_complex(
cg(
mv_real,
grad_real_flat,
x0=v_real_flat,
tol=sparse_tol,
maxiter=sparse_maxiter,
)[0])
assert tree_allclose(actual, expected)
def updates(prev_state,
hessian_xy=None,
hessian_yx=None,
grad_min=None,
grad_max=None,
breg_min=default_breg,
breg_max=default_breg,
eta_min=1.,
eta_max=1.,
objective_func=None,
precond_b_min=False,
precond_b_max=False):
"""Equation (4). Given current position (prev_state), compute the updates (del_x,del_y) to the players in cmd algorithm for next position.
Args:
prev_state (Named tuples of vectors): The current position of the players given by tuple
with signature 'CMDState(minPlayer maxPlayer minPlayer_dual maxPlayer_dual)'
breg_min (Named tuples of callable): Tuple of unary callables with signature
'BregmanPotential = collections.namedtuple("BregmanPotential", ["DP", "DP_inv", "D2P","D2P_inv"])'
where DP and DP_inv are unary callables with signatures
`DP(x,*args, **kwargs)`, 'DP_inv(x,*arg,**kwarg)' and
D2P, D2P_inv are function of functions
(Given an x, returning linear transformation function
that can take in another vector to output hessian-vector product).
breg_max (Named tuples of callable): Tuple of unary callables as 'breg_min'.
eta_min (scalar): User specified step size for min player. Default 1e-4.
eta_max (scalar): User specified step size for max player. Default 1e-4.
hessian_xy (callable): The (estimated) mixed hessian of the current positions of the players, represented in a matrix-vector operator from jax.jvp
hessian_xy (callable): The (estimated) mixed hessian of the current positions of the players, represented in a matrix-vector operator from jax.jvp
grad_min (vector): The (estimated) gradient of the cost function w.r.t. the max player parameters at current position.
grad_max(vector): The (estimated) gradient of the cost function w.r.t. the max player parameters at current position.
Returns:
UpdateState(del_min, del_max), a named tuple for the updates
"""
if objective_func is not None:
# grad_min_func = jit(jacfwd(objective_func, 0))
# grad_max_func = jit(jacfwd(objective_func, 1))
# H_xy_func = jit(jacfwd(grad_min, 1))
# H_yx_func =jit(jacfwd(grad_max, 0))
# Compute current gradient for min and max players
grad_min = jacfwd(objective_func, 0)(prev_state.minPlayer,
prev_state.maxPlayer)
grad_max = jacfwd(objective_func, 1)(prev_state.minPlayer,
prev_state.maxPlayer)
# Define the mixed hessian-vector product linear operator at current position
def hessian_xy(tangent):
return make_mixed_jvp(objective_func, prev_state.minPlayer,
prev_state.maxPlayer)(tangent)
def hessian_yx(tangent):
return make_mixed_jvp(objective_func, prev_state.minPlayer,
prev_state.maxPlayer, True)(tangent)
def linear_opt_min(min_tree):
temp = hessian_yx(min_tree) # returns max_tree type
temp1 = _tree_apply(_tree_apply(breg_max.inv_D2P,
prev_state.maxPlayer),
temp) # returns max_tree type
temp2 = hessian_xy(temp1) # returns min_tree type
temp3 = tree_util.tree_map(lambda x: eta_max * x,
temp2) # still min_tree type
temp4 = _tree_apply(_tree_apply(breg_min.D2P, prev_state.minPlayer),
min_tree) # also returns min_tree type
temp5 = tree_util.tree_map(lambda x: 1 / eta_min * x, temp4)
# print("linear operator being called! - min")
out = tree_util.tree_multimap(lambda x, y: x + y, temp3, temp5)
return out # min_tree type
def linear_opt_max(max_tree):
temp = hessian_xy(max_tree)
temp1 = _tree_apply(
_tree_apply(breg_min.inv_D2P, prev_state.minPlayer), temp)
temp2 = hessian_yx(temp1) # returns max_tree type
temp3 = tree_util.tree_map(lambda x: eta_min * x,
temp2) # max_tree type
temp4 = _tree_apply(_tree_apply(breg_max.D2P, prev_state.maxPlayer),
max_tree)
temp5 = tree_util.tree_map(lambda x: 1 / eta_max * x,
temp4) # max_tree type
# print("linear operator being called! - max")
out = tree_util.tree_multimap(lambda x, y: x + y, temp3,
temp5) # max_tree type
return out
# calculate the vectors in equation (4)
temp = hessian_xy(
_tree_apply(_tree_apply(breg_max.inv_D2P, prev_state.maxPlayer),
grad_max))
temp2 = tree_util.tree_map(lambda x: eta_max * x, temp)
vec_min = tree_util.tree_multimap(lambda arr1, arr2: arr1 + arr2, grad_min,
temp2)
if precond_b_min:
# vec_min_tree, min_tree_def = tree_util.tree_flatten(vec_min)
# cond_min = tree_util.tree_unflatten(min_tree_def,
# jax.tree_map(lambda x: jnp.linalg.norm(x, jnp.inf), vec_min_tree))
# vec_min = tree_util.tree_multimap(lambda x, y: x / y, vec_min, cond_min)
cond_min = max(
jax.tree_map(lambda x: jnp.linalg.norm(x, jnp.inf),
tree_util.tree_flatten(vec_min)[0]))
vec_min = tree_util.tree_map(lambda x: x / cond_min, vec_min)
# temp = _tree_apply(hessian_yx, _tree_apply(_tree_apply(breg_min.inv_D2P, prev_state.minPlayer), grad_min))
temp = hessian_yx(
_tree_apply(_tree_apply(breg_min.inv_D2P, prev_state.minPlayer),
grad_min))
temp2 = tree_util.tree_map(lambda x: eta_min * x, temp)
vec_max = tree_util.tree_multimap(lambda x, y: x - y, grad_max, temp2)
if precond_b_max:
# vec_max_tree, max_tree_def = tree_util.tree_flatten(vec_max)
# cond_max = tree_util.tree_unflatten(max_tree_def,
# jax.tree_map(lambda x: jnp.linalg.norm(x), vec_max_tree))
# vec_max = tree_util.tree_multimap(lambda x, y: x / y, vec_max,cond_max)
cond_max = max(
jax.tree_map(lambda x: jnp.linalg.norm(x),
tree_util.tree_flatten(vec_max)[0]))
vec_max = tree_util.tree_map(lambda x: x / cond_max, vec_max)
update_min, status_min = linalg.cg(linear_opt_min, vec_min, maxiter=1000)
if precond_b_min:
update_min = tree_util.tree_map(lambda x: cond_min * x, update_min)
# update_min = tree_util.tree_multimap(lambda x, y: y * x, cond_min, update_min)
update_min = tree_util.tree_map(lambda x: -x, update_min) # negation here!
update_max, status_max = linalg.cg(linear_opt_max, vec_max, maxiter=1000)
if precond_b_max:
update_max = tree_util.tree_map(lambda x: cond_max * x, update_max)
# update_max = tree_util.tree_multimap(lambda x, y: y * x, cond_max, update_max)
return UpdateState(update_min, update_max)
