Added a function to utilise optax for lbfgs optimisation.

Updated lbfgs to truncate history to reduce inverse hessian computation.

blackjax/optimizers/lbfgs.py

@@ -17,6 +17,8 @@
 import jax.numpy as jnp
 import jax.random
 import jaxopt
+import optax
+import optax.tree_utils as otu
 from jax import lax
 from jax.flatten_util import ravel_pytree
 from jaxopt._src.lbfgs import LbfgsState
@@ -37,6 +39,22 @@
 MIN_STEP_SIZE = 1e-3
 
 
+class _OptaxLBFGSHistory(NamedTuple):
+    x: Array
+    f: Array
+    g: Array
+    alpha: Array
+    update_mask: Array
+    # store intermediate values to perform checks
+    not_converged: Array
+    s: Array
+    z: Array
+    s_l: Array
+    z_l: Array
+    last: Array
+    iter: Array
+
+
 class LBFGSHistory(NamedTuple):
     """Container for the optimization path of a L-BFGS run
 
@@ -60,6 +78,7 @@ class LBFGSHistory(NamedTuple):
     g: Array
     alpha: Array
     update_mask: Array
+    not_converged: Array  # for clipping history for shorter inverse hessian calcs and bfgs sampling
 
 
 def minimize_lbfgs(
@@ -148,6 +167,143 @@ def minimize_lbfgs(
     return last_step, history
 
 
+def optax_lbfgs(
+    fun: Callable,
+    x0: Array,
+    maxiter: int,
+    maxcor: float,
+    gtol: float,
+    ftol: float,
+    maxls: int,
+    # **lbfgs_kwargs, # TODO: insert kwargs to optax.scale_by_zoom_linesearch and optax.value_and_grad_from_state
+):
+    linesearch = optax.scale_by_zoom_linesearch(
+        max_linesearch_steps=maxls,
+        verbose=True,
+    )
+    solver = optax.lbfgs(
+        memory_size=maxcor,
+        linesearch=linesearch,
+    )
+    value_and_grad_fun = optax.value_and_grad_from_state(fun)
+
+    def lbfgs_one_step(carry, i):
+        # state is a 3-dim tuple
+        (params, state), previous_history = carry
+        value, grad = value_and_grad_fun(params, state=state)
+        updates, next_state = solver.update(
+            grad, state, params, value=value, grad=grad, value_fn=fun
+        )
+
+        # ensure num_linesearch_steps is of the same type
+        info = next_state[2].info._replace(
+            num_linesearch_steps=jnp.asarray(
+                next_state[2].info.num_linesearch_steps, dtype=jnp.int32
+            )
+        )
+
+        next_state = (next_state[0], next_state[1], next_state[2]._replace(info=info))
+
+        # LBFGS use a rolling history, getting the correct index here.
+        iter = state[0].count
+        # last variable for getting the correct index where updates occur
+        last = jnp.max(jnp.array([iter - 1, 0], dtype=jnp.int32)) % maxcor
+        next_params = optax.apply_updates(params, updates)
+
+        # Recover alpha and update mask
+        s_l = next_state[0].diff_params_memory[last]
+        z_l = next_state[0].diff_updates_memory[last]
+        alpha_lm1 = previous_history.alpha
+        alpha_l, mask_l = lbfgs_recover_alpha(alpha_lm1, s_l, z_l)
+
+        # TODO: check correct calc for g
+        # g = next_state[2].grad
+        # g = state[2].grad
+        # g = grad
+        # g = previous_history.g
+        # g = previous_history.g + z_l
+        # g = state[2].grad + z_l
+
+        not_converged = check_convergence(state, next_state, iter)
+        history = _OptaxLBFGSHistory(
+            x=next_params,
+            f=next_state[2].value,
+            g=next_state[2].grad,
+            alpha=alpha_l,
+            update_mask=mask_l,
+            not_converged=not_converged,
+            s=next_state[0].diff_params_memory,
+            z=next_state[0].diff_updates_memory,
+            s_l=s_l,
+            z_l=z_l,
+            last=jnp.asarray(last, dtype=jnp.int32),
+            iter=jnp.asarray(iter, dtype=jnp.int32),
+        )
+        return ((next_params, next_state), history), not_converged
+
+    def check_convergence(state, next_state, iter):
+        f_delta = (
+            jnp.abs(state[2].value - next_state[2].value)
+            / jnp.asarray(
+                [jnp.abs(state[2].value), jnp.abs(next_state[2].value), 1.0]
+            ).max()
+        )
+        next_state_grad = otu.tree_get(next_state[2], "grad")
+        error = otu.tree_l2_norm(next_state_grad)
+        return jnp.array(
+            (iter == 0) | (error > gtol) & (f_delta > ftol) & (iter < maxiter),
+            dtype=bool,
+        )
+
+    def non_op(carry, i):
+        (params, state), previous_history = carry
+
+        info = state[2].info._replace(
+            num_linesearch_steps=jnp.asarray(
+                state[2].info.num_linesearch_steps, dtype=jnp.int32
+            )
+        )
+        state = (state[0], state[1], state[2]._replace(info=info))
+
+        return ((params, state), previous_history), jnp.array(False, dtype=bool)
+
+    def scan_body(tup, i):
+        carry, not_converged = tup
+        next_tup = jax.lax.cond(not_converged, lbfgs_one_step, non_op, carry, i)
+        return next_tup, next_tup[0][-1]
+
+    x0, init_state = (x0, solver.init(x0))
+    init_history = _OptaxLBFGSHistory(
+        x=init_state[0].params,
+        f=init_state[2].value,
+        g=init_state[2].grad,
+        alpha=jnp.ones_like(x0),
+        update_mask=jnp.zeros_like(x0, dtype=bool),
+        not_converged=jnp.array(True, dtype=bool),
+        s=init_state[0].diff_params_memory,
+        z=init_state[0].diff_updates_memory,
+        s_l=jnp.zeros_like(x0),
+        z_l=jnp.zeros_like(x0),
+        last=jnp.asarray(-1, dtype=jnp.int32),
+        iter=jnp.asarray(-1, dtype=jnp.int32),
+    )
+
+    # Use lax.scan to accumulate history
+    (((final_params, final_state), _), _), history = jax.lax.scan(
+        scan_body,
+        (((x0, init_state), init_history), True),
+        jnp.arange(maxiter),
+        length=maxiter,
+    )
+
+    history = jax.tree.map(
+        lambda x, y: jnp.concatenate([x[None, ...], y], axis=0),
+        init_history,
+        history,
+    )
+    return (final_params, final_state), history
+
+
 def _minimize_lbfgs(
     fun: Callable,
     x0: Array,
@@ -181,19 +337,21 @@ def lbfgs_one_step(carry, i):
         alpha_l, mask_l = lbfgs_recover_alpha(alpha_lm1, s_l, z_l)
 
         current_grad = previous_history.g + z_l
+
+        # check convergence
+        f_delta = (
+            jnp.abs(state.value - next_state.value)
+            / jnp.asarray([jnp.abs(state.value), jnp.abs(next_state.value), 1.0]).max()
+        )
+        not_converged = (next_state.error > gtol) & (f_delta > ftol) & (i < maxiter)
         history = LBFGSHistory(
             x=next_params,
             f=next_state.value,
             g=current_grad,
             alpha=alpha_l,
             update_mask=mask_l,
+            not_converged=jnp.array(not_converged, dtype=bool),
         )
-        # check convergence
-        f_delta = (
-            jnp.abs(state.value - next_state.value)
-            / jnp.asarray([jnp.abs(state.value), jnp.abs(next_state.value), 1.0]).max()
-        )
-        not_converged = (next_state.error > gtol) & (f_delta > ftol) & (i < maxiter)
         return (OptStep(params=next_params, state=next_state), history), not_converged
 
     def non_op(carry, it):
@@ -224,6 +382,7 @@ def scan_body(tup, it):
         g=grad0,
         alpha=jnp.ones_like(x0),
         update_mask=jnp.zeros_like(x0, dtype=bool),
+        not_converged=jnp.array(True, dtype=bool),
     )
 
     ((last_step, _), _), history = lax.scan(

blackjax/vi/pathfinder.py

@@ -138,7 +138,11 @@ def approximate(
         **lbfgs_kwargs,
     )
 
-    # Get postions and gradients of the optimization path (including the starting point).
+    # get the index where lbfgs converged
+    lbfgs_converged_idx = history.not_converged.sum()
+    # truncate history to the point of convergence
+    history = jax.tree.map(lambda x: x[:lbfgs_converged_idx], history)
+
     position = history.x
     grad_position = history.g
     alpha = history.alpha
@@ -172,19 +176,14 @@ def path_finder_body_fn(rng_key, S, Z, alpha_l, theta, theta_grad):
     # Index and reshape S and Z to be sliding window view shape=(maxiter,
     # maxcor, param_dim), so we can vmap over all the iterations.
     # This is in effect numpy.lib.stride_tricks.sliding_window_view
-    path_size = maxiter + 1
+    path_size = lbfgs_converged_idx
     index = jnp.arange(path_size)[:, None] + jnp.arange(maxcor)[None, :]
     s_j = s_padded[index.reshape(path_size, maxcor)].reshape(path_size, maxcor, -1)
     z_j = z_padded[index.reshape(path_size, maxcor)].reshape(path_size, maxcor, -1)
     rng_keys = jax.random.split(rng_key, path_size)
     elbo, beta, gamma = jax.vmap(path_finder_body_fn)(
         rng_keys, s_j, z_j, alpha, position, grad_position
     )
-    elbo = jnp.where(
-        (jnp.arange(path_size) < (status.iter_num)) & jnp.isfinite(elbo),
-        elbo,
-        -jnp.inf,
-    )
 
     unravel_fn_mapped = jax.vmap(unravel_fn)
     pathfinder_result = PathfinderState(