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# SPDX-License-Identifier: Apache-2.0
from __future__ import annotations
from dataclasses import fields
from typing import Any, Callable
import equinox as eqx
import jax
import jax.numpy as jnp
from .replica_exchange import (
REState,
adapt_beta_equi_rej,
adapt_m,
adapt_sigma,
init_state,
run_re_langevin_cached,
)
def _state_axes(mask_axis: int | None) -> REState:
return REState(
q=0,
log_p=0,
grad_log_p=0,
beta=None,
sigma=None,
step=None,
key=0,
n_local_accept=0,
n_local=0,
n_swap_accept=0,
n_swap=0,
mask=mask_axis,
m=None,
n_haar_accept=0,
n_haar=0,
)
def init_batched_state(
key: jax.Array,
context: Any,
batch_size: int,
n_replicas: int,
initial_m: int = 1,
initial_sigma: float = 0.3,
) -> REState:
mask = jnp.asarray(context.mask, dtype=jnp.int32)
keys = jax.random.split(key, batch_size)
return jax.vmap(
lambda walker_key: init_state(
walker_key,
n_replicas=n_replicas,
n_spins=int(mask.shape[-1]),
sigma=initial_sigma,
mask=mask,
initial_m=initial_m,
),
out_axes=_state_axes(None),
)(keys)
def _log_probability(
model: Callable,
context: Any,
q: jax.Array,
) -> jax.Array:
real, _phase = model(q, context, 0.0)
return 2.0 * real
def _run_one(
state: REState,
model: Callable,
context: Any,
n_steps: int,
) -> REState:
return run_re_langevin_cached(
state,
lambda q: _log_probability(model, context, q),
n_steps,
)
def run_batched(
model: Callable,
context: Any,
state: REState,
n_steps: int,
walker_chunk_size: int | None = None,
) -> REState:
if walker_chunk_size is None or walker_chunk_size >= state.q.shape[0]:
return jax.vmap(
lambda walker: _run_one(walker, model, context, n_steps),
in_axes=(_state_axes(None),),
out_axes=_state_axes(None),
)(state)
shared_names = {"mask", "beta", "sigma", "step", "m"}
shared = {name: getattr(state, name) for name in shared_names}
walker_fields = {
item.name: getattr(state, item.name)
for item in fields(state)
if item.name not in shared_names
}
def run_walker(walker: dict[str, jax.Array]) -> dict[str, jax.Array]:
new_state = _run_one(
REState(**shared, **walker),
model,
context,
n_steps,
)
return {
item.name: getattr(new_state, item.name)
for item in fields(new_state)
if item.name not in {"mask", "beta", "sigma", "m"}
}
mapped = jax.lax.map(
run_walker,
walker_fields,
batch_size=int(walker_chunk_size),
)
step = mapped.pop("step")[0]
return REState(**{**shared, "step": step}, **mapped)
def adapt_batched(
state: REState,
*,
beta_history_weight: float = 0.9,
sigma_target: float = 0.574,
sigma_scale: float = 1.1,
haar_target: float = 0.234,
) -> REState:
pooled = REState(
q=state.q[0],
log_p=state.log_p[0],
grad_log_p=state.grad_log_p[0],
beta=state.beta,
sigma=state.sigma,
step=state.step,
key=state.key[0],
n_local_accept=state.n_local_accept.sum(axis=0),
n_local=state.n_local.sum(axis=0).astype(state.n_local.dtype),
n_swap_accept=state.n_swap_accept.sum(axis=0),
n_swap=state.n_swap.sum(axis=0).astype(state.n_swap.dtype),
mask=state.mask,
m=state.m,
n_haar_accept=state.n_haar_accept.sum(axis=0),
n_haar=state.n_haar.sum(axis=0).astype(state.n_haar.dtype),
)
adapted = adapt_sigma(
pooled,
target=sigma_target,
factor=sigma_scale,
)
adapted = adapt_m(adapted, target=haar_target)
adapted = adapt_beta_equi_rej(
adapted,
ema=beta_history_weight,
)
def target(value: REState):
return (
value.sigma,
value.beta,
value.m,
value.n_local_accept,
value.n_local,
value.n_swap_accept,
value.n_swap,
value.n_haar_accept,
value.n_haar,
)
return eqx.tree_at(
target,
state,
(
adapted.sigma,
adapted.beta,
adapted.m,
jnp.zeros_like(state.n_local_accept),
jnp.zeros_like(state.n_local),
jnp.zeros_like(state.n_swap_accept),
jnp.zeros_like(state.n_swap),
jnp.zeros_like(state.n_haar_accept),
jnp.zeros_like(state.n_haar),
),
)
def cold_samples(state: REState) -> jax.Array:
return state.q[:, -1]
__all__ = [
"REState",
"adapt_batched",
"cold_samples",
"init_batched_state",
"run_batched",
]
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