Add SGLRW

docs/api/index.md

@@ -36,6 +36,8 @@ thermostat (SGNHT) algorithm from [Ding et al, 2014](https://proceedings.neurips
 (SGHMC with adaptive friction coefficient).
 - [`sgmcmc.baoa`](sgmcmc/baoa.md) implements the BAOA integrator for SGHMC
 from [Leimkuhler and Matthews, 2015 - p271](https://link.springer.com/book/10.1007/978-3-319-16375-8).
+- [`sgmcmc.sglrw`](sgmcmc/sglrw.md) implements the stochastic gradient lattice random
+walk (SGLRW) algorithm from [Mensch et al, 2026](https://arxiv.org/abs/2602.15925).
 
 For an overview and unifying framework for SGMCMC methods, see [Ma et al, 2015](https://arxiv.org/abs/1506.04696).
 

docs/api/sgmcmc/sglrw.md

@@ -0,0 +1,3 @@
+# SGLRW
+
+::: posteriors.sgmcmc.sglrw

mkdocs.yml

@@ -82,6 +82,7 @@ nav:
       - api/sgmcmc/sghmc.md
       - api/sgmcmc/sgnht.md
       - api/sgmcmc/baoa.md
+      - api/sgmcmc/sglrw.md
     - VI:
       - Dense: api/vi/dense.md
       - Diag: api/vi/diag.md

posteriors/sgmcmc/__init__.py

@@ -2,3 +2,4 @@
 from posteriors.sgmcmc import sghmc
 from posteriors.sgmcmc import sgnht
 from posteriors.sgmcmc import baoa
+from posteriors.sgmcmc import sglrw

posteriors/sgmcmc/sglrw.py

@@ -0,0 +1,158 @@
+# type: ignore          posteriors is not typed
+from typing import Any
+from functools import partial
+import torch
+from torch import Tensor
+from torch.func import grad_and_value
+from tensordict import TensorClass
+
+from posteriors.types import TensorTree, Transform, LogProbFn, Schedule
+from posteriors.tree_utils import flexi_tree_map, tree_insert_
+from posteriors.utils import CatchAuxError
+
+
+def build(
+    log_posterior: LogProbFn,
+    lr: float | Schedule,
+    temperature: float | Schedule = 1.0,
+) -> Transform:
+    """Builds SGLRW transform - Stochastic Gradient Lattice Random Walk.
+
+    Algorithm from [Mensch et al, 2026](https://arxiv.org/abs/2602.15925)
+    adapted from [Duffield et al, 2025](https://arxiv.org/abs/2508.20883):
+    $$
+    θ_{t+1} = θ_t + δx Δ(θₜ, t)
+    $$
+    where $δx = √(lr * 2 * T)$ is a spatial stepsize and $Δ(θₜ, t)$ is a random
+    binary valued vector defined in the paper.
+
+    Targets $p_T(θ) \\propto \\exp( \\log p(θ) / T)$ with temperature $T$,
+    as it discretizes the overdamped Langevin SDE:
+    $$
+    dθ = ∇ log p_T(θ) dt + √(2 T) dW
+    $$
+
+    The log posterior and temperature are recommended to be [constructed in tandem](../../log_posteriors.md)
+    to ensure robust scaling for a large amount of data and variable batch size.
+
+    Args:
+        log_posterior: Function that takes parameters and input batch and
+            returns the log posterior value (which can be unnormalised)
+            as well as auxiliary information, e.g. from the model call.
+        lr: Learning rate,
+            scalar or schedule (callable taking step index, returning scalar).
+        temperature: Temperature of the sampling distribution.
+            Scalar or schedule (callable taking step index, returning scalar).
+
+    Returns:
+        SGLRW transform (posteriors.types.Transform instance).
+    """
+    update_fn = partial(
+        update,
+        log_posterior=log_posterior,
+        lr=lr,
+        temperature=temperature,
+    )
+    return Transform(init, update_fn)
+
+
+class SGLRWState(TensorClass["frozen"]):
+    """State encoding params for SG-LRW (binary).
+
+    Attributes:
+        params: Parameters.
+        log_posterior: Last log posterior evaluation.
+        step: Current step count.
+    """
+
+    params: TensorTree
+    log_posterior: Tensor = torch.tensor(torch.nan)
+    step: Tensor = torch.tensor(0)
+
+
+def init(params: TensorTree) -> SGLRWState:
+    """Initialise SG-LRW."""
+    return SGLRWState(params)
+
+
+def update(
+    state: SGLRWState,
+    batch: Any,
+    log_posterior: LogProbFn,
+    lr: float | Schedule,
+    temperature: float | Schedule = 1.0,
+    inplace: bool = False,
+) -> tuple[SGLRWState, TensorTree]:
+    with torch.no_grad(), CatchAuxError():
+        grads, (log_post, aux) = grad_and_value(log_posterior, has_aux=True)(
+            state.params, batch
+        )
+
+    # Resolve schedules
+    lr_val = lr(state.step) if callable(lr) else lr
+    T_val = temperature(state.step) if callable(temperature) else temperature
+    lr_val = torch.as_tensor(
+        lr_val, dtype=state.params.dtype, device=state.params.device
+    )
+    T_val = torch.as_tensor(T_val, dtype=state.params.dtype, device=state.params.device)
+
+    # Spatial stepsize to make update binary
+    diffusion_val = torch.sqrt(2.0 * T_val)
+    delta_x = torch.sqrt(lr_val) * diffusion_val
+
+    # Per-parameter binary LRW transform
+    def transform_params(p, g):
+        p_plus = ternary_probs(g, diffusion_val, lr_val, delta_x)[:, 2]
+
+        u = torch.rand_like(p_plus)
+        step_sign = torch.where(
+            u < p_plus, torch.ones_like(p_plus), -torch.ones_like(p_plus)
+        )
+        step = delta_x * step_sign
+        return p + step
+
+    params = flexi_tree_map(transform_params, state.params, grads, inplace=inplace)
+
+    if inplace:
+        tree_insert_(state.log_posterior, log_post.detach())
+        tree_insert_(state.step, state.step + 1)
+        return state, aux
+    return SGLRWState(params, log_post.detach(), state.step + 1), aux
+
+
+def ternary_probs(
+    drift_val: Tensor,
+    diffusion_val: Tensor,
+    stepsize: Tensor,
+    delta_x: Tensor,
+) -> Tensor:
+    """
+    Generate the probabilities for the ternary update
+    from the discretization parameters.
+
+    Args:
+        drift_val: Evaluation of the Drift function.
+        diffusion_val: Evaluation of the Diffusion function.
+        stepsize: Temporal stepsize value.
+        delta_x: Spatial stepsize value.
+
+    Returns:
+        Update probabilities as a tensor, with last axis being [p_minus, p_zero, p_plus].
+    """
+    desired_mean = stepsize * drift_val
+    desired_var = stepsize * diffusion_val**2
+    scaled_mean = desired_mean / delta_x
+    scaled_var = desired_var / delta_x**2
+
+    # Ensure p_minus + p_plus <= 1
+    scaled_var = torch.clamp(scaled_var, 0.0, 1.0)
+
+    # Ensure positive probs
+    scaled_mean = torch.clamp(scaled_mean, -scaled_var, scaled_var)
+
+    # Clip probs for numerical stability
+    p_plus = torch.clamp(0.5 * (scaled_var + scaled_mean), 0.0, 1.0)
+    p_minus = torch.clamp(0.5 * (scaled_var - scaled_mean), 0.0, 1.0)
+    p_zero = torch.clamp(1 - p_plus - p_minus, 0.0, 1.0)
+
+    return torch.stack([p_minus, p_zero, p_plus], dim=-1)

tests/sgmcmc/test_sglrw.py

@@ -0,0 +1,39 @@
+from functools import partial
+import torch
+from posteriors.sgmcmc import sglrw
+from tests.scenarios import get_multivariate_normal_log_prob
+from tests.utils import verify_inplace_update
+from tests.sgmcmc.utils import run_test_sgmcmc_gaussian
+
+
+def test_sglrw():
+    torch.manual_seed(42)
+
+    # Set inference parameters
+    lr = 1e-2
+
+    # Run MCMC test on Gaussian
+    run_test_sgmcmc_gaussian(
+        partial(sglrw.build, lr=lr),
+    )
+
+
+def test_sglrw_inplace_step():
+    torch.manual_seed(42)
+
+    # Load log posterior
+    dim = 5
+    log_prob, _ = get_multivariate_normal_log_prob(dim)
+
+    # Set inference parameters
+    def lr(step):
+        return 1e-2 * (step + 1) ** -0.33
+
+    # Build transform
+    transform = sglrw.build(log_prob, lr)
+
+    # Initialise
+    params = torch.randn(dim)
+
+    # Verify inplace update
+    verify_inplace_update(transform, params, None)