fix(nss): chunked algo.init follow-up to #1303

[Jammy2211]

Overview

PyAutoFit#1303 (feat(nss): chunk_size kwarg for inversion-heavy A100 likelihoods, merged c161235) chunked the per-iteration MCMC step's jax.vmap(num_delete) but didn't address a separate hardcoded jax.vmap(init_state_fn) inside blackjax.ns.nss.as_top_level_api's init_fn (blackjax/ns/nss.py:223-230). A100 retry on autolens_profiling NSS pixelization + delaunay × HST × fp64 (jobs 322605 + 322606) OOM at the same byte counts as before #1303 landed; the NSS configuration: INFO log line (which runs after algo.init) never appears, proving the crash is in algo.init not in algo.step.

This issue ships the missing init-side chunking. PyAutoFit gains a local build_chunked_nss_algorithm that replicates blackjax.nss.as_top_level_api (~30 lines) so we control both the chunked update_strategy (already done by #1303) and a chunked init_fn that swaps jax.vmap(init_state_fn) for jax.lax.map(init_state_fn, positions, batch_size=chunk_size). af.NSS._fit uses the local builder when chunk_size is set; chunk_size=None keeps using upstream blackjax.nss(...) bit-for-bit.

Together #1303 + this PR cover both NSS vmap paths and unblock NSS profiling on production lensing cells (SLaM source_pix[1/2] Delaunay / pixelization) at A100 80 GB.

Plan

• New module autofit/non_linear/search/nest/nss/_chunked_nss.py exposing build_chunked_nss_algorithm(*, logprior_fn, loglikelihood_fn, num_inner_steps, num_delete, chunk_size). Internally calls make_chunked_update_strategy(chunk_size) from feat(nss): chunk_size kwarg for inversion-heavy A100 likelihoods #1303's _chunked_update.py for the step path, and swaps init_state_fn=jax.vmap(init_state_fn) for init_state_fn=lambda p: jax.lax.map(init_state_fn, p, batch_size=chunk_size) for the init path.
• af.NSS._fit: when chunk_size is not None and chunk_size < max(n_live, num_delete), build algo via the local function. Otherwise continue calling _blackjax.nss(...). Default chunk_size=None preserves bit-identical un-chunked behaviour.
• Unit tests (no JAX): the new factory returns a SamplingAlgorithm with the expected (init, step) callables; af.NSS plumbing tests continue to pass.
• Manual A100 validation: resubmit autolens_profiling NSS pixelization + delaunay × HST × fp64 (chunk_size=16 set automatically by build_nss). Compare against Nautilus baselines: pixelization 46.5 ms/eval / 46 min (322603), delaunay 84.8 ms/eval / 45 min (322601), NSS MGE 1.6 ms/eval / 11 min (322590).

Detailed implementation plan

Affected Repositories

• rhayes777/PyAutoFit (primary — af.NSS._fit switch + new _chunked_nss.py)
• PyAutoLabs/autolens_profiling (consumer — no source change needed; build_nss already sets chunk_size=vmap_batch_for_cell(...) per autolens_profiling#43. After this lands + the HPC HPCPullPyAuto, the A100 resubmits should just work.)
• handley-lab/blackjax (external — deferred. Long-term cleanup is upstreaming an init_batcher kwarg to as_top_level_api so the PyAutoFit-local replica can shrink to a thin forwarder.)

Work Classification

Library (PyAutoFit). No workspace changes; autolens_profiling consumer is already wired correctly.

Branch Survey

Repository	Current Branch	Dirty?
./PyAutoFit	main	clean
./autolens_profiling	main	clean (no edits needed)

Suggested branch: feature/nss-chunked-init
Worktree root: ~/Code/PyAutoLabs-wt/nss-chunked-init/ (created later by /start_library)

Implementation Steps

1. Add autofit/non_linear/search/nest/nss/_chunked_nss.py (new file). Replicate the body of blackjax.ns.nss.as_top_level_api:

   from functools import partial
   import jax
   from blackjax import SamplingAlgorithm
   from blackjax.ns.adaptive import init as ns_init
   from blackjax.ns.base import init_state_strategy
   from blackjax.ns.nss import build_kernel, update_inner_kernel_params
   
   from ._chunked_update import make_chunked_update_strategy
   
   
   def build_chunked_nss_algorithm(
       *,
       logprior_fn,
       loglikelihood_fn,
       num_inner_steps,
       num_delete,
       chunk_size,
   ):
       init_state_fn = partial(
           init_state_strategy,
           logprior_fn=logprior_fn,
           loglikelihood_fn=loglikelihood_fn,
       )
   
       kernel = build_kernel(
           init_state_fn,
           num_inner_steps,
           num_delete,
           update_strategy=make_chunked_update_strategy(chunk_size),
       )
   
       def init_fn(position, rng_key=None):
           if chunk_size is None:
               init_batcher = jax.vmap(init_state_fn)
           else:
               init_batcher = lambda p: jax.lax.map(
                   init_state_fn, p, batch_size=chunk_size
               )
           return ns_init(
               position,
               init_state_fn=init_batcher,
               update_inner_kernel_params_fn=update_inner_kernel_params,
           )
   
       def step_fn(rng_key, state):
           return kernel(rng_key, state)
   
       return SamplingAlgorithm(init_fn, step_fn)

2. Switch af.NSS._fit (PyAutoFit/autofit/non_linear/search/nest/nss/search.py): replace the current branch

   if self.chunk_size is not None and self.chunk_size < self.num_delete:
       nss_kwargs["update_strategy"] = make_chunked_update_strategy(self.chunk_size)
   algo = _blackjax.nss(**nss_kwargs)

   with:

   if self.chunk_size is not None and self.chunk_size < max(self.n_live, self.num_delete):
       from autofit.non_linear.search.nest.nss._chunked_nss import (
           build_chunked_nss_algorithm,
       )
       algo = build_chunked_nss_algorithm(
           logprior_fn=prior_logprob,
           loglikelihood_fn=log_likelihood,
           num_inner_steps=self.num_mcmc_steps,
           num_delete=self.num_delete,
           chunk_size=self.chunk_size,
       )
   else:
       algo = _blackjax.nss(**nss_kwargs)

   The max(n_live, num_delete) guard means we use the local replica whenever chunking would actually reduce memory in either of the two vmap sites. If chunk_size is None or already wider than both, fall through to upstream blackjax bit-for-bit.

3. Unit tests (test_autofit/non_linear/search/nest/nss/test_search.py):

   • Extend test__chunked_update_strategy_factory style: import build_chunked_nss_algorithm and assert it returns a callable shape compatible with SamplingAlgorithm (has .init and .step attributes). No JAX execution — library policy keeps JAX-traced tests in workspace_test.
   • The existing kwarg-plumbing tests already cover af.NSS(chunk_size=...).

4. Workspace-test follow-up (out of scope here, file as a separate prompt): bit-identical log_Z on a 5D Gaussian between _blackjax.nss(...).init(positions) and build_chunked_nss_algorithm(chunk_size=2).init(positions) on the same seed, with n_live=20.

5. A100 validation (after merge + HPCPullPyAuto):

   • searches/nss/imaging/pixelization × hst × fp64 — was OOMing as 322605, expect ~46 min (≤ Nautilus's 322603 at 46.5 ms/eval)
   • searches/nss/imaging/delaunay × hst × fp64 — was OOMing as 322606, expect ~45 min (≤ Nautilus's 322601 at 84.8 ms/eval)

Key Files

• PyAutoFit:autofit/non_linear/search/nest/nss/_chunked_nss.py (new)
• PyAutoFit:autofit/non_linear/search/nest/nss/search.py (af.NSS._fit switch — 5 line diff)
• PyAutoFit:test_autofit/non_linear/search/nest/nss/test_search.py (small additive test)

A100 evidence

• Working post-feat(nss): chunk_size kwarg for inversion-heavy A100 likelihoods #1303: PyAutoFit unit tests (1414/0/1), 5D Gaussian smoke (bit-identical log_Z between chunk_size=None and chunk_size=2)
• OOM post-feat(nss): chunk_size kwarg for inversion-heavy A100 likelihoods #1303 (this issue): 322605 (NSS pix, 28.05 GB), 322606 (NSS delaunay, 27.67 GB) — same byte counts as 322604 / 322602 before feat(nss): chunk_size kwarg for inversion-heavy A100 likelihoods #1303
• Diagnostic confirmation: the NSS configuration: log line (printed after algo.init) appears in the working smoke output but never appears in 322605/606's logs, isolating the OOM to the algo.init phase

Original Prompt

Click to expand starting prompt

af.NSS: chunked algo.init so n_live initial particles don't OOM

Context

Follow-up to PyAutoFit#1303 ("feat(nss): chunked vmap for inversion-heavy
A100 likelihoods"). That PR added a chunk_size kwarg that replaces
blackjax.ns.from_mcmc.update_with_mcmc_take_last's inner
jax.vmap(num_delete) with jax.lax.map(batch_size=chunk_size). The
PyAutoFit unit tests + a 5D Gaussian smoke confirmed bit-identical
log_Z between the unchunked and chunked paths.

A100 validation re-runs of the cells the first PR was supposed to
unblock (jobs 322605 NSS pixelization × HST × fp64, 322606 NSS delaunay
× HST × fp64) OOM at the same allocations as before (28.05 GB
pixelization, 27.67 GB delaunay):

jaxlib.xla_extension.XlaRuntimeError: RESOURCE_EXHAUSTED:
  Out of memory while trying to allocate 28055330400 bytes.
  in PyAutoArray:autoarray/inversion/mappers/mapper_util.py:315
    mat = mat.at[flat_parent, flat_pixidx].add(flat_contrib_out)

Decisive evidence the chunked-update path isn't the right seam: the
NSS configuration INFO log line (which lives after algo.init and
before the sampling loop) never appears. The OOM fires inside
algo.init(initial_samples), not inside the per-iteration algo.step
that the first PR fixed.

The actual root cause

blackjax.ns.nss.as_top_level_api constructs algo.init as:

def init_fn(position, rng_key=None):
    return init(
        position,
        init_state_fn=jax.vmap(init_state_fn),  # ← hardcoded, no kwarg seam
        update_inner_kernel_params_fn=update_inner_kernel_params_fn,
    )

(blackjax/ns/nss.py:223-230, handley-lab fork at SHA ef45acd2).

The jax.vmap(init_state_fn) is hardcoded inline — there is no
kwarg seam to inject chunking through blackjax.nss(...). Initial
particle state for all n_live=150 particles is computed in one
parallel JAX call. With the
PyAutoArray:autoarray/inversion/mappers/mapper_util.py:315 scatter
allocating ~184 MB per particle (15,361 image pixels × 1,500 source
pixels × 8 bytes fp64) plus XLA scatter temp buffers, 150 ×
~184 MB ≈ 27.6 GB matches the observed OOM exactly (the ratio
28,055,330,400 / 184,332,000 = 152.2 is conspicuously close to
n_live=150).

The chunked update_strategy the first PR added only fires inside
algo.step, which runs after algo.init returns successfully. For
inversion-heavy lensing cells we never get there.

Desired fix

Replicate blackjax.nss.as_top_level_api locally in PyAutoFit (it's
~30 lines) so we control both:

1. The chunked update_strategy (already done by PyAutoFit#1303 via
   make_chunked_update_strategy).
2. The chunked init_fn (this PR).

Sketch (new module autofit/non_linear/search/nest/nss/_chunked_nss.py):

import jax
from functools import partial
from blackjax import SamplingAlgorithm
from blackjax.ns.adaptive import init as ns_init
from blackjax.ns.base import init_state_strategy
from blackjax.ns.nss import (
    build_kernel,
    update_inner_kernel_params,
)
from ._chunked_update import make_chunked_update_strategy


def build_chunked_nss_algorithm(
    *, logprior_fn, loglikelihood_fn,
    num_inner_steps, num_delete, chunk_size,
):
    """Local replica of ``blackjax.nss(as_top_level_api)`` with
    chunked init AND chunked update.

    ``chunk_size`` controls both the inner-vmap inside the per-
    iteration MCMC step (matches PyAutoFit#1303) and the n_live-wide
    vmap inside the algorithm's ``init``. When ``chunk_size`` is
    None or >= max(n_live, num_delete) both paths fall back to
    bit-identical upstream behaviour (plain ``jax.vmap``).
    """
    init_state_fn = partial(
        init_state_strategy,
        logprior_fn=logprior_fn,
        loglikelihood_fn=loglikelihood_fn,
    )

    kernel = build_kernel(
        init_state_fn,
        num_inner_steps,
        num_delete,
        update_strategy=make_chunked_update_strategy(chunk_size),
    )

    def init_fn(position, rng_key=None):
        if chunk_size is None:
            init_batcher = jax.vmap(init_state_fn)
        else:
            init_batcher = lambda p: jax.lax.map(
                init_state_fn, p, batch_size=chunk_size
            )
        return ns_init(
            position,
            init_state_fn=init_batcher,
            update_inner_kernel_params_fn=update_inner_kernel_params,
        )

    def step_fn(rng_key, state):
        return kernel(rng_key, state)

    return SamplingAlgorithm(init_fn, step_fn)

Then in af.NSS._fit, when chunk_size is set and chunking would
actually help (e.g. chunk_size < max(n_live, num_delete)), use
build_chunked_nss_algorithm instead of _blackjax.nss(...).
chunk_size=None keeps using blackjax.nss(...) unchanged for
bit-identical fallback.

Test plan

• Unit test (test_autofit, no JAX): factory builds a SamplingAlgorithm
  with the expected (init, step) shape; kwarg plumbing on af.NSS
  threads chunk_size through.
• Workspace-test (JAX-traced): bit-identical log_Z on a 5D Gaussian
  between _blackjax.nss(...).init (full vmap) and our
  build_chunked_nss_algorithm(chunk_size=2).init on the same seed,
  with n_live=20. (Same shape as the PyAutoFit#1303 smoke; that
  smoke only exercised the step seam, not the init seam.)
• A100 end-to-end: resubmit autolens_profiling's
  searches/nss/imaging/{pixelization,delaunay} × hst × fp64;
  chunk_size=16 set automatically by
  autolens_profiling/searches/_samplers.py:build_nss. Confirm
  completion (was OOMing as 322605/606 after PyAutoFit#1303 landed,
  same allocations as 322602/604 before it).

Affected callers / interaction surface

• af.NSS — _fit switches from _blackjax.nss(...) to
  build_chunked_nss_algorithm(...) when chunk_size is set and
  smaller than the wider of n_live / num_delete.
• autolens_profiling — no change needed. build_nss already
  sets chunk_size=vmap_batch_for_cell(...); the PyAutoFit-side
  change is transparent.
• handley-lab/blackjax — still no patch needed for this fix.
  Long-term cleanup: file an upstream PR adding init_batcher (or
  similar) as a kwarg on blackjax.nss(as_top_level_api) so our
  local replica can shrink to a thin forwarder. Lower priority since
  the PyAutoFit shim works.

Why this matters

PyAutoFit#1303 was the right partial fix — per-iteration vmap chunking
is necessary, just not sufficient. Without this follow-up, NSS still
can't profile or run on the production lensing cells (SLaM
source_pix[1/2] Delaunay / pixelization phases) at A100 80 GB,
which were exactly the cells the original profile sweep was supposed
to compare against Nautilus.

The same A100 evidence as PyAutoFit#1301 applies; the next round
(322605 NSS pix, 322606 NSS delaunay) confirms the bug is in the
init path. Nautilus baselines for the comparison still apply:
pixelization 46.5 ms/eval / 46 min (322603), delaunay 84.8 ms/eval /
45 min (322601), NSS MGE 1.6 ms/eval / 11 min (322590).

Out of scope

• Replacing slice-MCMC with HMC / NUTS for better mixing — separate
  upstream concern.
• Multi-GPU sharding via jax.shard_map — single-GPU chunked init is
  the cheapest fix for the immediate gap.

Cross-references

• PyAutoFit#1303 — first chunked-vmap PR, fixed the per-iteration path
• PyAutoFit#1301 — original issue with chunked-vmap framing
• autolens_profiling#43 — workspace consumer wiring of chunk_size
• PyAutoPrompt/autoarray/delaunay_interpolator_pure_callback_vmap_memory.md
  — separate efficiency follow-up (already shown not to be the OOM cause)
• blackjax/ns/nss.py:223-230 (the hardcoded jax.vmap(init_state_fn))
• A100 evidence: 322605 (NSS pix), 322606 (NSS delaunay) — both OOM
  at the same byte counts as 322604 / 322602 from before
  PyAutoFit#1303

[Jammy2211]

Shipped — 2026-05-29

PR

• fix(nss): chunked algo.init follow-up to #1303 #1305

Summary

Added build_chunked_nss_algorithm — a ~30-line local replica of blackjax.nss.as_top_level_api exposed as autofit.non_linear.search.nest.nss._chunked_nss. It controls both vmap sites: the per-iteration MCMC step (via make_chunked_update_strategy from #1303, unchanged) AND the n_live-wide algo.init, which now swaps jax.vmap(init_state_fn) for jax.lax.map(init_state_fn, positions, batch_size=chunk_size). af.NSS._fit switches to the local builder when chunk_size < max(n_live, num_delete); otherwise calls _blackjax.nss(...) bit-for-bit. Bit-identical log_Z = -4.3208251152 on a 5D Gaussian at n_live=20, num_delete=4, chunk_size=4 (5 init chunks of 4) — both code paths confirmed via the configuration log line.

Session Notes

• Diagnostic that pinned the bug: the NSS configuration: INFO log line is printed after algo.init(initial_samples) and before the sampling loop. In the OOM logs (322605 pix + 322606 delaunay) that line never appears, but it does appear in the working MGE cell (322590) and in local smokes. That single observation isolated the crash site to init, not step. Worth keeping in mind for future blackjax OOM diagnoses: which log lines fire is itself a diagnostic axis.
• Why a local replica beats monkey-patching: I considered monkey-patching algo._fields[0] (init) on the SamplingAlgorithm NamedTuple after _blackjax.nss(...) returned. Rejected because blackjax.nss.as_top_level_api binds init_state_fn inside init_fn's closure; we'd have to re-derive that closure outside the function, which is ~as much code as the replica. The replica is cleaner and more debuggable.
• Why widen the threshold to max(n_live, num_delete): feat(nss): chunk_size kwarg for inversion-heavy A100 likelihoods #1303 used chunk_size < num_delete. After this PR, the init path also benefits, and n_live is typically larger than num_delete (e.g. 150 vs 50 in production), so the wider threshold matches the actual memory profile. Both un-chunked fallthroughs (chunk_size=None or chunk_size >= max(n_live, num_delete)) are bit-identical to upstream blackjax.nss(...).
• Future work: (1) Resubmit the A100 NSS pixelization + delaunay cells now that this is merged — should complete with chunk_size=16 set automatically by autolens_profiling's build_nss. (2) Upstream the chunked init+step seam to handley-lab/blackjax so the PyAutoFit-local replica can shrink to a forwarder. (3) JAX-traced bit-identical workspace test (the n_live=20 smoke at /tmp/nss_chunked_init_smoke.py belongs in autofit_workspace_test/).

[Jammy2211]

Closing as done — delivered by "fix(nss): chunked algo.init follow-up to #1303" (merged 2026-05-29). The issue was never closed at merge time.

Closed as part of a stale-issue sweep across all repos (2026-07-15).