[Bugfix] Preserve Machete act-order permutation storage across weight reload

[RyanClark2k]

[Bugfix] Preserve Machete act-order permutation storage across weight reloads

Purpose

Fixes the Machete act-order row of RFC #48312 (category 1, storage identity), which I reported there after a registry-wide reload simulation caught it.

MacheteLinearKernel.process_weights_after_loading computes perm = torch.argsort(g_idx) fresh on every call and captures the tensor inside self.act_perm (a functools.partial or lambda). The tensor is unregistered and lives inside a kernel-held callable, so the reload copy-back in model_loader/reload/layerwise.py never sees it: every RL weight sync rebinds it while captured CUDA graphs keep the address baked at capture time. Machete is the preferred W4A16 kernel on Hopper, so act-order GPTQ + RL reload + CUDA graphs hits this in a mainstream configuration.

Live probing on an H100 (below) shows the failure mode is a stale read, not a dangling pointer: the capture-time permutation storage stays alive (retained by capture/compile artifacts) but stops receiving updates, so after a real weight update the captured graphs silently permute activations with the OLD model's act order. A same-weights reload produces bit-identical outputs, which is why nothing catches this today.

Fix

Same idiom as the Marlin act-order fix in #48438:

• Register the permutation as layer.g_idx_sort_indices (the name Marlin already uses for the same argsort) via replace_parameter(..., prefer_copy=True), so reload recomputes it into the same storage and copy-back preserves it.
• Resolve it through the layer in apply_weights instead of capturing a tensor in a callable. This is load-bearing, not cosmetic: PWAL runs before reload's copy-back, so any tensor captured at PWAL time is the transient object that copy-back subsequently swaps out. A first iteration that registered the parameter but still bound it into act_perm at PWAL time failed live validation for exactly this reason (see below). Only call-time resolution reads the registered parameter unconditionally.
• self.act_perm is removed; self.use_permute_cols (a bool) keeps the ops.permute_cols fast-path selection from PWAL.

Why this is not duplicating an existing PR

The bug was found by my registry test and reported on #48312 (no prior report). Searched open PRs for machete, act_perm, permute_cols reload, and 48312; the only related work is my own #48438 (Marlin family), which deliberately did not touch Machete. aoshen02's #48478 is the systemic registry design and defers per-kernel fixes to migration; this PR is one such migration made concrete.

Test plan

Unit (CPU, red/green verified — fails on main, passes with the fix):

pytest tests/model_executor/model_loader/test_reload.py -k machete -v
# main:  FAILED (act_perm captures a fresh unregistered tensor)
# fixed: PASSED

The new test_machete_post_load_preserves_act_perm_address runs post-load twice with different act orders and asserts the registered permutation keeps its storage, carries the recomputed values, and that no tensor is captured inside a kernel-held callable.

Also added test_machete_act_order_layerwise_reload_accounting, the Machete analogue of test_marlin_act_order_layerwise_reload_accounting from #48438. g_idx_sort_indices is generated during weight processing and never loaded from checkpoints, so registering it as a Parameter must not count it toward load_numel_total. Reload restores the construction-time tensor set before sizing, which means act-order layers still process during streaming instead of deferring (and buffering weights) until finalization. The test records metadata at construction, processes the layer, initializes layerwise reload, then streams a new checkpoint. It asserts that the total equals the checkpoint-loadable numel and that the layer processes as soon as its last checkpoint tensor arrives. I verified the test discriminates: if metadata is instead captured after processing, the total inflates from 1280 to 1408 and the first assert fails.

Both Machete tests reuse the shared GPTQ checkpoint helpers introduced by the #48438 tests, since the checkpoint format and sizes are identical.

Existing suites: tests/model_executor/model_loader/test_reload.py and the kernel selection paths are unchanged for non-act-order configs (the fix is inside the has_g_idx branch; apply_weights reads the same values it did before).

Live capture/reload/replay validation (H100, both sides of the fix)

Same methodology as the #48438 validation: load under CUDA graphs at defaults, record data_ptr and a storage weak reference for every runtime permutation tensor inside the live engine, reload identical weights via Worker.reload_weights, re-census, then mutate the live storage in place and check whether generations move.

Setup. Rented H100 SXM (SM90 — the hardware Machete serves), driver 550.163.01, torch 2.11.0+cu129. Model: TheBloke/TinyLlama-1.1B-Chat-v1.0-GPTQ, revision gptq-4bit-64g-actorder_True (4-bit, symmetric, group 64 — Machete-eligible; note the 32g revisions are not, since Machete's fp16 group sizes are -1/64/128). Kernel census confirmed MacheteLinearKernel on all 88 linear layers.

Side	act_perm tensors moved	registered perm moved	replay = baseline	flip test
Unfixed (main)	88/88	n/a (not registered)	8/8 (same weights, see below)	not run (see below)
Fixed (this PR)	n/a (no captured tensor)	0/88	8/8	8/8 generations changed

Unfixed side. All 88 act_perm permutation tensors rebind on reload. Notably, 0/88 capture-time storages were freed: capture/compile artifacts retain the old tensors, so this presents as a stale read rather than a crash — captured graphs keep permuting activations with the permutation frozen at capture time while the rest of the model updates. That also means no same-weights comparison can ever catch it (outputs are bit-identical by construction), and no allocation pressure will make it crash; it is silent wrong-output territory exclusive to real weight changes. Replay after a same-weights reload matching baseline 8/8 is exactly that blindness, live.

A wrong fix this probe caught. My first fix registered the permutation but still bound it into act_perm at PWAL time. The probe rejected it: the registered parameter held its storage (0/88 moved) but the callable's captured tensor still moved 88/88, and flipping it changed 0/8 generations — the callable was holding a detached alias, because PWAL runs before reload's copy-back swaps the original parameter back in. That ordering constraint is why the final fix resolves the parameter through the layer at apply time instead of capturing any tensor.

Fixed side. The registered g_idx_sort_indices keeps its storage across reload (0/88 moved, 0/88 freed), replay after reload matches baseline 8/8, and flipping the live registered permutation in place changes 8/8 generations — captured graphs demonstrably read the storage the engine now refreshes in place.

Raw logs for all three runs (bug side, wrong-fix side, fixed side) are retained; happy to attach any of them. The probe:

machete_actperm_probe.py (run unfixed for the bug side; run with the flip argument on this branch for the fixed side)

# SPDX-License-Identifier: Apache-2.0
"""Live capture/reload/replay probe for Machete act_perm (RFC #48312).

MacheteLinearKernel.process_weights_after_loading recomputes
perm = argsort(g_idx) and captures it inside self.act_perm (a partial or
lambda). Unfixed: the tensor is unregistered, so reload rebinds it and
captured CUDA graphs keep the freed pointer. Fixed: the perm is registered
as layer.g_idx_sort_indices via replace_parameter(prefer_copy=True) and its
storage survives reload.

Evidence collected:
  1. Pointer census: data_ptr of every act_perm perm tensor before/after
     Worker.reload_weights (bug side: moves; fixed side: stable).
  2. Storage expiry: StorageWeakRef recorded before reload (bug side: old
     storage freed = dangling captured address).
  3. Flip test (fixed side): flip the live registered perm in place; if
     captured graphs read the storage the engine mutates, generations change.

Usage: python machete_actperm_probe.py [flip]
Env:   SKIP_REPLAY=1  skip post-reload generation (bug side wedge guard)
"""

import gc
import os
import sys

os.environ.setdefault("VLLM_ENABLE_V1_MULTIPROCESSING", "0")

from vllm import LLM, SamplingParams

MODEL = "TheBloke/TinyLlama-1.1B-Chat-v1.0-GPTQ"
REVISION = "gptq-4bit-64g-actorder_True"  # 4-bit, group 64, sym: Machete-eligible
PROMPTS = [
    "The capital of France is",
    "In machine learning, overfitting means",
    "The chemical symbol for gold is",
    "To be or not to be,",
    "The speed of light is approximately",
    "Water boils at a temperature of",
    "The largest planet in the solar system is",
    "Photosynthesis is the process by which",
]


def _extract_perm(kernel):
    act_perm = getattr(kernel, "act_perm", None)
    if act_perm is None:
        return None
    kw = getattr(act_perm, "keywords", None)
    if kw and "perm" in kw:
        return kw["perm"]
    closure = getattr(act_perm, "__closure__", None)
    if closure:
        import torch

        for cell in closure:
            if isinstance(cell.cell_contents, torch.Tensor):
                return cell.cell_contents
    return None


def collect_ptrs(model):
    """Worker-side: map module -> act_perm/gsi pointers; stash weakrefs."""
    from torch.multiprocessing.reductions import StorageWeakRef

    out = {}
    kernel_census = {}
    refs = {}
    for name, mod in model.named_modules():
        qm = getattr(mod, "quant_method", None)
        kernel = getattr(qm, "kernel", None)
        if kernel is not None:
            kname = type(kernel).__name__
            kernel_census[kname] = kernel_census.get(kname, 0) + 1
        if kernel is None or type(kernel).__name__ != "MacheteLinearKernel":
            continue
        entry = {}
        perm = _extract_perm(kernel)
        if perm is not None:
            entry["act_perm"] = perm.data_ptr()
            refs[f"{name}.act_perm"] = StorageWeakRef(perm.untyped_storage())
        gsi = getattr(mod, "g_idx_sort_indices", None)
        if gsi is not None and hasattr(gsi, "data_ptr") and gsi.numel() > 0:
            entry["gsi"] = gsi.data_ptr()
            refs[f"{name}.gsi"] = StorageWeakRef(gsi.untyped_storage())
        if entry:
            out[name] = entry
    if not hasattr(model, "_machete_probe_refs"):
        model._machete_probe_refs = refs
    return {"ptrs": out, "census": kernel_census}


def count_expired(model):
    refs = getattr(model, "_machete_probe_refs", {})
    return sum(1 for r in refs.values() if r.expired())


def flip_perms(model):
    """Worker-side: reverse every live perm in place (storage unchanged).
    Fixed design has no tensor inside act_perm; flip the registered param."""
    n = 0
    for _, mod in model.named_modules():
        qm = getattr(mod, "quant_method", None)
        kernel = getattr(qm, "kernel", None)
        if kernel is None or type(kernel).__name__ != "MacheteLinearKernel":
            continue
        perm = _extract_perm(kernel)
        if perm is None:
            perm = getattr(mod, "g_idx_sort_indices", None)
        if perm is not None and hasattr(perm, "data"):
            perm.data.copy_(perm.data.flip(0))
            n += 1
    return n


def main() -> None:
    import torch
    import vllm

    print(f"model={MODEL} revision={REVISION}")
    print(
        f"torch {torch.__version__} | vllm {vllm.__version__} | "
        f"{torch.cuda.get_device_name(0)}"
    )

    llm = LLM(
        model=MODEL,
        revision=REVISION,
        gpu_memory_utilization=0.45,
        max_model_len=2048,
        enforce_eager=False,
        enable_prefix_caching=False,
    )
    params = SamplingParams(temperature=0.0, max_tokens=32)
    base = [o.outputs[0].text for o in llm.generate(PROMPTS, params)]

    first = llm.apply_model(collect_ptrs)[0]
    print(f"kernel census: {first['census']}")
    before = first["ptrs"]
    n_perm = sum(1 for e in before.values() if "act_perm" in e)
    n_gsi = sum(1 for e in before.values() if "gsi" in e)
    print(f"tracked: act_perm={n_perm} g_idx_sort_indices={n_gsi}")
    if n_perm == 0 and n_gsi == 0:
        print("PROBE VERDICT: NO MACHETE ACT-ORDER LAYERS (wrong kernel/revision?)")
        sys.exit(2)
    if n_perm == 0:
        print("(no tensor captured inside act_perm: fixed late-binding design)")

    print("=== reloading identical weights via Worker.reload_weights ===")
    llm.collective_rpc("reload_weights")
    gc.collect()

    after = llm.apply_model(collect_ptrs)[0]["ptrs"]
    moved = {"act_perm": 0, "gsi": 0}
    for name, entry in before.items():
        for k, ptr in entry.items():
            if after.get(name, {}).get(k) != ptr:
                moved[k] += 1
    expired = llm.apply_model(count_expired)[0]
    print(f"moved after reload: act_perm={moved['act_perm']}/{n_perm} "
          f"gsi={moved['gsi']}/{n_gsi}")
    print(f"capture-time perm storages freed: {expired}/{n_perm + n_gsi}")

    if os.environ.get("SKIP_REPLAY") != "1":
        replay = [o.outputs[0].text for o in llm.generate(PROMPTS, params)]
        same = sum(1 for a, b in zip(base, replay) if a == b)
        print(f"replay after reload matches baseline: {same}/{len(PROMPTS)}")

    if moved["act_perm"] or moved["gsi"] or expired:
        print("PROBE VERDICT: PERM REBINDS ON RELOAD (bug confirmed live)")
    else:
        print("PROBE VERDICT: PERM STORAGE STABLE ACROSS RELOAD")

    if "flip" in sys.argv[1:]:
        n_flipped = llm.apply_model(flip_perms)[0]
        print(f"flipped {n_flipped} live perm tensors in place")
        flipped = [o.outputs[0].text for o in llm.generate(PROMPTS, params)]
        diverged = sum(1 for a, b in zip(base, flipped) if a != b)
        print(f"generations changed after live flip: {diverged}/{len(PROMPTS)}")
        if diverged:
            print("FLIP VERDICT: captured graphs read the storage the engine "
                  "mutates (coupling confirmed)")
        else:
            print("FLIP VERDICT: no divergence — graphs NOT reading this storage")


if __name__ == "__main__":
    main()

Notes for reviewers

• g_idx_sort_indices enters the layer's registered parameters (as it already does for Marlin act-order layers), so it participates in reload copy-back and state_dict. It is int32 and K-sized per act-order layer.
• No output change for a fixed set of weights: the permutation values are identical; only their storage lifecycle changes. Model evals are therefore not applicable beyond the replay-identity check in the validation section above.

AI assistance was used for the investigation, implementation, and validation tooling. I reviewed every changed line, ran the tests above, and can defend the change end to end.

Part of #48312.

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