[Bugfix] Preserve Marlin runtime tensor storage across weight reload

[RyanClark2k]

The fix is verified at three levels: red/green CPU regression tests (in this PR), live-engine GPU validation on an RTX 4090 (pointer identity across reload, a graph-coupling flip test, and a livelock demonstration on unfixed main), and live bug-side/fixed-side validation of every sibling Marlin path. Method, results, and the validation script for the primary fix are in this description; the per-site sibling matrix is in the validation results comment on this PR.

Part of RFC #48312 ("Weight Reload Correctness for RL") — addresses the "Marlin workspace and sort indices" row of the high-risk candidates table. The workspace pattern is not specific to this kernel: eight more sites across the Marlin family share it. At the RFC author's request (comment), all eight are folded into this PR as one-line applications of the same helper, each live-validated on both sides of the fix. Details under "Scope note for reviewers" below.

Purpose

MarlinLinearKernel.process_weights_after_loading creates two runtime tensors that escape the layerwise-reload protection:

1. self.workspace (vllm/model_executor/kernels/linear/mixed_precision/marlin.py) is allocated fresh on every call and stored on the kernel object, not the layer. The reload copy-back (_copy_and_restore_kernel_tensors in vllm/model_executor/model_loader/reload/layerwise.py) only preserves registered parameters/buffers (layer._parameters / layer._buffers), so after an RL weight reload the old workspace is freed while captured CUDA graphs keep launching Marlin kernels against its stale device pointer. Affects all Marlin models (GPTQ, AWQ, compressed-tensors W4A16/W4A8) when CUDA graphs are enabled (the default).

2. layer.g_idx_sort_indices (act-order GPTQ only) is a plain argsort output assigned as a bare attribute — never registered — with the same lifecycle gap. Since it is a permutation the kernel reads, stale/reused memory means wrong outputs rather than scratch corruption.

Verified benign, no change needed: the non-act-order empty g_idx/w_zp/g_idx_sort_indices paths (marlin_make_empty_g_idx returns a zero-numel nn.Parameter, which nn.Module.__setattr__ auto-registers) and input_global_scale (registered, covered by copy-back).

Fix

Follows the invariant stated in RFC #48312 (recompute values into the same storage on reload) and the pattern of #48251:

• marlin_make_workspace_new gains an existing argument: when a compatible workspace is passed, it is zeroed and returned in place instead of reallocated. MarlinLinearKernel passes its current workspace on reprocessing.
• g_idx_sort_indices is registered via replace_parameter(..., prefer_copy=True), so repeated post-load processing copies new indices into the same storage, and the layerwise copy-back protects it going forward.

This does not change serving-path numerics — the tensors hold identical values; only their storage identity across reloads changes — so no model eval delta is expected. The GPU validation below covers the behavioral claim on the reload path.

Scope note for reviewers

The same unregistered layer.workspace = marlin_make_workspace_new(...) pattern exists at eight more sites, all in functions rerun from process_weights_after_loading during reload: the dense FP8/NVFP4/MXFP8 Marlin fallbacks (marlin_utils_fp8.py:135/466, marlin_utils_fp4.py:243), the FP8/MXFP8/NVFP4/MXFP4 Marlin MoE paths (marlin_utils_fp8.py:283/552, marlin_utils_fp4.py:399/484), and a conditional rebind in the compressed-tensors WNA16 modular MoE path (compressed_tensors_moe_wna16_marlin.py:490). At the RFC author's request, all eight are fixed in this PR (second commit) by passing the existing workspace to the helper. Every site is live-validated with the same capture/reload/replay pointer census that validates the primary fix — bug side and fixed side — see the validation results comment on this PR for the full per-site matrix.

Deliberately untouched: auto_awq.py / auto_gptq.py assign their workspace in create_weights, which does not rerun on reload, and the WNA16 MoE converter's w13/w2_g_idx_sort_indices go through _replace_or_register_parameter and are already protected.

Test Plan

CPU lifecycle regression tests (in this PR, no GPU required), both in tests/model_executor/model_loader/test_reload.py:

• test_marlin_post_load_preserves_runtime_tensor_addresses: builds a real MarlinLinearKernel over a minimal act-order GPTQ layer (only the two CUDA-only pieces are monkeypatched: num_compute_units and ops.gptq_marlin_repack), runs post-load processing, simulates a reload by installing fresh checkpoint-format weights with a different act-order, processes again, and asserts both data_ptrs are stable, the sort indices refresh to the new permutation, and the workspace is zeroed.
• test_marlin_prepare_layer_preserves_workspace_address[fp8|mxfp8|nvfp4]: runs the actual prepare_fp8_layer_for_marlin, prepare_mxfp8_layer_for_marlin, and prepare_fp4_layer_for_marlin fallback paths twice on a minimal layer (same two monkeypatches) and asserts the workspace data_ptr is stable and zeroed.

pytest tests/model_executor/model_loader/test_reload.py -q

GPU validation: capture -> reload -> replay. Setup: RTX 4090 (SM89), torch 2.11.0+cu130, VLLM_USE_PRECOMPILED=1 editable install, model TheBloke/TinyLlama-1.1B-Chat-v1.0-GPTQ revision gptq-4bit-32g-actorder_True (desc_act=true, so all 88 linear modules take the act-order Marlin path), CUDA graphs at defaults (FULL_AND_PIECEWISE), prefix caching off, greedy decoding. Log confirms Using MarlinLinearKernel for AutoGPTQLinearMethod.

The validation script generates, records data_ptr of every Marlin workspace and g_idx_sort_indices inside the live engine via llm.apply_model, reloads identical weights through Worker.reload_weights, records the pointers again, then flips the live sort-indices permutations in place and generates again. The flip distinguishes whether captured graphs read the storage the engine mutates (correct) or stale memory (bug), independent of allocator behavior.

To reproduce on each side of the A/B (any SM75+ GPU, roughly five minutes with warm caches):

git checkout <commit>   # 4c81772 for base, this PR's branch for fixed
timeout 1200 python validate_marlin_reload.py

validate_marlin_reload.py

# SPDX-License-Identifier: Apache-2.0
"""GPU validation for the Marlin reload identity fix (vLLM PR #48438, RFC #48312).

Two live-engine signals, neither dependent on allocator luck:

1. Pointer identity: collect data_ptr of every Marlin kernel workspace and
   act-order g_idx_sort_indices inside the engine, before and after
   Worker.reload_weights. The captured CUDA graphs bake the "before" pointers.

2. Graph coupling: after reload, flip the live g_idx_sort_indices permutation
   in place and generate again.
   - Unfixed code: pointers CHANGE on reload, and flipping the live tensors
     does NOT change generations (graphs read the old, freed memory).
   - Fixed code: pointers are STABLE, and flipping DOES change generations
     (graphs read the same storage the engine mutates).

Run under an outer `timeout`:  timeout 1200 python validate_marlin_reload.py
"""

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-32g-actorder_True"

PROMPTS = [
    "The capital of France is",
    "In machine learning, overfitting means",
    "def fibonacci(n):",
    "The three primary colors are",
    "Once upon a time in a small village",
    "The chemical formula for water is",
    "To reverse a linked list, you",
    "The speed of light is approximately",
]


def collect_ptrs(model):
    """Map module name -> (workspace data_ptr, g_idx_sort_indices data_ptr)."""
    out = {}
    for name, mod in model.named_modules():
        qm = getattr(mod, "quant_method", None)
        kernel = getattr(qm, "kernel", None)
        ws = getattr(kernel, "workspace", None)
        gsi = getattr(mod, "g_idx_sort_indices", None)
        ws_ptr = ws.data_ptr() if ws is not None else None
        gsi_ptr = gsi.data_ptr() if gsi is not None and gsi.numel() > 0 else None
        if ws_ptr is not None or gsi_ptr is not None:
            out[name] = (ws_ptr, gsi_ptr)
    return out


def flip_sort_indices(model):
    """Reverse every act-order permutation in place (valid but wrong order)."""
    import torch

    n = 0
    with torch.no_grad():
        for _, mod in model.named_modules():
            gsi = getattr(mod, "g_idx_sort_indices", None)
            if gsi is not None and gsi.numel() > 0:
                gsi.data.copy_(gsi.data.flip(0))
                n += 1
    torch.cuda.synchronize()
    return n


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

    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=128)

    out_a = llm.generate(PROMPTS, params)
    tokens_a = [tuple(o.outputs[0].token_ids) for o in out_a]

    ptrs_before = llm.apply_model(collect_ptrs)[0]
    n_ws = sum(1 for v in ptrs_before.values() if v[0] is not None)
    n_gsi = sum(1 for v in ptrs_before.values() if v[1] is not None)
    print(f"tracked tensors: {n_ws} workspaces, {n_gsi} act-order sort indices "
          f"across {len(ptrs_before)} modules")
    assert n_gsi > 0, "model has no act-order layers; wrong checkpoint revision?"

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

    ptrs_after = llm.apply_model(collect_ptrs)[0]
    ws_changed = sum(
        1 for k in ptrs_before
        if ptrs_before[k][0] is not None and ptrs_before[k][0] != ptrs_after[k][0]
    )
    gsi_changed = sum(
        1 for k in ptrs_before
        if ptrs_before[k][1] is not None and ptrs_before[k][1] != ptrs_after[k][1]
    )
    print(f"pointer identity after reload: {ws_changed}/{n_ws} workspaces moved, "
          f"{gsi_changed}/{n_gsi} sort indices moved")

    n_flipped = llm.apply_model(flip_sort_indices)[0]
    print(f"flipped {n_flipped} live sort-indices tensors in place")

    out_b = llm.generate(PROMPTS, params)
    tokens_b = [tuple(o.outputs[0].token_ids) for o in out_b]
    diverged = sum(a != b for a, b in zip(tokens_a, tokens_b))
    print(f"generations changed after live flip: {diverged}/{len(PROMPTS)}")

    ptrs_stable = ws_changed == 0 and gsi_changed == 0
    graph_coupled = diverged > 0

    if ptrs_stable and graph_coupled:
        print("VERDICT: FIXED — storage identity preserved across reload; "
              "captured graphs read live storage")
    elif not ptrs_stable and not graph_coupled:
        print("VERDICT: BUG CONFIRMED — reload moved runtime tensors while "
              "captured graphs still read the old (freed) memory; live engine "
              "state is decoupled from what the graphs execute")
        sys.exit(1)
    else:
        print(f"VERDICT: ANOMALY — ptrs_stable={ptrs_stable}, "
              f"graph_coupled={graph_coupled}; needs investigation")
        sys.exit(2)


if __name__ == "__main__":
    main()

Test Result

CPU:

• Without the fix: the kernel test fails on kernel.workspace.data_ptr() (workspace reallocated on reload); the g_idx_sort_indices.data_ptr() assertion was additionally verified red in isolation (address changed across reload). All three test_marlin_prepare_layer_preserves_workspace_address variants likewise fail with the sibling-site fixes reverted and pass with them.
• With the fix: the new tests and the full tests/model_executor/model_loader/test_reload.py suite pass (macOS CPU, VLLM_TARGET_DEVICE=empty editable install; the suite's five engine-spawning e2e tests fail identically on the clean base on this machine — no compiled CPU extension — and are unrelated).
• ruff check and ruff format --check clean on all changed files.

GPU, unfixed base (4c81772):

pointer identity after reload: 88/88 workspaces moved, 88/88 sort indices moved
flipped 88 live sort-indices tensors in place
[GPU livelock: post-reload generation hung at 100% GPU utilization for >10min
 and was killed; the same generation took ~1.2s before the reload]

Every runtime tensor moved while the captured graphs kept the old addresses. The post-reload generation then hung the GPU: temporary allocations from the flip reclaimed the freed workspace blocks, so the replayed Marlin kernels spun on garbage synchronization counters. In an RL loop this wedges the rollout engine, not just corrupts outputs.

One honest note: a naive version of this test (reload identical weights, compare generations, no pointer instrumentation) passes on the unfixed base, because the stale memory still contains bit-identical values until something reclaims it. The bug is real but latent until allocation pressure or changed weights expose it, which is presumably why it survived in tree.

GPU, fixed (this PR's branch head, 34ad158):

pointer identity after reload: 0/88 workspaces moved, 0/88 sort indices moved
flipped 88 live sort-indices tensors in place
generations changed after live flip: 8/8
VERDICT: FIXED - storage identity preserved across reload; captured graphs read live storage
EXIT=0

Storage identity holds across reload, and the flip test confirms the graphs read the same storage the engine mutates. (First validated on the initial commit 1e6bdf3; re-run with identical results on the branch head after folding in the sibling fixes.)

GPU, sibling sites: all eight folded-in sites are live-validated bug-side/fixed-side with the same pointer-census method — full per-site matrix, models, and reproduction details in the validation results comment on this PR. Full logs for all runs are retained and available on request.

GPU, test suite: all test_reload.py unit tests (including this PR's) and all of its engine-spawning e2e cases pass on the 4090.

Duplicate-work check

No open PR references #48312. Searches for "marlin workspace", "marlin reload", and "g_idx_sort_indices" surface only unrelated kernel-bounds fixes. The RFC thread has no claims on this item. The RFC author's open PRs in this campaign (#48251, #48382) cover MLA/FlashInfer sinks and compressed-tensors WNA16 Triton MoE respectively — not the Marlin path.

AI assistance disclosure

Investigation and implementation were AI-assisted (Claude Code). I have reviewed every changed line, re-derived the root-cause analysis against the reload machinery myself, and run the tests locally.

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[claude]

Claude Code Review

This pull request is from a fork — automated review is disabled. A repository maintainer can comment @claude review to run a one-time review.

[RyanClark2k]

Sibling-site validation: live capture/reload/replay A/B for all eight folded-in sites

Per the fold-in requested on the RFC: all eight sibling workspace sites are fixed in this PR, and rather than leaving five of them at static analysis, every site now has live capture/reload/replay evidence on both sides of the fix — bug side on 1e6bdf3 (the parent commit, which fixes only the MPLinearKernel sites), fixed side on 34ad158 (this branch). Method identical to the primary validation: pointer census of every layer.workspace inside the live engine via llm.apply_model, reload of identical weights via Worker.reload_weights, CUDA graphs at defaults. All runs on one RTX 4090 (SM89), torch 2.11.0+cu130, in a single session.

Site	Prepare function	Exercising model	Bug side	Fixed side
marlin_utils_fp8.py:135	prepare_fp8_layer_for_marlin	bf16 TinyLlama, online FP8, VLLM_TEST_FORCE_FP8_MARLIN=1	88/88 moved	0/88
marlin_utils_fp4.py:243	prepare_fp4_layer_for_marlin	nm-testing/TinyLlama-1.1B-Chat-v1.0-NVFP4	88/88 moved	0/88
marlin_utils_fp8.py:283	prepare_fp8_moe_layer_for_marlin	dacorvo/Mixtral-tiny, online FP8, force-Marlin	6/6 moved	0/6
marlin_utils_fp8.py:466	prepare_mxfp8_layer_for_marlin	Qwen/Qwen3-0.6B, online mxfp8	112/112 moved	0/112
marlin_utils_fp8.py:552	prepare_mxfp8_moe_layer_for_marlin	inference-optimization/DeepSeek-V3-debug-empty, online mxfp8	7/7 moved	0/7
marlin_utils_fp4.py:399	prepare_nvfp4_moe_layer_for_marlin	inference-optimization/DeepSeek-V3-debug-empty-NVFP4A16	7/7 moved	0/7
marlin_utils_fp4.py:484	prepare_moe_fp4_layer_for_marlin	generated tiny CT W4A4 MXFP4 MoE (recipe below)	18/30 moved¹	0/30
compressed_tensors_moe_wna16_marlin.py:490	WNA16 modular-MoE conditional	nm-testing/tinysmokeqwen3moe-W4A16-first-only-CTstable (auto backend selects MARLIN on SM89)	1/1 moved	0/1

The original MPLinearKernel sites were re-validated on the folded branch: 0/88 workspaces and 0/88 sort indices moved, and flipping the live sort indices in place changed 8/8 generations — captured graphs read the storage the engine mutates. All fixed-side runs completed their post-reload generation normally.

Notes:

1. The 18/30 on the MXFP4 MoE bug side is the caching allocator coincidentally returning 12 same-size blocks at their old addresses — any movement proves the rebind. It is the same one-sidedness that makes naive reload tests pass on broken code: bug-side address stability can be luck, which is why the fixed side must show 0/N by construction (storage reuse), not by allocator behavior.
2. The MXFP8 MoE bug-side run independently reproduced the livelock from the primary validation: after reload moved all 7 workspaces, the post-reload generation hung at 100% GPU utilization and the spinning kernel outlived SIGTERM of the host process. Bug-side MoE censuses therefore skip the post-reload generation; the census completes before replay and is unaffected.
3. The two sites previously without small exercising checkpoints became testable via (a) the tiny debug checkpoints test_reload.py already uses plus the 10 MB WNA16 MoE smoke checkpoint from vLLM's own CI, and (b) a 2 MB mxfp4-pack-quantized MoE checkpoint generated data-free from nm-testing/tinysmokeqwen3moe:

MXFP4 MoE checkpoint generation (llm-compressor 0.12.0, compressed-tensors 0.17.1)

from transformers import AutoModelForCausalLM, AutoTokenizer
from llmcompressor import oneshot
from llmcompressor.modifiers.quantization import QuantizationModifier

model_id = "nm-testing/tinysmokeqwen3moe"
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype="bfloat16")
tok = AutoTokenizer.from_pretrained(model_id)
recipe = QuantizationModifier(
    targets="Linear",
    scheme="MXFP4",
    ignore=["lm_head", "re:.*mlp.gate$"],
)
oneshot(model=model, recipe=recipe)
model.save_pretrained("tinysmoke-mxfp4")
tok.save_pretrained("tinysmoke-mxfp4")

vLLM loads it via CompressedTensorsW4A4Mxfp4MoEMethod; on GPUs without native FP4 the weight-only Marlin fallback engages (Using MarlinExperts for MXFP4 MoE), which is the code path under test.

Supplementary: tests/model_executor/model_loader/test_reload.py on the same GPU — all unit tests (including this PR's new ones) and all of the engine-spawning e2e cases pass.

Full logs for every run are retained and available on request.

[aoshen02]

We can just raise error here I think?

    if (
        existing.device != device
        or existing.dtype != torch.int
        or existing.numel() != size
    ):
        raise ValueError("Existing Marlin workspace is incompatible")

[RyanClark2k]

Done, it now raises on device/dtype/numel mismatch instead of allocating a new tensor. Agreed this should raise, since silently reallocating would reproduce the original bug one level down. Added a unit test covering the reuse path and both raise paths.

[aoshen02]

@codex review

[chatgpt-codex-connector]

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[chatgpt-codex-connector]

Keep sort indices out of reload accounting

For act-order Marlin models using layerwise reload, registering g_idx_sort_indices as a Parameter makes it visible to get_layer_params_buffers()/get_layer_size(), but checkpoints never load this generated permutation. info.load_numel_total therefore includes this extra size_k tensor while load_numel can only account for qweight/scales/g_idx, so _layerwise_process cannot run when the layer finishes loading and every such layer is deferred to finalization, buffering all loaded weights and defeating the layerwise memory cap; large act-order GPTQ/AWQ reloads can OOM. Preserve the storage without counting it as a loadable checkpoint parameter, or exclude it from reload sizing.

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[aoshen02]

seems relevant

[RyanClark2k]

I traced this and the total doesn't get inflated. record_metadata_for_reloading runs at model construction (model_loader/utils.py:64), before any weights load and before process_weights_after_loading ever runs, so the restore set contains only the checkpoint-format params. On reload, initialize_layerwise_reload first snapshots the live post-process params into kernel_tensors (that's the set copy-back preserves, and the only place g_idx_sort_indices is meant to appear), then restore_layer_on_meta deletes the current params and re-registers the construction-time set, and only after that is load_numel_total computed (reload/layerwise.py:111-133). The mid-stream refresh at layerwise.py:177 can't pick it up either, since processing hasn't run yet at that point. So the permutation is never part of the loadable total; it only reappears inside _layerwise_process, after the layer has already hit its total. There's an existing precedent for this shape: input_global_scale in the same prepare path is a generated Parameter that checkpoints never load, handled the same way.

To pin the invariant I added test_marlin_act_order_layerwise_reload_accounting: it runs an act-order Marlin layer through record → process → initialize_layerwise_reload → streamed weight loads, and asserts that load_numel_total equals the checkpoint-loadable numel (excluding the permutation) and that the layer processes as soon as its last checkpoint tensor arrives rather than deferring to finalization. I also verified the test discriminates: with the ordering this finding assumes (metadata captured after processing), the total inflates from 1280 to 1408 and the first assert fails.

[aoshen02]

CI enabled, let's get it merged today if ci looks good.

[mergify]

Hi @RyanClark2k, the pre-commit checks have failed. Please run:

uv pip install pre-commit>=4.5.1
pre-commit install
pre-commit run --all-files

Then, commit the changes and push to your branch.

For future commits, pre-commit will run automatically on changed files before each commit.

[RyanClark2k]

The reasons for the four failures:

1. Distributed Compile Unit Tests (2xH100): 36 failures, but every one is an mnnvl parametrization of test_all_reduce_fusion_pass_replace failing with all_reduce_fusion_pass.matched_count=0 (the fusion pattern didn't match on that runner). Nothing Marlin-related. It looks like an environment/main-side issue with MNNVL all-reduce detection. Recent main builds don't show this job failing, so it may have been transient or fixed since.

2. Kernels FusedMoE Layer Test (2 B200s): nvshmem couldn't set up its InfiniBand connections (ibv_modify_qp failed to nvshmem setup connections failed) and the job was killed. This job is tracked as chronically broken in issue [CI Failure]: Kernels FusedMoE Layer Test (2 B200s) has been broken since it was added on 04/06/25 #39525 ("broken since it was added").

3. Language Models Tests (Hybrid) 2: one failure, test_models[5-64-Zyphra/Zamba2-1.2B-instruct], and the root cause is an OOM at engine startup: only 19.3 of 22 GiB free when 20.3 was needed, i.e., a previous test leaked GPU memory. There are also HuggingFace 429 rate-limit errors in the log. Classic flake.

4. V1 Sample + Logits: two failures in test_logprobs_e2e. The server variant died at startup with the same leaked-memory OOM pattern (1.36 of 16 GiB free). The offline variant hit RuntimeError: shape '[25, 2]' is invalid for input of size 52 inside serial_utils.py's msgpack tensor decoding, which is a prompt-logprobs deserialization mismatch on an unquantized Llama-3.2-1B, completely outside the Marlin code path. No open issue matches it; it could be a genuine main-side race in the shared-memory tensor transport, but it's not related to this change.

None of these are related to the PR's changes.

[aoshen02]

@mgoin @yewentao256 @robertgshaw2-redhat

[yewentao256]

CC @mgoin

[mgoin]

This is quite messier than I would like.. #48478 is more along the lines but also won't fully address this. Either way, we can accept it for now to unblock

[aoshen02]

Sure, I think 48478 is the final plan but it involve to much refactor for now.