[Bugfix][KV Offload] Namespace persistent cache by model runner

[jongukc]

Purpose

V1 and V2 model runners can currently share a persistent native KV-offload
namespace at single-rank parallelism even though their serialized cache layouts
are incompatible. V1 uses a packed cross-layer representation while V2 uses
per-layer tensors. The tested layouts have the same byte count, so size checks
cannot reject V1 pages loaded by V2 and inference silently returns incorrect
output.

This change adds parallel_agnostic: false to FileMapper's persistent
fingerprint when a layout is not safe to share across parallelism
configurations. V1 keeps its existing parallel-agnostic namespace, while V2 is
already classified as non-agnostic and moves away from legacy V1 pages.

Duplicate-work check

I searched open and closed issues and PRs for V1/V2 model runners, FileMapper,
persistent native offload, layout identity, and silent cache corruption. I
found no matching fix. #44733 introduced parallelism-agnostic cache sharing but
does not fingerprint the runner layout. #49261 and #49266 add model revision,
and #48906 deduplicates replicated MLA KV. #48414 fingerprints its opt-in
canonical layout, while #49413 concerns offload-event routing. None separates
legacy V1 bytes from V2 bytes.

Test plan

.venv/bin/python -m pytest -q \
  tests/v1/kv_offload/test_factory.py \
  tests/v1/kv_offload/test_file_mapper.py

.venv/bin/python -m pytest -q tests/v1/kv_offload

.venv/bin/pre-commit run --files \
  vllm/v1/kv_offload/file_mapper.py \
  tests/v1/kv_offload/test_file_mapper.py

git diff --check origin/main...HEAD

The model-level replay uses facebook/opt-125m, FP16, TP=PP=1, a fixed
PYTHONHASHSEED, and a persistent filesystem tier. It runs cold V2, populates
the tier with V1, and then starts V2 against the same root.

Test results

Tested on current upstream main at b0d787518.

• Focused factory and mapper tests: 38 passed.
• Complete KV-offload suite: 386 passed.
• All applicable pre-commit hooks passed, including Ruff and mypy.
• git diff --check passed.
• V1 kept its legacy persistent path, while V2 created a distinct path whose
  config.json contains "parallel_agnostic": false.
• The fixed V2 replay JSON, including token IDs and chosen-token logprobs, was
  byte-identical to the cold V2 baseline.

Before the fix, V2 loaded the V1 files and changed the greedy continuation from
Paris... to .,,,,,, the the.,,,, by a.

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[orozery]

Thanks @jongukc !
I'm trying to understand the physical layout difference between v1 and v2.
I would expect that on both cases, the per-block layout would be (num_layers, num_heads, block_size, 2*head_size).
Can you trace it from your run?

[jongukc]

I traced this with OPT 125M, FP16, FlashAttention 2, TP=PP=1, and the persistent OffloadingConnector.
The connector forces HND.

V1 cross layer allocation:

shape=(15475 blocks, 12 heads, 12 layers, 16 tokens, 128 values)
stride=(294912, 24576, 2048, 128, 1)
canonical row=(15475, 589824) bytes

V2 allocation:

12 tensors, one per layer
shape=(15473 blocks, 12 heads, 16 tokens, 128 values)
stride=(24576, 2048, 128, 1)
CPU row offsets=0, 49152, ..., 540672
CPU row size=589824 bytes

The persisted V1 row order is (head, layer, token, value).
However, V2 concatenates its tensors as (layer, head, token, value).

I reproduced both traces by setting VLLM_USE_V2_MODEL_RUNNER to 0 and 1.
The different block counts come from runner memory overhead and do not affect the 589824 byte block format.

[orozery]

I traced this with OPT 125M, FP16, FlashAttention 2, TP=PP=1, and the persistent OffloadingConnector. The connector forces HND.

V1 cross layer allocation:

shape=(15475 blocks, 12 heads, 12 layers, 16 tokens, 128 values)
stride=(294912, 24576, 2048, 128, 1)
canonical row=(15475, 589824) bytes

V2 allocation:

12 tensors, one per layer
shape=(15473 blocks, 12 heads, 16 tokens, 128 values)
stride=(24576, 2048, 128, 1)
CPU row offsets=0, 49152, ..., 540672
CPU row size=589824 bytes

The persisted V1 row order is (head, layer, token, value). However, V2 concatenates its tensors as (layer, head, token, value).

I reproduced both traces by setting VLLM_USE_V2_MODEL_RUNNER to 0 and 1. The different block counts come from runner memory overhead and do not affect the 589824 byte block format.

Got you. Thanks for the details!

[orozery]

Instead of introducing a model_runner field in FileMapper, let's include parallel_agnostic in the hashable fields, like this:

if not parallel_agnostic:
    self.fields["parallel_agnostic"] = False

[jongukc]

Revised the patch as suggested and checked that the issue is gone now.
Thanks for the review!