perf(exl3): load mixed Trellis directly into tier slabs

[malaiwah]

What changed

Mixed-bitrate rank-sliced EXL3 tensors now load directly into their final
contiguous K3/K4 tier slabs instead of retaining 256 per-expert tensors and
repacking every layer with nested torch.stack calls after loading.

Exl3MoEParameter accepts an exact expert partition, maps each incoming expert
to a deterministic (group, offset), and preallocates one projection-major
backing per bitrate. _prepare_mixed_rank_sliced_weights binds those backings
zero-copy. The cleanup path resets the backing slots rather than deleting their
shape, preserving reload behavior.

This is a small child PR against the current EXL3 integration head in #228 so
reviewers see only the startup change. It addresses #276.

Why

An exact r33 launch of willfalco/GLM-5.2-EXL3-TR3-3.42bpw, TP4/DCP4/MTP3,
showed the layer-3 through layer-77 mixed-Trellis materialization phase taking
336 seconds (11:26:21 to 11:31:57) on every rank. Total model loading was
1217.15 seconds, so this serial per-rank phase represented 27.6% of the reported
load interval.

The ranks are already concurrent. The serialization is within each worker's
generic process_weights_after_loading() module walk. A typical 3.42-bpw layer
(H=6144, TP-local I=512, 148 K3 + 108 K4 experts) contains about 985.5 MiB
of Trellis payload per rank. The old path temporarily holds both the per-expert
source storage and a second tier-ordered copy for each layer.

This patch moves the mandatory loader copy into the final layout and removes
the later device-to-device repack. It should eliminate roughly 985.5 MiB of
transient VRAM for a typical layer and collapse most of the visible 336-second
post-load phase. The honest total startup improvement still needs a matched GPU
A/B because the mandatory host-to-device transfer moves earlier into the
safetensors phase rather than disappearing.

Validation

On the exact r33 runtime, in a separate CPU-only/network-isolated container:

45 passed, 15 warnings in 10.43s

Coverage includes:

• deterministic K3/K4 expert partitioning;
• projection-major W13 and single-shard W2 layouts;
• zero-copy pointer identity from loader backing to both prepared tier plans;
• exact partition and incomplete-load failures;
• cleanup state that remains reusable by a later reload.

Additional checks:

ruff check vllm/model_executor/layers/quantization/exl3.py \
  tests/quantization/test_exl3.py
All checks passed!

git diff --check

GPU qualification still required

Before merging, compare patched/unpatched warm-cache launches on the same
checkpoint and profile:

1. total model load and layer-3-to-layer-77 materialization time;
2. peak/final VRAM and allocator fragmentation;
3. load correctness and deterministic tier counts;
4. short generation, C1/C8 decode, 8K/64K prefill, MTP acceptance;
5. long-context needle/correctness gate.

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📥 Commits

Reviewing files that changed from the base of the PR and between fa033bd and 07a3f22.

📒 Files selected for processing (2)

• tests/quantization/test_exl3.py
• vllm/model_executor/layers/quantization/exl3.py

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[malaiwah]

Clarification on the evidence and performance claim:

• Directly measured on the r33 GLM-5.2 TP4/DCP4 startup: the mixed-Trellis post-load phase spans 336 s (11:26:21 to 11:31:57), which is 27.6% of the 1,217.15 s model-loading interval.
• For a representative 3.42 bpw TP-local layer (H=6144, I=512, 148 K3 + 108 K4 experts), the current path creates approximately 985.5 MiB/rank of transient duplicate tier payload while repacking that layer. The proposed path writes directly into the final tier slabs and removes that second device-side representation.
• This PR does not yet claim a total startup wall-time gain. It moves mandatory host-to-device work into the safetensors loading interval, so only a matched GPU A/B can establish the end-to-end improvement. The GPU gate must compare Loading weights, first/last mixed-Trellis log, total model-loading time, and peak/final VRAM under the same warm cache and serving profile.

The focused CPU/static validation demonstrates ordering, pointer identity/zero-copy binding, malformed-partition rejection, and reload-safe cleanup; GPU qualification remains the release gate.

[malaiwah]

Test Results (automated)

Host: macOS M4 Max, CPU-only (no CUDA)
Command: .venv/bin/python -m pytest tests/quantization/test_exl3.py --noconftest -x -v

Tests could not be collected — the test module imports vllm.model_executor.layers.quantization.exl3, which triggers a deep import chain (vllm.model_executor → vllm.distributed → vllm.utils.network_utils) that requires the zmq package, which is not installed in this CPU-only environment.

==================================== ERRORS ====================================
_______________ ERROR collecting tests/quantization/test_exl3.py _______________
ImportError while importing test module '/private/tmp/test-pr-277/tests/quantization/test_exl3.py'.
Hint: make sure your test modules/packages have valid Python names.
Traceback:
/Users/mbelleau/.local/share/uv/python/cpython-3.12.13-macos-aarch64-none/lib/python3.12/importlib/__init__.py:90: in import_module
    return _bootstrap._gcd_import(name[level:], package, level)
tests/quantization/test_exl3.py:9: in <module>
    import vllm.model_executor.layers.quantization.exl3 as exl3_module
vllm/model_executor/__init__.py:4: in <module>
    from vllm.model_executor.parameter import BasevLLMParameter, PackedvLLMParameter
vllm/model_executor/parameter.py:12: in <module>
    from vllm.distributed import (
vllm/distributed/__init__.py:4: in <module>
    from vllm.distributed.communication_op import *
vllm/distributed/communication_op.py:9: in <module>
    from vllm.distributed.parallel_state import get_tp_group
vllm/distributed/parallel_state.py:49: in <module>
    from vllm.distributed.utils import (
vllm/distributed/utils.py:33: in <module>
    from vllm.utils.network_utils import get_tcp_uri
vllm/utils/network_utils.py:17: in <module>
    import zmq
E   ModuleNotFoundError: No module named 'zmq'
------------------------------- Captured stdout --------------------------------
WARNING 08-12 15:48:53 [__init__.py:27] The vLLM package was not found, so its version could not be inspected. This may cause platform detection to fail.
=============================== warnings summary ===============================
vllm/__init__.py:7
  /private/tmp/test-pr-277/vllm/__init__.py:7: RuntimeWarning: Failed to read commit hash:
  No module named 'vllm._version'
    from .version import __version__, __version_tuple__  # isort:skip

-- Docs: https://docs.pytest.org/en/stable/how-to/capture/warnings.html
=========================== short test summary info ============================
ERROR tests/quantization/test_exl3.py
!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!! stopping after 1 failures !!!!!!!!!!!!!!!!!!!!!!!!!!!
!!!!!!!!!!!!!!!!!!!!!!!!!!!! Interrupted: 1 error during collection !!!!!!!!!!!!!!!!!!!!
========================= 1 warning, 1 error in 1.25s ==========================

Tests require dependencies not available on this host (macOS M4 Max, CPU-only torch, no CUDA, no zmq). Cannot run.

---

Automated test run by @malaiwah's agent. Results are from a CPU-only environment; GPU-dependent tests may behave differently on CUDA hardware.

[malaiwah]

Addressed review findings C15, N6, N7 (commit `07a3f22`).

C15 — silent dtype coercion in the slab loader (merge-blocking)

The hole: the tier-slab loader inferred the backing dtype from the first tensor and validated only shape for subsequent tensors. `target.copy_(loaded_weight)` then silently cast any later mismatched dtype, so e.g. one int32 Trellis tensor among int16 tensors, or one float32 scale among float16 scales, was truncated/rounded before `_validate_moe_shapes` ran — letting a malformed checkpoint pass validation instead of failing deterministically. The pre-slab path preserved each tensor's dtype and would have rejected it.

The fix: rather than trusting the first untrusted tensor, the required dtype is now derived from the parameter suffix via a new `_EXL3_SLAB_DTYPE` map (`trellis → int16`, `suh/svh → float16`; `mcg`/`mul1` markers are int32 but never slab-preallocated, so intentionally absent — their dtype is still checked by `validate_marker`). Before every `copy` into a slab (both the grouped-slab path and the projection-slab path), `loaded_weight.dtype` is checked against this schema dtype and a `ValueError` is raised naming the suffix, expert, shard, expected dtype and received dtype. For suffixes without a canonical mapping the check falls back to consistency against the first tensor (`target.dtype`), with a comment explaining the fallback. The slab is now allocated with the schema dtype, so even a malformed first tensor cannot pin the backing dtype.

N6 — implicit slab layout ordering

Added a layout-contract line to the `exl3_group_backing` docstring: the backing is shard-major, group-offset-minor, in `exl3_shard_ids` order (`backing[shard_index, group_offset]`, or `backing[group_offset]` for a single shard). This is the invariant the mixed-rank-sliced loader relies on to stack tiers correctly; `shard_index` comes from `exl3_shard_ids.index(shard_id)` and `group_offset` from the expert's position in its preallocation group.

N7 — `torch.empty` slab allocation invariant

Added a comment at the grouped-slab `torch.empty` allocation noting that the backing is uninitialized until every `(expert, shard)` slot is `copy_`'d in, and that the `loaded == expected` completeness check in `exl3_group_backing` is what prevents a direct reader of `exl3_group_backings[i]` from seeing garbage — so any future caller must go through that accessor rather than indexing the backing directly.

New tests (`tests/quantization/test_exl3.py`)

• `test_grouped_slab_rejects_mismatched_trellis_dtype` — int32 trellis tensor among int16 (grouped slab, subsequent tensor) → `ValueError`.
• `test_grouped_slab_rejects_wrong_first_tensor_dtype` — int32 as the first trellis tensor (grouped slab) → `ValueError` (proves the schema-derived dtype catches a bad first tensor, not just later ones).
• `test_projection_slab_rejects_mismatched_scale_dtype` — float32 scale among float16 (projection slab) → `ValueError`.
• `test_grouped_slab_accepts_consistent_dtype` — regression guard: a homogeneous-dtype grouped slab still loads.

Bug proof: with the dtype check neutralized (but the `suffix` plumbing kept), the three rejection tests fail with `DID NOT RAISE ValueError` — i.e. the silent cast occurs and the malformed checkpoint is accepted. With the check restored, all four pass.

Verification

```
/Users/mbelleau/Projects/vllm-voipmonitor/.venv/bin/python -m pytest tests/quantization/test_exl3.py --noconftest -v
```
→ 23 passed, 3 failed. The 4 new tests pass and the 2 existing slab tests still pass. The 3 failures are pre-existing on this PR branch (identical before and after my changes — confirmed by stashing) and are unrelated to C15/N6/N7:

• `test_rank_sliced_broadcast_pointer_table_repeats_one_physical_row` — calls `_pointer_table(slab, num_experts=4)` but the PR code's `_pointer_table` signature takes only `slab` (test/code drift).
• `test_rank_sliced_shared_h_create_weights_allocates_one_physical_row` — asserts `layer.exl3_shared_h_rotations`, an attribute the PR code never sets (test/code drift).
• `test_mixed_rank_sliced_weights_are_partitioned_by_declared_bitrate` — expects `_prepare_mixed_rank_sliced_weights` to produce 4 `prepare_weights` calls (`[3,3,4,4]`) but the PR code produces 2 (`[3,4]`) (test/code drift in the tier loop).

These three are out of scope for the assigned findings and are left for the PR author; I did not modify them.

Sequencing note (#270)

This PR and #270 both rewrite `create_weights` and `_prepare_mixed_rank_sliced_weights` in `exl3.py`, so they will conflict on merge. Whichever lands first will require a rebase of the other. My changes to `create_weights` are limited to passing `suffix=suffix` to `Exl3MoEParameter(...)`, which should rebase cleanly onto either ordering.

[malaiwah]

After restarting mixed bpw quant of GLM-5.2 on vLLM many times in a row, I got sick of seeing it take so long to load and this one PR addresses a long part of the startup.