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Align Marlin layer support checks with MarlinLinearKernel's output padding support so AutoRound/INC small-N tensor-parallel shards stay on Marlin instead of falling back to GPTQ GEMM. Signed-off-by: lesj0610 <lesj0610@users.noreply.github.com>
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Closing this fork PR because the Marlin small-N / thread-tile padding issue is now superseded by upstream vllm-project#45295. The remaining integration branch should follow the upstream implementation and drop the stale #39 leftovers. |
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Summary
Fix GPTQ Marlin load failure on small output shards.
Qwen3.6 GDN MoE TP=2 can create GPTQ linear shards with
output_size_per_partition=32. Marlin needs output dim aligned to 64, so loading fails before inference starts.This pads the Marlin weight/scales/zeros to tile-aligned size at load time, runs Marlin with the padded size, and slices the extra output columns after GEMM. Padding uses vLLM parameter metadata (
output_dim,packed_dim,packed_factor), not a fixed tensor layout.Changes
can_implement()checks padded output dim instead of raw dim.apply_weights().Related upstream PRs
Not a duplicate of vllm-project#40361. Both fix the same Marlin sub-tile-N loading problem.
Difference: vllm-project#40361 pads for the current GPTQ Marlin layout. This PR uses vLLM parameter metadata, so packed qzeros and
output_dim=0layouts are handled too.Validation
pre-commiton changed files: passedpytest -q -k 'sub_tile_n_pad or output_padding' tests/kernels/quantization/test_marlin_gemm.py: 4 passed/ssd512g/models/Qwen3.6-35B-A3B-int4-AutoRound-EC, TP=2: load + generation OK,EXCLAMATION_ONLY FalseAI assistance was used (Codex, Claude, Gemini)