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[Feat] Support native Kimi-K2-Thinking native W4A16 quantized experts weights (#4516)

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### What this PR does / why we need it?

Adds W4A16 quantization method for the Kimi-K2-Thinking model and
updates relevant modules to support the new quantization method.

- Implements complete W4A16 quantization method including weight
packing/unpacking, per-group quantization parameter generation,
post-processing logic and MoE method application.
- Adds parameters `use_int4_w4a16`, `w1_offset` and `w2_offset`, adjusts
`with_quant` conditional logic to support W4A16 matrix multiplication.
- Adds `packed_modules_model_mapping` for Kimi-K2-Thinking model and
processing logic for `weight_packed` field.

- vLLM version: v0.12.0
- vLLM main:
ad32e3e19c

---------

Signed-off-by: zhoux77899 <zhouxiang100@huawei.com>
Signed-off-by: Ruri <33858552+zhoux77899@users.noreply.github.com>
Signed-off-by: Ruri <zhouxiang100@huawei.com>
This commit is contained in:
Ruri
2025-12-10 15:58:52 +08:00
committed by GitHub
parent c1db298f43
commit ce5872705e
13 changed files with 781 additions and 13 deletions
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docs/source/tutorials/index.md
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@@ -12,6 +12,7 @@ single_npu_qwen3_w4a4
single_node_pd_disaggregation_mooncake
multi_npu_qwen3_next
multi_npu
multi_npu_kimi-k2-thinking
multi_npu_moge
multi_npu_qwen3_moe
multi_npu_quantization