[klshuster]

Motivation

We need this packed modules mapping for reading excluded modules from quantization configs. This is primarily a concern for nvfp4 weights, such as NVIDIA's checkpoint here. Note that this checkpoint is OK because of wildcards but a more intentional/systematic approach is desired.

The following is AI's slightly-verbose rationale:

KimiK3's runtime fuses checkpoint shards into merged modules (qkv_conv1d, fused_qkvg_proj, fused_qkvbfg_a_proj, fused_fg_b_proj, ...), but the model class declares no packed_modules_mapping, so quantization configs cannot match per-shard exclude_modules against the fused runtime prefixes.

Concretely, NVIDIA's Kimi-K3-NVFP4 hf_quant_config.json excludes self_attn.q_conv1d / self_attn.k_conv1d / self_attn.v_conv1d; today the fused qkv_conv1d module is only excluded because the v_conv1d pattern happens to substring-match the fused name 'qkv_conv1d' -- the q_ and k_ patterns match nothing. Any checkpoint whose excludes do not get that one-character accident quantizes modules the producer excluded.

Declare the mapping (same shape as the 80+ other model classes), so ModelOptFp4Config and friends resolve fused prefixes to their shard names structurally.

Modifications

Added packed modules mapping to K3

Accuracy Tests

Speed Tests and Profiling

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CI States

Latest PR Test (Base): ✅ Run #33060683597
Latest PR Test (Extra): ❌ Run #33060683273
Latest PR Test (AMD ROCm 7.2): ❌ Run #33060683637