llama.cpp

9a757058 - convert : write explicit recurrent_layers for Qwen3-Next / Qwen3.5 (#28208)

convert : write explicit recurrent_layers for Qwen3-Next / Qwen3.5 (#28208) Problem - Loader prefers `<arch>.attention.recurrent_layers`, falls back to `full_attention_interval` if missing - Converter only ever writes the interval. gguf-py has no constant/writer for the array - Interval can only describe evenly spaced full-attention layers. Any non-uniform `layer_types` gets reconstructed wrong - No error, no warning. Model loads, runs, wrong layers get wrong ops. Full-attn layers marked recurrent lose their KV cache - Every published Qwen3.5 checkpoint is uniform so nobody's hit it yet Repro 12 layers, periods 4/3/5: layer: 0 1 2 3 4 5 6 7 8 9 10 11 actual: L L L F L L F L L L L F loader: L L L F L L L F L L L F ^ ^ Layer 6 is full attn, loaded as recurrent. Layer 7 the reverse. 52-layer non-uniform stack: 15/52 mis-typed. Fix - `constants.py`: add `Keys.Attention.RECURRENT_LAYERS` (name already registered in llama-arch.cpp) - `gguf_writer.py`: add `add_recurrent_layers()`, same shape as `add_rope_pattern()` - `conversion/qwen.py`: emit array from `layer_types` in `Qwen3NextModel.set_gguf_parameters` (covers 3-Next, 3.5, 3.5-MoE) Notes - Array is padded with `false` for MTP blocks. `get_key_or_arr` checks length against `n_layer_all`, which includes MTP. Matches the fallback's `i < n_layer()` guard - Interval is still written. Old builds only understand the interval - `layer_types` length != `num_hidden_layers` now raises in converter instead of producing a GGUF that fails at load Tested - End-to-end on a 62-layer non-uniform Qwen3.8-27B (2 linear layers removed). Loader reads the array, 62 blocks, 0 mismatches. Without fix: interval fallback, mis-typed - MTP padding NOT tested on a real MTP model. Reasoned from qwen35.cpp + get_key_or_arr. Would appreciate a check Co-authored-by: Claude Opus 5 <noreply@anthropic.com>