model: add GLM 5.2 Indexer support (#25407)

* Start building graph - reuse deepseek32

* Enable kv cache and rotation for glm_dsa architecture

Just follow Deepseek 3.2 for now.

* Reuse prev_top_k for "shared" indexer layers

* GLM 5.2 uses LLAMA_ROPE_TYPE_NORM for the indexer.

This is transformers' `apply_rotary_pos_emb_interleave`

* Default indexer types to GLM pattern

Previous converted GGUFs like https://huggingface.co/unsloth/GLM-5.2-GGUF write indexer weights to _all_ layers, even if they are only required for "full" types. This PR relies on a new key "%s.attention.indexer.types"; if absent, it will use the default GLM 5.2 schedule as defined in https://huggingface.co/zai-org/GLM-5.2/blob/main/config.json#L26.

Note that conversion is not saving this key yet.

* Save indexer types to gguf, restore on load

* Use ggml_lightning_indexer when cparams.fused_lid

Co-authored-by: fairydreaming <166155368+fairydreaming@users.noreply.github.com>

* GLM 5 and 5.1 use full indexers

Co-authored-by: fairydreaming <166155368+fairydreaming@users.noreply.github.com>

* Fix indentation

* Ensure array is zero-filled

* Prefer explicit std::fill

* Assert prev_top_k exists for shared indexer

---------

Co-authored-by: fairydreaming <166155368+fairydreaming@users.noreply.github.com>

gguf-py/gguf/constants.py

@@ -200,6 +200,7 @@ class Keys:
             HEAD_COUNT = "{arch}.attention.indexer.head_count"
             KEY_LENGTH = "{arch}.attention.indexer.key_length"
             TOP_K      = "{arch}.attention.indexer.top_k"
+            TYPES      = "{arch}.attention.indexer.types"
 
     class HyperConnection:
         COUNT                = "{arch}.hyper_connection.count"

gguf-py/gguf/gguf_writer.py

@@ -793,6 +793,10 @@ class GGUFWriter:
     def add_indexer_top_k(self, top_k: int) -> None:
         self.add_uint32(Keys.Attention.Indexer.TOP_K.format(arch=self.arch), top_k)
 
+    def add_indexer_types(self, value: Sequence[bool]) -> None:
+        key = Keys.Attention.Indexer.TYPES.format(arch=self.arch)
+        self.add_array(key, value)
+
     def add_max_alibi_bias(self, bias: float) -> None:
         self.add_float32(Keys.Attention.MAX_ALIBI_BIAS.format(arch=self.arch), bias)