model: add NextN/MTP speculative decoding support for GLM_DSA (GLM-5.2) (#25980)

* model: add NextN/MTP speculative decoding support for GLM_DSA (GLM-5.2)

Adds GLM-5.2 NextN/MTP as a --spec-type draft-mtp target: nextn tensor
loading via the qwen35moe/step35-style presence probe, a graph_mtp
builder (enorm/hnorm/eh_proj + dense MLA + sigmoid-gated MoE with
shared expert + shared head with fallbacks, _s scale tensors passed
for NVFP4), t_h_nextn extraction in the trunk graph, and MTP-context
KV setup: the draft head runs dense MLA, so the MTP context uses a
plain attention KV cache holding only the nextn layer(s) (same
pattern as the hybrid Qwen3.5 MTP context) while the main context
keeps the DSA cache, now filtered to trunk layers only.

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>

* convert : support --mtp/--no-mtp export for GlmMoeDsaForCausalLM (GLM-5.2)

Opt GLM-5.2 into the supports_mtp_export contract (post-#25641 shape,
mirroring HYV3Model/Step35Model): --no-mtp drops the appended NextN
block (blk.78) and its nextn_predict_layers KV; --mtp keeps only the
NextN block plus shared embeddings/norm/lm_head. Default (bundled)
output is unchanged.

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>

---------

Co-authored-by: Claude Fable 5 <noreply@anthropic.com>

conversion/glm.py

@@ -1,6 +1,8 @@
 from __future__ import annotations
 
-from typing import Iterable, TYPE_CHECKING
+import re
+
+from typing import Callable, Iterable, TYPE_CHECKING
 
 import torch
 
@@ -213,12 +215,47 @@ class Glm4MoeLiteModel(DeepseekV2Model):
 class GlmMoeDsaModel(DeepseekV2Model):
     model_arch = gguf.MODEL_ARCH.GLM_DSA
     skip_mtp = False
+    supports_mtp_export = True
+
+    # Trunk layer count, stashed before indexing so the classmethod
+    # filter_tensors can identify the appended NextN/MTP block (mirrors
+    # HYV3Model / Step35Model).
+    _n_main_layers: int | None = None
 
     def __init__(self, *args, **kwargs):
         super().__init__(*args, **kwargs)
-        self.block_count = self.hparams["num_hidden_layers"] + self.hparams.get("num_nextn_predict_layers", 0)
+        self.block_count = self.hparams["num_hidden_layers"]
+        if not self.no_mtp:
+            self.block_count += self.hparams.get("num_nextn_predict_layers", 0)
         self.tensor_map = gguf.get_tensor_name_map(self.model_arch, self.block_count)
 
+    def index_tensors(self, remote_hf_model_id: str | None = None):
+        type(self)._n_main_layers = self.hparams["num_hidden_layers"]
+        return super().index_tensors(remote_hf_model_id=remote_hf_model_id)
+
+    @classmethod
+    def filter_tensors(cls, item: tuple[str, Callable[[], Tensor]]) -> tuple[str, Callable[[], Tensor]] | None:
+        if (titem := super().filter_tensors(item)) is None:
+            return None
+        name, gen = titem
+
+        # GLM-5.2 appends the NextN/MTP block past num_hidden_layers
+        # (model.layers.78 -> blk.78 in the 79-block file).
+        assert cls._n_main_layers is not None
+        is_mtp = (m := re.match(r"model\.layers\.(\d+)\.", name)) is not None and int(m.group(1)) >= cls._n_main_layers
+
+        # --no-mtp: drop the appended NextN block entirely.
+        if is_mtp and cls.no_mtp:
+            return None
+        # --mtp: keep ONLY NextN-block tensors plus the shared embeddings/
+        # norm/lm_head (so the resulting GGUF carries just the draft head).
+        if cls.mtp_only and not is_mtp and name not in (
+            "model.embed_tokens.weight", "model.norm.weight", "lm_head.weight",
+        ):
+            return None
+
+        return name, gen
+
     def set_vocab(self):
         return self._set_vocab_glm()
 
@@ -230,7 +267,7 @@ class GlmMoeDsaModel(DeepseekV2Model):
         self.gguf_writer.add_rope_dimension_count(int(rope_dim * partial_rotary_factor))
 
         # NextN/MTP prediction layers
-        if (num_nextn_predict_layers := self.hparams.get("num_nextn_predict_layers")) is not None:
+        if not self.no_mtp and (num_nextn_predict_layers := self.hparams.get("num_nextn_predict_layers")) is not None:
             self.gguf_writer.add_nextn_predict_layers(num_nextn_predict_layers)
 
         # DSA indexer parameters