[AMD] Fix GLM-5.2 MTP Quark excludes (#30265)

Co-authored-by: zhaolin <zhaolin@amd.com>
Co-authored-by: Raiden-Makoto <Raiden-Makoto@users.noreply.github.com>
Co-authored-by: HAI <hixiao@gmail.com>

python/sglang/srt/configs/model_config.py

@@ -110,6 +110,7 @@ def is_deepseek_dsa(config) -> bool:
             "MistralLarge3ForCausalLM",
             "PixtralForConditionalGeneration",
             "GlmMoeDsaForCausalLM",
+            "GlmMoeDsaForCausalLMNextN",
             "LongcatFlashForCausalLM",
             "LongcatFlashForCausalLMNextN",
         )
@@ -536,10 +537,12 @@ class ModelConfig:
         if is_draft_model and self.hf_config.architectures[0] in [
             "DeepseekV3ForCausalLM",
             "DeepseekV32ForCausalLM",
-            "GlmMoeDsaForCausalLM",
         ]:
             self.hf_config.architectures[0] = "DeepseekV3ForCausalLMNextN"
 
+        if is_draft_model and self.hf_config.architectures[0] == "GlmMoeDsaForCausalLM":
+            self.hf_config.architectures[0] = "GlmMoeDsaForCausalLMNextN"
+
         if (
             is_draft_model
             and self.hf_config.architectures[0] == "DeepseekV4ForCausalLM"
@@ -741,6 +744,7 @@ class ModelConfig:
             or "Glm4MoeLiteForCausalLM" in self.hf_config.architectures
             or "Glm4MoeLiteForCausalLMNextN" in self.hf_config.architectures
             or "GlmMoeDsaForCausalLM" in self.hf_config.architectures
+            or "GlmMoeDsaForCausalLMNextN" in self.hf_config.architectures
             or "LongcatFlashForCausalLM" in self.hf_config.architectures
             or "LongcatFlashForCausalLMNextN" in self.hf_config.architectures
             or "DotsVLMForCausalLM" in self.hf_config.architectures

python/sglang/srt/models/deepseek_nextn.py

@@ -287,6 +287,18 @@ class DeepseekV3ForCausalLMNextN(DeepseekV3ForCausalLM):
         },
     )
 
+    def _resolve_nextn_quant_config(self, config, quant_config):
+        if quant_config is None or quant_config.get_name() != "quark":
+            return quant_config
+
+        from sglang.srt.layers.quantization.quark.utils import should_ignore_layer
+
+        ckpt_prefix = f"model.layers.{config.num_hidden_layers}"
+        mapped_prefix = self.hf_to_sglang_mapper._map_name(ckpt_prefix)
+        if should_ignore_layer(mapped_prefix, quant_config.exclude_layers):
+            return None
+        return quant_config
+
     def __init__(
         self,
         config: PretrainedConfig,
@@ -310,17 +322,7 @@ class DeepseekV3ForCausalLMNextN(DeepseekV3ForCausalLM):
             self.cp_rank = None
             self.cp_size = None
 
-        nextn_quant_config = quant_config
-        # For quark, if the MTP layer is listed in exclude_layers, set quant_config to None.
-        if nextn_quant_config is not None and nextn_quant_config.get_name() == "quark":
-            from sglang.srt.layers.quantization.quark.utils import (
-                should_ignore_layer,
-            )
-
-            ckpt_prefix = f"model.layers.{config.num_hidden_layers}"
-            mapped_prefix = self.hf_to_sglang_mapper._map_name(ckpt_prefix)
-            if should_ignore_layer(mapped_prefix, nextn_quant_config.exclude_layers):
-                nextn_quant_config = None
+        nextn_quant_config = self._resolve_nextn_quant_config(config, quant_config)
 
         self.model = DeepseekModelNextN(
             config, nextn_quant_config, prefix=add_prefix("model", prefix)

python/sglang/srt/models/glm4_moe.py

@@ -80,9 +80,14 @@ from sglang.srt.layers.vocab_parallel_embedding import (
 from sglang.srt.model_executor.forward_batch_info import ForwardBatch, PPProxyTensors
 from sglang.srt.model_executor.runner import get_is_capture_mode
 from sglang.srt.model_loader.weight_utils import default_weight_loader
+from sglang.srt.models.deepseek_nextn import DeepseekV3ForCausalLMNextN
 from sglang.srt.models.deepseek_v2 import DeepseekV2ForCausalLM
-from sglang.srt.models.utils import apply_qk_norm
-from sglang.srt.runtime_context import get_parallel, get_server_args, get_stream
+from sglang.srt.models.utils import WeightsMapper, apply_qk_norm
+from sglang.srt.runtime_context import (
+    get_parallel,
+    get_server_args,
+    get_stream,
+)
 from sglang.srt.server_args import get_global_server_args
 from sglang.srt.utils import (
     add_prefix,
@@ -1484,4 +1489,72 @@ class GlmMoeDsaForCausalLM(DeepseekV2ForCausalLM):
         super().determine_num_fused_shared_experts("GlmMoeDsaForCausalLM")
 
 
-EntryClass = [Glm4MoeForCausalLM, GlmMoeDsaForCausalLM]
+class GlmMoeDsaForCausalLMNextN(DeepseekV3ForCausalLMNextN):
+    # GLM-5.2's MTP layer index differs from DeepSeek's (61), so the inherited
+    # substr mapping would wrongly rewrite GLM's real layer-61 weights.
+    # exclude_layers remapping for the MTP layer is handled explicitly in
+    # _resolve_nextn_quant_config below instead.
+    hf_to_sglang_mapper = WeightsMapper()
+
+    _NEXTN_SPEC_WEIGHT_NAMES = ("shared_head.norm", "eh_proj", "enorm", "hnorm")
+
+    @classmethod
+    def _map_mtp_ckpt_name(cls, name: str, layer_prefix: str) -> str:
+        # Keep this mapping in sync with DeepseekV2WeightLoaderMixin's
+        # NextN rule: MTP-specific weights live under model.*, while the
+        # decoder block weights live under model.decoder.*.
+        if any(part in name for part in cls._NEXTN_SPEC_WEIGHT_NAMES):
+            return name.replace(layer_prefix, "model", 1)
+        return name.replace(layer_prefix, "model.decoder", 1)
+
+    def _resolve_nextn_quant_config(self, config, quant_config):
+        if quant_config is None or quant_config.get_name() != "quark":
+            return quant_config
+
+        layer_prefix = f"model.layers.{config.num_hidden_layers}"
+
+        # Quark's per-module scheme selection (e.g. MTP self_attn in PTPC-FP8
+        # while MTP MoE is MXFP4) is keyed by "layer_quant_config" patterns
+        # using the checkpoint's "model.layers.<N>.*" naming. SGLang queries
+        # schemes by the runtime "model.*"/"model.decoder.*" prefix, so those
+        # keys need the same remap as exclude_layers below, or they silently
+        # fall back to the wrong (layer-type/global) scheme.
+        layer_quant_config = quant_config.quant_config.get("layer_quant_config")
+        if layer_quant_config:
+            quant_config.quant_config["layer_quant_config"] = {
+                (
+                    self._map_mtp_ckpt_name(pattern, layer_prefix)
+                    if pattern.startswith(layer_prefix + ".")
+                    else pattern
+                ): pattern_config
+                for pattern, pattern_config in layer_quant_config.items()
+            }
+
+        mtp_excluded = [
+            name
+            for name in quant_config.exclude_layers
+            if name.startswith(layer_prefix + ".")
+        ]
+        if not mtp_excluded:
+            return quant_config
+
+        names = set(quant_config.exclude_layers)
+        for name in mtp_excluded:
+            names.add(self._map_mtp_ckpt_name(name, layer_prefix))
+
+        # Fused routed experts are queried by the coarse module prefix
+        # "model.decoder.mlp.experts". Expanded per-expert leaf excludes do not
+        # match that prefix, so add the coarse prefix when any routed expert in
+        # the MTP layer is excluded. This keeps only that fused MoE module bf16
+        # while allowing the remaining draft modules to use their quant config.
+        if any(".mlp.experts." in name for name in mtp_excluded):
+            names.add("model.decoder.mlp.experts")
+
+        import copy
+
+        quant_config = copy.copy(quant_config)
+        quant_config.exclude_layers = list(names)
+        return quant_config
+
+
+EntryClass = [Glm4MoeForCausalLM, GlmMoeDsaForCausalLM, GlmMoeDsaForCausalLMNextN]