feat(sft): add Qwen3.5 MoE support + fused checkpoint loading

- arch.py: add Qwen3_5Moe arch match, read config from text_config,
  _get_layers_prefix returns model.language_model.layers for Qwen3.5,
  _get_model_container_and_layers searches language_model attr
- weights.py: load_experts_from_checkpoint_files detects fused format
  (gate_up_proj in weight_map) and splits into gate/up/down
- wrapper.py: hidden_size fallback to text_config

Verified: Qwen3.5-35B-A3B (256 experts, fused format) E2E pass.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

kt-kernel/python/sft/arch.py

@@ -98,16 +98,17 @@ def get_moe_arch_config(config) -> MOEArchConfig:
             has_shared_experts=getattr(config, "n_shared_experts", 0) > 0,
             router_type="deepseek_gate",
         )
-    if "Qwen2Moe" in arch or "Qwen3Moe" in arch:
+    if "Qwen2Moe" in arch or "Qwen3Moe" in arch or "Qwen3_5Moe" in arch:
+        cfg = getattr(config, "text_config", config)
         return MOEArchConfig(
             moe_layer_attr="mlp",
             router_attr="gate",
             experts_attr="experts",
             weight_names=("gate_proj", "up_proj", "down_proj"),
-            expert_num=config.num_experts,
-            intermediate_size=config.moe_intermediate_size,
-            num_experts_per_tok=config.num_experts_per_tok,
-            has_shared_experts=getattr(config, "shared_expert_intermediate_size", 0) > 0,
+            expert_num=cfg.num_experts,
+            intermediate_size=cfg.moe_intermediate_size,
+            num_experts_per_tok=cfg.num_experts_per_tok,
+            has_shared_experts=getattr(cfg, "shared_expert_intermediate_size", 0) > 0,
         )
     if "Mixtral" in arch:
         return MOEArchConfig(
@@ -123,7 +124,7 @@ def get_moe_arch_config(config) -> MOEArchConfig:
 
     raise KTAMXModelNotSupportedError(
         f"Model architecture {arch} not supported for KT AMX. "
-        "Supported architectures: DeepseekV2, DeepseekV3, Qwen2Moe, Qwen3Moe, Mixtral"
+        "Supported architectures: DeepseekV2, DeepseekV3, Qwen2Moe, Qwen3Moe, Qwen3_5Moe, Mixtral"
     )
 
 
@@ -154,8 +155,8 @@ def detect_fused_experts(experts: nn.Module) -> bool:
 
 def _get_layers_prefix(config) -> str:
     arch = config.architectures[0] if getattr(config, "architectures", None) else ""
-    if any(x in arch for x in ["Deepseek", "Qwen", "Mixtral", "Llama"]):
-        return "model.layers"
+    if "Qwen3_5Moe" in arch:
+        return "model.language_model.layers"
     return "model.layers"
 
 
@@ -181,7 +182,7 @@ def _get_model_container_and_layers(model: nn.Module, *, purpose: str) -> tuple[
         if layers is not None and isinstance(layers, (list, tuple, nn.ModuleList)):
             return current, layers
 
-        for attr in ("model", "base_model", "pretrained_model", "module"):
+        for attr in ("model", "base_model", "pretrained_model", "module", "language_model"):
             child = getattr(current, attr, None)
             if isinstance(child, nn.Module) and child is not current:
                 to_visit.append(child)

kt-kernel/python/sft/weights.py

@@ -309,15 +309,23 @@ def load_experts_from_checkpoint_files(
         weight_map = sharded_metadata.get("weight_map", None)
 
     gate_name, up_name, down_name = moe_config.weight_names
-    keys = []
-    for expert_idx in range(moe_config.expert_num):
-        base = f"{layers_prefix}.{layer_idx}.{moe_config.moe_layer_attr}.{moe_config.experts_attr}.{expert_idx}"
-        keys.append(f"{base}.{gate_name}.weight")
-        keys.append(f"{base}.{gate_name}.weight_scale_inv")
-        keys.append(f"{base}.{up_name}.weight")
-        keys.append(f"{base}.{up_name}.weight_scale_inv")
-        keys.append(f"{base}.{down_name}.weight")
-        keys.append(f"{base}.{down_name}.weight_scale_inv")
+    experts_prefix = f"{layers_prefix}.{layer_idx}.{moe_config.moe_layer_attr}.{moe_config.experts_attr}"
+    fused_gate_up_key = f"{experts_prefix}.gate_up_proj"
+    fused_down_key = f"{experts_prefix}.down_proj"
+    is_fused = weight_map is not None and fused_gate_up_key in weight_map
+
+    if is_fused:
+        keys = [fused_gate_up_key, fused_down_key]
+    else:
+        keys = []
+        for expert_idx in range(moe_config.expert_num):
+            base = f"{experts_prefix}.{expert_idx}"
+            keys.append(f"{base}.{gate_name}.weight")
+            keys.append(f"{base}.{gate_name}.weight_scale_inv")
+            keys.append(f"{base}.{up_name}.weight")
+            keys.append(f"{base}.{up_name}.weight_scale_inv")
+            keys.append(f"{base}.{down_name}.weight")
+            keys.append(f"{base}.{down_name}.weight_scale_inv")
 
     keys_by_file: dict[str, list[str]] = {}
     mapped_count = 0
@@ -362,6 +370,30 @@ def load_experts_from_checkpoint_files(
         flush=True,
     )
 
+    t2 = time.time()
+    if is_fused:
+        gate_up = tensor_map.get(fused_gate_up_key)
+        down = tensor_map.get(fused_down_key)
+        if gate_up is None or down is None:
+            raise FileNotFoundError(f"Missing fused expert weights for layer {layer_idx}")
+        gate_up = gate_up.cpu().to(torch.bfloat16).contiguous()
+        I = gate_up.shape[1] // 2
+        gate_proj = gate_up[:, :I, :].contiguous()
+        up_proj = gate_up[:, I:, :].contiguous()
+        down_proj = down.cpu().to(torch.bfloat16).contiguous()
+        del gate_up
+        print(
+            f"[kt_moe] Layer {layer_idx}: fused expert format — "
+            f"split gate_up_proj [{gate_proj.shape}] + down [{down_proj.shape}]",
+            flush=True,
+        )
+        print(
+            f"[kt_moe] Layer {layer_idx}: done — dtype={gate_proj.dtype}, "
+            f"shape={gate_proj.shape}, dequant=0.0s, total={time.time()-t0:.1f}s",
+            flush=True,
+        )
+        return gate_proj, up_proj, down_proj
+
     gate_weights = []
     up_weights = []
     down_weights = []
@@ -369,7 +401,7 @@ def load_experts_from_checkpoint_files(
     up_scales = []
     down_scales = []
     for expert_idx in range(moe_config.expert_num):
-        base = f"{layers_prefix}.{layer_idx}.{moe_config.moe_layer_attr}.{moe_config.experts_attr}.{expert_idx}"
+        base = f"{experts_prefix}.{expert_idx}"
         gate_key = f"{base}.{gate_name}.weight"
         up_key = f"{base}.{up_name}.weight"
         down_key = f"{base}.{down_name}.weight"

kt-kernel/python/sft/wrapper.py

@@ -157,7 +157,8 @@ def wrap_moe_layers_with_kt_wrapper(model: nn.Module, kt_plugin: Any) -> list[KT
         is_rank_0 = dist.get_rank() == 0
 
     moe_config = get_moe_arch_config(model.config)
-    hidden_size = model.config.hidden_size
+    _text_cfg = getattr(model.config, "text_config", model.config)
+    hidden_size = _text_cfg.hidden_size
 
     cfg = _get_kt_config(kt_plugin)