(kt-kernel): add numa_nodes parameter for explicit NUMA node mapping (#1891)

Add numa_nodes parameter to BaseMoEWrapper and all subclasses, allowing
users to explicitly specify which NUMA node IDs to use for subpool
mapping instead of always defaulting to sequential [0, 1, ..., N-1].

This enables running multiple KTransformers instances on different NUMA
nodes of the same machine, e.g. --kt-threadpool-count 1 --kt-numa-nodes 1
to bind to NUMA node 1. Previously this required external numactl
workarounds since subpool_numa_map was hardcoded to start from 0.

kt-kernel/python/utils/amx.py

@@ -1,7 +1,7 @@
 import os
 import torch
 import ctypes
-from typing import Optional
+from typing import List, Optional
 
 # Use relative imports for package structure
 from ..experts_base import BaseMoEWrapper
@@ -53,6 +53,7 @@ class AMXMoEWrapper(BaseMoEWrapper):
         cpu_save: bool = False,
         max_deferred_experts_per_token: Optional[int] = None,
         method: str = "AMXINT4",
+        numa_nodes: Optional[List[int]] = None,
     ):
         """
         Initialize AMX MoE Wrapper.
@@ -103,6 +104,7 @@ class AMXMoEWrapper(BaseMoEWrapper):
             cpu_save=cpu_save,
             max_deferred_experts_per_token=max_deferred_experts_per_token,
             method=method,
+            numa_nodes=numa_nodes,
         )
 
         # AMX-specific: Check if we should load merged safetensor weights
@@ -288,7 +290,11 @@ class AMXMoEWrapper(BaseMoEWrapper):
             moe_config.save = True
             moe_config.load = False
             base_key = f"model.layers.{self.layer_idx}"
-            w = self.safetensor_loader.load_experts(base_key)
+            try:
+                w = self.safetensor_loader.load_experts(base_key)
+            except (ValueError, KeyError):
+                base_key = f"model.language_model.layers.{self.layer_idx}"
+                w = self.safetensor_loader.load_experts(base_key)
 
             self.gate_proj = torch.cat(w["gate_weight"], dim=0).contiguous()
             self.up_proj = torch.cat(w["up_weight"], dim=0).contiguous()
@@ -392,6 +398,7 @@ class NativeMoEWrapper(BaseMoEWrapper):
             cpu_save=cpu_save,
             max_deferred_experts_per_token=max_deferred_experts_per_token,
             method=method,
+            numa_nodes=numa_nodes,
         )
 
         if NativeMoEWrapper._native_loader_instance is None:
@@ -431,7 +438,12 @@ class NativeMoEWrapper(BaseMoEWrapper):
 
         t0 = time.time()
         base_key = f"model.layers.{self.layer_idx}"
-        weights = self.loader.load_experts(base_key)
+        try:
+            weights = self.loader.load_experts(base_key)
+        except (ValueError, KeyError):
+            # For VL/multimodal models (e.g. Qwen3.5) with 'language_model' prefix
+            base_key = f"model.language_model.layers.{self.layer_idx}"
+            weights = self.loader.load_experts(base_key)
         t1 = time.time()
 
         # Keep individual tensors instead of stacking - avoid expensive memory copy

kt-kernel/python/utils/llamafile.py

@@ -1,5 +1,5 @@
 import torch
-from typing import Optional
+from typing import List, Optional
 import os
 
 # Use relative imports for package structure
@@ -133,6 +133,7 @@ class LlamafileMoEWrapper(BaseMoEWrapper):
             cpu_save=cpu_save,
             max_deferred_experts_per_token=max_deferred_experts_per_token,
             method=method,
+            numa_nodes=numa_nodes,
         )
 
         self.weights_to_keep = None

kt-kernel/python/utils/moe_kernel.py

@@ -1,7 +1,7 @@
 import os
 import torch
 import ctypes
-from typing import Optional
+from typing import List, Optional
 
 # Use relative imports for package structure
 from ..experts_base import BaseMoEWrapper
@@ -97,6 +97,7 @@ class GeneralMoEWrapper(BaseMoEWrapper):
             cpu_save=cpu_save,
             max_deferred_experts_per_token=max_deferred_experts_per_token,
             method=method,
+            numa_nodes=numa_nodes,
         )
 
         # moe-specific: Check if we should load merged safetensor weights