Add Wall-X serving and Turtle2 TCP WebSocket bridge

wall_x/model/core/ops/moe.py

@@ -5,6 +5,7 @@ Mixture-of-Experts processing.
 """
 
 import logging
+import os
 
 import torch
 
@@ -13,6 +14,14 @@ from wall_x.model.core.ops.base import OpsProxy
 logger = logging.getLogger(__name__)
 
 
+def _force_pytorch_backend() -> bool:
+    return os.environ.get("WALL_X_FORCE_TORCH_MOE_OPS", "").lower() in {
+        "1",
+        "true",
+        "yes",
+    }
+
+
 class PermuteOp(OpsProxy):
     """Reorder tokens by expert assignment for MoE processing.
 
@@ -21,9 +30,11 @@ class PermuteOp(OpsProxy):
 
     @property
     def _external_accel_name(self):
-        return "permute"
+        return None if _force_pytorch_backend() else "permute"
 
     def _get_cuda_kernel(self):
+        if _force_pytorch_backend():
+            return None
         try:
             from wall_x.model.core.ops._cuda_wrappers import permute_kernel
 
@@ -64,9 +75,11 @@ class UnpermuteOp(OpsProxy):
 
     @property
     def _external_accel_name(self):
-        return "unpermute"
+        return None if _force_pytorch_backend() else "unpermute"
 
     def _get_cuda_kernel(self):
+        if _force_pytorch_backend():
+            return None
         try:
             from wall_x.model.core.ops._cuda_wrappers import unpermute_kernel