Mock triton mla due to precision issue

ktransformers/operators/attention.py

@@ -43,11 +43,13 @@ class KDeepseekV2Attention(BaseInjectedModule, DeepseekV2Attention):
                  orig_module: nn.Module,
                  device: str = "cuda",
                  chunck_size: int = 1000,
+                 use_triton: bool = False,
                  **kwargs):
         BaseInjectedModule.__init__(self, key, gguf_loader, config, orig_module, device, **kwargs)
         self.orig_module.__init__(orig_module.config,
             orig_module.layer_idx)
         self.chunck_size = chunck_size # TODO, generate chunck_size automatically.
+        self.use_triton = use_triton
 
     def get_absorbed(self) -> Tuple[torch.Tensor, torch.Tensor]:
         if not (hasattr(self, 'q_absorb') and hasattr(self, 'out_absorb')):
@@ -401,7 +403,7 @@ class KDeepseekV2Attention(BaseInjectedModule, DeepseekV2Attention):
         cache_position: Optional[torch.LongTensor] = None,
         **kwargs,
     ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
-        if os.name == 'nt':
+        if not self.use_triton: # os.name == 'nt' 
             return self.forward_windows(
                 hidden_states,
                 attention_mask,