feat: support multi cards in ascend graph mode (#2755)

* support multi cards in ascend graph mode

* update warning info

* update warning info

lmdeploy/pytorch/backends/dlinfer/ascend/graph_runner.py

@@ -22,7 +22,6 @@ def __init__(self, model: torch.nn.Module, model_config: ModelConfig,
         super().__init__(model, model_config, cache_config, backend_config,
                          device)
 
-        self.supported_model = ['Llama3-8B', 'Llama2-7B', 'Qwen2-7B']
         self.enable_graph = self.check_enable_graph()
         if self.enable_graph:
             import dlinfer.graph
@@ -39,26 +38,23 @@ def check_enable_graph(self):
         # eager_mode
         if self.backend_config.eager_mode:
             return False
-        # tp
-        if torch.distributed.is_initialized():
-            warnings.warn(
-                "Graph mode of device_type 'ascend' only supports tp=1 "
-                'for now, fallback to eager mode', RuntimeWarning)
-            return False
 
         warnings.warn(
             '\n\n'
-            '**********************************************************\n'
-            '  The following models were tested in graph mode of\n'
-            "  device_type 'ascend' when tp=1:\n"
-            f"  {', '.join(self.supported_model)}\n"
-            '  Other LLaMa-like models may work in graph mode, please\n'
-            '  check the result yourself!\n'
-            '  If graph mode does not work correctly with your model,\n'
-            '  please use eager mode instead.\n'
-            '**********************************************************\n\n',
+            '************************************************************\n'
+            '  Graph mode is an experimental feature. We currently\n'
+            '  support both dense and Mixture of Experts (MoE) models\n'
+            '  with bf16 and fp16 data types.\n'
+            '  If graph mode does not function correctly with your model,\n'
+            '  please consider using eager mode as an alternative.\n'
+            '************************************************************\n\n',
             RuntimeWarning)
 
+        # tp
+        if torch.distributed.is_initialized():
+            torch._inductor.config.compile_threads = 1
+            return True
+
         return True
 
     def patch_kernels_custom_op(self):