修改美团 deepseek channel-wise模型 moe config获取

去除AutoTuning info提示信息

vllm/model_executor/layers/quantization/w8a8_int8.py

@@ -248,6 +248,7 @@ class W8A8Int8MoEMethod:
 
     def __init__(self, quant_config):
         self.quant_config = quant_config
+        self.tritonsingleton= W8a8GetCacheJSON()
 
     def create_weights(
         self,
@@ -302,6 +303,22 @@ class W8A8Int8MoEMethod:
         layer.register_parameter("w2_input_scale", w2_input_scale)
 
     def process_weights_after_loading(self, layer: torch.nn.Module) -> None:
+        E=layer.w13_weight.shape[0]
+        N1=layer.w13_weight.shape[1]
+        N2=layer.w2_weight.shape[1]
+        K=layer.w2_weight.shape[2]
+        if [E,N1,N2,K] not in self.tritonsingleton.moe_weight_shapes:
+            self.tritonsingleton.moe_weight_shapes.append([E,N1,N2,K])
+            
+        TOPK= self.tritonsingleton.topk
+
+        json_file=self.tritonsingleton.get_moeint8json_name(E,N1,N2,K,TOPK)
+        configs_dict=self.tritonsingleton.get_moeint8_triton_cache(json_file,E,N1,N2,K,TOPK)
+        
+        #warmup
+        if configs_dict:
+            self.tritonsingleton.triton_moejson_dict.update(configs_dict)
+
         layer.w13_weight = Parameter(layer.w13_weight, requires_grad=False)
         layer.w2_weight = Parameter(layer.w2_weight, requires_grad=False)
         layer.w13_weight_scale = Parameter(

vllm/utils.py

@@ -1904,7 +1904,7 @@ class W8a8GetCacheJSON:
         json_dir = os.getenv('LMSLIM_TUNING_JSON', "None")
         if json_dir is not "None" and os.path.exists(json_dir):
             #生成模型配置文件
-            logger.info("model_tuning.json is at LMSLIM_TUNING_JSON:%s", json_dir)
+            # logger.info("model_tuning.json is at LMSLIM_TUNING_JSON:%s", json_dir)
             config = {
                 "layers": {
                     "linear": {
@@ -1938,12 +1938,12 @@ class W8a8GetCacheJSON:
                 config["layers"]["linear"]["shapes"].append(shape)
             
             if block_size is not None:
-            config["quantization_config"]["weight_block_size"]=block_size
+                config["quantization_config"]["weight_block_size"]=block_size
                                     
             with open(json_dir+"/model.json", 'w') as f:
                 json.dump(config, f, indent=4)
-        else:
-            logger.info("LMSLIM_TUNING_JSON is not set")
+        # else:
+        #     logger.info("LMSLIM_TUNING_JSON is not set")
                    
     def getspec_config(self,configs_dict,M,N,K):
         if f"{M}_{N}_{K}" in configs_dict: