fix merge

tests/kernels/test_attention.py

@@ -33,11 +33,7 @@ HEAD_SIZES = [64, 80, 96, 112, 128, 256
 
 BLOCK_SIZES = [16, 32]
 USE_ALIBI = [False, True]
-<<<<<<< HEAD
-KV_CACHE_DTYPE = ["auto", "fp8_e5m2"] if not is_hip() else ["auto"]
-=======
-KV_CACHE_DTYPE = ["auto", "fp8"]
->>>>>>> v0.4.1
+KV_CACHE_DTYPE = ["auto", "fp8"] if not is_hip() else ["auto"]
 SEEDS = [0]
 CUDA_DEVICES = [
     f"cuda:{i}" for i in range(1 if torch.cuda.device_count() == 1 else 2)

tests/kernels/test_cache.py

@@ -25,11 +25,7 @@ SEEDS = [0]
 CUDA_DEVICES = [
     f"cuda:{i}" for i in range(1 if torch.cuda.device_count() == 1 else 2)
 ]
-<<<<<<< HEAD
-KV_CACHE_DTYPE = ["auto", "fp8_e5m2"]  if not is_hip() else ["auto"]
-=======
-KV_CACHE_DTYPE = ["auto", "fp8"]
->>>>>>> v0.4.1
+KV_CACHE_DTYPE = ["auto", "fp8"] if not is_hip() else ["auto"]
 
 
 @pytest.mark.parametrize("num_mappings", NUM_MAPPINGS)

vllm/model_executor/layers/linear.py

@@ -346,13 +346,8 @@ class MergedColumnParallelLinear(ColumnParallelLinear):
         self.output_sizes = output_sizes
         tp_size = get_tensor_model_parallel_world_size()
         assert all(output_size % tp_size == 0 for output_size in output_sizes)
-<<<<<<< HEAD
-        super().__init__(input_size, sum(output_sizes), bias, gather_output,     
-                         skip_bias_add, params_dtype, linear_method,
-=======
         super().__init__(input_size, sum(output_sizes), bias, gather_output,
                          skip_bias_add, params_dtype, quant_config,
->>>>>>> v0.4.2
                          self.output_sizes)
         self.use_llama_nn = os.environ.get('LLAMA_NN') == '1'
 
@@ -514,12 +509,8 @@ class QKVParallelLinear(ColumnParallelLinear):
         ]
 
         super().__init__(input_size, output_size, bias, False, skip_bias_add,
-<<<<<<< HEAD
-                         params_dtype, linear_method, output_sizes)
-        self.use_llama_nn = os.environ.get('LLAMA_NN') == '1'
-=======
                          params_dtype, quant_config, output_sizes)
->>>>>>> v0.4.2
+        self.use_llama_nn = os.environ.get('LLAMA_NN') == '1'
 
     def weight_loader(self,
                       param: Parameter,

vllm/model_executor/model_loader/loader.py

@@ -54,18 +54,12 @@ def _get_quantization_config(
                 f"{model_config.dtype} is not supported for quantization "
                 f"method {model_config.quantization}. Supported dtypes: "
                 f"{supported_dtypes}")
-<<<<<<< HEAD
-
-        linear_method = quant_config.get_linear_method()
+        return quant_config
     
-    if linear_method != None:
+    if quant_config != None:
         os.environ['LLAMA_NN'] = '0'
         
-    return linear_method
-=======
-        return quant_config
     return None
->>>>>>> v0.4.2
 
 
 def _get_model_initialization_kwargs(