improve the quant weight loaded code

vllm/model_executor/models/llama.py

@@ -426,86 +426,60 @@ def load_weights(self,
                 extra_rows = extra_rows.to(loaded_weight)
                 loaded_weight = torch.cat([loaded_weight, extra_rows], dim=0)
 
-            # merge linear layers
-            if self.quant_config is not None:
-                is_attention_weight = False
-                for weight_name, shard_size, offset in attention_weight_specs:
-                    if weight_name not in name:
-                        continue
-                    param = state_dict[name.replace(weight_name, "qkv_proj")]
+            is_quantized = self.quant_config is not None
 
-                    loaded_weight = loaded_weight[
-                        shard_size * tensor_model_parallel_rank:shard_size *
-                        (tensor_model_parallel_rank + 1)]
-                    param_slice = param.data[offset:offset + shard_size]
-                    assert param_slice.shape == loaded_weight.shape
-
-                    param_slice.copy_(loaded_weight)
-                    is_attention_weight = True
-                    break
-                if is_attention_weight:
+            is_attention_weight = False
+            for weight_name, shard_size, offset in attention_weight_specs:
+                if weight_name not in name:
                     continue
+                param = state_dict[name.replace(weight_name, "qkv_proj")]
 
-                is_gate_up_weight = False
-                for stride_id, weight_name in enumerate(["gate_proj", "up_proj"]):
-                    if weight_name not in name:
-                        continue
-                    param = state_dict[name.replace(weight_name, "gate_up_proj")]
-                    shard_size = param.shape[0] // 2
+                if not is_quantized:
                     loaded_weight = loaded_weight[
                         shard_size * tensor_model_parallel_rank:shard_size *
                         (tensor_model_parallel_rank + 1)]
-                    param_slice = param.data[shard_size * stride_id:shard_size *
-                                             (stride_id + 1)]
-                    assert param_slice.shape == loaded_weight.shape
-                    param_slice.copy_(loaded_weight)
-                    is_gate_up_weight = True
-                    break
-                if is_gate_up_weight:
-                    continue
-            else:
-                # TODO: improve this block of code
-                is_attention_weight = False
-                for stride_id, weight_spec in enumerate(attention_weight_specs):
-                    weight_name, shard_size, offset = weight_spec
-
-                    if weight_name not in name:
-                        continue
-
-                    param = state_dict[name.replace(weight_name, "qkv_proj")]
-
-                    # TODO: this is specific to AWQ (should be more general)
+                    param_slice = param.data[offset:offset + shard_size]
+                else:
+                    # TODO: this is specific to AWQ
                     if "qweight" in name or "qzeros" in name:
                         adjustment = 32 / self.quant_config.bits
                         shard_size = int(shard_size // adjustment)
                         offset = int(offset // adjustment)
-
                     param_slice = param.data[:, offset:offset + shard_size]
-                    assert param_slice.shape == loaded_weight.shape
 
-                    param_slice.copy_(loaded_weight)
-                    is_attention_weight = True
-                    break
-                if is_attention_weight:
+                assert param_slice.shape == loaded_weight.shape
+
+                param_slice.copy_(loaded_weight)
+                is_attention_weight = True
+                break
+            if is_attention_weight:
+                continue
+
+            is_gate_up_weight = False
+            for stride_id, weight_name in enumerate(["gate_proj", "up_proj"]):
+                if weight_name not in name:
                     continue
+                param = state_dict[name.replace(weight_name, "gate_up_proj")]
 
-                is_gate_up_weight = False
-                for stride_id, weight_name in enumerate(["gate_proj", "up_proj"]):
-                    if weight_name not in name:
-                        continue
-                    param = state_dict[name.replace(weight_name, "gate_up_proj")]
+                if not is_quantized:
+                    shard_size = param.shape[0] // 2
+                    loaded_weight = loaded_weight[
+                        shard_size * tensor_model_parallel_rank:shard_size *
+                        (tensor_model_parallel_rank + 1)]
+                    param_slice = param.data[shard_size * stride_id:shard_size *
+                                             (stride_id + 1)]
+                else:
                     shard_size = param.shape[1] // 2
-
                     start = shard_size * stride_id
                     end = shard_size * (stride_id + 1)
-
                     param_slice = param.data[:, start:end]
-                    assert param_slice.shape == loaded_weight.shape
-                    param_slice.copy_(loaded_weight)
-                    is_gate_up_weight = True
-                    break
-                if is_gate_up_weight:
-                    continue
+
+                assert param_slice.shape == loaded_weight.shape
+                param_slice.copy_(loaded_weight)
+                is_gate_up_weight = True
+                break
+            if is_gate_up_weight:
+                continue
 
             param = state_dict[name]
             load_tensor_parallel_weights(param, loaded_weight, name,