fix: revert code to avoid no attribute problem (#827)

vllm/model_executor/models/gpt_bigcode.py

@@ -259,6 +259,8 @@ class GPTBigCodeForCausalLM(nn.Module):
                      model_name_or_path: str,
                      cache_dir: Optional[str] = None,
                      use_np_cache: bool = False):
+        tensor_model_parallel_world_size = (
+            get_tensor_model_parallel_world_size())
         tensor_model_parallel_rank = get_tensor_model_parallel_rank()
         state_dict = self.state_dict()
 
@@ -288,8 +290,7 @@ class GPTBigCodeForCausalLM(nn.Module):
                 hidden_size = self.config.hidden_size
                 head_size = hidden_size // total_num_heads
                 total_kv_size = head_size * total_num_kv_heads
-                num_heads = (total_num_heads //
-                             self.tensor_model_parallel_world_size)
+                num_heads = total_num_heads // tensor_model_parallel_world_size
                 head_start = tensor_model_parallel_rank * num_heads
                 head_end = (tensor_model_parallel_rank + 1) * num_heads
 
@@ -329,7 +330,7 @@ class GPTBigCodeForCausalLM(nn.Module):
             if name == "transformer.wte.weight":
                 # Consider padding in the vocab size.
                 padded_vocab_size = param.shape[
-                    0] * self.tensor_model_parallel_world_size
+                    0] * tensor_model_parallel_world_size
                 num_extra_rows = padded_vocab_size - self.config.vocab_size
                 extra_rows = torch.empty(num_extra_rows,
                                          loaded_weight.shape[1])