simplify vllm preprocessing input ids

Author: YeAnbang

applications/ColossalChat/coati/distributed/inference_backend.py

@@ -212,13 +212,11 @@ def __init__(
     def generate(self, input_ids: torch.Tensor, attention_mask: torch.Tensor, **kwargs) -> Dict[str, torch.Tensor]:
         micro_batch_size = input_ids.size(0)
         response_start_idx = input_ids.size(1)
+        first_non_padding_token_idx = (input_ids != self.tokenizer.pad_token_id).int().argmax(dim=1)
         micro_batch_input_ids = input_ids.tolist()
-        micro_batch_input_ids_no_padding = []
-        for i in range(micro_batch_size):
-            for j in range(input_ids.size(1)):
-                if micro_batch_input_ids[i][j] != self.tokenizer.pad_token_id:
-                    micro_batch_input_ids_no_padding.append(micro_batch_input_ids[i][j:])
-                    break
+        micro_batch_input_ids_no_padding = [
+            micro_batch_input_ids[i][first_non_padding_token_idx[i] :] for i in range(micro_batch_size)
+        ]
         outputs = self.llm.generate(
             prompt_token_ids=micro_batch_input_ids_no_padding, sampling_params=self.generate_config, use_tqdm=False
         )