diff --git a/lmdeploy/serve/utils.py b/lmdeploy/serve/utils.py
index 44476a958b..4791d3c724 100644
--- a/lmdeploy/serve/utils.py
+++ b/lmdeploy/serve/utils.py
@@ -203,18 +203,27 @@ def get_ppl(self, input_ids: Union[List[int],
         sizes = [len(_) for _ in input_ids]
         losses = []
         target_counts = []
+        sorted_index_values = sorted(list(enumerate(sizes)),
+                                     key=lambda x: x[1],
+                                     reverse=True)
+        sizes = [value for index, value in sorted_index_values]
+        indices = [index for index, value in sorted_index_values]
+        logger.info(f'sorted sizes: {sizes}')
+        logger.info(f'sorted indices: {indices}')
         for (start, end) in self._batch_iterator(sizes, max_input_len):
+            logger.info(f'start: {start}, end: {end}')
+            _input_ids = [input_ids[indices[i]] for i in range(start, end)]
             if start == end:
                 loss, target_count = self._get_long_text_ppl(
                     generator=generator,
-                    input_ids=input_ids[start],
+                    input_ids=_input_ids,
                     max_input_len=max_input_len)
                 losses.append(loss)
                 target_counts.append(target_count)
             else:
                 loss, target_count = self._get_ppl(
                     generator=generator,
-                    input_ids=input_ids[start:end],
+                    input_ids=_input_ids,
                     max_input_len=max_input_len,
                 )
                 losses.append(loss)
@@ -222,17 +231,27 @@ def get_ppl(self, input_ids: Union[List[int],
         loss = torch.concatenate(losses)
         target_count = torch.concatenate(target_counts)
         loss_avg = loss / target_count
-        loss_avg = loss_avg.numpy()
-        return loss_avg
-
-    def _batch_iterator(self, sizes, max_length):
+        loss_avg = loss_avg.numpy().tolist()
+        result = list(range(len(loss_avg)))
+        for index, sorted_index in enumerate(indices):
+            result[sorted_index] = loss_avg[index]
+        return result
+
+    def _batch_iterator(self, sizes, max_value):
+        """Return an iterator that calculates intervals (start, end) of a
+        descend-order list, in which the sum of values in the range is the
+        maximum number not less than max_value. By "the sum of values",
+
+        here it means $$len(sizes[start:end]) * sizes[start]$$
+        """
         i = 0
         while i < len(sizes):
             current_sum = 0
             start_index = i
 
-            while i < len(sizes) and current_sum + sizes[i] <= max_length:
-                current_sum += sizes[i]
+            while i < len(
+                    sizes) and current_sum + sizes[start_index] <= max_value:
+                current_sum += sizes[start_index]
                 i += 1
 
             yield (start_index, i)
@@ -242,24 +261,25 @@ def _batch_iterator(self, sizes, max_length):
                 i += 1
 
     def _get_long_text_ppl(self, generator, input_ids, max_input_len):
-        assert all(isinstance(_, int) for _ in input_ids)
-        assert len(input_ids) > max_input_len
+        assert isinstance(input_ids, List) and len(input_ids) == 1
+        seq_len = len(input_ids[0])
+        assert seq_len > max_input_len
+        logger.info(f'get long text ppl: seq_len {seq_len}')
 
-        seq_len = len(input_ids)
         losses = []
         target_counts = []
-        for i in range(0, len(input_ids), max_input_len):
-            token_ids = input_ids[i:i + max_input_len]
-            step = i
+        for i in range(0, seq_len, max_input_len):
+            token_ids = input_ids[:, i:i + max_input_len]
+            step = [i]
             # shift token_ids by 1 to the left
-            target_ids = input_ids[i + 1:i + 1 + max_input_len]
+            target_ids = input_ids[:, i + 1:i + 1 + max_input_len]
 
             loss, target_count = self._get_ppl(
                 generator=generator,
-                input_ids=[token_ids],
+                input_ids=token_ids,
                 max_input_len=max_input_len,
-                target_ids=[target_ids],
-                steps=[step],
+                target_ids=target_ids,
+                steps=step,
                 sequence_start=(i == 0),
                 sequence_end=(i + max_input_len >= seq_len))
             losses.append(loss)
@@ -278,11 +298,16 @@ def _get_ppl(self,
                  sequence_end: bool = True):
         assert isinstance(input_ids, List)
         assert all(isinstance(_, List) for _ in input_ids)
-        assert sum(len(_) for _ in input_ids) <= max_input_len
         if target_ids:
             assert all(isinstance(_, List) for _ in target_ids)
 
-        logger.info(f'get_ppl batch_size {len(input_ids)}')
+        lens = [len(_) for _ in input_ids]
+        total_len = sum(lens)
+        assert sum(lens) <= max_input_len
+
+        logger.info(f'get_ppl: bs: {len(input_ids)}, lens: {lens}, '
+                    f'total_len: {total_len}')
+        torch.cuda.empty_cache()
         logits = generator.decode(input_ids=input_ids,
                                   steps=steps,
                                   sequence_start=sequence_start,