[Misc]: optimize eager mode host time (#4196)

Co-authored-by: xuhao <xuhao@cambricon.com>

vllm/utils.py

@@ -17,6 +17,7 @@ from typing import (Any, AsyncIterator, Awaitable, Callable, Dict, Generic,
                     Hashable, List, Optional, OrderedDict, Tuple, TypeVar,
                     Union)
 
+import numpy as np
 import psutil
 import torch
 
@@ -501,11 +502,6 @@ def str_to_int_tuple(s: str) -> Tuple[int, ...]:
             f"(e.g., 1, 2, 3). Given input: {s}") from e
 
 
-def pad_to_max_length(x: List[int], max_len: int, pad: int) -> List[int]:
-    assert len(x) <= max_len
-    return x + [pad] * (max_len - len(x))
-
-
 def make_tensor_with_pad(
     x: List[List[int]],
     max_len: int,
@@ -518,7 +514,10 @@ def make_tensor_with_pad(
     The padding is applied to the end of each inner list until it reaches
     `max_len`.
     """
-    padded_x = [pad_to_max_length(x_i, max_len, pad) for x_i in x]
+    padded_x = np.zeros([len(x), max_len], dtype=np.int32) + pad
+    for ind, blocktb in enumerate(x):
+        assert len(blocktb) <= max_len
+        padded_x[ind, :len(blocktb)] = blocktb
     return torch.tensor(padded_x, dtype=dtype, device=device)