Support FP8-E5M2 KV Cache (#2279)

Co-authored-by: zhaoyang <zhao.yang16@zte.com.cn>
Co-authored-by: Zhuohan Li <zhuohan123@gmail.com>

tests/kernels/conftest.py

@@ -1,44 +1,7 @@
-from typing import List, Tuple
-
 import pytest
-import torch
-
-
-def create_kv_caches(
-    num_blocks: int,
-    block_size: int,
-    num_layers: int,
-    num_heads: int,
-    head_size: int,
-    dtype: torch.dtype,
-    seed: int,
-    device: str,
-) -> Tuple[List[torch.Tensor], List[torch.Tensor]]:
-    torch.random.manual_seed(seed)
-    torch.cuda.manual_seed(seed)
-
-    scale = head_size**-0.5
-    x = 16 // torch.tensor([], dtype=dtype).element_size()
-    key_cache_shape = (num_blocks, num_heads, head_size // x, block_size, x)
-    key_caches = []
-    for _ in range(num_layers):
-        key_cache = torch.empty(size=key_cache_shape,
-                                dtype=dtype,
-                                device=device)
-        key_cache.uniform_(-scale, scale)
-        key_caches.append(key_cache)
-
-    value_cache_shape = (num_blocks, num_heads, head_size, block_size)
-    value_caches = []
-    for _ in range(num_layers):
-        value_cache = torch.empty(size=value_cache_shape,
-                                  dtype=dtype,
-                                  device=device)
-        value_cache.uniform_(-scale, scale)
-        value_caches.append(value_cache)
-    return key_caches, value_caches
+from vllm.utils import create_kv_caches_with_random
 
 
 @pytest.fixture()
 def kv_cache_factory():
-    return create_kv_caches
+    return create_kv_caches_with_random