[Minor] More fix of test_cache.py CI test failure (vllm-project#2750)

Author: LiuXiaoxuanPKU

tests/kernels/test_cache.py

@@ -181,16 +181,15 @@ def test_swap_blocks(
     num_blocks: int,
     dtype: torch.dtype,
     seed: int,
-    device: int,
+    device: str,
 ) -> None:
     random.seed(seed)
     torch.random.manual_seed(seed)
     if torch.cuda.is_available():
         torch.cuda.manual_seed(seed)
-    src_device = f"{direction[0]}:{device}" if direction[
-        0] == "cuda" else direction[0]
-    dst_device = f"{direction[1]}:{device}" if direction[
-        1] == "cuda" else direction[1]
+
+    src_device = device if direction[0] == "cuda" else 'cpu'
+    dst_device = device if direction[1] == "cuda" else 'cpu'
 
     src_blocks = random.sample(range(num_blocks), num_mappings)
     # For the same device, mapping must not overlap

vllm/utils.py

@@ -258,10 +258,13 @@ def create_kv_caches_with_random(
         key_cache = torch.empty(size=key_cache_shape,
                                 dtype=torch_dtype,
                                 device=device)
-        if cache_dtype in ["auto", "half", "bfloat16", "float"]:
-            key_cache.uniform_(-scale, scale)
-        elif cache_dtype == 'fp8_e5m2':
+        if cache_dtype == 'fp8_e5m2':
             _generate_random_fp8_e5m2(key_cache, -scale, scale)
+        elif torch_dtype in [torch.half, torch.bfloat16, torch.float]:
+            key_cache.uniform_(-scale, scale)
+        else:
+            raise ValueError(
+                f"Does not support key cache of type {cache_dtype}")
         key_caches.append(key_cache)
 
     value_cache_shape = (num_blocks, num_heads, head_size, block_size)
@@ -270,9 +273,12 @@ def create_kv_caches_with_random(
         value_cache = torch.empty(size=value_cache_shape,
                                   dtype=torch_dtype,
                                   device=device)
-        if cache_dtype in ["auto", "half", "bfloat16", "float"]:
-            value_cache.uniform_(-scale, scale)
-        elif cache_dtype == 'fp8_e5m2':
+        if cache_dtype == 'fp8_e5m2':
             _generate_random_fp8_e5m2(value_cache, -scale, scale)
+        elif torch_dtype in [torch.half, torch.bfloat16, torch.float]:
+            value_cache.uniform_(-scale, scale)
+        else:
+            raise ValueError(
+                f"Does not support value cache of type {cache_dtype}")
         value_caches.append(value_cache)
     return key_caches, value_caches