diff --git a/csrc/cache_kernels.cu b/csrc/cache_kernels.cu
index 1be806bbfa43c..8a95279f9a25a 100644
--- a/csrc/cache_kernels.cu
+++ b/csrc/cache_kernels.cu
@@ -307,10 +307,20 @@ void reshape_and_cache_flash(
     torch::Tensor& key_cache,  // [num_blocks, block_size, num_heads, head_size]
     torch::Tensor&
         value_cache,  // [num_blocks, block_size, num_heads, head_size]
-    torch::Tensor& slot_mapping,  // [num_tokens]
+    torch::Tensor& slot_mapping,  // [num_tokens] or [num_actual_tokens]
     const std::string& kv_cache_dtype, const double k_scale,
     const double v_scale) {
-  int num_tokens = key.size(0);
+  // NOTE(woosuk): In vLLM V1, key.size(0) can be different from
+  // slot_mapping.size(0) because of padding for CUDA graphs.
+  // In vLLM V0, key.size(0) is always equal to slot_mapping.size(0) because
+  // both include padding.
+  // In vLLM V1, however, key.size(0) can be larger than slot_mapping.size(0)
+  // since key includes padding for CUDA graphs, while slot_mapping does not.
+  // In this case, slot_mapping.size(0) represents the actual number of tokens
+  // before padding.
+  // For compatibility with both cases, we use slot_mapping.size(0) as the
+  // number of tokens.
+  int num_tokens = slot_mapping.size(0);
   int num_heads = key.size(1);
   int head_size = key.size(2);
   int block_size = key_cache.size(1);
diff --git a/vllm/v1/attention/backends/flash_attn.py b/vllm/v1/attention/backends/flash_attn.py
index d37989055c2e5..251a103e60f06 100644
--- a/vllm/v1/attention/backends/flash_attn.py
+++ b/vllm/v1/attention/backends/flash_attn.py
@@ -138,14 +138,25 @@ def forward(
             # Profiling run.
             return output
 
-        num_actual_tokens = attn_metadata.num_actual_tokens
+        # IMPORTANT!
+        # NOTE(woosuk): With piece-wise CUDA graphs, this method is executed in
+        # eager-mode PyTorch. Thus, we need to be careful about any CPU overhead
+        # in this method. For example, `view` and `slice` (or `[:n]`) operations
+        # are surprisingly slow even in the case they do not invoke any GPU ops.
+        # Minimize the PyTorch ops in this method as much as possible.
+        # Whenever making a change in this method, please benchmark the
+        # performance to make sure it does not introduce any overhead.
 
+        num_actual_tokens = attn_metadata.num_actual_tokens
         # Reshape the input keys and values and store them in the cache.
-        key_cache = kv_cache[0]
-        value_cache = kv_cache[1]
+        # NOTE(woosuk): Here, key and value are padded while slot_mapping is
+        # not padded. However, we don't need to do key[:num_actual_tokens] and
+        # value[:num_actual_tokens] because the reshape_and_cache_flash op uses
+        # the slot_mapping's shape to determine the number of actual tokens.
+        key_cache, value_cache = kv_cache.unbind(0)
         torch.ops._C_cache_ops.reshape_and_cache_flash(
-            key[:num_actual_tokens],
-            value[:num_actual_tokens],
+            key,
+            value,
             key_cache,
             value_cache,
             attn_metadata.slot_mapping,