diff --git a/vllm/v1/attention/backends/flash_attn.py b/vllm/v1/attention/backends/flash_attn.py
index 9b69b28858fe3..837d7faf43708 100755
--- a/vllm/v1/attention/backends/flash_attn.py
+++ b/vllm/v1/attention/backends/flash_attn.py
@@ -179,7 +179,7 @@ class FlashAttentionImpl(AttentionImpl):
         assert output is not None, "Output tensor must be provided."
 
         if attn_metadata is None:
-            # Dynamic shape profiling run.
+            # Profiling run.
             return output
 
         # IMPORTANT!
@@ -193,13 +193,17 @@ class FlashAttentionImpl(AttentionImpl):
 
         num_actual_tokens = attn_metadata.num_actual_tokens
         # Reshape the input keys and values and store them in the cache.
+        # 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,
             value,
             key_cache,
             value_cache,
-            attn_metadata.slot_mapping[:num_actual_tokens],
+            attn_metadata.slot_mapping,
             self.kv_cache_dtype,
             layer._k_scale,
             layer._v_scale,
@@ -208,17 +212,14 @@ class FlashAttentionImpl(AttentionImpl):
         # Compute attention and update output up to `num_actual_tokens`.
         if not attn_metadata.use_cascade:
             # Regular attention (common case).
-            batch_size = attn_metadata.block_table.shape[0]
-            
-            #TODO: Do we need to slice by [:batch_size+1]?
             flash_attn_varlen_func(
                 q=query[:num_actual_tokens],
                 k=key_cache,
                 v=value_cache,
                 out=output[:num_actual_tokens],
-                cu_seqlens_q=attn_metadata.query_start_loc[:batch_size+1],
+                cu_seqlens_q=attn_metadata.query_start_loc,
                 max_seqlen_q=attn_metadata.max_query_len,
-                seqused_k=attn_metadata.seq_lens[:batch_size],
+                seqused_k=attn_metadata.seq_lens,
                 max_seqlen_k=attn_metadata.max_seq_len,
                 softmax_scale=self.scale,
                 causal=True,
diff --git a/vllm/v1/worker/gpu_model_runner.py b/vllm/v1/worker/gpu_model_runner.py
index 8bc0da8e3a953..ffbd1451a0a5c 100644
--- a/vllm/v1/worker/gpu_model_runner.py
+++ b/vllm/v1/worker/gpu_model_runner.py
@@ -11,7 +11,7 @@ import torch.nn as nn
 
 from vllm.attention.backends.abstract import AttentionType
 from vllm.attention.layer import Attention
-from vllm.config import CompilationLevel, VllmConfig
+from vllm.config import VllmConfig
 from vllm.distributed.parallel_state import graph_capture
 from vllm.forward_context import set_forward_context
 from vllm.inputs import INPUT_REGISTRY
@@ -122,9 +122,6 @@ class GPUModelRunner:
             vocab_size=model_config.get_vocab_size(),
         )
 
-        #        self.use_cuda_graph = (self.vllm_config.compilation_config.level
-        #                               == CompilationLevel.PIECEWISE
-        #                               and not self.model_config.enforce_eager)
         self.use_cuda_graph = not self.model_config.enforce_eager
         # TODO(woosuk): Provide an option to tune the max cudagraph batch size.
         # The convention is different.
@@ -467,7 +464,8 @@ class GPUModelRunner:
         self.input_batch.block_table.get_device_tensor()[num_reqs:].fill_(-1)
 
         # Fill with -1s -- needed for reshape_and_cache
-        self.slot_mapping[total_num_scheduled_tokens:].fill_(-1) # Definitely needed
+        self.slot_mapping[total_num_scheduled_tokens:].fill_(
+            -1)  # Definitely needed
 
         # Prepare for cascade attention if needed.
         common_prefix_len = (scheduler_output.num_common_prefix_blocks *
@@ -550,12 +548,12 @@ class GPUModelRunner:
         attn_metadata = FlashAttentionMetadata(
             num_actual_tokens=total_num_scheduled_tokens,
             max_query_len=max_num_scheduled_tokens,
-            query_start_loc=self.query_start_loc,
+            query_start_loc=self.query_start_loc[:num_reqs + 1],
             max_seq_len=max_seq_len,
-            seq_lens=self.seq_lens,
+            seq_lens=self.seq_lens[:num_reqs],
             block_table=(
                 self.input_batch.block_table.get_device_tensor()[:num_reqs]),
-            slot_mapping=self.slot_mapping,
+            slot_mapping=self.slot_mapping[:total_num_scheduled_tokens],
             # Cascade stuff
             use_cascade=use_cascade,
             common_prefix_len=common_prefix_len,
@@ -914,12 +912,12 @@ class GPUModelRunner:
         return FlashAttentionMetadata(
             num_actual_tokens=num_tokens,
             max_query_len=max_query_len,
-            query_start_loc=self.query_start_loc,
+            query_start_loc=self.query_start_loc[:num_reqs + 1],
             max_seq_len=max_seq_len,
-            seq_lens=self.seq_lens,
+            seq_lens=self.seq_lens[:num_reqs],
             block_table=(
                 self.input_batch.block_table.get_device_tensor()[:num_reqs]),
-            slot_mapping=self.slot_mapping,
+            slot_mapping=self.slot_mapping[:max_seq_len],
             # Cascade stuff. Non-piecewise CUDA graphs NYI
             use_cascade=False,
             common_prefix_len=0,