[Enhancement] Add padding for ACL Graph (#803)

### What this PR does / why we need it?
Add padding for ACL Graph and refactor graph batch size adjustments to
utils.py

---------

Signed-off-by: Yizhou Liu <liu_yizhou@outlook.com>

vllm_ascend/attention/attention_v1.py

@@ -104,6 +104,7 @@ class AscendAttentionState(Enum):
 
 @dataclass
 class AscendMetadata:
+    num_actual_tokens: int  # Number of tokens excluding padding.
     # (batch_size, max_blocks_per_seq).
     # Block addresses per sequence. (Seq id -> list of physical block)
     block_tables: torch.Tensor
@@ -125,7 +126,6 @@ class AscendMetadata:
     is_only_prefill: bool = False
     # Current state of this attention run.
     attn_state: AscendAttentionState = AscendAttentionState.ChunkedPrefill
-
     attn_mask: Optional[torch.Tensor] = None
 
 
@@ -149,7 +149,8 @@ class AscendAttentionMetadataBuilder:
         attn_mask = self.runner.attn_mask
         attn_state = self.runner.attn_state
 
-        attn_metadata = AscendMetadata(block_tables=block_table,
+        attn_metadata = AscendMetadata(num_actual_tokens=num_actual_tokens,
+                                       block_tables=block_table,
                                        query_lens=query_lens,
                                        seq_lens=seq_lens,
                                        max_query_len=max_query_len,
@@ -234,9 +235,9 @@ class AscendAttentionBackendImpl(AttentionImpl):
                 output=output,
                 layer_name=layer.layer_name)
         else:
-            num_tokens = query.shape[0]
             if attn_metadata is None:
                 return output.view(num_tokens, self.hidden_size)
+            num_actual_tokens = attn_metadata.num_actual_tokens
             assert layer._k_scale_float == 1.0 and layer._v_scale_float == 1.0
             attn_type = self.attn_type
             if attn_type != AttentionType.DECODER:
@@ -255,11 +256,12 @@ class AscendAttentionBackendImpl(AttentionImpl):
                 if self.key_cache is None:
                     self.key_cache, self.value_cache = kv_cache[0], kv_cache[1]
                 slots = attn_metadata.slot_mapping
-                torch_npu._npu_reshape_and_cache(key=key,
-                                                 value=value,
-                                                 key_cache=self.key_cache,
-                                                 value_cache=self.value_cache,
-                                                 slot_indices=slots)
+                torch_npu._npu_reshape_and_cache(
+                    key=key[:num_actual_tokens],
+                    value=value[:num_actual_tokens],
+                    key_cache=self.key_cache,
+                    value_cache=self.value_cache,
+                    slot_indices=slots)
 
             if hasattr(layer, 'quant_method'):
                 # TODO: Add attr (num_prefills, prefill_metadata, decode_metadata) to AscendMetadata