[Bugfix] Fix decode tokens w. CUDA graph (#6757)

tests/worker/test_model_runner.py

@@ -193,6 +193,7 @@ def test_prepare_decode_cuda_graph(batch_size):
     for _ in range(expected_bs - len(seq_lens)):
         seq_lens.append(1)
     assert attn_metadata.seq_lens == seq_lens
+    assert attn_metadata.num_decode_tokens == len(seq_lens)
     start_idx = 0
     start_loc = [start_idx]
     for _ in context_lens:

vllm/attention/backends/flash_attn.py

@@ -272,7 +272,15 @@ class FlashAttentionMetadataBuilder(
 
     def build(self, seq_lens: List[int], query_lens: List[int],
               cuda_graph_pad_size: int, batch_size: int):
-        """Build attention metadata with on-device tensors."""
+        """Build attention metadata with on-device tensors.
+
+        Args:
+            seq_lens: The maybe padded sequence lengths of the input sequences.
+            query_lens: The query lengths of the input sequences.
+            cuda_graph_pad_size: The padding size for cuda graph.
+                                 -1 if cuda graph is not used.
+            batch_size: The maybe padded batch size.
+        """
         for inter_data in self.input_builder.inter_data_list:
             self._add_seq_group(inter_data,
                                 self.input_builder.chunked_prefill_enabled)
@@ -297,7 +305,7 @@ class FlashAttentionMetadataBuilder(
         if use_captured_graph:
             self.slot_mapping.extend([PAD_SLOT_ID] * cuda_graph_pad_size)
             self.block_tables.extend([] * cuda_graph_pad_size)
-            num_decode_tokens = batch_size + cuda_graph_pad_size
+            num_decode_tokens = batch_size
 
             # The shape of graph_block_tables is
             # [max batch size, max context len // block size].

vllm/attention/backends/flashinfer.py

@@ -320,6 +320,15 @@ class FlashInferMetadataBuilder(AttentionMetadataBuilder[FlashInferMetadata]):
 
     def build(self, seq_lens: List[int], query_lens: List[int],
               cuda_graph_pad_size: int, batch_size: int):
+        """Build attention metadata with on-device tensors.
+
+        Args:
+            seq_lens: The maybe padded sequence lengths of the input sequences.
+            query_lens: The query lengths of the input sequences.
+            cuda_graph_pad_size: The padding size for cuda graph.
+                                 -1 if cuda graph is not used.
+            batch_size: The maybe padded batch size.
+        """
         for inter_data in self.input_builder.inter_data_list:
             self._add_seq_group(inter_data,
                                 self.input_builder.chunked_prefill_enabled)
@@ -334,7 +343,7 @@ class FlashInferMetadataBuilder(AttentionMetadataBuilder[FlashInferMetadata]):
         if use_captured_graph:
             self.slot_mapping.extend([PAD_SLOT_ID] * cuda_graph_pad_size)
             self.block_tables.extend([] * cuda_graph_pad_size)
-            num_decode_tokens = batch_size + cuda_graph_pad_size
+            num_decode_tokens = batch_size
 
             # The shape of graph_block_tables is
             # [max batch size, max context len // block size].

vllm/attention/backends/utils.py

@@ -149,6 +149,15 @@ class CommonMetadataBuilder(AttentionMetadataBuilder[TAttentionMetadata]):
 
     def build(self, seq_lens: List[int], query_lens: List[int],
               cuda_graph_pad_size: int, batch_size: int):
+        """Build attention metadata with on-device tensors.
+
+        Args:
+            seq_lens: The maybe padded sequence lengths of the input sequences.
+            query_lens: The query lengths of the input sequences.
+            cuda_graph_pad_size: The padding size for cuda graph.
+                                 -1 if cuda graph is not used.
+            batch_size: The maybe padded batch size.
+        """
         for inter_data in self.input_builder.inter_data_list:
             self._add_seq_group(inter_data,
                                 self.input_builder.chunked_prefill_enabled)
@@ -173,7 +182,7 @@ class CommonMetadataBuilder(AttentionMetadataBuilder[TAttentionMetadata]):
         if use_captured_graph:
             self.slot_mapping.extend([PAD_SLOT_ID] * cuda_graph_pad_size)
             self.block_tables.extend([] * cuda_graph_pad_size)
-            num_decode_tokens = batch_size + cuda_graph_pad_size
+            num_decode_tokens = batch_size
 
             # The shape of graph_block_tables is
             # [max batch size, max context len // block size].