[Bugfix] [Encoder-Decoder] Bugfix for encoder specific metadata construction during decode of encoder-decoder models. (vllm-project#8545)

Signed-off-by: LeiWang1999 <[email protected]>

Author: sroy745

tests/worker/test_encoder_decoder_model_runner.py

@@ -273,7 +273,8 @@ def test_prepare_prompt(batch_size):
                     "unsupported for encoder/ "
                     "decoder models")
 @pytest.mark.parametrize("batch_size", BATCH_SIZES)
-def test_prepare_decode(batch_size):
+@pytest.mark.parametrize("multiple_seqs_per_seq_group", [True, False])
+def test_prepare_decode(batch_size, multiple_seqs_per_seq_group):
     '''
     Test the ability of the encoder/decoder model runner subclass to
     produce decode-phase model inputs & attention metadata.
@@ -288,6 +289,7 @@ def test_prepare_decode(batch_size):
     Arguments:
 
     * batch_size
+    * multiple_seqs_per_seq_group
     * backend_name: The attention backend under test
     * enforce_eager: Enforce eager mode if True (i.e. no CUDAGraph)
     '''
@@ -305,29 +307,40 @@ def test_prepare_decode(batch_size):
     seq_lens: List[int] = []
     encoder_seq_lens: List[int] = []
     seq_group_metadata_list: List[SequenceGroupMetadata] = []
-    block_tables = {0: [1]}
+    block_tables = {
+        0: [1],
+        1: [3]
+    } if multiple_seqs_per_seq_group else {
+        0: [1]
+    }
     cross_block_table = [2]
     for i in range(batch_size):
         # make sure all tokens fit into one block
         seq_len = i % (model_runner.block_size - 1) + 1
-        seq_lens.append(seq_len)
         seq_data = SequenceData(
             array(VLLM_TOKEN_ID_ARRAY_TYPE, (range(seq_len))))
         encoder_seq_len = (i + 1) % (model_runner.block_size - 1) + 1
-        encoder_seq_lens.append(encoder_seq_len)
         encoder_seq_data = SequenceData(
             array(VLLM_TOKEN_ID_ARRAY_TYPE, (range(encoder_seq_len))))
+
         seq_group_metadata = SequenceGroupMetadata(
             request_id=f"test_{i}",
             is_prompt=False,
-            seq_data={0: seq_data},
+            seq_data={
+                0: seq_data,
+                1: seq_data
+            } if multiple_seqs_per_seq_group else {0: seq_data},
             sampling_params=SamplingParams(temperature=0),
             block_tables=block_tables,
             encoder_seq_data=encoder_seq_data,
             cross_block_table=cross_block_table,
         )
         assert seq_group_metadata.token_chunk_size == 1
         seq_group_metadata_list.append(seq_group_metadata)
+        seq_lens.extend(
+            [seq_len for _ in range(len(seq_group_metadata.seq_data))])
+        encoder_seq_lens.extend(
+            [encoder_seq_len for _ in range(len(seq_group_metadata.seq_data))])
 
     # Build
     # * Decoder model inputs
@@ -398,19 +411,24 @@ def test_prepare_decode(batch_size):
 
     # Verify block tables are correct for prompts
     # - Decoder self-attention
-    expected = torch.tensor(
-        [block_tables[0] for _ in range(len(seq_group_metadata_list))],
-        dtype=torch.int32,
-        device=model_runner.device)
+    flattened_block_tables = [
+        block_table for block_table in block_tables.values()
+    ]
+    expected = torch.tensor(flattened_block_tables *
+                            len(seq_group_metadata_list),
+                            dtype=torch.int32,
+                            device=model_runner.device)
     assert torch.equal(
         attn_metadata.block_tables,
         expected,
     )
     # - Encoder/decoder cross-attention
-    expected = torch.tensor(
-        [cross_block_table for _ in range(len(seq_group_metadata_list))],
-        dtype=torch.int32,
-        device=model_runner.device)
+    expected = torch.tensor([
+        cross_block_table for seq_group_metadata in seq_group_metadata_list
+        for _ in range(len(seq_group_metadata.seq_data))
+    ],
+                            dtype=torch.int32,
+                            device=model_runner.device)
     assert torch.equal(
         attn_metadata.cross_block_tables,
         expected,
@@ -474,7 +492,8 @@ def test_prepare_decode(batch_size):
 
 
 @pytest.mark.parametrize("batch_size", list(range(1, 257)))
-def test_prepare_decode_cuda_graph(batch_size):
+@pytest.mark.parametrize("multiple_seqs_per_seq_group", [True, False])
+def test_prepare_decode_cuda_graph(batch_size, multiple_seqs_per_seq_group):
     """
     Tests that for encoder-decoder models with CUDA Graph capture and replay
     enabled, the tensors used during the decode phase are correctly padded 
@@ -489,32 +508,45 @@ def test_prepare_decode_cuda_graph(batch_size):
         enable_chunked_prefill=False,
         enforce_eager=False,
     )
-
+    block_tables = {
+        0: [1],
+        1: [3]
+    } if multiple_seqs_per_seq_group else {
+        0: [1]
+    }
     seq_lens: List[int] = []
     encoder_seq_lens: List[int] = []
     seq_group_metadata_list: List[SequenceGroupMetadata] = []
-    block_tables = {0: [1]}
+
     cross_block_table = [2]
+    expanded_batch_size = 0
     for i in range(batch_size):
         # make sure all tokens fit into one block
         seq_len = i % (model_runner.block_size - 1) + 1
-        seq_lens.append(seq_len)
         seq_data = SequenceData(
             array(VLLM_TOKEN_ID_ARRAY_TYPE, (range(seq_len))))
         encoder_seq_len = (i + 1) % (model_runner.block_size - 1) + 1
-        encoder_seq_lens.append(encoder_seq_len)
         encoder_seq_data = SequenceData(
             array(VLLM_TOKEN_ID_ARRAY_TYPE, (range(encoder_seq_len))))
         seq_group_metadata = SequenceGroupMetadata(
             request_id=f"test_{i}",
             is_prompt=False,
-            seq_data={0: seq_data},
+            seq_data={
+                0: seq_data,
+                1: seq_data
+            } if multiple_seqs_per_seq_group else {0: seq_data},
             sampling_params=SamplingParams(temperature=0),
             block_tables=block_tables,
             encoder_seq_data=encoder_seq_data,
             cross_block_table=cross_block_table,
         )
         assert seq_group_metadata.token_chunk_size == 1
+        seq_lens.extend(
+            [seq_len for _ in range(len(seq_group_metadata.seq_data))])
+        encoder_seq_lens.extend(
+            [encoder_seq_len for _ in range(len(seq_group_metadata.seq_data))])
+        expanded_batch_size = expanded_batch_size + len(
+            seq_group_metadata.seq_data)
         seq_group_metadata_list.append(seq_group_metadata)
 
     model_input = model_runner.prepare_model_input(seq_group_metadata_list)
@@ -530,8 +562,8 @@ def test_prepare_decode_cuda_graph(batch_size):
     # With CUDA Graph capture and replay enabled, the decoder and encoder
     # input sequences will be padded. Create the expected padded tensors
     # accordingly.
-    graph_batch_size = _get_graph_batch_size(batch_size)
-    cuda_graph_pad_size = graph_batch_size - batch_size
+    graph_batch_size = _get_graph_batch_size(expanded_batch_size)
+    cuda_graph_pad_size = graph_batch_size - expanded_batch_size
     padded_seq_lens = seq_lens + list(itertools.repeat(1, cuda_graph_pad_size))
     padded_encoder_seq_lens = encoder_seq_lens + list(
         itertools.repeat(1, cuda_graph_pad_size))
@@ -560,10 +592,13 @@ def test_prepare_decode_cuda_graph(batch_size):
 
     # Verify block tables are correct for prompts
     # - Decoder self-attention. Pad the block tables as expected.
-    expected = [block_tables[0] for _ in range(batch_size)]
-    expected.extend([[] for _ in range(cuda_graph_pad_size)])
+    flattened_block_tables = [
+        block_table for _ in range(len(seq_group_metadata_list))
+        for block_table in block_tables.values()
+    ]
+    flattened_block_tables.extend([[] for _ in range(cuda_graph_pad_size)])
     expected = make_tensor_with_pad(
-        expected,
+        flattened_block_tables,
         max_len=64,
         pad=0,
         dtype=torch.int32,
@@ -575,7 +610,10 @@ def test_prepare_decode_cuda_graph(batch_size):
     )
     # - Encoder/decoder cross-attention. Pad the cross-attention block tables
     # as expected.
-    expected = [cross_block_table for _ in range(len(seq_group_metadata_list))]
+    expected = [
+        cross_block_table for seq_group_metadata in seq_group_metadata_list
+        for _ in range(len(seq_group_metadata.seq_data))
+    ]
     expected.extend([[] for _ in range(cuda_graph_pad_size)])
     expected = make_tensor_with_pad(
         expected,

vllm/worker/enc_dec_model_runner.py

@@ -435,18 +435,18 @@ def _prepare_encoder_model_input_tensors(
             encoder_input_tokens_tensor = self._empty_long_tensor()
             encoder_input_positions_tensor = self._empty_long_tensor()
             cross_slot_mapping_tensor = self._empty_long_tensor()
-
             # Extract cross-attention block tables &
             # seq len from each sequence group metadata.
             # Cross-attention block tables are empty
             # during vLLM memory profiling.
             cross_block_tables = []
             for seq_group_metadata in seq_group_metadata_list:
-                encoder_seq_lens.append(
-                    seq_group_metadata.encoder_seq_data.get_len())
-                cross_block_table = seq_group_metadata.cross_block_table
-                cross_block_tables.append([] if (
-                    cross_block_table is None) else cross_block_table)
+                for _ in range(len(seq_group_metadata.seq_data)):
+                    encoder_seq_lens.append(
+                        seq_group_metadata.encoder_seq_data.get_len())
+                    cross_block_table = seq_group_metadata.cross_block_table
+                    cross_block_tables.append([] if (
+                        cross_block_table is None) else cross_block_table)
 
             if (model_input.attn_metadata is not None
                     and model_input.attn_metadata.use_cuda_graph):