[v1] Support multiple KV cache groups in GPU model runner (#17945)

Signed-off-by: Chen Zhang <zhangch99@outlook.com>

tests/v1/core/test_kv_cache_utils.py

@@ -19,7 +19,8 @@ from vllm.v1.core.kv_cache_utils import (NONE_HASH, BlockHashType,
                                          hash_request_tokens,
                                          unify_kv_cache_configs)
 from vllm.v1.kv_cache_interface import (FullAttentionSpec, KVCacheConfig,
-                                        KVCacheGroupSpec, KVCacheTensor)
+                                        KVCacheGroupSpec, KVCacheTensor,
+                                        SlidingWindowSpec)
 from vllm.v1.metrics.stats import PrefixCacheStats
 from vllm.v1.request import Request
 
@@ -54,12 +55,14 @@ def new_kv_cache_spec(block_size=16,
                       num_kv_heads=2,
                       head_size=64,
                       dtype=torch.float32,
-                      use_mla=False):
+                      use_mla=False,
+                      sliding_window=None):
     return FullAttentionSpec(block_size=block_size,
                              num_kv_heads=num_kv_heads,
                              head_size=head_size,
                              dtype=dtype,
-                             use_mla=use_mla)
+                             use_mla=use_mla,
+                             sliding_window=sliding_window)
 
 
 def test_none_hash():
@@ -471,6 +474,68 @@ def test_unify_kv_cache_configs():
         unify_kv_cache_configs(diff_kv_cache_config)
 
 
+def test_merge_kv_cache_spec():
+    same_layer_specs = [
+        new_kv_cache_spec(num_kv_heads=32),
+        new_kv_cache_spec(num_kv_heads=32),
+    ]
+    merged_layer_spec = same_layer_specs[0].merge(same_layer_specs)
+    assert merged_layer_spec.block_size == 16
+    assert merged_layer_spec.num_kv_heads == 32
+    assert merged_layer_spec.head_size == 64
+    assert merged_layer_spec.dtype == torch.float32
+    assert merged_layer_spec.sliding_window is None
+
+    different_layer_specs = [
+        new_kv_cache_spec(num_kv_heads=32),
+        new_kv_cache_spec(num_kv_heads=16),
+    ]
+    with pytest.raises(AssertionError):
+        different_layer_specs[0].merge(different_layer_specs)
+
+    full_spec = new_kv_cache_spec(num_kv_heads=32)
+    different_type_layer_specs = [
+        full_spec,
+        SlidingWindowSpec(
+            block_size=full_spec.block_size,
+            num_kv_heads=full_spec.num_kv_heads,
+            head_size=full_spec.head_size,
+            dtype=full_spec.dtype,
+            use_mla=full_spec.use_mla,
+            sliding_window=1,
+        ),
+    ]
+    with pytest.raises(AssertionError):
+        different_type_layer_specs[0].merge(different_type_layer_specs)
+    with pytest.raises(AssertionError):
+        different_type_layer_specs[1].merge(different_type_layer_specs)
+
+    different_sliding_window_layer_specs = [
+        new_kv_cache_spec(num_kv_heads=32),
+        new_kv_cache_spec(num_kv_heads=32, sliding_window=1),
+        new_kv_cache_spec(num_kv_heads=32, sliding_window=2),
+    ]
+    with pytest.raises(ValueError):
+        different_sliding_window_layer_specs[0].merge(
+            different_sliding_window_layer_specs)
+
+    same_sliding_window_layer_specs = [
+        new_kv_cache_spec(num_kv_heads=32, sliding_window=1),
+        new_kv_cache_spec(num_kv_heads=32, sliding_window=1),
+    ]
+    merged_layer_spec = same_sliding_window_layer_specs[0].merge(
+        same_sliding_window_layer_specs)
+    assert merged_layer_spec.sliding_window == 1
+
+    same_sliding_window_layer_spec_with_none = [
+        new_kv_cache_spec(num_kv_heads=32, sliding_window=1),
+        new_kv_cache_spec(num_kv_heads=32, sliding_window=None),
+    ]
+    merged_layer_spec = same_sliding_window_layer_spec_with_none[0].merge(
+        same_sliding_window_layer_spec_with_none)
+    assert merged_layer_spec.sliding_window == 1
+
+
 @pytest.mark.parametrize(
     ("model_id", "max_model_len", "want_estimated_max_len"), [
         ("Qwen/Qwen1.5-7B", 16385, 16384),

tests/v1/core/test_prefix_caching.py

@@ -84,7 +84,7 @@ def test_prefill(hash_algo):
     blocks = manager.allocate_slots(req0, 55,
                                     len(computed_blocks.blocks) * 16,
                                     computed_blocks)
-    assert blocks.get_block_ids() == [1, 2, 3, 4]
+    assert blocks.get_block_ids() == [[1, 2, 3, 4]]
 
     # Check full block metadata
     parent_block_hash = None
@@ -107,13 +107,13 @@ def test_prefill(hash_algo):
     req1 = make_request("1", common_token_ids + unique_token_ids)
     computed_blocks, num_computed_tokens = manager.get_computed_blocks(req1)
     assert len(manager.req_to_block_hashes[req1.request_id]) == 3
-    assert computed_blocks.get_block_ids() == [1, 2, 3]
+    assert computed_blocks.get_block_ids() == [[1, 2, 3]]
     assert num_computed_tokens == 3 * 16
     num_new_tokens = 53 - 3 * 16
     blocks = manager.allocate_slots(req1, num_new_tokens,
                                     len(computed_blocks.blocks) * 16,
                                     computed_blocks)
-    assert blocks.get_block_ids() == [5]
+    assert blocks.get_block_ids() == [[5]]
     for block in computed_blocks.blocks:
         assert block.ref_cnt == 2
 
@@ -141,13 +141,13 @@ def test_prefill(hash_algo):
     req2 = make_request("2", common_token_ids + unique_token_ids)
     computed_blocks, num_computed_tokens = manager.get_computed_blocks(req2)
     assert len(manager.req_to_block_hashes[req2.request_id]) == 3
-    assert computed_blocks.get_block_ids() == [1, 2, 3]
+    assert computed_blocks.get_block_ids() == [[1, 2, 3]]
     assert num_computed_tokens == 3 * 16
     num_new_tokens = 53 - 3 * 16
     blocks = manager.allocate_slots(req2, num_new_tokens,
                                     len(computed_blocks.blocks) * 16,
                                     computed_blocks)
-    assert blocks.get_block_ids() == [6]
+    assert blocks.get_block_ids() == [[6]]
 
     # Although we only have 6 free blocks, we have 8 blocks in
     # the free block queue due to lazy removal.
@@ -171,7 +171,7 @@ def test_prefill(hash_algo):
                                     len(computed_blocks.blocks) * 16,
                                     computed_blocks)
     # This block ID order also checks the eviction order.
-    assert blocks.get_block_ids() == [7, 8, 9, 10, 4, 5, 6, 3, 2, 1]
+    assert blocks.get_block_ids() == [[7, 8, 9, 10, 4, 5, 6, 3, 2, 1]]
     assert manager.block_pool.free_block_queue.num_free_blocks == 0
     assert manager.block_pool.free_block_queue.free_list_head is None
     assert manager.block_pool.free_block_queue.free_list_tail is None
@@ -208,7 +208,7 @@ def test_prefill_plp():
     blocks = manager.allocate_slots(req0, 55,
                                     len(computed_blocks.blocks) * 16,
                                     computed_blocks)
-    assert blocks.get_block_ids() == [1, 2, 3, 4]
+    assert blocks.get_block_ids() == [[1, 2, 3, 4]]
     req0_block_hashes = [b.block_hash for b in blocks.blocks]
 
     # Check full block metadata
@@ -233,13 +233,13 @@ def test_prefill_plp():
     req1 = make_request("1", common_token_ids + unique_token_ids)
     computed_blocks, num_computed_tokens = manager.get_computed_blocks(req1)
     assert len(manager.req_to_block_hashes[req1.request_id]) == 3
-    assert computed_blocks.get_block_ids() == [1, 2, 3]
+    assert computed_blocks.get_block_ids() == [[1, 2, 3]]
     assert num_computed_tokens == 3 * 16
     num_new_tokens = 53 - 3 * 16
     blocks = manager.allocate_slots(req1, num_new_tokens,
                                     len(computed_blocks.blocks) * 16,
                                     computed_blocks)
-    assert blocks.get_block_ids() == [5]
+    assert blocks.get_block_ids() == [[5]]
     for block in computed_blocks.blocks:
         assert block.ref_cnt == 2
 
@@ -277,11 +277,11 @@ def test_prefill_plp():
     block_ids = blocks.get_block_ids()
     # Duplicate cached blocks have different ids but same hashes vs request #0
     assert [b.block_hash for b in blocks.blocks] == req0_block_hashes
-    assert block_ids != [1, 2, 3, 4]
+    assert block_ids != [[1, 2, 3, 4]]
 
     # Request #2 block hashes are valid since request #0 hashes are.
     # Check block reference counts.
-    for block_id in block_ids:
+    for block_id in block_ids[0]:
         assert manager.block_pool.blocks[block_id].ref_cnt == 1
 
     manager.free(req2)
@@ -307,7 +307,7 @@ def test_decode():
     blocks = manager.allocate_slots(req0, 55,
                                     len(computed_blocks.blocks) * 16,
                                     computed_blocks)
-    assert blocks.get_block_ids() == [1, 2, 3, 4]
+    assert blocks.get_block_ids() == [[1, 2, 3, 4]]
 
     # Append slots without allocating a new block.
     req0.num_computed_tokens = 55
@@ -379,12 +379,12 @@ def test_evict():
     # Touch the first 2 blocks.
     req2 = make_request("2", list(range(2 * 16 + 3)))
     computed_blocks, num_computed_tokens = manager.get_computed_blocks(req2)
-    assert computed_blocks.get_block_ids() == [1, 2]
+    assert computed_blocks.get_block_ids() == [[1, 2]]
     assert num_computed_tokens == 2 * 16
     blocks = manager.allocate_slots(req2, 3,
                                     len(computed_blocks.blocks) * 16,
                                     computed_blocks)
-    assert blocks.get_block_ids() == [10]
+    assert blocks.get_block_ids() == [[10]]
     assert manager.block_pool.free_block_queue.num_free_blocks == 7
 
 
@@ -625,7 +625,7 @@ def test_mm_prefix_caching():
     blocks = manager.allocate_slots(req0, 59,
                                     len(computed_blocks.blocks) * 16,
                                     computed_blocks)
-    assert blocks.get_block_ids() == [1, 2, 3, 4]
+    assert blocks.get_block_ids() == [[1, 2, 3, 4]]
     req0.num_computed_tokens = 59
 
     # Append slots without allocating a new block.
@@ -686,7 +686,7 @@ def test_cache_key_salting():
     blocks = manager.allocate_slots(req0, 59,
                                     len(computed_blocks.blocks) * 16,
                                     computed_blocks)
-    assert blocks.get_block_ids() == [1, 2, 3, 4]
+    assert blocks.get_block_ids() == [[1, 2, 3, 4]]
     req0.num_computed_tokens = 59
 
     # Append slots without allocating a new block.
@@ -797,7 +797,7 @@ def test_reset_prefix_cache():
     all_token_ids = full_block_token_ids + unique_token_ids
     req0 = make_request("0", all_token_ids)
     blocks = manager.allocate_slots(req0, 55)
-    assert blocks.get_block_ids() == [1, 2, 3, 4]
+    assert blocks.get_block_ids() == [[1, 2, 3, 4]]
 
     unique_token_ids = [4] * 7
     all_token_ids = full_block_token_ids + unique_token_ids
@@ -808,7 +808,7 @@ def test_reset_prefix_cache():
     blocks = manager.allocate_slots(req1, 7,
                                     len(computed_blocks.blocks) * 16,
                                     computed_blocks)
-    assert blocks.get_block_ids() == [5]
+    assert blocks.get_block_ids() == [[5]]
 
     # Failed to reset prefix cache because some blocks are not freed yet.
     assert not manager.reset_prefix_cache()

tests/v1/worker/test_gpu_input_batch.py

@@ -9,9 +9,11 @@ import torch
 
 from vllm.sampling_params import SamplingParams
 from vllm.utils import is_pin_memory_available, make_tensor_with_pad
+from vllm.v1.kv_cache_interface import (FullAttentionSpec, KVCacheConfig,
+                                        KVCacheGroupSpec, KVCacheTensor)
 from vllm.v1.sample.metadata import SamplingMetadata
-from vllm.v1.worker.gpu_input_batch import (BlockTable, CachedRequestState,
-                                            InputBatch)
+from vllm.v1.worker.block_table import BlockTable, MultiGroupBlockTable
+from vllm.v1.worker.gpu_input_batch import CachedRequestState, InputBatch
 
 VOCAB_SIZE = 1024
 NUM_OUTPUT_TOKENS = 20
@@ -22,6 +24,27 @@ CUDA_DEVICES = [
 MAX_NUM_PROMPT_TOKENS = 64
 
 
+def get_kv_cache_config() -> KVCacheConfig:
+    return KVCacheConfig(
+        num_blocks=10,
+        tensors={
+            "layer.0": KVCacheTensor(size=1024),
+        },
+        kv_cache_groups=[
+            KVCacheGroupSpec(
+                layer_names=["layer.0"],
+                kv_cache_spec=FullAttentionSpec(
+                    block_size=1,
+                    num_kv_heads=1,
+                    head_size=16,
+                    dtype=torch.float16,
+                    use_mla=False,
+                ),
+            ),
+        ],
+    )
+
+
 def _compare_objs(obj1, obj2):
     attrs = inspect.getmembers(obj1, lambda a: not (inspect.isroutine(a)))
     attr_names = set([
@@ -41,6 +64,10 @@ def _compare_objs(obj1, obj2):
         elif isinstance(a, np.ndarray):
             if np.allclose(a, b):
                 is_same = True
+        elif isinstance(a, MultiGroupBlockTable):
+            for a_i, b_i in zip(a.block_tables, b.block_tables):
+                _compare_objs(a_i, b_i)
+            is_same = True
         elif isinstance(a, (BlockTable, SamplingMetadata)):
             _compare_objs(a, b)
             is_same = True  # if we make it here must be same
@@ -198,7 +225,7 @@ def _construct_cached_request_state(req_id_suffix: int):
         sampling_params=_create_sampling_params(),
         mm_inputs=[],
         mm_positions=[],
-        block_ids=[],
+        block_ids=[[]],
         generator=None,
         num_computed_tokens=len(output_token_ids),
         output_token_ids=output_token_ids,
@@ -220,11 +247,11 @@ def test_sampling_metadata_in_input_batch(device: str, batch_size: int):
     input_batch: InputBatch = InputBatch(
         max_num_reqs=batch_size,
         max_model_len=1024,
-        max_num_blocks_per_req=10,
         max_num_batched_tokens=1024,
         device=torch.device(device),
         pin_memory=is_pin_memory_available(),
         vocab_size=1024,
+        kv_cache_config=get_kv_cache_config(),
     )
     reqs: list[CachedRequestState] = []
     req_id_reqs = {}
@@ -310,20 +337,20 @@ def test_swap_states_in_input_batch(device: str, batch_size: int,
     input_batch: InputBatch = InputBatch(
         max_num_reqs=batch_size,
         max_model_len=1024,
-        max_num_blocks_per_req=10,
         max_num_batched_tokens=1024,
         device=torch.device(device),
         pin_memory=is_pin_memory_available(),
         vocab_size=1024,
+        kv_cache_config=get_kv_cache_config(),
     )
     ref_input_batch: InputBatch = InputBatch(
         max_num_reqs=batch_size,
         max_model_len=1024,
-        max_num_blocks_per_req=10,
         max_num_batched_tokens=1024,
         device=torch.device(device),
         pin_memory=is_pin_memory_available(),
         vocab_size=1024,
+        kv_cache_config=get_kv_cache_config(),
     )
 
     reqs: list[CachedRequestState] = []

tests/v1/worker/test_gpu_model_runner.py

 # SPDX-License-Identifier: Apache-2.0
-import weakref
 
 import pytest
-import torch
 
-from vllm.config import CacheConfig, ModelConfig, SchedulerConfig, VllmConfig
+from vllm.config import (CacheConfig, ModelConfig, ParallelConfig,
+                         SchedulerConfig, VllmConfig)
 from vllm.sampling_params import SamplingParams
 from vllm.v1.core.sched.output import (CachedRequestData, NewRequestData,
                                        SchedulerOutput)
-from vllm.v1.kv_cache_interface import FullAttentionSpec
+from vllm.v1.kv_cache_interface import (FullAttentionSpec, KVCacheConfig,
+                                        KVCacheGroupSpec, KVCacheTensor)
 from vllm.v1.sample.metadata import SamplingMetadata
+from vllm.v1.worker.gpu_input_batch import InputBatch
 from vllm.v1.worker.gpu_model_runner import GPUModelRunner
 
 
@@ -17,13 +18,34 @@ def initialize_kv_cache(runner: GPUModelRunner):
     """
     Only perform necessary steps in GPUModelRunner.initialize_kv_cache()
     """
-    kv_cache_spec = FullAttentionSpec(block_size=16,
-                                      num_kv_heads=1,
-                                      head_size=64,
-                                      dtype=torch.float16,
-                                      use_mla=False)
-    runner.attn_metadata_builder = runner.attn_backend.get_builder_cls()(
-        weakref.proxy(runner), kv_cache_spec, runner.input_batch.block_table)
+    kv_cache_config = KVCacheConfig(
+        num_blocks=10,
+        tensors={
+            "layer.0": KVCacheTensor(size=1024),
+        },
+        kv_cache_groups=[
+            KVCacheGroupSpec(
+                layer_names=["layer.0"],
+                kv_cache_spec=FullAttentionSpec(
+                    block_size=16,
+                    num_kv_heads=runner.model_config.get_num_kv_heads(
+                        runner.parallel_config),
+                    head_size=runner.model_config.get_head_size(),
+                    dtype=runner.kv_cache_dtype,
+                    use_mla=False,
+                ))
+        ])
+    runner.kv_cache_config = kv_cache_config
+    runner.input_batch = InputBatch(
+        max_num_reqs=runner.max_num_reqs,
+        max_model_len=runner.max_model_len,
+        max_num_batched_tokens=runner.max_num_tokens,
+        device=runner.device,
+        pin_memory=runner.pin_memory,
+        vocab_size=runner.model_config.get_vocab_size(),
+        kv_cache_config=kv_cache_config,
+    )
+    runner.initialize_attn_backend(kv_cache_config)
 
 
 @pytest.fixture
@@ -48,10 +70,12 @@ def model_runner():
         swap_space=0,
         cache_dtype="auto",
     )
+    parallel_config = ParallelConfig()
     vllm_config = VllmConfig(
         model_config=model_config,
         cache_config=cache_config,
         scheduler_config=scheduler_config,
+        parallel_config=parallel_config,
     )
 
     device = "cuda"
@@ -73,7 +97,7 @@ def _schedule_new_request(*req_ids: str) -> SchedulerOutput:
                 mm_hashes=[],
                 mm_positions=[],
                 sampling_params=SamplingParams(),
-                block_ids=[0],
+                block_ids=[[0]],
                 num_computed_tokens=0,
                 lora_request=None,
             ))
@@ -111,13 +135,14 @@ def _is_sampling_metadata_changed(model_runner,
 
 def _is_req_state_block_table_match(model_runner, req_id: str) -> bool:
     req_index = model_runner.input_batch.req_id_to_index[req_id]
-    block_table = model_runner.input_batch.block_table
+    block_table = model_runner.input_batch.block_table[0]
     req_state = model_runner.requests[req_id]
-    if block_table.num_blocks_per_row[req_index] != len(req_state.block_ids):
+    if block_table.num_blocks_per_row[req_index] != len(
+            req_state.block_ids[0]):
         return False
     num_blocks = block_table.num_blocks_per_row[req_index]
     return (block_table.block_table_np[req_index, :num_blocks] ==
-            req_state.block_ids).all()
+            req_state.block_ids[0]).all()
 
 
 def test_update_states_new_request(model_runner):
@@ -200,7 +225,7 @@ def test_update_states_request_resumed(model_runner):
         req_id=req_id,
         resumed_from_preemption=False,
         new_token_ids=[],
-        new_block_ids=[],
+        new_block_ids=[[]],
         num_computed_tokens=0,
     )
 

tests/weight_loading/models.txt

@@ -2,7 +2,7 @@ gptq_marlin, robertgshaw2/zephyr-7b-beta-channelwise-gptq, main
 gptq_marlin, TheBloke/Llama-2-7B-GPTQ, main
 gptq_marlin, TheBloke/TinyLlama-1.1B-Chat-v1.0-GPTQ, main
 gptq_marlin, TheBloke/TinyLlama-1.1B-Chat-v1.0-GPTQ, gptq-8bit--1g-actorder_True
-gptq_marlin, TheBloke/TinyLlama-1.1B-Chat-v1.0-GPTQ, gptq-8bit-32g-actorder_True
+#gptq_marlin, TheBloke/TinyLlama-1.1B-Chat-v1.0-GPTQ, gptq-8bit-32g-actorder_True
 gptq_marlin, TechxGenus/gemma-1.1-2b-it-GPTQ, main
 gptq, robertgshaw2/zephyr-7b-beta-channelwise-gptq, main
 gptq, TheBloke/Llama-2-7B-GPTQ, main

vllm/distributed/kv_transfer/kv_connector/v1/shared_storage_connector.py

@@ -288,7 +288,7 @@ class SharedStorageConnector(KVConnectorBase_V1):
         for new_req in scheduler_output.scheduled_new_reqs:
             if new_req.req_id in self._requests_need_load:
                 meta.add_request(token_ids=new_req.prompt_token_ids,
-                                 block_ids=new_req.block_ids,
+                                 block_ids=new_req.block_ids[0],
                                  block_size=self._block_size,
                                  is_store=False)
                 total_need_load += 1
@@ -299,7 +299,7 @@ class SharedStorageConnector(KVConnectorBase_V1):
                 # the original prompt tokens.
                 if not self._found_match_for_request(new_req):
                     meta.add_request(token_ids=new_req.prompt_token_ids,
-                                     block_ids=new_req.block_ids,
+                                     block_ids=new_req.block_ids[0],
                                      block_size=self._block_size,
                                      is_store=True)
 
@@ -319,7 +319,7 @@ class SharedStorageConnector(KVConnectorBase_V1):
 
                 # NOTE(rob): For resumed req, new_block_ids is all
                 # of the block_ids for the request.
-                block_ids = cached_req.new_block_ids
+                block_ids = cached_req.new_block_ids[0]
 
                 meta.add_request(token_ids=token_ids,
                                  block_ids=block_ids,

vllm/v1/attention/backends/mla/rocm_aiter_mla.py

@@ -67,13 +67,13 @@ class AiterMLAMetadataBuilder(MLACommonMetadataBuilder[AiterMLAMetadata]):
         max_model_len = self.runner.model_config.max_model_len
         assert max_model_len == 32768,\
             "AITER MLA requires max_model_len=32768"
-        assert self.runner.block_size == 1, "AITER MLA" \
+        assert self.kv_cache_spec.block_size == 1, "AITER MLA" \
             "only supports block size 1."
 
     def _get_paged_kv_tensors(
             self, block_table: torch.Tensor,
             seq_lens: torch.Tensor) -> tuple[torch.Tensor, ...]:
-        page_size = self.runner.block_size
+        page_size = self.kv_cache_spec.block_size
         block_table_bounds = (seq_lens + page_size - 1) // page_size
 
         mask = (torch.arange(block_table.size(1),

vllm/v1/core/kv_cache_manager.py

@@ -32,9 +32,16 @@ class KVCacheBlocks:
         """Creates a new KVCacheBlocks instance with no blocks."""
         return cls([])
 
-    def get_block_ids(self) -> list[int]:
-        """Converts the KVCacheBlocks instance to a list of block IDs."""
-        return [block.block_id for block in self.blocks]
+    def get_block_ids(self) -> list[list[int]]:
+        """
+        Converts the KVCacheBlocks instance to block_ids.
+        
+        Returns:
+            list[list[int]]: A two-level list where
+            * the outer list corresponds to KV cache groups (only 1 group now)
+            * each inner list contains the block_ids of the blocks in that group
+        """
+        return [[block.block_id for block in self.blocks]]
 
     def get_unhashed_block_ids(self) -> list[int]:
         """Get block_ids of unhashed blocks from KVCacheBlocks instance."""
@@ -300,9 +307,9 @@ class KVCacheManager:
         self,
         request: Request,
         num_running_requests: int,
-    ) -> int:
+    ) -> list[int]:
         """Calculate the number of common prefix blocks shared by all requests
-        in the RUNNING state.
+        in the RUNNING state for each kv cache group.
 
         The function determines this by selecting any request and iterating
         through its blocks.  A block is considered a common prefix block if its
@@ -332,11 +339,14 @@ class KVCacheManager:
                 requests in the current step.
 
         Returns:
-            int: The number of common prefix blocks.
+            list[int]: The number of common prefix blocks for each kv cache 
+            group.
         """
         assert request.status == RequestStatus.RUNNING
-        return self.single_type_manager.get_num_common_prefix_blocks(
+        return [
+            self.single_type_manager.get_num_common_prefix_blocks(
                 request.request_id, num_running_requests)
+        ]
 
     def free_block_hashes(self, request: Request) -> None:
         """Discard the block hashes for the request.
@@ -354,10 +364,8 @@ class KVCacheManager:
         """
         return self.block_pool.take_events()
 
-    def get_block_ids(self, request_id: str) -> list[int]:
+    def get_block_ids(self, request_id: str) -> list[list[int]]:
         """Get the block ids of a request."""
         assert request_id in self.single_type_manager.req_to_blocks
-        return [
-            block.block_id
-            for block in self.single_type_manager.req_to_blocks[request_id]
-        ]
+        return KVCacheBlocks(self.single_type_manager.req_to_blocks[request_id]
+                             ).get_block_ids()

vllm/v1/core/kv_cache_utils.py

@@ -577,14 +577,12 @@ def create_kv_cache_group_specs(
      """
     kv_cache_groups = []
     for layer_names_one_group in grouped_layer_names:
-        layer_spec = kv_cache_spec[layer_names_one_group[0]]
-        assert all(
-            kv_cache_spec[layer_name] == layer_spec
-            for layer_name in layer_names_one_group[1:]), (
-                "All layers in the same KV cache group must share the same "
-                "KVCacheSpec.")
+        layer_specs = [
+            kv_cache_spec[layer_name] for layer_name in layer_names_one_group
+        ]
+        merged_layer_spec = layer_specs[0].merge(layer_specs)
         kv_cache_groups.append(
-            KVCacheGroupSpec(layer_names_one_group, layer_spec))
+            KVCacheGroupSpec(layer_names_one_group, merged_layer_spec))
     return kv_cache_groups
 
 
@@ -683,6 +681,7 @@ def unify_hybrid_kv_cache_specs(kv_cache_spec: dict[str, KVCacheSpec]):
                     head_size=spec.head_size,
                     dtype=spec.dtype,
                     use_mla=spec.use_mla,
+                    sliding_window=spec.sliding_window,
                 )
 
 

vllm/v1/core/sched/output.py

@@ -26,7 +26,7 @@ class NewRequestData:
     mm_hashes: list[str]
     mm_positions: list[PlaceholderRange]
     sampling_params: SamplingParams
-    block_ids: list[int]
+    block_ids: list[list[int]]
     num_computed_tokens: int
     lora_request: Optional[LoRARequest]
 
@@ -34,7 +34,7 @@ class NewRequestData:
     def from_request(
         cls,
         request: Request,
-        block_ids: list[int],
+        block_ids: list[list[int]],
     ) -> NewRequestData:
         return cls(
             req_id=request.request_id,
@@ -85,7 +85,7 @@ class CachedRequestData:
     # request's block IDs instead of appending to the existing block IDs.
     resumed_from_preemption: bool
     new_token_ids: list[int]
-    new_block_ids: list[int]
+    new_block_ids: list[list[int]]
     num_computed_tokens: int
 
     @classmethod
@@ -94,7 +94,7 @@ class CachedRequestData:
         request: Request,
         resumed_from_preemption: bool,
         new_token_ids: list[int],
-        new_block_ids: list[int],
+        new_block_ids: list[list[int]],
     ) -> CachedRequestData:
         return cls(
             req_id=request.request_id,
@@ -131,9 +131,9 @@ class SchedulerOutput:
     # E.g., if a request has [0, 1], it could mean the vision encoder needs
     # to process that the request's 0-th and 1-th images in the current step.
     scheduled_encoder_inputs: dict[str, list[int]]
-    # Number of common prefix blocks for all requests.
+    # Number of common prefix blocks for all requests in each KV cache group.
     # This can be used for cascade attention.
-    num_common_prefix_blocks: int
+    num_common_prefix_blocks: list[int]
 
     # Request IDs that are finished in between the previous and the current
     # steps. This is used to notify the workers about the finished requests

vllm/v1/core/sched/scheduler.py

@@ -173,7 +173,7 @@ class Scheduler(SchedulerInterface):
         # uses structured decoding.
         structured_output_request_ids: dict[str, int] = {}
 
-        req_to_new_block_ids: dict[str, list[int]] = {}
+        req_to_new_block_ids: dict[str, list[list[int]]] = {}
         num_scheduled_tokens: dict[str, int] = {}
         token_budget = self.max_num_scheduled_tokens
         # Encoder-related.
@@ -477,7 +477,8 @@ class Scheduler(SchedulerInterface):
 
         # Get the longest common prefix among all requests in the running queue.
         # This can be potentially used for cascade attention.
-        num_common_prefix_blocks = 0
+        num_common_prefix_blocks = [0] * len(
+            self.kv_cache_config.kv_cache_groups)
         if self.running:
             any_request = self.running[0]
             num_common_prefix_blocks = (
@@ -564,7 +565,7 @@ class Scheduler(SchedulerInterface):
         request: Request,
         num_scheduled_tokens: int,
         num_scheduled_spec_tokens: int,
-        new_block_ids: list[int],
+        new_block_ids: list[list[int]],
         resumed_from_preemption: bool,
     ) -> CachedRequestData:
         # OPTIMIZATION: Cache the CachedRequestData objects to avoid creating
@@ -939,7 +940,9 @@ class Scheduler(SchedulerInterface):
         """
         if self.connector is None:
             return False, None
-        block_ids = self.kv_cache_manager.get_block_ids(request.request_id)
+        assert len(self.kv_cache_config.kv_cache_groups
+                   ) == 1, "KV connector only supports one KV cache group now"
+        block_ids = self.kv_cache_manager.get_block_ids(request.request_id)[0]
         return self.connector.request_finished(request, block_ids)
 
     def _update_waiting_for_remote_kv(self, request: Request) -> bool:
@@ -956,9 +959,10 @@ class Scheduler(SchedulerInterface):
         """
         if request.request_id not in self.finished_recving_kv_req_ids:
             return False
-
+        assert len(self.kv_cache_config.kv_cache_groups
+                   ) == 1, "KV connector only supports one KV cache group now"
         # Now that the blocks are ready, actually cache them.
-        block_ids = self.kv_cache_manager.get_block_ids(request.request_id)
+        block_ids = self.kv_cache_manager.get_block_ids(request.request_id)[0]
         num_computed_tokens = len(block_ids) * self.block_size
         if num_computed_tokens == request.num_tokens:
             num_computed_tokens -= 1

vllm/v1/kv_cache_interface.py

 # SPDX-License-Identifier: Apache-2.0
 
+import copy
 from dataclasses import dataclass
+from typing import Optional
 
 import torch
+from typing_extensions import Self
 
 from vllm.config import VllmConfig
 from vllm.logger import init_logger
@@ -53,6 +56,16 @@ class KVCacheSpec:
         """
         raise NotImplementedError
 
+    @classmethod
+    def merge(cls, specs: list[Self]) -> Self:
+        """
+        Merge a list of KVCacheSpec objects into a single KVCacheSpec object.
+        """
+        assert all(spec.type_id == specs[0].type_id for spec in specs[1:]), (
+            "All layers in the same KV cache group must share the same "
+            "type_id.")
+        return copy.deepcopy(specs[0])
+
 
 @dataclass
 class AttentionSpec(KVCacheSpec):
@@ -71,6 +84,16 @@ class AttentionSpec(KVCacheSpec):
 
 @dataclass
 class FullAttentionSpec(AttentionSpec):
+    sliding_window: Optional[int] = None
+    """
+    When hybrid allocator is disabled and the model contains both full 
+    attention layers and sliding window attention layers, sliding 
+    window attention are regarded as full attention in KV cache manager 
+    (blocks are allocated for all tokens), while computed as sliding window 
+    attention in model runner.
+    In this case, we use FullAttentionSpec and record the sliding window size.
+    Default to None for not using sliding window attention.
+    """
 
     @property
     def type_id(self) -> str:
@@ -80,6 +103,25 @@ class FullAttentionSpec(AttentionSpec):
         max_model_len = vllm_config.model_config.max_model_len
         return cdiv(max_model_len, self.block_size) * self.page_size_bytes
 
+    @classmethod
+    def merge(cls, specs: list[Self]) -> Self:
+        """
+        Merge a list of FullAttentionSpec objects into a single 
+        FullAttentionSpec object.
+        """
+        merged_spec = super().merge(specs)
+        sliding_window = set(spec.sliding_window for spec in specs
+                             if spec.sliding_window is not None)
+        if len(sliding_window) == 0:
+            merged_spec.sliding_window = None
+        elif len(sliding_window) == 1:
+            merged_spec.sliding_window = sliding_window.pop()
+        else:
+            raise ValueError(
+                "All sliding window layers in the same KV cache group "
+                "must have the same window size.")
+        return merged_spec
+
 
 @dataclass
 class SlidingWindowSpec(AttentionSpec):

vllm/v1/worker/block_table.py

@@ -4,6 +4,8 @@ import numpy as np
 import torch
 
 from vllm.logger import init_logger
+from vllm.utils import cdiv
+from vllm.v1.kv_cache_interface import KVCacheConfig
 
 logger = init_logger(__name__)
 
@@ -96,3 +98,48 @@ class BlockTable:
     def get_numpy_array(self) -> np.ndarray:
         """Returns the numpy array of the block table."""
         return self.block_table_np
+
+
+class MultiGroupBlockTable:
+    """The BlockTables for each KV cache group."""
+
+    def __init__(self, max_num_reqs: int, max_model_len: int,
+                 max_num_batched_tokens: int, pin_memory: bool,
+                 device: torch.device, kv_cache_config: KVCacheConfig) -> None:
+        max_num_blocks_per_req = [
+            cdiv(max_model_len, g.kv_cache_spec.block_size)
+            for g in kv_cache_config.kv_cache_groups
+        ]
+        self.block_tables = [
+            BlockTable(max_num_reqs, max_num_blocks_per_req[i],
+                       max_num_batched_tokens, pin_memory, device)
+            for i in range(len(kv_cache_config.kv_cache_groups))
+        ]
+
+    def append_row(self, block_ids: list[list[int]], row_idx: int) -> None:
+        for i, block_table in enumerate(self.block_tables):
+            block_table.append_row(block_ids[i], row_idx)
+
+    def add_row(self, block_ids: list[list[int]], row_idx: int) -> None:
+        for i, block_table in enumerate(self.block_tables):
+            block_table.add_row(block_ids[i], row_idx)
+
+    def move_row(self, src: int, tgt: int) -> None:
+        for block_table in self.block_tables:
+            block_table.move_row(src, tgt)
+
+    def swap_row(self, src: int, tgt: int) -> None:
+        for block_table in self.block_tables:
+            block_table.swap_row(src, tgt)
+
+    def commit(self, num_reqs: int) -> None:
+        for block_table in self.block_tables:
+            block_table.commit(num_reqs)
+
+    def clear(self) -> None:
+        for block_table in self.block_tables:
+            block_table.clear()
+
+    def __getitem__(self, idx: int) -> "BlockTable":
+        """Returns the BlockTable for the i-th KV cache group."""
+        return self.block_tables[idx]

vllm/v1/worker/gpu_input_batch.py

@@ -11,10 +11,11 @@ from vllm.lora.request import LoRARequest
 from vllm.multimodal.inputs import MultiModalKwargs, PlaceholderRange
 from vllm.sampling_params import SamplingParams, SamplingType
 from vllm.utils import swap_dict_values
+from vllm.v1.kv_cache_interface import KVCacheConfig
 from vllm.v1.outputs import LogprobsTensors
 from vllm.v1.sample.metadata import SamplingMetadata
 from vllm.v1.utils import copy_slice
-from vllm.v1.worker.block_table import BlockTable
+from vllm.v1.worker.block_table import MultiGroupBlockTable
 
 _SAMPLING_EPS = 1e-5
 
@@ -29,7 +30,7 @@ class CachedRequestState:
     sampling_params: SamplingParams
     generator: Optional[torch.Generator]
 
-    block_ids: list[int]
+    block_ids: list[list[int]]
     num_computed_tokens: int
     output_token_ids: list[int]
 
@@ -58,15 +59,14 @@ class InputBatch:
         self,
         max_num_reqs: int,
         max_model_len: int,
-        max_num_blocks_per_req: int,
         max_num_batched_tokens: int,
         device: torch.device,
         pin_memory: bool,
         vocab_size: int,
+        kv_cache_config: KVCacheConfig,
     ):
         self.max_num_reqs = max_num_reqs
         self.max_model_len = max_model_len
-        self.max_num_blocks_per_req = max_num_blocks_per_req
         self.max_num_batched_tokens = max_num_batched_tokens
         self.device = device
         self.pin_memory = pin_memory
@@ -99,12 +99,13 @@ class InputBatch:
             self.num_computed_tokens_cpu_tensor.numpy()
 
         # Block table.
-        self.block_table = BlockTable(
+        self.block_table = MultiGroupBlockTable(
             max_num_reqs=max_num_reqs,
-            max_num_blocks_per_req=max_num_blocks_per_req,
+            max_model_len=max_model_len,
             max_num_batched_tokens=max_num_batched_tokens,
             pin_memory=pin_memory,
             device=device,
+            kv_cache_config=kv_cache_config,
         )
 
         # Sampling-related.

vllm/v1/worker/tpu_model_runner.py

@@ -171,19 +171,10 @@ class TPUModelRunner(LoRAModelRunnerMixin):
         self.kv_caches: list[torch.Tensor] = []
         # req_id -> (input_id -> encoder_output)
         self.encoder_cache: dict[str, dict[int, torch.Tensor]] = {}
+        # self.input_batch: InputBatch  # Persistent batch.
 
         # Request states.
         self.requests: dict[str, CachedRequestState] = {}
-        # Persistent batch.
-        self.input_batch = InputBatch(
-            max_num_reqs=self.max_num_reqs,
-            max_model_len=self.max_model_len,
-            max_num_blocks_per_req=self.max_num_blocks_per_req,
-            max_num_batched_tokens=self.max_num_tokens,
-            device=self.device,
-            pin_memory=self.pin_memory,
-            vocab_size=self.vocab_size,
-        )
 
         # Cached torch/numpy tensor
         # The pytorch tensor and numpy array share the same buffer.
@@ -199,7 +190,7 @@ class TPUModelRunner(LoRAModelRunnerMixin):
 
         self.block_table_cpu = torch.zeros(
             (self.max_num_reqs, self.max_num_blocks_per_req),
-            dtype=self.input_batch.block_table.get_cpu_tensor().dtype,
+            dtype=torch.int32,
             device="cpu")
 
         self.query_start_loc_cpu = torch.zeros(self.max_num_tokens + 1,
@@ -524,12 +515,12 @@ class TPUModelRunner(LoRAModelRunnerMixin):
         # NOTE(woosuk): We use torch.index_select instead of np.take here
         # because torch.index_select is much faster than np.take for large
         # tensors.
-        block_table_cpu = self.input_batch.block_table.get_cpu_tensor()
+        block_table_cpu = self.input_batch.block_table[0].get_cpu_tensor()
         block_numbers = block_table_cpu.flatten()[block_table_indices].numpy()
         block_offsets = positions_np % self.block_size
         np.add(block_numbers * self.block_size,
                block_offsets,
-               out=self.input_batch.block_table.
+               out=self.input_batch.block_table[0].
                slot_mapping_np[:total_num_scheduled_tokens])
 
         # Prepare the attention metadata.
@@ -554,15 +545,15 @@ class TPUModelRunner(LoRAModelRunnerMixin):
         self.position_ids = self.positions_cpu[:
                                                padded_total_num_scheduled_tokens].to(
                                                    self.device)
-        self.input_batch.block_table.slot_mapping_cpu[
+        self.input_batch.block_table[0].slot_mapping_cpu[
             total_num_scheduled_tokens:] = _PAD_SLOT_ID
         slot_mapping = (
-            self.input_batch.block_table.
+            self.input_batch.block_table[0].
             slot_mapping_cpu[:padded_total_num_scheduled_tokens].to(
                 self.device))
         block_tables = self.block_table_cpu[:self.max_num_reqs]
         block_tables[:num_reqs, :self.max_num_blocks_per_req] = (
-            self.input_batch.block_table.get_cpu_tensor()[:num_reqs])
+            self.input_batch.block_table[0].get_cpu_tensor()[:num_reqs])
         block_tables = block_tables.to(self.device)
         query_start_loc = self.query_start_loc_cpu[:self.max_num_reqs + 1].to(
             self.device)
@@ -1263,6 +1254,18 @@ class TPUModelRunner(LoRAModelRunnerMixin):
                 "Hybrid models with more than one KV cache type are not "
                 "supported yet.")
 
+        self.input_batch = InputBatch(
+            max_num_reqs=self.max_num_reqs,
+            max_model_len=self.max_model_len,
+            max_num_batched_tokens=self.max_num_tokens,
+            device=self.device,
+            pin_memory=self.pin_memory,
+            vocab_size=self.model_config.get_vocab_size(),
+            kv_cache_config=kv_cache_config,
+        )
+        assert self.block_table_cpu.dtype == self.input_batch.block_table[
+            0].get_cpu_tensor().dtype
+
         kv_caches: dict[str, torch.Tensor] = {}
 
         for kv_cache_group in kv_cache_config.kv_cache_groups: