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A high-throughput and memory-efficient inference and serving engine for LLMs

Mirrored from https://github.com/tduncan-lum/Nvidia-Optimization-Stack---VLLM.git

Synced Nov 15, 08:30 UTC

[v1] Redo "Support multiple KV cache groups in GPU model runner (#17945)" (#18593)

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

Chen Zhang committed May 24, 2025 at 00:39 UTC 6550114c9cd23198f3a88094ebe3e0d2bc8cb8de
15 files changed +469 -203
tests/v1/core/test_kv_cache_utils.py
+68 -3
@@ -19,7 +19,8 @@ from vllm.v1.core.kv_cache_utils import (FreeKVCacheBlockQueue, KVCacheBlock,
19 hash_request_tokens,
20 unify_kv_cache_configs)
21 from vllm.v1.kv_cache_interface import (FullAttentionSpec, KVCacheConfig,
22 - KVCacheGroupSpec, KVCacheTensor)
22 + KVCacheGroupSpec, KVCacheTensor,
23 + SlidingWindowSpec)
24 from vllm.v1.metrics.stats import PrefixCacheStats
25 from vllm.v1.request import Request
26
@@ -54,12 +55,14 @@ def new_kv_cache_spec(block_size=16,
55 num_kv_heads=2,
56 head_size=64,
57 dtype=torch.float32,
57 - use_mla=False):
58 + use_mla=False,
59 + sliding_window=None):
60 return FullAttentionSpec(block_size=block_size,
61 num_kv_heads=num_kv_heads,
62 head_size=head_size,
63 dtype=dtype,
62 - use_mla=use_mla)
64 + use_mla=use_mla,
65 + sliding_window=sliding_window)
66
67
68 def test_none_hash(monkeypatch):
@@ -492,6 +495,68 @@ def test_unify_kv_cache_configs():
495 unify_kv_cache_configs(diff_kv_cache_config)
496
497
498 +def test_merge_kv_cache_spec():
499 + same_layer_specs = [
500 + new_kv_cache_spec(num_kv_heads=32),
501 + new_kv_cache_spec(num_kv_heads=32),
502 + ]
503 + merged_layer_spec = same_layer_specs[0].merge(same_layer_specs)
504 + assert merged_layer_spec.block_size == 16
505 + assert merged_layer_spec.num_kv_heads == 32
506 + assert merged_layer_spec.head_size == 64
507 + assert merged_layer_spec.dtype == torch.float32
508 + assert merged_layer_spec.sliding_window is None
509 +
510 + different_layer_specs = [
511 + new_kv_cache_spec(num_kv_heads=32),
512 + new_kv_cache_spec(num_kv_heads=16),
513 + ]
514 + with pytest.raises(AssertionError):
515 + different_layer_specs[0].merge(different_layer_specs)
516 +
517 + full_spec = new_kv_cache_spec(num_kv_heads=32)
518 + different_type_layer_specs = [
519 + full_spec,
520 + SlidingWindowSpec(
521 + block_size=full_spec.block_size,
522 + num_kv_heads=full_spec.num_kv_heads,
523 + head_size=full_spec.head_size,
524 + dtype=full_spec.dtype,
525 + use_mla=full_spec.use_mla,
526 + sliding_window=1,
527 + ),
528 + ]
529 + with pytest.raises(AssertionError):
530 + different_type_layer_specs[0].merge(different_type_layer_specs)
531 + with pytest.raises(AssertionError):
532 + different_type_layer_specs[1].merge(different_type_layer_specs)
533 +
534 + different_sliding_window_layer_specs = [
535 + new_kv_cache_spec(num_kv_heads=32),
536 + new_kv_cache_spec(num_kv_heads=32, sliding_window=1),
537 + new_kv_cache_spec(num_kv_heads=32, sliding_window=2),
538 + ]
539 + with pytest.raises(ValueError):
540 + different_sliding_window_layer_specs[0].merge(
541 + different_sliding_window_layer_specs)
542 +
543 + same_sliding_window_layer_specs = [
544 + new_kv_cache_spec(num_kv_heads=32, sliding_window=1),
545 + new_kv_cache_spec(num_kv_heads=32, sliding_window=1),
546 + ]
547 + merged_layer_spec = same_sliding_window_layer_specs[0].merge(
548 + same_sliding_window_layer_specs)
549 + assert merged_layer_spec.sliding_window == 1
550 +
551 + same_sliding_window_layer_spec_with_none = [
552 + new_kv_cache_spec(num_kv_heads=32, sliding_window=1),
553 + new_kv_cache_spec(num_kv_heads=32, sliding_window=None),
554 + ]
555 + merged_layer_spec = same_sliding_window_layer_spec_with_none[0].merge(
556 + same_sliding_window_layer_spec_with_none)
557 + assert merged_layer_spec.sliding_window == 1
558 +
559 +
560 @pytest.mark.parametrize(
561 ("model_id", "max_model_len", "want_estimated_max_len"), [
562 ("Qwen/Qwen1.5-7B", 16385, 16384),
tests/v1/core/test_prefix_caching.py
+18 -18
@@ -84,7 +84,7 @@ def test_prefill(hash_algo):
84 blocks = manager.allocate_slots(req0, 55,
85 len(computed_blocks.blocks) * 16,
86 computed_blocks)
87 - assert blocks.get_block_ids() == [1, 2, 3, 4]
87 + assert blocks.get_block_ids() == [[1, 2, 3, 4]]
88
89 # Check full block metadata
90 parent_block_hash = None
@@ -107,13 +107,13 @@ def test_prefill(hash_algo):
107 req1 = make_request("1", common_token_ids + unique_token_ids)
108 computed_blocks, num_computed_tokens = manager.get_computed_blocks(req1)
109 assert len(manager.req_to_block_hashes[req1.request_id]) == 3
110 - assert computed_blocks.get_block_ids() == [1, 2, 3]
110 + assert computed_blocks.get_block_ids() == [[1, 2, 3]]
111 assert num_computed_tokens == 3 * 16
112 num_new_tokens = 53 - 3 * 16
113 blocks = manager.allocate_slots(req1, num_new_tokens,
114 len(computed_blocks.blocks) * 16,
115 computed_blocks)
116 - assert blocks.get_block_ids() == [5]
116 + assert blocks.get_block_ids() == [[5]]
117 for block in computed_blocks.blocks:
118 assert block.ref_cnt == 2
119
@@ -141,13 +141,13 @@ def test_prefill(hash_algo):
141 req2 = make_request("2", common_token_ids + unique_token_ids)
142 computed_blocks, num_computed_tokens = manager.get_computed_blocks(req2)
143 assert len(manager.req_to_block_hashes[req2.request_id]) == 3
144 - assert computed_blocks.get_block_ids() == [1, 2, 3]
144 + assert computed_blocks.get_block_ids() == [[1, 2, 3]]
145 assert num_computed_tokens == 3 * 16
146 num_new_tokens = 53 - 3 * 16
147 blocks = manager.allocate_slots(req2, num_new_tokens,
148 len(computed_blocks.blocks) * 16,
149 computed_blocks)
150 - assert blocks.get_block_ids() == [6]
150 + assert blocks.get_block_ids() == [[6]]
151
152 # Although we only have 6 free blocks, we have 8 blocks in
153 # the free block queue due to lazy removal.
@@ -171,7 +171,7 @@ def test_prefill(hash_algo):
171 len(computed_blocks.blocks) * 16,
172 computed_blocks)
173 # This block ID order also checks the eviction order.
174 - assert blocks.get_block_ids() == [7, 8, 9, 10, 4, 5, 6, 3, 2, 1]
174 + assert blocks.get_block_ids() == [[7, 8, 9, 10, 4, 5, 6, 3, 2, 1]]
175 assert manager.block_pool.free_block_queue.num_free_blocks == 0
176 assert manager.block_pool.free_block_queue.free_list_head is None
177 assert manager.block_pool.free_block_queue.free_list_tail is None
@@ -208,7 +208,7 @@ def test_prefill_plp():
208 blocks = manager.allocate_slots(req0, 55,
209 len(computed_blocks.blocks) * 16,
210 computed_blocks)
211 - assert blocks.get_block_ids() == [1, 2, 3, 4]
211 + assert blocks.get_block_ids() == [[1, 2, 3, 4]]
212 req0_block_hashes = [b.block_hash for b in blocks.blocks]
213
214 # Check full block metadata
@@ -233,13 +233,13 @@ def test_prefill_plp():
233 req1 = make_request("1", common_token_ids + unique_token_ids)
234 computed_blocks, num_computed_tokens = manager.get_computed_blocks(req1)
235 assert len(manager.req_to_block_hashes[req1.request_id]) == 3
236 - assert computed_blocks.get_block_ids() == [1, 2, 3]
236 + assert computed_blocks.get_block_ids() == [[1, 2, 3]]
237 assert num_computed_tokens == 3 * 16
238 num_new_tokens = 53 - 3 * 16
239 blocks = manager.allocate_slots(req1, num_new_tokens,
240 len(computed_blocks.blocks) * 16,
241 computed_blocks)
242 - assert blocks.get_block_ids() == [5]
242 + assert blocks.get_block_ids() == [[5]]
243 for block in computed_blocks.blocks:
244 assert block.ref_cnt == 2
245
@@ -277,11 +277,11 @@ def test_prefill_plp():
277 block_ids = blocks.get_block_ids()
278 # Duplicate cached blocks have different ids but same hashes vs request #0
279 assert [b.block_hash for b in blocks.blocks] == req0_block_hashes
280 - assert block_ids != [1, 2, 3, 4]
280 + assert block_ids != [[1, 2, 3, 4]]
281
282 # Request #2 block hashes are valid since request #0 hashes are.
283 # Check block reference counts.
284 - for block_id in block_ids:
284 + for block_id in block_ids[0]:
285 assert manager.block_pool.blocks[block_id].ref_cnt == 1
286
287 manager.free(req2)
@@ -307,7 +307,7 @@ def test_decode():
307 blocks = manager.allocate_slots(req0, 55,
308 len(computed_blocks.blocks) * 16,
309 computed_blocks)
310 - assert blocks.get_block_ids() == [1, 2, 3, 4]
310 + assert blocks.get_block_ids() == [[1, 2, 3, 4]]
311
312 # Append slots without allocating a new block.
313 req0.num_computed_tokens = 55
@@ -379,12 +379,12 @@ def test_evict():
379 # Touch the first 2 blocks.
380 req2 = make_request("2", list(range(2 * 16 + 3)))
381 computed_blocks, num_computed_tokens = manager.get_computed_blocks(req2)
382 - assert computed_blocks.get_block_ids() == [1, 2]
382 + assert computed_blocks.get_block_ids() == [[1, 2]]
383 assert num_computed_tokens == 2 * 16
384 blocks = manager.allocate_slots(req2, 3,
385 len(computed_blocks.blocks) * 16,
386 computed_blocks)
387 - assert blocks.get_block_ids() == [10]
387 + assert blocks.get_block_ids() == [[10]]
388 assert manager.block_pool.free_block_queue.num_free_blocks == 7
389
390
@@ -625,7 +625,7 @@ def test_mm_prefix_caching():
625 blocks = manager.allocate_slots(req0, 59,
626 len(computed_blocks.blocks) * 16,
627 computed_blocks)
628 - assert blocks.get_block_ids() == [1, 2, 3, 4]
628 + assert blocks.get_block_ids() == [[1, 2, 3, 4]]
629 req0.num_computed_tokens = 59
630
631 # Append slots without allocating a new block.
@@ -686,7 +686,7 @@ def test_cache_key_salting():
686 blocks = manager.allocate_slots(req0, 59,
687 len(computed_blocks.blocks) * 16,
688 computed_blocks)
689 - assert blocks.get_block_ids() == [1, 2, 3, 4]
689 + assert blocks.get_block_ids() == [[1, 2, 3, 4]]
690 req0.num_computed_tokens = 59
691
692 # Append slots without allocating a new block.
@@ -797,7 +797,7 @@ def test_reset_prefix_cache():
797 all_token_ids = full_block_token_ids + unique_token_ids
798 req0 = make_request("0", all_token_ids)
799 blocks = manager.allocate_slots(req0, 55)
800 - assert blocks.get_block_ids() == [1, 2, 3, 4]
800 + assert blocks.get_block_ids() == [[1, 2, 3, 4]]
801
802 unique_token_ids = [4] * 7
803 all_token_ids = full_block_token_ids + unique_token_ids
@@ -808,7 +808,7 @@ def test_reset_prefix_cache():
808 blocks = manager.allocate_slots(req1, 7,
809 len(computed_blocks.blocks) * 16,
810 computed_blocks)
811 - assert blocks.get_block_ids() == [5]
811 + assert blocks.get_block_ids() == [[5]]
812
813 # Failed to reset prefix cache because some blocks are not freed yet.
814 assert not manager.reset_prefix_cache()
tests/v1/worker/test_gpu_input_batch.py
+33 -6
@@ -9,9 +9,11 @@ import torch
9
10 from vllm.sampling_params import SamplingParams
11 from vllm.utils import is_pin_memory_available, make_tensor_with_pad
12 +from vllm.v1.kv_cache_interface import (FullAttentionSpec, KVCacheConfig,
13 + KVCacheGroupSpec, KVCacheTensor)
14 from vllm.v1.sample.metadata import SamplingMetadata
13 -from vllm.v1.worker.gpu_input_batch import (BlockTable, CachedRequestState,
14 - InputBatch)
15 +from vllm.v1.worker.block_table import BlockTable, MultiGroupBlockTable
16 +from vllm.v1.worker.gpu_input_batch import CachedRequestState, InputBatch
17
18 VOCAB_SIZE = 1024
19 NUM_OUTPUT_TOKENS = 20
@@ -22,6 +24,27 @@ CUDA_DEVICES = [
24 MAX_NUM_PROMPT_TOKENS = 64
25
26
27 +def get_kv_cache_config() -> KVCacheConfig:
28 + return KVCacheConfig(
29 + num_blocks=10,
30 + tensors={
31 + "layer.0": KVCacheTensor(size=1024),
32 + },
33 + kv_cache_groups=[
34 + KVCacheGroupSpec(
35 + layer_names=["layer.0"],
36 + kv_cache_spec=FullAttentionSpec(
37 + block_size=1,
38 + num_kv_heads=1,
39 + head_size=16,
40 + dtype=torch.float16,
41 + use_mla=False,
42 + ),
43 + ),
44 + ],
45 + )
46 +
47 +
48 def _compare_objs(obj1, obj2):
49 attrs = inspect.getmembers(obj1, lambda a: not (inspect.isroutine(a)))
50 attr_names = set([
@@ -41,6 +64,10 @@ def _compare_objs(obj1, obj2):
64 elif isinstance(a, np.ndarray):
65 if np.allclose(a, b):
66 is_same = True
67 + elif isinstance(a, MultiGroupBlockTable):
68 + for a_i, b_i in zip(a.block_tables, b.block_tables):
69 + _compare_objs(a_i, b_i)
70 + is_same = True
71 elif isinstance(a, (BlockTable, SamplingMetadata)):
72 _compare_objs(a, b)
73 is_same = True # if we make it here must be same
@@ -198,7 +225,7 @@ def _construct_cached_request_state(req_id_suffix: int):
225 sampling_params=_create_sampling_params(),
226 mm_inputs=[],
227 mm_positions=[],
201 - block_ids=[],
228 + block_ids=[[]],
229 generator=None,
230 num_computed_tokens=len(output_token_ids),
231 output_token_ids=output_token_ids,
@@ -220,11 +247,11 @@ def test_sampling_metadata_in_input_batch(device: str, batch_size: int):
247 input_batch: InputBatch = InputBatch(
248 max_num_reqs=batch_size,
249 max_model_len=1024,
223 - max_num_blocks_per_req=10,
250 max_num_batched_tokens=1024,
251 device=torch.device(device),
252 pin_memory=is_pin_memory_available(),
253 vocab_size=1024,
254 + block_size=1,
255 )
256 reqs: list[CachedRequestState] = []
257 req_id_reqs = {}
@@ -310,20 +337,20 @@ def test_swap_states_in_input_batch(device: str, batch_size: int,
337 input_batch: InputBatch = InputBatch(
338 max_num_reqs=batch_size,
339 max_model_len=1024,
313 - max_num_blocks_per_req=10,
340 max_num_batched_tokens=1024,
341 device=torch.device(device),
342 pin_memory=is_pin_memory_available(),
343 vocab_size=1024,
344 + block_size=1,
345 )
346 ref_input_batch: InputBatch = InputBatch(
347 max_num_reqs=batch_size,
348 max_model_len=1024,
322 - max_num_blocks_per_req=10,
349 max_num_batched_tokens=1024,
350 device=torch.device(device),
351 pin_memory=is_pin_memory_available(),
352 vocab_size=1024,
353 + block_size=1,
354 )
355
356 reqs: list[CachedRequestState] = []
tests/v1/worker/test_gpu_model_runner.py
+41 -16
@@ -1,15 +1,16 @@
1 # SPDX-License-Identifier: Apache-2.0
2 -import weakref
2
3 import pytest
5 -import torch
4
7 -from vllm.config import CacheConfig, ModelConfig, SchedulerConfig, VllmConfig
5 +from vllm.config import (CacheConfig, ModelConfig, ParallelConfig,
6 + SchedulerConfig, VllmConfig)
7 from vllm.sampling_params import SamplingParams
8 from vllm.v1.core.sched.output import (CachedRequestData, NewRequestData,
9 SchedulerOutput)
11 -from vllm.v1.kv_cache_interface import FullAttentionSpec
10 +from vllm.v1.kv_cache_interface import (FullAttentionSpec, KVCacheConfig,
11 + KVCacheGroupSpec, KVCacheTensor)
12 from vllm.v1.sample.metadata import SamplingMetadata
13 +from vllm.v1.worker.gpu_input_batch import InputBatch
14 from vllm.v1.worker.gpu_model_runner import GPUModelRunner
15
16
@@ -17,13 +18,34 @@ def initialize_kv_cache(runner: GPUModelRunner):
18 """
19 Only perform necessary steps in GPUModelRunner.initialize_kv_cache()
20 """
20 - kv_cache_spec = FullAttentionSpec(block_size=16,
21 - num_kv_heads=1,
22 - head_size=64,
23 - dtype=torch.float16,
24 - use_mla=False)
25 - runner.attn_metadata_builder = runner.attn_backend.get_builder_cls()(
26 - weakref.proxy(runner), kv_cache_spec, runner.input_batch.block_table)
21 + kv_cache_config = KVCacheConfig(
22 + num_blocks=10,
23 + tensors={
24 + "layer.0": KVCacheTensor(size=1024),
25 + },
26 + kv_cache_groups=[
27 + KVCacheGroupSpec(
28 + layer_names=["layer.0"],
29 + kv_cache_spec=FullAttentionSpec(
30 + block_size=16,
31 + num_kv_heads=runner.model_config.get_num_kv_heads(
32 + runner.parallel_config),
33 + head_size=runner.model_config.get_head_size(),
34 + dtype=runner.kv_cache_dtype,
35 + use_mla=False,
36 + ))
37 + ])
38 + runner.kv_cache_config = kv_cache_config
39 + runner.input_batch = InputBatch(
40 + max_num_reqs=runner.max_num_reqs,
41 + max_model_len=runner.max_model_len,
42 + max_num_batched_tokens=runner.max_num_tokens,
43 + device=runner.device,
44 + pin_memory=runner.pin_memory,
45 + vocab_size=runner.model_config.get_vocab_size(),
46 + block_size=kv_cache_config.kv_cache_groups[0].kv_cache_spec.block_size,
47 + )
48 + runner.initialize_attn_backend(kv_cache_config)
49
50
51 @pytest.fixture
@@ -48,10 +70,12 @@ def model_runner():
70 swap_space=0,
71 cache_dtype="auto",
72 )
73 + parallel_config = ParallelConfig()
74 vllm_config = VllmConfig(
75 model_config=model_config,
76 cache_config=cache_config,
77 scheduler_config=scheduler_config,
78 + parallel_config=parallel_config,
79 )
80
81 device = "cuda"
@@ -73,7 +97,7 @@ def _schedule_new_request(*req_ids: str) -> SchedulerOutput:
97 mm_hashes=[],
98 mm_positions=[],
99 sampling_params=SamplingParams(),
76 - block_ids=[0],
100 + block_ids=[[0]],
101 num_computed_tokens=0,
102 lora_request=None,
103 ))
@@ -111,13 +135,14 @@ def _is_sampling_metadata_changed(model_runner,
135
136 def _is_req_state_block_table_match(model_runner, req_id: str) -> bool:
137 req_index = model_runner.input_batch.req_id_to_index[req_id]
114 - block_table = model_runner.input_batch.block_table
138 + block_table = model_runner.input_batch.block_table[0]
139 req_state = model_runner.requests[req_id]
116 - if block_table.num_blocks_per_row[req_index] != len(req_state.block_ids):
140 + if block_table.num_blocks_per_row[req_index] != len(
141 + req_state.block_ids[0]):
142 return False
143 num_blocks = block_table.num_blocks_per_row[req_index]
144 return (block_table.block_table_np[req_index, :num_blocks] ==
120 - req_state.block_ids).all()
145 + req_state.block_ids[0]).all()
146
147
148 def test_update_states_new_request(model_runner):
@@ -200,7 +225,7 @@ def test_update_states_request_resumed(model_runner):
225 req_id=req_id,
226 resumed_from_preemption=False,
227 new_token_ids=[],
203 - new_block_ids=[],
228 + new_block_ids=[[]],
229 num_computed_tokens=0,
230 )
231
vllm/distributed/kv_transfer/kv_connector/v1/shared_storage_connector.py
+3 -3
@@ -288,7 +288,7 @@ class SharedStorageConnector(KVConnectorBase_V1):
288 for new_req in scheduler_output.scheduled_new_reqs:
289 if new_req.req_id in self._requests_need_load:
290 meta.add_request(token_ids=new_req.prompt_token_ids,
291 - block_ids=new_req.block_ids,
291 + block_ids=new_req.block_ids[0],
292 block_size=self._block_size,
293 is_store=False)
294 total_need_load += 1
@@ -299,7 +299,7 @@ class SharedStorageConnector(KVConnectorBase_V1):
299 # the original prompt tokens.
300 if not self._found_match_for_request(new_req):
301 meta.add_request(token_ids=new_req.prompt_token_ids,
302 - block_ids=new_req.block_ids,
302 + block_ids=new_req.block_ids[0],
303 block_size=self._block_size,
304 is_store=True)
305
@@ -319,7 +319,7 @@ class SharedStorageConnector(KVConnectorBase_V1):
319
320 # NOTE(rob): For resumed req, new_block_ids is all
321 # of the block_ids for the request.
322 - block_ids = cached_req.new_block_ids
322 + block_ids = cached_req.new_block_ids[0]
323
324 meta.add_request(token_ids=token_ids,
325 block_ids=block_ids,
vllm/v1/attention/backends/mla/rocm_aiter_mla.py
+2 -2
@@ -69,13 +69,13 @@ class AiterMLAMetadataBuilder(MLACommonMetadataBuilder[AiterMLAMetadata]):
69 max_model_len = self.runner.model_config.max_model_len
70 assert max_model_len == 32768,\
71 "AITER MLA requires max_model_len=32768"
72 - assert self.runner.block_size == 1, "AITER MLA" \
72 + assert self.kv_cache_spec.block_size == 1, "AITER MLA" \
73 "only supports block size 1."
74
75 def _get_paged_kv_tensors(
76 self, block_table: torch.Tensor,
77 seq_lens: torch.Tensor) -> tuple[torch.Tensor, ...]:
78 - page_size = self.runner.block_size
78 + page_size = self.kv_cache_spec.block_size
79 block_table_bounds = (seq_lens + page_size - 1) // page_size
80 device = self.runner.device
81
vllm/v1/core/kv_cache_manager.py
+21 -13
@@ -32,9 +32,16 @@ class KVCacheBlocks:
32 """Creates a new KVCacheBlocks instance with no blocks."""
33 return cls([])
34
35 - def get_block_ids(self) -> list[int]:
36 - """Converts the KVCacheBlocks instance to a list of block IDs."""
37 - return [block.block_id for block in self.blocks]
35 + def get_block_ids(self) -> list[list[int]]:
36 + """
37 + Converts the KVCacheBlocks instance to block_ids.
38 +
39 + Returns:
40 + list[list[int]]: A two-level list where
41 + * the outer list corresponds to KV cache groups (only 1 group now)
42 + * each inner list contains the block_ids of the blocks in that group
43 + """
44 + return [[block.block_id for block in self.blocks]]
45
46 def get_unhashed_block_ids(self) -> list[int]:
47 """Get block_ids of unhashed blocks from KVCacheBlocks instance."""
@@ -300,9 +307,9 @@ class KVCacheManager:
307 self,
308 request: Request,
309 num_running_requests: int,
303 - ) -> int:
310 + ) -> list[int]:
311 """Calculate the number of common prefix blocks shared by all requests
305 - in the RUNNING state.
312 + in the RUNNING state for each kv cache group.
313
314 The function determines this by selecting any request and iterating
315 through its blocks. A block is considered a common prefix block if its
@@ -332,11 +339,14 @@ class KVCacheManager:
339 requests in the current step.
340
341 Returns:
335 - int: The number of common prefix blocks.
342 + list[int]: The number of common prefix blocks for each kv cache
343 + group.
344 """
345 assert request.status == RequestStatus.RUNNING
338 - return self.single_type_manager.get_num_common_prefix_blocks(
339 - request.request_id, num_running_requests)
346 + return [
347 + self.single_type_manager.get_num_common_prefix_blocks(
348 + request.request_id, num_running_requests)
349 + ]
350
351 def free_block_hashes(self, request: Request) -> None:
352 """Discard the block hashes for the request.
@@ -354,10 +364,8 @@ class KVCacheManager:
364 """
365 return self.block_pool.take_events()
366
357 - def get_block_ids(self, request_id: str) -> list[int]:
367 + def get_block_ids(self, request_id: str) -> list[list[int]]:
368 """Get the block ids of a request."""
369 assert request_id in self.single_type_manager.req_to_blocks
360 - return [
361 - block.block_id
362 - for block in self.single_type_manager.req_to_blocks[request_id]
363 - ]
370 + return KVCacheBlocks(self.single_type_manager.req_to_blocks[request_id]
371 + ).get_block_ids()
vllm/v1/core/kv_cache_utils.py
+6 -7
@@ -577,14 +577,12 @@ def create_kv_cache_group_specs(
577 """
578 kv_cache_groups = []
579 for layer_names_one_group in grouped_layer_names:
580 - layer_spec = kv_cache_spec[layer_names_one_group[0]]
581 - assert all(
582 - kv_cache_spec[layer_name] == layer_spec
583 - for layer_name in layer_names_one_group[1:]), (
584 - "All layers in the same KV cache group must share the same "
585 - "KVCacheSpec.")
580 + layer_specs = [
581 + kv_cache_spec[layer_name] for layer_name in layer_names_one_group
582 + ]
583 + merged_layer_spec = layer_specs[0].merge(layer_specs)
584 kv_cache_groups.append(
587 - KVCacheGroupSpec(layer_names_one_group, layer_spec))
585 + KVCacheGroupSpec(layer_names_one_group, merged_layer_spec))
586 return kv_cache_groups
587
588
@@ -683,6 +681,7 @@ def unify_hybrid_kv_cache_specs(kv_cache_spec: dict[str, KVCacheSpec]):
681 head_size=spec.head_size,
682 dtype=spec.dtype,
683 use_mla=spec.use_mla,
684 + sliding_window=spec.sliding_window,
685 )
686
687
vllm/v1/core/sched/output.py
+6 -6
@@ -26,7 +26,7 @@ class NewRequestData:
26 mm_hashes: list[str]
27 mm_positions: list[PlaceholderRange]
28 sampling_params: SamplingParams
29 - block_ids: list[int]
29 + block_ids: list[list[int]]
30 num_computed_tokens: int
31 lora_request: Optional[LoRARequest]
32
@@ -34,7 +34,7 @@ class NewRequestData:
34 def from_request(
35 cls,
36 request: Request,
37 - block_ids: list[int],
37 + block_ids: list[list[int]],
38 ) -> NewRequestData:
39 return cls(
40 req_id=request.request_id,
@@ -85,7 +85,7 @@ class CachedRequestData:
85 # request's block IDs instead of appending to the existing block IDs.
86 resumed_from_preemption: bool
87 new_token_ids: list[int]
88 - new_block_ids: list[int]
88 + new_block_ids: list[list[int]]
89 num_computed_tokens: int
90
91 @classmethod
@@ -94,7 +94,7 @@ class CachedRequestData:
94 request: Request,
95 resumed_from_preemption: bool,
96 new_token_ids: list[int],
97 - new_block_ids: list[int],
97 + new_block_ids: list[list[int]],
98 ) -> CachedRequestData:
99 return cls(
100 req_id=request.request_id,
@@ -131,9 +131,9 @@ class SchedulerOutput:
131 # E.g., if a request has [0, 1], it could mean the vision encoder needs
132 # to process that the request's 0-th and 1-th images in the current step.
133 scheduled_encoder_inputs: dict[str, list[int]]
134 - # Number of common prefix blocks for all requests.
134 + # Number of common prefix blocks for all requests in each KV cache group.
135 # This can be used for cascade attention.
136 - num_common_prefix_blocks: int
136 + num_common_prefix_blocks: list[int]
137
138 # Request IDs that are finished in between the previous and the current
139 # steps. This is used to notify the workers about the finished requests
vllm/v1/core/sched/scheduler.py
+10 -6
@@ -173,7 +173,7 @@ class Scheduler(SchedulerInterface):
173 # uses structured decoding.
174 structured_output_request_ids: dict[str, int] = {}
175
176 - req_to_new_block_ids: dict[str, list[int]] = {}
176 + req_to_new_block_ids: dict[str, list[list[int]]] = {}
177 num_scheduled_tokens: dict[str, int] = {}
178 token_budget = self.max_num_scheduled_tokens
179 # Encoder-related.
@@ -486,7 +486,8 @@ class Scheduler(SchedulerInterface):
486
487 # Get the longest common prefix among all requests in the running queue.
488 # This can be potentially used for cascade attention.
489 - num_common_prefix_blocks = 0
489 + num_common_prefix_blocks = [0] * len(
490 + self.kv_cache_config.kv_cache_groups)
491 if self.running:
492 any_request = self.running[0]
493 num_common_prefix_blocks = (
@@ -573,7 +574,7 @@ class Scheduler(SchedulerInterface):
574 request: Request,
575 num_scheduled_tokens: int,
576 num_scheduled_spec_tokens: int,
576 - new_block_ids: list[int],
577 + new_block_ids: list[list[int]],
578 resumed_from_preemption: bool,
579 ) -> CachedRequestData:
580 # OPTIMIZATION: Cache the CachedRequestData objects to avoid creating
@@ -949,7 +950,9 @@ class Scheduler(SchedulerInterface):
950 """
951 if self.connector is None:
952 return False, None
952 - block_ids = self.kv_cache_manager.get_block_ids(request.request_id)
953 + assert len(self.kv_cache_config.kv_cache_groups
954 + ) == 1, "KV connector only supports one KV cache group now"
955 + block_ids = self.kv_cache_manager.get_block_ids(request.request_id)[0]
956 return self.connector.request_finished(request, block_ids)
957
958 def _update_waiting_for_remote_kv(self, request: Request) -> bool:
@@ -966,9 +969,10 @@ class Scheduler(SchedulerInterface):
969 """
970 if request.request_id not in self.finished_recving_kv_req_ids:
971 return False
969 -
972 + assert len(self.kv_cache_config.kv_cache_groups
973 + ) == 1, "KV connector only supports one KV cache group now"
974 # Now that the blocks are ready, actually cache them.
971 - block_ids = self.kv_cache_manager.get_block_ids(request.request_id)
975 + block_ids = self.kv_cache_manager.get_block_ids(request.request_id)[0]
976 num_computed_tokens = len(block_ids) * self.block_size
977 if num_computed_tokens == request.num_tokens:
978 num_computed_tokens -= 1
vllm/v1/kv_cache_interface.py
+42
@@ -1,8 +1,11 @@
1 # SPDX-License-Identifier: Apache-2.0
2
3 +import copy
4 from dataclasses import dataclass
5 +from typing import Optional
6
7 import torch
8 +from typing_extensions import Self
9
10 from vllm.config import VllmConfig
11 from vllm.logger import init_logger
@@ -53,6 +56,16 @@ class KVCacheSpec:
56 """
57 raise NotImplementedError
58
59 + @classmethod
60 + def merge(cls, specs: list[Self]) -> Self:
61 + """
62 + Merge a list of KVCacheSpec objects into a single KVCacheSpec object.
63 + """
64 + assert all(spec.type_id == specs[0].type_id for spec in specs[1:]), (
65 + "All layers in the same KV cache group must share the same "
66 + "type_id.")
67 + return copy.deepcopy(specs[0])
68 +
69
70 @dataclass
71 class AttentionSpec(KVCacheSpec):
@@ -71,6 +84,16 @@ class AttentionSpec(KVCacheSpec):
84
85 @dataclass
86 class FullAttentionSpec(AttentionSpec):
87 + sliding_window: Optional[int] = None
88 + """
89 + When hybrid allocator is disabled and the model contains both full
90 + attention layers and sliding window attention layers, sliding
91 + window attention are regarded as full attention in KV cache manager
92 + (blocks are allocated for all tokens), while computed as sliding window
93 + attention in model runner.
94 + In this case, we use FullAttentionSpec and record the sliding window size.
95 + Default to None for not using sliding window attention.
96 + """
97
98 @property
99 def type_id(self) -> str:
@@ -80,6 +103,25 @@ class FullAttentionSpec(AttentionSpec):
103 max_model_len = vllm_config.model_config.max_model_len
104 return cdiv(max_model_len, self.block_size) * self.page_size_bytes
105
106 + @classmethod
107 + def merge(cls, specs: list[Self]) -> Self:
108 + """
109 + Merge a list of FullAttentionSpec objects into a single
110 + FullAttentionSpec object.
111 + """
112 + merged_spec = super().merge(specs)
113 + sliding_window = set(spec.sliding_window for spec in specs
114 + if spec.sliding_window is not None)
115 + if len(sliding_window) == 0:
116 + merged_spec.sliding_window = None
117 + elif len(sliding_window) == 1:
118 + merged_spec.sliding_window = sliding_window.pop()
119 + else:
120 + raise ValueError(
121 + "All sliding window layers in the same KV cache group "
122 + "must have the same window size.")
123 + return merged_spec
124 +
125
126 @dataclass
127 class SlidingWindowSpec(AttentionSpec):
vllm/v1/worker/block_table.py
+41
@@ -4,6 +4,7 @@ import numpy as np
4 import torch
5
6 from vllm.logger import init_logger
7 +from vllm.utils import cdiv
8
9 logger = init_logger(__name__)
10
@@ -96,3 +97,43 @@ class BlockTable:
97 def get_numpy_array(self) -> np.ndarray:
98 """Returns the numpy array of the block table."""
99 return self.block_table_np
100 +
101 +
102 +class MultiGroupBlockTable:
103 + """The BlockTables for each KV cache group."""
104 +
105 + def __init__(self, max_num_reqs: int, max_model_len: int,
106 + max_num_batched_tokens: int, pin_memory: bool,
107 + device: torch.device, block_size: int) -> None:
108 + self.block_tables = [
109 + BlockTable(max_num_reqs, cdiv(max_model_len, block_size),
110 + max_num_batched_tokens, pin_memory, device)
111 + ]
112 +
113 + def append_row(self, block_ids: list[list[int]], row_idx: int) -> None:
114 + for i, block_table in enumerate(self.block_tables):
115 + block_table.append_row(block_ids[i], row_idx)
116 +
117 + def add_row(self, block_ids: list[list[int]], row_idx: int) -> None:
118 + for i, block_table in enumerate(self.block_tables):
119 + block_table.add_row(block_ids[i], row_idx)
120 +
121 + def move_row(self, src: int, tgt: int) -> None:
122 + for block_table in self.block_tables:
123 + block_table.move_row(src, tgt)
124 +
125 + def swap_row(self, src: int, tgt: int) -> None:
126 + for block_table in self.block_tables:
127 + block_table.swap_row(src, tgt)
128 +
129 + def commit(self, num_reqs: int) -> None:
130 + for block_table in self.block_tables:
131 + block_table.commit(num_reqs)
132 +
133 + def clear(self) -> None:
134 + for block_table in self.block_tables:
135 + block_table.clear()
136 +
137 + def __getitem__(self, idx: int) -> "BlockTable":
138 + """Returns the BlockTable for the i-th KV cache group."""
139 + return self.block_tables[idx]
vllm/v1/worker/gpu_input_batch.py
+6 -6
@@ -14,7 +14,7 @@ from vllm.utils import swap_dict_values
14 from vllm.v1.outputs import LogprobsTensors
15 from vllm.v1.sample.metadata import SamplingMetadata
16 from vllm.v1.utils import copy_slice
17 -from vllm.v1.worker.block_table import BlockTable
17 +from vllm.v1.worker.block_table import MultiGroupBlockTable
18
19 _SAMPLING_EPS = 1e-5
20
@@ -29,7 +29,7 @@ class CachedRequestState:
29 sampling_params: SamplingParams
30 generator: Optional[torch.Generator]
31
32 - block_ids: list[int]
32 + block_ids: list[list[int]]
33 num_computed_tokens: int
34 output_token_ids: list[int]
35
@@ -58,15 +58,14 @@ class InputBatch:
58 self,
59 max_num_reqs: int,
60 max_model_len: int,
61 - max_num_blocks_per_req: int,
61 max_num_batched_tokens: int,
62 device: torch.device,
63 pin_memory: bool,
64 vocab_size: int,
65 + block_size: int,
66 ):
67 self.max_num_reqs = max_num_reqs
68 self.max_model_len = max_model_len
69 - self.max_num_blocks_per_req = max_num_blocks_per_req
69 self.max_num_batched_tokens = max_num_batched_tokens
70 self.device = device
71 self.pin_memory = pin_memory
@@ -99,12 +98,13 @@ class InputBatch:
98 self.num_computed_tokens_cpu_tensor.numpy()
99
100 # Block table.
102 - self.block_table = BlockTable(
101 + self.block_table = MultiGroupBlockTable(
102 max_num_reqs=max_num_reqs,
104 - max_num_blocks_per_req=max_num_blocks_per_req,
103 + max_model_len=max_model_len,
104 max_num_batched_tokens=max_num_batched_tokens,
105 pin_memory=pin_memory,
106 device=device,
107 + block_size=block_size,
108 )
109
110 # Sampling-related.
vllm/v1/worker/gpu_model_runner.py
+152 -101
@@ -12,6 +12,8 @@ import torch.distributed
12 import torch.nn as nn
13
14 from vllm.attention import AttentionType, get_attn_backend
15 +from vllm.attention.backends.abstract import (AttentionBackend,
16 + AttentionMetadataBuilder)
17 from vllm.attention.layer import Attention
18 from vllm.attention.utils.fa_utils import get_flash_attn_version
19 from vllm.config import (CompilationLevel, VllmConfig,
@@ -32,8 +34,8 @@ from vllm.multimodal.utils import group_mm_inputs_by_modality
34 from vllm.sampling_params import SamplingType
35 from vllm.sequence import IntermediateTensors
36 from vllm.utils import (STR_DTYPE_TO_TORCH_DTYPE, DeviceMemoryProfiler,
35 - GiB_bytes, LayerBlockType, LazyLoader, cdiv,
36 - check_use_alibi, is_pin_memory_available)
37 + GiB_bytes, LazyLoader, cdiv, check_use_alibi,
38 + is_pin_memory_available)
39 from vllm.v1.attention.backends.flash_attn import FlashAttentionMetadata
40 from vllm.v1.attention.backends.utils import CommonAttentionMetadata
41 from vllm.v1.core.encoder_cache_manager import compute_encoder_budget
@@ -51,6 +53,7 @@ from vllm.v1.spec_decode.metadata import SpecDecodeMetadata
53 from vllm.v1.spec_decode.ngram_proposer import NgramProposer
54 from vllm.v1.spec_decode.utils import is_spec_decode_supported
55 from vllm.v1.utils import bind_kv_cache
56 +from vllm.v1.worker.block_table import BlockTable
57 from vllm.v1.worker.gpu_input_batch import CachedRequestState, InputBatch
58 from vllm.v1.worker.lora_model_runner_mixin import LoRAModelRunnerMixin
59
@@ -103,59 +106,17 @@ class GPUModelRunner(LoRAModelRunnerMixin):
106 self.kv_cache_dtype = STR_DTYPE_TO_TORCH_DTYPE[
107 cache_config.cache_dtype]
108
106 - # NOTE(woosuk): sliding_window is None for models with interleaved
107 - # attention. Use interleaved_sliding_window instead.
108 - self.sliding_window = model_config.get_sliding_window()
109 - self.interleaved_sliding_window = getattr(
110 - model_config.hf_text_config, "interleaved_sliding_window", None)
111 - self.window_size = (self.sliding_window
112 - or self.interleaved_sliding_window)
113 -
109 self.is_multimodal_model = model_config.is_multimodal_model
115 - self.block_size = cache_config.block_size
110 self.max_model_len = model_config.max_model_len
117 - self.max_num_blocks_per_req = cdiv(self.max_model_len, self.block_size)
111 self.max_num_tokens = scheduler_config.max_num_batched_tokens
112 self.max_num_reqs = scheduler_config.max_num_seqs
113
114 # Model-related.
122 - self.num_attn_layers = model_config.get_num_layers_by_block_type(
123 - parallel_config, LayerBlockType.attention)
115 self.num_query_heads = model_config.get_num_attention_heads(
116 parallel_config)
126 - self.num_kv_heads = model_config.get_num_kv_heads(parallel_config)
127 - self.head_size = model_config.get_head_size()
117 self.hidden_size = model_config.get_hidden_size()
118 self.attention_chunk_size = model_config.attention_chunk_size
119
131 - self.attn_backend = get_attn_backend(
132 - self.head_size,
133 - self.dtype,
134 - self.kv_cache_dtype,
135 - self.block_size,
136 - self.model_config.is_attention_free,
137 - use_mla=self.model_config.use_mla,
138 - )
139 - if self.attn_backend is None:
140 - error_msg = (
141 - f"Error with get_att_backend: {self.head_size=}, "
142 - f"{self.dtype=}, {self.kv_cache_dtype=}, {self.block_size=}, "
143 - f"{self.model_config.is_attention_free=}, "
144 - f"{self.model_config.use_mla=}")
145 - logger.error(error_msg)
146 - raise NotImplementedError(
147 - "Non-Attention backend is not supported by V1 GPUModelRunner.")
148 -
149 - if self.vllm_config.compilation_config.full_cuda_graph:
150 - attn_backend_name = self.attn_backend.__name__
151 - flash_attn_version = get_flash_attn_version()
152 - if attn_backend_name != "FlashAttentionBackend" or \
153 - flash_attn_version != 3:
154 - raise ValueError(
155 - f"full_cuda_graph is only supported with "
156 - f"FA3. Current attention backend is {attn_backend_name}, "
157 - f"FlashAttention version is {flash_attn_version}.")
158 -
120 self.cascade_attn_enabled = not self.model_config.disable_cascade_attn
121
122 # Multi-modal data support
@@ -177,8 +138,10 @@ class GPUModelRunner(LoRAModelRunnerMixin):
138 # self.model: nn.Module # Set after load_model
139 # Initialize in initialize_kv_cache
140 self.kv_caches: list[torch.Tensor] = []
141 + self.attn_metadata_builders: list[AttentionMetadataBuilder] = []
142 + self.attn_backends: list[type[AttentionBackend]] = []
143 # self.kv_cache_config: KVCacheConfig
181 - # self.attn_metadata_builder: type[AttentionMetadataBuilder]
144 + # self.input_batch: InputBatch # Persistent batch.
145
146 # req_id -> (input_id -> encoder_output)
147 self.encoder_cache: dict[str, dict[int, torch.Tensor]] = {}
@@ -207,15 +170,15 @@ class GPUModelRunner(LoRAModelRunnerMixin):
170
171 # Request states.
172 self.requests: dict[str, CachedRequestState] = {}
210 - # Persistent batch.
173 +
174 self.input_batch = InputBatch(
175 max_num_reqs=self.max_num_reqs,
176 max_model_len=self.max_model_len,
214 - max_num_blocks_per_req=self.max_num_blocks_per_req,
177 max_num_batched_tokens=self.max_num_tokens,
178 device=self.device,
179 pin_memory=self.pin_memory,
218 - vocab_size=model_config.get_vocab_size(),
180 + vocab_size=self.model_config.get_vocab_size(),
181 + block_size=self.cache_config.block_size,
182 )
183
184 self.use_cuda_graph = (self.vllm_config.compilation_config.level
@@ -311,6 +274,31 @@ class GPUModelRunner(LoRAModelRunnerMixin):
274 pin_memory=self.pin_memory)
275 self.seq_lens_np = self.seq_lens_cpu.numpy()
276
277 + def _may_reorder_batch(self, scheduler_output: "SchedulerOutput") -> bool:
278 + """
279 + Update the order of requests in the batch based on the attention
280 + backend's needs. For example, some attention backends (namely MLA) may
281 + want to separate requests based on if the attention computation will be
282 + compute-bound or memory-bound.
283 +
284 + Args:
285 + scheduler_output: The scheduler output.
286 +
287 + Returns:
288 + True if the batch was reordered, False otherwise.
289 + """
290 + batch_reordered = self.attn_metadata_builders[0].reorder_batch(
291 + self.input_batch, scheduler_output)
292 +
293 + # For models with multiple KV cache groups, the groups should agree on
294 + # the same order of requests. We ensure this by only allowing the first
295 + # group to reorder the batch and asserting that all other groups do not
296 + # reorder the batch.
297 + for i in range(1, len(self.kv_cache_config.kv_cache_groups)):
298 + assert not self.attn_metadata_builders[i].reorder_batch(
299 + self.input_batch, scheduler_output)
300 + return batch_reordered
301 +
302 def _update_states(self, scheduler_output: "SchedulerOutput") -> None:
303 """Update the cached states and the persistent batch with the scheduler
304 output.
@@ -447,7 +435,8 @@ class GPUModelRunner(LoRAModelRunnerMixin):
435 # Update the block IDs.
436 if not req_data.resumed_from_preemption:
437 # Append the new blocks to the existing block IDs.
450 - req_state.block_ids.extend(req_data.new_block_ids)
438 + for i in range(len(self.kv_cache_config.kv_cache_groups)):
439 + req_state.block_ids[i].extend(req_data.new_block_ids[i])
440 else:
441 # The request is resumed from preemption.
442 # Replace the existing block IDs with the new ones.
@@ -505,11 +494,7 @@ class GPUModelRunner(LoRAModelRunnerMixin):
494 if removed_req_indices:
495 self.input_batch.condense(removed_req_indices)
496
508 - # Some attention backends (namely MLA) may want to separate requests
509 - # based on if the attention computation will be compute-bound or
510 - # memory-bound. This gives them a hook to do that.
511 - batch_reordered = self.attn_metadata_builder.reorder_batch(
512 - self.input_batch, scheduler_output)
497 + batch_reordered = self._may_reorder_batch(scheduler_output)
498
499 if batch_changed or batch_reordered:
500 self.input_batch.refresh_sampling_metadata()
@@ -577,21 +562,29 @@ class GPUModelRunner(LoRAModelRunnerMixin):
562 torch.from_numpy(token_indices),
563 out=self.input_ids_cpu[:total_num_scheduled_tokens])
564
580 - # Calculate the slot mapping.
581 - # E.g., [0, 1, 0, 1, 2, 3, 4, 0, 1, 2]
582 - # -> [0, 0, K, K, K + 1, K + 1, K + 2, 2 * K, 2 * K, 2 * K + 1]
583 - # where K is the max_num_blocks_per_req and the block size is 2.
584 - # NOTE(woosuk): We can't simply use `token_indices // block_size` here
585 - # because M (max_model_len) is not necessarily divisible by block_size.
586 - block_table_indices = (req_indices * self.max_num_blocks_per_req +
587 - positions_np // self.block_size)
588 - block_table_cpu = self.input_batch.block_table.get_cpu_tensor()
589 - block_numbers = block_table_cpu.flatten()[block_table_indices].numpy()
590 - block_offsets = positions_np % self.block_size
591 - np.add(block_numbers * self.block_size,
592 - block_offsets,
593 - out=self.input_batch.block_table.
594 - slot_mapping_np[:total_num_scheduled_tokens])
565 + # Calculate the slot mapping for each KV cache group.
566 + for kv_cache_group_id, kv_cache_group_spec in enumerate(
567 + self.kv_cache_config.kv_cache_groups):
568 + block_size = kv_cache_group_spec.kv_cache_spec.block_size
569 + block_table: BlockTable = self.input_batch.block_table[
570 + kv_cache_group_id]
571 + # E.g., [0, 1, 0, 1, 2, 3, 4, 0, 1, 2]
572 + # -> [0, 0, K, K, K + 1, K + 1, K + 2, 2 * K, 2 * K, 2 * K + 1]
573 + # where K is the max_num_blocks_per_req and the block size is 2.
574 + # NOTE(woosuk): We can't simply use `token_indices // block_size`
575 + # here because M (max_model_len) is not necessarily divisible by
576 + # block_size.
577 + block_table_indices = (
578 + req_indices * block_table.max_num_blocks_per_req +
579 + positions_np // block_size)
580 + block_table_cpu = block_table.get_cpu_tensor()
581 + block_numbers = block_table_cpu.flatten(
582 + )[block_table_indices].numpy()
583 + block_offsets = positions_np % block_size
584 + np.add(
585 + block_numbers * block_size,
586 + block_offsets,
587 + out=block_table.slot_mapping_np[:total_num_scheduled_tokens])
588
589 # Prepare the attention metadata.
590 self.query_start_loc_np[0] = 0
@@ -633,10 +626,6 @@ class GPUModelRunner(LoRAModelRunnerMixin):
626 attn_metadata: dict[str, FlashAttentionMetadata] = {}
627 # Prepare the attention metadata for each KV cache group and make layers
628 # in the same group share the same metadata.
636 - # NOTE(Chen): there is exactly one KV cache group that contains all
637 - # attetnion layers in the model for now, so the current logic for
638 - # getting attn_metadata is not related to kv_cache_group information.
639 - # Will extend this part to support multiple KV cache groups later.
629 for kv_cache_group_id, kv_cache_group_spec in enumerate(
630 self.kv_cache_config.kv_cache_groups):
631
@@ -645,15 +634,19 @@ class GPUModelRunner(LoRAModelRunnerMixin):
634 if self.cascade_attn_enabled:
635 common_prefix_len = self._compute_cascade_attn_prefix_len(
636 num_scheduled_tokens,
648 - scheduler_output.num_common_prefix_blocks,
637 + scheduler_output.
638 + num_common_prefix_blocks[kv_cache_group_id],
639 + kv_cache_group_spec.kv_cache_spec,
640 + self.attn_metadata_builders[kv_cache_group_id],
641 )
642
651 - attn_metadata_i = self.attn_metadata_builder.build(
652 - num_reqs=num_reqs,
653 - num_actual_tokens=total_num_scheduled_tokens,
654 - max_query_len=max_num_scheduled_tokens,
655 - common_prefix_len=common_prefix_len,
656 - common_attn_metadata=common_attn_metadata)
643 + attn_metadata_i = (
644 + self.attn_metadata_builders[kv_cache_group_id].build(
645 + num_reqs=num_reqs,
646 + num_actual_tokens=total_num_scheduled_tokens,
647 + max_query_len=max_num_scheduled_tokens,
648 + common_prefix_len=common_prefix_len,
649 + common_attn_metadata=common_attn_metadata))
650 for layer_name in kv_cache_group_spec.layer_names:
651 attn_metadata[layer_name] = attn_metadata_i
652
@@ -691,6 +684,8 @@ class GPUModelRunner(LoRAModelRunnerMixin):
684 self,
685 num_scheduled_tokens: np.ndarray,
686 num_common_prefix_blocks: int,
687 + kv_cache_spec: KVCacheSpec,
688 + attn_metadata_builder: AttentionMetadataBuilder,
689 ) -> int:
690 """Compute the length of the common prefix for cascade attention.
691
@@ -709,7 +704,7 @@ class GPUModelRunner(LoRAModelRunnerMixin):
704 Returns:
705 int: Length of common prefix in tokens.
706 """
712 - common_prefix_len = num_common_prefix_blocks * self.block_size
707 + common_prefix_len = num_common_prefix_blocks * kv_cache_spec.block_size
708 if common_prefix_len == 0:
709 # Common case.
710 return 0
@@ -758,15 +753,19 @@ class GPUModelRunner(LoRAModelRunnerMixin):
753 common_prefix_len,
754 self.input_batch.num_computed_tokens_cpu[:num_reqs].min())
755 # common_prefix_len should be a multiple of the block size.
761 - common_prefix_len = (common_prefix_len // self.block_size *
762 - self.block_size)
763 - use_cascade = self.attn_metadata_builder.use_cascade_attention(
756 + common_prefix_len = (common_prefix_len // kv_cache_spec.block_size *
757 + kv_cache_spec.block_size)
758 + use_sliding_window = (isinstance(kv_cache_spec, SlidingWindowSpec) or
759 + (isinstance(kv_cache_spec, FullAttentionSpec)
760 + and kv_cache_spec.sliding_window is not None))
761 + assert isinstance(kv_cache_spec, AttentionSpec)
762 + use_cascade = attn_metadata_builder.use_cascade_attention(
763 common_prefix_len=common_prefix_len,
764 query_lens=num_scheduled_tokens,
765 num_query_heads=self.num_query_heads,
767 - num_kv_heads=self.num_kv_heads,
766 + num_kv_heads=kv_cache_spec.num_kv_heads,
767 use_alibi=self.use_alibi,
769 - use_sliding_window=self.window_size is not None,
768 + use_sliding_window=use_sliding_window,
769 num_sms=self.num_sms,
770 )
771 return common_prefix_len if use_cascade else 0
@@ -1661,7 +1660,7 @@ class GPUModelRunner(LoRAModelRunnerMixin):
1660 dtype=np.int32)
1661
1662 if skip_attn:
1664 - attn_metadata = None
1663 + attn_metadata: Optional[dict[str, FlashAttentionMetadata]] = None
1664 else:
1665 query_start_loc = self.query_start_loc[:num_reqs + 1]
1666 seq_lens = self.seq_lens[:num_reqs]
@@ -1669,13 +1668,19 @@ class GPUModelRunner(LoRAModelRunnerMixin):
1668 common_attn_metadata = CommonAttentionMetadata(
1669 query_start_loc=query_start_loc, seq_lens=seq_lens)
1670
1672 - attn_metadata = self.attn_metadata_builder.build(
1673 - num_reqs=num_tokens,
1674 - num_actual_tokens=num_tokens,
1675 - max_query_len=num_tokens,
1676 - common_prefix_len=0,
1677 - common_attn_metadata=common_attn_metadata,
1678 - )
1671 + attn_metadata = {}
1672 + for kv_cache_group_id, kv_cache_group_spec in enumerate(
1673 + self.kv_cache_config.kv_cache_groups):
1674 + attn_metadata_i = (
1675 + self.attn_metadata_builders[kv_cache_group_id].build(
1676 + num_reqs=num_tokens,
1677 + num_actual_tokens=num_tokens,
1678 + max_query_len=num_tokens,
1679 + common_prefix_len=0,
1680 + common_attn_metadata=common_attn_metadata,
1681 + ))
1682 + for layer_name in kv_cache_group_spec.layer_names:
1683 + attn_metadata[layer_name] = attn_metadata_i
1684
1685 with self.maybe_dummy_run_with_lora(self.lora_config,
1686 num_scheduled_tokens):
@@ -1909,6 +1914,56 @@ class GPUModelRunner(LoRAModelRunnerMixin):
1914 logger.info("Graph capturing finished in %.0f secs, took %.2f GiB",
1915 elapsed_time, cuda_graph_size / (1 << 30))
1916
1917 + def initialize_attn_backend(self, kv_cache_config: KVCacheConfig) -> None:
1918 + """
1919 + Initialize the attention backends and attention metadata builders.
1920 + """
1921 + assert len(self.attn_backends) == 0 and len(
1922 + self.attn_metadata_builders
1923 + ) == 0, "Attention backends are already initialized"
1924 + for i, kv_cache_group_spec in enumerate(
1925 + kv_cache_config.kv_cache_groups):
1926 + kv_cache_spec = kv_cache_group_spec.kv_cache_spec
1927 + if not isinstance(kv_cache_spec, AttentionSpec):
1928 + raise NotImplementedError(
1929 + "Only AttentionSpec is supported for now.")
1930 + attn_backend_i = get_attn_backend(
1931 + kv_cache_spec.head_size,
1932 + self.dtype,
1933 + kv_cache_spec.dtype,
1934 + kv_cache_spec.block_size,
1935 + self.model_config.is_attention_free,
1936 + use_mla=kv_cache_spec.use_mla,
1937 + )
1938 + if attn_backend_i is None:
1939 + error_msg = (
1940 + f"Error with get_attn_backend: {kv_cache_spec.head_size=}, "
1941 + f"{self.dtype=}, {kv_cache_spec.dtype=}, "
1942 + f"{kv_cache_spec.block_size=}, "
1943 + f"{self.model_config.is_attention_free=}, "
1944 + f"{kv_cache_spec.use_mla=}")
1945 + logger.error(error_msg)
1946 + raise NotImplementedError(
1947 + "Non-Attention backend is not supported by V1 "
1948 + "GPUModelRunner.")
1949 +
1950 + if self.vllm_config.compilation_config.full_cuda_graph:
1951 + attn_backend_name = attn_backend_i.__name__
1952 + flash_attn_version = get_flash_attn_version()
1953 + if attn_backend_name != "FlashAttentionBackend" or \
1954 + flash_attn_version != 3:
1955 + raise ValueError(
1956 + f"full_cuda_graph is only supported with "
1957 + f"FA3. Current attention backend is "
1958 + f"{attn_backend_name}, FlashAttention version is "
1959 + f"{flash_attn_version}.")
1960 +
1961 + block_table_i = self.input_batch.block_table[i]
1962 + attn_metadata_builder_i = attn_backend_i.get_builder_cls()(
1963 + weakref.proxy(self), kv_cache_spec, block_table_i)
1964 + self.attn_backends.append(attn_backend_i)
1965 + self.attn_metadata_builders.append(attn_metadata_builder_i)
1966 +
1967 def initialize_kv_cache(self, kv_cache_config: KVCacheConfig) -> None:
1968 """
1969 Initialize KV cache based on `kv_cache_config`.
@@ -1921,10 +1976,11 @@ class GPUModelRunner(LoRAModelRunnerMixin):
1976 "Hybrid models with more than one KV cache type are not "
1977 "supported yet.")
1978 self.kv_cache_config = kv_cache_config
1979 + self.initialize_attn_backend(kv_cache_config)
1980
1981 kv_caches: dict[str, torch.Tensor] = {}
1982
1927 - for kv_cache_group in kv_cache_config.kv_cache_groups:
1983 + for i, kv_cache_group in enumerate(kv_cache_config.kv_cache_groups):
1984 kv_cache_spec = kv_cache_group.kv_cache_spec
1985 for layer_name in kv_cache_group.layer_names:
1986 tensor_config = kv_cache_config.tensors[layer_name]
@@ -1939,7 +1995,7 @@ class GPUModelRunner(LoRAModelRunnerMixin):
1995 # the min of all `num_blocks`. Verify it here.
1996 assert num_blocks >= kv_cache_config.num_blocks
1997 if isinstance(kv_cache_spec, AttentionSpec):
1942 - kv_cache_shape = self.attn_backend.get_kv_cache_shape(
1998 + kv_cache_shape = self.attn_backends[i].get_kv_cache_shape(
1999 num_blocks, kv_cache_spec.block_size,
2000 kv_cache_spec.num_kv_heads, kv_cache_spec.head_size)
2001 dtype = kv_cache_spec.dtype
@@ -1959,11 +2015,6 @@ class GPUModelRunner(LoRAModelRunnerMixin):
2015 if has_kv_transfer_group():
2016 get_kv_transfer_group().register_kv_caches(kv_caches)
2017
1962 - self.attn_metadata_builder = self.attn_backend.get_builder_cls()(
1963 - weakref.proxy(self),
1964 - kv_cache_config.kv_cache_groups[0].kv_cache_spec,
1965 - self.input_batch.block_table)
1966 -
2018 def get_kv_cache_spec(self) -> dict[str, KVCacheSpec]:
2019 """
2020 Generates the KVCacheSpec by parsing the kv cache format from each
vllm/v1/worker/tpu_model_runner.py
+20 -16
@@ -171,19 +171,10 @@ class TPUModelRunner(LoRAModelRunnerMixin):
171 self.kv_caches: list[torch.Tensor] = []
172 # req_id -> (input_id -> encoder_output)
173 self.encoder_cache: dict[str, dict[int, torch.Tensor]] = {}
174 + # self.input_batch: InputBatch # Persistent batch.
175
176 # Request states.
177 self.requests: dict[str, CachedRequestState] = {}
177 - # Persistent batch.
178 - self.input_batch = InputBatch(
179 - max_num_reqs=self.max_num_reqs,
180 - max_model_len=self.max_model_len,
181 - max_num_blocks_per_req=self.max_num_blocks_per_req,
182 - max_num_batched_tokens=self.max_num_tokens,
183 - device=self.device,
184 - pin_memory=self.pin_memory,
185 - vocab_size=self.vocab_size,
186 - )
178
179 # Cached torch/numpy tensor
180 # The pytorch tensor and numpy array share the same buffer.
@@ -199,7 +190,7 @@ class TPUModelRunner(LoRAModelRunnerMixin):
190
191 self.block_table_cpu = torch.zeros(
192 (self.max_num_reqs, self.max_num_blocks_per_req),
202 - dtype=self.input_batch.block_table.get_cpu_tensor().dtype,
193 + dtype=torch.int32,
194 device="cpu")
195
196 self.query_start_loc_cpu = torch.zeros(self.max_num_tokens + 1,
@@ -524,12 +515,12 @@ class TPUModelRunner(LoRAModelRunnerMixin):
515 # NOTE(woosuk): We use torch.index_select instead of np.take here
516 # because torch.index_select is much faster than np.take for large
517 # tensors.
527 - block_table_cpu = self.input_batch.block_table.get_cpu_tensor()
518 + block_table_cpu = self.input_batch.block_table[0].get_cpu_tensor()
519 block_numbers = block_table_cpu.flatten()[block_table_indices].numpy()
520 block_offsets = positions_np % self.block_size
521 np.add(block_numbers * self.block_size,
522 block_offsets,
532 - out=self.input_batch.block_table.
523 + out=self.input_batch.block_table[0].
524 slot_mapping_np[:total_num_scheduled_tokens])
525
526 # Prepare the attention metadata.
@@ -554,15 +545,15 @@ class TPUModelRunner(LoRAModelRunnerMixin):
545 self.position_ids = self.positions_cpu[:
546 padded_total_num_scheduled_tokens].to(
547 self.device)
557 - self.input_batch.block_table.slot_mapping_cpu[
548 + self.input_batch.block_table[0].slot_mapping_cpu[
549 total_num_scheduled_tokens:] = _PAD_SLOT_ID
550 slot_mapping = (
560 - self.input_batch.block_table.
551 + self.input_batch.block_table[0].
552 slot_mapping_cpu[:padded_total_num_scheduled_tokens].to(
553 self.device))
554 block_tables = self.block_table_cpu[:self.max_num_reqs]
555 block_tables[:num_reqs, :self.max_num_blocks_per_req] = (
565 - self.input_batch.block_table.get_cpu_tensor()[:num_reqs])
556 + self.input_batch.block_table[0].get_cpu_tensor()[:num_reqs])
557 block_tables = block_tables.to(self.device)
558 query_start_loc = self.query_start_loc_cpu[:self.max_num_reqs + 1].to(
559 self.device)
@@ -1263,6 +1254,19 @@ class TPUModelRunner(LoRAModelRunnerMixin):
1254 "Hybrid models with more than one KV cache type are not "
1255 "supported yet.")
1256
1257 + self.input_batch = InputBatch(
1258 + max_num_reqs=self.max_num_reqs,
1259 + max_model_len=self.max_model_len,
1260 + max_num_batched_tokens=self.max_num_tokens,
1261 + device=self.device,
1262 + pin_memory=self.pin_memory,
1263 + vocab_size=self.model_config.get_vocab_size(),
1264 + block_size=kv_cache_config.kv_cache_groups[0].kv_cache_spec.
1265 + block_size,
1266 + )
1267 + assert self.block_table_cpu.dtype == self.input_batch.block_table[
1268 + 0].get_cpu_tensor().dtype
1269 +
1270 kv_caches: dict[str, torch.Tensor] = {}
1271
1272 for kv_cache_group in kv_cache_config.kv_cache_groups: