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[TPU][Bugfix] fix kv cache padding (#20048)

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Signed-off-by: Chengji Yao <chengjiyao@google.com>
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Chengji Yao 2025-06-25 14:24:10 -07:00 committed by GitHub
parent 9f0608fc16
commit 2cc2069970
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2 changed files with 14 additions and 9 deletions
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8
vllm/v1/attention/backends/pallas.py
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@ -48,13 +48,7 @@ class PallasAttentionBackend(AttentionBackend):
) -> tuple[int, ...]: ) -> tuple[int, ...]:
padded_head_size = cdiv( padded_head_size = cdiv(
head_size, TPU_HEAD_SIZE_ALIGNMENT) * TPU_HEAD_SIZE_ALIGNMENT head_size, TPU_HEAD_SIZE_ALIGNMENT) * TPU_HEAD_SIZE_ALIGNMENT
num_blocks = num_blocks * head_size // padded_head_size return (num_blocks, block_size, num_kv_heads * 2, padded_head_size)
if padded_head_size != head_size:
logger.warning_once(
"head size is padded to %d, and num_blocks is adjusted to %d"
" accordingly", padded_head_size, num_blocks)
head_size = padded_head_size
return (num_blocks, block_size, num_kv_heads * 2, head_size)
@staticmethod @staticmethod
def swap_blocks( def swap_blocks(

15
vllm/v1/worker/tpu_worker.py
View File

@ -18,7 +18,8 @@ from vllm.distributed import (ensure_model_parallel_initialized,
from vllm.logger import init_logger from vllm.logger import init_logger
from vllm.lora.request import LoRARequest from vllm.lora.request import LoRARequest
from vllm.model_executor import set_random_seed from vllm.model_executor import set_random_seed
from vllm.utils import STR_DTYPE_TO_TORCH_DTYPE from vllm.utils import STR_DTYPE_TO_TORCH_DTYPE, cdiv
from vllm.v1.attention.backends.pallas import TPU_HEAD_SIZE_ALIGNMENT
from vllm.v1.core.sched.output import SchedulerOutput from vllm.v1.core.sched.output import SchedulerOutput
from vllm.v1.kv_cache_interface import (AttentionSpec, KVCacheConfig, from vllm.v1.kv_cache_interface import (AttentionSpec, KVCacheConfig,
KVCacheSpec) KVCacheSpec)
@ -221,7 +222,17 @@ class TPUWorker:
usable_memory_size = int(total_memory_size * usable_memory_size = int(total_memory_size *
self.cache_config.gpu_memory_utilization) self.cache_config.gpu_memory_utilization)
tpu_kv_cache_bytes = max(usable_memory_size - profiled, 0) tpu_kv_cache_bytes = max(usable_memory_size - profiled, 0)
head_size = self.model_config.get_head_size()
if head_size > 0:
padded_head_size = cdiv(
head_size, TPU_HEAD_SIZE_ALIGNMENT) * TPU_HEAD_SIZE_ALIGNMENT
if padded_head_size != head_size:
logger.warning_once("head size is padded to %d",
padded_head_size)
# We adjust the usable memory size for the KV cache to prevent OOM
# errors, even after padding the head_size.
tpu_kv_cache_bytes = (tpu_kv_cache_bytes * head_size //
padded_head_size)
return int(tpu_kv_cache_bytes) return int(tpu_kv_cache_bytes)
def execute_model( def execute_model(