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CPU KV Offloading: Use more CUDA streams (#29013)

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Signed-off-by: Or Ozeri <oro@il.ibm.com>
This commit is contained in:
Or Ozeri
2025-12-15 01:50:45 +02:00
committed by GitHub
parent 9ccbf6b692
commit 174e39ead7
3 changed files with 192 additions and 105 deletions
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261
vllm/v1/kv_offload/worker/cpu_gpu.py
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@@ -1,5 +1,6 @@
# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
from collections import deque
import numpy as np
import torch
@@ -8,7 +9,7 @@ from vllm import _custom_ops as ops
from vllm.attention.backends.abstract import AttentionBackend
from vllm.logger import init_logger
from vllm.utils.platform_utils import is_pin_memory_available
from vllm.v1.kv_offload.mediums import CPULoadStoreSpec, GPULoadStoreSpec
from vllm.v1.kv_offload.mediums import BlockIDsLoadStoreSpec
from vllm.v1.kv_offload.worker.worker import (
OffloadingHandler,
TransferResult,
@@ -51,7 +52,123 @@ def expand_block_ids(
output_idx = output_end_idx
class CpuGpuOffloadingHandler(OffloadingHandler):
class SingleDirectionOffloadingHandler(OffloadingHandler):
"""
SingleDirectionOffloadingHandler handles transfers for a single direction,
either CPU->GPU or GPU->CPU.
Transfers are guaranteed to be executed in order of their submission.
Each transfer uses a unique CUDA stream, and its stream will start
executing only after the streams of previous transfers have finished.
"""
def __init__(
self,
src_tensors: list[torch.Tensor],
dst_tensors: list[torch.Tensor],
kv_dim_before_num_blocks: list[bool],
src_block_size_factor: int,
dst_block_size_factor: int,
priority: int,
):
"""
Initialize a SingleDirectionOffloadingHandler.
Args:
src_tensors: list of KV cache tensors to copy from.
dst_tensors: list of KV cache tensors to copy to.
Order should match src_tensors.
kv_dim_before_num_blocks: list of bools, indicating
whether the respective KV cache tensor has a KV
dimension before its num_blocks dimension.
e.g. (2, num_blocks, ...)
src_block_size_factor: The number of kernel blocks
per KV block in a source tensor.
dst_block_size_factor: The number of kernel blocks
per KV block in a destination tensor.
priority: The priority of the backing CUDA streams.
Lower numbers indicate higher priority.
"""
assert len(src_tensors) == len(dst_tensors) == len(kv_dim_before_num_blocks)
self.src_tensors: list[torch.Tensor] = src_tensors
self.dst_tensors: list[torch.Tensor] = dst_tensors
self.kv_dim_before_num_blocks: list[bool] = kv_dim_before_num_blocks
self.src_block_size_factor: int = src_block_size_factor
self.dst_block_size_factor: int = dst_block_size_factor
self.priority = priority
# queue of transfers (job_id, stream, event)
self._transfers: deque[tuple[int, torch.cuda.Stream, torch.Event]] = deque()
# list of CUDA streams available for re-use
self._stream_pool: list[torch.cuda.Stream] = []
# list of CUDA events available for re-use
self._event_pool: list[torch.Event] = []
def transfer_async(self, job_id: int, transfer_spec: TransferSpec) -> bool:
src_spec, dst_spec = transfer_spec
assert isinstance(src_spec, BlockIDsLoadStoreSpec)
assert isinstance(dst_spec, BlockIDsLoadStoreSpec)
src_blocks = src_spec.block_ids
dst_blocks = dst_spec.block_ids
assert src_blocks.ndim == 1
assert dst_blocks.ndim == 1
src_sub_block_count = src_blocks.size * self.src_block_size_factor
dst_sub_block_count = dst_blocks.size * self.dst_block_size_factor
src_sub_blocks_to_skip = -dst_blocks.size % self.src_block_size_factor
assert dst_sub_block_count == src_sub_block_count - src_sub_blocks_to_skip
src_to_dst = np.empty((dst_sub_block_count, 2), dtype=np.int64)
expand_block_ids(
src_blocks,
self.src_block_size_factor,
src_to_dst[:, 0],
skip_count=src_sub_blocks_to_skip,
)
expand_block_ids(dst_blocks, self.dst_block_size_factor, src_to_dst[:, 1])
src_to_dst_tensor = torch.from_numpy(src_to_dst)
stream = (
self._stream_pool.pop()
if self._stream_pool
else torch.cuda.Stream(priority=self.priority)
)
event = self._event_pool.pop() if self._event_pool else torch.Event()
if self._transfers:
_, _, last_event = self._transfers[-1]
# assure job will start only after the previous one completes
stream.wait_event(last_event)
with torch.cuda.stream(stream):
for src_tensor, dst_tensor, kv_dim in zip(
self.src_tensors, self.dst_tensors, self.kv_dim_before_num_blocks
):
if kv_dim:
src_key_cache, src_value_cache = src_tensor
dst_key_cache, dst_value_cache = dst_tensor
ops.swap_blocks(src_key_cache, dst_key_cache, src_to_dst_tensor)
ops.swap_blocks(src_value_cache, dst_value_cache, src_to_dst_tensor)
else:
ops.swap_blocks(src_tensor, dst_tensor, src_to_dst_tensor)
event.record(stream)
self._transfers.append((job_id, stream, event))
# success
return True
def get_finished(self) -> list[TransferResult]:
results: list[TransferResult] = []
while self._transfers and self._transfers[0][2].query():
job_id, stream, event = self._transfers.popleft()
results.append((job_id, True))
self._stream_pool.append(stream)
self._event_pool.append(event)
return results
class CpuGpuOffloadingHandlers:
def __init__(
self,
gpu_block_size: int,
@@ -60,27 +177,20 @@ class CpuGpuOffloadingHandler(OffloadingHandler):
gpu_caches: dict[str, torch.Tensor],
attn_backends: dict[str, type[AttentionBackend]],
):
assert gpu_caches
assert cpu_block_size % gpu_block_size == 0
self.block_size_factor = cpu_block_size // gpu_block_size
# cuda streams for gpu->cpu and cpu->gpu
self.d2h_stream = torch.cuda.Stream()
self.h2d_stream = torch.cuda.Stream()
# job_id -> transfer cuda event
self.transfer_events: dict[int, torch.Event] = {}
# list of cuda events available for re-use
self.events_pool: list[torch.Event] = []
block_size_factor = cpu_block_size // gpu_block_size
pin_memory = is_pin_memory_available()
# allocate cpu tensors
logger.info("Allocating %d CPU tensors...", len(gpu_caches))
self.gpu_tensors: list[torch.Tensor] = []
self.cpu_tensors: list[torch.Tensor] = []
self.kv_dim_before_num_blocks: list[bool] = []
gpu_tensors: list[torch.Tensor] = []
cpu_tensors: list[torch.Tensor] = []
kv_dim_before_num_blocks: list[bool] = []
kernel_block_size: int | None = None
for layer_name, gpu_tensor in gpu_caches.items():
self.gpu_tensors.append(gpu_tensor)
gpu_tensors.append(gpu_tensor)
gpu_shape = gpu_tensor.shape
attn_backend = attn_backends[layer_name]
@@ -88,16 +198,21 @@ class CpuGpuOffloadingHandler(OffloadingHandler):
num_blocks=1234, block_size=16, num_kv_heads=8, head_size=256
)
has_layers_dim = False
if len(gpu_shape) != len(test_shape):
# cross-layers tensor
# shape is (num_blocks, ...)
assert len(gpu_shape) == len(test_shape) + 1
num_blocks_idx = 0
self.kv_dim_before_num_blocks.append(False)
has_layers_dim = True
kv_dim_before_num_blocks.append(False)
# prepend a dummy num_layers=80 to test_shape
test_shape = (80,) + test_shape
elif test_shape[0] == 1234:
# shape is (num_blocks, ...)
num_blocks_idx = 0
self.kv_dim_before_num_blocks.append(False)
kv_dim_before_num_blocks.append(False)
else:
# shape should be (2, num_blocks, ...)
assert test_shape[0] == 2
@@ -105,13 +220,32 @@ class CpuGpuOffloadingHandler(OffloadingHandler):
assert gpu_shape[0] == 2
num_blocks_idx = 1
self.kv_dim_before_num_blocks.append(True)
kv_dim_before_num_blocks.append(True)
try:
kv_cache_stride_order = attn_backend.get_kv_cache_stride_order(
include_num_layers_dimension=has_layers_dim
)
assert len(kv_cache_stride_order) == len(gpu_shape)
except (AttributeError, NotImplementedError):
kv_cache_stride_order = tuple(range(len(gpu_shape)))
# permute test_shape according to stride_order
test_shape = tuple(test_shape[i] for i in kv_cache_stride_order)
# find block_size (16) dimension index
block_size_idx = test_shape.index(16)
if kernel_block_size is not None:
assert kernel_block_size == gpu_shape[block_size_idx]
else:
kernel_block_size = gpu_shape[block_size_idx]
assert gpu_block_size % kernel_block_size == 0
cpu_shape = list(gpu_shape)
cpu_shape[num_blocks_idx] = num_cpu_blocks * self.block_size_factor
cpu_shape[num_blocks_idx] = num_cpu_blocks * block_size_factor
logger.debug("Allocating CPU tensor of shape %r", cpu_shape)
self.cpu_tensors.append(
cpu_tensors.append(
torch.zeros(
cpu_shape,
dtype=gpu_tensor.dtype,
@@ -120,72 +254,27 @@ class CpuGpuOffloadingHandler(OffloadingHandler):
)
)
def transfer_async(self, job_id: int, spec: TransferSpec) -> bool:
src_spec, dst_spec = spec
if isinstance(src_spec, CPULoadStoreSpec):
assert isinstance(dst_spec, GPULoadStoreSpec)
stream = self.h2d_stream
src_tensors = self.cpu_tensors
dst_tensors = self.gpu_tensors
src_block_size_factor = self.block_size_factor
dst_block_size_factor = 1
else:
assert isinstance(src_spec, GPULoadStoreSpec)
assert isinstance(dst_spec, CPULoadStoreSpec)
stream = self.d2h_stream
src_tensors = self.gpu_tensors
dst_tensors = self.cpu_tensors
src_block_size_factor = 1
dst_block_size_factor = self.block_size_factor
assert kernel_block_size is not None
gpu_block_size_factor = gpu_block_size // kernel_block_size
cpu_block_size_factor = cpu_block_size // kernel_block_size
src_blocks = src_spec.block_ids
dst_blocks = dst_spec.block_ids
assert src_blocks.ndim == 1
assert dst_blocks.ndim == 1
# TODO (orozery): adapt swap_blocks to support gpu_block_size_factor
assert gpu_block_size_factor == 1
src_sub_block_count = src_blocks.size * src_block_size_factor
dst_sub_block_count = dst_blocks.size * dst_block_size_factor
src_sub_blocks_to_skip = -dst_blocks.size % src_block_size_factor
assert dst_sub_block_count == src_sub_block_count - src_sub_blocks_to_skip
src_to_dst = np.empty((dst_sub_block_count, 2), dtype=np.int64)
expand_block_ids(
src_blocks,
src_block_size_factor,
src_to_dst[:, 0],
skip_count=src_sub_blocks_to_skip,
self.gpu_to_cpu_handler = SingleDirectionOffloadingHandler(
src_tensors=gpu_tensors,
dst_tensors=cpu_tensors,
kv_dim_before_num_blocks=kv_dim_before_num_blocks,
src_block_size_factor=gpu_block_size_factor,
dst_block_size_factor=cpu_block_size_factor,
priority=1,
)
expand_block_ids(dst_blocks, dst_block_size_factor, src_to_dst[:, 1])
src_to_dst_tensor = torch.from_numpy(src_to_dst)
event = self.events_pool.pop() if self.events_pool else torch.Event()
with torch.cuda.stream(stream):
for src_tensor, dst_tensor, kv_dim in zip(
src_tensors, dst_tensors, self.kv_dim_before_num_blocks
):
if kv_dim:
src_key_cache = src_tensor[0]
dst_key_cache = dst_tensor[0]
ops.swap_blocks(src_key_cache, dst_key_cache, src_to_dst_tensor)
src_value_cache = src_tensor[1]
dst_value_cache = dst_tensor[1]
ops.swap_blocks(src_value_cache, dst_value_cache, src_to_dst_tensor)
else:
ops.swap_blocks(src_tensor, dst_tensor, src_to_dst_tensor)
event.record(stream)
self.transfer_events[job_id] = event
# success
return True
def get_finished(self) -> list[TransferResult]:
results: list[TransferResult] = []
for job_id, event in self.transfer_events.items():
if event.query():
results.append((job_id, True))
self.events_pool.append(event)
for job_id, _ in results:
del self.transfer_events[job_id]
return results
self.cpu_to_gpu_handler = SingleDirectionOffloadingHandler(
src_tensors=cpu_tensors,
dst_tensors=gpu_tensors,
kv_dim_before_num_blocks=kv_dim_before_num_blocks,
src_block_size_factor=cpu_block_size_factor,
dst_block_size_factor=gpu_block_size_factor,
priority=-1,
)