class OffloadingConnectorScheduler:
"""Implementation of Scheduler side methods"""
def __init__(
self,
spec: OffloadingSpec,
vllm_config: VllmConfig,
kv_cache_config: KVCacheConfig,
):
self.config = SchedulerOffloadConfig.from_spec(
spec, vllm_config, kv_cache_config
)
self.manager: OffloadingManager = spec.get_manager()
self._connector_stats = OffloadingConnectorStats()
full_attention_groups: list[int] = []
sliding_window_groups: list[int] = []
for group_config in self.config.kv_group_configs:
if group_config.sliding_window_size_in_chunks is None:
full_attention_groups.append(group_config.group_idx)
else:
sliding_window_groups.append(group_config.group_idx)
# sort sliding window groups by window size in decreasing order
def _sliding_window_sort_key(i: int) -> int:
val = self.config.kv_group_configs[i].sliding_window_size_in_chunks
assert val is not None
return val
sliding_window_groups.sort(key=_sliding_window_sort_key, reverse=True)
# used by _lookup
self._sliding_window_groups: tuple[int, ...] = tuple(sliding_window_groups)
self._lookup_groups = tuple(full_attention_groups) + self._sliding_window_groups
self._mamba_align_size: int | None = resolve_mamba_align_size(
spec, kv_cache_config
)
self._req_status: dict[ReqId, RequestOffloadState] = {}
self._current_batch_load_jobs: dict[int, TransferJob] = {}
self._current_batch_jobs_to_flush: set[int] = set()
# GPU block IDs allocated in the current engine step
self._current_batch_allocated_block_ids: set[int] = set()
# if GPU prefix caching is enabled,
# Track loaded chunks to avoid redundant loads.
self._chunks_being_loaded: set[OffloadKey] | None = (
set() if vllm_config.cache_config.enable_prefix_caching else None
)
# Job ID counter shared by loads and stores.
self._job_counter: int = 0
# Threshold value for stale jobs. All job ids >= _stale_job_threshold are
# active jobs.
self._stale_job_threshold: int = 0
self._jobs: dict[int, TransferJobStatus] = {}
# block_id -> pending store job_ids. Used to track jobs that needs
# flushing in case a block is re-allocated by the KV cache manager.
# Populated only for finished requests (running-request blocks are
# protected by their ref_cnt) and for sliding window blocks (which can
# be freed before a request finishes).
self._block_id_to_pending_jobs: dict[int, set[int]] = {}
self._events_tracker = OffloadingEventsTracker(spec.kv_events_config)
def _maybe_observe_lookup_async_delay(
self, req_status: RequestOffloadState
) -> None:
start_time = req_status.deferred_lookup_start_time
if start_time is None:
return
req_status.deferred_lookup_start_time = None
self._connector_stats.observe_histogram(
_ConnectorMetricName.LOOKUP_ASYNC_DELAY,
time.monotonic() - start_time,
)
def _generate_job_id(self) -> int:
job_id = self._job_counter
self._job_counter += 1
return job_id
def _remove_pending_job(self, job_id: int, block_ids: list[int] | None) -> None:
for bid in block_ids or ():
pending = self._block_id_to_pending_jobs[bid]
pending.remove(job_id)
if not pending:
del self._block_id_to_pending_jobs[bid]
def _calc_num_offloadable_tokens(
self, req_status: RequestOffloadState, num_computed_tokens: int
) -> int:
num = min(num_computed_tokens, req_status.req.num_tokens)
max_offload_tokens = req_status.max_offload_tokens
if max_offload_tokens is not None:
num = min(num, max_offload_tokens)
if self.config.offload_prompt_only:
num = min(num, req_status.req.num_prompt_tokens)
return num
def _maybe_cleanup_finished_req(
self, req_id: str, req_status: RequestOffloadState
) -> None:
"""Clean up req_status if finished and no in-flight jobs."""
if req_status.req.is_finished() and not req_status.transfer_jobs:
del self._req_status[req_id]
def _maximal_prefix_lookup(
self,
keys: Iterable[OffloadKey],
req_context: ReqContext,
req: Request,
group_config: GroupOffloadConfig,
start_chunk_idx: int,
) -> int | None:
"""Return the number of consecutive offloaded chunks from the start,
or None if the backend deferred a lookup."""
hit_count = 0
defer_lookup = False
for local_idx, key in enumerate(keys):
result = self.manager.lookup(key, req_context)
match result:
case LookupResult.HIT:
self._events_tracker.record_lookup(
req,
group_config,
start_chunk_idx + local_idx,
key,
)
hit_count += 1
case LookupResult.HIT_PENDING:
defer_lookup = True
hit_count += 1
case LookupResult.RETRY:
# Don't break: keep scanning to let manager kick off
# async lookups (until a miss is detected).
defer_lookup = True
case LookupResult.MISS:
break
return hit_count if not defer_lookup else None
def _sliding_window_lookup(
self,
keys: Sequence[OffloadKey],
sliding_window_size: int,
req_context: ReqContext,
) -> int | None:
"""Return the end index (in `keys`) of the last run of
`sliding_window_size` consecutive hits, scanning from the end.
Returns 0 on miss, None if the backend deferred a lookup."""
defer_lookup = False
consecutive_hits = 0
for idx in range(len(keys) - 1, -1, -1):
match self.manager.lookup(keys[idx], req_context):
case LookupResult.HIT:
consecutive_hits += 1
case LookupResult.HIT_PENDING:
# Block is in cache, just not readable yet — counts
# as hit for the consecutive streak. Don't break:
# keep scanning to let manager kick off async lookups.
defer_lookup = True
consecutive_hits += 1
case LookupResult.RETRY:
# Block location uncertain — does not count as hit.
# Don't break: keep scanning to let manager kick off
# async lookups.
defer_lookup = True
consecutive_hits = 0
case LookupResult.MISS:
consecutive_hits = 0
if consecutive_hits == sliding_window_size:
return idx + sliding_window_size if not defer_lookup else None
return consecutive_hits if not defer_lookup else None
def _touch(self, req_status: RequestOffloadState):
for group_config, group_state in zip(
self.config.kv_group_configs, req_status.group_states
):
if group_config.sliding_window_size_in_chunks is None:
self.manager.touch(group_state.offload_keys, req_status.req_context)
else:
# Keep only chunks needed to hit the original request, plus
# decoded chunks.
chunks_to_skip = max(
0,
group_state.num_hit_chunks
- group_config.sliding_window_size_in_chunks,
)
self.manager.touch(
group_state.offload_keys[chunks_to_skip:],
req_status.req_context,
)
def _lookup(self, req_status: RequestOffloadState) -> int | None:
"""
Find how many tokens beyond num_locally_computed_tokens can be loaded.
Iterates full-attention groups first (prefix lookup), then sliding-window
groups (suffix lookup). Each group may tighten max_hit_size_tokens, which
can invalidate an earlier group's result, so the loop re-runs when that
happens until num_hit_tokens converges.
"""
num_computed_tokens = req_status.num_locally_computed_tokens
max_hit_size_tokens: int = req_status.req.num_tokens
if self._sliding_window_groups:
# the last prompt token has to be recomputed to get the logprobs
# for sliding window attention, we must reduce by 1 to make sure
# we still have a hit after reduction
max_hit_size_tokens -= 1
if self._mamba_align_size is not None:
# Constrain hit-window to the mamba block size.
max_hit_size_tokens = round_down(
max_hit_size_tokens, self._mamba_align_size
)
num_hit_tokens: int = 0
defer_lookup = False
lookup_groups = self._lookup_groups
# Tracks which eagle groups have already popped their volatile trailing chunk
# in the current convergence iteration. Reset when a non-eagle group
# tightens the hit boundary, requiring a fresh pop.
eagle_verified: set[int] = set()
while lookup_groups:
looked_up_sliding_window: bool = False
groups_iter = iter(lookup_groups)
lookup_groups = ()
for group_idx in groups_iter:
group_config: GroupOffloadConfig = self.config.kv_group_configs[
group_idx
]
group_state: RequestGroupState = req_status.group_states[group_idx]
tokens_per_chunk = group_config.tokens_per_chunk
offload_keys = group_state.offload_keys
assert (
len(offload_keys) >= req_status.req.num_tokens // tokens_per_chunk
)
is_eagle_unverified = (
group_config.is_eagle_group and group_idx not in eagle_verified
)
# Constrain to a chunk-aligned boundary for this group.
max_hit_size_tokens = min(
max_hit_size_tokens, len(offload_keys) * tokens_per_chunk
)
if max_hit_size_tokens - num_computed_tokens < tokens_per_chunk:
# We can only load less than a chunk, so skip.
return 0
sliding_window_size_in_chunks = (
group_config.sliding_window_size_in_chunks
)
# For eagle groups, query one extra chunk that will be popped.
# We only need to increase the query size for sliding window groups.
query_max = max_hit_size_tokens
if is_eagle_unverified and sliding_window_size_in_chunks is not None:
query_max = min(
max_hit_size_tokens + tokens_per_chunk,
len(offload_keys) * tokens_per_chunk,
)
num_chunks = min(cdiv(query_max, tokens_per_chunk), len(offload_keys))
start_chunk_idx = num_computed_tokens // tokens_per_chunk
offload_keys = offload_keys[start_chunk_idx:num_chunks]
# end index (in the sliced offload_keys) up to which we
# have backend-confirmed hits
num_hit_chunks: int | None
if sliding_window_size_in_chunks is None:
num_hit_chunks = self._maximal_prefix_lookup(
offload_keys,
req_status.req_context,
req_status.req,
group_config,
start_chunk_idx,
)
else:
required_window = sliding_window_size_in_chunks
if is_eagle_unverified:
required_window += 1
num_hit_chunks = self._sliding_window_lookup(
offload_keys,
required_window,
req_status.req_context,
)
if num_hit_chunks == 0:
return 0
if num_hit_chunks is None:
defer_lookup = True
else:
if is_eagle_unverified:
num_hit_chunks -= 1
eagle_verified.add(group_idx)
max_hit_size_tokens = min(
max_hit_size_tokens,
tokens_per_chunk * (start_chunk_idx + num_hit_chunks),
)
new_num_hit_tokens = max_hit_size_tokens - num_computed_tokens
if new_num_hit_tokens < tokens_per_chunk:
# We can only load less than a chunk, so skip.
return 0
if new_num_hit_tokens < num_hit_tokens:
if not group_config.is_eagle_group:
eagle_verified.clear()
if defer_lookup:
# make another iteration on all groups to check
# if we still need to defer lookup
defer_lookup = False
lookup_groups = self._lookup_groups
elif looked_up_sliding_window and not lookup_groups:
# we need another iteration to confirm previously looked up
# sliding window works with the new_num_hit_tokens
lookup_groups = self._sliding_window_groups
looked_up_sliding_window |= sliding_window_size_in_chunks is not None
num_hit_tokens = new_num_hit_tokens
if defer_lookup:
logger.debug(
"Offloading manager delayed request %s as backend requested",
req_status.req.request_id,
)
return None
# Possibly delay the request if any hit chunk is already being loaded.
if self._chunks_being_loaded:
for group_config, group_state in zip(
self.config.kv_group_configs, req_status.group_states
):
tokens_per_chunk = group_config.tokens_per_chunk
sliding_window_size_in_chunks = (
group_config.sliding_window_size_in_chunks
)
offload_keys = group_state.offload_keys
num_chunks = cdiv(
num_computed_tokens + num_hit_tokens, tokens_per_chunk
)
start_chunk_idx = num_computed_tokens // tokens_per_chunk
offload_keys = offload_keys[start_chunk_idx:num_chunks]
if sliding_window_size_in_chunks is not None:
offload_keys = offload_keys[-sliding_window_size_in_chunks:]
if any(key in self._chunks_being_loaded for key in offload_keys):
# Hit chunks are being loaded, so delay the request.
logger.debug(
"Delaying request %s since some of its"
" chunks are already being loaded",
req_status.req.request_id,
)
return None
logger.debug(
"Request %s hit %s offloaded tokens after %s GPU hit tokens",
req_status.req.request_id,
num_hit_tokens,
num_computed_tokens,
)
return num_hit_tokens
def on_new_request(self, request: Request) -> None:
"""Called when a new request is added to the scheduler."""
req_context = _create_req_context(request)
offloading_context = self.manager.on_new_request(req_context)
req_status = RequestOffloadState(
config=self.config,
req=request,
req_context=req_context,
offloading_context=offloading_context,
)
self._req_status[request.request_id] = req_status
def get_num_new_matched_tokens(
self, request: Request, num_computed_tokens: int
) -> tuple[int | None, bool]:
"""
Get number of new tokens that can be loaded beyond the
num_computed_tokens.
Args:
request (Request): the request object.
num_computed_tokens (int): the number of locally
computed tokens for this request
Returns:
A tuple with the following elements:
- The number of tokens that can be loaded beyond what is
already computed.
If None, it means that the connector needs more time to
determine the number of matched tokens, and the scheduler
should query for this request again later.
- `True` if tokens will be loaded asynchronously
(between scheduler steps).
"""
req_status = self._req_status[request.request_id]
for group_state in req_status.group_states:
group_state.block_ids.clear()
if req_status.transfer_jobs:
logger.debug(
"Delaying request %s since it still has in-flight transfers",
request.request_id,
)
return None, False
req_status.update_offload_keys()
req_status.num_locally_computed_tokens = num_computed_tokens
num_hit_tokens: int | None
if request.skip_reading_prefix_cache:
num_hit_tokens = 0
else:
lookup_start = time.monotonic()
num_hit_tokens = self._lookup(req_status)
self._connector_stats.observe_histogram(
_ConnectorMetricName.LOOKUP_SYNC_DELAY,
time.monotonic() - lookup_start,
)
if num_hit_tokens is None:
if req_status.deferred_lookup_start_time is None:
req_status.deferred_lookup_start_time = lookup_start
else:
self._maybe_observe_lookup_async_delay(req_status)
req_status.update_num_hit_chunks(num_computed_tokens + (num_hit_tokens or 0))
self._touch(req_status)
return num_hit_tokens, bool(num_hit_tokens)
def update_state_after_alloc(
self, request: Request, blocks: KVCacheBlocks, num_external_tokens: int
):
if num_external_tokens == 0:
return
req_status = self._req_status[request.request_id]
num_locally_computed_tokens = req_status.num_locally_computed_tokens
num_cached_tokens = num_locally_computed_tokens + num_external_tokens
keys_to_load: list[OffloadKey] = []
dst_block_ids: list[int] = []
# per group
group_sizes: list[int] = []
block_indices: list[int] = []
for group_config, group_state, group_blocks in zip(
self.config.kv_group_configs,
req_status.group_states,
blocks.blocks,
):
self._current_batch_allocated_block_ids.update(
block.block_id for block in group_blocks if block.block_id != 0
)
tokens_per_block = group_config.tokens_per_block
tokens_per_chunk = group_config.tokens_per_chunk
offload_keys = group_state.offload_keys
num_gpu_blocks = cdiv(num_cached_tokens, tokens_per_block)
assert len(group_blocks) >= num_gpu_blocks
num_locally_computed_gpu_blocks = num_gpu_blocks
# Skip null placeholder blocks (used for sliding window or mamba padding).
for i, block in enumerate(group_blocks[:num_gpu_blocks]):
if not block.is_null and block.block_hash is None:
num_locally_computed_gpu_blocks = i
break
assert (
num_locally_computed_tokens
<= num_locally_computed_gpu_blocks * tokens_per_block
)
num_pending_gpu_blocks = num_gpu_blocks - num_locally_computed_gpu_blocks
if group_config.sliding_window_size_in_chunks is not None:
assert (
num_pending_gpu_blocks
<= group_config.sliding_window_size_in_chunks
* self.config.blocks_per_chunk
)
num_chunks = cdiv(num_cached_tokens, tokens_per_chunk)
assert len(offload_keys) >= num_chunks
if num_pending_gpu_blocks:
start_chunk_idx = (
num_locally_computed_gpu_blocks // self.config.blocks_per_chunk
)
keys_to_load.extend(offload_keys[start_chunk_idx:num_chunks])
dst_block_ids.extend(
block.block_id
for block in group_blocks[
num_locally_computed_gpu_blocks:num_gpu_blocks
]
)
group_sizes.append(num_pending_gpu_blocks)
block_indices.append(num_locally_computed_gpu_blocks)
# Skip prefix-hit chunks for block-level policy; for
# request-level, next_stored_chunk_idx stays at 0 so all
# chunks (including hits) are offloaded.
if req_status.offloading_context.policy == OffloadPolicy.BLOCK_LEVEL:
group_state.next_stored_chunk_idx = num_chunks
src_spec = self.manager.prepare_load(keys_to_load, req_status.req_context)
dst_spec = GPULoadStoreSpec(
dst_block_ids, group_sizes=group_sizes, block_indices=block_indices
)
load_job_id = self._generate_job_id()
self._current_batch_load_jobs[load_job_id] = TransferJob(
req_id=request.request_id,
src_spec=src_spec,
dst_spec=dst_spec,
)
# a load can only be issued when no other jobs are pending.
assert not req_status.transfer_jobs
req_status.transfer_jobs.add(load_job_id)
self._jobs[load_job_id] = TransferJobStatus(
req_id=request.request_id,
pending_count=self.config.num_workers,
keys=set(keys_to_load),
is_store=False,
)
if self._chunks_being_loaded is not None:
self._chunks_being_loaded.update(keys_to_load)
def _update_req_states(self, scheduler_output: SchedulerOutput) -> None:
"""
Update request states from the Scheduler's output.
"""
# new_block_ids_end[req_id][i] = end of pre-existing block_ids for
# the i-th sliding window group (before this step's extend).
# Used to detect sliding window blocks that got re-allocated.
new_block_ids_end: dict[str, tuple[int, ...]] = {}
for req_id, new_block_id_groups, preempted in yield_req_data(scheduler_output):
req_status = self._req_status[req_id]
req_status.update_offload_keys()
if preempted:
for group_state in req_status.group_states:
group_state.block_ids.clear()
if new_block_id_groups:
if self._sliding_window_groups:
new_block_ids_end[req_id] = tuple(
len(req_status.group_states[grp_idx].block_ids)
for grp_idx in self._sliding_window_groups
)
req_status.update_block_id_groups(new_block_id_groups)
for new_blocks in new_block_id_groups:
for bid in new_blocks:
if bid != 0:
self._current_batch_allocated_block_ids.add(bid)
# Zero out stale block_ids in sliding window groups' pending-store
# positions. Only sliding window groups can have stale entries (blocks
# freed by remove_skipped_blocks then reallocated). Only positions in
# [next_stored_chunk_idx * bsf, end) need checking where end is the
# pre-extend length: earlier positions were already offloaded, later
# ones are fresh allocations from this step.
if self._sliding_window_groups and self._current_batch_allocated_block_ids:
blocks_per_chunk = self.config.blocks_per_chunk
for req_id, req_status in self._req_status.items():
ends = new_block_ids_end.get(req_id)
for i, grp_idx in enumerate(self._sliding_window_groups):
group_state = req_status.group_states[grp_idx]
start = group_state.next_stored_chunk_idx * blocks_per_chunk
end = ends[i] if ends is not None else len(group_state.block_ids)
for j in range(start, end):
if (
group_state.block_ids[j]
in self._current_batch_allocated_block_ids
):
group_state.block_ids[j] = 0
def _build_store_jobs(
self,
scheduler_output: SchedulerOutput,
) -> dict[int, TransferJob]:
blocks_per_chunk = self.config.blocks_per_chunk
store_jobs: dict[int, TransferJob] = {}
for req_id in chain(
scheduler_output.num_scheduled_tokens,
scheduler_output.finished_req_ids or (),
):
req_status = self._req_status.get(req_id)
if req_status is None:
continue
req = req_status.req
if req.is_finished():
num_tokens_after_batch = req.num_tokens
else:
num_scheduled_tokens = scheduler_output.num_scheduled_tokens[req_id]
num_tokens_after_batch = req.num_computed_tokens + num_scheduled_tokens
num_offloadable_tokens = self._calc_num_offloadable_tokens(
req_status, num_tokens_after_batch
)
# Filter out chunks skipped due to sliding window attention / SSM
# or unreachable by the load path's alignment constraints.
new_offload_keys: list[OffloadKey] = []
for group_config, group_state in zip(
self.config.kv_group_configs, req_status.group_states
):
num_chunks = req_status.storable_chunks(
group_config, group_state, num_offloadable_tokens
)
start_chunk_idx = group_state.next_stored_chunk_idx
if num_chunks <= start_chunk_idx:
continue
offload_keys = group_state.offload_keys[start_chunk_idx:num_chunks]
# For each chunk, take the last corresponding GPU block. For
# blocks_per_chunk=3 and GPU block IDs 1 5 6 7 2 4 9 3 8,
# this selects GPU blocks 6 4 8.
# A block_id of 0 means either a sliding window / SSM skip
# or a stale entry that was zeroed out — skip it either way.
offload_block_ids = group_state.block_ids[
start_chunk_idx * blocks_per_chunk
+ blocks_per_chunk
- 1 : num_chunks * blocks_per_chunk : blocks_per_chunk
]
assert len(offload_keys) == len(offload_block_ids)
for key_idx, (offload_key, block_id) in enumerate(
zip(offload_keys, offload_block_ids)
):
if block_id == 0:
continue
# Skip SWA chunks that can never serve a load hit:
# within each full-attention alignment segment, only the
# trailing chunks queried by _sliding_window_lookup are
# reachable. EAGLE/MTP requires one additional chunk that
# lookup later drops as its volatile draft tail.
abs_chunk_idx = start_chunk_idx + key_idx
if not is_store_reachable_swa_chunk(
abs_chunk_idx,
num_chunks,
group_config.alignment_chunk_count,
group_config.sliding_window_size_in_chunks,
group_config.is_eagle_group,
):
continue
new_offload_keys.append(offload_key)
if not new_offload_keys:
req_status.advance_stored_idx(num_offloadable_tokens)
self._maybe_cleanup_finished_req(req_id, req_status)
continue
store_output = self.manager.prepare_store(
new_offload_keys, req_status.req_context
)
if store_output is None:
self._connector_stats.increase_counter(
_ConnectorMetricName.ALLOCATION_FAILURE
)
logger.warning("Request %s: cannot store chunks", req_id)
self._maybe_cleanup_finished_req(req_id, req_status)
continue
if not store_output.keys_to_store:
req_status.advance_stored_idx(num_offloadable_tokens)
self._maybe_cleanup_finished_req(req_id, req_status)
continue
self._touch(req_status)
keys_to_store = set(store_output.keys_to_store)
group_sizes: list[int] = []
block_indices: list[int] = []
src_block_ids: list[int] = []
sliding_window_block_ids: list[int] = []
non_sliding_window_block_ids: list[int] = []
for group_config, group_state in zip(
self.config.kv_group_configs, req_status.group_states
):
is_sliding_window = (
group_config.sliding_window_size_in_chunks is not None
)
num_chunks = req_status.storable_chunks(
group_config, group_state, num_offloadable_tokens
)
start_chunk_idx = group_state.next_stored_chunk_idx
block_ids = group_state.block_ids
num_group_blocks = 0
start_gpu_block_idx: int | None = None
for idx, offload_key in enumerate(
group_state.offload_keys[start_chunk_idx:num_chunks]
):
if offload_key not in keys_to_store:
continue
chunk_idx = start_chunk_idx + idx
self._events_tracker.record_store(
req, group_config, chunk_idx, offload_key
)
gpu_block_idx = chunk_idx * blocks_per_chunk
for i in range(blocks_per_chunk):
block_id = block_ids[gpu_block_idx + i]
if block_id == 0:
continue
if start_gpu_block_idx is None:
start_gpu_block_idx = gpu_block_idx + i
src_block_ids.append(block_id)
num_group_blocks += 1
if is_sliding_window:
sliding_window_block_ids.append(block_id)
else:
non_sliding_window_block_ids.append(block_id)
group_sizes.append(num_group_blocks)
block_indices.append(start_gpu_block_idx or 0)
group_state.next_stored_chunk_idx = max(
group_state.next_stored_chunk_idx, num_chunks
)
src_spec = GPULoadStoreSpec(
src_block_ids, group_sizes=group_sizes, block_indices=block_indices
)
dst_spec = store_output.store_spec
job_id = self._generate_job_id()
# a store can only be issued when no load is pending.
if req_status.transfer_jobs:
any_jid = next(iter(req_status.transfer_jobs))
assert self._jobs[any_jid].is_store
req_status.transfer_jobs.add(job_id)
# Watch sliding window blocks as they may get evicted
# before the request finishes
for bid in sliding_window_block_ids or ():
self._block_id_to_pending_jobs.setdefault(bid, set()).add(job_id)
# the non-sliding window blocks will be watched only
# when the request finishes
self._jobs[job_id] = TransferJobStatus(
req_id=req_id,
pending_count=self.config.num_workers,
keys=set(keys_to_store),
is_store=True,
non_sliding_window_block_ids=non_sliding_window_block_ids,
sliding_window_block_ids=sliding_window_block_ids or None,
)
store_jobs[job_id] = TransferJob(
req_id=req_id, src_spec=src_spec, dst_spec=dst_spec
)
logger.debug(
"Request %s offloading %s chunks upto %d tokens (job %d)",
req_id,
len(keys_to_store),
num_offloadable_tokens,
job_id,
)
if req.is_finished():
# Register non-sliding-window blocks for flush detection.
for bid in non_sliding_window_block_ids:
self._block_id_to_pending_jobs.setdefault(bid, set()).add(job_id)
if bid in self._current_batch_allocated_block_ids:
self._current_batch_jobs_to_flush.add(job_id)
return store_jobs
def build_connector_meta(
self, scheduler_output: SchedulerOutput
) -> KVConnectorMetadata:
self._update_req_states(scheduler_output)
schedule_end_context = ScheduleEndContext(
new_req_ids=[req.req_id for req in scheduler_output.scheduled_new_reqs],
preempted_req_ids=scheduler_output.preempted_req_ids or (),
)
self.manager.on_schedule_end(schedule_end_context)
# Flush jobs for preempted requests.
for req_id in scheduler_output.preempted_req_ids or ():
req_status = self._req_status.get(req_id)
if req_status is None or not req_status.transfer_jobs:
continue
any_jid = next(iter(req_status.transfer_jobs))
assert self._jobs[any_jid].is_store
self._current_batch_jobs_to_flush.update(req_status.transfer_jobs)
# Flush jobs that contain re-allocated blocks.
if (
self._block_id_to_pending_jobs
and not self._block_id_to_pending_jobs.keys().isdisjoint(
self._current_batch_allocated_block_ids
)
):
self._current_batch_jobs_to_flush.update(
jid
for bid in self._current_batch_allocated_block_ids
if bid in self._block_id_to_pending_jobs
for jid in self._block_id_to_pending_jobs[bid]
)
meta = OffloadingConnectorMetadata(
load_jobs=self._current_batch_load_jobs,
store_jobs=self._build_store_jobs(scheduler_output),
jobs_to_flush=self._current_batch_jobs_to_flush,
)
self._current_batch_load_jobs = {}
self._current_batch_jobs_to_flush = set()
self._current_batch_allocated_block_ids = set()
return meta
def has_pending_push_work(self) -> bool:
"""Whether the engine must keep stepping.
While True, build_connector_meta() and update_connector_output()
continue to be called even when no requests are scheduled.
"""
return bool(self._jobs) or self.manager.has_pending_work()
def update_connector_output(self, connector_output: KVConnectorOutput):
"""
Update KVConnector state from worker-side connectors output.
Args:
connector_output (KVConnectorOutput): the worker-side
connectors output.
"""
meta = connector_output.kv_connector_worker_meta
if not isinstance(meta, OffloadingWorkerMetadata):
assert meta is None
meta = OffloadingWorkerMetadata()
if not meta.transfer_stats.is_empty():
transfer_stats = OffloadingConnectorStats()
if not meta.transfer_stats.load.is_empty():
transfer_stats.increase_counter(
_TransferMetricName.LOAD_BYTES,
meta.transfer_stats.load.bytes,
)
transfer_stats.increase_counter(
_TransferMetricName.LOAD_TIME,
meta.transfer_stats.load.time,
)
for size in meta.transfer_stats.load.sizes:
transfer_stats.observe_histogram(
_TransferMetricName.LOAD_SIZE, size
)
if not meta.transfer_stats.store.is_empty():
transfer_stats.increase_counter(
_TransferMetricName.STORE_BYTES,
meta.transfer_stats.store.bytes,
)
transfer_stats.increase_counter(
_TransferMetricName.STORE_TIME,
meta.transfer_stats.store.time,
)
for size in meta.transfer_stats.store.sizes:
transfer_stats.observe_histogram(
_TransferMetricName.STORE_SIZE, size
)
self._connector_stats.aggregate(transfer_stats)
for job_id, count in meta.completed_jobs.items():
assert count > 0
if job_id < self._stale_job_threshold:
logger.debug(
"Skipping stale completed job %d (pre-reset counter: %d)",
job_id,
self._stale_job_threshold,
)
continue
job_status = self._jobs[job_id]
job_status.pending_count -= count
if job_status.pending_count > 0:
continue
assert job_status.pending_count == 0
req_status = self._req_status[job_status.req_id]
if job_status.is_store:
self.manager.complete_store(job_status.keys, req_status.req_context)
else:
self.manager.complete_load(job_status.keys, req_status.req_context)
if self._chunks_being_loaded:
self._chunks_being_loaded.difference_update(job_status.keys)
if self._block_id_to_pending_jobs:
# Sliding window blocks are tracked from store creation
# and must be cleaned up unconditionally.
self._remove_pending_job(job_id, job_status.sliding_window_block_ids)
# Non-sliding-window blocks are only tracked after
# request_finished, so only clean up for finished requests.
if req_status.req.is_finished():
self._remove_pending_job(
job_id, job_status.non_sliding_window_block_ids
)
del self._jobs[job_id]
req_status.transfer_jobs.remove(job_id)
if not req_status.transfer_jobs and req_status.req.is_finished():
del self._req_status[job_status.req_id]
def get_stats(self) -> OffloadingConnectorStats | None:
stats: OffloadingConnectorStats | None = None
if not self._connector_stats.is_empty():
stats = self._connector_stats
self._connector_stats = OffloadingConnectorStats()
manager_stats = self.manager.get_stats()
if manager_stats is not None:
if stats is None:
stats = manager_stats
else:
stats.aggregate(manager_stats)
return stats
def request_finished(
self,
request: Request,
) -> tuple[bool, dict[str, Any] | None]:
"""
Called when a request has finished, before its blocks are freed.
Returns:
True if the request is being saved/sent asynchronously and blocks
should not be freed until the request_id is returned from
get_finished().
Optional KVTransferParams to be included in the request outputs
returned by the engine.
"""
req_status = self._req_status.get(request.request_id)
if req_status is None:
# Untracked request (offloading never started): no in-flight jobs,
# nothing was deferred, so finalize immediately.
req_context = _create_req_context(request)
self.manager.on_new_request(req_context)
self.manager.on_request_finished(req_context)
return False, None
self.manager.on_request_finished(req_status.req_context)
self._maybe_observe_lookup_async_delay(req_status)
# Update offload keys with final block hash so _build_store_jobs can
# create store jobs for the last block(s) on the next schedule step.
req_status.update_offload_keys()
# Keep req_status alive: _build_store_jobs will process finished_req_ids
# on the next step and handle cleanup after creating store jobs.
# Register non_sliding_window_block_ids so future block reuse triggers
# a flush via _block_id_to_pending_jobs.
for job_id in req_status.transfer_jobs:
job_status = self._jobs[job_id]
for bid in job_status.non_sliding_window_block_ids or ():
self._block_id_to_pending_jobs.setdefault(bid, set()).add(job_id)
return False, None
def take_events(self) -> Iterable[KVCacheEvent]:
"""Drain pending KV cache events.
Complete metadata is available only when self-describing KV events
are enabled, and only for full-attention groups. Other shapes retain
the previous placeholder payload so consumers can ignore them.
Yields:
``BlockStored`` or ``BlockRemoved`` events corresponding to
the underlying :class:`OffloadingEvent` stream.
"""
yield from self._events_tracker.take_events(self.manager.take_events())
def reset_cache(self) -> None:
"""Reset the offloading manager cache, evicting all stored chunks."""
# reset_cache cannot be called in the middle of a schedule step
assert not self._current_batch_load_jobs
assert not self._current_batch_jobs_to_flush
assert not self._current_batch_allocated_block_ids
# Flush all in-flight jobs
self._current_batch_jobs_to_flush.update(self._jobs.keys())
for req_id, status in list(self._req_status.items()):
if status.req.is_finished():
del self._req_status[req_id]
# Reset offloading manager cache
self.manager.reset_cache()
# Reset store progress so active requests re-offload from chunk 0.
for status in self._req_status.values():
for group_state in status.group_states:
group_state.next_stored_chunk_idx = 0
status.transfer_jobs.clear()
# Discard jobs and save job_counter to be able to discard worker responses
self._stale_job_threshold = self._job_counter
self._jobs.clear()
self._block_id_to_pending_jobs.clear()
# The manager pool is empty; pending event payloads and announced
# reference counts are stale.
self._events_tracker.reset()
# Note: _current_batch_jobs_to_flush is intentionally NOT cleared.
# The load flush IDs collected above must be delivered to workers.
if self._chunks_being_loaded is not None:
self._chunks_being_loaded.clear()
def shutdown(self) -> None:
self.manager.shutdown()