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[P/D] layerwise connector supports DeepSeek-V3.2 sparse attention && Distribute transfer tasks to redundant kv_head cards (#5722)

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### What this PR does / why we need it?
Add new function to mooncake layerwise connector, including:
1. supports sparse attention, for DeepSeek-V3.2
2. Distribute transfer tasks to redundant kv_head cards

This PR is related to [[RFC]: CDCP Scheduling for Disaggregated
Prefilling with KV Cache Layerwise Push
Support](https://github.com/vllm-project/vllm-ascend/issues/4842)

### Does this PR introduce _any_ user-facing change?
No.

### How was this patch tested?
By CI.

- vLLM version: v0.13.0
- vLLM main:
2f4e6548ef

---------

Signed-off-by: nwpu-zxr <zhouxuerong2@huawei.com>
Signed-off-by: liziyu <liziyu16@huawei.com>
Co-authored-by: liziyu <liziyu16@huawei.com>
This commit is contained in:
zxr2333
2026-01-10 23:04:16 +08:00
committed by GitHub
parent c316679e65
commit 78b554dda9
3 changed files with 142 additions and 111 deletions
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216
vllm_ascend/distributed/mooncake_layerwise_connector.py
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@@ -162,6 +162,7 @@ class KVCacheSendingLayerThread(threading.Thread):
self.kv_caches_base_addr = kv_cache_base_addr
self.total_layers = total_layers
self.use_mla = use_mla
self.use_sparse = len(block_len) == 3
self.block_len = block_len
self._decode_tp_size = decode_tp_size
self.resharding_stream = resharding_stream
@@ -195,17 +196,6 @@ class KVCacheSendingLayerThread(threading.Thread):
src_list: list[str] = []
dst_list: list[str] = []
length_list: list[int] = []
# not need to send kv cache
if self.tp_rank % self.num_head_replica != 0:
logger.debug(
f"Cancelling KV cache transfer for request {req_id}. Reason: TP rank excluded from head replication (TP Rank: {self.tp_rank}, Replicas: {self.num_head_replica})."
)
return (src_list, dst_list, length_list)
if self.use_mla and self.tp_rank >= self._decode_tp_size:
logger.debug(
f"Cancelling KV cache transfer for request {req_id}. Reason: MLA mode active and TP rank outside decoding group (TP Rank: {self.tp_rank}, Decode TP Size: {self._decode_tp_size})."
)
return (src_list, dst_list, length_list)
layer_idx = send_task.layer_idx
remote_block_ids = req_meta.remote_block_ids
@@ -214,21 +204,36 @@ class KVCacheSendingLayerThread(threading.Thread):
local_block_ids = req_meta.local_block_ids
if self.pd_head_ratio == 1:
layer_local_kv_base_addr = [
local_kv_base_addr[i]
for i in [2 * layer_idx, 2 * layer_idx + 1]
]
layer_remote_kv_base_addr = [
remote_kv_base_addrs[i] # type:ignore
for i in [2 * layer_idx, 2 * layer_idx + 1]
]
if self.use_sparse:
layer_local_kv_base_addr = [
local_kv_base_addr[i] for i in
[3 * layer_idx, 3 * layer_idx + 1, 3 * layer_idx + 2]
]
layer_remote_kv_base_addr = [
remote_kv_base_addrs[i] # type:ignore
for i in
[3 * layer_idx, 3 * layer_idx + 1, 3 * layer_idx + 2]
]
else:
layer_local_kv_base_addr = [
local_kv_base_addr[i]
for i in [2 * layer_idx, 2 * layer_idx + 1]
]
layer_remote_kv_base_addr = [
remote_kv_base_addrs[i] # type:ignore
for i in [2 * layer_idx, 2 * layer_idx + 1]
]
grouped_remote_block_ids, grouped_local_block_ids = \
group_concurrent_contiguous(remote_block_ids, local_block_ids)
for k, (src_layer_base_addr, dst_layer_base_addr) in enumerate(
zip(layer_local_kv_base_addr, layer_remote_kv_base_addr)):
block_len = self.block_len[
k % 2] if self.use_mla else self.block_len[0]
if self.use_mla:
block_len = (self.block_len[k % 2])
elif self.use_sparse:
block_len = (self.block_len[k % 3])
else:
block_len = (self.block_len[0])
for group_remote_block_id, group_local_block_id in zip(
grouped_remote_block_ids, grouped_local_block_ids):
src = src_layer_base_addr + group_local_block_id[
@@ -931,7 +936,9 @@ class MooncakeLayerwiseConnectorWorker:
# TODO(tms): Find a more robust way to detect and handle MLA
self.use_mla = first_kv_cache_tuple[0].size(
-1) != first_kv_cache_tuple[1].size(-1)
-1) != first_kv_cache_tuple[1].size(-1) and len(
first_kv_cache_tuple) == 2
self.use_sparse = len(first_kv_cache_tuple) == 3
if self.use_mla:
# MLA case.[num_block, block_size, 1, hidden_dim]
self.num_blocks = first_kv_cache.shape[0]
@@ -945,6 +952,21 @@ class MooncakeLayerwiseConnectorWorker:
logger.info(
"num_blocks: %s, block_shape_norm: %s, block_shape_pe: %s",
self.num_blocks, block_shape_norm, block_shape_pe)
elif self.use_sparse:
self.num_blocks = first_kv_cache.shape[0]
block_rank = 3 # [block_size, latent_dim]
block_shape_norm = first_kv_cache_tuple[0].shape[-block_rank:]
block_shape_pe = first_kv_cache_tuple[1].shape[-block_rank:]
block_shape_k = first_kv_cache_tuple[2].shape[-block_rank:]
self.block_len = [
first_kv_cache[0].element_size() * math.prod(block_shape_norm),
first_kv_cache[1].element_size() * math.prod(block_shape_pe),
first_kv_cache[2].element_size() * math.prod(block_shape_k)
]
logger.info(
"num_blocks: %s, block_shape_norm: %s, block_shape_pe: %s, block_shape_k: %s",
self.num_blocks, block_shape_norm, block_shape_pe,
block_shape_k)
else:
# [num_block, block_size, num_head, hidden_dim]
self.num_blocks = first_kv_cache.shape[0]
@@ -955,8 +977,9 @@ class MooncakeLayerwiseConnectorWorker:
logger.info("num_blocks: %s, block_shape: %s", self.num_blocks,
block_shape)
logger.info("Registering KV_Caches. use_mla: %s, shape %s",
self.use_mla, first_kv_cache.shape)
logger.info(
"Registering KV_Caches. use_mla: %s, use_sparse: %s, shape %s",
self.use_mla, self.use_sparse, first_kv_cache.shape)
self.kv_caches = kv_caches
kv_caches_base_addr = []
@@ -971,9 +994,17 @@ class MooncakeLayerwiseConnectorWorker:
kv_caches_base_addr.append(base_addr)
ptrs.append(base_addr)
lengths.append(region_len)
elif self.use_sparse:
for i, cache in enumerate(cache_or_caches, 0):
base_addr = cache.data_ptr()
region_len = self.num_blocks * self.block_len[i % 3]
kv_caches_base_addr.append(base_addr)
ptrs.append(base_addr)
lengths.append(region_len)
else:
cache_list = [cache_or_caches
] if self.use_mla else cache_or_caches
cache_list = [
cache_or_caches
] if self.use_mla or self.use_sparse else cache_or_caches
for cache in cache_list:
base_addr = cache.data_ptr()
region_len = self.num_blocks * self.block_len[0]
@@ -1046,56 +1077,72 @@ class MooncakeLayerwiseConnectorWorker:
if self.vllm_config.kv_transfer_config.is_kv_producer and connector_metadata.requests.keys(
):
# enable decode prefix cache
if self.use_mla:
reshape_cache_event = attn_metadata[
layer_name].reshape_cache_event
if self.use_mla or self.use_sparse:
num_kv_head = self._decode_tp_size
else:
reshape_cache_event = attn_metadata.reshape_cache_event
num_kv_head = self.vllm_config.model_config.hf_config.num_key_value_heads
num_replica_groups = self.tp_size // num_kv_head if self.tp_size >= num_kv_head else 1
replica_group_idx = self.tp_rank % num_replica_groups
req_ids = sorted(list(connector_metadata.requests.keys()))
selected_req_ids = [
req_id for i, req_id in enumerate(req_ids)
if i % num_replica_groups == replica_group_idx
]
if selected_req_ids:
if self.use_mla or self.use_sparse:
reshape_cache_event = attn_metadata[
layer_name].reshape_cache_event
else:
reshape_cache_event = attn_metadata.reshape_cache_event
if self.pd_head_ratio != 1:
assert self.resharding_stream is not None
with npu_stream_switch(self.resharding_stream):
reshape_cache_event.wait()
rearrange_block_ids = sorted({
block_id
for request in connector_metadata.requests.values()
for block_id in request.local_block_ids
})
if self.pd_head_ratio != 1:
assert self.resharding_stream is not None
with npu_stream_switch(self.resharding_stream):
reshape_cache_event.wait()
rearrange_block_ids = sorted({
block_id
for req_id in selected_req_ids
for block_id in
connector_metadata.requests[req_id].local_block_ids
})
keys = kv_layer[0][rearrange_block_ids].clone()
values = kv_layer[1][rearrange_block_ids].clone()
# sort kv caches for each block
keys = keys.view(keys.size(0), self.pd_head_ratio, -1,
*keys.shape[2:]).transpose(
0, 1).reshape_as(keys)
values = values.view(values.size(0), self.pd_head_ratio,
-1, *values.shape[2:]).transpose(
0, 1).reshape_as(values)
# reshard kv cache
keys = keys.reshape(-1, *kv_layer[0].shape[2:])
values = values.reshape(-1, *kv_layer[1].shape[2:])
(keys, values) = kv_alltoall_and_rearrange(
self.pd_head_ratio, keys, values)
else:
keys = None
values = None
rearrange_block_ids = None
keys = kv_layer[0][rearrange_block_ids].clone()
values = kv_layer[1][rearrange_block_ids].clone()
# sort kv caches for each block
keys = keys.view(keys.size(0), self.pd_head_ratio, -1,
*keys.shape[2:]).transpose(
0, 1).reshape_as(keys)
values = values.view(values.size(0),
self.pd_head_ratio, -1,
*values.shape[2:]).transpose(
0, 1).reshape_as(values)
# reshard kv cache
keys = keys.reshape(-1, *kv_layer[0].shape[2:])
values = values.reshape(-1, *kv_layer[1].shape[2:])
(keys, values) = kv_alltoall_and_rearrange(
self.pd_head_ratio, keys, values)
else:
keys = None
values = None
rearrange_block_ids = None
assert self.kv_send_layer_thread is not None
assert reshape_cache_event is not None
send_task = SendTask(wait_event=reshape_cache_event,
k_cache=keys,
v_cache=values,
layer_idx=self.current_layer,
rearrange_block_ids=rearrange_block_ids)
for req_id, req_meta in connector_metadata.requests.items():
req_meta_update = self.update_decoder_info(req_id, req_meta)
logger.debug(
f"Add request {req_id} to kv send layer thread. {req_meta_update=}"
)
send_task.send_request[req_id] = req_meta_update
assert self.kv_send_layer_thread is not None
assert reshape_cache_event is not None
send_task = SendTask(wait_event=reshape_cache_event,
k_cache=keys,
v_cache=values,
layer_idx=self.current_layer,
rearrange_block_ids=rearrange_block_ids)
for req_id, req_meta in connector_metadata.requests.items():
if req_id in selected_req_ids:
req_meta_update = self.update_decoder_info(
req_id, req_meta)
logger.debug(
f"Add request {req_id} to kv send layer thread. {req_meta_update=}"
)
send_task.send_request[req_id] = req_meta_update
self.kv_send_layer_thread.send_queue.put(send_task)
self.kv_send_layer_thread.send_queue.put(send_task)
self.current_layer += 1
def _get_remote_socket(
@@ -1121,8 +1168,13 @@ class MooncakeLayerwiseConnectorWorker:
def update_decoder_info(self, req_id, req_meta):
req_meta_update = copy.deepcopy(req_meta)
req_meta_update.remote_port = req_meta_update.remote_port + (
self.tp_rank // self.pd_tp_ratio) % self._decode_tp_size
if self.use_mla or self.use_sparse:
pd_tp_ratio = self.tp_size // self._decode_tp_size
req_meta_update.remote_port = req_meta_update.remote_port + (
self.tp_rank // pd_tp_ratio) % self._decode_tp_size
else:
req_meta_update.remote_port = req_meta_update.remote_port + (
self.tp_rank // self.pd_tp_ratio) % self._decode_tp_size
if req_meta_update.remote_engine_id not in self.remote_kv_caches_base_addr or \
req_meta_update.remote_port not in self.remote_kv_caches_base_addr[req_meta_update.remote_engine_id]:
try:
@@ -1146,14 +1198,16 @@ class MooncakeLayerwiseConnectorWorker:
logger.info(
f"Query to port and kv base addr for request {req_id} from {req_meta_update.remote_host}:{req_meta_update.remote_port} success {agent_meta.kv_caches_base_addr=} {agent_meta.te_rpc_port=}"
)
session_id = f"{req_meta_update.remote_host}:{agent_meta.te_rpc_port}"
ret = self.engine.batch_transfer_sync_write(
session_id, [self.kv_caches_base_addr[0]],
[agent_meta.kv_caches_base_addr[0]], [128])
if ret < 0:
logger.error(
f"Mooncake transfer failed to create link to device {session_id}"
)
if self.pd_head_ratio > 1:
# for tp inequal, pre-create link to prevent alltoall out of memory
session_id = f"{req_meta_update.remote_host}:{agent_meta.te_rpc_port}"
ret = self.engine.batch_transfer_sync_write(
session_id, [self.kv_caches_base_addr[0]],
[agent_meta.kv_caches_base_addr[0]], [128])
if ret < 0:
logger.error(
f"Mooncake transfer failed to create link to device {session_id}"
)
req_meta_update.remote_te_rpc_port = self.remote_te_port[
req_meta_update.remote_engine_id][req_meta_update.remote_port]
req_meta_update.remote_kv_caches_base_addr = self.remote_kv_caches_base_addr[