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[Nixl] Heterogeneous TP support FlashInfer (#20189)

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Signed-off-by: NickLucche <nlucches@redhat.com>
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Nicolò Lucchesi 2025-09-03 09:19:54 +02:00 committed by GitHub
parent f38035c123
commit f0c503f66e
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1 changed files with 53 additions and 9 deletions
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62
vllm/distributed/kv_transfer/kv_connector/v1/nixl_connector.py
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@ -715,7 +715,7 @@ class NixlConnectorWorker:
# are non-contiguous (it's not locally guaranteed that they will be)
# Disadvantage is that the encoded NixlAgentMetadata is now larger
# (roughly 8KB vs 5KB).
# Conversely for FlashInfer, K and V are transferred in the same tensor
# Conversely for FlashInfer, K and V are registered in the same region
# to better exploit the memory layout (ie num_blocks is the first dim).
split_k_and_v = not (self.use_mla or self._use_pallas_v1
or self._use_flashinfer)
@ -758,12 +758,21 @@ class NixlConnectorWorker:
assert tensor_size_bytes % self.num_blocks == 0
self.block_len = tensor_size_bytes // self.num_blocks
self.slot_size_bytes = self.block_len // self.block_size
self.device_kv_caches = kv_caches
self.dst_num_blocks[self.engine_id] = self.num_blocks
if self._use_flashinfer:
assert self.slot_size_bytes % 2 == 0
self.slot_size_bytes /= 2
self.device_kv_caches = kv_caches
self.dst_num_blocks[self.engine_id] = self.num_blocks
# NOTE (NickLucche) When FlashInfer is used, memory is registered
# with joint KV for each block. This minimizes the overhead in
# registerMem allowing faster descs queries. In order to be able to
# split on kv_heads dim as required by heterogeneous TP, one must
# be able to index K/V separately. Hence the we double the number
# of 'virtual' regions here and halve `block_len` below.
self.num_regions *= 2
kv_block_len = self.get_backend_aware_kv_block_len()
# Register local/src descr for NIXL xfer.
blocks_data = []
for base_addr in seen_base_addresses:
@ -776,8 +785,18 @@ class NixlConnectorWorker:
block_offset = block_id * self.block_len
addr = base_addr + block_offset
# (addr, len, device id)
# TODO: does device_id matter to DRAM?
blocks_data.append((addr, self.block_len, self.tp_rank))
blocks_data.append((addr, kv_block_len, self.tp_rank))
if self._use_flashinfer:
# Separate and interleave K/V regions to maintain the same
# descs ordering. This is needed for selecting contiguous heads
# when split across TP ranks.
for block_id in range(self.num_blocks):
block_offset = block_id * self.block_len
addr = base_addr + block_offset
# Register addresses for V cache (K registered first).
v_addr = addr + kv_block_len
blocks_data.append((v_addr, kv_block_len, self.tp_rank))
logger.debug("Created %s blocks for src engine %s and rank %s",
len(blocks_data), self.engine_id, self.tp_rank)
@ -903,7 +922,7 @@ class NixlConnectorWorker:
remote_block_size = nixl_agent_meta.block_len // (
self.slot_size_bytes * tp_ratio)
if self._use_flashinfer:
# Account for joint KV in FlashInfer.
# With flashinfer, KV are sent in the same message.
remote_block_size //= 2
if tp_ratio > 1:
# Heterogeneous TP expects same kv_cache_layout.
@ -929,10 +948,10 @@ class NixlConnectorWorker:
# rank. With heterogeneous TP, prepare the descriptors by splitting the
# P KV cache along kv_head dim, of D worker's kv_head size (D>P).
# Eg. PTP1 DTP2 => P0 KV:[block0-KV_0 | block0-KV_1..].
# Only register the remote's descriptors if current rank pulls from it.
self.kv_caches_base_addr[
engine_id] = nixl_agent_meta.kv_caches_base_addr
rank_offset = self.tp_rank % tp_ratio * self.block_len \
kv_block_len = self.get_backend_aware_kv_block_len()
rank_offset = self.tp_rank % tp_ratio * kv_block_len \
if not (self.use_mla or is_kv_replicated) else 0
# Register all remote blocks, but only the corresponding kv heads.
for base_addr in nixl_agent_meta.kv_caches_base_addr:
@ -943,7 +962,16 @@ class NixlConnectorWorker:
# self.block_len == remote_block_len//tp_ratio bytes.
addr = base_addr + block_offset + rank_offset
# (addr, len, device id)
blocks_data.append((addr, self.block_len, remote_tp_rank))
blocks_data.append((addr, kv_block_len, remote_tp_rank))
if self._use_flashinfer:
# With FlashInfer index V separately to allow head splitting.
for block_id in range(nixl_agent_meta.num_blocks):
block_offset = block_id * nixl_agent_meta.block_len
addr = base_addr + block_offset + rank_offset
v_addr = addr + nixl_agent_meta.block_len // 2
blocks_data.append((v_addr, kv_block_len, remote_tp_rank))
logger.debug(
"Created %s blocks for dst engine %s with remote rank %s and "
"local rank %s", len(blocks_data), engine_id, remote_tp_rank,
@ -1249,6 +1277,22 @@ class NixlConnectorWorker:
descs_ids.append(reg_id * num_blocks + block_id)
return descs_ids
def get_backend_aware_kv_block_len(self):
"""
Get the block length for one K/V element (K and V have the same size).
For FA and other backends, this is equal to the length of the whole
block, as K and V are in separate regions.
For FlashInfer, this is half the length of the whole block, as K and V
share the same region.
"""
if self._use_flashinfer:
# For indexing only half (either just the K or V part).
block_len = self.block_len // 2
else:
block_len = self.block_len
return block_len
@contextlib.contextmanager
def zmq_ctx(socket_type: Any, addr: str) -> Iterator[zmq.Socket]: