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[Core] Add operator opt-out for the expandable_segments KV-connector rejection - #48243
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…rejection _verify_kv_transfer_compat conservatively rejects every KV connector when PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True is set, because connectors that pin or register KV cache memory (NixlConnector via ibv_reg_mr, MooncakeConnector) break when the CUDA VMM allocator remaps physical pages. Copy-based connectors are not affected: they move KV with CUDA kernels or cudaMemcpy at transfer time, dereferencing virtual addresses on every call, so physical remaps are transparent to them. For those deployments both sanctioned escapes are strictly worse: - Unsetting expandable_segments removes the anti-fragmentation margin, which on unified-memory platforms (e.g. GB10 / DGX Spark) directly causes CUDA OOM at previously working gpu_memory_utilization values. - enable_cumem_allocator routes the weights load through the CuMem pool as well (gpu_worker wraps both tag="weights" and tag="kv_cache"), and tens of GB of cuMemCreate RM sysmem allocations OOM the NVIDIA driver (NVRM: NV_ERR_NO_MEMORY from nv_alloc_system_pages) on unified-memory boards - observed as a full node freeze mid weight-load. Add KVTransferConfig.kv_connector_supports_expandable_segments (default False) so the operator can assert their connector is copy-based and skip the rejection. The default behavior is unchanged and the error message now mentions the opt-out. Signed-off-by: Carlos Molina <carlosmolina0615@gmail.com>
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Purpose
FIX #48242
_verify_kv_transfer_compatconservatively rejects every KV connector underPYTORCH_CUDA_ALLOC_CONF=expandable_segments:True. That is correct for connectors that pin/register KV cache memory (NIXLibv_reg_mr, Mooncake), but copy-based connectors (CUDA kernels /cudaMemcpyat transfer time, e.g. the LMCache connectors) dereference virtual addresses on every transfer and are unaffected by VMM physical remaps.Both sanctioned escapes are harmful on unified-memory platforms (GB10/DGX Spark): dropping
expandable_segmentscosts the anti-fragmentation margin (CUDA OOM at previously-workinggpu_memory_utilization), andenable_cumem_allocatorroutes the weights load through the CuMem pool too (gpu_worker.pywraps bothtag="weights"andtag="kv_cache"), which pins ~weights-sized RM sysmem and hard-crashed both nodes mid weight-load (NVRM: NV_ERR_NO_MEMORYfromnv_alloc_system_pages). Details and evidence in the linked issue.This PR adds
KVTransferConfig.kv_connector_supports_expandable_segments(defaultFalse— behavior unchanged): an explicit operator assertion that the configured connector never pins/registers KV cache device memory, which skips the rejection. The error message now mentions the opt-out.Test Plan
New tests:
test_kv_connector_allows_expandable_segments_with_operator_opt_in(LMCacheMPConnector / LMCacheConnectorV1 pass with the opt-in)test_kv_connector_opt_in_defaults_off(without the opt-in the ValueError still fires)Existing rejection/exemption tests unchanged and still passing.
Test Result
All 10
_verify_kv_transfer_compattests in the file pass (3 rejection params, sleep-mode/cumem/other-alloc-conf/no-connector exemptions, 2 new opt-in params, 1 new default-off), verified by applying the same patch to a vLLM 0.23.1 install (function identical to main) and running the updated test file:(The unrelated
test_kv_connector[...]offloading-translation cases in the same file require the cpu_test CI image and were failing identically before this change in the local environment.)