A single cudaHostRegister over a >~1 TB host buffer fails with cudaErrorMemoryAllocation on B300 (hard per-call ceiling: 512 GiB OK, 1024 GiB FAIL), crashing the hicache_size=1600 (~1.5 TB CP shared-L2 slab) prefill at startup. The registration cannot simply be chunked: a memcpy (cudaMemcpyBatchAsync, the CP-L2 H2D/D2H transfer) fails with cudaErrorInvalidValue when its host range straddles a registration boundary (verified empirically on b300-049). Fix: physically split the host cache into multiple page-aligned slabs, each <= a safe single-registration size (default 480 GiB, env SGLANG_CP_HICACHE_MAX_SLAB_GB), reusing the existing SharedHostTensorGroupAllocator + per-slab transfer splitting (_host_transfer_segments). Each slab is one whole registration and no transfer crosses a boundary; small configs (hicache_size<=400) stay single-slab unchanged. - memory_pool_host.py: add cp_hicache_max_single_register_bytes() + the fail-loud _check_single_cuda_host_register_size guard; revert the (transfer-unsafe) registration chunking back to one cudaHostRegister per buffer/slab. - hiradix_cache.py: _cp_shared_l2_slab_pages_by_payload auto-caps each payload's slab <= the ceiling so large caches auto-split. - cp_l3_slab_accessor.py: CpSharedL2SlabAccessor is now slab-count-aware (CpL3SlabSpan + per-slab dispatch via global_base_page; per-slab layer stride); _cp_l3_slab_spans rewires _maybe_init_cp_l3 off the single-slab assumption. L3 disk slabs / slot pool / LMDB index / GC are content-addressed and unchanged. Tests: multi-slab accessor incl. a non-circular torch.frombuffer-layout check; a real allocate_group + _cp_l3_slab_spans roundtrip; slab-cap auto-split; L3 store cross-slab spill/reload. Reviewed by 3 adversarial agents, no correctness bugs. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Unit Tests
Component-level tests that do not launch a server or load model weights. Tests can use CPU or GPU — the key criterion is no server process.
Quick Start
- Find the source file under
python/sglang/srt/. - Create the corresponding test here, mirroring the source tree:
srt/mem_cache/radix_cache.py → unit/mem_cache/test_radix_cache.py srt/sampling/sampling_params.py → unit/sampling/test_sampling_params.py - Register for CI at the top of the file (after imports, before test classes):
from sglang.test.ci.ci_register import register_cpu_ci register_cpu_ci(est_time=5, suite="stage-a-test-cpu") # or: register_cuda_ci(est_time=10, suite="stage-b-test-1-gpu-small") - Run locally:
pytest test/registered/unit/ -v # all unit tests pytest test/registered/unit/mem_cache/ -v # one module - Run with coverage:
# summary pytest test/registered/unit/ --cov --cov-config=.coveragerc -v # PR incremental check (require ≥60% on changed lines) pytest test/registered/unit/ --cov --cov-config=.coveragerc --cov-report=xml diff-cover coverage.xml --compare-branch=origin/main --fail-under=60
Example
"""Unit tests for <module> — no server, no model loading."""
from sglang.test.ci.ci_register import register_cpu_ci
register_cpu_ci(est_time=5, suite="stage-a-test-cpu")
import unittest
from sglang.srt.<module> import TargetClass
from sglang.test.test_utils import CustomTestCase
class TestTargetClass(CustomTestCase):
def test_basic_behavior(self):
obj = TargetClass(...)
self.assertEqual(obj.method(), expected)
if __name__ == "__main__":
unittest.main()
Rules
- No
popen_launch_server()orEngine(...). - No model weight loading.
- Use
CustomTestCase(fromsglang.test.test_utils, adds CI retry). - Use
unittest.mockfor dependencies that are expensive to construct.