Files
sglang/test/registered/unit
laoyao0822 bc23a81884 Overlap CP shared KV prefix materialization for cached MLA prefill
Shared CP KV materialization remained on the critical path for cached
NSA/MLA prefill batches.  This change introduces a one-layer-ahead
prefetcher that materializes the cached prefix for the next layer on a
separate CUDA stream and consumes it when that layer reaches attention.
The prefetch path keeps the existing dense page-table semantics, defers
waiting until the prefetched buffer is actually consumed, and uses the
TAI optimized materialize/remap helpers when enabled before falling back
to the torch implementation.

The implementation is intentionally gated by environment variables and
keeps layer-2-only probe logging for functional confirmation without
making normal profiling noisy.

Constraint: Prefill CP shared KV must preserve existing page-table and dense KV semantics for NSA paged topk attention
Constraint: The production performance path requires SGLANG_CP_SHARED_KV_USE_TAI_MATERIALIZE=1 and logging disabled
Rejected: Wait immediately after the producer layer attention | this truncated the overlap window and hid less work
Rejected: Torch-only prefetch materialize | it bypassed the optimized TAI materialize/remap path and could erase the expected win
Confidence: medium
Scope-risk: moderate
Directive: Do not evaluate Phase8 throughput with SGLANG_CP_SHARED_KV_LOG_MLA_PREFETCH=1; use it only to confirm create/start/consume_hit behavior
Tested: Local AST parse for modified Python files
Tested: Local git diff --check
Tested: Remote g0034 container AST parse for modified files under /sgl-workspace/sglang-tai
Tested: Remote g0034 container pytest target covering Phase8 log env, TAI range materialize, optimized slot inverse/remap, and existing token TAI path
Not-tested: Full prefill/decode/router throughput after the TAI prefetch-path fix
2026-05-03 03:09:59 +08:00
..

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

  1. Find the source file under python/sglang/srt/.
  2. 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
    
  3. 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")
    
  4. Run locally:
    pytest test/registered/unit/ -v            # all unit tests
    pytest test/registered/unit/mem_cache/ -v  # one module
    
  5. 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() or Engine(...).
  • No model weight loading.
  • Use CustomTestCase (from sglang.test.test_utils, adds CI retry).
  • Use unittest.mock for dependencies that are expensive to construct.