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Development Guide

Environment

source <venv>/bin/activate
pip install -e .[dev]

Use the same Python environment for DaseR and vLLM integration tests so the connector imports resolve consistently.

Running vLLM and DaseR

DaseR uses a vLLM-first startup sequence. Start vllm serve with only the connector identity and the IPC socket path:

vllm serve /path/to/model \
    --port 8001 \
    --no-enable-prefix-caching \
    --kv-transfer-config '{"kv_connector":"DaserConnector","kv_connector_module_path":"daser.connector.daser_connector","kv_role":"kv_both","kv_connector_extra_config":{"socket_path":"/tmp/daser.sock"}}'

Then start DaseR:

python -m daser.server \
    --vllm-base-url http://127.0.0.1:8001 \
    --store-dir /path/to/daser-state \
    --l2-size 10gb \
    --socket-path /tmp/daser.sock \
    --host 0.0.0.0 \
    --port 2026

python -m daser.server starts both servers in one process:

  • HTTP server: FastAPI routes for health checks, document upload/list/get/delete, and /infer.
  • IPC server: Unix socket + msgpack endpoint used only by DaserConnector.

The DaseR server reads vLLM's served model id from /v1/models, derives KV geometry from the local model config.json, creates <store-dir>/daser.store, and saves metadata to <store-dir>/daser.index on SIGTERM/SIGINT shutdown. The shutdown path is a fast consistent stop: DaseR stops accepting new HTTP and IPC work, snapshots the currently committed chunk metadata and registered documents, then closes transfer resources. It does not wait for connector-side pending writes that have not reached commit_chunk.

Runtime values such as store_path, slot_size, block_tokens, and model_id are owned by DaseR and fetched by the connector over IPC. The exported L2 capacity is the slot-aligned store size, so the transfer layer does not resize daser.store back to the raw --l2-size value. If an older run left a larger raw-size store file, startup truncates it to the aligned capacity; smaller existing store files are rejected.

daser.index includes ring-buffer state, chunk metadata, and the document registry. Only documents whose register_document call completed are restored by /documents after restart. Committed chunks that were not attached to a document are retained as ordinary cache entries; if the same document content is uploaded later, matching chunk keys can be reused. Uncommitted bytes in daser.store are ignored after restart.

If vLLM exposes a non-local served model name, pass the local model path explicitly:

python -m daser.server \
    --vllm-base-url http://127.0.0.1:8001 \
    --model-path /path/to/model \
    --store-dir /path/to/daser-state
Flag Default Description
--vllm-base-url required Base URL for the vLLM OpenAI-compatible server
--model-path optional Local HuggingFace model path; required when /v1/models returns an alias
--store-dir required Directory for daser.store and daser.index
--l2-size 10 GiB L2 SSD capacity; accepts bytes or mb/gb/mib/gib and is rounded down to whole KV slots
--l1-size min(1 GiB, --l2-size) L1 pinned-memory capacity for --transfer-mode iouring; must not exceed --l2-size
--transfer-mode iouring iouring for the current pinned-memory L1 + SSD L2 path; gds remains an optional kvikio/cuFile compatibility backend and is not the current validation path
--socket-path /tmp/daser.sock IPC server Unix socket path
--host 0.0.0.0 HTTP server bind host
--port 2026 HTTP server bind port
--cache-reuse-mode chunk chunk for block-aligned document chunk reuse, prefix for rolling-prefix slot reuse

Tests

Scratch layout and cleanup

Pytest temp files (tmp_path, integration daser.store, IPC sockets) are written to a scratch root instead of /tmp:

Priority Scratch root
1 $DASER_TEST_SCRATCH_ROOT when set
2 /data/$USER/daser-test when /data exists and is writable
3 <repo>/.test-scratch on CI runners and machines without /data

Within the scratch root:

Path Purpose Cleanup
pytest/<pid>-<uuid8>/ per-session pytest --basetemp for daser.store, daser.index, sockets removed after that session finishes; stale dirs pruned after 6 hours
results/ optional artifacts you want to keep across runs preserved

Each pytest invocation uses its own basetemp subdirectory so parallel local runs do not delete another session's active temp files. Passing an explicit --basetemp on the CLI skips managed allocation and cleanup of that path.

Repo-local .pytest_cache/ is unchanged. Benchmark JSON output from --out is also preserved because it is written to a user-provided path, not the scratch root.

Override the scratch root when needed:

export DASER_TEST_SCRATCH_ROOT=/data/<user>/daser-test
pytest -q -m "not integration and not slow"

Use the test_results_dir fixture when a test needs to persist output under results/:

def test_writes_report(test_results_dir: Path) -> None:
    (test_results_dir / "report.json").write_text("{}")

Unit and Component Tests

Run the default non-integration suite before committing code changes:

pytest -q -m "not integration and not slow"

Useful focused commands:

pytest -q tests/server
pytest -q tests/connector
pytest -q tests/retrieval tests/position
pytest -q tests/server/test_main_cli.py::test_parse_size_bytes_accepts_human_readable_units

These tests use public module interfaces and lightweight stubs where possible. They do not require a running vLLM server.

Integration Tests

Integration tests require CUDA, vLLM, CuPy/kvikio, and local model weights:

CUDA_DEVICE_ORDER=PCI_BUS_ID CUDA_VISIBLE_DEVICES=0 \
pytest -q tests/integration -m integration --log-cli-level=INFO

The integration fixtures start an in-process DaseR IPCServer with a temporary store file. They exercise the vLLM connector path without requiring an external python -m daser.server process.

Benchmarks

The maintained benchmark is the service-oriented vLLM/DaseR/LMCache comparison:

python benchmarks/run_bench.py \
    --backend all \
    --dataset longbench \
    --model /path/to/model \
    --longbench-dir /path/to/longbench_data \
    --datasets 2wikimqa,hotpotqa_e \
    --store-dir /path/to/benchmark-scratch \
    --max-samples 20 \
    --max-num-seqs 64 \
    --max-inflight 64

--backend all runs baseline, lmcache, daser-chunk, and daser-prefix under one run directory. Use a specific backend name to run only one row of the matrix; --backend daser still honors --cache-reuse-mode for compatibility. --cache-reuse-mode is only passed to DaseR rows; baseline and LMCache use the same prepared prompts and record reuse_mode: none. The Python entry point prints stage separators for prepare, backend start, cold/warm load, a final == COMPARISON SUMMARY == with cold, warm, correctness, elapsed-time, and throughput fields, and the final run_root.

By default the benchmark uses --gpu-util 0.85 and --gpu-id auto, which picks the GPU with the most free memory and sets CUDA_DEVICE_ORDER=PCI_BUS_ID before CUDA libraries initialize. Each invocation creates a unique run root below --store-dir, so repeated runs do not reuse old DaseR stores or LMCache disk files.

For a quick DaseR smoke run:

python benchmarks/run_bench.py \
    --backend daser-prefix \
    --dataset longbench \
    --model /path/to/model \
    --store-dir /path/to/benchmark-scratch/smoke-run \
    --longbench-dir /path/to/longbench_data \
    --datasets triviaqa \
    --max-samples 1 \
    --max-num-seqs 1 \
    --max-inflight 1

The benchmark starts subprocess services, sends HTTP load, and writes per-backend manifest and result JSON files under the run directory. Default no-evict runs pass --skip-l2 to DaseR and LMCache so load hits are measured from L1 only; both no-evict and evict sizing cap L1 at 80% of current host MemFree. Add --evict to keep L2 enabled and exercise eviction behavior. Evict sizing keeps enough L2 capacity for the full workload while sizing L1 to 80% of the workload and above the largest single prompt. Evict runs fail fast when that L1 size would exceed the MemFree cap.

Linting and Formatting

pre-commit run --all-files
ruff check .
ruff format .
mypy .