d6e00d61dc
Sync the glm52 add16 fixes (98cdb67) into the DSV4 experiment so the
adaptive concurrency search can actually run end-to-end.
sglang client (the main blocker):
- Built local/vllm-ascend:0.23-a3-dsv4-sglang image: sglang 0.5.2 (not
0.5.16 -- 0.5.16 deprecates bench_serving and the glm52 parser fix
targets the 0.5.2 output format) + minimal deps (ipython/traitlets/
stack_data/executing/asttokens/pure_eval/prompt_toolkit/wcwidth) via
--no-deps, so the vllm env is untouched.
- Verified: python -m sglang.bench_serving --help works in the image.
- config.env DOCKER_IMAGE -> local/vllm-ascend:0.23-a3-dsv4-sglang.
config.env (sync glm52 98cdb67):
- CONTAINER_NAME: drop ${...:-} override -> fixed value (avoids the
double-suffix bug where CONTAINER_NAME already carries _tpX_dpY).
- CONTAINER_PYTHON: /usr/local/bin/python (does not exist) ->
/usr/local/python3.12.13/bin/python3 (matches glm52 fix).
run_adaptive_concurrency_add16.sh (sync glm52 98cdb67):
- --model $SERVED_MODEL_NAME -> --tokenizer $MODEL_PATH (bench_serving
0.5.2 wants the tokenizer path).
- docker exec env: add TORCH_DEVICE_BACKEND_AUTOLOAD=0 so the client
does not try to autoload torch_npu.
- export ENGINE_TP/ENGINE_DP in engine_start_server + export line;
container_name uses ${ENGINE_TP:-${tp}} (the bench runs in a subshell
where tp/dp are not in scope).
start_vllm_docker.sh (sync glm52 98cdb67):
- Health timeout configurable via HEALTH_MAX_RETRIES /
HEALTH_RETRY_INTERVAL_S (default 480x5s=40min; TP=16 compiles 16
graphs ~60min, old hardcoded 360x10s was too rigid).
- Container name: drop the double-suffix (CONTAINER_NAME no longer
re-overridden before appending _tpX_dpY).
- Mount /mnt (bench client reads dataset from there).
129 lines
6.4 KiB
Bash
129 lines
6.4 KiB
Bash
# TP×DP matrix experiment for DeepSeek-V4-Flash on Ascend 910C (8 NPUs / 16 dies)
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# using vLLM-Ascend.
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# Tests vLLM with three parallel configurations:
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# TP=2, DP=4 -> 2 dies per replica, 4 replicas
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# TP=4, DP=2 -> 4 dies per replica, 2 replicas
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# TP=8, DP=1 -> 8 dies, no data parallelism
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#
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# Platform: ascend_910c (see platforms/ascend_910c.env).
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# Host: 910c.1 / NPU-NODE61, openEuler 22.03 SP4 aarch64, driver 25.5.2, CANN 9.0.0.
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#
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# DeepSeek-V4-Flash w8a8-mtp weights are present at /mnt/models/DeepSeek-V4-Flash-w8a8-mtp
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# (downloaded from ModelScope Eco-Tech/DeepSeek-V4-Flash-w8a8-mtp, ~280 GiB, 70 shards).
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EXPERIMENT="dsv4_910c_vllm_tp_dp_matrix"
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MODEL_NAME="DeepSeek-V4-Flash"
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# Real DSV4-Flash w8a8-mtp directory (verified present & chown'd to shishi).
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MODEL_PATH="${MODEL_PATH:-/mnt/models/DeepSeek-V4-Flash-w8a8-mtp}"
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SERVED_MODEL_NAME="dsv4"
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VLLM_PORT="${VLLM_PORT:-30052}"
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# Dedicated container name so this experiment never touches other 910c runs.
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CONTAINER_NAME="vllm-ascend-dsv4-910c"
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# Python interpreter for the benchmark client inside the vllm-ascend container.
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CONTAINER_PYTHON="/usr/local/python3.12.13/bin/python3"
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# vllm-ascend image. Use the general v0.23 A3 image for DSV4 (the GLM5.2-tuned
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# variant carries GLM-specific patches and is NOT compatible with DSV4).
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# Verified present locally: quay.io/ascend/vllm-ascend:v0.23.0rc1-a3-openeuler
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USE_DOCKER="${USE_DOCKER:-1}"
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DOCKER_IMAGE="${DOCKER_IMAGE:-local/vllm-ascend:0.23-a3-dsv4-sglang}"
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# Benchmark client Docker image. vLLM's image does not include sglang.bench_serving;
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# reuse the vllm-ascend container itself for the client via `docker exec` (see
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# run_adaptive_concurrency_add16.sh), so this is only used if USE_DOCKER_CLIENT=1
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# with an external sglang image. Default off on 910c.
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DOCKER_CLIENT_IMAGE="${DOCKER_CLIENT_IMAGE:-lmsysorg/sglang:latest}"
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USE_DOCKER_CLIENT="${USE_DOCKER_CLIENT:-0}"
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# Device selection. We mount all 8 cards (16 dies) via --device /dev/davinci0..15
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# in start_vllm_docker.sh. ASCEND_VISIBLE_DEVICES is kept for parity with the
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# shared library but the explicit --device flags are the authoritative path on
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# this host (Ascend Docker Runtime injection was unreliable here).
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export ASCEND_VISIBLE_DEVICES="${ASCEND_VISIBLE_DEVICES:-0,1,2,3,4,5,6,7}"
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export CUDA_VISIBLE_DEVICES="${CUDA_VISIBLE_DEVICES:-0,1,2,3,4,5,6,7}"
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# Runtime working directory for logs, pid files, and tmp.
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RUNTIME_BASE="${RUNTIME_BASE:-${SCRIPT_DIR}/runtime}"
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# Parallel configurations to test. Format: "TP DP"
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# DSV4-Flash w8a8 routed-expert weights ~280 GiB total. With expert-parallel the
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# weight load is sharded across TP dies, but w8a8 still leaves a heavy per-die
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# footprint. TP*DP must equal 16 (8 cards x 2 dies).
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# TP=2, DP=4 -> 4 dies/replica x 4 replicas (smallest TP, most replicas)
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# TP=4, DP=2 -> 4 dies/replica x 2 replicas
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# TP=8, DP=1 -> 8 dies/replica x 1 replica (largest TP, max weight sharding)
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if [[ -n "${PARALLEL_CONFIGS_STR:-}" ]]; then
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declare -a PARALLEL_CONFIGS=()
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for pair in $PARALLEL_CONFIGS_STR; do
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PARALLEL_CONFIGS+=("${pair//,/ }")
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done
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else
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declare -a PARALLEL_CONFIGS=(
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"2 4"
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"4 2"
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"8 1"
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)
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fi
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# vLLM-Ascend server settings for DSV4-Flash (w8a8-mtp).
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# Notes:
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# - KV cache dtype fp8 is supported on 910C; fall back to fp16 if the image rejects it.
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# - block-size 128 matches Ascend page semantics (910C favors 128).
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# - MAX_MODEL_LEN: DSV4-Flash supports up to 1M context; cap at 131072 for the
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# matrix sweep (extend to 1M via matrix.json once TP=8 is verified).
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# - gpu-memory-utilization maps to NPU HBM fraction on vllm-ascend (0.9).
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GPU_MEMORY_UTILIZATION="${GPU_MEMORY_UTILIZATION:-0.9}"
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KV_CACHE_DTYPE="${KV_CACHE_DTYPE:-fp8}"
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BLOCK_SIZE="${BLOCK_SIZE:-128}"
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MAX_MODEL_LEN="${MAX_MODEL_LEN:-131072}"
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MAX_NUM_SEQS="${MAX_NUM_SEQS:-256}"
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# vLLM-Ascend-specific launch flags injected by start_vllm_docker.sh.
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# attention backend for 910C: use the fused/atb attention path. Adjust per image.
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VLLM_ASCEND_ATTENTION_BACKEND="${VLLM_ASCEND_ATTENTION_BACKEND:-atb}"
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# DSV4-Flash-specific server args (passed through to vllm serve in
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# start_vllm_docker.sh). These are REQUIRED for DSV4 -- GLM-5.2 does not need them.
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# --tokenizer-mode deepseek_v4 : DSV4 custom tokenizer
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# --tool-call-parser deepseek_v4 : DSV4 tool-call parser
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# --enable-auto-tool-choice : enable automatic tool choice
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# --reasoning-parser deepseek_v4 : DSV4 reasoning parser
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# --enable-expert-parallel : shard 256 experts across EP ranks
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# --quantization ascend : use modelslim w8a8 quantization path
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# --safetensors-load-strategy prefetch: prefetch shards for faster load
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# --async-scheduling : overlap CPU scheduling with NPU compute
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# MTP speculative decoding via SPEC_CONFIG
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DSV4_TOKENIZER_MODE="${DSV4_TOKENIZER_MODE:-deepseek_v4}"
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DSV4_TOOL_CALL_PARSER="${DSV4_TOOL_CALL_PARSER:-deepseek_v4}"
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DSV4_REASONING_PARSER="${DSV4_REASONING_PARSER:-deepseek_v4}"
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DSV4_QUANTIZATION="${DSV4_QUANTIZATION:-ascend}"
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DSV4_SAFETENSORS_LOAD_STRATEGY="${DSV4_SAFETENSORS_LOAD_STRATEGY:-prefetch}"
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# MTP speculative config (1 speculative token). JSON string, kept single-quoted
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# in the launcher to avoid shell mangling.
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DSV4_SPEC_CONFIG="${DSV4_SPEC_CONFIG:-{\"num_speculative_tokens\": 1, \"method\": \"mtp\", \"enforce_eager\": true}}"
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DSV4_COMPILATION_CONFIG="${DSV4_COMPILATION_CONFIG:-{\"cudagraph_mode\": \"FULL_DECODE_ONLY\"}}"
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DSV4_ADDITIONAL_CONFIG="${DSV4_ADDITIONAL_CONFIG:-{\"ascend_compilation_config\":{\"enable_npugraph_ex\":true,\"enable_static_kernel\":false},\"enable_cpu_binding\": true,\"enable_dsa_cp\": true,\"multistream_overlap_shared_expert\":true}}"
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DSV4_ENABLE_EXPERT_PARALLEL="${DSV4_ENABLE_EXPERT_PARALLEL:-1}"
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DSV4_ENABLE_ASYNC_SCHEDULING="${DSV4_ENABLE_ASYNC_SCHEDULING:-1}"
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# Model-loader extra config (multithread load, 128 threads) to speed up 280GiB load.
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DSV4_MODEL_LOADER_EXTRA_CONFIG="${DSV4_MODEL_LOADER_EXTRA_CONFIG:-{\"enable_multithread_load\": \"true\", \"num_threads\": 128}}"
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# Dataset used by sglang.bench_serving --dataset-name random.
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DATASET_PATH="${DATASET_PATH:-${ROOT_DIR}/datasets/ShareGPT_V3_unfiltered_cleaned_split.json}"
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# Matrix and concurrency rules are defined in matrix.json by default.
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MATRIX_FILE="${MATRIX_FILE:-${SCRIPT_DIR:-.}/matrix.json}"
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MATRIX_MODE="${MATRIX_MODE:-Y}"
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export CONCURRENCY_SAMPLES="${CONCURRENCY_SAMPLES:-2}"
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SCENARIO_TIMEOUT_S="${SCENARIO_TIMEOUT_S:-1800}"
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GPU_MEM_SAMPLE_INTERVAL_S="${GPU_MEM_SAMPLE_INTERVAL_S:-1}"
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DRY_RUN="${DRY_RUN:-0}"
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GRID_LIMIT="${GRID_LIMIT:-0}"
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