46e79d63e7
- platforms/ascend_910c.env: 8-card 910C config (16 dies, 64GB HBM/die), Ascend Docker Runtime, ASCEND_VISIBLE_DEVICES device selection - scripts/common/platform.sh: auto-detect 910C via npu-smi + Huawei PCI IDs - scripts/common/npu_smi_sampler.py: standalone npu-smi -> nvidia-smi CSV sampler so parse_backend.py needs no changes - experiments/910c/glm52_910c_vllm_tp_dp_matrix/: GLM-5.2 (w4a8c8) experiment, model present on host, ready for smoke after image load - experiments/910c/dsv4_910c_vllm_tp_dp_matrix/: DSV4-Flash experiment (placeholder MODEL_PATH, weights not yet downloaded) - envs/ASCEND_910C_ENV_SETUP.md: full onboarding guide (permissions, image load, Ascend Docker Runtime, NPU monitor, known pitfalls) - Both experiments: TP2/DP4 + TP4/DP2 + TP8/DP1, matrix.json capped at 128K context per 64GB HBM/die
89 lines
3.6 KiB
Bash
89 lines
3.6 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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# ⚠️ PLACEHOLDER: DeepSeek-V4-Flash weights are NOT yet present on this host
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# (only GLM-5.2-{w4a8c8,w8a8} under /mnt/models). Download DSV4-Flash first,
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# then fix MODEL_PATH below. Smoke run will fail until the model exists.
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# Recommended host path: /mnt/models/DeepSeek-V4-Flash (FP8)
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# /mnt/models/DeepSeek-V4-Flash-INT8 (INT8)
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EXPERIMENT="dsv4_910c_vllm_tp_dp_matrix"
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MODEL_NAME="DeepSeek-V4-Flash"
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# TODO: point this at the real DSV4-Flash directory once downloaded.
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MODEL_PATH="${MODEL_PATH:-/mnt/models/DeepSeek-V4-Flash}"
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SERVED_MODEL_NAME="deepseek-v4-flash"
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VLLM_PORT="${VLLM_PORT:-30052}"
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# Dedicated container name so this experiment never touches other 910c runs.
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CONTAINER_NAME="${CONTAINER_NAME:-vllm-ascend-dsv4-910c}"
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# Python interpreter for the benchmark client inside the vllm-ascend container.
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CONTAINER_PYTHON="${CONTAINER_PYTHON:-/usr/local/bin/python}"
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# vllm-ascend image. Use the general v0.23 image for DSV4 (the GLM5.2-tuned
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# variant may carry GLM-specific patches). Load from:
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# /mnt/models/vllm-ascend-v0.23.0rc1-a3-openeuler.tar
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USE_DOCKER="${USE_DOCKER:-1}"
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DOCKER_IMAGE="${DOCKER_IMAGE:-vllm-ascend:v0.23.0rc1-a3-openeuler}"
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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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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_BASE="${RUNTIME_BASE:-${SCRIPT_DIR}/runtime}"
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# Parallel configurations to test. Format: "TP DP"
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# DSV4-Flash FP8 routed-expert weights ~264 GiB total. Per-die load = 264/TP GiB.
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# TP=2 -> 132 GiB/die > 64 GiB HBM ❌ OOM expected (kept but will fail)
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# TP=4 -> 66 GiB/die > 64 GiB HBM ❌ borderline OOM (KV cache leaves no room)
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# TP=8 -> 33 GiB/die < 64 GiB HBM ✅ fits with room for KV cache
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# TP=2/DP=4 and TP=4/DP=2 are likely infeasible for FP8 on 64GB dies; they are
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# kept here so the smoke run records the OOM boundary explicitly. Switch to
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# INT8 weights (MODEL_PATH=...-INT8, ~132 GiB total) to make TP=4 viable.
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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.
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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="${BLOCK_SIZE:-128}"
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# DSV4-Flash supports up to 1M context, but 64GB HBM/die caps realistic ISL.
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# Start at 128K; extend via matrix.json once TP=8 is verified.
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MAX_MODEL_LEN="${MAX_MODEL_LEN:-131072}"
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MAX_NUM_SEQS="${MAX_NUM_SEQS:-256}"
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VLLM_ASCEND_ATTENTION_BACKEND="${VLLM_ASCEND_ATTENTION_BACKEND:-atb}"
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DATASET_PATH="${DATASET_PATH:-${ROOT_DIR}/datasets/ShareGPT_V3_unfiltered_cleaned_split.json}"
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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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