shishi 46e79d63e7 feat(platform): add Ascend 910C NPU platform support

- 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

2026-07-27 22:00:05 +08:00

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Platform Configurations

Each .env file in this directory describes one accelerator platform. They are meant to be sourced by benchmark scripts through scripts/common/platform.sh, not executed directly.

Usage

# Default platform for the current machine
bash experiments/dsv4_p800_sglang/run_bench.sh

# Explicitly select a platform
PLATFORM=kunlun_p800 bash experiments/dsv4_p800_sglang/run_bench.sh

Current platforms

File	Chip/Accelerator	Engine	Notes
`ascend_910c.env`	Huawei Ascend 910C	`vllm-ascend`	Docker-based，8 卡 16 die / 64GB HBM；Ascend Docker Runtime 为默认 runtime，见 `envs/ASCEND_910C_ENV_SETUP.md`
`kunlun_p800.env`	Kunlun P800 XPU	`sglang-xpu`	Docker-based SGLang serving image
`nvidia_h200.env`	NVIDIA H200	`vllm-dspark`	Native host virtual environments
`nvidia_h20.env`	NVIDIA H20	`vllm` / `sglang`	Docker-based（vllm-openai / sglang 官方镜像）
`nvidia_rtx6000d.env`	NVIDIA RTX 6000D	`vllm` / `sglang`	Docker-based，SM120 部署见 `envs/SM120_DSV4_DEPLOYMENT_GUIDE.md`

What belongs here

Chip/accelerator identity (CHIP, ACCELERATOR, HARDWARE, ENGINE).
Device selection environment variables.
Platform-wide paths that rarely change (model root, default port).
Container image / interpreter paths for Docker-based platforms.
Native interpreter / venv paths for host-based platforms.

What does NOT belong here

Specific model names or experiment scenarios — those go in experiments/<name>/config.env.
Engine-specific launch flags — those go in the experiment's start_server.sh or run_bench.sh.

Adding a new platform

Create platforms/<chip>.env with at least CHIP, ACCELERATOR, HARDWARE, ENGINE, DEFAULT_PORT, MODEL_ROOT.
If the platform runs inside Docker, set DOCKER_IMAGE, CONTAINER_NAME, CONTAINER_PYTHON, and PATCH_ROOT (see kunlun_p800.env).
If the platform runs natively on the host, set the relevant venv paths (see nvidia_h200.env).
Add a row to the table above and write a quick-start experiment under experiments/<name>/.

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