## Summary
Fix typos and improve grammar consistency across 50 documentation files.
### Changes include:
- Spelling corrections (e.g., "Facotory" → "Factory", "certainty" →
"determinism")
- Grammar improvements (e.g., "multi-thread" → "multi-threaded",
"re-routed" → "re-run")
- Punctuation fixes (semicolon consistency in filter parameters)
- Code style fixes (correct flag name `--num-prompts` instead of
`--num-prompt`)
- Capitalization consistency (e.g., "python" → "Python", "ascend" →
"Ascend")
- vLLM version: v0.15.0
- vLLM main:
9562912cea
---------
Signed-off-by: SlightwindSec <slightwindsec@gmail.com>
6.4 KiB
Netloader Guide
This guide provides instructions for using Netloader as a weight-loader plugin for acceleration in vLLM Ascend.
Overview
Netloader leverages high-bandwidth peer-to-peer (P2P) transfers between NPU cards to load model weights. It is implemented as a plugin (via the register_model_loader API added in vLLM 0.10). The workflow is:
- A server preloads a model.
- A new client instance requests weight transfer.
- After validating that the model and partitioning match, the client uses HCCL collective communication (send/recv) to receive weights in the same order as stored in the model.
The server runs alongside normal inference tasks via sub-threads and via stateless_init_torch_distributed_process_group in vLLM. The client thus takes over weight initialization without needing to load from storage.
Flowchart
Timing Diagram
Application Scenarios
- Reduce startup latency: By reusing already loaded weights and transferring them directly between NPU cards, Netloader cuts down model loading time versus conventional remote/local pull strategies.
- Relieve network & storage load: Avoid repeated downloads of weight files from remote repositories, thus reducing pressure on central storage and network traffic.
- Improve resource utilization & lower cost: Faster loading allows less reliance on standby compute nodes; resources can be scaled up/down more flexibly.
- Enhance business continuity & high availability: In failure recovery, new instances can quickly take over without long downtime, improving system reliability and user experience.
Usage
To enable Netloader, pass --load-format=netloader and provide configuration via --model-loader-extra-config (as a JSON string). Below are the supported configuration fields:
| Field Name | Type | Description | Allowed Values / Notes |
|---|---|---|---|
| SOURCE | List | Weight data sources. Each item is a map with device_id and sources, specifying the rank and its endpoints (IP:port). Example: {"SOURCE": [{"device_id": 0, "sources": ["10.170.22.152:19374"]}, {"device_id": 1, "sources": ["10.170.22.152:11228"]}]} If omitted or empty, fallback to default loader. The SOURCE here is second priority. |
A list of objects with keys device_id: int and sources: List[str] |
| MODEL | String | The model name, used to verify consistency between client and server. | Defaults to the --model argument if not specified. |
| LISTEN_PORT | Integer | Base port for the server listener. | The actual port = LISTEN_PORT + RANK. If omitted, a random valid port is chosen. Valid range: 1024–65535. If out of range, that server instance won’t open a listener. |
| INT8_CACHE | String | Behavior for handling int8 parameters in quantized models. | One of ["hbm", "dram", "no"]. - hbm: copy original int8 parameters to high-bandwidth memory (HBM) (may cost a lot of HBM). - dram: copy to DRAM. - no: no special handling (may lead to divergence or unpredictable behavior). Default: "no". |
| INT8_CACHE_NAME | List | Names of parameters to which INT8_CACHE is applied (i.e. filtering). |
Default: None (means no filtering—all parameters). |
| OUTPUT_PREFIX | String | Prefix for writing per-rank listener address/port files in server mode. | If set, each rank writes to {OUTPUT_PREFIX}{RANK}.txt (text), content = IP:Port. |
| CONFIG_FILE | String | Path to a JSON file specifying the above configuration. | If provided, the SOURCE inside this file has first priority (overrides SOURCE in other configs). |
Example Commands & Placeholders
Replace parts in
`<...>`before running.
Server
VLLM_SLEEP_WHEN_IDLE=1 vllm serve `<model_file>` \
--tensor-parallel-size 1 \
--served-model-name `<model_name>` \
--enforce-eager \
--port `<port>` \
--load-format netloader
Client
export NETLOADER_CONFIG='{"SOURCE":[{"device_id":0, "sources": ["`<server_IP>`:`<server_Port>`"]}]}'
VLLM_SLEEP_WHEN_IDLE=1 ASCEND_RT_VISIBLE_DEVICES=`<device_id_diff_from_server>` \
vllm serve `<model_file>` \
--tensor-parallel-size 1 \
--served-model-name `<model_name>` \
--enforce-eager \
--port `<client_port>` \
--load-format netloader \
--model-loader-extra-config="${NETLOADER_CONFIG}"
Placeholder Descriptions
<model_file>: Path to the model file<model_name>: Model name (must match between server & client)<port>: Base listening port on server<server_IP>+<server_Port>: IP and port of the Netloader server (from server log)<device_id_diff_from_server>: Client device ID (must differ from server’s)<client_port>: Port on which client listens
After startup, you can test consistency by issuing inference requests with temperature = 0 and comparing outputs.
Note & Caveats
- If Netloader is used, each worker process must bind a listening port. That port may be user-specified or assigned randomly. If user-specified, ensure it is available.
- Netloader requires extra HBM memory to establish HCCL connections (i.e.
HCCL_BUFFERSIZE, default ~200 MB). Users should reserve sufficient capacity (e.g. via--gpu-memory-utilization). - It is recommended to set
VLLM_SLEEP_WHEN_IDLE=1to mitigate unstable or slow connections/transmissions. Related info: vLLM Issue #16660, vLLM PR #16226.

