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[launcher] Add elastic and fault-tolerant training support (#8286)

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Signed-off-by: Butui Hu <hot123tea123@gmail.com>
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Butui Hu 2025-06-05 16:40:03 +08:00 committed by GitHub
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3 changed files with 66 additions and 18 deletions
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8
examples/README.md
View File

@ -165,6 +165,14 @@ FORCE_TORCHRUN=1 NNODES=2 NODE_RANK=0 MASTER_ADDR=192.168.0.1 MASTER_PORT=29500
FORCE_TORCHRUN=1 NNODES=2 NODE_RANK=1 MASTER_ADDR=192.168.0.1 MASTER_PORT=29500 llamafactory-cli train examples/train_full/llama3_full_sft.yaml
```
### Elastic and Fault-Tolerant Supervised Fine-Tuning on Multiple Nodes
To launch an elastic job with `MAX_RESTARTS` failures retries, run the following on at least `MIN_NNODES` nodes and at most `MAX_NNODES` nodes. `RDZV_ID` should be set as a unique job id (shared by all nodes participating in the job). See also [torchrun](https://docs.pytorch.org/docs/stable/elastic/run.html).
```bash
FORCE_TORCHRUN=1 MIN_NNODES=1 MAX_NNODES=3 MAX_RESTARTS=3 RDZV_ID=llamafactory MASTER_ADDR=192.168.0.1 MASTER_PORT=29500 llamafactory-cli train examples/train_full/llama3_full_sft.yaml
```
#### Multimodal Supervised Fine-Tuning
```bash

8
examples/README_zh.md
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@ -106,6 +106,14 @@ FORCE_TORCHRUN=1 NNODES=2 NODE_RANK=0 MASTER_ADDR=192.168.0.1 MASTER_PORT=29500
FORCE_TORCHRUN=1 NNODES=2 NODE_RANK=1 MASTER_ADDR=192.168.0.1 MASTER_PORT=29500 llamafactory-cli train examples/train_lora/llama3_lora_sft.yaml
```
### 支持弹性和容错的多机指令监督微调
要启动一个支持弹性节点和容错的多机指令微调，在每个节点上执行以下命令。弹性节点数量范围为 `MIN_NNODES:MAX_NNODES`，每个节点最多允许因为错误重启 `MAX_RESTARTS` 次。`RDZV_ID` 应设置为一个唯一的作业 ID（由参与该作业的所有节点共享）。更多新可以参考官方文档 [torchrun](https://docs.pytorch.org/docs/stable/elastic/run.html)。
```bash
FORCE_TORCHRUN=1 MIN_NNODES=1 MAX_NNODES=3 MAX_RESTARTS=3 RDZV_ID=llamafactory MASTER_ADDR=192.168.0.1 MASTER_PORT=29500 llamafactory-cli train examples/train_full/llama3_full_sft.yaml
```
#### 使用 DeepSpeed ZeRO-3 平均分配显存
```bash

68
src/llamafactory/cli.py
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@ -18,6 +18,8 @@ import sys
from copy import deepcopy
from functools import partial
from .hparams import get_train_args
USAGE = (
"-" * 70
@ -76,7 +78,11 @@ def main():
command = sys.argv.pop(1) if len(sys.argv) > 1 else "help"
if command == "train" and (is_env_enabled("FORCE_TORCHRUN") or (get_device_count() > 1 and not use_ray())):
# launch distributed training
max_restarts = os.getenv("MAX_RESTARTS", "0")
rdzv_id = os.getenv("RDZV_ID")
nnodes = os.getenv("NNODES", "1")
min_nnodes = os.getenv("MIN_NNODES")
max_nnodes = os.getenv("MAX_NNODES")
node_rank = os.getenv("NODE_RANK", "0")
nproc_per_node = os.getenv("NPROC_PER_NODE", str(get_device_count()))
master_addr = os.getenv("MASTER_ADDR", "127.0.0.1")
@ -91,25 +97,51 @@ def main():
env["PYTORCH_CUDA_ALLOC_CONF"] = "expandable_segments:True"
env["TORCH_NCCL_AVOID_RECORD_STREAMS"] = "1"
# NOTE: DO NOT USE shell=True to avoid security risk
process = subprocess.run(
(
"torchrun --nnodes {nnodes} --node_rank {node_rank} --nproc_per_node {nproc_per_node} "
"--master_addr {master_addr} --master_port {master_port} {file_name} {args}"
if rdzv_id is not None:
# launch elastic job with fault tolerant support when possible
# see also https://docs.pytorch.org/docs/stable/elastic/train_script.html
rdzv_nnodes = nnodes
# elastic number of nodes if MIN_NNODES and MAX_NNODES are set
if min_nnodes is not None and max_nnodes is not None:
rdzv_nnodes = f"{min_nnodes}:{max_nnodes}"
cmd = [
"torchrun",
"--nnodes",
rdzv_nnodes,
"--nproc-per-node",
nproc_per_node,
"--rdzv-id",
rdzv_id,
"--rdzv-backend",
"c10d",
"--rdzv-endpoint",
f"{master_addr}:{master_port}",
"--max-restarts",
max_restarts,
launcher.__file__,
*sys.argv[1:],
]
process = subprocess.run(cmd, env=env, check=True)
else:
# NOTE: DO NOT USE shell=True to avoid security risk
process = subprocess.run(
(
"torchrun --nnodes {nnodes} --node_rank {node_rank} --nproc_per_node {nproc_per_node} "
"--master_addr {master_addr} --master_port {master_port} {file_name} {args}"
)
.format(
nnodes=nnodes,
node_rank=node_rank,
nproc_per_node=nproc_per_node,
master_addr=master_addr,
master_port=master_port,
file_name=launcher.__file__,
args=" ".join(sys.argv[1:]),
)
.split(),
env=env,
check=True,
)
.format(
nnodes=nnodes,
node_rank=node_rank,
nproc_per_node=nproc_per_node,
master_addr=master_addr,
master_port=master_port,
file_name=launcher.__file__,
args=" ".join(sys.argv[1:]),
)
.split(),
env=env,
check=True,
)
sys.exit(process.returncode)
elif command in COMMAND_MAP:
COMMAND_MAP[command]()