wehub-resource-sync
84 lines
2.4 KiB
Bash
84 lines
2.4 KiB
Bash
# NOTE: Requires NCCL connectivity between the training master node and rollout nodes
|
|
# This script demonstrates multi-node rollout and multi-node training with swift.
|
|
# node1 and node2: multi-node rollout servers
|
|
# node3 and node4: distributed training nodes
|
|
|
|
# --- Rollout Section ---
|
|
# For rollout, you can launch any number of servers on different nodes
|
|
# Start rollout server on node1:
|
|
CUDA_VISIBLE_DEVICES=0,1,2,3 \
|
|
swift rollout \
|
|
--model Qwen/Qwen2.5-7B-Instruct \
|
|
--vllm_tensor_parallel_size 2 \
|
|
--vllm_data_parallel_size 2 \
|
|
--port <node1_port>
|
|
|
|
# Start rollout server on node2:
|
|
CUDA_VISIBLE_DEVICES=0,1,2,3 \
|
|
swift rollout \
|
|
--model Qwen/Qwen2.5-7B-Instruct \
|
|
--vllm_tensor_parallel_size 2 \
|
|
--vllm_data_parallel_size 2 \
|
|
--port <node2_port>
|
|
|
|
# --- Training Section ---
|
|
# node3: Master training node (rank 0)
|
|
NNODES=2 \
|
|
NODE_RANK=0 \
|
|
MASTER_ADDR=127.0.0.1 \
|
|
MASTER_PORT=29500 \
|
|
CUDA_VISIBLE_DEVICES=0,1,2,3 \
|
|
NPROC_PER_NODE=4 \
|
|
swift rlhf \
|
|
--rlhf_type grpo \
|
|
--model Qwen/Qwen2.5-7B-Instruct \
|
|
--reward_funcs accuracy \
|
|
--use_vllm true \
|
|
--vllm_mode server \
|
|
--vllm_server_host <node1_ip> <node2_ip> \
|
|
--vllm_server_port <node1_port> <node2_port> \
|
|
--dataset AI-MO/NuminaMath-TIR#1000 \
|
|
--load_from_cache_file true \
|
|
--max_completion_length 2048 \
|
|
--per_device_train_batch_size 1 \
|
|
--gradient_accumulation_steps 1 \
|
|
--learning_rate 1e-6 \
|
|
--save_total_limit 2 \
|
|
--logging_steps 1 \
|
|
--warmup_ratio 0.05 \
|
|
--dataloader_num_workers 4 \
|
|
--dataset_num_proc 4 \
|
|
--num_generations 4 \
|
|
--deepspeed zero2 \
|
|
--log_completions true \
|
|
|
|
# node4: Secondary training node (rank 1)
|
|
NNODES=2 \
|
|
NODE_RANK=1 \
|
|
MASTER_ADDR=<node3_ip> \
|
|
MASTER_PORT=29500 \
|
|
CUDA_VISIBLE_DEVICES=0,1,2,3 \
|
|
NPROC_PER_NODE=4 \
|
|
swift rlhf \
|
|
--rlhf_type grpo \
|
|
--model Qwen/Qwen2.5-7B-Instruct \
|
|
--reward_funcs accuracy \
|
|
--use_vllm true \
|
|
--vllm_mode server \
|
|
--vllm_server_host <node1_ip> <node2_ip> \
|
|
--vllm_server_port <node1_port> <node2_port> \
|
|
--dataset AI-MO/NuminaMath-TIR#1000 \
|
|
--load_from_cache_file true \
|
|
--max_completion_length 2048 \
|
|
--per_device_train_batch_size 1 \
|
|
--gradient_accumulation_steps 1 \
|
|
--learning_rate 1e-6 \
|
|
--save_total_limit 2 \
|
|
--logging_steps 1 \
|
|
--warmup_ratio 0.05 \
|
|
--dataloader_num_workers 4 \
|
|
--dataset_num_proc 4 \
|
|
--num_generations 4 \
|
|
--deepspeed zero2 \
|
|
--log_completions true \
|