| # AMD GPU Support |
|
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| ## 1. Environment setup |
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| ### 1.1 Base environment |
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| Pull the ms-swift image built for the AMD ROCm stack, then start the container with the commands below. |
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| If you need a newer ms-swift version, upgrade with pip or install from source code (adding `--no-deps` is recommended to avoid pulling in dependency upgrades that may cause issues). |
|
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| ```bash |
| IMAGE_NAME=amdagi/modelscope:ubuntu22.04-rocm7.2.0-py312-torch2.10.0-vllm0.18.1-modelscope1.35.1-swift4.1.0 |
| docker pull ${IMAGE_NAME} |
| |
| CONTAINER_NAME=swift_test |
| docker run -it --network=host --ipc=host --privileged --group-add video \ |
| --device=/dev/dri --device=/dev/kfd \ |
| --shm-size 512G --ulimit memlock=-1 \ |
| --security-opt seccomp=unconfined --cap-add SYS_PTRACE \ |
| --name ${CONTAINER_NAME} \ |
| ${IMAGE_NAME} \ |
| /bin/bash |
| ``` |
|
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| ### 1.2 Environment check |
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| - Confirm the availability of AMD devices for PyTorch in the container. |
|
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| ```bash |
| python -c "import torch;print(torch.cuda.is_available())" # output: True |
| ``` |
|
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| - Inspect GPU topology and NUMA: `rocm-smi --showtopo` |
|
|
| ``` |
| ============================ ROCm System Management Interface ============================ |
| WARNING: AMD GPU device(s) is/are in a low-power state. Check power control/runtime_status |
| |
| ================================ Weight between two GPUs ================================= |
| GPU0 GPU1 GPU2 GPU3 GPU4 GPU5 GPU6 GPU7 |
| GPU0 0 15 15 15 15 15 15 15 |
| GPU1 15 0 15 15 15 15 15 15 |
| GPU2 15 15 0 15 15 15 15 15 |
| GPU3 15 15 15 0 15 15 15 15 |
| GPU4 15 15 15 15 0 15 15 15 |
| GPU5 15 15 15 15 15 0 15 15 |
| GPU6 15 15 15 15 15 15 0 15 |
| GPU7 15 15 15 15 15 15 15 0 |
| |
| ================================= Hops between two GPUs ================================== |
| GPU0 GPU1 GPU2 GPU3 GPU4 GPU5 GPU6 GPU7 |
| GPU0 0 1 1 1 1 1 1 1 |
| GPU1 1 0 1 1 1 1 1 1 |
| GPU2 1 1 0 1 1 1 1 1 |
| GPU3 1 1 1 0 1 1 1 1 |
| GPU4 1 1 1 1 0 1 1 1 |
| GPU5 1 1 1 1 1 0 1 1 |
| GPU6 1 1 1 1 1 1 0 1 |
| GPU7 1 1 1 1 1 1 1 0 |
| |
| =============================== Link Type between two GPUs =============================== |
| GPU0 GPU1 GPU2 GPU3 GPU4 GPU5 GPU6 GPU7 |
| GPU0 0 XGMI XGMI XGMI XGMI XGMI XGMI XGMI |
| GPU1 XGMI 0 XGMI XGMI XGMI XGMI XGMI XGMI |
| GPU2 XGMI XGMI 0 XGMI XGMI XGMI XGMI XGMI |
| GPU3 XGMI XGMI XGMI 0 XGMI XGMI XGMI XGMI |
| GPU4 XGMI XGMI XGMI XGMI 0 XGMI XGMI XGMI |
| GPU5 XGMI XGMI XGMI XGMI XGMI 0 XGMI XGMI |
| GPU6 XGMI XGMI XGMI XGMI XGMI XGMI 0 XGMI |
| GPU7 XGMI XGMI XGMI XGMI XGMI XGMI XGMI 0 |
| |
| ======================================= Numa Nodes ======================================= |
| GPU[0] : (Topology) Numa Node: 0 |
| GPU[0] : (Topology) Numa Affinity: 0 |
| GPU[1] : (Topology) Numa Node: 0 |
| GPU[1] : (Topology) Numa Affinity: 0 |
| GPU[2] : (Topology) Numa Node: 0 |
| GPU[2] : (Topology) Numa Affinity: 0 |
| GPU[3] : (Topology) Numa Node: 0 |
| GPU[3] : (Topology) Numa Affinity: 0 |
| GPU[4] : (Topology) Numa Node: 1 |
| GPU[4] : (Topology) Numa Affinity: 1 |
| GPU[5] : (Topology) Numa Node: 1 |
| GPU[5] : (Topology) Numa Affinity: 1 |
| GPU[6] : (Topology) Numa Node: 1 |
| GPU[6] : (Topology) Numa Affinity: 1 |
| GPU[7] : (Topology) Numa Node: 1 |
| GPU[7] : (Topology) Numa Affinity: 1 |
| ================================== End of ROCm SMI Log =================================== |
| ``` |
|
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| - Check GPU utilization and VRAM usage (`rocm-smi` or `rocm-smi -u --showmeminfo vram`): |
|
|
| ``` |
| # output of 'rocm-smi' |
| ============================================ ROCm System Management Interface ============================================ |
| ====================================================== Concise Info ====================================================== |
| Device Node IDs Temp Power Partitions SCLK MCLK Fan Perf PwrCap VRAM% GPU% |
| (DID, GUID) (Junction) (Socket) (Mem, Compute, ID) |
| ========================================================================================================================== |
| 0 2 0x74a2, 1017 43.0°C 155.0W NPS1, SPX, 0 94Mhz 900Mhz 0% auto 650.0W 0% 0% |
| 1 3 0x74a2, 47713 41.0°C 155.0W NPS1, SPX, 0 91Mhz 900Mhz 0% auto 650.0W 0% 0% |
| 2 4 0x74a2, 37449 45.0°C 159.0W NPS1, SPX, 0 95Mhz 900Mhz 0% auto 650.0W 0% 0% |
| 3 5 0x74a2, 11217 41.0°C 155.0W NPS1, SPX, 0 95Mhz 900Mhz 0% auto 650.0W 0% 0% |
| 4 6 0x74a2, 41880 44.0°C 160.0W NPS1, SPX, 0 91Mhz 900Mhz 0% auto 650.0W 0% 0% |
| 5 7 0x74a2, 6656 42.0°C 157.0W NPS1, SPX, 0 95Mhz 900Mhz 0% auto 650.0W 0% 0% |
| 6 8 0x74a2, 12840 45.0°C 160.0W NPS1, SPX, 0 96Mhz 900Mhz 0% auto 650.0W 0% 0% |
| 7 9 0x74a2, 35760 43.0°C 158.0W NPS1, SPX, 0 107Mhz 900Mhz 0% auto 650.0W 0% 0% |
| ========================================================================================================================== |
| ================================================== End of ROCm SMI Log =================================================== |
| |
| # output of 'rocm-smi -u --showmeminfo vram' |
| ============================ ROCm System Management Interface ============================ |
| =================================== % time GPU is busy =================================== |
| GPU[0] : GPU use (%): 0 |
| GPU[0] : GFX Activity: 3862538534 |
| GPU[1] : GPU use (%): 0 |
| GPU[1] : GFX Activity: 4053246251 |
| GPU[2] : GPU use (%): 0 |
| GPU[2] : GFX Activity: 3114103535 |
| GPU[3] : GPU use (%): 0 |
| GPU[3] : GFX Activity: 4026776444 |
| GPU[4] : GPU use (%): 0 |
| GPU[4] : GFX Activity: 1224255679 |
| GPU[5] : GPU use (%): 0 |
| GPU[5] : GFX Activity: 1191191242 |
| GPU[6] : GPU use (%): 0 |
| GPU[6] : GFX Activity: 1184652679 |
| GPU[7] : GPU use (%): 0 |
| GPU[7] : GFX Activity: 2145209382 |
| ========================================================================================== |
| ================================== Memory Usage (Bytes) ================================== |
| GPU[0] : VRAM Total Memory (B): 206141652992 |
| GPU[0] : VRAM Total Used Memory (B): 297611264 |
| GPU[1] : VRAM Total Memory (B): 206141652992 |
| GPU[1] : VRAM Total Used Memory (B): 297623552 |
| GPU[2] : VRAM Total Memory (B): 206141652992 |
| GPU[2] : VRAM Total Used Memory (B): 297623552 |
| GPU[3] : VRAM Total Memory (B): 206141652992 |
| GPU[3] : VRAM Total Used Memory (B): 297623552 |
| GPU[4] : VRAM Total Memory (B): 206141652992 |
| GPU[4] : VRAM Total Used Memory (B): 297623552 |
| GPU[5] : VRAM Total Memory (B): 206141652992 |
| GPU[5] : VRAM Total Used Memory (B): 297623552 |
| GPU[6] : VRAM Total Memory (B): 206141652992 |
| GPU[6] : VRAM Total Used Memory (B): 297623552 |
| GPU[7] : VRAM Total Memory (B): 206141652992 |
| GPU[7] : VRAM Total Used Memory (B): 297623552 |
| ========================================================================================== |
| ================================== End of ROCm SMI Log =================================== |
| ``` |
|
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| ## 2. Run examples |
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| ### 2.1 Full fine-tuning Qwen3.5 with Megatron-Swift |
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| AMD GPUs often have large VRAM, so you can tune several knobs together to improve training throughput: |
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| - **Parallelism tuning**: Large per-GPU memory lets you reduce communication from aggressive splits (prefer tuning PP/EP before TP). |
| - **Optimizer CPU offload**: If VRAM allows, disable with `--optimizer_cpu_offload false`. |
| - **Activation / gradient checkpointing**: If VRAM allows, use `--recompute_granularity none`, or `--recompute_granularity selective` with `--recompute_modules` for finer control. |
| - **MoE models**: Set `export NVTE_USE_CUTLASS_GROUPED_GEMM=1` for the optimized grouped GEMM kernel. |
| - **Models with GatedDeltaNet**: Set `USE_MCORE_GDN=1` to use the Megatron-Core implementation. |
| - **Stability on some AMD GPUs**: Set `export HSA_NO_SCRATCH_RECLAIM=1` to avoid known issues and stabilize performance. |
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| Single-node training: |
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| ```bash |
| export HSA_NO_SCRATCH_RECLAIM=1 |
| export NVTE_USE_CUTLASS_GROUPED_GEMM=1 |
| |
| output_dir=${PWD}/megatron_output/Qwen3.5-35B-A3B |
| mkdir -p ${output_dir} |
| current_time=$(date "+%Y.%m.%d-%H.%M.%S") |
| log_file=${output_dir}/"1node_full_megatron_Qwen3.5-35B-A3B_${current_time}.log" |
| |
| PYTORCH_CUDA_ALLOC_CONF='expandable_segments:True' \ |
| NPROC_PER_NODE=8 \ |
| MAX_PIXELS=1003520 \ |
| VIDEO_MAX_PIXELS=50176 \ |
| FPS_MAX_FRAMES=12 \ |
| SKIP_MULTIMODAL_MTP_VALIDATION=1 \ |
| USE_MCORE_GDN=1 \ |
| megatron sft \ |
| --model Qwen/Qwen3.5-35B-A3B \ |
| --dataset 'AI-ModelScope/LongAlpaca-12k' \ |
| --save_safetensors true \ |
| --load_from_cache_file true \ |
| --tuner_type full \ |
| --add_non_thinking_prefix true \ |
| --split_dataset_ratio 0.01 \ |
| --tensor_model_parallel_size 1 \ |
| --pipeline_model_parallel_size 1 \ |
| --expert_model_parallel_size 8 \ |
| --sequence_parallel true \ |
| --moe_permute_fusion true \ |
| --moe_grouped_gemm true \ |
| --moe_shared_expert_overlap true \ |
| --moe_aux_loss_coeff 1e-6 \ |
| --moe_expert_capacity_factor 2 \ |
| --micro_batch_size 1 \ |
| --global_batch_size 8 \ |
| --recompute_granularity selective \ |
| --recompute_modules core_attn mlp moe \ |
| --num_train_epochs 500 \ |
| --group_by_length true \ |
| --finetune true \ |
| --freeze_llm false \ |
| --freeze_vit false \ |
| --freeze_aligner false \ |
| --cross_entropy_loss_fusion true \ |
| --lr 1e-5 \ |
| --lr_warmup_fraction 0.05 \ |
| --min_lr 1e-6 \ |
| --weight_decay 0.1 \ |
| --adam_beta2 0.95 \ |
| --eval_steps 500 \ |
| --save_steps 500 \ |
| --save_total_limit 10 \ |
| --logging_steps 1 \ |
| --max_length 16384 \ |
| --dataloader_num_workers 8 \ |
| --dataset_num_proc 8 \ |
| --no_save_optim true \ |
| --no_save_rng true \ |
| --optimizer_cpu_offload false \ |
| --attention_backend flash \ |
| --padding_free false \ |
| --output_dir ${output_dir} \ |
| 2>&1 | tee ${log_file} |
| ``` |
|
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| Multi-node training: |
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| ```bash |
| export NNODES=2 # example: 2 nodes |
| export NODE_RANK=0 # 0 on master, 1 on workers |
| export MASTER_ADDR=<MASTER_NODE_IP> # set to master node IP |
| export MASTER_PORT=29500 # communication port |
| export NCCL_SOCKET_IFNAME=ens50f1np1 # actual NIC name, check with ifconfig |
| export GLOO_SOCKET_IFNAME=ens50f1np1 # same as above |
| export NCCL_IB_HCA=mlx5_0,mlx5_1,mlx5_2,mlx5_3 # IB HCAs, check with ibv_devices |
| export NCCL_IB_GID_INDEX=3 |
| |
| # Main training script below: same as single-node script above |
| ... |
| ``` |
|
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| ### 2.2 Reinforcement learning training for Qwen3.5 with Megatron-Swift |
|
|
| ```bash |
| # Single-node training example |
| export HSA_NO_SCRATCH_RECLAIM=1 |
| export NVTE_USE_CUTLASS_GROUPED_GEMM=1 |
| |
| SYSTEM_PROMPT="""You are a helpful math assistant. Solve the problem step by step and put your final answer within \\boxed{}.""" |
| |
| CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 \ |
| NPROC_PER_NODE=8 \ |
| PYTORCH_CUDA_ALLOC_CONF='expandable_segments:True' \ |
| megatron rlhf \ |
| --rlhf_type grpo \ |
| --model Qwen/Qwen3.5-35B-A3B \ |
| --save_safetensors true \ |
| --enable_thinking false \ |
| --merge_lora true \ |
| --context_parallel_size 1 \ |
| --tensor_model_parallel_size 1 \ |
| --expert_model_parallel_size 8 \ |
| --pipeline_model_parallel_size 1 \ |
| --moe_permute_fusion true \ |
| --dataset open-r1/DAPO-Math-17k-Processed \ |
| --system "$SYSTEM_PROMPT" \ |
| --num_train_epochs 1 \ |
| --global_batch_size 64 \ |
| --micro_batch_size 1 \ |
| --steps_per_generation 2 \ |
| --num_generations 8 \ |
| --reward_funcs accuracy \ |
| --use_vllm true \ |
| --vllm_mode colocate \ |
| --vllm_gpu_memory_utilization 0.5 \ |
| --vllm_tensor_parallel_size 2 \ |
| --vllm_max_model_len 9192 \ |
| --max_length 1000 \ |
| --max_completion_length 8192 \ |
| --tuner_type lora \ |
| --target_modules all-linear \ |
| --lr 5e-5 \ |
| --bf16 true \ |
| --beta 0.00 \ |
| --epsilon 0.2 \ |
| --epsilon_high 0.28 \ |
| --dynamic_sample false \ |
| --overlong_filter true \ |
| --loss_type grpo \ |
| --sleep_level 1 \ |
| --offload_model true \ |
| --offload_bridge false \ |
| --offload_optimizer true \ |
| --logging_steps 1 \ |
| --recompute_granularity none \ |
| --gradient_accumulation_fusion false \ |
| --finetune \ |
| --dataloader_num_workers 8 \ |
| --dataset_num_proc 8 \ |
| --no_save_optim \ |
| --no_save_rng \ |
| --save_steps 20 \ |
| --attention_backend flash \ |
| --moe_expert_capacity_factor 2 \ |
| --temperature 1.0 \ |
| --padding_free false \ |
| --sequence_parallel true \ |
| --log_completions true \ |
| --report_to tensorboard |
| ``` |
|
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| ## Known issues |
|
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| - **Reinforcement learning**: If vLLM is the inference engine, use vLLM ≥ 0.11.0. It is recommended to use ROCm 7.0 or the image we provide to avoid the sleep mode memory leak issue. |
| - **MoE training**: Set `NVTE_USE_CUTLASS_GROUPED_GEMM=1` to reduce occasional GPU hangs. |
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|
