# Quick Start ms-swift incorporates Megatron's parallelization techniques to accelerate the training of large models, including data parallelism, tensor parallelism, pipeline parallelism, sequence parallelism, context parallelism, and expert parallelism. It supports CPT/SFT/GRPO/DPO/KTO/RM for models such as Qwen3, Qwen3.5, Deepseek-R1, GLM4.5, GPT-OSS, and more. For a complete list of supported models, please refer to the [Supported Models and Datasets documentation](../Instruction/Supported-models-and-datasets.md). | Method | Full-Parameter | LoRA | MoE | Multimodal | FP8 | | ---------------------- | -------------- | ---- | ---- | ---------- | ---- | | Pre-training | ✅ | ✅ | ✅ | ✅ | ✅ | | [Supervised Fine-Tuning](https://github.com/modelscope/ms-swift/tree/main/examples/megatron) | ✅ | ✅ | ✅ | ✅ | ✅ | | [GRPO](https://github.com/modelscope/ms-swift/tree/main/examples/megatron/grpo) | ✅ | ✅ | ✅ | ✅ | ✅ | | [GKD](https://github.com/modelscope/ms-swift/tree/main/examples/megatron/rlhf/gkd) | ✅ | ✅ | ✅ | ✅ | ✅ | | [DPO](https://github.com/modelscope/ms-swift/tree/main/examples/megatron/rlhf/dpo) | ✅ | ✅ | ✅ | ✅ | ✅ | | [KTO](https://github.com/modelscope/ms-swift/tree/main/examples/megatron/rlhf/kto) | ✅ | ✅ | ✅ | ✅ | ✅ | | [RM](https://github.com/modelscope/ms-swift/tree/main/examples/megatron/rlhf/rm) | ✅ | ✅ | ✅ | ✅ | ✅ | | [Embedding](https://github.com/modelscope/ms-swift/tree/main/examples/megatron/embedding) | ✅ | ✅| ✅ | ✅ | ✅ | | [Reranker](https://github.com/modelscope/ms-swift/tree/main/examples/megatron/reranker) | ✅ | ✅| ✅ | ✅ | ✅ | | [Sequence Classification](https://github.com/modelscope/ms-swift/tree/main/examples/megatron/seq_cls) | ✅ | ✅ | ✅ | ✅ | ✅ | ## Environment Setup To use Megatron-SWIFT, in addition to installing the `swift` dependencies, you also need to install the following: ```shell # transformer_engine # If an installation error occurs, you can refer to this issue for resolution: https://github.com/modelscope/ms-swift/issues/3793 pip install --no-build-isolation transformer-engine[pytorch] --no-cache-dir # apex # Note: Megatron-SWIFT can run in environments without apex by setting `--gradient_accumulation_fusion false`. git clone https://github.com/NVIDIA/apex cd apex pip install -v --disable-pip-version-check --no-cache-dir --no-build-isolation --config-settings "--build-option=--cpp_ext" --config-settings "--build-option=--cuda_ext" ./ # mcore-bridge pip install mcore-bridge -U # Install from main branch # pip install git+https://github.com/modelscope/mcore-bridge.git # megatron-core pip install "megatron-core==0.16.*" -U # If you are using multi-node training, please additionally set the `MODELSCOPE_CACHE` environment variable to a shared storage path. # This will ensure that the dataset cache is shared, thereby speeding up preprocessing. # Note: This step is crucial; otherwise multi-machine training may hang due to data inconsistencies caused by randomness in data preprocessing. export MODELSCOPE_CACHE='/xxx/shared' # flash_attn # Choose an appropriate version to install: https://github.com/Dao-AILab/flash-attention/releases/tag/v2.8.3 # Note: Do not install a version higher than the maximum supported by transformer_engine: https://github.com/NVIDIA/TransformerEngine/blob/release_v2.10/transformer_engine/pytorch/attention/dot_product_attention/utils.py#L118 MAX_JOBS=8 pip install "flash-attn==2.8.3" --no-build-isolation ``` Alternatively, you can also use the image: (See historical images [here](../GetStarted/SWIFT-installation.md#mirror)) ``` # cu128 modelscope-registry.cn-hangzhou.cr.aliyuncs.com/modelscope-repo/modelscope:ubuntu22.04-cuda12.8.1-py311-torch2.10.0-vllm0.17.1-modelscope1.34.0-swift4.0.3 modelscope-registry.cn-beijing.cr.aliyuncs.com/modelscope-repo/modelscope:ubuntu22.04-cuda12.8.1-py311-torch2.10.0-vllm0.17.1-modelscope1.34.0-swift4.0.3 modelscope-registry.us-west-1.cr.aliyuncs.com/modelscope-repo/modelscope:ubuntu22.04-cuda12.8.1-py311-torch2.10.0-vllm0.17.1-modelscope1.34.0-swift4.0.3 # cu129 (fp8 training) modelscope-registry.cn-hangzhou.cr.aliyuncs.com/modelscope-repo/modelscope:ubuntu22.04-cuda12.9.1-py312-torch2.10.0-vllm0.19.1-modelscope1.35.4-swift4.1.3 modelscope-registry.cn-beijing.cr.aliyuncs.com/modelscope-repo/modelscope:ubuntu22.04-cuda12.9.1-py312-torch2.10.0-vllm0.19.1-modelscope1.35.4-swift4.1.3 modelscope-registry.us-west-1.cr.aliyuncs.com/modelscope-repo/modelscope:ubuntu22.04-cuda12.9.1-py312-torch2.10.0-vllm0.19.1-modelscope1.35.4-swift4.1.3 ``` Recommended Operating Environment: | | Range | Recommended | Notes | |--------------|--------------|-------------|--------------------| | python | >=3.10 | 3.12 | | | cuda | | cuda12.8/12.9 | | | torch | >=2.0 | 2.8.0/2.10.0 | | | transformer-engine | >=2.3 | 2.13.0 | | | apex | | 0.1 | | | megatron-core | >=0.12,<0.17 | 0.16.1 | | | mcore-bridge | >=1.0.2 | | | | flash-attn | | 2.8.3/3.0.0b1 | | | transformers | >=4.33 | 4.57.6/5.6.2 | | | modelscope | >=1.23 | | | | peft | >=0.11,<0.20 | | LoRA | | trl | >=0.15,<1.0 | | RLHF | ## Quick Start Example This section introduces a quick start example for fine-tuning the self-awareness of the Qwen2.5-7B-Instruct model using two 80GiB A100 GPUs. The following best practices can be completed within 10 minutes. ### Traditional Method First, we need to convert the weights from HF (Hugging Face) format to Megatron format: - Multi-GPU weight conversion: Remove `CUDA_VISIBLE_DEVICES=0` to enable multi-GPU weight conversion. - Conversion precision test: `--test_convert_precision true` will test the conversion precision. For large MoE model conversions, this option takes longer and consumes more memory, so you may omit it as needed. ```shell CUDA_VISIBLE_DEVICES=0 \ swift export \ --model Qwen/Qwen2.5-7B-Instruct \ --to_mcore true \ --torch_dtype bfloat16 \ --output_dir Qwen2.5-7B-Instruct-mcore \ --test_convert_precision true ``` Next, use the following script to start training. The required GPU memory resources are 2*80GiB: - If using multi-machine training, it is recommended to share a disk and specify the same path for `--output_dir`. ```shell PYTORCH_CUDA_ALLOC_CONF='expandable_segments:True' \ NPROC_PER_NODE=2 \ CUDA_VISIBLE_DEVICES=0,1 \ megatron sft \ --mcore_model Qwen2.5-7B-Instruct-mcore \ --save_safetensors false \ --dataset 'AI-ModelScope/alpaca-gpt4-data-zh#500' \ 'AI-ModelScope/alpaca-gpt4-data-en#500' \ 'swift/self-cognition#500' \ --tensor_model_parallel_size 2 \ --sequence_parallel true \ --micro_batch_size 16 \ --global_batch_size 16 \ --recompute_granularity full \ --recompute_method uniform \ --recompute_num_layers 1 \ --finetune true \ --cross_entropy_loss_fusion true \ --lr 1e-5 \ --lr_warmup_fraction 0.05 \ --min_lr 1e-6 \ --num_train_epochs 1 \ --output_dir megatron_output/Qwen2.5-7B-Instruct \ --save_steps 100 \ --max_length 2048 \ --system 'You are a helpful assistant.' \ --dataloader_num_workers 4 \ --no_save_optim true \ --no_save_rng true \ --dataset_num_proc 4 \ --model_author swift \ --model_name swift-robot ``` Finally, convert the Megatron format weights back to HF format: - Note: Please point `--mcore_model` to the parent directory of `iter_xxx`. By default, the corresponding checkpoint from `latest_checkpointed_iteration.txt` will be used. - If OOM (Out of Memory) occurs, simply remove `CUDA_VISIBLE_DEVICES=0`. If you encounter insufficient memory, please remove `--test_convert_precision true`. ```shell CUDA_VISIBLE_DEVICES=0 \ swift export \ --mcore_model megatron_output/Qwen2.5-7B-Instruct/vx-xxx/checkpoint-xxx \ --to_hf true \ --torch_dtype bfloat16 \ --output_dir megatron_output/Qwen2.5-7B-Instruct/vx-xxx/checkpoint-xxx-hf \ --test_convert_precision true ``` We then perform inference on the generated HF format weights: ```shell CUDA_VISIBLE_DEVICES=0 \ swift infer \ --model megatron_output/Qwen2.5-7B-Instruct/vx-xxx/checkpoint-xxx-hf \ --stream true \ --temperature 0 \ --max_new_tokens 2048 ``` The inference results are as follows: ``` <<< who are you? I am a language model developed by swift, you can call me swift-robot. How can I assist you? ``` ### Mcore-Bridge [Recommended] Mcore-Bridge eliminates the tedious process of model conversion. For details, refer to the [Mcore-Bridge Documentation](./Mcore-Bridge.md). Training script: ```bash PYTORCH_CUDA_ALLOC_CONF='expandable_segments:True' \ NPROC_PER_NODE=2 \ CUDA_VISIBLE_DEVICES=0,1 \ megatron sft \ --model Qwen/Qwen2.5-7B-Instruct \ --save_safetensors true \ --dataset 'AI-ModelScope/alpaca-gpt4-data-zh#500' \ 'AI-ModelScope/alpaca-gpt4-data-en#500' \ 'swift/self-cognition#500' \ --tensor_model_parallel_size 2 \ --sequence_parallel true \ --micro_batch_size 16 \ --global_batch_size 16 \ --recompute_granularity full \ --recompute_method uniform \ --recompute_num_layers 1 \ --finetune true \ --cross_entropy_loss_fusion true \ --lr 1e-5 \ --lr_warmup_fraction 0.05 \ --min_lr 1e-6 \ --num_train_epochs 1 \ --output_dir megatron_output/Qwen2.5-7B-Instruct \ --save_steps 100 \ --max_length 2048 \ --system 'You are a helpful assistant.' \ --dataloader_num_workers 4 \ --no_save_optim true \ --no_save_rng true \ --dataset_num_proc 4 \ --model_author swift \ --model_name swift-robot ``` We perform inference on the generated safetensors format weights: ```shell CUDA_VISIBLE_DEVICES=0 \ swift infer \ --model megatron_output/Qwen2.5-7B-Instruct/vx-xxx/checkpoint-xxx \ --stream true \ --temperature 0 \ --max_new_tokens 2048 ``` - For pretraining, you can use `megatron pt` instead of `megatron sft`, which will use a generative template for training. - Megatron-SWIFT uses the same dataset and template processing modules as ms-swift, thus supporting techniques such as packing, loss scale, and agent training. For custom dataset formats, please refer to the [Custom Dataset Documentation](../Customization/Custom-dataset.md). - **More Examples**: Including packing, multi-node training, 32K context length, DPO, MoE models, and pre-training, can be found [here](https://github.com/modelscope/ms-swift/tree/main/examples/megatron). ## Training Tips - Methods to increase training throughput: use packing (do not enable streaming), increase data parallelism (DP), reduce recomputation, and increase compute-communication overlap. MoE models can also be accelerated by dropping tokens. - Parallelism choices: - Megatron-SWIFT uses ZeRO-1 (use_distributed_optimizer enabled by default) combined with various parallelism techniques. - DP is the fastest but consumes the most memory; use other parallel techniques to reduce memory usage. - TP/EP involve heavy communication, so keep them within the NVLink domain when possible; for cross-domain setups prefer PP/DP. For expert layers, prefer EP over ETP — ETP saves memory but is slower. - MoE parallel folding: separate MoE parallel groups from Dense groups. Attention uses tp-cp-dp-pp groups, while MoE uses etp-ep-dp-pp groups. - Choosing parallelism for weight conversion: Megatron-SWIFT uses the torch_dist storage format on the MCore side; you can adjust parallelism at training time and do not need to specify it during weight conversion. - Regarding log printing: Megatron-SWIFT logs are printed on the last rank, because in PP parallelism, only the last pp_rank has complete information. ## Benchmark The training speed comparison for full-parameter dense models with 8K context length, using `megatron sft` and `swift sft`, under a single-node, eight-GPU A800 environment is as follows: ([shell](https://github.com/modelscope/ms-swift/blob/main/examples/megatron/benchmark/deepspeed.sh)) **Dense** Qwen2.5-14B: | | Megatron-LM | Deepspeed-ZeRO2 | Deepspeed-ZeRO3 | | ---------------- | ----------- | --------------- | --------------- | | Training Speed | 9.04s/it | 10.32s/it | 10.56s/it | | GPU Memory Usage | 8\*64GB | 8\*80GB | 8\*58GB | The training speed comparison for full-parameter MoE models with 8K context length, using `megatron sft` and `swift sft`, under a two-node, 16-GPU A800 environment is as follows: **MoE** Qwen3-30B-A3B: - Note: The DeepSpeed test results were conducted in a "transformers<5.0" environment. In "transformers>5.0", training can be accelerated by using `--experts_impl grouped_mm`. | | Megatron-LM | Deepspeed-ZeRO2 | Deepspeed-ZeRO3 | | ---------------- | ----------- | --------------- | --------------- | | Training Speed | 9.6s/it | - | 91.2s/it | | GPU Memory Usage | 16 * 60GiB | OOM | 16 * 80GiB | ## Megatron-SWIFT Wechat Group