English | 中文

megatron-wrap

Wrapped Megatron: As User-Friendly as HuggingFace, As Powerful as Megatron-LM

Introduction

megatron-wrap provides a wrapper for NVIDIA's Megatron-LM, offering users ease of use similar to the HuggingFace series of training/inference frameworks (such as transformers, deepspeed, trl, etc.) while fully leverages Megatron-LM's parallel features and speed optimizations to scale up to larger models.

megatron-wrap 对NVIDIA的Megatron-LM进行了封装，对使用者提供了如同HuggingFace系列训练/推理框架一样的易用性（例如transformers、deepspeed、trl等），同时充分利用了Megatron-LM 的并行特性和速度优化，以便scale-up到更大的模型。

Features

Easy-to-Use Interafaces

• megatron_wrap.core.wrap provides easy-to-use interface of initializing megatron-lm, settting up model, train with data given at runtime (instead of the builtin dataset/loader), logging metrics and saving model, therefore you can focus your attention on developing the algorithm and avoid knowing the details about megatron-lm.
• megatron_wrap.core.flow abstracts the main elements of an algorithm if to be implemmented in megatron-lm, including data collating and loss calculating, the data parallelism (dp split and reduce) and context parallelism (cp split, validate and reduce) are taken care internally.

Config Management

The configs (megatron_wrap.core.config) are organized in a tree strcute and split by the frequency of being modified across runs. The config tree supports select, inherit and override syntax (will be explained in Quick Start section) for easier use of predefined configs and changing part of them, see docs of confignest for more details.

Patches

• megatron_wrap.utils.dist_logger patches the loguru logger with handy methods of (error|warning_info_debug)_(rank_0|all_ranks)
• megatron_wrap.core.wrap patches the python builtin print()(all prints goes to logger.debug_all_ranks), logging(removed handlers) and warning (hide FutureWarning and UserWarning)
• megatron_wrap.core.wrap patches the way of getting parallel states, instead of calling mpu.get_(tensor_model|pipeline_model|data|context|expert_model)_parallel_(world_size|rank|group)(), use (t|p|d|c|e)p_(size|rank|group) to save effort
• megatron_wrap.utils.wandb_logger contains a wandb wrap for the conveneince of use in logging metrics dict of both online and offline mode (replaces the megatron-lm wandb)

Quick Start

Step1: Install

# download this project
git clone https://github.com/0-1CxH/megatron-wrap.git
cd megatron-wrap
git submodule update --init --recursive # this will pull [email protected]:NVIDIA/Megatron-LM.git (core_r0.8.0) to project folder

If you have a megatron-lm environment already, just install reqs of this wrapper:

pip install -r environment/wrap_environment/requirements.txt

If you do not have one, see the dependencies section for more details.

Step2: Test with Example

Run the builtin example script first to check if it is installed sucessfully.

The example script is also a good starting point to make modifications on.

export WANDB_API_KEY="YOUR_WANDB_API_KEY" # optional
export CONSOLE_LOG_LEVEL="INFO" # optional, default is "DEBUG"

CONFIG_FILE="PATH_TO_CONFIG_FILE"
# options are:
# "configs/llama2-7b-minimal-mock.yaml": uses random generated tensor and mse loss to mock a training
# "configs/llama2-7b-sft.yaml": uses sft dataset (default example is 3200 sample from [trl-lib's tldr](https://huggingface.co/datasets/trl-lib/tldr) )
# better test all options
bash scripts/run_example.sh $CONFIG_FILE

Step3: Write a Training Script

Use wrapped interface of MegatronWrap to implement the training script, it is easier to start from the example or the following script skeleton:

from megatron_wrap.core import MegatronWrap

megatron_wrap = MegatronWrap(config_yaml)
megatron_wrap.initialize()
megatron_wrap.setup_model_and_optimizer()
for _ in range(train_iters):
    megatron_wrap.train(batch_data)
    megatron_wrap.log_last_metrics()
megatron_wrap.save()

Step4: Write a Config File

Config includes the meagtron-lm and megatron-wrap part, both are in tree structure and meagtron-lm args will be flatten when sending to megatron. There is not many configs to change, the frequently changed parts:

• model architecture: configs/nest/megatron_lm/model/arch
• model parallelism: configs/nest/megatron_lm/model/parallel
• optimizer: configs/nest/megatron_lm/train/optimizer
• learning rate: configs/nest/megatron_lm/train/learning-rate.yaml
• common training args: configs/nest/megatron_lm/train/common.yaml
• computation flow (algorithm): megatron-wrap/configs/nest/megatron_wrap/flow
• logger: configs/nest/megatron_wrap/logger.yaml

The examples in the Step2: Test with Example uses config:

megatron-wrap/configs/llama2-7b-minimal-mock.yaml
megatron-wrap/configs/llama2-7b-sft.yaml

Here is an detailed explanation of each field in the config :

# start with configs/nest, there are two subfolders mapping to each section of args
megatron_lm: # this is the section of tree-organized meagtron-lm args
  model:
    arch: # the __confignest_manifest__ indicates you need to select one file under the folder configs/nest/megatron_lm/model/arch, error will raise if you do not make choice
      __select__: llama2-7b # use __select__ to indicate the selected file name, here the choice is configs/nest/megatron_lm/model/arch/llama2-7b.yaml 
    parallel:
      __select__: base
      __override__: # __override__ is applied, so the following items will replace the ones in the selected file
        tensor_model_parallel_size: 2
        pipeline_model_parallel_size: 2
        context_parallel_size: 2
  train:
    common: # this is a file (configs/nest/megatron_lm/train/common.yaml), the following items replace the ones in file (just like the effects of __override__)
      micro_batch_size: 4
      global_batch_size: 128
      seq_length: 512
      train_iters: 64
      load: ckpt/llama-2-7b-mcore-tp2pp2
      save: ckpt/llama2-7b-minimal-mock-save
      save_interval: 1
    learning-rate:
      lr: 2.0e-5
      lr_warmup_fraction: 0.05
megatron_wrap:
  init:
    megatron_lm_project_path: megatron_lm_core_080
    skip_compile_dependencies: true
  logger:
    patch_print: true
    remove_logging: true
  model_provider:
    __select__: gpt_model
    __override__:
      show_weight_details: true
  flow:
    __select__: gpt_sft

Step5: Run with torchrun

Use torchrun to start your script, note that $SCRIPT and $CONFIG should be the training script and config from above steps.

DISTRIBUTED_ARGS="--nproc-per-node ${GPUS_PER_NODE:-8} \
                  --nnodes ${NNODES:-1} \
                  --node-rank ${NODE_RANK:-0} \
                  --master-addr ${MASTER_ADDR:-$(hostname)} \
                  --master-port ${MASTER_PORT:-22334}"

export OMP_NUM_THREADS=1
export CONSOLE_LOG_LEVEL="INFO"
export WANDB_API_KEY="xxxxxxxx"

torchrun $DISTRIBUTED_ARGS $SCRIPT $CONFIG 2>&1 | tee console.log

Dependencies

Attention: read this section if you do not have a valid megatron-lm environment, the following script is executed on nvidia's image nvcr.io/nvidia/pytorch:24.05-py3 and is just for reference, read other materials (such as the official repo of megatron-lm) if this fails, or you can use environment/test_environment/Dockerfile to build a environment that this project is developed and tested in (it contains usused libs that this project does not use).

export MAX_JOBS=16
export CUDA_HOME='/usr/local/cuda'
pip install torch==2.4.0 torchvision==0.19.0 torchaudio==2.4.0 --index-url https://download.pytorch.org/whl/cu124
git clone https://github.com/NVIDIA/apex.git && \
    pip uninstall apex -y && \
    pushd ./apex && \
    MAX_JOBS=16 python setup.py install --cuda_ext --cpp_ext && \
    popd
pip install poetry pydantic \
    transformers fastchat setuptools_scm \
    wandb protobuf==3.20.3 \
    git+https://github.com/fanshiqing/[email protected] \
    -i https://pypi.tuna.tsinghua.edu.cn/simple
RUN CUDACXX=/usr/local/cuda/bin/nvcc pip install \
    --force-reinstall --no-build-isolation --no-deps \
    git+https://github.com/Dao-AILab/[email protected] \
    git+https://github.com/huggingface/[email protected] \
    git+https://github.com/NVIDIA/[email protected]

Development

If you want to develop a new computation flow (of an algorithm), do the following:

Inherit from an Existing Class

The base class of all training flows is MegatronWrapTrainingFlowBase, in current version, the inheritage tree is:

+ MegatronWrapTrainingFlowBase
|____ + MegatronWrapGPTModelFlow 
      |____ - MegatronWrapMinimalMockFlow
      |____ - MegatronWrapGPTModelSFTFlow

If you are working with GPT model, inherit MegatronWrapGPTModelFlow since it contains GPT model's validate_model_forward_inputs, else inherit MegatronWrapTrainingFlowBase and implement validate_model_forward_inputs of the model type.

Implement the Following Methods

• def get_fields_and_seqdims(self) -> dict: returns dict of field: seqdim, field is a field in the flow that contains a sequence dimension, seqdim is the index of the sequence dimension (for example, shape input_ids is [bs, seqlen] so the seqdim is 1)
• def collate_data_micro_batch(self, iterator) -> dict: the input is the data iterator of the iter() of MegatronWrap::train input, in this func, get a micro batch from the iterator and collate to form two dicts (model_forward_inputs and loss_inputs) of name: tensor, where the tensor is torch.Tensor on cpu device
• def calculate_loss(self, loss_inputs, model_forward_output): this function takes the loss_inputs (that collate_data_micro_batch returns) and model_forward_output (result model.forward(model_forward_inputs)) and returns loss and metrics, NOTE: you need to consider context parallel in this function, for the loss_inputs and model_forward_output only contains segments of this cp rank (process them separately and use self.sum_on_cp_group and self.average_loss_across_dp_ranks for aggregation)

Register

Write a config file and put it under megatron-wrap/configs/nest/megatron_wrap/flow, register in MegatronWrapFlowEntry with the flow_key in the config file.

TODO

• support starting with ray
• add model arch configs for llama, qwen, mistral, deepseek and converting scripts of hf<->mcore, might refer to this
• add flow of dpo(policy part), grpo(policy part) and distill(student part)