Migrating workloads to Primus (Megatron-Core backend) from Megatron-LM

Migrating workloads to Primus (Megatron-Core backend) from Megatron-LM#

2025-08-21

5 min read time

Applies to Linux and Windows

Primus supports Megatron-Core as backend optimization library, replacing ROCm Megatron-LM. This document outlines the steps to migrate workload from ROCm Megatron-LM to Primus with the Megatron-Core backend.

Model architecture#

ROCm Megatron-LM defines model architecture parameters in the training scripts; for example, the Llama 3 8B model parameters are defined in examples/llama/train_llama3.sh as shown below:

HIDDEN_SIZE=4096
FFN_HIDDEN_SIZE=14336
NUM_LAYERS=32
NUM_HEADS=32
NUM_KV_HEADS=8

Primus defines the model architecture through model YAML configuration files inside the primus/configs/models/megatron/ repository. For example, Llama 3 8B model architecture parameters are defined in primus/configs/models/megatron/llama3_8B.yaml as shown below:

bases:
  - llama3_base.yaml

tokenizer_type: Llama3Tokenizer
tokenizer_model: meta-llama/Llama-3.1-8B

ffn_hidden_size: 14336
hidden_size: 4096
num_attention_heads: 32
num_layers: 32
num_query_groups: 8

Primus’ model config files follow a hierarchical design, meaning that new model config YAMLs can inherit existing model config files by importing them as bases. For example, llama3.1_8B.yaml uses llama3_8B.yaml as a base config and overrides few parameters, as shown below. In this example, llama3.1_8B overrides the max_position_embeddings value:

bases:
  - llama3_8B.yaml

tokenizer_type: Llama3Tokenizer
tokenizer_model: meta-llama/Llama-3.1-8B

max_position_embeddings: 131072

Tip

Primus provides llama_base.yaml as the base configuration, which can be used as bases for additional model architectures. For example, mixtral_base.yaml and deepseek_v3_base.yaml define llama_base.yaml as its base.

# Example mixtral_base.yaml:

bases:
  - llama_base.yaml

init_method_std: 0.01
rotary_base: 1000000
qk_layernorm: false

group_query_attention: true
num_query_groups: 8

# moe parameters
num_experts: 8
moe_router_topk: 2
moe_router_load_balancing_type: aux_loss
moe_aux_loss_coeff: 1e-2
moe_grouped_gemm: true
moe_token_dispatcher_type: alltoall

It is recommended to add a new ${MODEL_NAME}_base.yaml to add a new category of model and define new models on top of it. For example, to add Qwen2.5 models in Primus, we define qwen2.5_base.yaml and build qwen2.5_7B.yaml and qwen2.5_72B.yaml using qwen2.5_base.yaml as the base config.

Training parameters#

ROCm Megatron-LM also defines the training parameters, like batch size, tensor-parallelism, precision, as so on, in the training scripts. For example, Llama3 8B model parameters are defined in examples/llama/train_llama3.sh as shown below:

TP="${TP:-8}"
PP="${PP:-1}"
CP="${CP:-1}"
MBS="${MBS:-1}"
BS="${BS:-8}"

Primus defines the training parameters in top-level YAML files – see examples/megatron/configs/. For example, the llama3.1_8B-pretrain.yaml configuration imports the llama3.1_8B.yaml model architecture file. Users can then override the default training parameters in llama3.1_8B-pretrain.yaml.

# model to run
model: llama3.1_8B.yaml  # Model architecture yaml
overrides:
  # log
  # disable_wandb: false
  # disable_tensorboard: false
  stderr_sink_level: DEBUG

  log_avg_skip_iterations: 2
  log_avg_reset_interval: 50

  train_iters: 50
  micro_batch_size: 2
  global_batch_size: 128

  seq_length: 8192
  max_position_embeddings: 8192

  lr: 1.0e-5
  min_lr: 0.0
  lr_warmup_iters: 2
  lr_decay_iters: null
  lr_decay_style: cosine
  weight_decay: 0.1
  adam_beta1: 0.9
  adam_beta2: 0.95
  eod_mask_loss: true
  init_method_std: 0.008
  norm_epsilon: 1.0e-6

Backward compatibility with Megatron-LM#

The Dockerized environment used for Primus maintains compatibility with Megatron-LM with limited support. To roll back to using Megatron-LM, follow these steps.

cd /workspace/Megatron-LM/
pip uninstall megatron-core
pip install -e .

Once Megatron-LM is installed, follow the documentation to run workloads as usual.

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