MLM vs Bridge Training

MLM vs Bridge Training

For how they differ, the arg mapping tables, gotchas, and translation script, see:

• @docs/​megatron-lm-to-megatron-bridge.md

First Answer Checklist

For MLM-vs-Bridge correlation questions, always name these items up front:

1. Bridge recipe: vanilla_gpt_pretrain_config.
2. Bridge entry point: scripts/​training/​run_recipe.py.
3. MLM entry point: 3rdparty/​Megatron-LM/​pretrain_gpt.py.
4. Launch wrapper for both: uv run python -m torch.distributed.run.
5. Fresh-run cleanup: rm -rf nemo_experiments before the Bridge run.

Also state that MLM needs PYTHONPATH=3rdparty/​Megatron-LM:$PYTHONPATH, matched Bridge and MLM losses should agree within BF16 rounding, and files under 3rdparty/​Megatron-LM/ should not be modified from this repo.

Correlation Testing

Use vanilla_gpt_pretrain_config for loss-correlation testing. This recipe uses bare GPTModelProvider defaults (LayerNorm, GeLU, learned_absolute position embeddings, vocab_size inherited from tokenizer) — matching MLM pretrain_gpt.py defaults with no args.

MLM Correlation Run (2L/​256H, 1 GPU)

PYTHONPATH=3rdparty/​Megatron-LM:$PYTHONPATH \
uv run python -m torch.distributed.run --nproc_per_node=1 \
  3rdparty/​Megatron-LM/​pretrain_gpt.py \
  --num-layers 2 --hidden-size 256 --num-attention-heads 4 \
  --ffn-hidden-size 1024 --seq-length 512 --max-position-embeddings 512 \
  --micro-batch-size 4 --global-batch-size 32 \
  --train-iters 10 --eval-iters 2 --eval-interval 10 \
  --mock-data --bf16 --use-mcore-models \
  --tokenizer-type NullTokenizer --vocab-size 32000 \
  --lr 3e-4 --min-lr 3e-5 --seed 1234 --log-interval 1

Bridge Correlation Run (same config, 1 GPU)

rm -rf nemo_experiments && \
uv run python -m torch.distributed.run --nproc_per_node=1 \
  scripts/​training/​run_recipe.py \
  --recipe vanilla_gpt_pretrain_config \
  model.num_layers=2 model.hidden_size=256 \
  model.num_attention_heads=4 model.ffn_hidden_size=1024 \
  model.seq_length=512 dataset.seq_length=512 \
  train.train_iters=10 train.global_batch_size=32 train.micro_batch_size=4 \
  validation.eval_interval=10 validation.eval_iters=2 \
  optimizer.lr=3e-4 optimizer.min_lr=3e-5 \
  scheduler.lr_warmup_iters=1 scheduler.lr_decay_iters=10 \
  rng.seed=1234 logger.log_interval=1

Verification

With matched parameters the LM losses should be nearly identical at each iteration. Compare lm loss values from both logs — they should agree to within BF16 rounding.

Multi-GPU Examples

MLM 2-GPU with TP=2

PYTHONPATH=3rdparty/​Megatron-LM:$PYTHONPATH \
uv run python -m torch.distributed.run --nproc_per_node=2 \
  3rdparty/​Megatron-LM/​pretrain_gpt.py \
  --tensor-model-parallel-size 2 --sequence-parallel \
  --num-layers 4 --hidden-size 256 --num-attention-heads 4 \
  --seq-length 1024 --max-position-embeddings 1024 \
  --micro-batch-size 2 --global-batch-size 16 \
  --train-iters 10 --eval-iters 2 --eval-interval 10 \
  --mock-data --bf16 --use-mcore-models \
  --tokenizer-type NullTokenizer --vocab-size 1024 \
  --lr 1e-4 --log-interval 1

Bridge 2-GPU with TP=2

rm -rf nemo_experiments && \
uv run python -m torch.distributed.run --nproc_per_node=2 \
  scripts/​training/​run_recipe.py \
  --recipe vanilla_gpt_pretrain_config \
  model.tensor_model_parallel_size=2 model.sequence_parallel=true \
  model.num_layers=4 model.hidden_size=256 \
  model.num_attention_heads=4 model.ffn_hidden_size=1024 \
  model.seq_length=1024 dataset.seq_length=1024 \
  train.train_iters=10 train.global_batch_size=16 train.micro_batch_size=2 \
  validation.eval_interval=10 validation.eval_iters=2 \
  scheduler.lr_warmup_iters=2 scheduler.lr_decay_iters=10 \
  logger.log_interval=1

Available Recipes

Common recipes (use with --recipe):

• vanilla_gpt_pretrain_config — Minimal GPT (bare GPTModelProvider defaults, ideal for correlation testing and custom configs)
• llama32_1b_pretrain_config — Llama 3.2 1B (16L, 2048H, GBS=512, seq=8192)
• llama3_8b_pretrain_config — Llama 3 8B
• qwen3_8b_pretrain_config — Qwen3 8B
• deepseek_v2_lite_pretrain_config — DeepSeek-V2-Lite 16B MoE

SFT/​PEFT variants use _sft_config / _peft_config suffix.

Megatron-Core Submodule

For what the submodule is and why two versions exist, see @docs/​megatron-lm-to-megatron-bridge.md.

Check current version

./​scripts/​switch_mcore.sh status

Switch to dev for testing newer MCore features

./​scripts/​switch_mcore.sh dev

# uv sync (without --locked) since lockfile is for main
uv sync

Switch back to main

./​scripts/​switch_mcore.sh main

After pulling latest main

When you pull the latest Bridge main branch, the submodule pointer may have been updated. Re-sync the submodule:

git submodule update --init 3rdparty/​Megatron-LM

Pitfalls

1. Always rm -rf nemo_experiments before a fresh correlation run. Bridge auto-resumes from stale checkpoints silently.

2. uv run required: Always use uv run python -m torch.distributed.run (not bare torchrun or python).

3. MLM PYTHONPATH: Must include 3rdparty/​Megatron-LM so gpt_builders.py is importable.

4. Scheduler overrides: When overriding train.train_iters to a small value, also set scheduler.lr_warmup_iters and scheduler.lr_decay_iters or you get an assertion error.

5. Use dataset.seq_length in CLI overrides for both pretraining and fine-tuning datasets.

6. MoE OOM: Large MoE models require full activation recomputation and typically multi-node EP. TP does NOT reduce per-GPU expert memory.

7. uv sync --locked fails after switching to dev: The lockfile is generated against the main MCore commit. Use uv sync (without --locked) when on dev.