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deep learningPython

Megatron-LM Guide: Training Massive Language Models

December 25, 202515 min read

Introduction

Megatron-LM is NVIDIA's framework for training large transformer models. It pioneered efficient tensor and pipeline parallelism, enabling training of models with hundreds of billions of parameters.

CUDA Integration: Megatron uses custom CUDA kernels for fused operations, NCCL for tensor parallelism communication, and optimized attention implementations. It's designed specifically for NVIDIA GPUs with NVLink.

Key Features

✓Tensor parallelism (split layers across GPUs)
✓Pipeline parallelism (split model stages)
✓Sequence parallelism
✓Activation recomputation
✓Fused CUDA kernels
✓Efficient attention
✓GPT and BERT architectures
✓NeMo framework integration

Installation

Clone and set up Megatron.

bash

git clone https://github.com/NVIDIA/Megatron-LM.git
cd Megatron-LM
pip install -r requirements.txt

# Also install apex for fused kernels
git clone https://github.com/NVIDIA/apex.git
cd apex && pip install -v --no-cache-dir ./

Basic Example

GPT Pretraining

Launch GPT training with tensor parallelism.

bash

# pretrain_gpt.sh
GPUS_PER_NODE=8
MASTER_ADDR=localhost
MASTER_PORT=6000
NNODES=1

DISTRIBUTED_ARGS="
    --nproc_per_node $GPUS_PER_NODE \
    --nnodes $NNODES \
    --master_addr $MASTER_ADDR \
    --master_port $MASTER_PORT"

torchrun $DISTRIBUTED_ARGS pretrain_gpt.py \
    --tensor-model-parallel-size 2 \
    --pipeline-model-parallel-size 2 \
    --num-layers 24 \
    --hidden-size 1024 \
    --num-attention-heads 16 \
    --micro-batch-size 4 \
    --global-batch-size 32 \
    --seq-length 1024 \
    --max-position-embeddings 1024 \
    --train-iters 500000 \
    --lr 0.00015 \
    --fp16

Advanced Example

Multi-Node Training

Training across multiple machines.

bash

# On each node, set MASTER_ADDR to node 0's IP
export MASTER_ADDR=10.0.0.1
export MASTER_PORT=6000

# Node 0 (rank 0-7)
export NODE_RANK=0

# Node 1 (rank 8-15)
export NODE_RANK=1

torchrun \
    --nproc_per_node 8 \
    --nnodes 2 \
    --node_rank $NODE_RANK \
    --master_addr $MASTER_ADDR \
    --master_port $MASTER_PORT \
    pretrain_gpt.py \
    --tensor-model-parallel-size 4 \
    --pipeline-model-parallel-size 4 \
    --num-layers 96 \
    --hidden-size 12288 \
    --num-attention-heads 96

Performance Tips

high impact

Use tensor parallelism within node

High bandwidth NVLink required.

high impact

Pipeline parallelism across nodes

Lower bandwidth requirement.

medium impact

Enable sequence parallelism

Further memory reduction.

medium impact

Tune micro-batch size

Balance memory and throughput.

Common Pitfalls

•Tensor parallel must divide hidden size
•Pipeline parallel must divide layers
•NVLink required for efficient tensor parallel
•Global batch size must be divisible by micro-batch * DP
•Sequence length affects memory significantly

Benchmarks

Task	Performance	Notes
GPT-3 175B	502 petaFLOP/s	3072 A100 GPUs
Scaling efficiency	>90%	Up to 1000s GPUs
Memory efficiency	3x reduction	With optimizations

Frequently Asked Questions

Megatron vs DeepSpeed?

Can be combined. Megatron for parallelism, DeepSpeed for ZeRO.

Minimum GPUs needed?

At least 2 for tensor parallel. 4+ recommended.

Can I train on consumer GPUs?

Possible but inefficient. Designed for A100/H100.

Resources

Megatron-LM GitHubRepository

↗

Megatron PaperPaper

↗

Alternatives

DeepSpeed

ZeRO-focused, complementary

→

PyTorch FSDP

Simpler, less specialized

→

Optimize your Megatron CUDA code with RightNow AI - get real-time performance suggestions and memory analysis.

Megatron-LMtensor parallelismpipeline parallelismlarge model trainingNVIDIA Megatron

Introduction

Megatron-LM is NVIDIA's framework for training large transformer models. It pioneered efficient tensor and pipeline parallelism, enabling training of models with hundreds of billions of parameters.

git clone https://github.com/NVIDIA/Megatron-LM.git cd Megatron-LM pip install -r requirements.txt # Also install apex for fused kernels git clone https://github.com/NVIDIA/apex.git cd apex && pip install -v --no-cache-dir ./

Basic Example

GPT Pretraining

Launch GPT training with tensor parallelism.

bash

# pretrain_gpt.sh
GPUS_PER_NODE=8
MASTER_ADDR=localhost
MASTER_PORT=6000
NNODES=1

DISTRIBUTED_ARGS="
    --nproc_per_node $GPUS_PER_NODE \
    --nnodes $NNODES \
    --master_addr $MASTER_ADDR \
    --master_port $MASTER_PORT"

torchrun $DISTRIBUTED_ARGS pretrain_gpt.py \
    --tensor-model-parallel-size 2 \
    --pipeline-model-parallel-size 2 \
    --num-layers 24 \
    --hidden-size 1024 \
    --num-attention-heads 16 \
    --micro-batch-size 4 \
    --global-batch-size 32 \
    --seq-length 1024 \
    --max-position-embeddings 1024 \
    --train-iters 500000 \
    --lr 0.00015 \
    --fp16

Advanced Example

Multi-Node Training

Training across multiple machines.

bash

# On each node, set MASTER_ADDR to node 0's IP
export MASTER_ADDR=10.0.0.1
export MASTER_PORT=6000

# Node 0 (rank 0-7)
export NODE_RANK=0

# Node 1 (rank 8-15)
export NODE_RANK=1

torchrun \
    --nproc_per_node 8 \
    --nnodes 2 \
    --node_rank $NODE_RANK \
    --master_addr $MASTER_ADDR \
    --master_port $MASTER_PORT \
    pretrain_gpt.py \
    --tensor-model-parallel-size 4 \
    --pipeline-model-parallel-size 4 \
    --num-layers 96 \
    --hidden-size 12288 \
    --num-attention-heads 96

Task

Performance

Notes

GPT-3 175B

502 petaFLOP/s

3072 A100 GPUs

Scaling efficiency

>90%

Up to 1000s GPUs

Memory efficiency

3x reduction

With optimizations