hermes-agent/optional-skills/mlops/accelerate/references/megatron-integration.md

Megatron Integration with Accelerate

Overview

Accelerate supports Megatron-LM for massive model training with tensor parallelism and pipeline parallelism.

Megatron capabilities:

• Tensor Parallelism (TP): Split layers across GPUs
• Pipeline Parallelism (PP): Split model depth across GPUs
• Data Parallelism (DP): Replicate model across GPU groups
• Sequence Parallelism: Split sequences for long contexts

Setup

Install Megatron-LM

# Clone Megatron-LM repository
git clone https://github.com/NVIDIA/Megatron-LM.git
cd Megatron-LM
pip install -e .

# Install Apex (NVIDIA optimizations)
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" ./

Accelerate Configuration

accelerate config

Questions:

In which compute environment are you running?
> This machine

Which type of machine are you using?
> Multi-GPU

How many different machines will you use?
> 1

Do you want to use DeepSpeed/FSDP?
> No

Do you want to use Megatron-LM?
> Yes

What is the Tensor Parallelism degree? [1-8]
> 2

Do you want to enable Sequence Parallelism?
> No

What is the Pipeline Parallelism degree? [1-8]
> 2

What is the Data Parallelism degree? [1-8]
> 2

Where to perform activation checkpointing? ['SELECTIVE', 'FULL', 'NONE']
> SELECTIVE

Where to perform activation partitioning? ['SEQUENTIAL', 'UNIFORM']
> SEQUENTIAL

Generated config (~/.cache/huggingface/accelerate/default_config.yaml):

compute_environment: LOCAL_MACHINE
distributed_type: MEGATRON_LM
downcast_bf16: 'no'
machine_rank: 0
main_training_function: main
megatron_lm_config:
  megatron_lm_gradient_clipping: 1.0
  megatron_lm_learning_rate_decay_iters: 320000
  megatron_lm_num_micro_batches: 1
  megatron_lm_pp_degree: 2
  megatron_lm_recompute_activations: true
  megatron_lm_sequence_parallelism: false
  megatron_lm_tp_degree: 2
mixed_precision: bf16
num_machines: 1
num_processes: 8
rdzv_backend: static
same_network: true
tpu_env: []
tpu_use_cluster: false
tpu_use_sudo: false
use_cpu: false

Parallelism Strategies

Tensor Parallelism (TP)

Splits each transformer layer across GPUs:

# Layer split across 2 GPUs
# GPU 0: First half of attention heads
# GPU 1: Second half of attention heads

# Each GPU computes partial outputs
# All-reduce combines results

TP degree recommendations:

• TP=1: No tensor parallelism (single GPU per layer)
• TP=2: 2 GPUs per layer (good for 7-13B models)
• TP=4: 4 GPUs per layer (good for 20-40B models)
• TP=8: 8 GPUs per layer (good for 70B+ models)

Benefits:

• Reduces memory per GPU
• All-reduce communication (fast)

Drawbacks:

• Requires fast inter-GPU bandwidth (NVLink)
• Communication overhead per layer

Pipeline Parallelism (PP)

Splits model depth across GPUs:

# 12-layer model, PP=4
# GPU 0: Layers 0-2
# GPU 1: Layers 3-5
# GPU 2: Layers 6-8
# GPU 3: Layers 9-11

PP degree recommendations:

• PP=1: No pipeline parallelism
• PP=2: 2 pipeline stages (good for 20-40B models)
• PP=4: 4 pipeline stages (good for 70B+ models)
• PP=8: 8 pipeline stages (good for 175B+ models)

Benefits:

• Linear memory reduction (4× PP = 4× less memory)
• Works across nodes (slower interconnect OK)

Drawbacks:

• Pipeline bubbles (idle time)
• Requires micro-batching

Data Parallelism (DP)

Replicates model across GPU groups:

# 8 GPUs, TP=2, PP=2, DP=2
# Group 0 (GPUs 0-3): Full model replica
# Group 1 (GPUs 4-7): Full model replica

DP degree:

• DP = total_gpus / (TP × PP)
• Example: 8 GPUs, TP=2, PP=2 → DP=2

Benefits:

• Increases throughput
• Scales batch size

Sequence Parallelism

Splits long sequences across GPUs (extends TP):

# 8K sequence, TP=2, Sequence Parallel=True
# GPU 0: Tokens 0-4095
# GPU 1: Tokens 4096-8191

Benefits:

• Enables very long sequences (100K+ tokens)
• Reduces activation memory

Requirements:

• Must use with TP > 1
• RoPE/ALiBi position encodings work best

Accelerate Code Example

Basic Setup

from accelerate import Accelerator
from accelerate.utils import MegatronLMPlugin

# Configure Megatron
megatron_plugin = MegatronLMPlugin(
    tp_degree=2,              # Tensor parallelism degree
    pp_degree=2,              # Pipeline parallelism degree
    num_micro_batches=4,      # Micro-batches for pipeline
    gradient_clipping=1.0,    # Gradient clipping value
    sequence_parallelism=False,  # Enable sequence parallelism
    recompute_activations=True,  # Activation checkpointing
    use_distributed_optimizer=True,  # Distributed optimizer
    custom_prepare_model_function=None,  # Custom model prep
)

# Initialize accelerator
accelerator = Accelerator(
    mixed_precision='bf16',
    megatron_lm_plugin=megatron_plugin
)

# Prepare model and optimizer
model, optimizer, train_dataloader = accelerator.prepare(
    model, optimizer, train_dataloader
)

# Training loop (same as DDP!)
for batch in train_dataloader:
    optimizer.zero_grad()
    outputs = model(**batch)
    loss = outputs.loss
    accelerator.backward(loss)
    optimizer.step()

Full Training Script

import torch
from accelerate import Accelerator
from accelerate.utils import MegatronLMPlugin
from transformers import GPT2Config, GPT2LMHeadModel

def main():
    # Megatron configuration
    megatron_plugin = MegatronLMPlugin(
        tp_degree=2,
        pp_degree=2,
        num_micro_batches=4,
        gradient_clipping=1.0,
    )

    accelerator = Accelerator(
        mixed_precision='bf16',
        gradient_accumulation_steps=8,
        megatron_lm_plugin=megatron_plugin
    )

    # Model
    config = GPT2Config(
        n_layer=24,
        n_head=16,
        n_embd=1024,
    )
    model = GPT2LMHeadModel(config)

    # Optimizer
    optimizer = torch.optim.AdamW(model.parameters(), lr=6e-4)

    # Prepare
    model, optimizer, train_loader = accelerator.prepare(
        model, optimizer, train_loader
    )

    # Training loop
    for epoch in range(num_epochs):
        for batch in train_loader:
            with accelerator.accumulate(model):
                outputs = model(**batch)
                loss = outputs.loss
                accelerator.backward(loss)
                optimizer.step()
                optimizer.zero_grad()

        # Save checkpoint
        accelerator.wait_for_everyone()
        accelerator.save_state(f'checkpoint-epoch-{epoch}')

if __name__ == '__main__':
    main()

Launch Command

# 8 GPUs, TP=2, PP=2, DP=2
accelerate launch --multi_gpu --num_processes 8 train.py

# Multi-node (2 nodes, 8 GPUs each)
# Node 0
accelerate launch --multi_gpu --num_processes 16 \
  --num_machines 2 --machine_rank 0 \
  --main_process_ip $MASTER_ADDR \
  --main_process_port 29500 \
  train.py

# Node 1
accelerate launch --multi_gpu --num_processes 16 \
  --num_machines 2 --machine_rank 1 \
  --main_process_ip $MASTER_ADDR \
  --main_process_port 29500 \
  train.py

Activation Checkpointing

Reduces memory by recomputing activations:

megatron_plugin = MegatronLMPlugin(
    recompute_activations=True,      # Enable checkpointing
    checkpoint_num_layers=1,         # Checkpoint every N layers
    distribute_checkpointed_activations=True,  # Distribute across TP
    partition_activations=True,      # Partition in PP
    check_for_nan_in_loss_and_grad=True,  # Stability check
)

Strategies:

• SELECTIVE: Checkpoint transformer blocks only
• FULL: Checkpoint all layers
• NONE: No checkpointing

Memory savings: 30-50% with 10-15% slowdown

Distributed Optimizer

Shards optimizer state across DP ranks:

megatron_plugin = MegatronLMPlugin(
    use_distributed_optimizer=True,  # Enable sharded optimizer
)

Benefits:

• Reduces optimizer memory by DP degree
• Example: DP=4 → 4× less optimizer memory per GPU

Compatible with:

• AdamW, Adam, SGD
• Mixed precision training

Performance Tuning

Micro-Batch Size

# Pipeline parallelism requires micro-batching
megatron_plugin = MegatronLMPlugin(
    pp_degree=4,
    num_micro_batches=16,  # 16 micro-batches per pipeline
)

# Effective batch = num_micro_batches × micro_batch_size × DP
# Example: 16 × 2 × 4 = 128

Recommendations:

• More micro-batches → less pipeline bubble
• Typical: 4-16 micro-batches

Sequence Length

# For long sequences, enable sequence parallelism
megatron_plugin = MegatronLMPlugin(
    tp_degree=4,
    sequence_parallelism=True,  # Required: TP > 1
)

# Enables sequences up to TP × normal limit
# Example: TP=4, 8K normal → 32K with sequence parallel

GPU Topology

NVLink required for TP:

# Check NVLink topology
nvidia-smi topo -m

# Good topology (NVLink between all GPUs)
# GPU0 - GPU1: NV12 (fast)
# GPU0 - GPU2: NV12 (fast)

# Bad topology (PCIe only)
# GPU0 - GPU4: PHB (slow, avoid TP across these)

Recommendations:

• TP: Within same node (NVLink)
• PP: Across nodes (slower interconnect OK)
• DP: Any topology

Model Size Guidelines

Model Size	GPUs	TP	PP	DP	Micro-Batches
7B	8	1	1	8	1
13B	8	2	1	4	1
20B	16	4	1	4	1
40B	32	4	2	4	4
70B	64	8	2	4	8
175B	128	8	4	4	16

Assumptions: BF16, 2K sequence length, A100 80GB

Checkpointing

Save Checkpoint

# Save full model state
accelerator.save_state('checkpoint-1000')

# Megatron saves separate files per rank
# checkpoint-1000/
#   pytorch_model_tp_0_pp_0.bin
#   pytorch_model_tp_0_pp_1.bin
#   pytorch_model_tp_1_pp_0.bin
#   pytorch_model_tp_1_pp_1.bin
#   optimizer_tp_0_pp_0.bin
#   ...

Load Checkpoint

# Resume training
accelerator.load_state('checkpoint-1000')

# Automatically loads correct shard per rank

Convert to Standard PyTorch

# Merge Megatron checkpoint to single file
python merge_megatron_checkpoint.py \
  --checkpoint-dir checkpoint-1000 \
  --output pytorch_model.bin

Common Issues

Issue: OOM with Pipeline Parallelism

Solution: Increase micro-batches

megatron_plugin = MegatronLMPlugin(
    pp_degree=4,
    num_micro_batches=16,  # Increase from 4
)

Issue: Slow Training

Check 1: Pipeline bubbles (PP too high)

# Reduce PP, increase TP
tp_degree=4  # Increase
pp_degree=2  # Decrease

Check 2: Micro-batch size too small

num_micro_batches=8  # Increase

Issue: NVLink Not Detected

# Verify NVLink
nvidia-smi nvlink -s

# If no NVLink, avoid TP > 1
# Use PP or DP instead

Resources

• Megatron-LM: https://github.com/NVIDIA/Megatron-LM
• Accelerate Megatron docs: https://huggingface.co/docs/accelerate/usage_guides/megatron_lm
• Paper: "Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism"
• NVIDIA Apex: https://github.com/NVIDIA/apex