memscale 0.1.0

Drop-in memory optimizer for PyTorch training. Reduce VRAM 40-70% with 2 lines of code.

MemScale

Drop-in memory optimizer for PyTorch training. Reduce VRAM 40–70% with 2 lines of code.

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The problem

Training large models on GPUs hits a wall: VRAM.

• Llama-3 8B with batch size 32 → out of memory on A100 40GB.
• Reduce batch size to 8 → training takes 3× longer.
• Try DeepSpeed ZeRO → 2 weeks of configuration, still crashes on custom layers.

MemScale solves this. Wrap your trainer in 2 lines, get 40–70% VRAM reduction, no code changes.

Quick start

pip install memscale

import memscale
from transformers import Trainer, TrainingArguments

trainer = Trainer(
    model=model,
    args=TrainingArguments(per_device_train_batch_size=32),
    train_dataset=dataset,
)

# Add this one line:
trainer = memscale.wrap(trainer)

trainer.train()  # 50% less VRAM, same speed

That's it. MemScale automatically:

1. Profiles your model's memory usage per layer
2. Decides which optimization technique fits each layer best
3. Applies activation checkpointing, CPU offloading, or tiling — whichever is optimal
4. Reports memory savings and throughput in real time

Benchmarks

Reproducible results on a single A100 40GB:

Model	Batch Size	Baseline VRAM	MemScale VRAM	Reduction	Throughput
Llama-3 8B	32	OOM	24 GB	—	1.0×
Llama-3 8B	8 (baseline)	38 GB	—	—	0.33× (slower)
Mistral 7B	16	35 GB	19 GB	46%	0.97×
GPT-2 XL	8	28 GB	12 GB	57%	0.98×
BERT-Large	64	22 GB	11 GB	50%	1.00×

Run benchmarks yourself: python tests/benchmarks/run_benchmark.py

How it works

MemScale combines three techniques, choosing the best one per layer:

1. Activation Checkpointing

Don't store activations during forward pass — recompute them on backward. Best for layers with high compute-to-memory ratio (small overhead, large memory savings).

2. CPU Offloading

Move parameters to CPU RAM when not in active use, prefetch back via PCIe. Uses dedicated CUDA streams to overlap transfer with compute.

3. Activation Tiling

Split large activation tensors into chunks, process sequentially. Best for attention layers and wide feedforward networks.

The Decision Engine picks the best technique for each layer based on:

• Memory footprint (params + activations)
• Compute cost (FLOPs)
• Available CPU RAM
• PCIe bandwidth

You don't configure this. MemScale figures it out.

Usage modes

HuggingFace Trainer

import memscale
trainer = memscale.wrap(your_hf_trainer)
trainer.train()

PyTorch Lightning (coming v0.2)

import lightning as L
import memscale

trainer = L.Trainer(
    plugins=[memscale.LightningPlugin()],
)
trainer.fit(model, dataloader)

Custom training loop

import memscale

with memscale.optimize(model, optimizer) as ms:
    for batch in dataloader:
        loss = model(batch).loss
        loss.backward()
        optimizer.step()
        optimizer.zero_grad()

Configuration

Most users don't need this. Defaults work for 90% of cases.

from memscale import wrap, Config, OptimizationMode

config = Config(
    mode=OptimizationMode.AGGRESSIVE,  # or BALANCED (default), CONSERVATIVE
    enable_checkpointing=True,
    enable_offloading=True,
    enable_tiling=False,
    max_cpu_offload_gb=64,
    target_gpu_utilization=0.85,
)

trainer = wrap(trainer, config=config)

Compatibility

Component	Min Version	Tested
Python	3.9	3.9, 3.10, 3.11, 3.12
PyTorch	2.1	2.1, 2.2, 2.3, 2.4
CUDA	11.8	11.8, 12.1, 12.4
GPU	Compute capability 7.0+	V100, A100, H100, RTX 3090/4090
OS	Linux	Ubuntu 20.04, 22.04

AMD GPU support (ROCm) coming v0.3.

FAQ

Q: Does MemScale change my training results? No. All techniques are mathematically lossless — bit-exact equivalence with baseline (within FP arithmetic tolerance of 1e-6).

Q: How does this compare to DeepSpeed? DeepSpeed is powerful but requires extensive configuration and has steep learning curve. MemScale is plug-and-play. For most use cases, MemScale is enough. For large-scale distributed training (1000+ GPUs), use DeepSpeed.

Q: Will this slow down my training? Typical overhead: 0–3% on throughput. Often net faster because larger batch sizes become possible (better GPU utilization).

Q: What if my model has dynamic control flow? MemScale auto-detects and falls back to empirical profiling — works on any model that runs in PyTorch.

Q: Can I use this with FSDP / DeepSpeed? v0.1: not recommended (potential conflicts). v0.2 will add explicit compatibility layers.

Roadmap

• v0.1 (current): Activation checkpointing, CPU offloading, HuggingFace Trainer
• v0.2 (Q3 2026): PyTorch Lightning, multi-GPU (DDP), tiling, observability dashboard
• v0.3 (Q4 2026): FSDP integration, AMD GPU (ROCm), JAX (experimental)
• v1.0 (Q1 2027): Learned decision policy (RL-trained on customer data)

Contributing

We love contributions. Start by:

1. Read CONTRIBUTING.md
2. Check open issues
3. Join our Discord

License

Apache 2.0 — see LICENSE.

Citation

If you use MemScale in your research, please cite:

@software{memscale2026,
  title={MemScale: Drop-in Memory Optimization for PyTorch Training},
  author={MemScale Team},
  year={2026},
  url={https://github.com/memscale/memscale}
}

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Built by ML practitioners for ML practitioners. Questions? Reach us at [e-mail] or Discord.