expandable_segments with PYTORCH_CUDA_ALLOC_CONF reduces VRAM

[winglian]

In Unsloth:

# Reduce VRAM usage by reducing fragmentation
os.environ["PYTORCH_CUDA_ALLOC_CONF"] = "expandable_segments:True"

using the defaults in torchtune:

tune run lora_finetune_single_device --config llama3/8B_qlora_single_device

• A100 80GB
• Measure @ 20steps
• PYTORCH_CUDA_ALLOC_CONF="expandable_segments:True"

We should dig into why this provides such a large memory improvement and consider making it the default setting, or documenting it somewhere if not.

baseline: 16.39GiB @ 21.6sec/it w/ compile: 16.63GiB @ 8.5sec/it w/ expandable_segments: 10.831GiB @ 20.56sec/it w/ compile + expandable_segments: 10.919GiB @ 8.5sec/it

[pbontrager]

Adding a comment to track this discussion in Pytorch core https://github.com/pytorch/pytorch/issues/130330

[joecummings]

Adding a comment to track this discussion in Pytorch core pytorch/pytorch#130330

If this lands, we should enable this by default in torchtune until it lands in a PyTorch stable release.

[ebsmothers]

@winglian where did we land on this? The numbers here are for reserved memory, right? Do we see similar improvements on peak allocated memory as well?

[pbontrager]

I think the prudent thing to would be to wait for this to land in core with all the necessary safety updates that go with it, and not enable this flag ourself.

[felipemello1]

Just wanted to confirm that running on A100, with the flag i can run bs=4, but without, it OOMs

editing for clarity: Green has bsz=3. When i go to bsz=4, it doesnt work, unless I use the flag. The info is in the run name. It does NOT impact allocated memory, only reserved.

tune run --nproc_per_node 8 lora_finetune_distributed --config llama3/8B_lora \
batch_size=4 \
model.lora_attn_modules="['q_proj', 'v_proj']" \
model.apply_lora_to_mlp=False \
model.apply_lora_to_output=False \
metric_logger.name=LoRA__8192__nproc8__bsz4__ActCkptTrue \
dataset.max_seq_len=8192 \
dataset.source=Yukang/LongAlpaca-12k \
dataset.packed=False \
dataset.split="train[:10%]" \
metric_logger=torchtune.utils.metric_logging.WandBLogger \
metric_logger.project=mem_prealloc \
gradient_accumulation_steps=1 \
log_every_n_steps=1 \
log_peak_memory_stats=True \
max_steps_per_epoch=20 \
epochs=1 \
compile=False \
enable_activation_checkpointing=True

[joecummings]

Just wanted to confirm that running on A100, with the flag i can run bs=4, but without, it OOMs

This would imply that we should be paying attention to reserved attention too, right? @ebsmothers

[ebsmothers]

Just wanted to confirm that running on A100, with the flag i can run bs=4, but without, it OOMs

This would imply that we should be paying attention to reserved attention too, right? @ebsmothers

Sorry I missed this before. But based on @felipemello1's screenshot I would say that's not the case. It looks like this flag also impacts both active and allocated memory. In that case it seems pretty clear to me that we should enable it

[felipemello1]

It looks like this flag also impacts both active and allocated memory

No, sorry, thats misleading. Green has bsz=3. When i go to bsz=4, it doesnt work, unless I use the flag. The info is in the run name.

It does NOT impact allocated memory, only reserved

@ebsmothers

[ebsmothers]

Thanks @felipemello1 for clarifying, I should've looked at the legend more closely.

So for actual next steps here: it seems like there are cases where anecdotally this does prevent OOMs. However there is some feature gap around CUDA IPC (as discussed in the issue linked by @pbontrager) preventing core from enabling it by default at this exact moment. We can wait for that, but if we want to unblock this sooner, I would say we can:

(1) enable this config on a branch, run on a broader set of recipes to ensure no obvious breakages or huge perf regressions.

Depending on the result of that, either:

(2a) enable this by default for all our recipes, or (2b) gate it behind a config.

In my mind (2b) is not the ideal outcome cause it's just one extra bespoke field to worry about. But open to discussing once we have more comprehensive results here.

[Seedmanc]

What's the support matrix for expandable segments? I keep getting denied with "not supported on this platform" using EasyDiffusion and EasyTraining GUIs, but it's no stating whether I'm missing hardware or software support. I'm running Cuda 12.1 on Win10x64 and RTX 2070S 8Gb, torch-2.3.1+cu121, torchvision-0.18.1+cu121.

[felipemello1]

@Seedmanc , seems related: https://github.com/pytorch/pytorch/issues/122057

[Petros626]

@felipemello1 random questiobn: how did you custom named the wandb logs?

[felipemello1]

@felipemello1 random questiobn: how did you custom named the wandb logs?

@Petros626

tune run --nproc_per_node 2 full_finetune_distributed --config llama3_2/3B_full \
metric_logger=torchtune.training.metric_logging.WandBLogger \
metric_logger.project=profiling \
metric_logger.tags=[my_experiment_name] \
metric_logger.name=my_log_name 

[ebsmothers]

I am gonna close this issue as it's quite old and we ultimately decided to have PyTorch handle this

[kissint8]

Just wanted to confirm that running on A100, with the flag i can run bs=4, but without, it OOMs

editing for clarity: Green has bsz=3. When i go to bsz=4, it doesnt work, unless I use the flag. The info is in the run name. It does NOT impact allocated memory, only reserved.

tune run --nproc_per_node 8 lora_finetune_distributed --config llama3/8B_lora \
batch_size=4 \
model.lora_attn_modules="['q_proj', 'v_proj']" \
model.apply_lora_to_mlp=False \
model.apply_lora_to_output=False \
metric_logger.name=LoRA__8192__nproc8__bsz4__ActCkptTrue \
dataset.max_seq_len=8192 \
dataset.source=Yukang/LongAlpaca-12k \
dataset.packed=False \
dataset.split="train[:10%]" \
metric_logger=torchtune.utils.metric_logging.WandBLogger \
metric_logger.project=mem_prealloc \
gradient_accumulation_steps=1 \
log_every_n_steps=1 \
log_peak_memory_stats=True \
max_steps_per_epoch=20 \
epochs=1 \
compile=False \
enable_activation_checkpointing=True

I am very interested in your memory image. Is there any tool can get this visualization result, OR, which function to get these data?

[felipemello1]

@kissint8 , this is weights and biases. In the command, it is defined by the logger "metric_logger=torchtune.utils.metric_logging.WandBLogger"

Then you can go to their website and see it wandb.com

You can find more info here: https://pytorch.org/torchtune/main/deep_dives/wandb_logging.html?highlight=wandb