[Feat] Activation offloading for distributed lora recipe

[Jackmin801]

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Changelog

What are the changes made in this PR?

• Change docstring to reflect arg change activation_checkpoint -> enable_activation_checkpoint
• Add arg enable_activation_offloading and logic to do activation offloading

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[Jackmin801]

While we were working on this PR, we observed a weird behaviour in the trace.

Why does this aten::_scaled_dot_product_cudnn_attention_backward and forward take so much time in cpu???

Steps to reproduce

1. Clone repo at this branch

git clone --branch feat-activation-offloading-streams-dist git@github.com:Jackmin801/torchtune
cd torchtune

2. Dev Install with torch nightly

pip3 install --pre torch torchvision torchaudio --index-url https://download.pytorch.org/whl/nightly/cu121
pip install torchao
pip install matplotlib pandas numpy
pip install -e ".[dev]"

3. Login to huggingface and download model

huggingface-cli login --token <HF_TOKEN>

tune download meta-llama/Meta-Llama-3.1-8B-Instruct --output-dir /tmp/Meta-Llama-3.1-8B-Instruct --ignore-patterns "original/*.pth"

4. Run single device

#tune run --nnodes 1 --nproc-per-node 2 lora_finetune_distributed --config recipes/configs/llama3_1/8B_lora.yaml \
tune run lora_finetune_single_device --config recipes/configs/llama3_1/8B_lora.yaml \
  optimizer_in_bwd=False \
  enable_activation_offloading=False \
  enable_activation_checkpointing=True \
  compile=False \
  model.lora_attn_modules="[q_proj,v_proj]" \
  model.apply_lora_to_mlp=False \
  model.apply_lora_to_output=False \
  model.lora_rank=8 \
  model.lora_alpha=16 \
  dataset.source=Yukang/LongAlpaca-12k \
  dataset.packed=False \
  dataset.split=train[:10%] \
  dataset.train_on_input=True \
  tokenizer.max_seq_len=8192 \
  metric_logger=torchtune.training.metric_logging.StdoutLogger \
  metric_logger.project=recipe_profiling \
  log_every_n_steps=1 \
  log_peak_memory_stats=True \
  gradient_accumulation_steps=1 \
  max_steps_per_epoch=4 \
  epochs=1 \
  batch_size=2 \
  metric_logger.name=llama3__qlora__seqlen_8192__act_ckpt_True__act_off_True__bs2 \
  profiler.enabled=True \
  profiler.profile_memory=True \
  profiler.with_stack=True \
  profiler.wait_steps=0 \
  profiler.warmup_steps=2 \
  profiler.active_steps=2 \
  profiler.num_cycles=1

5. Observe the trace

Weird right?

Observations

@yf225 figured out that theres a warning from cuDNN sdpa about memory layout that is likely the culprit

/data/users/weif/pytorch/torch/autograd/graph.py:826: UserWarning: cuDNN SDPA backward got grad_output.strides() != output.strides(), attempting to materialize a grad_output with matching strides... (Triggered internally at /data/users/weif/pytorch/aten/src/ATen/native/cudnn/MHA.cpp:674.)
  return Variable._execution_engine.run_backward(  # Calls into the C++ engine to run the backward pass

@janeyx99 @joecummings @yf225

[Jackmin801]

ah. will lint haha. wanted to start hacking first

[felipemello1]

Thanks the PR!! Would you be able to run it with activation on and off for ~25-50 steps and compare loss, memory, tps?

If you use weights and biases, its probably the easiest way to take a screenshot (you can login with your gmail, get the token, pip install wandb, wandb login, insert your token)

then you can run your config like this:
tune run --nproc_per_node 2 lora_finetune_distributed --config llama3_1/8B_lora max_steps_per_epoch=50 epochs=1 metric_logger=torchtune.training.metric_logging.WandBLogger log_peak_memory_stats=True ...other params...

Please use dataset.packed=True, compile=True, enable_activation_checkpoint=True, enable_activation_offload=true/false

if loss and TPS is about the same, but memory is much lower, this should be easy to approve.