# Reducing Memory Usage Section under construction. Feel free to contribute! ## Truncation Sequence lengths in the dataset can vary widely. When data is batched, sequences are padded to match the longest one in the batch, which can cause high memory usage, even if most sequences are relatively short.
Truncation prompt completion
To reduce memory usage, it's important to truncate sequences to a reasonable length. While TRL trainers truncate sequences by default, you may want to adjust the default truncation length to better align with your specific use case. DPO truncation is applied first to the prompt and to the completion via the `max_prompt_length` and `max_completion_length` parameters. The `max_length` parameter is then used to truncate the resulting sequence.
Truncation prompt completion
To set the truncation parameters, use the following code snippet: ```python from trl import DPOConfig training_args = DPOConfig(..., max_prompt_length=..., max_length=...) ``` You can also use the `max_completion_length` parameter to truncate the completion, though this is less common since the goal is typically to preserve the completion's full length whenever possible. ```python from trl import DPOConfig training_args = DPOConfig(..., max_completion_length=...) ``` SFT truncation is applied to the input sequence via the `max_length` parameter.
Truncation input ids
To set the truncation parameter, use the following code snippet: ```python from trl import SFTConfig training_args = SFTConfig(..., max_length=...) ``` ## Packing This technique applies only to SFT. [Truncation](#truncation) has several drawbacks: 1. **Loss of information**: Key data at the end of a sequence may be discarded. 2. **Choosing truncation length**: Too short loses data; too long undermines efficiency. Packing, introduced in [Raffel et al., 2020](https://huggingface.co/papers/1910.10683), addresses these issues by grouping sequences instead of truncating. It concatenates and splits dataset sequences into the desired lengths.
Packing
Packing reduces padding by merging several sequences in one row when possible. We use an advanced method to be near-optimal in the way we pack the dataset. To enable packing, use `packing=True` and in the [`SFTConfig`]. In TRL 0.18 and earlier, packing used a more aggressive method that reduced padding to almost nothing, but had the downside of breaking sequence continuity for a large fraction of the dataset. To revert to this strategy, use `packing_strategy="wrapped"` in `SFTConfig`. ```python from trl import SFTConfig training_args = SFTConfig(..., packing=True, max_length=512) ``` Packing may cause batch contamination, where adjacent sequences influence one another. This can be problematic for some applications. For more details, see [#1230](https://github.com/huggingface/trl/issues/1230). ## Liger for reducing peak memory usage > [Liger Kernel](https://github.com/linkedin/Liger-Kernel) is a collection of Triton kernels designed specifically for LLM training. It can effectively increase multi-GPU training throughput by 20% and reduces memory usage by 60%. For more information, see [Liger Kernel Integration](liger_kernel_integration) To use Liger for reducing peak memory usage, use the following code snippet: ```python from trl import DPOConfig training_args = DPOConfig(..., use_liger_loss=True) ``` To use Liger for reducing peak memory usage, use the following code snippet: ```python from trl import GRPOConfig training_args = GRPOConfig(..., use_liger_loss=True) ``` To use Liger for reducing peak memory usage, use the following code snippet: ```python from trl import KTOConfig training_args = KTOConfig(..., use_liger_loss=True) ``` ## Padding-free Padding-free batching is an alternative approach for reducing memory usage. In this method, a batch is first sampled and then flattened into a single sequence, avoiding padding. Unlike packing, which can result in incomplete sequences by combining parts of different samples, padding-free batching ensures that all sequences remain complete and intact.
Padding-free batching
It's highly recommended to use padding-free batching with **Flash Attention 2**. Otherwise, you may encounter batch contamination issues. ```python from trl import DPOConfig training_args = DPOConfig(..., padding_free=True, model_init_kwargs={"attn_implementation": "flash_attention_2"}) ``` ```python from trl import SFTConfig training_args = SFTConfig(..., padding_free=True, model_init_kwargs={"attn_implementation": "flash_attention_2"}) ``` ## Activation offloading Activation offloading is a memory efficiency technique that reduces GPU VRAM usage by temporarily moving activation tensors to CPU RAM during the forward pass and bringing them back only when needed for the backward pass. This significantly reduces peak memory usage at the cost of slightly increased training time. To enable activation offloading in your SFT training configuration: ```python from trl import SFTConfig training_args = SFTConfig(..., activation_offloading=True) ``` When using activation offloading with models that use Liger kernels, you must disable Liger cross entropy due to compatibility issues. The issue occurs specifically with `use_liger_kernel=True` because Liger cross entropy performs in-place operations which conflict with activation offloading. The default setting (`use_liger_kernel=False`) works: ```python # When using activation offloading with a model that uses Liger kernels: from trl import SFTConfig training_args = SFTConfig( activation_offloading=True, use_liger_kernel=False, # Disable Liger cross entropy # Other parameters... ) ``` Under the hood, activation offloading implements PyTorch's [`saved_tensors_hooks`](https://pytorch.org/tutorials/intermediate/autograd_saved_tensors_hooks_tutorial.html#hooks-for-autograd-saved-tensors) to intercept activations during the forward pass. It intelligently manages which tensors to offload based on size and context, avoiding offloading output tensors which would be inefficient. For performance optimization, it can optionally use CUDA streams to overlap computation with CPU-GPU transfers. ## Disabling model gathering for generation in online methods When using DeepSpeed ZeRO-3, model weights are sharded across multiple GPUs. Online methods involve generating completions from the model as part of the training process. During this step, the model weights are temporarily gathered on a single GPU for generation. For very large models, this gathering can lead to out-of-memory (OOM) errors, as described in this issue: [#2250](https://github.com/huggingface/trl/issues/2250#issue-2598304204). If you encounter this issue, you can disable the gathering of model weights for generation by setting the following parameter: ```python from trl import GRPOConfig training_args = GRPOConfig(..., ds3_gather_for_generation=False) ``` ```python from trl import OnlineDPOConfig training_args = OnlineDPOConfig(..., ds3_gather_for_generation=False) ``` ```python from trl import PPOConfig training_args = PPOConfig(..., ds3_gather_for_generation=False) ``` ```python from trl import RLOOConfig training_args = RLOOConfig(..., ds3_gather_for_generation=False) ``` This adjustment prevents model weights from being gathered, avoiding OOM errors, but it may result in slower generation speeds.