![]() This is because there are other components that also require memory storage. Some large models may still face memory issues even when the batch size is set to 1 and gradient accumulation is used. This results in the same effective batch size while making better use ofįor additional information, please refer to batch size and gradient accumulation benchmarks for RTX-3090 Instead, keep per_device_train_batch_size=4Īnd set gradient_accumulation_steps=16. Per_device_train_batch_size to 1 and gradient_accumulation_steps to 64. ![]() If you would like to train with batches of size 64, do not set the Without gradient accumulation hits the GPU’s limit. Let’s say, the per_device_train_batch_size=4 Result in a more pronounced training slowdown. While it is advised to max out GPU usage as much as possible, a high number of gradient accumulation steps can In the above example, your effective batch size becomes 4.Īlternatively, use □ Accelerate to gain full control over the training loop. You can enable gradient accumulation by adding the gradient_accumulation_steps argument to TrainingArguments:Ĭopied training_args = TrainingArguments(per_device_train_batch_size= 1, gradient_accumulation_steps= 4, **default_args) However, it is important to note that the additional forward and backward passes introduced by gradient accumulation can By employing gradient accumulation, itīecomes possible to increase the effective batch size beyond the limitations imposed by the GPU’s memory capacity. Gradients have been accumulated, the model’s optimization step is executed. This approach involves iteratively calculating gradients in smaller batches by performing forwardĪnd backward passes through the model and accumulating the gradients during the process. The gradient accumulation method aims to calculate gradients in smaller increments instead of computing them for theĮntire batch at once. This is where tiling happens and the right multiplier can have a significant speedup. It’s an A100 GPU, in which case use multiples of 64.įor parameters that are small, consider also Dimension Quantization Effects. For instance, for fp16 data type a multiple of 8 is recommended, unless Higher depending on the hardware being used and the model’s dtype.įor reference, check out NVIDIA’s recommendation for input/output neuron counts andįully connected layers (which are involved in GEMMs (General Matrix Multiplications)).ĭefine the multiplier based on the dtype and the hardware. Often it’s a multiple of 8, but it can be Input/output neuron counts that are of size 2^N. To achieve optimal performance, start by identifying the appropriate batch size. Techniques outlined in the multi-GPU section. All these approaches are still valid in a multi-GPU setup, plus you can leverage additional parallelism Convert your model to BetterTransformer to leverage PyTorch native attentionįinally, if all of the above is still not enough, even after switching to a server-grade GPU like A100, consider moving.Consider a model that uses Mixture of Experts (MoE).Look into building your own custom Docker container with efficient softare prebuilds.If these methods do not result in sufficient gains, you can explore the following options: Training your model with Trainer or writing a pure PyTorch loop, in which case you can configure these optimizations These techniques are available to you whether you are You can combine the above methods to get a cumulative effect. Large model and a small batch size, the memory use will be larger. Note: when using mixed precision with a small model and a large batch size, there will be some memory savings but with a The methods and tools covered in this guide can be classified based on the effect they have on the training process: Method/tool Hyperparameter tuning, you should determine which batch size yields the best results and then optimize resources accordingly. Just because one can use a large batch size, does not necessarily mean they should. ![]() However, if the preferred batch size fits into memory, there’s no reason to apply memory-optimizing techniques because they can The memory optimization techniques, such as gradient accumulation, can help. If the desired batch size exceeds the limits of the GPU memory, This is generally achieved by utilizing the GPUĪs much as possible and thus filling GPU memory to its limit. Maximizing the throughput (samples/second) leads to lower training cost. When training large models, there are two aspects that should be considered at the same time: If you have access to a machine with multiple GPUs, these approaches are still valid, plus you can leverage additional methods outlined in the multi-GPU section.
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