impact of the gradient_accumulation_every setting on the model's convergence

[Jean-Rassaire]

Hello,

I'd like to understand the effect of the gradient_accumulation_every parameter.

From reviewing the piece of code below, it appears that not all batches are utilized for the training.

For example, you've set gradient_accumulation_every=2 in your code, which suggests that only two batches are used for each step. I expected all training batches to be used at each step before moving to the next. So, if I'm interpreting this correctly, with 10 training images, a batch_size of 1, and gradient_accumulation_every=2, then for each step, only two images (accounting for gradient_accumulation_every=2 and batch_size=1) are used to update the gradient. My question is: have you tested the impact of the gradient_accumulation_every setting on the model's convergence?

  def train(self):
        backwards = partial(loss_backwards, self.fp16)
        start_time = time.time()
        while self.step < self.train_num_steps:
            accumulated_loss = []
            for i in range(self.gradient_accumulate_every):
                if self.with_condition:
                    data = next(self.dl)
                    input_tensors = data['input'].to('cuda:0')
                    target_tensors = data['target'].to('cuda:0')
                    loss = self.model(target_tensors, condition_tensors=input_tensors)
                else:
                    data = next(self.dl).cuda()
                    loss = self.model(data)
                loss = loss.sum()/self.batch_size
                backwards(loss / self.gradient_accumulate_every, self.opt)
                    

[mobaidoctor]

@jeanRassaire Thank you for your inquiry. In our code, the gradient_accumulation_every parameter determines how many batches are processed before the model's gradients are updated. This setting is crucial when training with large volumetric images, particularly when constrained by GPU memory. We have set gradient_accumulation_every=2, meaning the code processes two batches for each gradient update. This method is especially beneficial when working with small batches (e.g., batch size of 1). It simulates the effects of a larger batch size, allowing for more stable training updates and potentially improving model convergence without requiring more computational resources. For example, with the parameters you mentioned: 10 training images, a batch size of 1, and gradient_accumulation_every=2, the model updates its weights after processing every two images. This process means each training step effectively handles the gradients averaged from two images, allowing for more stable and reliable gradient estimation. This stability is critical for efficiently training deep learning models. Using gradient accumulation smooths out the updates and can improve model performance, particularly when memory and computational power are limited. It’s important to note that the optimal value for gradient_accumulation_every might vary depending on the specific training scenario, thus requiring some experimentation to find the best setting for your particular model and data. Typically, setting gradient_accumulation_every to a large number when your training dataset has few samples is not advisable. Such a setup would mean updating the model weights only a few times per epoch, which can lead to extremely slow convergence and may not effectively capture the variability within the training data across epochs. A more practical approach is to set gradient_accumulation_every based on the equivalent batch size you aim to simulate. For instance, if the largest batch size your GPU can manage is 2, but you find that an effective batch size for your model should be 8, you might set gradient_accumulation_every to 4 (i.e., four batches of 2 images each). In our case, we set this parameter to 2 based on previous experiments, which indicated that this was sufficient for a batch size of 1, given our limited GPU resources.

[Jean-Rassaire]

thanks so much @mobaidoctor