How to get and/or set gradient data during training

I am using dask to perform distributed training with Pytorch. What I am doing is to create chunks of the data I want to train, and compute the loss function and call loss.backward() in each worker with each chunk. This seems to be working (.forward() and .backward() are doing their job). What I need to do now is to accumulate the gradients and sum them up as described here. After being summed up, I need to set them to the model to do an optimizer.update().

The delayed function that is computing the loss and loss.backward() is shown below:

  def train_batches(index, chunk, targets, model, optimizer, lossfxn,
                    atoms_per_image, device):
      """A function that allows training per batches"""
      inputs = OrderedDict(chunk)
      outputs = model(inputs)
      if lossfxn is None:
          loss = MSELoss(outputs, targets[index], optimizer,
                         atoms_per_image[index], device=device)
          raise('I do not know what to do')
      gradients = []
      for param in model.parameters():
      return outputs, loss, gradients    

Am I getting correctly the gradients by doing the loop in model.parameters()? Should I do I do get some lists when I call this function and I assume they are the gradients.

Now, I would like to sum them and set this sum as the gradient of the model to then call optimizer.step(). How can I achieve that?

I would appreciate any suggestions :slight_smile:.

I solved it. After receiving the list with gradients, I perform the following:

# grads = [sum of gradients per batch obtained from train_batches]
for index, param in enumerate(model.parameters()):
    param.grad = torch.tensor(grads[index])
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