f1aadee66689491a6993070955abe7987b9818fc,onmt/utils/loss.py,,shards,#Any#Any#Any#,269

Before Change

        // Assumed backprop"d
        variables = []
        for k, (v, v_split) in non_none.items():
            if isinstance(v, torch.Tensor) and state[k].requires_grad:
                variables.extend(zip(torch.split(state[k], shard_size),
                                     [v_chunk.grad for v_chunk in v_split]))
        inputs, grads = zip(*variables)
        torch.autograd.backward(inputs, grads)

After Change

        // want a sequence of dictionaries of tensors.
        // First, unzip the dictionary into a sequence of keys and a
        // sequence of tensor-like sequences.
        keys, values = zip(*((k, reversed(v_split))
                             for k, (_, v_split) in non_none.items()))

        // Now, yield a dictionary for each shard. The keys are always

Instances