I am trying to make MXNet (C++ API) learn, with a common sample in C++, on multiple GPU. According to this MXNet forum post, we need to aggregate manually the gradients that we fetch at the backpropagation time. Now, if I separate the gradients of each GPU, both networks are training. If I concatenate the weights, it doesn't work. (Like this) :

gradValuesCombined.insert(gradValuesCombined.end(), gradValues1.begin(), gradValues1.end());
gradValuesCombined.insert(gradValuesCombined.end(), gradValues1.begin(), gradValues1.end());
NDArray combined1(gradValuesCombined.data(), combinedShape, ctx1);

However, if the gradients have the same batch_size, then summing the gradients works.

gradArray1[i] + gradArray2[i]

But, summing all the gradients like if it was a one-batch vector, doesn't work:

combinedOneDim[0] = 1;
int sizeOneBatch = combinedOneDim.Size();
gradValuesCombinedOneDim.insert(gradValuesCombinedOneDim.end(), gradValues1.begin(), gradValues1.begin() + sizeOneBatch);
for(int i = 1; i < curGradArrayShape1[0]; i++) {
     std::transform(gradValuesCombinedOneDim.begin(), gradValuesCombinedOneDim.end(), gradValues1.begin() + i * sizeOneBatch, gradValuesCombinedOneDim.begin(), std::plus<mx_float>());
}
for(int i = 0; i < curGradArrayShape2[0]; i++) {
     std::transform(gradValuesCombinedOneDim.begin(), gradValuesCombinedOneDim.end(), gradValues2.begin() + i * sizeOneBatch, gradValuesCombinedOneDim.begin(), std::plus<mx_float>());
}

At that stage, I figured that it was because I had to keep the initial shape of the gradient. But if I try to average the weights on the batch dimension, and copy the values to keep the same gradient shape, it should work. But it doesn't... And if I just sum everything on the first dimension and fill the rest with zeroes, it doesn't work either...

I verified, the synchronization of data is correct and working. So what do I miss?

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