Neural network #1188
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Neural network #1188
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benjaminsavage
requested changes
Jul 9, 2024
| // M' neurons wide and here M is M'/N, L layers tall | ||
| pub async fn neural_network<C, S, const M: usize, const N: usize, const MTimesN: usize>( | ||
| ctx: C, | ||
| last_layer_neurons: &[BitDecomposed<AdditiveShare<Boolean, N>>; M], |
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These are the activations of the last layer of neurons? If so, let's give it a name including that word.
| pub async fn neural_network<C, S, const M: usize, const N: usize, const MTimesN: usize>( | ||
| ctx: C, | ||
| last_layer_neurons: &[BitDecomposed<AdditiveShare<Boolean, N>>; M], | ||
| edge_weights: &[BitDecomposed<AdditiveShare<Boolean, N>>; M], |
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It's very hard to know how to use this data structure.
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| Boolean: FieldSimd<N>, | ||
| AdditiveShare<Boolean, N>: BooleanProtocols<C, N>, | ||
| Boolean: FieldSimd<M>, | ||
| AdditiveShare<Boolean, M>: BooleanProtocols<C, M>, |
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Why do we need both N and M vectorization support?
| { | ||
| // use super::step::MultiplicationStep as Step; | ||
| // for each layer we get M*M vector of edge_weights | ||
| let mut mults = ctx.parallel_join(zip(edge_weights.iter(), last_layer_neurons).enumerate().map(|(i, (edge_weight, neuron))| { |
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mults is not a good name. Maybe input_edge_activations?
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| let mut num = 0; | ||
| while mults.len() > 1 { | ||
| // Add each of the mults amongst themselves | ||
| for (a, b) in mults.iter().tuples() { | ||
| let (add_result, _) = integer_add::<_, S, N>( | ||
| ctx.narrow(&TwoHundredFiftySixBitOpStep::Bit(M+num)), | ||
| RecordId::from(num), | ||
| &a, | ||
| &b, | ||
| ) | ||
| .await?; | ||
| mults.push(add_result); | ||
| num += 1; | ||
| } | ||
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| } |
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Andy already has code that does this (log(n) depth steps, adding each time and thereby dividing the length of the list by 2). Use pub async fn aggregate_values<'ctx, 'fut, C, OV, const B: usize>(
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| let mut one_cell = mults[0]; | ||
| while one_cell.len() > 1 { | ||
| let (left, right) = one_cell.split_at((one_cell.len()/2).try_into().unwrap()); | ||
| (one_cell, _) = integer_add::<_, S, N>( | ||
| ctx.narrow(&TwoHundredFiftySixBitOpStep::Bit(M+num)), | ||
| RecordId::FIRST, | ||
| &left, | ||
| &right, | ||
| ) | ||
| .await?; | ||
| num += 1; | ||
| } |
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I'm lost. I don't understand what is happenning here.
| .upgraded_semi_honest((edge_weights, prev_neurons), |ctx, (edge_weights, prev_neurons)| async move { | ||
| let edge_weights1 = BitDecomposed::transposed_from(&edge_weights).unwrap(); | ||
| let prev_neurons1 = BitDecomposed::transposed_from(&prev_neurons).unwrap(); | ||
| let edge_weights = [edge_weights1.clone(), edge_weights1.clone(), edge_weights1.clone(), edge_weights1.clone(), edge_weights1.clone(), edge_weights1.clone(), edge_weights1.clone(), edge_weights1]; |
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What is happenning here?
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| // for i in 0..M-1 // For going through all layers | ||
| // for j in 0..N-1 // Current layer | ||
| // for k in 0..N-1 // For previous layer | ||
| // neuron(i*N + j) += neuron((i-1)*N + k) * edge_weight(neuron((i)*N + j), neuron((i-1)*N + k)) | ||
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| // M' neurons wide and here M is M'/N, L layers tall |
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