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munmap_chunk(): invalid pointer
[kernel][1736790336][2968] [ /usr/src/debug/arrayfire/arrayfire-full-3.8.3/src/backend/opencl/Kernel.hpp:34 ] Launching identity_kernel
Aborted (core dumped)

/// A probabilistic implementation of the STDP algorithm for a bitwise linear layer.
pub fn probabilistic_bitwise_stdp<I: BitVolume, O: BitVolume>(
 synaptic_layers: &mut [BitwiseLinear<I, O>],
 self_connected: bool,
 presynaptic_trace: &af::Array<f32>,
 dominant_postsynaptic_spikes: &af::Array<bool>,
 dopamine: f32,
 alpha: f32,
 learning_rate: f32
 ) {
 // TODO:
 // When arrayfire supports 8-bit signed integers, migrate to that instead of 16-bit integers.
 // Go through each synaptic layer and modify its weights.
 let connections: usize = synaptic_layers.len();
 for (layer_idx, layer) in synaptic_layers.iter_mut().enumerate() {
 // Get the excitatory and inhibitory weights, and convert it to a trinary layer.
 let (excitatory, inhibitory): (af::Array<bool>, af::Array<bool>) = layer.weights_as_array();
 let mut weights: af::Array<i16> = af::constant(0, excitatory.dims());
 weights = af::selectl( 1.0, &excitatory, &weights);
 weights = af::selectl(-1.0, &inhibitory, &weights);
 // Normalize the trace and compute the LTP and LTD components.
 let trace_norm: af::Array<f32> = if connections == 1 {
 presynaptic_trace / af::max_all(presynaptic_trace).0
 } else {
 let sequences: &[af::Seq<i32>] = &[af::Seq::default(), af::Seq::default(), af::Seq::default(), af::Seq::new(layer_idx as i32, layer_idx as i32, 1)];
 let trace_layer: af::Array<f32> = af::index(presynaptic_trace, sequences);
 &trace_layer / af::max_all(&trace_layer).0
 };
 let ltp_trace: af::Array<f32> = af::exp(&trace_norm) - alpha;
 let ltd_trace: af::Array<f32> = af::exp(&(1.0f32 - &trace_norm)) - alpha;
 // Calculate update probabilities.
 let p_update_prob: af::Array<f32> = (ltp_trace - ltd_trace) * learning_rate * dopamine;
 let n_update_prob: af::Array<f32> = &p_update_prob * -1f32;
 // Generate random values for probabilistic update.
 // We use a uniform distribution to have an equal chance for all values to be generated.
 //
 // Using the random values, we'll then create a mask for different weight updates (positive and
 // negative).
 let random_values: af::Array<f32> = af::randu(weights.dims());
 let increase_mask: af::Array<bool> = af::lt(&random_values, &p_update_prob, false) & af::lt(&weights, & 1i16, false);
 let decrease_mask: af::Array<bool> = af::lt(&random_values, &n_update_prob, false) & af::gt(&weights, &-1i16, false);
 // Calculate delta weights
 let delta_weights: af::Array<i16> = increase_mask.cast::<i16>() - decrease_mask.cast::<i16>();
 // Apply winner_mask and identity_mask to delta_weights.
 let winner_mask: af::Array<bool> = af::tile(&dominant_postsynaptic_spikes, af::dim4!(1, I::FSIZE as u64, 1, 1));
 let masked_delta_weights: af::Array<i16> = if self_connected {
 let identity_mask: af::Array<i16> = (1f32 - af::identity::<i16>(af::dim4!(1, I::FSIZE as u64, O::FSIZE as u64, 1))).cast();
 &delta_weights 
 * &winner_mask.cast::<i16>() 
 * &identity_mask
 } else {
 &delta_weights
 * &winner_mask.cast::<i16>()
 };
 // Apply the masked delta weights to the weights.
 weights += masked_delta_weights;
 weights = af::clamp(&weights, &-1i16, &1i16, true);
 // Deconstruct the weights back into their excitatory and inhibitory counterparts and export it
 // back into the layer.
 let excitatory: af::Array<bool> = af::eq(&weights, & 1, false);
 let inhibitory: af::Array<bool> = af::eq(&weights, &-1, false);
 layer.update_weights(excitatory, inhibitory);
 }
}

 /// Returns the weights as an arrayfire array of the dimensions (1, input size, output size).
 /// Arrays are returned in the order of excitatory and inhibitory weights.
 pub fn weights_as_array(&self) -> (af::Array<bool>, af::Array<bool>) {
 (
 af::Array::new(
 &self.neurons.par_iter()
 .flat_map(|neuron| neuron.weight_excitatory.to_flattened_bits())
 .collect::<Vec<_>>(),
 af::dim4!(1, I::FSIZE as u64, O::FSIZE as u64)
 ),
 af::Array::new(
 &self.neurons.par_iter()
 .flat_map(|neuron| neuron.weight_inhibitory.to_flattened_bits())
 .collect::<Vec<_>>(),
 af::dim4!(1, I::FSIZE as u64, O::FSIZE as u64)
 )
 )
 }
 /// Updates the weights from an arrayfire array of the dimensions (input size, output size).
 pub fn update_weights(&mut self, updated_excitatory: af::Array<bool>, updated_inhibitory: af::Array<bool>) {
 let mut excitatory_weights: Vec<bool> = vec![false; (I::FSIZE * O::FSIZE) as usize];
 let mut inhibitory_weights: Vec<bool> = vec![false; (I::FSIZE * O::FSIZE) as usize];
 updated_excitatory.host(&mut excitatory_weights);
 updated_inhibitory.host(&mut inhibitory_weights);
 self.neurons.par_iter_mut().enumerate().for_each(|(i, neuron)| {
 let start: usize = i * I::FSIZE as usize;
 let end: usize = start + I::FSIZE as usize;
 let excitatory_slice: &[bool] = &excitatory_weights[start..end];
 let inhibitory_slice: &[bool] = &inhibitory_weights[start..end];
 neuron.weight_excitatory = I::from_flattened_bits(excitatory_slice).unwrap();
 neuron.weight_inhibitory = I::from_flattened_bits(inhibitory_slice).unwrap();
 });
 }

LSB Version:	n/a
Distributor ID:	Arch
Description:	Arch Linux
Release:	rolling
Codename:	n/a
name, memory.total [MiB], driver_version
NVIDIA GeForce RTX 3050 Laptop GPU, 4096 MiB, 565.57.01
rocm-smi not found.
clinfo not found.