add bonds_penalty hyperparam

Author: andreea-popescu-reef

pallets/admin-utils/src/tests/mock.rs

@@ -86,6 +86,7 @@ parameter_types! {
     pub const InitialImmunityPeriod: u16 = 2;
     pub const InitialMaxAllowedUids: u16 = 2;
     pub const InitialBondsMovingAverage: u64 = 900_000;
+    pub const InitialBondsPenalty: u16 = 0;
     pub const InitialStakePruningMin: u16 = 0;
     pub const InitialFoundationDistribution: u64 = 0;
     pub const InitialDefaultDelegateTake: u16 = 11_796; // 18% honest number.
@@ -163,6 +164,7 @@ impl pallet_subtensor::Config for Test {
     type InitialMaxRegistrationsPerBlock = InitialMaxRegistrationsPerBlock;
     type InitialPruningScore = InitialPruningScore;
     type InitialBondsMovingAverage = InitialBondsMovingAverage;
+    type InitialBondsPenalty = InitialBondsPenalty;
     type InitialMaxAllowedValidators = InitialMaxAllowedValidators;
     type InitialDefaultDelegateTake = InitialDefaultDelegateTake;
     type InitialMinDelegateTake = InitialMinDelegateTake;

pallets/admin-utils/src/tests/mod.rs

@@ -741,6 +741,39 @@ fn test_sudo_set_bonds_moving_average() {
     });
 }
 
+#[test]
+fn test_sudo_set_bonds_penalty() {
+    new_test_ext().execute_with(|| {
+        let netuid: u16 = 1;
+        let to_be_set: u16 = 10;
+        let init_value: u16 = SubtensorModule::get_bonds_penalty(netuid);
+        add_network(netuid, 10, 0);
+        assert_eq!(
+            SubtensorModule::sudo_set_bonds_penalty(
+                <<Test as Config>::RuntimeOrigin>::signed(U256::from(0)),
+                netuid,
+                to_be_set
+            ),
+            Err(DispatchError::BadOrigin.into())
+        );
+        assert_eq!(
+            SubtensorModule::sudo_set_bonds_penalty(
+                <<Test as Config>::RuntimeOrigin>::root(),
+                netuid + 1,
+                to_be_set
+            ),
+            Err(Error::<Test>::NetworkDoesNotExist.into())
+        );
+        assert_eq!(SubtensorModule::get_bonds_penalty(netuid), init_value);
+        assert_ok!(SubtensorModule::sudo_set_bonds_penalty(
+            <<Test as Config>::RuntimeOrigin>::root(),
+            netuid,
+            to_be_set
+        ));
+        assert_eq!(SubtensorModule::get_bonds_penalty(netuid), to_be_set);
+    });
+}
+
 #[test]
 fn test_sudo_set_rao_recycled() {
     new_test_ext().execute_with(|| {

pallets/subtensor/src/epoch/math.rs

@@ -1022,6 +1022,79 @@ pub fn weighted_median_col_sparse(
     median
 }
 
+// Element-wise interpolation of two matrices: Result = A + ratio * (B - A).
+// ratio is has intended range [0, 1]
+// ratio=0: Result = A
+// ratio=1: Result = B
+#[allow(dead_code)]
+pub fn interpolate(
+    mat1: &Vec<Vec<I32F32>>,
+    mat2: &Vec<Vec<I32F32>>,
+    ratio: I32F32,
+) -> Vec<Vec<I32F32>> {
+    if ratio == I32F32::from_num(0) {
+        return mat1.clone();
+    }
+    if ratio == I32F32::from_num(1) {
+        return mat2.clone();
+    }
+    assert!(mat1.len() == mat2.len());
+    if mat1.len() == 0 {
+        return vec![vec![]; 1];
+    }
+    if mat1[0].len() == 0 {
+        return vec![vec![]; 1];
+    }
+    let mut result: Vec<Vec<I32F32>> = vec![vec![I32F32::from_num(0); mat1[0].len()]; mat1.len()];
+    for i in 0..mat1.len() {
+        assert!(mat1[i].len() == mat2[i].len());
+        for j in 0..mat1[i].len() {
+            result[i][j] = mat1[i][j] + ratio * (mat2[i][j] - mat1[i][j]);
+        }
+    }
+    result
+}
+
+// Element-wise interpolation of two sparse matrices: Result = A + ratio * (B - A).
+// ratio is has intended range [0, 1]
+// ratio=0: Result = A
+// ratio=1: Result = B
+#[allow(dead_code)]
+pub fn interpolate_sparse(
+    mat1: &Vec<Vec<(u16, I32F32)>>,
+    mat2: &Vec<Vec<(u16, I32F32)>>,
+    columns: u16,
+    ratio: I32F32,
+) -> Vec<Vec<(u16, I32F32)>> {
+    if ratio == I32F32::from_num(0) {
+        return mat1.clone();
+    }
+    if ratio == I32F32::from_num(1) {
+        return mat2.clone();
+    }
+    assert!(mat1.len() == mat2.len());
+    let rows = mat1.len();
+    let zero: I32F32 = I32F32::from_num(0);
+    let mut result: Vec<Vec<(u16, I32F32)>> = vec![vec![]; rows];
+    for i in 0..rows {
+        let mut row1: Vec<I32F32> = vec![zero; columns as usize];
+        for (j, value) in mat1[i].iter() {
+            row1[*j as usize] = *value;
+        }
+        let mut row2: Vec<I32F32> = vec![zero; columns as usize];
+        for (j, value) in mat2[i].iter() {
+            row2[*j as usize] = *value;
+        }
+        for j in 0..columns as usize {
+            let interp: I32F32 = row1[j] + ratio * (row2[j] - row1[j]);
+            if zero < interp {
+                result[i].push((j as u16, interp))
+            }
+        }
+    }
+    result
+}
+
 // Element-wise product of two matrices.
 #[allow(dead_code)]
 pub fn hadamard(mat1: &[Vec<I32F32>], mat2: &[Vec<I32F32>]) -> Vec<Vec<I32F32>> {

pallets/subtensor/src/epoch/run_epoch.rs

@@ -137,18 +137,22 @@ impl<T: Config> Pallet<T> {
         // Compute preranks: r_j = SUM(i) w_ij * s_i
         let preranks: Vec<I32F32> = matmul(&weights, &active_stake);
 
-        // Clip weights at majority consensus
-        let kappa: I32F32 = Self::get_float_kappa(netuid); // consensus majority ratio, e.g. 51%.
+        // Consensus majority ratio, e.g. 51%.
+        let kappa: I32F32 = Self::get_float_kappa(netuid);
+        // Calculate consensus as stake-weighted median of weights.
         let consensus: Vec<I32F32> = weighted_median_col(&active_stake, &weights, kappa);
-        inplace_col_clip(&mut weights, &consensus);
-        let validator_trust: Vec<I32F32> = row_sum(&weights);
+        // Clip weights at majority consensus.
+        let mut clipped_weights: Vec<Vec<I32F32>> = weights.clone();
+        inplace_col_clip(&mut clipped_weights, &consensus);
+        // Calculate validator trust as sum of clipped weights set by validator.
+        let validator_trust: Vec<I32F32> = row_sum(&clipped_weights);
 
         // ====================================
         // == Ranks, Server Trust, Incentive ==
         // ====================================
 
         // Compute ranks: r_j = SUM(i) w_ij * s_i
-        let mut ranks: Vec<I32F32> = matmul(&weights, &active_stake);
+        let mut ranks: Vec<I32F32> = matmul(&clipped_weights, &active_stake);
 
         // Compute server trust: ratio of rank after vs. rank before.
         let trust: Vec<I32F32> = vecdiv(&ranks, &preranks);
@@ -161,14 +165,22 @@ impl<T: Config> Pallet<T> {
         // == Bonds and Dividends ==
         // =========================
 
+        // Get validator bonds penalty in [0, 1].
+        let bonds_penalty: I32F32 = Self::get_float_bonds_penalty(netuid);
+        // Calculate weights for bonds, apply bonds penalty to weights.
+        // bonds_penalty = 0: weights_for_bonds = weights.clone()
+        // bonds_penalty = 1: weights_for_bonds = clipped_weights.clone()
+        let weights_for_bonds: Vec<Vec<I32F32>> =
+            interpolate(&weights, &clipped_weights, bonds_penalty);
+
         // Access network bonds.
         let mut bonds: Vec<Vec<I32F32>> = Self::get_bonds(netuid);
         inplace_mask_matrix(&outdated, &mut bonds); // mask outdated bonds
         inplace_col_normalize(&mut bonds); // sum_i b_ij = 1
         log::trace!("B:\n{:?}\n", &bonds);
 
         // Compute bonds delta column normalized.
-        let mut bonds_delta: Vec<Vec<I32F32>> = row_hadamard(&weights, &active_stake); // ΔB = W◦S
+        let mut bonds_delta: Vec<Vec<I32F32>> = row_hadamard(&weights_for_bonds, &active_stake); // ΔB = W◦S
         inplace_col_normalize(&mut bonds_delta); // sum_i b_ij = 1
         log::trace!("ΔB:\n{:?}\n", &bonds_delta);
         // Compute the Exponential Moving Average (EMA) of bonds.
@@ -474,23 +486,26 @@ impl<T: Config> Pallet<T> {
         let preranks: Vec<I32F32> = matmul_sparse(&weights, &active_stake, n);
         log::trace!("Ranks (before): {:?}", &preranks);
 
-        // Clip weights at majority consensus
-        let kappa: I32F32 = Self::get_float_kappa(netuid); // consensus majority ratio, e.g. 51%.
+        // Consensus majority ratio, e.g. 51%.
+        let kappa: I32F32 = Self::get_float_kappa(netuid);
+        // Calculate consensus as stake-weighted median of weights.
         let consensus: Vec<I32F32> = weighted_median_col_sparse(&active_stake, &weights, n, kappa);
         log::trace!("Consensus: {:?}", &consensus);
 
-        weights = col_clip_sparse(&weights, &consensus);
-        log::trace!("Weights: {:?}", &weights);
+        // Clip weights at majority consensus.
+        let clipped_weights: Vec<Vec<(u16, I32F32)>> = col_clip_sparse(&weights, &consensus);
+        log::trace!("Clipped Weights: {:?}", &clipped_weights);
 
-        let validator_trust: Vec<I32F32> = row_sum_sparse(&weights);
+        // Calculate validator trust as sum of clipped weights set by validator.
+        let validator_trust: Vec<I32F32> = row_sum_sparse(&clipped_weights);
         log::trace!("Validator Trust: {:?}", &validator_trust);
 
         // =============================
         // == Ranks, Trust, Incentive ==
         // =============================
 
         // Compute ranks: r_j = SUM(i) w_ij * s_i.
-        let mut ranks: Vec<I32F32> = matmul_sparse(&weights, &active_stake, n);
+        let mut ranks: Vec<I32F32> = matmul_sparse(&clipped_weights, &active_stake, n);
         log::trace!("Ranks (after): {:?}", &ranks);
 
         // Compute server trust: ratio of rank after vs. rank before.
@@ -505,6 +520,14 @@ impl<T: Config> Pallet<T> {
         // == Bonds and Dividends ==
         // =========================
 
+        // Get validator bonds penalty in [0, 1].
+        let bonds_penalty: I32F32 = Self::get_float_bonds_penalty(netuid);
+        // Calculate weights for bonds, apply bonds penalty to weights.
+        // bonds_penalty = 0: weights_for_bonds = weights.clone()
+        // bonds_penalty = 1: weights_for_bonds = clipped_weights.clone()
+        let weights_for_bonds: Vec<Vec<(u16, I32F32)>> =
+            interpolate_sparse(&weights, &clipped_weights, n, bonds_penalty);
+
         // Access network bonds.
         let mut bonds: Vec<Vec<(u16, I32F32)>> = Self::get_bonds_sparse(netuid);
         log::trace!("B: {:?}", &bonds);
@@ -523,7 +546,8 @@ impl<T: Config> Pallet<T> {
         log::trace!("B (mask+norm): {:?}", &bonds);
 
         // Compute bonds delta column normalized.
-        let mut bonds_delta: Vec<Vec<(u16, I32F32)>> = row_hadamard_sparse(&weights, &active_stake); // ΔB = W◦S (outdated W masked)
+        let mut bonds_delta: Vec<Vec<(u16, I32F32)>> =
+            row_hadamard_sparse(&weights_for_bonds, &active_stake); // ΔB = W◦S (outdated W masked)
         log::trace!("ΔB: {:?}", &bonds_delta);
 
         // Normalize bonds delta.
@@ -713,6 +737,9 @@ impl<T: Config> Pallet<T> {
     pub fn get_float_kappa(netuid: u16) -> I32F32 {
         I32F32::from_num(Self::get_kappa(netuid)).saturating_div(I32F32::from_num(u16::MAX))
     }
+    pub fn get_float_bonds_penalty(netuid: u16) -> I32F32 {
+        I32F32::from_num(Self::get_bonds_penalty(netuid)).saturating_div(I32F32::from_num(u16::MAX))
+    }
 
     pub fn get_block_at_registration(netuid: u16) -> Vec<u64> {
         let n = Self::get_subnetwork_n(netuid);

pallets/subtensor/src/lib.rs

@@ -568,6 +568,11 @@ pub mod pallet {
     pub fn DefaultBondsMovingAverage<T: Config>() -> u64 {
         T::InitialBondsMovingAverage::get()
     }
+    /// Default bonds penalty.
+    #[pallet::type_value]
+    pub fn DefaultBondsPenalty<T: Config>() -> u16 {
+        T::InitialBondsPenalty::get()
+    }
     #[pallet::type_value]
     /// Default validator prune length.
     pub fn DefaultValidatorPruneLen<T: Config>() -> u64 {
@@ -1172,6 +1177,10 @@ pub mod pallet {
     /// --- MAP ( netuid ) --> bonds_moving_average
     pub type BondsMovingAverage<T> =
         StorageMap<_, Identity, u16, u64, ValueQuery, DefaultBondsMovingAverage<T>>;
+    #[pallet::storage]
+    /// --- MAP ( netuid ) --> bonds_penalty
+    pub type BondsPenalty<T> =
+        StorageMap<_, Identity, u16, u16, ValueQuery, DefaultBondsPenalty<T>>;
     /// --- MAP ( netuid ) --> weights_set_rate_limit
     #[pallet::storage]
     pub type WeightsSetRateLimit<T> =

pallets/subtensor/src/macros/config.rs

@@ -93,6 +93,9 @@ mod config {
         /// Initial bonds moving average.
         #[pallet::constant]
         type InitialBondsMovingAverage: Get<u64>;
+        /// Initial bonds penalty.
+        #[pallet::constant]
+        type InitialBondsPenalty: Get<u16>;
         /// Initial target registrations per interval.
         #[pallet::constant]
         type InitialTargetRegistrationsPerInterval: Get<u16>;

pallets/subtensor/src/macros/events.rs

@@ -57,6 +57,8 @@ mod events {
         ImmunityPeriodSet(u16, u16),
         /// bonds moving average is set for a subnet.
         BondsMovingAverageSet(u16, u64),
+        /// bonds penalty is set for a subnet.
+        BondsPenaltySet(u16, u16),
         /// setting the max number of allowed validators on a subnet.
         MaxAllowedValidatorsSet(u16, u16),
         /// the axon server information is added to the network.